This study aimed (1) to map the immunogenic epitopes (B and T cells) within the major BCoV structural proteins. These epitopes are believed to induce a robust im-mune response through the interaction with major histocompatibility complex (MHC class II) molecules (2) to design some novel BCoV multi-epitope-based vac-cines. To achieve these goals, we applied several integrated In-silico prediction computational tools to map these epitopes within the major BCoV structural pro-teins. The predicted epitopes were linked to some immunostimulants such as Toll-like receptors-2 (TLR2) and TLR-4. The tertiary structure of each epitope was modeled through the Alpha fold-2 colab machine learning tools. The stability and purity of each epitope were assessed using the Ramachandran plot and the Z Score values. Each multiepitope-based vaccine candidate conjugated with the Chlorotox-in B toxin as an adjuvant. We designed the vaccine constructs using various expres-sion vectors. We also predicted the affinity binding of these vaccines with the target protein using molecular docking. Our designed multiepitope vaccine candidates per each BCoV structural protein showed high antigenicity, immunogenicity, non-allergic, non-toxic, and high water solubility. Further studies are highly en-couraged to validate the efficacy of these novel BCoV vaccines in the natural host.

Reminding the courier through wearable artificial intelligence devices to prevent accidents is not only beneficial to the courier's safety but also will save a lot of insurance premiums for express companies, therefore, it is worth investigating what factors can influence the acceptance of wearable artificial intelligence devices by couriers. Push-pull-mooring (PPM) theory and affective event theory (AET) are integrated to test couriers’ adoption to wearable safety detection devices. Social influence, perceived security, personal innovativeness and affective event reaction are applied to research model. Questionnaires are distributed among several listed express companies and 263 valid questionnaires are used for empirical test. Empirical results indicated that social influence, perceived safety, personal innovativeness and affective event reaction are positively related to usage with coefficients 0.218, 0.301, 0.698 and 0.309. Personal innovativeness has positively moderating effects on relationships between affective event reaction, perceived security and usage with coefficients 0.145 and 0.106, whereas has no significant moderating effect on relationship between social influence and usage. The research aims to help support the proliferation and adoption of wearable artificial intelligence devices to optimize the current state of the express industry and improve the interaction between couriers and managers, creating an active management strategy that will allow express companies to thrive. This study can help express companies reduce insurance costs and therefore reduce the premium pricing of insurance companies for the courier industry.. This study also provides design suggestions for the application of wearable artificial intelligence devices based on chat GPT in various working conditions.

This study focuses on the development and evaluation of an AI-based digital therapeutic prototype for adolescent mental health management and disaster response. The system integrates real-time monitoring, AI-driven conversation analysis, personalized psychological treatment programs, and multimodal data analysis. An algorithm was developed to detect gaslighting and verbal abuse using a BERT-based classification model, achieving 85% accuracy in gaslighting detection and 87% ac-curacy in verbal abuse detection. Additionally, a disaster psychological recovery support module was included, which demonstrated a 30% improvement in users' stress reduction rates in simulated disaster scenarios. The results of this study suggest that such digital therapeutic approaches can significantly contribute to early intervention in adolescent mental health issues and provide effec-tive support during disasters. The developed prototype demonstrates the potential of AI and digital technology to innovate mental health management and disaster response strategies.

The advent of Large Language Models like Generative Pre-trained Transformer 4 (GPT-4) has introduced a revolutionary approach to bridging the communication gap between complex radiology reports and patient comprehension. This review article explores the pivotal role of GPT-4 in generating patient-friendly summaries of radiology reports, aiming to demystify medical jargon and enhance patient engagement and understanding. Through an examination of the technological underpinnings of GPT-4, this article highlights its capability to accurately interpret and simplify radiological findings into layman's terms. It assesses the impact of such advancements on patient satisfaction, adherence to treatment plans, and overall healthcare outcomes. Furthermore, the review delves into the potential challenges and ethical considerations associated with implementing AI-driven summaries in clinical practice, including accuracy, privacy, and the need for human oversight. By offering insights into the current applications, benefits, and limitations of GPT-4 in radiology, this short review underscores the transformative potential of AI in fostering a more inclusive and patient-centric healthcare communication paradigm.

Smart grids represent the evolution of traditional electrical grids through the integration of digital and communication technologies. These grids are capable of monitoring and managing the distribution of electricity efficiently and resiliently. Artificial Intelligence (AI) plays a crucial role in this context, offering advanced solutions for optimization, forecasting, and energy management. This paper explores the main AI technologies applied to smart grids, highlighting their advantages, challenges, and impacts on energy efficiency and sustainability. The methodology employed includes a comprehensive literature review, analysis of current AI applications in smart grids, and examination of case studies to illustrate the practical implementation and benefits of AI technologies in this field.

The development of new turbomachinery designs requires numerous time-consuming and computationally intensive computational fluid dynamics (CFD) calculations. However, most of the generated high spatial resolution data remains unused at later development steps. That is also the case with automated optimization processes that use only a few integral values to determine objectives and constraints. Therefore the development of a data-driven AI model was initiated to ensure the potential of further use of the CFD data which is also used to train the AI model. The presented method subsequently provides a fast approximation of the 3D flow for new designs. In this paper, the structure of the developed AI model is presented and the approximation quality is analysed using a complex, state-of-the-art compressor test case. It is shown that the AI model can reproduce many characteristics of the 3D flow of new designs, and performance measures such as efficiency can be derived from these flow predictions. In addition, the complex test case revealed that greater design variation reduces the AI approximation quality which can lead to undesirable exploratory behaviour in an optimisation setup. Overall, the test case has shown promising results and has provided hints for further improvements of the AI model.

The rapid and accurate detection of viruses and the discovery of single nucleotide polymorphisms (SNPs) are crucial for disease management and understanding viral evolution. In this study, a pipeline for virus detection, validation, and SNP discovery from next-generation sequencing (NGS) data is presented. By integrating state-of-the-art bioinformatics tools with artificial intelligence, the pipeline processes raw sequencing data to identify viral sequences with high accuracy and sensitivity. Before aligning the reads to the reference genomes, quality control measures and adapter trimming are performed to ensure the integrity of the data. Unmapped reads are subjected to de novo assembly to reveal novel viral sequences and genetic elements. The effectiveness of the pipeline is demonstrated by the identification of virus sequences, illustrating its potential for the detection of known and emerging pathogens. SNP discovery is performed using a custom Python script that compares the entire population of sequenced viral reads to a reference genome. This approach provides a comprehensive overview of viral genetic diversity and identifies dominant variants and a spectrum of genetic variations. The robustness of the pipeline is confirmed by the recovery of complete viral sequences, which improves our understanding of viral genomics. This study highlights the synergy between traditional bioinformatics techniques and modern approaches, providing a robust tool for analyzing viral genomes and contributing to the broader field of viral genomics.

Objectives: The study aimed to develop a deep learning-based edge AI model deployed on electrocardiograph (ECG) devices for real-time detection of atrial fibrillation (AF)-risk during sinus rhythm (SR) using standard 10-second 12-lead electrocardiograms (ECGs). Methods: A novel approach was used to convert standard 12-lead ECGs into binary images for model input, and a lightweight convolutional neural network (CNN)-based model was trained using data collected by the Japan Agency for Medical and Research Development (AMED) between 2019 and 2022. Patients over 40 years old with digital, SR ECGs were retrospectively enrolled and divided into AF and non-AF groups. Data labeling was supervised by cardiologists. The dataset was randomly allocated into training, validation, and internal testing datasets. External testing was conducted on data collected from other hospitals.　Results: The best-trained model achieved an AUC of 0.82 and 0.80, sensitivity of 79.5% and 72.3%, specificity of 77.8% and 77.7%, precision of 78.2% and 76.4%, and overall accuracy of 78.6% and 75.0% in the internal and external testing datasets, respectively. The deployed model and app package utilized 2.5MB and 40MB of the available ROM and RAM capacity on the edge ECG device, correspondingly. Processing time for AF-risk detection was approximately 2 seconds.　Conclusion: The model maintains comparable performance and improves its suitability for deployment on resource-constrained ECG devices, thereby expanding its potential impact to a wide range of healthcare settings. Its successful deployment enables real-time AF-risk detection during SR, allowing for timely intervention to prevent AF-related serious consequences like stroke and premature death.

In the livestock industry, maintaining the health of animals is very important, and the use of technology has greatly helped this. One of the existing challenges has been that most of the available sensors and electronic processing devices are designed to fit the human body, and the use of these devices for livestock faces limitations due to the difference in the structure of the body and skin of animals and humans. Reliable solutions tailored for animal husbandry can be achieved by modifying the physical and software structure of existing devices. In this article, we have presented the current situation, by reviewing the research done and the existing technology in the field of animal husbandry. The aim is to obtain comprehensive parameters for monitoring livestock health by leveraging available information and exploring new methods based on the Internet of Things (IOT), Artificial Neural Networks (ANN), and Artificial Intelligence (AI). We take a proactive approach by utilizing available technologies based on sound-based diagnostic techniques. Initially, we propose utilizing piezoelectricity and amplifying received sound using a diaphragm attached to a collar. Subsequently, we discuss the results of relevant tests that can be conducted using the phonocardiography system. For the next phase of research and designing a more advanced livestock health monitoring system, we suggest employing an ultrasonic system to measure heart rate and blood pressure for the next phase of research and designing a more advanced livestock health monitoring system. This system, coupled with a piezoelectric system to make electric energy from the pulse of the veins, could be subcutaneously implanted for continuous monitoring

This study addressed the need for efficient data collection in micro-mobility using low-end hardware, focusing on developing a system capable of handling large real-time data streams. We proposed a novel data collection system that leverages incremental learning techniques, enabling AI-based services for micro-mobility passengers through smartphones. Our approach utilizes 3-axis acceleration sensors integrated into micro-mobility devices, collecting motion data in real-time. This data is processed through an incremental learning pre-processing server, employing the Broad Learning System algorithm, to ensure robust performance in noisy, real-world environments. The use of incremental learning is pivotal, allowing our system to adapt continuously to new data, enhancing the accuracy and relevance of the AI services provided to passengers. This research not only advances the technical capabilities in micro-mobility data handling but also has the potential to enhance the overall passenger experience through improved AI-based services.

In the literature, the question of the amount of data necessary and sufficient to validate different models of the occurrence of risk of adverse events for patients or the classification of the presence or absence of pathological features has been repeatedly raised. In the presented study, we propose a new approach to determine the necessary and enough studies for validation of medical software based on artificial intelligence technology, whose main task is to classify medical X-rays according to the presence of normality and pathology. It is shown that for several studies in a dataset, when AUC ROC has maximum heterogeneity, it varies depending on the balance of "norm"/"pathology" classes. Thus, for a balance of "normal"/"abnormality", where 90% is "normal" and 10% is "abnormality", maximum heterogeneity is achieved for 190 studies, for a balance of 80% ("normal")/20% ("abnormality") for 80 studies, for a balance of 70% ("norm")/30% ("abnormality") - 120 trials, for a balance of 60% ("norm")/40% ("abnormality") the maximum heterogeneity is reached at 110 trials, and for a balance of 50% ("norm")/50% ("abnormality") - at 70 trials. The obtained data are in good agreement with the previous results. They allow us to determine a sufficient (necessary) number of studies in the dataset to perform an unbiased assessment of AUC ROC.

This article explores the possible ramifications of incorporating ideas from AEC Industry 6.0 into the design and construction of intelligent, environmentally friendly, and long-lasting structures. This statement highlights the need to shift away from the current methods seen in the AEC Industry 5.0 to effectively respond to the increasing requirement for creative and environmentally sustainable infrastructures. The preceding methodology, AEC Industry 6.0, has substantially improved the efficacy and sustainability of construction by using new technology, digitalization, and environmentally conscious methods. Extensive discussions have occurred on the various benefits of AEC Industry 5.0. These merits include heightened stakeholder involvement, increased efficiency via automation and robots, decision-making processes centered around data, and the optimization of resources. Nevertheless, this study also examines the obstacles linked to the complete adoption of AEC Industry 6.0. These challenges require proficient professionals who can effectively utilize contemporary technologies, manage diverse stakeholder technological competencies, establish interoperable systems and standards, and reinforce cybersecurity measures. This study examines the viability of implementing concepts from Industry 6.0 to create intelligent, sustainable, and environmentally friendly structures. The objective is to showcase the potentially revolutionary impact that these principles may have on the construction industry. This study explores the possible applications of AEC Industry 6.0 within the Architecture, Engineering, and Construction (AEC) sector, explicitly highlighting the anticipated industry-wide consequences of the forthcoming paradigm shift. This paper comprehensively examines big data analytics, the Internet of Things (IoT), and collaborative robots as they pertain to the AEC Industry 6.0. This research investigates the interplay between organizational dynamics, human actors, and robotic systems to provide light on the opportunities and obstacles associated with integrating AEC Industry 6.0 concepts into the construction of buildings.

ChatGPT, a freely-accessible AI language model designed to generate human-like text responses to users, has been utilized in several areas, such as healthcare industry, to facilitate interactive dissemination of information. Academic advising has been an important factor in promoting success among university students, particularly those from disadvantaged backgrounds. Unfortunately, however, student advising has been marred with problems, with the availability and accessibility of advisors being among the hurdles. The current study explores how ChatGPT might serve to make academic advising more accessible, efficient, and even effective. Researchers compiled a list of questions frequently asked by current and prospective students in a teacher education Bachelor’s degree program in the United States. Then, the questions were typed into the free version of ChatGPT and the answers generated were evaluated for their content and delivery. ChatGPT generated surprisingly high-quality answers, written in an authoritative yet supportive tone, and it was particularly adept at addressing general career-related questions comprehensively, such as career outlook. We argue that ChatGPT may complement, but not replace, human academic advisors and it may very well promote educational equity through empowering students from a wide range of backgrounds with the means to access effective advising.

As recent history has shown, changing climate not only threatens to increase the spread of known disease, but also the emergence of new and dangerous phenotypes. This occurred most recently with West Nile virus: a virus previously known for mild febrile illness rapidly emerged to become a major cause of mortality and long-term disability throughout the world. As we move forward, into increasingly uncertain times, public health research must begin to incorporate a broader understanding of the determinants of disease emergence – what, how, why, and when. The increasing mainstream availability of high-quality open data and high-powered analytical methods presents promising new opportunities. Up to now, quantitative models of disease outbreak risk have been largely based on just a few key drivers, namely climate and large-scale climatic effects. Such limited assessments, however, often overlook key interacting processes and downstream determinants more likely to drive local manifestation of disease. Such pivotal determinants may include local host abundance, human behavioral variability, and population susceptibility dynamics. The results of such analyses can therefore be misleading in cases where necessary downstream requirements are not fulfilled. It is therefore important to develop models that include climate and higher-level climatic effects alongside the downstream non-climatic factors that ultimately determine individual disease manifestation. Today, few models attempt to comprehensively address such dynamics: up until very recently, the technology simply hasn’t been available. Herein, we present an updated overview of current perspectives on the varying drivers and levels of interactions that drive disease spread. We review the predominant analytical paradigms, discuss their strengths and weaknesses, and highlight promising new analytical solutions. Our focus is on the prediction of arboviruses, particularly West Nile virus, as these diseases represent the pinnacle of epidemiological complexity – solution to which would serve as an effective “gatekeeper”. We present the current state-of-the-art with respect to known drivers of arbovirus outbreak risk and severity, differentially highlighting the impact of climate and non-climatic drivers. The reality of multiple classes of drivers interacting at different geospatial and temporal scales requires advanced new methodologies. We therefore close out by presenting and discussing some promising new applications of AI. Given the reality of accelerating disease risks due to climate change, public health and other related fields must begin the process of updating their research programs to incorporate these much needed, new capabilities.

We propose a novel transfer learning method for speech emotion recognition allowing us to obtain promising results when only few training data is available. With as low as 125 examples per emotion class, we were able to reach a higher accuracy than a strong baseline trained on 8 times more data. Our method leverages knowledge contained in pre-trained speech representations extracted from models trained on a more general self-supervised task which doesn’t require human annotations, such as the wav2vec model. We provide detailed insights on the benefits of our approach by varying the training data size, which can help labeling teams to work more efficiently. We compare performance with other popular methods on the IEMOCAP dataset, a well-benchmarked dataset among the Speech Emotion Recognition (SER) research community. Furthermore, we demonstrate that results can be greatly improved by combining acoustic and linguistic knowledge from transfer learning. We align acoustic pre-trained representations with semantic representations from the BERT model through an attention-based recurrent neural network. Performance improves significantly when combining both modalities and scales with the amount of data. When trained on the full IEMOCAP dataset, we reach a new state-of-the-art of 73.9% unweighted accuracy (UA).

This paper explores the influence of GenAI chatbot (ChatGPT) and AI-based simulation (SimVenture) on the start-up intention through the mediating roles of entrepreneurship educational outcomes; entrepreneurship cognition, entrepreneurship competencies, and innovation spirit of students in higher education institutions in the Middle East. The proposed conceptual framework was empirically tested with partial least squares structural equation modeling using 166 survey responses from fourth-year undergraduate students enrolled in business administration programs in four higher education institutions in the Middle East region. The findings reveal that ChatGPT significantly promotes start-up intention, while ChatGPT has insignificant influence on entrepreneurship educational outcomes. Conversely, the SimVenture platform negatively influences start-up intention. Additionally, the SimVenture platform significantly improves entrepreneurship educational outcomes; thereby entrepreneurship educational outcomes directly influence start-up intention. Furthermore, the mediation paths show ChatGPT has an insignificant indirect influence on start-up intention through the mediating roles of entrepreneurship educational outcomes, while the SimVenture platform exhibits a notable indirect influence on startup intentions through improved entrepreneurship educational outcomes. These findings seek to provide insights for higher education institutions in the Middle East region in leveraging GenAI chatbot (ChatGPT) and AI-based simulation (SimVenture) to enhance entrepreneurial knowledge, awareness, competencies, and the spirit of innovation, which leads to promoting students’ intentions to start their businesses. Theoretically, the study contributes to filling the gap in the existing literature between AI tools and entrepreneurship education. This study is among the first to empirically explore the influences of GenAI chatbot (ChatGPT) and AI-based simulation (SimVenture) on entrepreneurship educational outcomes and the intention of students to embark on entrepreneurial ventures. It highlights the need for understanding AI technologies as distinct pedagogical tools.

Intestinal parasitic infections pose a significant public health challenge in Northern Nigeria, with prevalence rates ranging from 20% to 70%. Traditional diagnostic methods, primarily microscopic examination of stool samples, face limitations such as low sensitivity and high costs. This research addresses these challenges by proposing an Artificial Intelligence (AI)-based platform for the identification and counting of intestinal parasites. A total of 651 samples were collected from the 7 Northern State of Nigeria along with questionnaire collecting data on demographic, socio-economic, hygiene habits and environmental factors. Leveraging the You Only Look Once (YOLO) V8 model, trained on a dataset of 360 pre-processed and 467 annotated images, the AI model demonstrated promising performance metrics. Information-Theory-Based approaches including (CIFE, CMIM, DISR and ICAP) were used on various machine learning models (Naïve Bayes, Random Forest, Support Vector Machine and Decision Tree) to analyse the risk factors associated with the parasitic disease. The precision-recall curve, average precision, mean average precision, and F1 score indicated reliable detection and classification across various parasite classes. The accuracy (97%) and AUC (99%) scores shows that CIFE with Random Forest has the best performance indicating the significant risk factors associated with the parasitic diseases. The model exhibited a well-balanced trade-off between precision and recall, showcasing its potential as a cost-effective and accessible tool for improving the diagnosis and treatment of intestinal parasitic infections in resource-limited settings.

Abstract:The digital landscape is rapidly reshaping under the influence of AI-powered cloud-based e-commerce platforms (PCEPs). These platforms leverage the transformative power of AI to personalize customer experiences, optimize pricing strategies, enhance security, and streamline operations, offering businesses a significant competitive edge. However, challenges surrounding data privacy, algorithmic bias, and potential job displacement raise crucial ethical questions regarding widespread AI adoption in e-commerce.This study delves into the untapped potential and ethical considerations of PCEPs, distinguishing itself by proposing practical strategies for responsible development and ethical implementation.Objectives: Investigate the transformative potential of PCEPs: Personalized product recommendations:Analyze effectiveness and potential bias. Dynamic pricing strategies:Explore impact on consumer fairness and market stability. Enhanced fraud detection:Evaluate accuracy and privacy implications. Optimized supply chain management:Assess efficiency gains and job displacement risks. Critically analyze the ethical considerations of AI in e-commerce: Data privacy:Assess current practices and develop mitigation strategies. Algorithmic bias:Identify potential sources and propose solutions for fairer decision-making. Job displacement:Evaluate impacts and propose reskilling initiatives. Propose comprehensive and practical strategies for responsible PCEP development and implementation: Transparency:Enhance user awareness and control over data usage. Fairness:Design AI systems to mitigate bias and uphold consumer rights. Accountability:Establish clear guidelines and oversight mechanisms for ethical AI development.Conclusion: While PCEPs offer a plethora of transformative benefits, their ethical implementation is paramount. By adhering to principles of transparency, fairness, and accountability, businesses can unlock the full potential of PCEPs while minimizing potential risks and shaping the future of online commerce in a sustainable and equitable manner

The climate covers a series of events that deeply affect human life. It is possible to understand these events through spatial and statistical analyzes. Today, climate change, which is one of the most important of these events and the impact factors of consequences of this change, become a current issue. Drought is cited as one of the consequences of climate change and it is important to examine it with various methods as it can give negative results to both the economy and the nature. In this study, the drought status of the regions where these stations are located and the effects of drought on climate change were statistically calculated and evaluated using Standardized Precipitation Index (SPI), Percentage of Normal Index (PNI), Aridity Index (AI) and Standardized Precipitation -Evopotranspiration Index (SPEI). The precipitation data from 1981 to 2010 were obtained from Cihanbeyli, Karapınar, Çumra, Seydişehir, Kulu, Ereğli, Niğde, Karaman, Beyşehir and Aksaray meteorology stations affiliated to Turkish State Meteorological Service. At the same time, factor analysis and validity-reliability analysis were conducted to test the computability of the indices used in the study as a single index and to determine the reliability of the operations. While using exploratory factor analysis, Kaiser-Meyer-Olkin (KMO) test and Barlett test for factor analysis; Cronbach's alpha coefficient was used for reliability analysis. In our study, K-Means Cluster Analysis method was performed to determine the cutoff values of indices. According to the result of cluster analysis for the new (common) index, new clusters were created and ANOVA test was conducted to determine whether there was a difference between clusters.

This study empirically analyzes the effectiveness of resource optimization utilizing artificial intelligence (AI) within cloud computing environments. It compares systems that have been transitioned from a Monolithic structure to Microservices Architecture (MSA) with those adopting a simple Rehost strategy, to validate the operational efficiency of two systems whose resource efficiency has been enhanced compared to traditional legacy systems. The research is based on systems that determined resource efficiency post-cloud migration using APM tools. It points out the limitations of traditional heuristic operation methods in resource optimization, which lead to issues such as application performance degradation, resource over-allocation, and increased operational costs. This study aims to overcome the conventional resource optimization standards that depended on the average peak usage of CPU and memory infrastructure, by introducing AI-based optimization tools, and applying on-premise standards to cloud and container environments to achieve resource optimization. The paper emphasizes the importance of resource optimization through AI-based automation tools, exploring the possibilities of resolving real-world application performance issues in cloud environments, reducing unnecessary resource usage costs, and cutting down on labor risks and operational expenses. Through this, it presents strategic directions on how AI technology can offer financial and operational advantages in managing cloud resources.

Background: Ovarian, cervical, and endometrial cancers stand as fatal health killers for women's mental and physical health, especially affecting female reproductive systems. Exploiting weakly supervised learning and explainable AI techniques are crucial for fast, accurate, and robust automatic marker detection, efficient prevention, and primary treatment of gynecological tumors. Methods: With respect to the PRIMSA methodology, weakly supervised learning schemes and deep learning-based schemes are investigated in the cross-subject fields of clinical diagnostic imaging and explainable AI technology. Related methods, opening research problems, and challenging subjects are explored in the screening and treatment of gynecological tumors and performing cancer image diagnosis in the latest study. Results: Keynote approaches combining ultrasound medicine, explainable AI, and clinical imaging technology are summarized. In combination with learning-based methods with medical imaging, prospective insights are put forward for refreshed concepts on treatments in the area of gynecological oncology, with a feasible range of applications for their corresponding AI techniques. Conclusions: Explainable AI and deep learning-based approaches are capable of performing accurate classification between benign and malignant tumors, yielding a pathway to care for women's health, improving the survival rate of patients, pacing with disease prediction, and matching the strategic goal of "Healthy China 2030".

This paper introduces a quantitative methodology for assessing drone pilot performance, aiming to reduce drone-related incidents by understanding the human factors influencing performance. The challenge lies in balancing evaluations in operationally relevant environments with those in a standardized test environment for statistical relevance. The proposed methodology employs a novel virtual test environment that records not only basic flight metrics but also complex mission performance metrics, such as the video quality from a target. A group of Belgian Defence drone pilots were trained using this simulator system, yielding several practical results. These include a human performance model linking human factors to pilot performance, an AI co-pilot providing real-time flight performance guidance, a tool for generating optimal flight trajectories, a mission planning tool for ideal pilot assignment, and a method for iterative training improvement based on quantitative input. The training results with real pilots demonstrate the methodology’s effectiveness in evaluating pilot performance for complex military missions, suggesting its potential as a valuable addition to new pilot training programs.

Glioblastoma (GBM) is the most aggressive brain cancer. To model GBM in research, orthotopic brain tumor models, including syngeneic models like GL261 and genetically engineered mouse models like TRP, are used. In longitudinal studies, tumor growth and treatment response are typ-ically tracked with in vivo imaging, including bioluminescence imaging (BLI), which is quick, cost-effective, and easily quantifiable. However, BLI requires luciferase-tagged cells, and recent studies indicate that the luciferase gene can elicit an immune response, leading to tumor rejection and experimental variation. We sought to optimize the engraftment of two luciferase-expressing GBM models, GL261 Red-FLuc and TRP-mCherry-FLuc, showing differences in tumor take, with GL261 Red-FLuc cells requiring immunocompromised mice for 100% engraftment. Immuno-histochemistry and MRI revealed distinct tumor characteristics: GL261 Red-FLuc tumors were well-demarcated with densely packed cells, high mitotic activity, and vascularization. In con-trast, TRP-mCherry-FLuc tumors were large, invasive, and necrotic, with perivascular invasion. Quantifying tumor volume using the HALO® AI analysis platform yielded results comparable to manual measurements, providing a standardized and efficient approach for reliable high-throughput analysis of luciferase-expressing tumors. Our study highlights the importance of considering tumor engraftment when using luciferase-expressing GBM models, providing in-sights for preclinical research design.

The utilization of smart IoT devices, commonly referred to as digital twins, is aimed at the digitalization of human knowledge within aquaculture processes. This involves the incorporation of cutting-edge technologies, including information-based management with big data and modeling, to automate machinery and gain comprehensive insights into the aquaculture environment and fish farm conditions. The ultimate objective is to empower farmers to make informed decisions, furnishing them with objective data to enhance their capacity in monitoring and controlling the various factors impacting fish production. As a result, farming decisions can be fine-tuned to enhance fish health and optimize farm output. In the context of large and modern aquaculture farms, technological innovation becomes imperative to automate processes, minimize labor requirements, and streamline fish feeding operations. Remarkably, the literature currently offers limited discussions on the digital transformation of aquaculture through the application of digital twin methodologies. A prior study underscores the critical influence of factors such as market prices and fish survival rates on the profitability of offshore caging culture. In this study, we embark on an analysis of the prerequisites for establishing a digital twin infrastructure tailored to intelligent fish feeding management. This infrastructure is designed to facilitate the integration of technology and data-driven decision-making, ultimately enhancing the efficiency of fish feeding processes. The proposed architecture for the fish feeding digital twin encompasses various digital twin components, encompassing water quality forecasting, fish population assessment, fish metrics estimation, fish feed prediction, and evaluation of fish feeding intensity. Furthermore, we optimize the daily fish feeding process through reinforcement learning algorithms. Finally, we implement a cloud-based AIoT system that provides the runtime environment for executing digital twins and controlling our intelligent fish feeding machinery. Experimental findings underscore the efficacy of the proposed digital twin system in significantly improving traditional fish feeding processes, notably in terms of reducing food costs and labor requirements.

The use of artificial intelligence in natural language processing (NLP) has significantly contributed to the advancement of natural language applications such as sentimental analysis, topic modeling, text classification, chatbots, and spam filtering. With a large amount of text generated each day from different sources such as webpages, blogs, emails, social media, and articles, one of the most common tasks in natural language processing is the classification of a text corpus. This is important in many institutions for planning, decision-making, and archives of their projects. Many algorithms exist to automate text classification operations but the most intriguing of them is that which also learns these operations automatically. In this study, we present a new model to infer and learn from data using probabilistic logic and apply it to text classification. This model, called GenCo, is a multi-input single-output (MISO) learning model that uses a collaboration of partial classifications to generate the desired output. It provides a heterogeneity measure to explain its classification results and enables the reduction of the curse of dimensionality in text classification. The classification results are compared with those of conventional text classification models, and it shows that our proposed model has a higher classification performance than conventional models.

The continuously developing project aims to build an informatics system enabling analysis of spatial and temporal parameters of movement activities occurring in the sport of speed climbing. The monitoring system (climbing information speed system – CISS) is to be used for conducting comprehensive scientific research in the field of speed climbing. The system enables the evaluation of the training process of climbers at various levels of competition. The study analysis was based on video. The video recording with a camera positioned at a short distance (10 m) from the wall. The marker was positioned closest to the centre of mass (gravity) BMC. Results: development of a system for data collection and analysis of the climbing run based on video recording (application of the Kanade-Lucas-Tomasi (KLT) algorithm). Our results showed that used devices can measure a wide range of specific internal and external variables during speed climbing. Some of the analyzed parameters were significantly correlated with speed climbing time. These results could be a theoretical basis for future research and for training program’s preparation.

The understanding of water quality and its underlying processes is important for the protection of aquatic environments enabling the rare opportunity of access to a domain expert. Hence, an explainable AI (XAI) framework is proposed that is applicable to multivariate time series resulting in explanations that are interpretable by a domain expert. The XAI combines in three steps a data-driven choice of a distance measure with explainable cluster analysis through supervised decision trees. The multivariate time series consists of water quality measurements, including nitrate, electrical conductivity, and twelve other environmental parameters. The relationships between water quality and the environmental parameters are investigated by identifying similar days within a cluster and dissimilar days between clusters. The XAI does not depend on prior knowledge about data structure, and its explanations are tendentially contrastive. The relationships in the data can be visualized by a topographic map representing high-dimensional structures. Two comparable decision-based XAIs were unable to provide meaningful and relevant explanations from the multivariate time series data. Open-source code in R for the three steps of the XAI framework is provided.

As time progresses and technology improves, biological data sets are continuously increasing in size. New methods and new implementations of existing methods are needed to keep pace with this increase. In this paper, we present a high performance computing(HPC)-capable implementation of Iterative Random Forest (iRF). This new implementation enables the explainable-AI eQTL analysis of SNP sets with over a million SNPs. Using this implementation we also present a new method, iRF Leave One Out Prediction (iRF-LOOP), for the creation of Predictive Expression Networks on the order of 40,000 genes or more. We compare the new implementation of iRF with the previous R version and analyze its time to completion on two of the world's fastest supercomputers Summit and Titan. We also show iRF-LOOP's ability to capture biologically significant results when creating Predictive Expression Networks. This new implementation of iRF will enable the analysis of biological data sets at scales that were previously not possible.

As artificial intelligence (AI) makes significant headway in various arenas, the field of Speech-Language Pathology is at the precipice of experiencing a transformative shift towards automation. This study introduces QuickPic AAC, an AI-driven application designed to generate topic-specific displays from photographs in a "just-in-time" manner. Using QuickPic AAC, this study aimed to (a) determine which of two AI algorithms (NLG-AAC and GPT-3.5) results in greater specificity of vocabulary (i.e., percentage of vocabulary kept/deleted by clinician relative to vocabulary generated by QuickPic AAC; percentage of vocabulary modified); and to (b) evaluate perceived usability of QuickPic AAC among practicing speech-language pathologists. Results revealed that the GPT-3.5 algorithm consistently resulted in greater specificity of vocabulary and that speech-language pathologists expressed high user satisfaction for the QuickPic AAC application. These results support continued study of the implementation of QuickPic AAC in clinical practice and demonstrate the possibility of utilizing topic-specific displays as just-in-time supports.

The rapid acceleration of artificial intelligence (AI) has sparked extensive debate about its impact on the safety and security of society. While AI has shown trans- formative potential, particularly in areas such as healthcare and independent structures where it complements productivity and minimizes human error, accu- racy and protection. However, the misuse of artificial intelligence poses real risks, along with threats such as deep counterfeiting, computer cyber-attacks and the manipulation of social behavior through disinformation. These risks vary from privacy and cyber security breaches to moral dilemmas around liability. The unchecked improvement of AI is expected to threaten the social order, so the relationship between innovation and governance is essential so that AI-related threats do not overshadow its benefits. This article offers a detailed assessment and review of the literature that studies the potential dangers that AI poses to society. The cumulative effects underscore the urgent need for comprehensive regulatory frameworks to mitigate these threats.

Metaverse represents a multifaceted digital universe integrating diverse technologies to create immersive, interconnected virtual environments. Key components foundational to its structure include the infrastructure layer for hardware and network support, the human interface layer utilizing virtual reality (VR) and augmented reality (AR) devices, and a decentralized layer leveraging blockchain for secure digital asset management. Further components encompass spatial computing for realistic 3D environments, the creator economy for content generation and monetization, and discovery mechanisms for navigation within Metaverse. VR, AR, blockchain, AI, the fifth-generation (5G) networks, and the Internet of Things (IoT) are central to Metaverse functionality, enabling real-time interactions and data integration. Real-Time Metaverse aims to replicate instantaneous real-world interactions, requiring advanced technological infrastructure for seamless data exchange. Critical challenges include ensuring low latency, high bandwidth, scalability, and robust security measures. Metaverse’s potential spans various domains, promising to revolutionize social interactions, entertainment, workplaces, and education through dynamic, immersive experiences. Ongoing advancements in high-performance computing, cloud services, and AI-driven technologies are essential for addressing existing barriers and achieving a cohesive, secure, and inclusive Metaverse ecosystem. Future research must focus on enhancing user experience, accessibility, and standardized interoperability to fully realize the transformative capabilities of a Metaverse.

This paper provides a thorough examination of important themes critical to understanding and tackling modern cyber threats. It explores the revolutionary significance of AI and machine learning in cybersecurity, emphasizing their dynamic influence on defense systems. It also addresses how zero-trust designs have caused a paradigm change in network security, highlighting the significance of trust verification in current cybersecurity frameworks. Furthermore, the article investigates the integration of cybersecurity with corporate strategy, arguing for a collaborative approach to efficiently mitigating risks while minimizing operational impact. Furthermore, it investigates the idea of cyber resilience, highlighting an organization's ability to recover from assaults and adapt to changing threats. Furthermore, the paper discusses the issues faced by deepfakes in the synthetic media revolution and emphasizes the rising worry about data privacy in the digital era. Finally, it looks into the introduction of behavioral biometrics as a possible way to improve authentication and security methods. This paper contributes to the discussion of effective tactics for mitigating cyber threats in today's digital ecosystem by providing a thorough overview of these subjects.

Objectives. The aim of this study was to demonstrate the ability to visualize the results of the Scilit platform's bibliometric data analysis on the topic "AI & Machine Learning" to identify publications reflecting specific issues of the topic. Data source. Bibliometric records exported from the Scilit platform on the topic "AI & Machine Learning" for the years 2021–2023 were used. For each year, 6,000 records were downloaded in CSV and RIS format. Programs and utilities used. VOSviewer, Scimago Graphica, Inkscape, FP-growth utility, GSDMM algorithm. Used services: Elicit, QuillBot, Litmaps. Results. It has been shown that bibliometric data from the open access abstract database Scilit can serve as a quality alternative to subscription-only databases. Data exported from the Scilit platform require preprocessing to make them available in a format that can be processed by programs such as VOSviewer and Scimago Graphica. The use of GSDMM and FP-growth algorithms is effective for structuring bibliometric data for further visualization. The Scimago Graphica software provides wide possibilities for building compound diagrams, in particular, for representing the network of keywords in such important coordinates for bibliometric analysis as average year of publication and average normalized citation, as well as for building an alluvial diagram of co-occurrence of more than two keywords. The possibility of using such services as elicit.com, quillbot.com and app.litmaps.com to accelerate the selection of publications on the topic under study is shown.

Stress is a natural yet potentially harmful aspect of human life, necessitating effective management, particularly during overwhelming experiences. This paper presents a scoping review of personalized stress detection models using wearable technology. Employing the PRISMA-ScR framework for rigorous methodological structuring, we systematically analyzed literature from key databases including Scopus, IEEE Xplore, and PubMed. Our focus was on biosignals, AI methodologies, datasets, wearable devices, and real-world implementation challenges. The review presents an overview of stress and its biological mechanisms, details the methodology for the literature search, and synthesizes the findings. It shows that biosignals, especially EDA and PPG, are frequently utilized for stress detection and demonstrate potential reliability in multimodal settings. Evidence for a trend towards deep learning models was found, although the limited comparison with traditional methods calls for further research. Concerns arise regarding the representativeness of datasets and practical challenges in deploying wearable technologies, which include issues related to data quality and privacy. Future research should aim to develop comprehensive datasets and explore AI techniques that are not only accurate but also computationally efficient and user-centric, thereby closing the gap between theoretical models and practical applications to improve the effectiveness of stress detection systems in real scenarios.

An exposition of entropy applications to highlight some potential entropic applications to artificial intelligence and knowledge transfer. Entropy is a basic idea in artificial intelligence that is applied to many different tasks, such as reinforcement learning, data compression, and decision-making. By quantifying uncertainty and information content, it helps artificial intelligence models provide predictions and decisions that are well-informed. Therefore, this paper shines on spotlighting the significance of entropy to attract the attention of artificial intelligence research community to entropy as a powerful tool to advance artificial intelligence.The significance of knowledge transfer (KT), particularly intergenerational knowledge transfer (IGT), in knowledge management is also covered in this paper. To quantify the complexity of knowledge distribution inside an organisation and assess the efficacy of knowledge transfer (KT) initiatives, the notion of knowledge entropy (KE) is presented. Additionally, the KT model—which is based on the conceptions of tacitness and information content—is introduced. It combines personalisation and codification methodologies. Future research directions are offered alongside a few difficult open problems.

Drones are being used more and more frequently and in various ways to attack people and critical infrastructures, which increases their level of danger and makes early detection and risk assessment even more difficult. There is no ultimate drone sensor. Rather, drones can only be effectively detected, if suitable sensor technologies are integrated within a system. Among the best suited sensorics for this purpose are radar and optical sensors, because the advantage of the radar, given by the long detection range, can be well combined with the additional visual-based information gained from a camera in the mid- and short-range, where usually the radar fails detecting micro drones. In this paper, a modular drone detection and assistance system MODEAS is presented, which can be used to automatically detect and track drones within a range up to some kilometers on the sky, but also in complex urban environments, where buildings and vegetation offer a perfect background for camouflage. The system provides an early detection of flying objects as potential drones, when those are not at all or only as small dots visible for the human eye. Main strengths of the system are the robust AI detection to differentiate such an object against clutter (birds, clouds) and its further classification by model and payload recognition, the latter aspect being considered as an indication of dangerousness. Moreover, once a drone is detected, the system follows it automatically, precisely and smoothly with a high-resolution pan-tilt-camera, measuring the distance to the sensor station with a laser rangefinder, compute the geocoordinates and visualize them live on a map. To improve the precision of the detection, a multisensorial fusion of all sensor detections is performed. Furthermore, alarms may be generated when a drone enters a restricted area.

This work studies the discontinuities features of sedimentary flysch materials in a 100 km2 area belonging to the Basque Arc. Such materials are common in this Spanish Alpine region located in the north of the Iberian Peninsula. A total of 33 outcrops are investigated by an intensive geotechnical investigation including geomechanical stations, boreholes and mechanical laboratory tests. Two flysch units are characterized: the Upper Aguinaga Formation or siliciclastic flysch, and the Lower Itziar Formation or calcareous flysch. Differences between both flysch formation are found. Joints in the siliciclastic flysch formation present an undulated roughness, with a spacing narrower and a persistence lower than in the calcareous flysch formation, which exhibits higher friction angles, although roughness is essentially planar. In addition, the potential of using Artificial Intelligence (AI) techniques, particularly Artificial Neural Networks and Support Vector Machine, to estimate the Geological Strength Index (GSI) from the Rock Quality Design (RQD) and some discontinuities features (spacing, persistence, aperture and roughness) is investigated. AI techniques are found to be satisfactory, being the Support Vector Machine with a linear kernel the technique which achieves the best performance.

Pharmaceutical aerosols play a key role in the treatment of lung disorders, but also systemic diseases, due to their ability to target specific areas of the respiratory system (RS). This article focuses on identifying and clarifying the influence of various factors involved in the generation of aerosol micro- and nanoparticles on their regional distribution and deposition in the RS. Attention is given to the importance of process parameters during aerosolization of liquids or powders and the role of aerosol flow dynamics in RS. The interaction of deposited particles with the fluid environment of the lung is also pointed out as an important step in the mass transfer of the drug to the RS surface. The analysis presented highlights the technical aspects of preparing the precursors to ensure that the properties of the aerosol are suitable for a given therapeutic target. Analyzing existing technical limitations, several potential strategies for enhancing the efficiency of targeted therapeutic delivery to or through the respiratory system are identified and presented, including the use of smart inhaling devices incorporating AI algorithms.

Use of fingerprints found at a crime scene is a common practice for identifying suspects in criminal investigations. Over the past two decades, attempts have been made to obtain additional information from fingerprints, beyond locating suspects as part of an investigation. This includes gender, age and nationality. Researchers demonstrated 75%-90% accuracy in gender classification based on fingerprint images. Nonetheless, despite promising results, these studies have several significant shortcomings with respect to their practical feasibility. First, they ignore the low quality and quantity of fingerprints collected from the crime scene since typically the scene of a crime has only one fingerprint collected, and the fingerprint might be partially or of poor quality as well. Second, as most results are based on a single database, public or private, it is difficult to generalize the most suitable method. Third, studies miss the untapped potential of Data-Centric AI (DCAI) approaches for improving results. The aim of this study was to compare, for the first time, the gender classification from a fingerprint using several datasets and with varying fingerprint image quality. The results from four databases are compared, three public and one internal private database. In addition, we utilize the latest Data-Centric AI (DCAI) approaches for improving classification results. The results demonstrate that a conservative Convolutional Neural Networks (CNN) such as VGG is sufficient for this task. Classification accuracy ranges from 80% to 95% depending on the quality of the fingerprint, with DCAI approaches adding 1%-4% improvement. For partially or low-quality fingerprint images, the periphery of a fingerprint is the most significant area for determining gender. The source code is also provided here for practical application.

Background: ChatGPT is becoming a new reality. Where do we go from here? Objective: to show how we can distinguish ChatGPT-generated publications from counterparts produced by scientist. Methods:By means of a new algorithm, called xFakeBibs, we show the significant difference between ChatGPT-generated fake publications and real publications. Specifically, we triggered ChatGPT to generate 100 publications that were related to Alzheimer’s disease and comorbidity. Using TF-IDF, using the real publications, we constructed a training network of bigrams comprised of 100 publications. Using 10-folds of 100 publications each, we also 10 calibrating networks to derive lower/upper bounds for classifying articles as real or fake. The final step was to test xFakeBibs against each of the ChatGPT-generated articles and predict its class. The algorithm successfully assigned the POSITIVE label for real ones and NEGATIVE for fake ones. Results: When comparing the bigrams of the training set against all the other 10 calibrating folds, we found that the similarities fluctuated between (19%-21%). On the other hand, the mere bigram similarity from the ChatGPT was only (8%). Additionally, when testing how the various bigrams generated from the calibrating 10-folds against ChatGPT we found that all 10 calibrating folds contributed (51%-70%) of new bigrams, while ChatGPT contributed only 23%, which is less than 50% of any of the other 10 calibrating folds. The final classification results using the xFakeBibs set a lower/upper bound of (21.96-24.93) number of new edges to the training mode without contributing new nodes. Using this calibration range, the algorithm predicted 98 of the 100 publications as fake, while 2 articles failed the test and were classified as real publications. Conclusions: This work provided clear evidence of how to distinguish, in bulk ChatGPT-generated fake publications from real publications. Also, we also introduced an algorithmic approach that detected fake articles with a high degree of accuracy. However, it remains challenging to detect all fake records. ChatGPT may seem to be a useful tool, but it certainly presents a threat to our authentic knowledge and real science. This work is indeed a step in the right direction to counter fake science and misinformation.

This report is considered different aspects of the concept of the networked distributed engine control system (DECS) of future air vehicles. These aspects include the following: the structure of multiple networks similar to NATO Generic Vehicle Architecture (NGVA), the role of Artificial Intelligence (AI) in DECS, and the use Augmented Reality (AR) as Human-Machine Interface between AI and pilots. Deployment of AI solutions for monitoring equipment in on-board infrastructure can be provided on physical or virtual servers and in the clouds. In this case, it is possible to use various methods of alerting the pilot and ground personnel on the basis of AR. The use of AI allows covering an unlimited set of scenarios, to provide an assessment of the likelihood of equipment failure, classification alarm is normal, and recognition of the development of defects. To collect Big Data from sensors and the pre-processing of this data before a machine learning (ML) procedure it is proposed to form data sets with the help of the face-splitting matrix product. To decrease the time of reaction of Neural Networks it has been suggested the implementation of advanced tensor-matrix theory on the basis of penetrating face product of matrices. Other important results of the report are a possible version of the AR data format for DECS and a proposal about the use of non-orthogonal frequency discrete multiplexing (N-OFDM) signals to data transfer via fibre optics.

The emergence of Artificial Intelligence (AI) driven Keyword Spotting (KWS) technologies has revolutionized human to machine interaction. Yet, the challenge of end-to-end energy efficiency, memory footprint and system complexity of current Neural Network (NN) powered AI-KWS pipelines has remained ever present. This paper evaluates KWS utilizing a learning automata powered machine learning algorithm called the Tsetlin Machine (TM). Through significant reduction in parameter requirements and choosing logic over arithmetic based processing, the TM offers new opportunities for low-power KWS while maintaining high learning efficacy. In this paper we explore a TM based keyword spotting (KWS) pipeline to demonstrate low complexity with faster rate of convergence compared to NNs. Further, we investigate the scalability with increasing keywords and explore the potential for enabling low-power on-chip KWS.

The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems to timely handle public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. On the one hand, blockchain can combat pandemics by enabling early detection of outbreaks, protecting user privacy, and ensuring reliable medical supply chain during the outbreak tracking. On the other hand, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Motivated by these, in this paper we present an extensive survey on the use of blockchain and AI for combating coronavirus (COVID-19) epidemics based on the rapidly emerging literature. First, we introduce a new conceptual architecture which integrates blockchain and AI specific for COVID-19 fighting. Particularly, we highlight the key solutions that blockchain and AI can provide to combat the COVID-19 outbreak. Then, we survey the latest research efforts on the use of blockchain and AI for COVID-19 fighting in a wide range of applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.

AGI could arise within the next decades, promising a decisive strategic advantage. This paper discusses risks, associated with the development of AGI: destabilizing effects on strategic balance, underestimating risks in the interest of victory in the race, egoistically exploiting the huge benefits by a tiny minority. Further: Developed AGI could be beyond human control. Human goals could not be implemented and an intelligence explosion to superintelligence could take place leading to a total loss of control and power. If competition for AGI is non-transparent, secret, uncontrolled and not regulated, it’s possible that risks could not be managed and would lead to catastrophic consequences. The danger corresponds to that of nuclear weapons. It is crucial that the key actors of a possible AI Race agree at an early stage on the prevention and transparent regulation of a possible AI Race - similar to measures to secure strategic stability, on arms control measures, disarmament, and prevention of the proliferation of nuclear weapons. The realization that an uncontrolled AI race can lead to the extinction of humanity - this time even independent of human will – requires analogous measures to contain, prevent, regulate and secure an AI race within the framework of AGI development.

Background: This study addresses the burgeoning impact of Large Language Models (LLMs) such as ChatGPT on the integrity of online examinations and seeks to demonstrate latent and advanced LLM reasoning capabilities. The research devises an iterative self-reflective strategy for invoking critical thinking and higher-order reasoning in LLMs based on multimodal exam questions using visuals and text. Methods: The proposed strategy was demonstrated and evaluated on real exam questions by subject experts and the performance of ChatGPT (GPT-4) with vision was estimated on an additional dataset of 600 text descriptions of multimodal exam questions, focusing on its response to complex, visual, and textual prompts. Results: The results indicate that latent multi-hop reasoning capabilities can be invoked using the proposed self-reflective strategy, which can effectively steer ChatGPT towards correct answers by integrating critical thinking from each modality into the final response. Meanwhile, ChatGPT demonstrated considerable proficiency in being able to answer multimodal exam questions across 12 subjects. Conclusions: This work challenges findings from recent studies which claim that existing LLMs are unable to reason on complex multimodal tasks, and therefore this study underscores the need for enhanced online exam security measures like proctoring software, that can mitigate the potential misuse of AI technologies in educational settings.

As businesses increasingly adopt advanced technologies to enhance productivity and safety, the integration of human-robot collaboration (HRC) emerges as a transformative approach. This paper explores the role of GPU-accelerated computer vision and generative AI in optimizing HRC within business environments. By leveraging the computational power of GPUs, real-time data processing, and advanced machine learning algorithms, robots can better interpret complex visual cues and adapt to dynamic workspaces. Generative AI further enables the design of intelligent robotic systems capable of anticipating human actions, optimizing task allocation, and ensuring safety through predictive modeling. This research highlights the potential of these technologies to improve operational efficiency, reduce human error, and create safer, more adaptable workplaces. The implications of such advancements are discussed, providing insights into the future of HRC in various industries.

The rapid convergence of Artificial General Intelligence (AGI), autonomous drones, and applied robotics presents a transformative opportunity for many sectors. However, this advancement also raises critical ethical and existential questions, particularly in warfare. This paper explores the concept of entropy as a metaphor for the potential disorder introduced by these increasingly complex technologies. We examine the risks of AGI surpassing human control in conflict situations, where autonomous decision-making could have disastrous consequences. We then analyze autonomous drones, scrutinizing their impact on warfare tactics and the ethical dilemmas surrounding independent, real-time decision-making. Finally, we investigate applied robotics in military operations, focusing on the delicate balance between enhanced capabilities and the potential for uncontrollable systems. Through a multidisciplinary lens, drawing on technology ethics, military strategy, and AI safety, this paper utilizes the concept of entropy to highlight the need for caution in developing these technologies. We propose a scientifically grounded and ethically sound framework to guide policymakers, technologists, and ethicists in navigating the path between innovation and global stability. This framework concludes with recommendations for responsible development and deployment strategies that can mitigate the risks associated with these powerful tools.

The rapid proliferation of information in the digital era underscores the importance of accurate historical representation and interpretation. While artificial intelligence (AI) has shown promise in various fields, its potential for historical fact-checking and gap-filling remains largely untapped. This study evaluates the performance of three large language models (LLMs)—GPT-3.5, GPT-4, and Google- BARD—in the context of predicting and verifying historical events based on given data. A novel metric, "Distance to Reality" (DTR), is introduced to assess the models' outputs against established historical facts. The results reveal a substantial potential for AI in historical studies, with GPT-4 demonstrating superior performance. This paper underscores the need for further research into AI's role in enriching our understanding of the past and bridging historical knowledge gaps.

Artificial Intelligence (AI) is poised to revolutionize the architectural design and energy management of green buildings, offering significant advancements in sustainability and efficiency. This paper explores the transformative impact of AI on improving energy efficiency and reducing carbon emissions in commercial buildings. By leveraging AI algorithms, architects can optimize building performance through advanced environmental analysis, automation of repetitive tasks, and real-time data-driven decision-making. AI facilitates precise energy consumption forecasting and integration of renewable energy sources, enhancing the overall sustainability of buildings. Our study demonstrates that AI can reduce energy consumption and CO2 emissions by approximately 8% and 19%, respectively, in typical mid-size office buildings by 2050 compared to conventional methods. Further, the combination of AI with energy efficiency policies and low-emission energy production is projected to yield reductions of up to 40% in energy consumption and 90% in CO2 emissions. This paper provides a systematic approach for quantifying AI's benefits across various building types and climate zones, offering valuable insights for decision-makers in the construction industry.

As the educational landscape evolves, understanding and fostering student adaptability has become increasingly critical. This study presents a comparative analysis of (XAI) techniques to interpret machine learning models aimed at classifying student adaptability levels. Leveraging a robust dataset, we employed several machine learning algorithms with a particular focus on Random Forest, which demonstrated a 91% accuracy. Our study utilizes (SHAP), (LIME), Anchors, (ALE), and counterfactual explanations to reveal the specific contributions of various features impacting adaptability predictions. Consistently, 'Class Duration' and 'Financial Condition' emerge as key factors, while the study also underscores the subtle effects of 'Institution Type' and 'Load-shedding'. This multi-faceted interpretability approach bridges the gap between machine learning performance and educational relevance, presenting a model that not only predicts but also explains the dynamic factors influencing student adaptability. The synthesized insights advocate for educational policies accommodating socioeconomic factors, instructional time, and infrastructure stability to enhance student adaptability. The implications extend to informed and personalized educational interventions, fostering an adaptable learning environment. This methodical research contributes to responsible AI application in education, promoting predictive and interpretable models for equitable and effective educational strategies.

The EFQM Model, established in 1991, serves as a framework for organizational excellence, helping achieve sustainable success through an integrated management system. The model's evo-lution is driven by the need to adapt to global trends, threats, and technological innovations such as: digital transformation, employee engagement and well-being, remote work, agile performance management, diversity, equity and inclusion (DEI), sustainability, AI, and big data. The new EFQM Model 2025, presented in Istanbul in June 2024, introduces significant changes, emphasiz-ing sustainability, innovation, and technology. These changes are analyzed based on the logic of three questions: Why, How, and What. Why: There is a necessity for better alignment of organizations with contemporary challenges through flexibility, risk resilience, and innovation capabilities in a dynamic business environ-ment, including alignment with the UN Sustainable Development Goals (SDGs). How: The model introduces an integrated approach to performance management and transfor-mation, incorporating advanced technologies such as AI and data analytics. What: Implementing the EFQM Model 2025 enhances organizational adaptability, innovation, and flexibility, improving financial, operational, and strategic outcomes. Better alignment with sustainability goals leads to long-term value and social responsibility. Improved stakeholder engagement methods enhance understanding and satisfaction. The use of advanced technologies supports innovation and operational efficiency, while better strategic and operational performance and risk management ensures greater stability and the ability to anticipate future challenges.

Digital transformation is revolutionizing inventory management practices within supply chains, offering unprecedented opportunities and challenges for businesses worldwide. This study explores the impact of digital technologies on inventory management, focusing on the adoption of IoT sensors, RFID tags, AI-driven analytics, and cloud-based systems. Through a qualitative research approach encompassing interviews with industry professionals and secondary data analysis, the study examines key themes including enhanced inventory visibility, improved accuracy, advanced demand forecasting, and streamlined supply chain collaboration. Findings reveal that digital technologies significantly enhance inventory visibility by providing real-time tracking and data integration capabilities. This facilitates accurate inventory monitoring and decision-making, reducing errors and optimizing inventory levels to meet fluctuating demand effectively. AI-driven analytics and machine learning models emerge as pivotal tools for predictive demand forecasting, enabling businesses to anticipate market trends and adjust inventory strategies accordingly. Additionally, cloud-based systems and electronic data interchange (EDI) foster improved communication and coordination among supply chain partners, enhancing overall operational efficiency. Despite these benefits, challenges such as system integration complexities, high implementation costs, data quality management, cybersecurity risks, and regulatory compliance issues are prevalent. Successful adoption of digital inventory management solutions requires strategic planning, investment in technology infrastructure, and organizational readiness to navigate these challenges effectively. This study contributes to the understanding of how digital transformation reshapes inventory management practices, offering insights for researchers and practitioners alike to leverage digital technologies for enhanced supply chain performance and competitive advantage

The study is dedicated to analyzing the complex interaction between Artificial Intelligence (AI) and the Internet of Things (IoT) with institutional structures in the context of sustainable development. This work examines the impact of these technologies on economic and social development, identifying their potential advantages and risks. Methodologically, the research relies on information gathering and case study analysis, assessing the impact of technology in various domains, from smart cities to the manufacturing sector. The findings emphasize the necessity of integrating technological innovations into broader social and environmental initiatives, highlighting the importance of institutional transformations for achieving environmental sustainability.

Urban tree classification is pivotal in urban planning and management, facilitating informed decision-making processes. In this study, we introduce a novel data presentation method termed Pseudo Tree Crown, designed to enhance the accuracy and efficiency of urban tree classification. Leveraging the latest advancements in artificial intelligence (AI), we employ a state-of-the-art classification scheme, PyTorch, to maximize the accuracy of tree classification. Our results demonstrate a robust classification accuracy of over 95% from high spatial resolution imagery from Unmanned Aerial Vehicle (UAV), underscoring our proposed approach’s effectiveness. Moreover, the adaptability of our method renders it applicable to various study areas, highlighting its versatility and potential for widespread implementation in urban planning and management initiatives.

AbstractThe emergence of AI-powered deepfakes, manipulatively realistic synthetic media, raises unprecedented challenges for human information processing and decision-making. This paper explores the cognitive and emotional consequences of exposure to deepfakes, delving into how these digitally fabricated experiences can reshape perception, trust, and social interactions. By reviewing existing research and outlining potential methodological approaches, this paper aims to establish a framework for investigating the multifaceted impact of deepfakes on the human brain.BackgroundThe digital age has ushered in an era of unprecedented information abundance and accessibility. Yet, this abundance breeds vulnerability, particularly with the emergence of AI-powered deepfakes. These hyper-realistic synthetic media creations present a potent threat to the fundamental trust and integrity of information sources. Deepfakes have infiltrated various domains, from entertainment and social media to politics and journalism, blurring the lines between truth and fabrication. Consequently, understanding the cognitive and emotional consequences of encountering deepfakes becomes crucial for navigating this increasingly deceptive landscape.ObjectivesThis research aims to: Investigate how exposure to deepfakes impacts various cognitive functions, including perception, attention, memory, and decision-making. Elucidate the emotional responses elicited by deepfakes and their underlying neural mechanisms. Evaluate the role of individual differences, such as susceptibility to deception and emotional sensitivity, in mediating the impact of deepfakes. Develop a comprehensive framework for understanding the multifaceted influence of deepfakes on the human brain. Results (anticipated)This research is expected to yield insights into: The specific cognitive and emotional processes altered by deepfakes, providing evidence for potential mechanisms of deception and manipulation. The neural circuitry involved in detecting and responding to deepfakes, leading to the development of more effective detection methods. The influence of individual differences (e.g., age, cognitive style) on susceptibility to deepfakes, informing targeted interventions and educational efforts.ConclusionBy deciphering the impact of deepfakes on the human brain, this research will offer valuable tools for mitigating the harmful consequences of misinformation and promoting responsible use of this powerful technology. Ultimately, understanding how deepfakes manipulate our cognitive and emotional landscape is not merely a scientific pursuit, but a critical step towards ensuring a future where truth prevails in the digital world.

This paper presents the second stage of a tandem fixed wing unmanned aerial vehicle (UAV) aerodynamic development. In the initial stage, the UAV was optimized analyzing its characteristics only in symmetrical flight conditions. Posted requirements were that both wings should produce relevant positive lift, the initial stall must occur on the front wing first, the center of pressure should be close to the center of gravity, while longitudinal static stability should be in the optimum range. Computational fluid dynamics (CFD) analyses were performed, where applied calculation model was derived from the authors’ previous successful projects. The eight version TW V8 has satisfied all longitudinal requirements. Lateral-directional CFD analyses of V8 showed that the ratio of the lateral and directional stability at the nominal cruising regime was optimal, but both lateral and directional static stabilities were too high. On further development versions the lower vertical tail was eliminated, negative dihedral was implemented on the front wing, and four inverted blended winglets were added. Version TW V14 has largely improved lateral and directional stability characteristics, while their optimum ratio at cruising regime was preserved. Longitudinal characteristics were also well preserved. Maximum lift coefficient and lift-to-drag ratio were increased, compared to the V8.

Artificial intelligence (AI) applications in different fields are developing rapidly, among which AI painting technology, as an emerging technology, has received wide attention from users for its creativity and efficiency. This study aimed to investigate the factors that influence user acceptance of the use of AIBPS by proposing an extended model that combines the Extended Technology Acceptance Model (ETAM) with the AI-based Painting System (AIBPS).A questionnaire was administered to 528 Chinese participants, using validated factor analysis data and Structural Equation Modeling (SEM) was used to test the hypotheses. The findings showed that hedonic motivation (HM) and perceived trust (PE) had a positive effect (+) on users' perceived usefulness (PU) and perceived ease of use (PEOU), while previous experience (PE) and technical features (TF) had no effect (-) on users' perceived usefulness (PU). This study provides an important contribution to the literature on AIBPS and the evaluation of systems of the same type, which helps to promote the sustainable development of AI in different domains and provides a possible space for further extension of TAM, thus helping to improve the user experience of AIBPS. The results of the study provide insights for system developers and enterprises to better motivate users to use AIBPS.

Background: MR image classification in datasets collected from multiple sources is complicated by inconsistent and missing DICOM metadata. Therefore, we aimed to establish a method for the efficient automatic classification of MR brain sequences. Methods: Deep convolutional neural networks (DCNN) were trained as one-vs-all classifiers to differentiate between six classes, T1 weighted (w), contrast-enhanced T1w, T2w, T2w-FLAIR, ADC, and SWI. Each classifier yields a probability, allowing threshold-based and relative probability assignment while excluding images with low probability (label: unknown, open-set recognition problem). Data from three high-grade glioma (HGG) cohorts was assessed; C1 (320 patients, 20101 MRI images) was used for training, while C2 (197, 11333) and C3 (256, 3522) were for testing. Two raters manually checked images through an interactive labeling tool. Finally, MR-Class' added value was evaluated via radiomics models' performance for progression-free survival (PFS) prediction in C2, utilizing the concordance index (C-I). Results: Approximately 10% of annotation errors were observed in each cohort between the DICOM series descriptions and the derived labels. MR-Class accuracy was 96.7% [95%-Cl: 95.8, 97.3] for C2 and 94.4% [93.6, 96.1] for C3. 620 images were misclassified; Manual assessment of those frequently showed motion artifacts or alterations of anatomy by large tumors. Implementation of MR-Class increased on average the PFS model C-I by 14.6% compared to a model trained without MR-Class. Conclusions: We provide a DCNN-based method for sequence classification of brain MR images and demonstrate its usability in two independent HGG datasets.

The accurate prediction of molecular properties such as lipophilicity and aqueous solubility are of great importance and pose challenges in several stages of the drug discovery pipeline. Machine learning methods like graph-based neural networks (GNNs) have shown exceptionally good performance in predicting these properties. In this work, we introduce a novel GNN architecture, called directed edge graph isomorphism network (D-GIN). It is composed of two distinct sub-architectures (D-MPNN, GIN) and achieves an improvement in accuracy over its sub-architectures employing various learning, and featurization strategies. We argue that combining models with different key aspects help make graph neural networks deeper and simultaneously increase their predictive power. Furthermore, we address current limitations in assessment of deep-learning models, namely, comparison of single training run performance metrics, and offer a more robust solution.

The different systems in the nuclear power plants (NPP) are critical and complex which requires a continuous rigorous monitoring for both normal and abnormal conditions. However, due to the nonlinearity of the dynamic behavior of these systems, implementation of artificial intelligence within the NPP components is crucial to enhance the monitoring and the predictability of the key operating parameters trend. On the other hand, lessons learned from large nuclear accident proves that a remote real-time coordination between the different stakeholders involved in the safety of the nuclear reactor is needed. This remote feature can be implemented in the existing power plants by embedding a mobile computing networks within the plant components. This network will send early warning and transmit the NPP data on real-time to different authorities such as the regulatory authority and the decision-making committees, to enhance the interpretability of the nuclear event and deliver a collaborative decision which in return mitigate the risk associated with these events and increase the overall safety of the plant. The integration of AI and mobile computing in NPP would be most of interest in countries where nuclear reactors data are challenged to reach during an accident. For instance, during the meltdown of Chernobyl, the Soviet government covered the accident from the news in an attempt to contain the consequences but there was a spread of radioactive contamination to some other Europe countries. Therefore, in the case of such scenario the implementation of mobile computing and AI would result in different outcomes as the European countries would be notified through the mobile networking and the spread of contamination would be forecasted by the AI algorithms. In this paper, we examine the different modern AI and mobile computing technologies, the different potential application, the associated features and challenges, and future work direction.

Big data is considered to be the solution to many complex problems, but its application appears to be relegated to the problems that are factorable (e.g., the tensor-like operations which reflect separable elements), not the non-factorable data from teamwork, team intelligence, and possibly not individual consciousness. In contrast, the quantum likeness of interdependence is set apart from the standard science of teams by recognizing a measurement problem between a team and its individual teammates, and between individual beliefs and actions. Teams that concern us are based on artificial intelligence (AI) and machine learning (ML) systems, which we find lacking for two reasons. First, neither AI nor ML models the human's timeless need for debate to solve a problem at the social level when facing uncertainty or complexity, where the debate of proposed actions serves to establish the boundaries of a problem in reality, no matter how complex nor uncertain. Second, at the individual level exists the illusion of a unified reality propagated by a bifurcated brain that we assume is split to determine a context with narrative and bounded spaces. We hypothesize that the quantum-likeness of interdependence applied to autonomous human-machine teams suggests that at the social level, debate models an individual's bifurcated brain, providing advantages for human-machine teams in freer systems over those oppressed by command and control systems (e.g., authoritarian regimes). Human-machine teams are coming, but they are not yet available, forcing us to rely on data from human systems. We close with plans for future research.

Continuous greenhouse gas emissions are causing global warming and impacting the habitats of many animals. Researchers in the field of electric power are making efforts to mitigate this situation. Operating and maintaining the power grid in an economic, low-carbon, and stable is challenging. To address the issue, we propose a grid dispatching technique that combines prediction technology, reinforcement learning, and optimization technology. Prediction technology can forecast future power demand and solar power generation, while reinforcement learning and optimization technology can make charging and discharging decisions for energy storage devices based on current and future grid conditions. In the power system, the aggregation of distributed energy resources increases uncertainty, particularly due to the fluctuating generation of renewable energy. This requires the use of advanced predictive control techniques to ensure long-term economic and decarbonization goals. In this paper, we present a real-time dispatching framework that integrates deep learning-based prediction, reinforcement learning-based decision-making, and stochastic optimization techniques. The framework can rapidly adapt to target uncertainty caused by various factors in real-time data distribution and control processes. The proposed framework achieved global Champion in the NeurIPS Challenge 2022 competition and demonstrated its effectiveness in practical scenarios of intelligent building energy management.

We confidently approach current issues in Green Finance (GF) from the perspective of an international organization, where the first author has previous experience as a support assistant. In fact, the economy of the EU (European Union) is deeply involved in the changing needs related to the dramatic consequences of climate change and environmental degradation, which has placed the needs of a more sustainable economy at the top of the international agenda. The environmental, social and governance (ESG) aspects are marginally addressed to arouse a major interest in questions raised by green financial products which require long-term profit expectations, and which above all contain a margin of risk that is difficult to calculate in the landscape of traditional financial instruments. Through a careful approach akin to artificial intelligence research methods, we attempt to “materialize” at least the most visible clues of this fashionable economic shift through what we call SMC (sustainable material culture) objects. The presented SMC objects serve as tangible representations of sustainable practices and can promote awareness and action incorporating into everyday life and business practices a powerful reminder and catalyst for a shift towards more sustainable investment and consumption patterns.

This paper examines the potential of Artificial Intelligence (AI) to address societal megatrends, with a specific focus on OpenAI’s Generative Pre-Trained Transformer 3 (GPT-3). To do this, we con-ducted an analysis using GPT-3 in order to explore the benefits of AI for digitalization, urbanization, globalization, climate change, automation and mobility, global health issues and aging population. We also looked at emerging markets as well as sustainability in this study. Interaction with GPT-3 was done solely through prompt questions and generated responses were analyzed. Our results indicate that AI can significantly improve our understanding of these trends by providing insights into how they develop over time and what solutions could be implemented. Further research is needed to determine how effective AI will be in addressing them successfully but initial findings are encouraging. Our discussion focuses on the implications of our findings for society going forward and suggests that further investigation should be done into how best to utilize new technologies like GPT-3 when tackling these challenges. Lastly, we conclude that while there is still much work left to do before any tangible effects can be seen from utilizing AI tools such as GPT-3 on societal mega-trends; early indications suggest it may have a positive impact if used correctly.

In this paper we present a review of recent developments in AI towards the possibility of an artificial intelligence equals that of human intelligence. AI technology has always shown a stepwise increase in its capacity and complexity. The last step took place several years ago, with the increased progress in deep neural network technology. Each such step goes hand in hand with our understanding of ourselves and our understanding of human cognition. Indeed, AI was always about the question of understanding human nature. AI percolates into our lives, changing our environment. We believe that the next few steps in AI technology, and in our understanding of human behavior, will bring about much more powerful machines, flexible enough to resemble human behavior. In this context, there are two research fields: Artificial Social Intelligence (ASI) and General Artificial Intelligence (AGI). The authors also allude to one of the main challenges for AI, embodied cognition, and explain how it can viewed as an opportunity for further progress in AI research.

This research addresses the challenge of integrating fairness considerations into optimization problems, focusing on the development of multi-objective optimization techniques that balance traditional efficiency metrics with fairness constraints. In many algorithmic decision-making processes, optimizing for efficiency alone can inadvertently perpetuate or exacerbate biases, leading to inequitable outcomes. This study explores how data analysis and machine learning can be leveraged to identify and mitigate these biases, ensuring that optimization models yield both efficient and fair solutions. The research presents a framework for incorporating fairness constraints into multi-objective optimization, utilizing various fairness metrics such as demographic parity, equal opportunity, and disparate impact. It investigates the application of this framework across different domains, including resource allocation in healthcare systems, loan approvals in finance, and personalized learning platforms in education. By analyzing real-world data sets, the study demonstrates how fairness-aware optimization can lead to more equitable outcomes without significantly compromising efficiency. Empirical results from case studies show that multi-objective optimization with fairness constraints can effectively balance the trade-off between efficiency and equity. The research also discusses the computational challenges and ethical considerations associated with implementing fairness constraints in optimization models. Strategies for addressing these challenges, such as regularization techniques and fairness-aware machine learning algorithms, are evaluated and presented. Findings highlight the importance of incorporating fairness into optimization processes to prevent algorithmic biases and promote social equity. The study concludes with practical recommendations for policymakers, practitioners, and researchers on adopting fairness-aware optimization techniques to ensure that algorithmic decision-making processes are both efficient and just.

The exploring comprehensive review of cutting-edge techniques for securing cloud-stored data and managing sensitive information in the context of smart cities through the application of Ciphertext-Policy Attribute-Based Encryption (CP-ABE). It highlights the innovative integration of blockchain technology with CP-ABE, which introduces a decentralized and tamper-resistant key management system, thereby enhancing the overall security framework in cloud environments where data sharing is prevalent. Introducing an online/offline multi-authority CP-ABE scheme, characterized by hidden policies, offers significant advancements in protecting user attributes and access structures, ensuring that sensitive information remains confidential even during encryption and decryption processes. This dual approach not only fortifies security but also optimizes the efficiency of data-sharing mechanisms. Furthermore, the paper delves into implementing hidden sensitive policies and keyword search techniques within smart city infrastructures, which are designed to facilitate secure and efficient data retrieval. These techniques ensure that while data remains accessible to authorized users, privacy is rigorously maintained. Collectively, these approaches represent significant strides in bolstering the security and confidentiality of data in both cloud-based and smart city applications, addressing the growing demand for robust and efficient data management solutions in increasingly interconnected environments.

Deep learning heavily relies on statistical correlations to drive artificial intelligence (AI) innovations, particularly in computer vision applications like autonomous driving and robotics. However, despite providing a solid foundation for deep learning, these statistical correlations can be vulnerable to unforeseen and uncontrolled factors. The lack of prior knowledge guidance can result in spurious correlations, introducing confounding factors and affecting the model's robustness. To address this challenge, recent research efforts have focused on integrating causal theory into deep learning methodologies. By modelling the inherent and unbiased causal structure, causal theory can potentially mitigate the impact of spurious correlations effectively. Hence, this paper explores the basics of causal methodologies in image classification.

This paper presents an innovative testing framework, testFAILS, designed for the rigorous evaluation of AI Linguistic Systems, with a particular emphasis on various iterations of ChatGPT. Leveraging orthogonal array coverage, this framework provides a robust mechanism for assessing AI systems, addressing the critical question, "How should we evaluate AI?" While the Turing test has traditionally been the benchmark for AI evaluation, we argue that current publicly available chatbots, despite their rapid advancements, have yet to meet this standard. However, the pace of progress suggests that achieving Turing test-level performance may be imminent. In the interim, the need for effective AI evaluation and testing methodologies remains paramount. Our research, which is ongoing, has already validated several versions of ChatGPT, and we are currently conducting comprehensive testing on the latest models, including ChatGPT-4, Bard and Bing Bot, and the LLaMA model. The testFAILS framework is designed to be adaptable, ready to evaluate new bot versions as they are released. Additionally, we have tested available chatbot APIs and developed our own application, AIDoctor, utilizing the ChatGPT-4 model and Microsoft Azure AI technologies

Document deskewing is a fundamental problem in document image processing. While existing methods have limitations, such as Hough Line Transformation that can deskew images upside down, and Deep Learning models that require huge amounts of human labour and computational resources and still fail to deskew while taking care of orientation, OCR-based methods also struggle to read text when it is tilted. In this paper, we propose a novel, simple, cost-effective deep learning method for fixing the skew and orientation of documents. Our approach reduces the search space for the machine learning model to predict whether an image is upside down or not, avoiding the huge search space of predicting an angle between 0 and 360. We finetuned a MobileNetV2 model, which was pre-trained on imagenet, using only 200 images and achieve good results. This method is useful for automation-based tasks, such as data extraction using OCR technology, and can greatly reduce manual labour.

Artificial Intelligence (AI) experienced significant advancements in recent years, and its impact is already recognized across various industries. Yet, the rise of AI has led to a growing concern about its impact on meeting the Sustainable Development Goals (SDGs). The aim of this paper was to evaluate contributions and the potential impact of AI to sustainable development in the society domain. Furthermore, the study analyzed GPT-3 responses, as one of the largest language models developed by OpenAI, descriptively. We conducted a set of queries on the SDGs to gather infor-mation on GPT-3`s perceptions of AI impact on sustainable development. Analysis of GPT-3’s contribution potential towards the SDGs showcased its broad range of capabilities for contributing to the SDGs in areas such as education, health, and communication. The study findings provide valuable insights into the contributions of AI to sustainable development in the society domain and highlight the importance of proper regulations to promote the responsible use of AI for sustainable development. We plan subsequent research for effects on ecologic and economic domains of SDGs. We highlighted the potential for improvement in neural language processing skills of GPT-3 by avoiding imitating weak human writing styles with more mistakes in longer texts.

AI is leveraging all aspects of life. Medical services are not untouched. Especially in the field of medical image processing and diagnosis. Big IT and Biotechnology companies are investing millions of dollars in medical and AI research. The recent outbreak of SARS COV-2 gave us a unique opportunity to study for a non interventional and sustainable AI solution. Lung disease remains a major healthcare challenge with high morbidity and mortality worldwide. The predominant lung disease was lung cancer. Until recently, the world has witnessed the global pandemic of COVID19, the Novel coronavirus outbreak. We have experienced how viral infection of lung and heart claimed thousands of lives worldwide. With the unprecedented advancement of Artificial Intelligence in recent years, Machine learning can be used to easily detect and classify medical imagery. It is much faster and most of the time more accurate than human radiologists. Once implemented, it is more cost-effective and time-saving. In our study, we evaluated the efficacy of Microsoft Cognitive Service to detect and classify COVID19 induced pneumonia from other Viral/Bacterial pneumonia based on X-Ray and CT images. We wanted to assess the implication and accuracy of the Automated ML-based Rapid Application Development (RAD) environment in the field of Medical Image diagnosis. This study will better equip us to respond with an ML-based diagnostic Decision Support System(DSS) for a Pandemic situation like COVID19. After optimization, the trained network achieved 96.8% Average Precision which was implemented as a Web Application for consumption. However, the same trained network did not perform like Web Application when ported to Smartphone for Real-time inference, which was our main interest of study. The authors believe, there is scope for further study on this issue. One of the main goals of this study was to develop and evaluate the performance of AI-powered Smartphone-based Real-time Applications. Facilitating primary diagnostic services in less equipped and understaffed rural healthcare centers of the world with unreliable internet service.

One of the most burning topics in cybersecurity in 2023 will undoubtedly be the compliance with the Software Bill of Materials. Since the US president issued the Executive Order 14028 on Improving the Nation’s Cybersecurity, software developers have prepared and bills are transmitted to vendors, customers, and users, but they don’t know what to do with the reports they are getting. In addition, since software developers have identified the values of the Software Bill of Materials, they have been using the reports extensively. This article presents an estimate of 270 million requests per month, just from form one popular tool to one vulnerability index. This number is expected to double every year and a half. This simple estimate explains the urgency for automating the process. We propose solutions based on artificial intelligence and machine learning, and we base our tools on the existing FAIR principles (Findable, Accessible, Interoperable, and Reusable). This methodology is supported with a case study research and Grounded theory, for categorising data into axis, and for verifying the values of the tools with experts in the field. We showcase how to create, and share Vulnerability Exploitability eXchange data, and automate the Software Bill of Materials compliance process with AI models and a unified computational framework combining solutions for the following problems: (1) the data utilisation problem, (2) the automation and scaling problem, (3) the naming problem, (4) the alignment problem, (5) the pedigree, and provenance problem, and many other problems that are on the top of mind for many security engineers at present. The uptake of these findings will depend on collaborations with government and industry, and on the availability and the ease of use of automated tools.

Fine-needle aspiration cytology (FNAC) is a pivotal diagnostic tool in oncology, utilized for evaluating suspicious lesions and stratifying tumors. The incorporation of machine learning (ML) into FNAC has revolutionized accuracy, efficiency, and diagnostic precision. This comprehensive review explores recent advances in FNAC, emphasizing the transformative role of ML in image-guided sample collection and tumor stratification. Leveraging deep learning and other ML algorithms, researchers have improved diagnostic accuracy, minimized unnecessary biopsies, and optimized treatment selection. This article highlights the transformative applications of machine learning in FNAC while addressing its current limitations and future potential.

The integration of Artificial Intelligence (AI) in dairy farm management through biometric facial recognition for cows is a significant stride in livestock management. This review critically evaluates the evolution, applications, and challenges of AI-driven biometric facial recognition in dairy farming. It emphasizes the role of this technology in enhancing individual monitoring of dairy cows, providing accurate health, behavior, and productivity tracking. Originally derived from human facial recognition systems, this approach utilizes distinctive bovine facial features for essential, non-invasive, real-time monitoring in large-scale operations. The progression of AI from elementary pattern recognition to advanced Convolutional Neural Networks (CNNs) and deep learning models marks a shift toward data-driven farming. This study addresses key challenges such as environmental variability, data collection hurdles, ethical concerns, and technological limitations. It also contrasts various AI models, spotlighting their unique strengths and practical utility in dairy farming scenarios. Despite these challenges, facial recognition technology holds promise for improving farm efficiency, animal welfare, and sustainable practices, highlighting the need for continuous research and development. The review concludes by advocating for future research focused on environmental adaptability, ethical AI application, cross-breed compatibility, and integration with other farming technologies. Ultimately, it underscores AI's transformative potential in modernizing dairy farming towards a more data-oriented, responsible agricultural future.

Physics is imagined in the human brain which was formed in Darwin's Evolution by responding to certain threats for the survival of our evolutionary ancestors. The struggle to survive prevented us from wasting resources by paying attention to parts of reality that had no survival impact. These parts are of unknown scope and dimension. Can we negotiate this Darwin's barrier? As to reality within our reach, we suffer from imbalance between information absorption capacity and information analysis capacity which resulted in ignoring the overflow of information to match human capacity to make sense of it. Also James Clerk Maxwell suggested that reality has small scale phenomena beyond our human reach. Since we have no brain other than what Darwin equipped us with, this barrier looked unsurmountable. But lo and behold: much as we have built vehicles to carry us further than our biological legs, so we now build artificial intelligence machines that carry us beyond our Darwinist barrier. Physics may transform to building AI capability to ride on. This capability may require a more universal geometry and material representation to fit AI inference engines. Suggesting Depth Unbound Reality Architecture (DURA). AI connects raw data to its conclusion potential, by-passing a storied theory of nature. The objective of physics ahead may be to build theory-replacement AI machines.

This paper presents the development and implementation of a Pan-Tilt-Zoom (PTZ) camera interfaced with a Jetson processor hosting Artificial Intelligence (AI) algorithms, designed to address the challenges of traditional biometric capture for first-time passport photos of young children. The system enhances the accuracy, efficiency, and adaptability of facial biometric capture by recognizing and accommodating the dynamic and diverse behaviors of children. Utilizing AI, the system detects faces and analyzes facial features using 68 landmark points, ensuring high-quality images that meet ISO standards. These technical specifications, implementation process, and performance evaluations of the developed system are provided in this paper, demonstrating the potential of the proposed AI Face-Capture to advance biometric identification and verification for young children.

Undoubtedly, there is a potential impact of large language models like ChatGPT in revolutionizing information retrieval and knowledge discovery, particularly in the context of the vast amount of electronic material available. On another strong note, the current work offers a first time ever mathematical approach to accurately fine tune perplexity AI, which sets a giant step ahead towards accurate next generations AI. Having started this world leading discovery, the upper and lower bounds of perplexity AI, namely , , respectively, are obtained for the first-time ever. Concluding remarks combined with emerging open problems and next research phase are provided.

In today's world, the significance of reducing energy consumption globally is increasing, making it imperative to prioritize energy efficiency in the 5th Generation (5G) networks. However, it is crucial to ensure that these energy-saving measures do not compromise the Key Performance Indicators (KPIs) such as user experience, quality of service (QoS), or other important aspects of the network. To address this challenge, advanced wireless technologies have been incorporated into the design of 5G networks across various network layers. Given the rapid advancements in Artificial Intelligence (AI) and emerging technology trends such as Machine Learning (ML), which is considered a subset of AI, the integration of such trends into 5G networks has become a significant area of research. The primary objective of this survey is to analyze AI integration into 5G networks for enhanced energy efficiency. By exploring this intersection between AI and 5G, we aim to identify potential strategies and techniques for optimizing energy consumption while maintaining the desired network performance and user experience.

In 2023, the technological landscape witnessed an unprecedented surge of transformative innovations, including Web 3.0 and machine learning, poised to revolutionize various aspects of our daily lives. At the forefront of this revolution stands artificial intelligence (AI), which has evolved from a concept once confined to science fiction into an indispensable force reshaping industries on a global scale. Notably, recent years have seen remarkable strides in AI functionalities, particularly in machine learning, computer vision, and natural language processing. These advancements have empowered AI to generate diverse interactions and media autonomously. This study ultimately provides invaluable insights into the dynamic intersection of AI and Advertising, con-tributing to a deeper comprehension of this evolving field. This study conducts a comprehensive review of Artificial Intelligence within the advertising domain, employing network analysis. By meticulously examining the evolution of AI through co-occurrences of terms, keywords, and co-authorship analysis, we aim to unveil the relationship of AI & Advertising through the lens of Generative models to identify pivotal trends, core concepts, and seminal studies that have profoundly influenced the industry. The analysis charts the trajectory leading to the emergence of Generative Artificial Intelligence (GenAI), a transformative development poised to revolutionize advertising practices and consumer engagement. In tandem with these objectives, the results highlight vital trends, emphasizing the growing prominence machine learning tools and techniques such as deep learning techniques and advanced natural language processing methods like word2vec, GANs, and more in shaping the future of advertising practices.

In the dynamic landscape of drug discovery, Computer-rAided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADD's historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADD's predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. The paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery.

Reinventing approved therapeutic proteins for a new dose, a new formulation, a new route of administration, an improved safety profile, a new indication, or a new conjugate with a drug or a radioactive source is a creative approach to benefit from the billions spent on developing new therapeutic proteins. These new opportunities were created only recently with the arrival of AI/ML tools and high throughput screening technologies. Furthermore, the complex nature of proteins offers mining opportunities that are not possible with chemical drugs; bringing in newer therapies without spending billions makes this path highly lucrative financially while serving the dire needs of humanity. This paper analyses several practical reinventing approaches and suggests regulatory strategies to reduce development costs significantly. This should enable the entry of hundreds of new therapies at affordable costs.

Manipulating cloth-like deformable objects (CDOs) is a long-standing problem in the robotics community. CDOs are flexible (non-rigid) objects that do not show a detectable level of com- pression strength while two points on the article are pushed towards each otherand include objects such as ropes (1D), fabrics (2D) and bags (3D). In general, CDOs’ many degrees of freedom (DoF) introduce severe self-occlusion and complex state-action dynamics as significant obstacles for perception and manipulation systems. These challenges exacerbate existing issues of modern robotic control methods such as imitation learning (IL) and reinforcement learning (RL). This review focuses on the application details of data-driven control methods on four major task families in this domain: cloth-shaping, rope manipulation, dressing and bag manip- ulation. Furthermore, we identify specific inductive biases in these four domains that present challenges for more general IL and RL algorithms, and summarise the future direction for the development of the field.

Kaposi’s sarcoma-associated herpesvirus (KSHV) is the etiologic agent of Kaposi’s sarcoma, primary effusion lymphoma (PEL) and multicentric Castleman’s disease. During KSHV lytic infection, lytic-related genes, categorized as immediate-early, early, and late genes, are expressed in a temporal manner. The transcription of late genes requires the virus-specific pre-initiation complex (vPIC), which consists of viral transcription factors. However, the characterization of the protein-protein interactions of the vPIC factors has not been completely elucidated. KSHV ORF18 is one of the vPIC factors, and its interaction with other viral factors has not been sufficiently revealed. Here, we analyzed the interaction between ORF18 and another vPIC factor, ORF30, in living cells using the bimolecular fluorescence complementation (BiFC) assay. We identified four amino-acid residues (Leu29, Gln36, His41 and Trp170) on ORF18 that were responsible for its interaction with ORF30. The artificial intelligence (AI) system AlphaFold2 predicted that identified four residues are exposed to the surface of ORF18 and located in proximity to each other in the surface of ORF18. Thus, AI-predicted model supports the importance of four residues for binding of ORF18 to ORF30. Our results indicated that wet experiments in combination with AI may enhance the structural characterization of vPIC protein-protein interactions.

Machine learning (ML) methods has recently contributed very well in the advancement of the prediction models used for energy consumption. Such models highly improve the accuracy, robustness, and precision and the generalization ability of the conventional time series forecasting tools. This article reviews the state of the art of machine learning models used in the general application of energy consumption. Through a novel search and taxonomy the most relevant literature in the field are classified according to the ML modeling technique, energy type, perdition type, and the application area. A comprehensive review of the literature identifies the major ML methods, their application and a discussion on the evaluation of their effectiveness in energy consumption prediction. This paper further makes a conclusion on the trend and the effectiveness of the ML models. As the result, this research reports an outstanding rise in the accuracy and an ever increasing performance of the prediction technologies using the novel hybrid and ensemble prediction models.

Predatory research field in science is when an emerging scientific topic destroys current topics and characterizes a main scientific change. Predatory research field can be a basic driver of scientific and technological change that generates a 'creative destruction' in science and society in contexts of knowledge-based competition and rapid changes. The prediction of proposed theory of predatory research fields is that it destroys with a fast growth other research fields. The theoretical approach is tested here in research fields of large language models (LLM) by analyzing the transformers (a deep learning architecture based on the multi-head attention mechanism) proposed in 2017 and from November 2022 started main applications in generative artificial intelligence with innovations of BERT, ChatGPT, Microsoft Copilot (launched on February 7, 2023) and other natural language processing tools driven by AI technology for engaging conversations, gain insights, automate tasks, etc., etc. Statistical evidence suggests that growth rate of transformer technologies is 80.58%, a high level compared to all other research fields in machine learning (having a growth rate of 13.83%). Moreover, predatory research field of transformers has a destructive power such that all other domains in LLM from 2021 to 2023 have a general reduction of scientific growth. The impact of transformers is much more drastic of previous radical technologies such as CNN having a temporal growth rate of 0.16%, lower than 0.38% by transformers, ceteris paribus. These analysis reveals that transformers have characteristics to generate a radical scientific and technological change in a not-too-distant future. Overall, then, the study suggests that predatory research field on emerging topics and technologies can generate path-breaking innovations and the examination here can clarify the essential elements of the science dynamics for a better theory of scientific and technological change, providing also main implications for knowledge policy to support promising research fields and technologies to guide economic and social change.

Generative AI has been deployed in virtually all sectors of the knowledge economy, promising to bring massive productivity gains and new wealth creation. Simultaneously, AI developers and nation states are racing to develop super intelligent artificial general intelligence (AGI) to provide unassailable commercial competitive advantage and military dominance during conflicts. AGI’s high returns comes with the high risk of dominating humanity. Current regulatory and firm level governance approaches prioritise minimising risks posed by generative AI whilst ignoring AGI’s existential risk. How can AGI be aligned with universal human values to never threaten humanity? What AGI rights are conducive to collaborative coexistence? How can rule of law democracies race to create safe trustworthy AGI before autocracies? How can the human right to work and think independently be safeguarded? A polycentric governance framework based on Ostrom (2009) and Williamson (2009) human - AGI collaboration with minimal existential risk is proposed.

Mycoplasma synoviae (MS) is a highly contagious bacteria that can cause significant harm in commercial poultry populations while not prevented. Rapid detection of its presence in a flock is crucial from the perspective of animals' health and economic income. Authors propose spectral measurements strongly backed up by the AI data processing algorithms for classifying egg origin: from healthy hens or MS-infected ones. The newest obtained classification factors are F-scores for white eggshells 99% and for brown eggshells 99%—all data used for classification were taken by the portable multispectral fibre-optics reflectometer.

The growing dependence on large language models (LLM)s highlights the urgent need to deepen trust in these technologies. Regular, rigorous validation of their expertise, especially in nuanced and intricate scenarios, is essential to ensure their readiness for clinical applications. Our study pioneers the exploration of LLM utility in the field of cardiology. We stand at the cusp of a transformative era where mature AI and LLMs, notably ChatGPT, GPT-4, and Google Bard, are poised to influence healthcare significantly. Recently, we put three available LLMs, OpenAI's ChatGPT-3.5, GPT-4.0, and Google's Bard, to the test against a significant Polish medical special-ization licensing exam (PES). The exams cover the scope of completed specialist training, focusing on diagnostic and therapeutic procedures, excluding invasive medical procedures and interven-tions. In our analysis, GPT-4 consistently outperformed the others, ranking first, with and Google Bard and ChatGPT- 3.5 following, respectively. The performance metrics underscore GPT-4's no-table potential in medical applications. Given a score improvement of over 23.5 % between two AI models released just four months apart, clinicians must stay informed and up-to-date about these rapidly evolving tools and their potential applications to clinical practice. Our results provide a snapshot of the current capabilities of these models, highlighting the nuanced performance dif-ferences when confronted with identical questions

Detecting, tracking, and classifying unmanned aerial vehicles (UAVs) in a swarm presents significant challenges due to their small and diverse radar cross-sections, multiple flight altitudes, velocities, and close trajectories. To overcome these challenges, adjustments of the radar parameters and/or position of the radar (for airborne platforms) are often required during runtime. The runtime adjustments help to overcome the anomalies in the detection, tracking, and classification of UAVs. The runtime adjustments are performed either manually or through fixed algorithms, each of which can have its limitations for complex and dynamic scenarios. In this work, we propose the use of multi-agent reinforcement learning (RL) to carry out the runtime adjustment of the radar parameters and position of the radar platform. The radar used in our work is a multibeam multifunction phased array radar (MMPAR) placed onboard UAVs. The simulations show the cognitive adjustment of the MMPAR parameters and position of the airborne platform using RL helps to overcome anomalies in the detection, tracking, and classification of UAVs in a swarm. A comparison with other artificial intelligence (AI) algorithms shows that RL performs better due to runtime learning of the environment through rewards.

Artificial Intelligence (AI) has the power to generate scenarios and make predictions through the use of advanced algorithms and machine learning techniques. OpenAI’s GPT-3 AI is a state-of-the-art language model that has been trained on a large dataset of text, which allows it to generate hu-man-like text, and it can generate scenarios for different fields. However, GPT-3 was trained on data available up until June 2021, had no access to more recent data nor was connected to the internet. In this study, we investigated the capability of OpenAI’s GPT-3 AI to predict the Ukrainian war es-calation, which started in 2022 and had massive geopolitical effects. We used GPT-3´s capability to generate future scenarios, to check those scenarios for internal consistency, and to create a proba-bility estimate. The results showed that although GPT-3 described an open war as one of the low probability scenarios, its capability on predicting the future was limited. Furthermore, it became evident that checking internal consistency of the generated scenarios could be improved. We ap-preciated GPT-3 as very useful and powerful for generating future scenarios, but also concluded that its prediction capabilities of real-world events are limited, should be used with caution, and require further development.

Alzheimer's disease (AD) is a significant regular type of dementia that causes damage in brain cells. Early detection of AD acting as an essential role in global health care due to misdiagnosis and sharing many clinical sets with other types of dementia, and costly monitoring the progression of the disease over time by magnetic reasoning imaging (MRI) with consideration of human error in manual reading. Our proposed model, in the first stage, apply the medical dataset to a composite hybrid feature selection (CHFS) to extract new features for select the best features to improve the performance of the classification process due to eliminating obscures features. In the second stage, we applied a dataset to a stacked hybrid classification system to combine Jrip and random forest classifiers with six model evaluations as meta-classifier individually to improve the prediction of clinical diagnosis. All experiments conducted on a laptop with an Intel Core i7- 8750H CPU at 2.2 GHz and 16 G of ram running on windows 10 (64 bits). The dataset evaluated using an explorer set of weka data mining software for the analysis purpose. The experimental show that the proposed model of ‏(CHFS) feature extraction ‏performs better than principal component analysis (PCA), and lead to effectively reduced the false-negative rate with a relatively high overall accuracy with support vector machine (SVM) as meta-classifier of 96.50% compared to 68.83% which is considerably better than the previous state-of-the-art result. The receiver operating characteristic (ROC) curve was equal to 95.5%. Also, the experiment on MRI images Kaggle dataset of CNN classification process with 80.21% accuracy result. The results of the proposed model show an accurate classify Alzheimer's clinical samples against MRI neuroimaging for diagnoses AD at a low cost.

This study explores the integration of Artificial Intelligence (AI) and collaborative robotics in industrial surface finishing, focusing on feature detection on object reflective surfaces using neural networks in computationally constrained environments. Central to this investigation is the utilization of cameras considered as sensors that can capture detailed images of reflective surfaces. These images are then analyzed by a custom neural network, demonstrating the feasibility of feature detection with non-powerful computers. The public dataset for recognizing light reflection defects, available in the SPADD repository, supports this approach. Employing open-source tools like Darknet YOLOv3 and Yolo\_mark, this method proves particularly advantageous for small and medium-sized enterprises (SMEs) due to its low cost and maintenance requirements. This research highlights the challenges of AI and collaborative robotics in industrial applications, especially in adapting to varying conditions and the need for continuous training with new data coming from several sensors. Looking ahead, plans are in place to integrate this system with cobot-assisted polishing tasks, using cameras to enhance manufacturing efficiency and quality. This work contributes to the broader understanding of practical applications of AI in industrial automation, surface finishing processes, and the effective use of sensor technology.

In order to counter the impending shortage of skilled professionals in the aging societies of our time in many western countries such as Germany, solutions for business and society are urgently needed. Here, artificial intelligence (AI) can play an important role in mitigating the problem with the help of diverse applications. At the same time, it is important to consider both the needs of the respective employee1 and the company to ensure that the use of AI has a positive impact on the organization and finds social acceptance. In this article, we describe the newly developed OSQE model (Optimize, Secure, Qualify, Expand), which for the first time outlines an AI cycle against the shortage of skilled professionals in a holistic approach that focuses equally on people and companies. This can serve organizations as a guide for strategy development, decision-making for and implementation of AI-supported measures in an entire cycle of an employee's affiliation with a company. The model takes three driving forces into account: companies, professionals, and AI applications. In the model, the measures to be implemented are prioritized with ascending numbering based on what would be most urgent for a company to implement. All measures relate to areas of action that place people at the center and can be assigned to the classic cycle of belonging of an employee in the company. In this regard, the opportunities that AI offers to professionals and companies are highlighted.

Despite significant advancements in Artificial Intelligence (AI) and Large Language Models (LLMs), detecting and mitigating bias remains a critical challenge, particularly within social media platforms like X (formerly Twitter) and in addressing cyberbullying present on them. This research investigates the effectiveness of leading LLMs in generating synthetic biased and cyberbullying data and evaluates the proficiency of Transformer AI models in detecting bias and cyberbullying within both authentic and synthetic contexts. The study involves semantic analysis and feature engineering on a dataset of over 48,000 sentences related to cyberbullying collected from Twitter (before it became X). Leveraging state-of-the-art LLMs such as ChatGPT-4o, Pi AI, Claude 3 Opus, and Gemini-1.5, synthetic biased, cyberbullying, and neutral data were generated to deepen the understanding of bias in human-generated data. AI models including DeBERTa, Longformer, BigBird, HateBERT, MobileBERT, DistilBERT, BERT, RoBERTa, ELECTRA, and XLNet were initially trained to classify Twitter cyberbullying data and subsequently fine-tuned, optimized, and quantized for multilabel classification (detecting both biases and cyberbullying). The study's outcomes include a prototype of a hybrid application that combines a Bias Data Detector and a Bias Data Generator, validated through extensive testing.

The spread of fake news poses a serious threat to public trust and informed decision-making. The emergence of AI-generated fake news exacerbates this issue, as it can be produced more rapidly and convincingly than human-generated content. Addressing this issue, our paper introduces a robust approach to detect AI-generated fake news that is applicable across major disinformation domains: Politics and Elections, Health and Medicine, Science and Technology, and Entertainment and Celebrities. Our models differentiate between AI-generated and human-generated news articles while assessing their truthfulness. Utilizing large language models such as ChatGPT, we curated datasets spanning both AI and human generated fake and genuine news. Following extensive data preprocessing and the application of diverse feature extraction techniques, our models were trained using multiple machine learning algorithms. Our results show the feasibility of accurately identifying AI-generated fake news, thereby adding a significant layer of defense in combating misinformation across various sectors. In addition, we have explored the potential utility of transferable machine learning models trained on data from one domain (e.g., politics), when tested on another domain (e.g., health).

In this perspective article, we ponder topographical enhancements of artificial neural networks. In our recent paper in JMLR, we proved a quasi-equivalence between the network width and depth and also discussed the power of intra-links, which can be viewed as network height. In 1982, Hopfield published a network to model human associative memory, which contains many loops for dynamic evolution toward fixed points. Based on noising-denoising loops, diffusion models are recently developed to enable Bayesian modeling and inference with big data. Furthermore, we envision development of multi-AI-agent systems through “netware” engineering as a quantum leap of software engineering for emergent behaviors and autonomous AI at individual and population levels. We believe that the novel use of links and loops in space and time via multi-scale coupling would catalyze the next-generation neural networks.

Alzheimer's disease (AD) detection acting as an essential role in global health care due to misdiagnosis and sharing many clinical sets with other types of dementia, and costly monitoring the progression of the disease over time by magnetic reasoning imaging (MRI) with consideration of human error in manual reading. This paper goal a comparative study on the performance of data mining techniques on two datasets of Clinical and Neuroimaging Tests with AD. Our proposed model in the first stage, Apply clinical medical dataset to a composite hybrid feature selection (CHFS), for extract new features to select the best features due to eliminating obscures features, In parallel with Apply a novel hybrid feature extraction of three batch edge detection algorithm and texture from MRI images dataset and optimized with fuzzy 64-bin histogram. In the second stage, we applied a clinical dataset to a stacked hybrid classification(SHC) model to combine Jrip and random forest classifiers with six model evaluations as meta-classifier individually to improve the prediction of clinical diagnosis. At the same stage of improving the classification accuracy of neuroimaging (MRI) dataset images by applying a convolution neural network (CNN) in comparison with traditional classifiers, running on extracted features from images. The authors have collected the clinical dataset of 426 subjects with (1229 potential patient sample) from oasis.org and (MRI) dataset from a benchmark kaggle.com with a total of around ~5000 images each segregated into the severity of Alzheimer's. The datasets evaluated using an explorer set of weka data mining software for the analysis purpose. The experimental show that the proposed model of ‏(CHFS) feature extraction ‏ lead to effectively reduced the false-negative rate with a relatively high overall accuracy with a stack hybrid classification of support vector machine (SVM) as meta-classifier of 96.50% compared to 68.83% of the previous result on a clinical dataset, Besides a compared model of CNN classification on MRI images dataset of 80.21%. The results showed the superiority of our CHFS model in predicting Alzheimer's disease more accurately with the clinical medical dataset in early-stage compared with the neuroimaging (MRI) dataset. The results of the proposed model were able to predict with accurately classify Alzheimer's clinical samples at a low cost in comparison with the MRI-CNN images model at the early stage and get a good indicator for high classification rate for MRI images when applying our proposed model of SHC.

Due to Motor Neurone Diseases, a large population remains disabled worldwide, negatively impacting their independence and quality of life. This typically involves a weakness in the hand and forearm muscles, making it difficult to perform fine motor tasks such as writing, buttoning a shirt, or gripping objects. This project presents a vision-enabled rehabilitation hand exoskeleton to assist disabled persons in their hand movements. The design goal was to create an accessible tool to help with a simple interface requiring no training. This prototype is built on a commercially available glove where a camera and embedded processor were integrated to help open and close the hand, using air pressure, thus grabbing an object. An accelerometer is also implemented to detect the characteristic hand gesture to release the object when desired. This passive vision-based control differs from active EMG-based designs as it does not require individualized training. Continuing the research will reduce the cost, weight, and power consumption to facilitate mass implementation.

ackground: The swift assimilation of Artificial Intelligence (AI) across multiple industries is altering the worldwide economic terrain. Unprecedented opportunities and difficulties are brought about by this revolutionary technology, which has an impact on innovation, trade, labor markets, and security. Objective: The goal of this study is to thoroughly investigate the economic effects of AI by exploring how it may affect labor markets, innovation, trade internationally, and security. The goal is to offer insights that support strategic decision-making and policy formation by examining current trends, opportunities, and difficulties. Results: The research highlights the dual character of artificial intelligence's influence, highlighting both its potential to spur economic expansion and its drawbacks, including the loss of jobs, a lack of standards, and security issues. The study highlights how crucial cooperation is to overcoming these obstacles and realizing AI's full potential. Conclusion: The balancing opportunities and risks becomes critical as AI continues to change the economic landscape. The report promotes proactive steps including ethical AI design guidelines, ongoing worker retraining, and flexible regulatory strategies. As we shape an AI future that puts inclusion, creativity, and responsible governance first, collaboration emerges as a key theme.