Accurate detection and state analysis of traffic flows are essential for effectively reconstructing traffic flows and reducing the risk of severe injury and fatality. For this reason, several studies on resolving traffic problems have proposed the use of crowdsourcing, in which drivers provide real-time traffic information using mobile devices, to monitor traffic conditions. Using data collected via crowdsourcing for traffic event detection has advantages in terms of improved accuracy and reduced time cost in collecting relevant data. In this paper, we propose a technique that employs crowdsourcing to collect traffic-related data and uses these data to detect events that influence traffic. The proposed technique uses various machine-learning methods to more accurately identify events and find accurate location information. Therefore, it is able to resolve problems typically encountered with conventionally provided location information, such as broadly defined locations or inaccurate location information. The proposed technique has advantages in terms of reducing time and cost while increasing accuracy. Its validity and effectiveness were also demonstrated through various performance evaluations.

Decision support systems based on machine learning models should be able to help users identify opportunities and threats. Popular model-agnostic explanation models can identify factors that support various predictions, answering questions like “What factors affect sales?” or “Why did sales decline?”, but do not highlight what a person should or could do to get a more desirable outcome. Counterfactual explanation approaches address intervention, and some even consider feasibility, but there has been no evaluation of their suitability for real-time applications, such as question answering. Here we address this gap by introducing a novel model-agnostic method that provides specific, feasible changes that would impact the outcomes of a complex black-box AI model for a given instance and assess its real-world utility by measuring its real-time performance and ability to find achievable changes. The method uses the instance of concern to generate high-precision explanations and then applies a secondary method to find minimally contrastive and maximally probable high-precision counterfactual explanations, while limiting contrasted features to changes that are achievable. We demonstrate that using this method, it is possible to find such explanations quickly enough for use in real-time systems. High-precision achievable minimally contrastive explanations would be useful in applications where people seek remedial actions or question how effective a proposed remedy is likely to be.

This paper describes preliminary results of measuring the impact of human body movements on plants. In particular, we analyze the influence of eurythmic gestures of human actors on lettuce and beans. In an eight week experiment, we exposed rows of lettuce and beans to weekly eurythmic movements (similar to Qi Gong) of a eurythmist, while at the same time measuring changes in voltage between the roots and leaves of lettuce and beans using the plant spikerbox (https://backyardbrains.com/products/plantspikerbox). We compared this experimental group of vegetables with a control group of vegetables whose voltage differential was also measured while never being exposed to eurythmy. Using t-tests, we found a clear difference between the experimental and the control group which was also verified with a machine learning model. In other words, the vegetables showed a noticeably different pattern in electric potentials in response to eurythmic gestures.

Protein molecules show varying degrees of flexibility throughout their three-dimensional structures. This flexibility is associated with the fluctuation of the torsion angle in proteins. The protein backbone can be defined mainly by two torsion angles, phi (φ) and psi (ψ). The fluctuation of torsion angles is derived from different NMR models’ variations in backbone torsion angles. The angle fluctuations in the cartesian coordinate space are used to characterize the protein’s structural flexibility. Fluctuation of torsion angle is useful in predicting protein function and structure when the torsion angles are used as restraints. This study aims to develop a machine learning method to predict torsion angle fluctuations directly from protein sequences. It explores various useful features such as disorder probability, position-specific scoring matrix profiles, secondary structure probabilities, monograms, bigram, position-specific estimated energy, half-sphere exposures, etc. Likewise, it explores well-known machine learning methods and proposes an optimized Light Gradient Boosting Machine Regressor (LightGBM) method, named TAFPred, to predict torsion angle fluctuations with the selected features. The proposed method achieves ten-fold cross-validated correlation coefficients of 0.746 and 0.737 and mean absolute errors of 0.114 and 0.123 for the angle fluctuation of φ and ψ, respectively, and attains an improvement of 10.08% in MAE, 24.83% in PCC in the phi angle, and 9.93% in MAE, 22.37% in PCC in psi angle compared to the state-of-the-art method proposed by Zhang et al.

High efficiency and safety are critical factors in ensuring optimal performance and reliability of systems and equipment across various industries. Fault Monitoring (FM) techniques play a pivotal role in this regard by continuously monitoring system performance and identifying the presence of faults or abnormalities. However, traditional FM methods face limitations in fully capturing the complex interactions within a system and providing real-time monitoring capabilities. To overcome these challenges, Digital Twin (DT) technology has emerged as a promising solution to enhance existing FM practices. By creating a virtual replica or digital copy of a physical equipment or system, DT offers the potential to revolutionize fault monitoring approaches. This paper aims to explore and discuss the diverse range of predictive methods utilized in DT and their implementations in FM across industries. Furthermore, it will showcase successful implementations of DT in FM across a wide array of industries, including manufacturing, energy, transportation, and healthcare The utilization of DT in FM enables a comprehensive understanding of system behavior and performance by leveraging real-time data, advanced analytics, and machine learning algorithms. By integrating physical and virtual components, DT facilitates the monitoring and prediction of faults, providing valuable insights into the system’s health and enabling proactive maintenance and decision-making.

The growth in the Internet of Things (IoT) devices in the healthcare sector enables the new era of the Internet of Medical Things (IoMT). However, IoT devices are susceptible to diverse cybersecurity attacks and threats that lead to negative consequences. Cyberattacks can harm not just the IoMT devices being used but also human life. Currently, several security solutions are proposed to enhance the security of the IoMT, which uses machine learning (ML) and blockchain. ML can be used to develop detection and classification methods to identify cyberattacks targeting IoMT devices in the healthcare sector. In addition, blockchain technology enables a decentralized approach to the healthcare system and eliminates some disadvantages of a centralized system, such as a single point of failure. This paper proposes a resilient security framework integrating a Tri-layered Neural Network (TNN) and blockchain technology in the healthcare domain. The TNN detects anomalies in data measured by medical sensors to find fraudulent data. Therefore, cyberattacks are detected and discarded from the IoMT system before data is processed at the fog layer. In addition, a blockchain network is used in the fog layer to ensure that the data is not altered, boosting the integrity and privacy of the medical data. The experimental results show that the TNN and blockchain models produce the expected result. Furthermore, the accuracy of the TNN model reached 99.99% on the F1-score accuracy metric.

The performance of modern wireless communication systems is highly dependent on the adoption of multiple antennas and the associated signal processing. In 5G and 6G networks, beamforming and beam management become challenging tasks due to aspects such as user mobility, increased number of antennas, and the adoption of higher frequencies. Artificial intelligence, and more specifically, machine learning, are efficient tools to reduce the complexity involved in generating beams and the overhead associated with beam management without sacrificing system performance. Therefore, AI-aided beamforming and beam management have received a lot of attention recently. This article presents a complete survey on this topic, emphasizing open problems and promising directions. The discussion includes architectural and signal processing aspects of modern beamforming and beam management. The article presents communication problems and respective solutions using centralized/decentralized, supervised/unsupervised, semi-supervised, active, federated, and reinforcement learning.

An artificial intelligence (AI)-based conversational large language model (LLM) was launched in November 2022 namely, “ChatGPT”. Despite the wide array of potential applications of LLMs in healthcare education, research and practice, several valid concerns were raised. The current systematic review aimed to investigate the possible utility of ChatGPT and to highlight its limitations in healthcare education, research and practice. Using the PRIMSA guidelines, a systematic search was conducted to retrieve English records in PubMed/MEDLINE and Google Scholar under the term “ChatGPT”. Eligibility criteria included the published research or preprints of any type that discussed ChatGPT in the context of healthcare education, research and practice. A total of 280 records were identified, and following full screening, a total of 60 records were eligible for inclusion. Benefits/applications of ChatGPT were cited in 51/60 (85.0%) records with the most common being the utility in scientific writing followed by benefits in healthcare research (efficient analysis of massive datasets, code generation and rapid concise literature reviews besides utility in drug discovery and development). Benefits in healthcare practice included cost saving, documentation, personalized medicine and improved health literacy. Concerns/possible risks of ChatGPT use were expressed in 58/60 (96.7%) records with the most common being the ethical issues including the risk of bias, plagiarism, copyright issues, transparency issues, legal issues, lack of originality, incorrect responses, limited knowledge, and inaccurate citations. Despite the promising applications of ChatGPT which can result in paradigm shifts in healthcare education, research and practice, the embrace of this application should be done with extreme caution. Specific applications of ChatGPT in health education include the promising utility in personalized learning tools and shift towards more focus on critical thinking and problem-based learning. In healthcare practice, ChatGPT can be valuable for streamlining the workflow and refining personalized medicine. Saving time for the focus on experimental design and enhancing research equity and versatility are the benefits in scientific research. Regarding authorship in scientific articles, as it currently stands, ChatGPT does not qualify to be listed as an author unless the ICJME/COPE guidelines are revised and amended. An initiative involving all stakeholders involved in healthcare education, research and practice is urgently needed to set a code of ethics and conduct on the responsible practices involving ChatGPT among other LLMs.

Machine learning methods have widely been applied to detect anomalies in machine and cutting tool behavior during lathe or milling. However, detecting anomalies in the workpiece itself did not get the same attention by researchers. That is why in this article, the authors present a pub-licly available multivariate time series dataset which was recorded during milling of 16MnCr5. Due to artificially introduced, though realistic anomalies in the workpiece the dataset can be ap-plied for anomaly detection. By using a convolutional autoencoder as a first model good results in detecting the location of the anomalies in the workpiece were achieved. Furthermore, milling tools with two different diameters where used which led to a dataset eligible for transfer learn-ing. The objective of this article is to provide researchers with a real-world time series dataset of the milling process which is suitable for modern machine learning research topics like anomaly detection and transfer learning.

The electroretinography (ERG) is a diagnostic test that measures the electrical activity of the retina in response to a light stimulus. This test has a long history and has been extensively studied among years. Dr. James Dewar recorded the first electroretinogram signal in 1873. Later he described signal analysis using 4 components, namely amplitude, and latency of a-wave and b-wave. Nowadays, the international electrophysiology community has established the standard for electroretinography in 2008. However, from the point of view of signal analysis, the major change did not happen. ERG analysis is still based on the 4 components evaluation. The article describes ERG database including the classification of signals by using advanced analysis of electroretinograms based on wavelet scalogram processing. To implement an extended analysis of the ERG, the parameters extracted from the wavelet scalogram of the signal were obtained using digital image processing and machine learning methods. The results of the study show that the proposed algorithm implements the classification of adult electroretinogram signals by 19% more accurately and pediatric signals by 20% more accurately than the classical algorithm. The promising use of ERG is differential diagnostics, which may also be used in preclinical toxicology and experimental modeling. The problem of developing methods for electrophysiological signals analysis in ophthalmology is associated with the complex morphological structure of electrophysiological signals components, due to the generation of retina cell electrical responses to light stimulus.

Globally, zoonotic diseases have been on the rise in recent years. Predictive modelling approaches have been successfully used in the literature to identify the underlying causes of these zoonotic diseases. We examine the latest research in the field of predictive modeling that verifies the growth of zoonotic pathogens and assesses the factors associated with their spread. The results of our survey indicate that popular mathematical models can successfully be used in modeling the growth rate of these pathogens under varying storage temperatures. Additionally, some of them are used for the assessment of the inactivation of these pathogens based on various conditions. Based on the results of our study, machine learning models and deep learning are commonly used to detect pathogens within food items and to predict the factors associated with the presence of the pathogens.

In the mid-layers of Deep Learning systems, clustered features tend to fit multiple classifications, which are filtered out during the final stages of object recognition. However, many misclassifications remain, regarded as errors of the system. This paper claims that tagging an entity incorrectly for reasons of similarity is evidence of spontaneous machine creativeness. According to the ratings of 40 design educators and researchers, AI-generated false-class inclusions produced creative design ideas, predicting the level of innovation value. These designers were not just anybody but came from a design school in Asia with a top position on the world ranking-lists. They entered an experiment in which 20 classification mistakes were framed as early-design ideas that were either human-made or intentionally suggested by creative AI. Many examples passed the Feigenbaum variant of the Turing test with a conceptual preference to creations supposedly done by human hand.

The urgency of the COVID19 pandemic caused a surge in related scientific literature. This surge made the manual exploration of scientific articles time-consuming and inefficient. Therefore, a range of exploratory search applications have been created to facilitate access to the available literature. In this survey, we give a short description of certain efforts in this direction and explore the different approaches that they used.

Electroencephalography (EEG) is a brain imaging technique in which electrodes are placed on the scalp. EEG signals are commonly decomposed into frequency bands called delta, theta, alpha, and beta.While these bands have been shown to be useful for characterizing various brain states, their utility as a one-size-fits-all analysis tool remains unclear. We present a two-part data-driven methodology for objectively determining the best EEG bands for a given dataset in this paper. First, a decision tree is used to estimate the optimal frequency band boundaries for reproducing the signal’s power spectrum for a predetermined number of bands. The optimal number of bands is then determined using an Akaike Information Criterion (AIC)-inspired quality score that balances goodness-of-fit with a small band count. Data-driven EEG band discovery may aid in objectively capturing key signal components and uncovering new indices of brain activity.

Researchers are concerned about the impact of fake news on democracy, while it could also escalate to life-threatening problems. Fake news continues to spread, so does people's behaviour and emotions about fake news via social media. This opens up the back door for cyber-criminals to entice people (i.e. taking advantage of victims' emotional and behavioural aspects) to click on fraudulent links (e.g. phishing links) associated with fake news when reading. Therefore, we investigate how people's emotional and behavioural features influence reading and diffusing fake news on social media. We proposed a classification model incorporating people's behavioural features and their emotions to better detect fake news in social media. Our results reveal that fake news has more negative emotions than legitimate ones and both title and the content of the news/posts are equally important. Furthermore, we have identified that there exist strong correlations between some of the behavioural and emotional features. Finally, we concluded that emotional and behavioural features are important for fake news classifications as they improve the accuracy of detecting fake news, and the findings of our study can ultimately be used to develop a risk score prediction model for fake news in social media.

The objective of this study was to correlate the binding of drugs on a very popular nanoparticulate polymeric matrix; PLGA nanoparticles with their main constitutional, electronic and physico-chemical descriptors. Gaussian Processes (GPs) was the artificial intelligence machine learning method that was utilized to fulfil this task. The method could successfully model the results where optimum values of the investigated descriptors of the loaded drugs were deduced. A percentage bias of 12.68 % ± 2.1 was obtained in predicting the binding energies of a group of test drugs. As a conclusion, GPs could successfully model the drugs-PLGA interactions associated with a good predicting power. The GPs-predicted binding energies (ΔG) can easily be projected to the drugs loading as was previously proven. Adopting the “Pharmaceutics Informatics” approach can save the pharmaceutical industry and the drug delivery scientists a lot of exerted resources, efforts and time.

Hateful and abusive speech presents a major challenge for all online social media platforms. Recent advances in Natural Language Processing and Natural Language Understanding allow more accurate detection of hate speech in textual streams. This study presents a multimodal approach to hate speech detection by combining Computer Vision and Natural Language processing models for abusive context detection. Our study focuses on Twitter messages and, more specifically, on hateful, xenophobic and racist speech in Greek aimed at refugees and migrants. In our approach we combine transfer learning and fine-tuning of Bidirectional Encoder Representations from Transformers (BERT) and Residual Neural Networks (Resnet). Our contribution includes the development of a new dataset for hate speech classification, consisting of tweet ids, along with the code to obtain their visual appearance, as they would have been rendered in a web browser. We have also released a pre-trained Language Model trained on Greek tweets, which has been used in our experiments. We report a consistently high level of accuracy (accuracy score=0.970, f1-score=0.947 in our best model) in racist and xenophobic speech detection.

The main bottleneck when performing computational fluid dynamics (CFD) simulations of combustion systems is the computation and integration of the highly non-linear and stiff chemical source terms. In recent times, machine learning has emerged as a promising tool to accelerate combustion chemistry, involving the use of regression models to predict the chemical source terms as functions of the thermochemical state of the system. However, combustion is a highly nonlinear phenomenon, and this often leads to divergence from the true solution when the neural network representation of chemical kinetics is integrated in time. This is because these approaches minimize the error during training without guaranteeing successful integration with ordinary differential equation (ODE) solvers. In this work, a novel neural ODE approach to combustion modeling, ChemNODE, is developed to address this issue. The source terms predicted by the neural network are integrated during training, and by backpropagating errors through the ODE solver, the neural network weights are adjusted accordingly to minimize the difference between the predicted and actual ODE solutions. It is shown that even when the dimensionality of the thermochemical manifold is trimmed to remove redundant species, the proposed approach accurately captures the correct physical behavior and reproduces the results obtained using the full chemical kinetic mechanism.

One of the oldest and common methods of diagnosing heart abnormalities is auscultation. Even for experienced medical doctors, it is not an easy task to detect abnormal patterns in the heart sounds. Most of the digital stethoscopes are now capable of recording and transferring the heart sounds. Moreover, it is proven that auscultation records can be classified as healthy or unhealthy via artificial intelligence techniques. In this work, an artificial intelligence-powered mobile application that works in a connectionless fashion is presented. According to the clinical experiments, the mobile application can detect heart abnormalities with approximately 92% accuracy which is comparable if not better than humans since only a small number of well-trained cardiologists can analyze auscultation records better than artificial intelligence. Using the diagnostic ability of artificial intelligence in a mobile application would change the classical way of auscultation for heart disease diagnosis.

Estimation of breeding values through Best Linear Unbiased Prediction (BLUP) using pedigree-based kinship and Marker-Assisted Selection (MAS) are the two fundamental breeding methods used before and after the introduction of genetic markers, respectively. The emergence of high-density genome-wide markers has led to the development of two parallel series of approaches inspired by BLUP and MAS, which are collectively referred to as Genomic Selection (GS). The first series of GS methods alters pedigree-based BLUP by replacing pedigree-based kinship with marker-based kinship in a variety of ways, including weighting markers by their effects in genome-wide association study (GWAS), joining both pedigree and marker-based kinship together in a single-step BLUP, and substituting individuals with groups in a compressed BLUP. The second series of GS methods estimates the effects for all genetic markers simultaneously. For the second series methods, the marker effects are summed together regardless of their individual significance. Instead of fitting individuals as random effects like in the BLUP series, the second series fits markers as random effects. Differing assumptions regarding the underlying distribution of these marker effects have resulted in the development of many Bayesian-based GS methods. This review highlights critical concept developments for both of these series and explores ongoing GS developments in machine learning, multiple trait selection, and adaptation for hybrid breeding. Furthermore, considering the increasing use and variety of GS methods in plant breeding programs, this review addresses important concerns for future GS development and application, such as the use of GWAS-assisted GS, the long-term effectiveness of GS methods, and the valid assessment of prediction accuracy.

This paper presents a methodology for predictive and prescriptive analytics of complex engineering systems. The methodology is based on a combination of physics-based and data-driven modeling using machine learning techniques. Combining these approaches results in a set of reliable, fast, and continuously updating models for prescriptive analytics. The methodology is demonstrated with a case study of a jet-engine power plant preventive maintenance and diagnostics of its flame tube.

Predicting the popularity of posts on social networks has taken on significant importance in recent years, and several social media management tools now offer solutions to improve and optimize the quality of published content and to enhance the attractiveness of companies and organizations. Scientific research has recently moved in this direction, with the aim of exploiting advanced techniques such as machine learning, deep learning, natural language processing, etc., to support such tools. In light of the above, in this work we aim to address the challenge of predicting the popularity of a future post on Instagram, by defining the problem as a classification task and by proposing an original approach based on Gradient Boosting and feature engineering, which led us to promising experimental results. The proposed approach exploits big data technologies for scalability and efficiency and is general enough to be applied to other social media as well.

We present an automated system for nutrient solution management. Prior arts usually measure only pH and EC of the nutrient solutions for maintenance. When EC drops, they just simply add concentrated nutrient to the horticulture bed. Such approach can maintain the density of nutrient solution but cannot maintain the rates of individual ion particles. To prevent nutrition related disorders, fertilization methods with ion selective electrodes are widely introduced. This trend measures individual ion concentration of nutrient solution to maintain appropriate nutrient composition by supplying only insufficient ions. Many researchers have suggested ISE based automated fertilization systems. However, they failed to control a chemical artifact called ion interference effect, which becomes greater at higher density. Our system measures individual concentration of multiple ions and add only deficient nutrients, while handling the ion interference effect issue. To ensure the performance of ion selective electrodes, the system also performs fully automated 3-point calibration 24 times a day. A machine learning algorithm is applied on the sensory parts to remove ion interference effect which make measurement of complex solution with ISE almost impossible. With automated calibration and signal processing technology, the system robustly and continuously maintains nutrient condition for plants. We suggest applying this system on closed hydroponic systems such as smart farms or plant factory, to reduce water consumption and to provide more appropriate environment for the crops.

Globally, there is massive uptake and explosion of data and challenge is to address issues like scale, pace, velocity, variety, volume and complexity of this big data. Considering the recent epidemic in China, modeling of COVID-19 epidemic for cumulative number of infected cases using data available in early phase was big challenge. Being COVID-19 pandemic during very short time span, it is very important to analyze the trend of these spread and infected cases. This chapter presents medical perspective of COVID-19 towards epidemiological triad and the study of state-of-the-art. The main aim this chapter is to present different predictive analytics techniques available for trend analysis, different models and algorithms and their comparison. Finally, this chapter concludes with the prediction of COVID-19 using Prophet algorithm indicating more faster spread in short term. These predictions will be useful to government and healthcare communities to initiate appropriate measures to control this outbreak in time.

Water quality forecasting is increasingly significant for agricultural management and environmental protection. Enormous amounts of water quality data are increasingly being collected by advanced sensors, which leads to an interest in using data-driven models for predicting trends in water quality. However, the unpredictable background noises introduced during water quality monitoring seriously degrade the performance of those models. Meanwhile, artificial neural networks (ANN) with feed-forward architecture lack the capability of maintaining and utilizing the accumulated temporal information, which leads to biased predictions in processing time series data. Hence, we propose a water quality predictive model based on a combination of Kernal Principal Component Analysis (kPCA) and Recurrent Neural Network (RNN) to forecast the trend of dissolved oxygen. Water quality variables are reconstructed based on kPCA method, which aims to reduce the noise from the raw sensory data and preserve actionable information. With the RNN's recurrent connections, our model can make use of the previous information in predicting the trend in the future. Data collected from Burnett River, Australia was applied to evaluate our kPCA-RNN model. The kPCA-RNN model achieved R2 scores up to 0.908, 0.823 and 0.671 for predicting the concentration of dissolved oxygen in the upcoming 1, 2 and 3 hours, respectively. Compared to current data-driven methods like ANN and SVR, the predictive accuracy of the kPCA-RNN model was at least 8 %, 17 % and 21 % better than the comparative models in these 3 cases. The study demonstrates the effectiveness of the kPAC-RNN modeling technique in predicting water quality variables with noisy sensory data.

Free Floating Car Sharing (FFCS) services are a flexible alternative to car ownership. These transportation services show highly dynamic usage both over different hours of the day, and across different city areas. In this work, we study the problem of predicting FFCS demand patterns -- a problem of great importance to an adequate provisioning of the service. We tackle both the prediction of the demand i) over time and ii) over space. We rely on months of real FFCS rides in Vancouver, which constitute our ground truth. We enrich this data with detailed socio-demographic information obtained from large open-data repositories to predict usage patterns. Our aim is to offer a thorough comparison of several machine learning algorithms in terms of accuracy and easiness of training, and to assess the effectiveness of current state-of-art approaches to address the prediction problem. Our results show that it is possible to predict the future usage with relative errors down to 10%, and the spatial prediction can be estimated with relative errors of about 40%. Our study also uncovered the socio-demographic features that most strongly correlate with FFCS usage, providing interesting insights for providers opening service in new regions.

Identifying human actions in complex scenes is widely considered as a challenging research problem due to the unpredictable behaviors and variation of appearances and postures. For extracting variations in motion and postures, trajectories provide meaningful way. However, simple trajectories are normally represented by vector of spatial coordinates. In order to identify human actions, we must exploit structural relationship between different trajectories. In this paper, we propose a method that divides the video into N number of segments and then for each segment we extract trajectories. We then compute trajectory descriptor for each segment which capture the structural relationship among different trajectories in the video segment. For trajectory descriptor, we project all extracted trajectories on the canvas. This will result in texture image which can store the relative motion and structural relationship among the trajectories. We then train Convolution Neural Network (CNN) to capture and learn the representation from dense trajectories. . Experimental results shows that our proposed method out performs state of the art methods by 90.01% on benchmark data set.

We apply deep convolutional neural networks (CNNs) to estimate wave breaking type from close-range monochrome infrared imagery of the surf zone. Image features are extracted using six popular CNN architectures developed for generic image feature extraction. Logistic regression on these features is then used to classify breaker type. The six CNN-based models are compared without and with augmentation, a process that creates larger training datasets using random image transformations. The simplest model performs optimally, achieving average classification accuracies of 89% and 93%, without and with image augmentation respectively. Without augmentation, average classification accuracies vary substantially with CNN model. With augmentation, sensitivity to model choice is minimized. A class activation analysis reveals the relative importance of image features to a given classification. During its passage, the front face and crest of a spilling breaker are more important than the back face. Whereas for a plunging breaker, the crest and back face of the wave are most important. This suggests that CNN-based models utilize the distinctive `streak' temperature patterns observed on the back face of plunging breakers for classification.

Disease classification based on biological data is an important area in bioinformatics and biomedical research. It helps the doctors and medical practitioners for the early detection of disease and support them as a computer-aided diagnostic tool for accurate diagnosis, prognosis, and treatment of disease. Earlier Microarray gene expression data have wide application for the classification of disease, but now Next-generation sequencing (NGS) has replaced the Microarray technology. From the last few years, RNA sequence (RNA-Seq) data are widely used for the transcriptomic analysis. Hence, RNA-Seq based classification of disease is in its infancy. In this article, we present a general framework for the classification of disease constructed on RNA-Seq data. This framework will guide the researchers to process RNA-Seq, extract relevant features and apply the appropriate classifier to classify any kind of disease.

Automated machine classifications of galaxies are necessary because the size of upcoming surveys will overwhelm human volunteers. We improve upon existing machine classification methods by adding the output of SpArcFiRe to the inputs of a machine learning model. We use the human classifications from Galaxy Zoo 1 (GZ1) to train a random forest of decision trees to reproduce the human vote distributions of the Spiral class. We prefer the random forest model over other black box models like neural networks because it allows us to trace post hoc the precise reasoning behind the classification of each galaxy. We find that, across a sample of 470,000 Sloan galaxies that are large enough that details could be seen if they were there, the combination of SpArcFiRe outputs with existing SDSS features provides a better machine classification than either one alone on comparison to Galaxy Zoo 1. We suggest that adding SpArcFiRe outputs as features to any machine learning algorithm will likely improve its performance.

Pounded yam eaten with any type of soup is a delicacy eaten by Nigerians. It is manually prepared by pounding yam, cocoyam or combination with mortar and pestle. Preparation takes 30-45 mins. Researches on the improvements have taken place to reduce the heavy burden on the pounded yam preparation. One of the efforts made is the fabrication of pounding machine from the Department of Engineering, Federal College of Agriculture, Akure, Nigeria. The aim of this study is to carry out the organoleptic analysis of pounded yam produced from the fabricated machine. A ten-man panelist was used to determine the organoleptic analysis of the pounded yam in terms of the appearance, taste, colour, odour and texture. The panelists were scored with a class of quality grading: 22.6-25.0 (excellent), 20.2-22.5 (good), 18.2-20.1 (satisfactory), 16.2-18.1 (less satisfactory), and <15.2 (unsatisfactory). Also, sensory attributes were scored as 1= unsatisfactory, 2= less satisfactory, 3= satisfactory, 4=good, and 5= excellent. The results were statistically analysed. From the results it was found that the mean results of appearance, colour, odour, taste, and texture were 4.55, 4.43, 4.15, 4.32, and 4.08 respectively. Coefficients of variation (%) were 16.89, 17.80, 21.65, 15.67, and 24.40. The Skewness of the parameters was in negative, while only odour has its Kurtosis in negative form (-0.37). All the panelists rated the appearance (19-24%) of all the samples high above all other parameters. The Pearson Coefficient of Correlation and Principal Component Analysis showed elements of relationships between the parameters. In conclusion, the machine pounded yam was scored above satisfactory, which means the fabricated machine was efficient.

In the paper is described the control electronics for an industrial pneumatic – hydraulic system based on a low-cost PLC. The developed system is a hydraulic pulse system and it generates series of high pressure hydraulic pulses (max. 200 bar). We describe requirements, an overall concept of the embedded control system, user interface, security features and network connectivity. In the description of the software solution we describe implementation of hierarchical ordered program threads (multithreaded program) and main control state machine. At the conclusion, we describe the calibration method of the system and calibration curves and we present the schematic diagram and a photo of a functional prototype of the system.

In traditional databases, the entity resolution problem (which is also known as deduplication), refers to the task of mapping multiple manifestations of virtual objects to its corresponding real-world entity. When addressing this problem, in both theory and practice, it is widely assumed that such sets of virtual object appear as the result of clerical errors, transliterations, missing or updated attributes, abbreviations, and so forth. In this paper, we address this problem under the assumption that this situation is caused by malicious actors operating in domains in which they do not wish to be identified, such as hacker forums and markets in which the participants are motivated to remain semi-anonymous (though they wish to keep their true identities secret, they find it useful for customers to identify their products and services). We are therefore in the presence of a different, even more challenging problem that we refer to as adversarial deduplication. In this paper, we study this problem via examples that arise from real-world data on malicious hacker forums and markets arising from collaborations with a cyber threat intelligence company focusing on understanding this kind of behavior. We argue that it is very difficult---if not impossible---to find ground truth data on which to build solutions to this problem, and develop a set of preliminary experiments based on training machine learning classifiers that leverage text analysis to detect potential cases of duplicate entities. Our results are encouraging as a first step towards building tools that human analysts can use to enhance their capabilities towards fighting cyber threats.

Errors during manufacture of high value components are not acceptable nowadays in driving industries such as energy and transportation. Sectors such as aerospace, automotive, shipbuilding, nuclear power, large science facilities or wind power manufacture complex and accurate components that demand close measurements and fast feedback into manufacturing processes. New measuring technologies are already available in machine tools, including integrated touch probes and fast interface capabilities. They shall provide the possibility to measure the workpiece during or after the manufacturing process, maintaining the original setup of the workpiece and avoiding the manufacturing process from being interrupted to transport the workpiece to a measuring position. However, the traceability of the measurement process on a machine tool is not ensured yet and measurement data is still not fully reliable for process control or product validation. Due to the similarity between a coordinate measuring machine and a machine tool, some of the methods applied for a correct assessment of uncertainty in coordinate measuring machines are adapted to the challenges of a machine tool. The scientific objective is to determine the uncertainty on a machine tool measurement and, in this way, convert it into a machine integrated traceable measuring process. This paper reviews the fundamentals of machine tool metrology.

Power consumption prediction is a crucial component in enhancing the efficiency and sustainability of building operations. This study investigates the impact of data collection frequency and model selection on the predictive accuracy of power consumption in two distinct building types: an Academic one with 15-minute interval data and an Industrial one with hourly data. Various machine learning models, including Support Vector Machine (SVM) with Radial and Sigmoid kernels, Random Forest (RF), and Deep Neural Networks (DNN), across different data splits and feature sets. Our analysis reveals that higher data collection frequency generally improves model performance, as indicated by lower RMSE, MAPE, and CV values, alongside higher R² scores. The inclusion of more historical power consumption features has also been found to have a more significant impact on the accuracy of predictions than including climate condition features. Moreover, the SVM-Radial model consistently outperformed others, particularly in capturing complex, non-linear patterns in the data. However, the DNN model, while competent in some metrics, showed elevated MAPE values, suggesting potential overfitting issues. These findings suggest that careful consideration of data frequency, features and model selection is essential for optimizing power prediction, contributing to more efficient power management strategies in building operations.

: Leveraging the sustainability of the power system market, researchers have developed various ML models for forecasting electricity demand. The LSSVM is well suited to handle complex non-linear power load series. However, the less optimal regularization parameter and the Gaussian kernel function in the LSSVM model have contributed to flawed forecasting accuracy and random generalization ability. Thus, these parameters of LSSVM need to be chosen appropriately using intelligent optimization algorithms. This study proposes a hybrid model based on the LSSVM optimized by the IBFOA for forecasting the daily electricity load in Peninsular Malaysia. The IBFOA for parameter optimization of LSSVM is introduced. The IBFOA based on the sine cosine equation is proposed to adjust the constant step size in the BFOA, which creates an imbalance between the exploration and exploitation during optimization. Finally, the load forecasting model based on LSSVM-IBFOA is constructed using MAPE as the objective function. Comparative analysis demonstrates the model, achieving the highest R2 (0.9880) and significantly reducing error metrics (MAPE, MAE, RMSE, MSE, NRMSE) compared to the baseline LSSVM (average reduction of 27.72% to 47.72%). Additionally, IBFOA exhibits faster convergence and higher accuracy compared to BFOA, highlighting the accuracy of LSSVM-IBFOA for short-term load forecasting.

Synchronous homopolar machines (SHM) with excitation winding on the stator have attracted the attention of researchers for many decades due to their high reliability. They are used in a variety of applications such as aircraft and railway generators and traction motors. At the same time, the three-dimensional structure of their magnetic circuit makes the problem of their analysis and optimization challenging. This problem becomes even more complicated when considering an SHM with hybrid excitation, with ferrite magnets in the rotor slots. The article proposes a mathematical procedure for optimizing a synchronous homopolar generator with hybrid excitation based on the single-criterion Nelder-Mead method and two-dimensional finite element analysis (FEA). The use of the Nelder-Mead method compared to multicriteria methods and 2D FEA compared to 3D FEA makes possible to significantly reduce the computational burden. As a result of optimization, the power loss and torque ripples of the generator are significantly reduced. The article also compares the characteristics of synchronous homopolar generators with and without ferrite magnets.

Leukemia is a form of blood cancer that results in an increase in the number of white blood cells in the body. The correct identification of Leukemia at any stage is essential. The current traditional approaches rely mainly on field expertise' knowledge, which is time consuming. The poor understanding and a long period of examination might damage the human body. In this situation an automated Leukemia identification delivers more reliable and accurate diagnostic information. To effectively diagnose acute lymphoblastic Leukemia from blood smears pictures, a new strategy based on traditional image analysis techniques with machine learning techniques, and a composite learning approach was constructed in this experiment. To identify the type of acute Leukemia first, four well-known machine learning models were utilized. It was discovered that Support Vector Machine (SVM) Provides the highest accuracy in this scenario. To boost the performance, a deep learning model Resnet50 was hybridized with SVM model. Finally, it was revealed that this composite approach achieved 99.9% accuracy.

The paper interprets a comparison of two mostly used techniques of a field-oriented control for 6-phase electric drives, with their pros and cons, as well as their differences in construction and behaviour. Both of these approaches have been realized. Frequency and step responses analysis have been demonstrated with a 6-phase permanent magnet synchronous machine. Experimental results have been compared with simulations based on a mathematical model.

The dynamic behaviour of a machine tool (MT) directly influences the machining performances. The adoption of lightweight structures may reduce the effects of undesired vibrations and increasing the workpiece quality. This paper aims to present and compare a set of hybrid materials that may be excellent candidate to fabricate the MT moving parts. The selected materials have high dynamic characteristics and capacity to damp mechanical vibrations. In this way, starting by the kinematic model of a milling machine that highlights the main critical factors, this study paper evaluates a number of prototypes made of Al Foam sandwiches (AFS), Al Corrugated sandwiches (ACS) and materials reinforced by carbon fibres (CFRP). A set of prototypes has been fabricated, represented the Z-axis ram of a commercial milling machine. The static and dynamical properties have been evaluated by using both FE simulations and experimental tests. The obtained results show that the proposed structures may be a valid alternative to the conventional materials of MT moving parts, increasing machining performances. In particular, the AFS prototype highlighted a damping coefficient that is 20 times greater than a conventional ram (e.g. steel). The CFRP structure is able to satisfy the machining requirements with a reduced weight of 48.5%, while the ACS prototype showed a good trade-off between stiffness and damping.

Stock price prediction is challenging due to global economic instability, high volatility and complexity of financial markets. Hence, this study compared several machine learning algorithms for stock market prediction and further examine the influence of sentiment analysis indicator in predicting stock price. Our results are in two-fold. Firstly, we present a lexicon-based sentiment analysis approach for identifying sentiment features and thus, evidence the correlation between the sentiment indicator and stock price movement. Secondly, we propose the use of GRUVader, an optimal gated recurrent unit networks for stock market prediction. Our findings suggest stand-alone models struggle when compared with AI-enhanced models. Thus, our paper made further recommendations on latter systems.

This paper presents a novel machine learning framework that applies evolutionary ensembles of artificial agents to mineralogical analysis and classification. The approach is based on hybridiza-tion techniques that combine diverse machine learning algorithms, creating large and effective communities of agents. These progressively mute and improve through crossover, hybridization, and selection, addressing the challenges of mineral recognition and classification from thin section images. By combining multiple machine learning techniques, the ensemble of agents autono-mously improves by evolving to adapt and enhance its ability to identify mineral species and classify different types of alterations. We detail the method, provide examples using synthetic and real data, and explore the potential to improve mineralogical analysis workflows through this dynamic, self-improving system.

Among women globally, breast cancer is a major cause of cancer-related death. Accurate and timely diagnosis is essential, and results can be significantly improved. A new era in image analysis has been brought about by the emergence of artificial intelligence (AI), which has made significant progress in the diagnosis and customization of treatment plans for breast cancer possible. This study aimed to develop a comprehensive AI framework for breast cancer detection by integrating convolutional neural networks (CNNs) for image analysis with an artificial neural network (ANN) for clinical data. Using a dataset of ultrasound and pathology images, along with clinical features from 569 patients, we trained CNN models to classify breast tissue as benign or malignant, and the ANN to process clinical data for the same task. The combined approach significantly enhanced the accuracy of breast cancer diagnosis, achieving over 96% accuracy by leveraging both imaging and clinical data. Class Activation Maps (CAM) and heatmaps provided valuable interpretability for CNN predictions, improving trust and transparency in the model’s decision-making process. The results demonstrate that the fusion of CNNs and ANNs enhances diagnostic accuracy and offers a promising tool for early breast cancer detection, while providing crucial insights for clinical validation.

The volatility of global energy markets, particularly electricity prices, plays a crucial role in influencing international economic activities. In the era of big data, machine learning has revolutionized the field of cancer research, particularly in analyzing gene expression data. This study explores the application of machine learning models to the GSE45827 dataset, which contains breast cancer gene expression profiles. With over 54,000 genes and 151 samples categorized into six classes, the dataset presents a high-dimensional challenge that is addressed using dimensionality reduction techniques such as Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE). The PCA method proved most effective in retaining the critical features of the data in lower dimensions, allowing for clearer visualization and enhanced model performance.The reduced dataset was then classified using the eXtreme Gradient Boosting (XGBoost) model, achieving promising multi-class classification results. The model demonstrated high precision, recall, and F1-scores across several classes, particularly excelling in classes 1, 2, and 5. However, certain classes, such as 0 and 4, exhibited lower recall, highlighting areas for further refinement. The integration of PCA and XGBoost not only improved the interpretability and computational efficiency of the model but also contributed to the accurate identification of breast cancer subtypes, emphasizing the importance of machine learning in cancer diagnosis and treatment.

In recent years, with the increasing impact of climate change, achieving the "dual carbon" goal has become increasingly urgent. Fossil fuel consumption is one of the main sources of carbon emissions and is closely related to economic development. Therefore, it is necessary to decouple economic growth from fossil fuel consumption as soon as possible. However, the small sample defect in fossil energy consumption panel data has caused great difficulties for machine learning algorithms. To address this defect, this study uses TimeGAN for data augmentation and compares the performance of six regression analysis methods (XGBoost, CatBoost, LGBM, KNN, linear regression, decision tree) in predicting fossil energy consumption. In addition, feature importance and SHAP are used simultaneously to explore the main driving factors of fossil energy consumption. The research results show that the decoupling index of most provinces fluctuated relatively little from 2011 to 2018. Compared to other machine learning algorithms, XGBoost and CatBoost perform better in predicting fossil energy consumption. Finally, based on two interpretable analysis methods, we found that population has the most significant impact on fossil energy consumption, followed by GDP, and urbanization rate is the least important. The research results provide important information for formulating energy sustainable development strategies and further discuss energy-saving solutions based on data methods.

In recent studies it has been shown that fluorescence lifetime imaging (FLIM) may reveal intracellular structural details in unstained cytological preparations which are not revealed by standard staining procedures. The aim of this investigation was to show whether FLIM images could reveal areas suggestive of polymerization in red blood cells (RBCs) of sickle cell disease (SCD) patients. We examined air-dried unstained blood films using auto-fluorescence FLIM images of 45 SCD patients and compared the results with those of 27 control persons without hematological disease. All control RBCs revealed homogeneous cytoplasm without any foci. Rounded non-sickled RBCs in SCD showed between zero and three small intensively fluorescent dots with higher lifetime values. In sickled RBCs we found additionally larger irregularly shaped intensively fluorescent areas with increased FLIM values. These areas were interpreted as equivalent to polymerized hemoglobin. The rounded non-sickled RBCs of SCD patients with homogeneous cytoplasm were not different from those of erythrocytes of control patients in light microscopy. Yet, variables from the local binary pattern transformed matrix of the FLIM values per pixel, showed significant differences between non-sickled RBCs and those of control cells. In a linear discriminant analysis, using local binary pattern transformed texture features (mean and entropy) of the erythrocyte cytoplasm of normal appearing cells, the final model could distinguish between SCD patients and control persons with an accuracy of 84.7% of the patients.

Machine unlearning, the process of selectively forgetting or removing the influence of specific data points from a machine learning model, is increasingly important for privacy and compliance with regulations like the GDPR. This paper explores the concept of exact unlearning, focusing on its implementation in models trained using convex and non-convex functions. Convex functions, due to their well-behaved optimization landscapes, lend themselves to efficient unlearning through methods such as inverse optimization, duality-based approaches, and incremental learning. In contrast, non-convex functions, common in deep learning models, present more complex challenges due to their multiple local minima and high-dimensional parameter spaces. Techniques like checkpoint-based retraining, gradient inversion, and meta-learning are discussed as viable, though computationally expensive, methods for non-convex exact unlearning. The paper also highlights real-world applications in fields such as finance and healthcare, where exact unlearning can enhance privacy and security without compromising model performance. Finally, it outlines key challenges and future research directions, particularly the need for more efficient unlearning algorithms in non-convex settings and the development of secure, adversarial-resistant methods for sensitive data removal.

Machine unlearning is a critical process designed to allow machine learning models to forget or remove specific data upon request, particularly for privacy protection. While the primary objective of unlearning is to safeguard sensitive information, it introduces substantial privacy and security risks. This paper explores the two major categories of threats to machine unlearning: privacy attacks and security attacks. Privacy attacks, such as membership inference and model inversion, exploit residual information to infer or reconstruct sensitive data that should have been erased. Security attacks, particularly specific data poisoning, involve the injection of malicious data into the training process, which may leave lingering effects even after the unlearning process. In this paper, we provide an in-depth examination of these threats and propose several defense mechanisms. Differential privacy, adversarial training, and query limitations are highlighted as key defenses against privacy attacks, while data validation, adversarial examples, and post-unlearning audits are critical in mitigating security risks. Additionally, we discuss emerging methodologies like data provenance tracking and fine-tuning as crucial for ensuring unlearning processes are thorough and effective. Through these analyses, we aim to provide a comprehensive framework for strengthening the security and privacy of machine unlearning systems, enabling their broader adoption across industries such as healthcare, finance, and AI-driven technologies.

There is an extensive body of research in the literature focusing on predicting the mechanical properties of concrete, such as compressive strength. Summarizing the current studies following the valuable contributions of researchers will serve as a guide for future studies and researchers. To this end, this study aims to identify the key authors, sources, institutions, and countries that have contributed to the prediction of concrete compressive strength. Additionally, it aims to provide researchers with comprehensive information on prominent research themes, trends, and gaps in the literature related to the prediction of concrete compressive strength. For this purpose, 2319 articles on the prediction of concrete compressive strength published from 2000 to 19th August 2024 were identified through the Scopus Database. The scientific measurement analyses were conducted using VOSviewer software. Upon reviewing the relevant research, it was found that machine learning methods are frequently used in predicting concrete compressive strength. In this context, the study will make significant contributions to the literature by examining leading institutions, countries, authors, and sources in the field, synthesizing data, and highlighting research areas, gaps, and trends related to concrete compressive strength prediction.

This review article explores the innovative application of software sensors for monitoring and controlling biosystems, emphasizing their advantage in managing the complexities of various biological processes. Biosystems—from cellular interactions to ecological dynamics—are characterized by intrinsic nonlinearity, temporal variability, and uncertainty, posing significant challenges for traditional monitoring approaches. A critical challenge highlighted is that what is typically measurable may not align with what needs to be monitored. Software sensors bridge this gap by combining hardware sensor data with advanced computational modelling techniques to indirectly infer hard-to-measure target variables, such as stress level, animal and human health indicators, and chemical soil properties. The article outlines advancements in sensor technologies and their integration into model-based monitoring and control systems, leveraging the capabilities of Internet of Things (IoT) devices, wearables, remote sensing, and smart sensors. It provides an overview of common methodologies for designing software sensors, focusing on the modelling process. The discussion contrasts hypothetico-deductive (mechanistic) models with inductive (data-driven) models, illustrating the trade-offs between model accuracy and interpretability. Specific case studies are presented, showcasing software sensor applications such as the use of Kalman filter in greenhouse control, remote detection of soil organic matter, and sound recognition algorithms for early detection of respiratory infections in animals. Key challenges in designing software sensors, including the complexity of biological systems, inherent temporal and individual variabilities, and the trade-offs between model simplicity and predictive performance, are also discussed. This article positions software sensor as remarkable tool for advancing biosystem management, driving forward the potential for sustainable practices in various sectors such as agriculture, healthcare, and environmental monitoring.

Digital twin technology, a cutting-edge approach that creates dynamic digital replicas of physical systems, is increasingly integral to various industrial applications. However, the effective deployment of digital twins often faces challenges associated with data availability, quality, and interoperability, which can impede the development and operational efficacy of these virtual counterparts. This paper presents a systematic review of the application of artificial intelligence (AI) in enhancing and managing digital twin technologies across multiple domains such as industry, healthcare, urban planning, business, education, technology, and others. It specifically examines how machine learning models, particularly neural networks and deep generative models, are being employed to optimize the creation, maintenance, and functionality of digital twins. The review explores the role of artificial intelligence in overcoming data-related limitations by providing robust, scalable data solutions for digital twins. Additionally, it addresses critical considerations of real-time data processing and system interoperability within these applications. This survey not only identifies the prevailing challenges and opportunities within this emerging field but also highlights potential future research directions that could further the integration of AI with digital twin technology. Through a detailed exploration of the intersection between AI and digital twins, this paper aims to contribute significantly to the knowledge base and encourage further innovations in this interdisciplinary area.

This research reviews a novel artificial intelligence (AI) based application called ‘TLDetect’, which filters and classifies anomalous glow curves (GCs) of thermoluminescent dosimeters (TLDs). Until recently, GCs review and correction in the lab were performed using an old in-house software, which uses Microsoft Access database and allows the laboratory technician to manually review and correct almost all GCs without any filtering. The newly developed application ‘TLDetect’ uses a modern SQL database and filters out only the necessary GCs for technician review. TLDetect first uses an Artificial Neural Network (ANN) model to filter out all regular GCs. Afterwards, it automatically classifies the rest of the GCs into five different anomaly classes. These five classes are defined by GCs typical patterns, i.e. high noise at either low or high temperature channels, untypical GC width (either wide or narrow), shifted GCs whether to the low or to the high temperatures, spikes, and the last class contains all other unclassified anomalies. By this automatic filtering and classification, the algorithm substantially reduces the amount of technician’s time of reviewing the GCs and makes the external dosimetry laboratory dose assessment process more repeatable, accurate and fast. Moreover, a database of GCs class anomalies distribution over time is saved along with all their relevant statistics, which can later assist with preliminary diagnosis of TLD reader hardware issues.

This study aims to evaluate the utilization and effectiveness of artificial intelligence (AI) applications in managing symptoms of anxiety and depression. The primary objectives are to identify current AI tools, analyze their practicality and efficacy, and assess their potential benefits and risks. A comprehensive literature review was conducted using databases such as ScienceDirect, Google Scholar, PubMed, and ResearchGate, focusing on publications from the last five years. The search utilized keywords including "artificial intelligence," "applications," "mental health," "anxiety," "LLMs" and "depression". Various AI tools, including chatbots, mobile applications, wearables, virtual reality settings, and large language models (LLMs), were examined and categorized based on their functions in mental health care. The findings indicate that AI applications, including LLMs, show significant promise in symptom management, offering accessible and personalized interventions that can complement traditional mental health treatments. Tools such as AI-driven chatbots, mobile apps, and LLMs have demonstrated efficacy in reducing symptoms of anxiety and depression, improving user engagement and mental health outcomes. LLMs, in particular, have shown potential in enhancing therapeutic chatbots, diagnostic tools, and personalized treatment plans by providing immediate support and resources, thus reducing the workload on mental health professionals. However, limitations include concerns over data privacy, the potential for over-reliance on technology, and the need for human oversight to ensure comprehensive care. Ethical considerations, such as data security and the balance between AI and human interaction, were also addressed. The study concludes that while AI, including LLMs, has the potential to significantly aid mental health care, it should be used as a complement to, rather than a replacement for, human therapists. Future research should focus on enhancing data security measures, integrating AI tools with traditional therapeutic methods, and exploring the long-term effects of AI interventions on mental health. Further investigation is also needed to evaluate the effectiveness of AI applications across diverse populations and settings.

Recent advancements in neural machine translation (NMT) offer promising potential for generating cross-language clinical natural language processing (NLP) resources. There is a pressing need to be able to foster the development of clinical NLP tools that extract key clinical entities in a comparable way for a multitude of medical application scenarios, hindered by lack of multilingual annotated data. This study explores the efficacy of using NMT and annotation projection techniques with expert in the loop validation to develop named entity recognition (NER) systems for an under resourced target language (Catalan) by leveraging Spanish clinical corpora annotated by domain experts. We employed a state-of-the-art NMT system to translate three clinical case corpora. The translated annotations were then projected onto the target language texts and subsequently validated and corrected by clinical domain experts. The efficacy of the resulting NER systems was evaluated against manually annotated test sets in the target language. Our findings indicate that this approach not only facilitates the generation of high-quality training data for the target language (Catalan) but also demonstrates the potential to extend this methodology to other languages, thereby enhancing multilingual clinical NLP resource development. The generated corpora and components are publicly accessible, providing potentially a valuable resource for further research and application in multilingual clinical settings: https://zenodo.org/doi/10.5281/zenodo.13133124.

Background: In recent years, computational aesthetics and neuroaesthetics have provided novel insights into understanding beauty. Building upon the findings of traditional aesthetics, this study aims to combine these two research methods to explore an interdisciplinary approach to studying aesthetics; Method: Abstract artworks were used as experimental materials. Based on traditional aesthetics and in combination, features of composition, tone, and texture were selected. Computational aesthetic methods were then employed to correspond these features to physical quantities: blank space, gray histogram, GLCM, LBP, and Gabor filters. An EEG experiment was carried out, in which participants conducted aesthetic evaluations of the experimental materials in different contexts (genuine, fake), and their EEG data was recorded to analyze the impact of various feature classes in the aesthetic evaluation process. Finally, a SVM was utilized to model the feature data, EEG data, context data, and subjective aesthetic evaluation data; Result: Behavioral data revealed that higher aesthetic ratings in the genuine context. EEG data indicated that genuine contexts elicited more negative deflections in the prefrontal lobes between 200-1000ms. Class II compositions demonstrated more positive deflections in the parietal lobes at 50-120ms, while Class I tones evoked more positive amplitudes in the occipital lobes at 200-300ms. Gabor features showed significant variations in the parieto-occipital area at an early stage. Class II LBP elicited a prefrontal negative wave with a larger amplitude. The results of the SVM models indicated that the model incorporating aesthetic subject and context data (ACC=0.76866) outperforms the model using only parameters of the aesthetic object (ACC=0.68657); Conclusion: A positive context tends to provide participants with a more positive aesthetic experience, but abstract artworks may not respond to this positivity. During aesthetic evaluation, the ERP data activated by different features show a trend from global to local. The SVM model based on multimodal data fusion effectively predicts aesthetics, further demonstrating the feasibility of the combined research approach of computational aesthetics and neuroaesthetics.

Nowadays and in the web era, there is a huge amount of information available due to automatically and easily generated websites. This information has many shared opinions from people toward different entities. Over the last decade there was a significant huge interest to determine the sentiment of any type of text especially on twitter. Twitter provides unstructured text that has many misspelling words, slang words and shortened forms. More complexity and challenges have come to the sentiment analysis researchers in the context of twitter. In this paper we conducted a literature review of the recent research of Twitter sentiment analysis on different languages. After that, we investigated challenges related to twitter sentiment analysis of Arabic language. Then we found a new solution for Sentiment analysis of Arabic twitter. So, we designed the main component of the proposed system and presented its algorithm. Finally, we performed many experiments under two kinds of datasets. The system achieves highest accuracy and shows many important results..

Generative adversarial networks (GANs) have become a recent and rapidly developing research topic in Machine Learning. Since their inception in 2014, a significant number of variants have been proposed to address various topics across many fields, and has particularly excelled not only in image and language processing, but also in the medical and data science domains. In this paper, we aim to highlight the significance and advance that these GAN models can introduce in the field of Business Economics, where they have yet to be fully developed. To this end, a review of the literature of GANs is presented in general together with a more specific review in the field of Business Economics wherein only a few papers can be found. Furthermore, the most relevant papers are analysed in order to provide an approach the opportunity to research into GANs in the field of Business Economics.

A typical orchard's mechanical operation consists of three or four stages: lining, digging for plantation, moving the seedling from nurseries to the farm, moving the seedling to the planting hole, and planting the seedling in the hole. However, the digging the planting hole is the most time-consuming operation. In fruit orchards, robots' usage is increasingly becoming more prevalent to increase operational efficiency. They offer practical and effective services to both industry and people, whether they are assigned to plant trees, reduce the use of chemical fertilizers, or carry heavy loads to relieve staff. Robots can operate for extended periods of time and can highly adept at repetitive tasks like planting many trees. The present study aims to identify the locations for planting trees in orchards using geographic information systems (GIS), to develop an autonomous drilling machine and use the developed robot to open planting holes. There isn't a comparable study on autonomous hole planting in the literature in this regard. The agricultural mobile robot is a four wheeled nonholonomic robot with differential steering and forwarding capability to stable target positions. The designed mobile robot can be used in fully autonomous, partially autonomous, or fully manual modes. The drilling system, which is a y-axis shifter driven by a DC motor with reducer includes an auger with a 2.1 HP gasoline engine. SOLIDWORKS software was used for designing and drawing of the mobile robot and drilling system. The Microsoft Visual Basic.NET programming language was used to create the robot navigation system and drilling mechanism software. The cross-track error (XTE), which determines the distances between the actual and desired holes positions, was utilized to analyze the steering accuracy of the mobile robot to the drilling spots. Consequently, the average of the arithmetic means was determined to be 4.35 cm, and the standard deviation was 1.73 cm. This figure indicates that the suggested system is effective for drilling plant holes in orchards.

Background: Generative artificial intelligence (Gen-AI) has emerged as a transformative tool in research and education. However, there is a mixed perception about its use. This study assessed the use, perception, prospect, and challenges of Gen-AI use in higher education. Methods: This is a prospective, cross-sectional survey of university students in the United Kingdom (UK) distributed online between January and April 2024. Demography and perception of Gen-AI and other AI tools were assessed and statistically analysed to assess the difference in perception between various subgroups. Results: A total of 136 students responded to the survey of which 59% (80) were male. The majority were aware of Gen-AI and other AI use in academia (61%) with 52% having personal experience of the tools. Grammar correction and idea generation were the two most common tasks of use, with 37% being regular users. Fifty-six percent of respondents agreed that AI gives an academic edge with 40% holding a positive overall perception about the use in academia. Comparatively, there was a statistically significant difference in overall perception between different age ranges (I² = 27.39; p = 0.002) and levels of education (I² = 20.07; p < 0.001). Also, 83% of students believe AI use will increase in academia with over half agreeing it should be integrated into learning. Plagiarism (33%), privacy issues (14%), and lack of clarity by the university (13%) remain the top concerns regarding the use of Gen-AI and other AI tools in academia. Conclusions: Gen-AI and other AI tools are being used and their use will continue to grow in higher education. While current use is challenging due mainly to plagiarism fear and lack of clarity by the university, most users believe AI should be integrated into the university curriculum.

Enhancing the management and monitoring of oil and gas processes demands developing precise predictive analytics techniques. Over the past two years, oil and its prediction have advanced significantly using conventional and modern Machine Learning techniques. Several review articles detail the developments in predictive maintenance and technical and non-technical aspects of influencing the uptake of big data. The absence of references for machine learning techniques impacts the effective optimization of predictive analytics in the oil and gas sector. This review paper offers readers thorough information on the latest machine learning methods utilized in this industry's predictive analytical modelling. The review covers forms of Machine Learning techniques used in predictive analytic modelling from 2021 to 2023 (91 articles). It provides an overview of the details of the papers that were reviewed, comprising of the model’s categories, the data's temporality, field, and name, the dataset's type, predictive analytics (classification or clustering or prediction), the models' input and output parameters, performance metrics, optimal model, and benefits and its drawbacks. Additionally, suggestions for future research directions are provided to raise the potential of the associated knowledge and increase the accuracy of oil and gas predictive analytics models.

Artificial Intelligence (AI) tools and techniques are comparatively recent advances of present-day globalized world. With their easy availability and friendly user interface AI has affected every sphere of human being. These tools are increasingly becoming popular because of the numerous benefits occurred to the society in many ways. However, there are not only the positive side of these technologies, but also many negative sides. Along with the development and adoption of AI, cyber-attacks are increased and disrupting the information system. Threat actors may misuse artificial intelligence. Hence, these tool designers are working and applying the ability of generative AI can tip the scales in cybersecurity. So, in the age of AI, there is always tension between the cyber attackers and the defenders to make a balanced information system. It is a general rule that defenders must be well in advance in their favor and keep ahead of adversaries. The United National and several governments for example the US, UK, and the big AI giants like Microsoft along with OpenAI, are working on emerging cyberattacks. This research paper explores the concept of information resilience in ML and AI systems, focusing on strategies to enhance their ability to withstand uncertainties, adversarial attacks, and data perturbations.

This study introduces a novel algorithm tailored for the precise detection of lower outliers in univariate datasets, particularly suited for scenarios with a single cluster. The approach leverages a combination of transformative techniques and advanced filtration methods to efficiently segregate anomalies from normal values. Notably, the algorithm emphasizes high precision, ensuring minimal false positives, and requires only a few parameters for configuration. Its unsupervised nature enables robust anomaly filtering without the need for extensive manual intervention. To validate its efficacy, the algorithm is rigorously tested using real-world data obtained from photovoltaic (PV) module strings with similar DC capacities, containing various anomalies. Results demonstrate the algorithm's capability to accurately identify lower outliers while maintaining computational efficiency and reliability in practical applications.

Background: Microscopic examination of acid-fast stained sputum smears for detecting mycobacterial acid-fast bacilli (AFB) remains the most economical and readily available method for laboratory diagnosis of tuberculosis (TB). However, this conventional approach has limitations, including low sensitivity and labor-intensive procedures. Methods: An automated microscopy system incorporating artificial intelligence (AI) and machine learning for AFB identification was evaluated. The study was conducted at an Infectious Disease Hospital in Jiangsu Province, China, utilizing an intelligent microscope system (TB-Scan, Wellgen Medical, Kaohsiung). A total of 1,000 sputum smears were included in the analysis, with the system capturing digital microscopic images and employing an image recognition model to automatically identify and classify AFB. Referee technicians provided the gold standard for discrepant results. Results: The automated system demonstrated an overall accuracy of 95.00% (950/1,000), sensitivity of 91.24% (177/194), and specificity of 95.91% (773/806). Notably, the system identified 21 smears as positive that were previously reported as negative, with referee technicians confirming 17 of these as true positive and retracting the test results accordingly. Recalculating the performance, the accuracy increased to 96.70% (967/1,000), sensitivity to 91.94% (194/211), and specificity to 97.97% (773/789), with a false negative rate of 8.06% (17/211) and a false positive rate of 2.03% (16/789). Conclusions: The incorporation of AI and machine learning into an automated microscopy system demonstrated the potential to enhance the sensitivity and efficiency of AFB detection in sputum smears compared to conventional manual microscopy. This approach holds promise for widespread application in TB diagnostics and potentially other fields requiring labor-intensive microscopic examination.

The causes of algal blooms in reservoirs are often complexly intertwined with chemical, physical, and biological factors such as the supply of nutrients. Observation of phytoplankton distribution with high spatiotemporal resolution is necessary to track the nutrient sources that cause algal blooms and to understand their behavior in response to wind and water temperature stratification. Observation from a UAV, which has excellent temporal and spatial resolution, is considered to be an effective method to obtain water quality information comprehensively. On the other hand, it is not only the growth of plankton that affects the color of the water surface but also turbidity. Furthermore, since the brightness value of passive sensors such as optical cameras changes depending on the amount of insolation, it is necessary to perform analysis after making corrections for this. In this study, we attempted to develop a method for estimating chlorophyll concentration from aerial images taken from UAVs using machine learning that takes into account brightness correction based on insolation and the spatial distribution of turbidity evaluated by satellite image analysis.

In this project the EEG – electroencephalogram - channel(s) will be characterized to diagnose PTSD – Post-traumatic stress disorder – cases. For this aim, we applied boosting methods including a combination of K-mean and Support Vector Machine (SVM) models to find the feature weights to detect the PTSD cases. We classified 32 channels of 12 subjects including 6 PTSD and 6 healthy controls using a 6-mean classifier. The linear SVM found the weights of distinguished channels within each subject for each cluster. It was found that the significant SVM weights of F4, F8, and Pz are smaller in PTSD than in healthy subjects. This new method can be used as a tool to better understand the interaction of EEG signals and diagnosis.

This paper describes a system that is used to gather data from Smart Home sensors measuring energy consumption, analyze and predict future consumption by using machine learning algorithm for prediction. It focuses on the architecture of the system as well as the need for such system to exist in practice – why the consumption should be measured at all and how that measurement can be put to good use for achieving energy efficiency.

Colorectal cancer (CRC) with KRAS mutation is prone to develop metachronous metastasis. However, effective measures for prediction and prevention of such clinical issue are currently unavailable. In this study, we confirmed that KRAS mutation is a risk factor for CRC metachronous metastasis through meta-analysis and public database analysis. Based on the ICGC-AGRO database, we have constructed a risk scoring model for predicting metachronous metastasis in KRAS-mutant CRC using machine learning, which was validated in public datasets. Furthermore, we discovered that KRAS inhibitors can effectively suppress the migration and invasion capabilities of high risk CRC cells by wound healing assay and transwell experiment, which was validated ex vivo and in vivo using organoids and splenic liver metastatic mouse model. Finally, RNA sequencing, RT-qPCR, and western blots have proven that KRAS inhibitors can suppress the epithelial-mesenchymal transition (EMT) and transforming growth factor beta (TGF-beta) signaling pathway in CRC. Collectively, our study suggests that KRAS inhibitors may inhibit the metastatic ability of CRC by suppressing EMT and the TGF-beta pathway, suggesting their potential for the prevention of metachronous metastasis in CRC with KRAS mutation.

Artificial intelligence (AI) has witnessed an exponential increase in its use in various applications. Recently, the academic community started to research and inject new AI-based approaches to provide solutions to traditional software engineering problems. However, a comprehensive and holistic understanding of the current status is missing. To close the above gap, synthetic knowledge synthesis was used to induce a research landscape of the contemporary research literature on the use of AI in software engineering. The synthesis resulted in 15 research categories and five themes, namely natural language processing in software engineering, use of artificial intelligence in the management of software development life cycle, use of machine learning in fault/defect prediction and effort estimation, employment of deep learning in intelligent software engineering and code management, and mining software repositories to improve software quality. The most productive country was China (n=2042), followed by the United States (n=1193), India (n=934), Germany (n=445), and Canada (n=381). A high percentage (n=47.4%) of papers were funded, showing a strong interest in this research topic. The convergence of AI and software engineering can significantly reduce needed resources, improve quality, increase user experience, and improve the well-being of software developers.

Accurately estimating shrub biomass in arid and semi-arid regions is critical for understanding ecosystem productivity and carbon stocks at both local and global scales. However, capturing the shrub biomass accurately by satellite observations is challenging due to the short and sparse features of shrubs. In this study, we presented a framework to estimate shrub biomass at a 10m spatial resolution by integrating ground in-situ data, unmanned aerial vehicle (UAV), Sentinel-2, and Sentinel-1 observations. The pilot study was conducted in the Helan mountains of Ningxia province, China. First, the spatial distribution map of shrubland and other land cover types was generated in 2023. Then, a prediction model of shrub biomass was developed in a Random Forest Regression (RFR) approach driven by different predicted variable datasets based on in-situ measurements, UAV, and satellite images. Finally, the developed model was used to produce the biomass map of shrubland in 2023. The uncertainty was characterized by creating a standard deviation (SD) map using the Leave-One-Out Cross-Validation (LOOCV) method in the shrub biomass estimates. The resultant shrubland distribution map in 2023 has an F1 score of 0.92. The shrub biomass model driven by spectral bands and vegetation indices (R2= 0.62) demonstrated superior performance than that only driven by spectral bands (R2=0.33) or vegetation indices (R2=0.55). The uncertainty of the estimated biomass was lower than 4%, with the lowest values (<2%) happening in the high shrub coverage (>30%) and biomass production (>30kg/m2) regions. Furthermore, the findings show that the magnitude of shrub biomass was affected by the aridity and precipitation significantly in the study region. This study provides a workflow to accurately monitor the biomass of shrublands in a complicated environment based on remote sensing images from multi-platform observations.

In this study on volume determination in waste sorting facilities, we evaluate the effectiveness of ultrasonic sensors and address waste-material-specific challenges. Although ultrasonic sensors offer a cost-effective automation solution, their accuracy is affected by irregular waste shapes, varied compositions, and environmental factors. Notable inconsistencies in volume measurements between storage bunkers and conveyor belts underscore the need for a comprehensive approach to standardize bale production. With prediction reliability being constrained by limited data sets, undocumented modifications to machine-settings and sensor failures, this task renders a challenging application area for Machine Learning. We explore related research and present dataset analyses from three distinct waste sorting facilities in Europe, addressing issues as sensor usability, data quality and material specifics. Our analysis suggests promising strategies and future directions for enhancing waste volume measurement accuracy, ultimately aiming to advance sustainable waste management.

The application of Explainable AI (XAI) to improve understanding of complex non-linear SST teleconnections and gain predictive skills is at the frontier of climate research. In this study, we attempt to refine the applications of XAI advancements to ENSO prediction by including SSTA in the western North Pacific (WNP) as a precursor. Our analyses indicate that the baseline accuracy increases from the 60% threshold previously reported for ENSO to &gt;85% for moderate warm, cold, and neutral ENSO states one year in advance when WNP SSTA is included. Precision increases to over 90% for higher magnitude El Niños and La Niñas. Experiments conducted at 4-year lead times yield a strong WNP SSTA signal. A moderate SSTA signal is observed at 3-year lead time with no predictability; however, predictability spikes to over 80% when stratospheric zonal winds are included with WNP SSTA. While additional scientific work is required to make robust connections between XAI and dynamical processes, our results demonstrate a potential to improve long term predictability of ENSO through the ability of XAI in capturing SST patterns evolving at extended spatial and temporal scales.

Genotypic testing is often recommended to improve the management of patients infected with human immunodeficiency virus (HIV). To help combat this major pandemic, next-generation sequencing (NGS) techniques are widely used to analyse resistance to antiretroviral drugs. In this study, we used a Vela Sentosa kit, which is usually used for the Ion Torrent personal genome machine (PGM) platform, to sequence HIV using the Illumina Miseq platform. After RNA extraction and reverse transcriptase-polymerase chain reaction (RT-PCR), minor modifications were applied to the Vela Sentosa kit to adapt it to Illumina Miseq platform. Analysis of the results showed the same mutations present in the samples using both sequencing platforms. The total number of reads varied from 185,069 to 752,343 and from 642,162 to 2,074,028 in the Ion Torrent PGM platform and the Illumina Miseq platform, respectively. The average depth was 21955 and 46856 for Ion Torrent PGM and Illumina Miseq platforms, respectively. The cost of sequencing a run of eight samples was closely similar between the two platforms (about 1 790 United States dollars [USD] for Illumina Miseq and about 1833 USD for Ion Torrent PGM platform). We have shown for the first time that it is possible to adapt and use the Vela Sentosa kit for the Illumina Miseq platform to obtain high quality results with a similar cost.

Historically the use of artificial intelligence (AI) in paediatrics dates back to 1984 with the intro-duction of a computer-assisted medical decision-making system called SHELP. Since then, research on the use of AI in pediatrics has become much more popular, and the number of reviewed publications largely increased. Consequently, a need for a holistic research landscape enabling researchers and other interested parties to gain insights into, deepen their knowledge about or inform them about the use of AI in pediatrics arised. To fill this gap a novel methodology named synthetic knowledge synthesis was applied. Using SKS we identified most prolific countries, in-stitutions, source titles, funding agencies, research themes, research gaps and hot spots.

There is a noticeable absence of health education among college students. This study aimed to evaluate the extent of general nutrition knowledge among Chinese university students and explore its association with eating attitudes. Data were collected from a group of 273 students in Spring of 2023, using a valid and reliable research instrument consisting of three sections: demographic variables, the General Nutrition Knowledge Questionnaire (GNKQ), and the Eating Attitudes Test (EAT-26). The results were analyzed using SPSS, with correlations and t-tests to examine the relationships between nutritional knowledge and dietary attitudes. Furthermore, the present study employed the Random Forest (RF) algorithm, a machine learning technique, utilizing the Mean Decrease Impurity (MDI) method to investigate the influence of various features on participants' eating attitudes. The findings revealed that Chinese university students had an average accuracy of over 60% in their nutritional knowledge, but their understanding of the relationship between diet and disease still needs improvement. Moreover, male students had significantly lower nutritional knowledge than female students, and there was a positive correlation between nutritional knowledge and parents’ income. The study also found a significant correlation between the level of nutritional knowledge and eating attitudes. RF results indicated that family income level exhibited the most substantial impact on the eating attitudes of the participants. The study highlights the need for nutrition education curriculum developers to focus more on improving students' nutritional knowledge, with particular attention given to male students, low-income individuals, and those with an abnormal BMI.

This study explores the dependence of drone acoustic detection systems performance on distance, using learning machines with different complexities, from simple linear discriminants, to deep neural networks, such as YAMNet, which exploits transfer learning. Other machine learning systems studied are the multilayer perceptron, support vector machines and random forest. These methods have been evaluated with a meticulously selected database that includes a wide variety of drone and interference sounds, previously processed by array signal processing and affected by ambient noise. Two strategies for training learning machines are considered. In the first one, they are trained with unattenuated signals, trying to preserve the information generated by the sound sources, but they are tested considering the attenuation with distance, achieving effective detection at distances up to 200 metres with some methods, especially linear discriminant. In all cases, the existence of interferences that hinder detection is considered. In the second strategy, the systems are trained and tested with attenuated signals as a function of distance. The effective detection range increases up to 300 metres for most methods and up to 500 metres for the YAMNet based detector. In addition, this second approach raises the possibility of developing specialised detectors for each distance range, thus significantly extending the effective detection range. These results underline the promising potential of acoustic detection of drones at various distances, encouraging further exploration in this research area.

AI applications are transforming the technology and digital sector, seamlessly integrating into the daily interactions of individuals with the digital world. Key entry points to the internet, such as search engines, are now complemented by recommendation systems that curate content on social networks, streaming platforms, and online marketplaces. Additionally, virtual assistants have become ubiquitous across various platforms. While these groundbreaking changes are gradually extending to several industries, there remains significant untapped potential for the application of artificial intelligence in diverse domains. The renewable energy sector, in particular, continually reinvents itself through research and innovation to meet the escalating demand. Positioned at the forefront of technological evolution, this industry is well-placed to effectively adopt and tailor the innovations of artificial intelligence to its specific needs. Presently, both emerging and established tools leverage elements of AI to enhance processes and introduce functionalities that were previously unattainable. If the current trajectory persists, AI-enhanced tools have the potential to assume a role as crucial to the future of engineering as computer-aided design (CAD) software is in contemporary times.

Within the past few decades, thousands of experiments have been performed to characterize waste and biomass to estimate the bioenergy potential and product identification. There is a need to develop an integrated process model based on experimental literature, and simulation to obtain suitable products. In this study municipal solid waste (MSW) characterization and integrated process model have been developed to optimize final products in a reactor system. The process model has two modes R&amp;D and reactor control (RC) to obtain suitable products including bio-oil, char, and gases. A database was integrated based on thermokinetics, machine learning and simulation models to optimize product efficiency. The experimental data includes thermogravimetric analysis, Fourier transform Infrared Spectroscopy, Gas chromatography, and Mass spectrometry, which are linked with pyrolysis experimental setup. Feedstock-product mapping models were incorporated into the database along with the temperature, heating rates, elemental analysis, and final product concentration, which are utilized for pyrolysis reactor setup. Product feasibility is conducted based on lifecycle cost, affordability, and product efficiency. The present work will bridge the gap between experimental study and decision making based on obtained products at several experimental conditions around the world.

As artificial intelligence continues to advance, researchers are increasingly using machine learning algorithms to study the factors that make people more susceptible to phishing scams. Most studies in this area have taken one of two approaches: either they explore statistical associations between various factors and susceptibility, or they use complex models such as deep neural networks to predict phishing behavior. However, these approaches have limitations in terms of providing practical insights for individuals to avoid future phishing attacks and delivering personalized explanations regarding their susceptibility to phishing. In this paper, we propose a machine learning approach that leverages explainable artificial intelligence techniques to examine the influence of human and demographic factors on susceptibility to phishing attacks. Our analysis reveals that psychological factors such as impulsivity and conscientiousness, as well as appropriate online security habits, significantly affect an individual's susceptibility to phishing attacks. Furthermore, our individualized case-by-case approach offers personalized recommendations on mitigating the risk of falling prey to phishing exploits, considering the specific circumstances of each individual.

The diagnosis and the pathology of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) remain under debate. However, there is a growing body of evidence for an autoimmune component in ME/CFS caused by the Epstein-Barr virus (EBV) and other viral infections. In this work, we took advantage of a large public dataset on the IgG antibodies to 3,054 EBV peptides to understand whether these immune responses could be used as putative biomarkers for disease diagnosis and triggers of pathological autoimmunity in ME/CFS patients using healthy controls (HCs) as a comparator cohort. We then aimed at predicting disease status of study participants using a Super Learner algorithm targeting an accuracy of 85% when splitting data into train and test datasets. When we compared data of all ME/CFS patients or data of a subgroup of these patients with non-infectious or unknown disease trigger to the dataset of HC, we could not find an antibody-based classifier that would meet the desired accuracy in the test dataset. In contrast, we could identify a 26-antibody classifier that could distinguish ME/CFS patients with an infectious disease trigger from HCs with 100% and 90% accuracies on the train and test sets, respectively. We finally performed a bioinformatic analysis of the EBV peptides associated with these 26 antibodies. We found no correlation between the importance metric of the selected antibodies in the classifier and the maximal sequence homology between human proteins and each EBV peptide recognized by these antibodies. In conclusion, these 26 antibodies against EBV have an effective potential for disease diagnosis of a subset of patients, but they are less likely to trigger pathological autoimmune responses that could explain the pathogenesis of ME/CFS.

On a global scale, food safety and security aspects entail to be considered throughout the farm to fork continuum considering food’s supply chain. Generally, the agri-food system is a multiplex network of interconnected features and processes, with a hard predictive rate, where maintaining the food’s safety is an indispensable element and is part of the Sustainable Development Goals (SDGs). It has led the scientific community to develop advanced applied analytical methods, such as Machine learning (ML) and Deep Learning (DL) techniques applied for assessing foodborne diseases. The main objective of this paper is to contribute to the development of the consensus version of ongoing research about the application of artificial intelligence tools in the domain of food-crops safety from an analytical point of view. Writing a comprehensive review for a more specific topic can also be challenging, especially when searching within the literature. To our knowledge, this review is the first to address this issue. This work consisted of conducting a unique and exhaustive study of the literature, using our TriScope Keywords-based Synthesis methodology. All available literature related to our topic was investigated according to our criteria of Inclusion and Exclusion. The final count of data papers was subject to deep reading and analysis to extract the necessary information to answer our research questions. Although many studies have been conducted, limited attention has been paid to outlining the applications of AI tools combined with analytical strategies for crop-based food safety specifically.

Microsoft has implemented several measures to defend against macro viruses, including the use of Anti-malware Scan Interface (AMSI) and automatic macro blocking. Nevertheless, evidence shows that threat actors have found ways to bypass these mechanisms. As a result, phishing emails continue to apply malicious macros as their essential attack vector. In this paper, we analyze 77 obfuscation features from the attacker’s point of view and extract 46 suspicious keywords in macro. We first combine the above two types of features to train machine learning models on a public dataset. Then, we carry out the same experiment on self-constructed dataset, a collection of newly discovered samples, to see if our proposed method could discover the unseen malicious macros. Experimental results demonstrate that, comparing with the existing researches, our proposed method has a higher detection rate and better consistency. Furthermore, ensemble multi-classifiers with distinct feature selection further improve the detection performance.

The relative position of the orchard robot to the rows of fruit trees is an important parameter for achieving autonomous navigations. The current methods for estimating the position parameters between rows of orchard robots obtain low parameter accuracy, and to address this problem, this paper proposes a machine vision-based method for detecting the relative position of orchard robots and fruit tree rows. Firstly, the fruit tree trunk is identified based on the improved YOLOv4 model; secondly, the camera coordinates of the tree trunk are calculated from the principle of binocular camera triangulation, and the ground projection coordinates of the tree trunk are obtained through coordinate conversion; finally, the midpoints of the projection coordinates of different sides are combined and the navigation path is obtained by linear fitting with the least squares method, and the position parameters of the orchard robot are obtained through calculation. The experimental results show that the average accuracy and average recall of the improved YOLOv4 model for fruit tree trunk detection are 97.05% and 95.42%, respectively, which are 5.92 and 7.91 percentage points higher than those of the original YOLOv4 model. The average errors of heading angle and lateral deviation estimates obtained based on the method in this paper are 0.57° and 0.02 m. The method can accurately calculate heading angle and lateral deviation values at different positions between rows, and can provide a reference for autonomous visual navigation of orchard robots.

Single-cell RNA sequencing (scRNA-seq) technology has significantly advanced our understanding of the diversity of cells and how this diversity is implicated in diseases. Yet, translating these findings across various scRNA-seq datasets poses challenges due to technical variability and dataset-specific biases. To overcome this, we present a novel approach that employs both an LLM-based framework and explainable machine learning to facilitate generalization across single-cell datasets and identify gene signatures to capture disease-driven transcriptional changes. Our approach uses scBERT, which harnesses shared transcriptomic features among cell types to establish consistent cell-type annotations across multiple scRNA-seq datasets. Additionally, we employ a symbolic regression algorithm to pinpoint highly relevant yet minimally redundant models and features for inferring a cell type’s disease state based on its transcriptomic profile. We ascertain the versatility of these cell-specific gene signatures across datasets, showcasing their resilience as molecular markers to pinpoint and characterize disease-associated cell types. Validation is carried out using four publicly available scRNA-seq datasets from both healthy individuals and those suffering from ulcerative colitis (UC). This demonstrates our approach’s efficacy in bridging disparities specific to different datasets, fostering comparative analyses. Notably, the simplicity and symbolic nature of the retrieved gene signatures facilitate their interpretability, allowing us to elucidate underlying molecular disease mechanisms using these models.

With the advancement of urbanization in China, effective control of pollutant emissions and air quality have become important goals in current environmental management. Nitrogen dioxide (NO2), as a precursor of tropospheric ozone and fine particulate matter, plays a significant role in atmospheric chemistry research and air pollution control. However, the uneven ground monitoring stations and low temporal resolution of polar-orbiting satellites set challenges for accurately assessing near-surface NO2. To address this issue, a spatiotemporal refined NO2 retrieval model was established for China using the geostationary satellite Himawari-8. The spatiotemporal characteristics of NO2 were analyzed and its contribution factors were explored. Firstly, seven Himawari-8 channels sensitive to NO2 were selected by using the forward feature selection based on information entropy. Subsequently, a 2DCNN-LSTM network model was constructed, incorporating the selected channels and meteorological variables as retrieval factors to estimate hourly NO2 in China from March 2018 to February 2020 (with a resolution of 0.05°, per hour). The performance evaluation demonstrated that the full-channel 2DCNN-LSTM model had good fitting capability and robustness (R2=0.74, RMSE=10.93), and further improvements were achieved after channel selection (R2=0.87, RMSE=6.84). The 10-fold cross-validation results indicated that the R2 between retrieval and measured values was above 0.85, the MAE was within 5.60, and the RMSE was within 7.90. R2 varied between 0.85 and 0.90, showing better validation at mid-day (R2=0.89) and in spring and fall transition seasons (R2 =0.88 and R2 =0.90). To investigate the cooperative effect of meteorological factors and other air pollutants on NO2, statistical methods (Beta coefficients) were used to test the factor interpretability. Meteorological factors as well as other pollutants were analyzed. From a statistical perspective, PM2.5, Boundary Layer Height, and O3 were found to have the largest impacts on near-surface NO2, with each standard deviation change in these factors leading to 0.28, 0.24, and 0.23 in standard deviations of near-surface NO2, respectively. Findings of the study contribute to a comprehensive understanding of the spatiotemporal distribution of NO2 and provide a scientific basis for formulating targeted air pollution policies.

Software Defined Networking (SDN) is positioning the standard for the management of networks due to its scalability and flexibility to program the network. The SDN provides many advantages but it also involves some specific security problems take down the controller using cyber attack and in result the whole network will shut down which makes it a single point of failure. In this paper, the DDoS attacks in SDN were detected using AI-enabled, machine and deep learning, models with some specific features for data-set under normal and DDoS traffic. In our approach, initial data-set is collected from 84 features on kaggle and then the 20 top most features are selected using permutation importance algorithm. The data-set were learned and tested with AI-enabled 5 models. Our experimental results showed that the use of machine learning based random forest model has achieved the highest accuracy rate of 99.97%, in DDoS attack detection in SDN. Our contributions through this study are, first, we found highest 20 attacks that absolutely contributed to DDoS attacks. Secondly, it can reduce the time and cost of comparing various learning models and performance required for determining a learning model suitable for DDoS detection. Finally, various experimental methods for evaluating the performance of the learning model are presented so that related researchers can utilize them.

Electroencephalography (EEG) signals provide valuable insights into various activities of the human brain, including the detection of mental stress, which is a complex physiological and psychological response. However, the challenge lies in identifying mental stress accurately while mitigating the limitations associated with a large number of EEG channels. This includes issues such as computational complexity, the risk of overfitting, and the increased setup time for electrode placement, which can be cumbersome for real-life applications. Therefore, it is crucial to develop EEG channel selection algorithms that enable the creation of a wearable device capable of assessing mental stress in real-life scenarios. This study introduces a novel channel selection method aimed at identifying highly accurate channels for detecting mental stress. Our approach, known as the Correlation Coefficient of Hjorth Parameters (CCHP), assesses the correlation between activity, mobility, and complexity in the time domain to nominate the most relevant channels. By selecting channels that exhibit high correlation with the stress task while being uncorrelated with each other, CCHP significantly reduces the number of EEG channels required, without compromising accuracy or performance. To evaluate the effectiveness of CCHP, we conducted experiments using the DEAP public dataset. Comparing our results with other recent algorithms that utilize the full set of EEG channels, CCHP achieved a superior classification accuracy of 81.56% using only eight EEG channels. Furthermore, CCHP outperformed existing channel selection methods by an impressive 8%. These findings strongly indicate that the CCHP algorithm shows great promise in the design of a wearable application for mental stress detection, utilizing a minimal number of EEG channels.

In this paper we derive a unified perspective for Optimal Transport (OT) theory and Mean Field Control (MFC) theory to analyse the learning process for Neural Networks algorithms in a high-dimensional framework. We consider Mean Field Neural Networks in the context of MFC theory, specifically the mean field formulation of OT theory that allows the development of highly efficient algorithms while providing a powerful tool in the context of explainable Artificial Intelligence.

Like the most of crops, especially sugarcane needs to be kept upright until it is harvested. Because, lodging of sugarcane has a significant negative effect on cane yield and sugar content of sugarcane. To keep sugarcane upright earthing up is an utmost intercultural operation. In Bangladesh, most of the operations for sugarcane cultivation including earthing up are generally done in traditional method using human labor which increases production costs as well as reduces the income of sugarcane growers. So, a cost effective two-wheeled tractor (2WT) mounted earthing up machine was developed in Bangladesh Sugarcrop Research Institute (BSRI), Pabna to reduce drudgery and cost of sugarcane production. Field tests were conducted in experimental sugarcane field at BSRI and technical and economic performances of the developed earthing up machine were also carried out based on the field test. Average effective field capacity and field efficiency were found 0.16 ha/hr and 77.41 %, respectively for the developed earthing up machine. The earthing up machine was not found economically viable when it is used only for earthing up operation. Besides, when the 2WT was being used as the main driver for other activities including earthing up operation, then the earthing up machine becomes economically beneficial with net cash flow, net present value, internal rate of return, benefit-cost ratio and payback period of BDT 148497/ha, BDT 23184, 3%, 3.81:1 and approximately 1 year, respectively. On the contrary, considering cost of only earthing up tool without 2WT, then it was found economically beneficial with net cash flow, internal rate of return, net present value, benefit-cost ratio and payback period of BDT 16428/ha, BDT 3053, 4.7%, 2.71:1 and approximately 2 years, respectively. Since 2WT is commonly used for versatile farming purposes. Therefore, versatile use of 2WT as prime mover for other machines including earthing up machine can make earthing up machine economically viable and beneficial for sugarcane growers.

With the rise of piano teaching in recent years, many people have joined the team of piano learners. However, the expensive cost of manual instruction and the unique one-on-one teaching model have made piano learning an extravagant event. Most existing approaches based on the audio modality aim to evaluate piano players' skills. Unfortunately, these methods ignored the information contained in the video, which led to a one-sided and simplistic evaluation of the piano player's skills. More recently, multimodal-based methods are proposed to assess the skill level of piano players using both video and audio information. However, existing multimodal approaches use shallow networks to extract video and audio features, which are deficient in extracting complex spatio-temporal and time-frequency features from piano performance. Furthermore, the fingering and the pitch-rhythm information of the piano performance is contained in the spatio-temporal and time-frequency features, respectively. In the paper, we propose a ResNet-based audio-visual fusion model that combines video and audio features to assess the skill level of piano players. Firstly, ResNet18-3D is used as the backbone network for our visual branches, which can extract feature information from the video data. Then, we consider ResNet18-2D as the backbone network of the aural branch and extract the feature information from the audio data. The extracted video features are fused with the audio features to generate multimodal features for the final piano skill evaluation. The experimental results on the PISA dataset show that our proposed audio-visual fusion model, with a validation accuracy of 70.80%, outperforms the state-of-the-art methods in both performance and efficiency. Then, we also explore the impact of different layers of ResNet on model performance, and the experimental results show that the audio-visual fusion model dealing with the piano skill assessment problem can make full use of both feature information when the number of video features is close to the number of audio features.

Data scarcity is a significant challenge in the field of Machine Learning (ML), as data collection can be expensive, time-consuming, and difficult, particularly in developing countries. This challenge is exaggerated on the need to use dataset for livestock disease predictions for early intervention and surveillance. To address this challenge, this paper presents a data synthesis method that has been used to accurately generate new data samples from few real-world data. With much data available to train the ML models, overfitting is eliminated. We present the use of Generative Adversarial Networks mainly the Conditional Tabular Generative Adversarial Network to synthesize categorical data for training machine learning models for prediction of the Pestes des Petits Ruminants (PPR) disease. The results showed that training score became 0.89 and the cross-validation score was 0.87 after synthesized data was used with Random Forest algorithm. The resulting dataset can be used to support the prediction and surveillance of the Pestes des Petits Ruminants (PPR) disease. The proposed method can also be applied to any domain with categorical data, and has the potential to improve the performance of machine learning models with increased data availability.

Conducting related research is crucial since there are still many problems that need to be resolved in the research on soft robots in the areas of material selection, structure design and manufacture, and drive control. Soft manipulators, a subset of soft robots, are now a popular area of study for many researchers. In comparison to typical manipulators, soft manipulators feature a high degree of gripping flexibility and a basic morphological structure. They are composed of flexible materials. It has a wide range of potential applications in healthcare, rehabilitation, bionics, and detection, and it can compensate for the drawbacks of rigid manipulators in some use scenarios. A modular soft-body torsional gripping system is developed after a torsional and gripping actuator is conceived, constructed, and its performance is examined. The torsion actuator and the grasping actuator can be combined in the system in a modular fashion. With the help of RGB-D vision algorithms, this multi-modular setup makes it possible to combine soft actuators with various twisting degrees and achieve exact gripping. Through pneumatic control, the target object is precisely grasped and rotated at various angles, enabling rotation of the target object in three dimensions.

This paper mainly used database technology, machine learning, thermodynamic calculation, experimental verification, etc., on integrated computational materials engineering. The interaction between different alloying elements and the strengthening effect of precipitated phases are investigated mainly for martensitic ageing steels. Modelling and parameter optimization were performed by machine learning, and the highest prediction accuracy was 98.58%. Investigated the influence of composition fluctuation on performance and correlation tests to analyze the influence of elements from multiple perspectives. Furthermore, screen out the three-component composition process parameters with composition and performance with high contrast. Thermodynamic calculations studied the effect of alloying element content on the nano-precipitation phase, Laves phase, and austenite in the material. The heat treatment process parameters of the new steel grade were also developed based on the phase diagram. A new type of martensitic ageing steel was prepared by selected vacuum arc melting. The sample with the highest overall mechanical properties had a yield strength of 1887 MPa, a tensile strength of 1907 MPa, and a hardness of 58 HRC. The sample with the highest plasticity had an elongation of 7.8%. The machine learning process for the accelerated design of new ultra-high tensile steels was found to be generalizable and reliable.

Abstract: Despite existing several techniques for distributed sensing (temperature and strain) using standard Single Mode optical Fiber (SMF), compensating or decoupling both effects is mandatory for many applications. Currently, most of the decoupling techniques require special optical fibers and are difficult to implement with high spatial resolution distributed techniques, such as ϕ-PA-OFDR. So, this work’s objective is to study the feasibility of decoupling temperature and strain out of a ϕ-PA-OFDR readouts taken over an SMF. For this purpose, the readouts will be subjected to a study using several Machine Learning algorithms, among them, Deep Neural Networks. The motivation which underlies this target is the current blockage in the widespread use of Fiber Optic Sensors in situations where both strain and temperature change, due to the coupled dependence of currently developed sensing methods. Instead of using other types of sensors or even other interrogation methods, the objective of this work is to analyze the available information in order to develop a sensing method capable of providing information about strain and temperature simultaneously.

In the last decade, the genetic engineering world has been shaken up by a relatively new genetic editing tool based on RNA-guided Nucleases (RGNs): the CRISPR/Cas9 system. Since the first report in 1987 and its characterization in 2007 as a bacterial defense mechanism, the interest and research on this system have grown exponentially. CRISPR systems provide immunity to bacteria against invading genetic material; however, with specific modifications in sequence and structure, it becomes a precise editing system that makes it possible to genetically modify almost any organism. There are diverse approaches regarding the refinement of these modifications, such as constructing more accurate nucleases, understanding the cellular context and facing the epigenetic conditions, or re-designing guide RNAs (gRNAs). Considering the critical importance for the correct CRISPR/Cas9 systems performance, our scope will emphasize in the latter approach. Hence, we present an overview of the past and the most recent guide RNA web-based design tools, highlighting their computational architecture and gRNA characteristics evolution through the years. Our study concisely explains the computational approaches that use machine learning techniques, deep neural networks, and large datasets of gRNA/target interactions to make possible both predictions and classifications directed to design, optimize, and create promising gRNAs suitable for future gene therapies.

The current epoch, known as the Anthropocene, is characterized by significant human impact on the global ecological system, including altered species survival and distribution patterns due to global warming resulting from industrialization. Accurately quantifying and assessing the extent of human impact on the environment is of utmost importance. To this end, four primary methods have been developed to measure human impact: (1) ecological footprint, (2) Human appropriation of net primary production (HANPP), (3) Planetary boundary, and (4) Living Planet Index. In this paper, we aim to provide a comprehensive understanding of these methods, including their strengths and weaknesses, to evaluate their efficacy in accurately assessing the impact of human activities on the global ecosystem. Furthermore, we explore the potential of utilizing novel machine learning modelling for future predictions in this area. By analyzing and comparing these methods, we can gain insight into the ways in which human activities impact the environment and use this knowledge to inform future policies and practices to mitigate negative effects.

Backgrounds: The field of Artificial Intelligence (AI) has seen a major shift in recent years due to the development of new Machine Learning (ML) models such as Generative Pre-trained Transformer (GPT) and AI which are progressively becoming part our everyday lives. GPT has achieved previously unheard-of levels of accuracy in most computerized language processing tasks and their chat-based variations. Aim: The aim of this study was to investigate the problem-solving abilities of ChatGPT using two sets of verbal insight problems, with a known performance level established by a sample of human participants. Material and Methods: A total of 30 problems labelled as “practice problems” and “transfer problems”, as listed by Ansburg and Dominowski (2000), were administered to ChatGPT. The answers provided by ChatGPT received a score of "0" for each problem answered incorrectly and a score of "1" for each correct response, as per the correct solution specified in Ansburg and Dominowski's (2000) study. The highest score that could be attributed to both the practice and transfer problems was 15 out of 15. In order to compare ChatGPT performance to the performance to that of human subjects, the solution rate for each problem (based on a sample of 20 subjects) was used, as indicated by Ansburg and Dominowski (2000). Results: The study highlighted that ChatGPT can be trained in out-of-the-box thinking and demonstrated potential in solving verbal insight problems. The global performance of ChatGPT equalled the most probable outcome for the human sample in both practice problems and transfer problems as well as upon their combination. Additionally, ChatGPT answer combinations were among the 5% of most probable outcomes for the human sample both when considering practice problems and pooled problem sets. These findings demonstrate that ChatGPT performance on both set of problems was in line with the mean rate of success of human subjects, indicating that it performed reasonably well. Conclusions: The use of transformer architecture and self-attention in ChatGPT may have helped to prioritize inputs while predicting, contributing to its potential in verbal insight problem-solving. ChatGPT has shown potential in solving insight problems, thus highlighting the importance of incorporating AI in psychological research. However, it is acknowledged that open challenges still exist. Indeed, further research is needed to fully understand the capabilities and limitations of AI in insight problem-solving.

Reliable and accurate flood prediction is a challenging task in poorly gauged basins due to data scarcity. Data is an essential component of any AI/ML model today, and the performance of such models hugely depends on the availability of sufficient amount of trusted, representative data. However, unlike a few well-studied rivers, most of the rivers in developing countries are still insufficiently monitored, which significantly hinges the design and development of advanced flood prediction models and early warning systems. This paper presents a multi-modal, sensor-based and near-real time river monitoring system to produce a multi-feature data set for the Kikuletwa river in Northern Tanzania, an area that heavily suffers from frequent floods. Our deployed system, which gather information about river depth levels and weather at several locations, aims at widening the ground truth of the river characteristics and eventually improve the accuracy of flood predictions. We provide details on the monitoring system used to gather the data as well as report on the methodology and the nature of the data. Finally, we present the relevance of the data set in the context of flood prediction, discussing the most suitable AI/ML-based forecasting approaches, while also highlighting some applications of the data set beyond flood warning systems.

Ovarian cancers are curable by surgical resection when discovered early enough. Unfortunately, most ovarian cancers are diagnosed in the later stages. One strategy to identify early ovarian tumors is to screen women who have the highest risk scores. This mini review summarizes the accuracy of different methods used to assess the risk of developing ovarian cancer, including family history, BRCA genetic tests, and polygenic risk scores. The accuracy of these is compared to the maximum theoretical accuracy, revealing a substantial gap. We suggest that this gap, or missing heritability, could be caused by epistatic interactions between genes. An alternative approach to computing genetic risk scores, using chromosomal-scale length variation should incorporate epistatic interactions. Future research in this area should focus on this and other alternative methods of characterizing genomes.

Background: There is an increasing interest in the role of EEG in neurorehabilitation. We primarily aimed to identify the knowledge base through highly influential studies. Our secondary aims were to imprint the relevant thematic hotspots, research trends, and social networks within the scientific community. Methods: We performed an electronic search in Scopus looking for studies reporting on rehabilitation in patients with neurological disabilities. The most influential papers outlined the knowledge base, while a word co-occurrence analysis imprinted the research hotspots. Likewise, co-citation analyses highlighted collaboration networks between Universities, authors, and countries. The results were presented in summary tables, burst detection plots, and geospatial maps. Finally, a content review based on the top-20 most cited articles completed our study. Results: Our current bibliometric study was based on 874 records from 420 sources. There was a vivid research interest in EEG use for neurorehabilitation, with an annual growth rate as high as 14.3%. The most influential paper was the study titled "Brain-computer interfaces, a review" by Nicolas-Alfonso LF and Gomez-Gill J, with 997 citations, followed by "Brain-computer interfaces in neurological rehabilitation" by Daly J. and Wolpaw JR (708 citations). The USA, Italy, and Germany were among the most productive countries. The research hotspots shifted with time from the use of “functional magnetic imaging” to EEG-based “brain-machine interface”, “motor imagery”, and “deep learning”. Conclusions: EEG constitutes the most significant input in brain-computer interfaces (BCI) and can be successfully used in the neurorehabilitation of patients with stroke, amyotrophic lateral sclerosis, and traumatic brain and spinal injury. EEG-based BCI facilitates training, communication, and control of wheelchair and exoskeletons. However, research is limited to specific scientific groups from developed countries. Evidence is expected to change with the broader availability of BCI and improvement in EEG filtering algorithms.

The evolving global SARS-CoV-2 pandemic emphasizes how unprepared we are for the emergence of the next lethal viral pathogen. A list of potential candidates was created by the World Health Organization which named Nipah virus infection as the highly lethal prototypic member of that list. Building on our earlier viral pandemic preparedness research into SARS-CoV-2 we have created computer simulations of Nipah virus infection complicated by encephalitis, the most common cause of Nipah-associated mortality. In the current experiments, we first created updated simulations of wild-type whole-brain organoids (aiWBO). Upon validation, the aiWBO were infected with the simulated Nipah virus genome. The Nipah encephalitis simulations (aiWBO-NiV) were then used to find optimal single, double, and triple protein combinations for candidates as potential targets for vaccine development. Our data suggest that the use of multi-viral proteins/epitopes chimera is the most promising approach to Nipah vaccine development and that employing artificial intelligence to guide the identification of promising vaccine candidates is an efficient and cost-effective strategy for future viral pandemic preparedness.