This article presents a comparative analysis of three Machine Learning models, namely Logistic Regression, Decision Tree Classifier, and Random Forest Classifier, for prostate cancer detection. The models were trained and evaluated using clinical data, and their performance was assessed using various evaluation metrics. The results show that Logistic Regression achieved the highest accuracy (90%) among the three models, followed by Random Forest Classifier (76.67%) and Decision Tree Classifier (73.33%). Similarly, Logistic Regression demonstrated superior precision (95.65%) and F1 Score (93.62%), indicating its effectiveness in identifying true positive cases. However, the Decision Tree Classifier exhibited higher recall for the negative class (83.33%) compared to the positive class (70.83%), while Random Forest Classifier showed balanced recall for both classes (66.67% for negative and 79.17% for positive). These findings suggest that Logistic Regression outperforms the other models in terms of accuracy and precision, while the Decision Tree Classifier and Random Forest Classifier provide better recall for certain classes. The results highlight the potential of Machine Learning in prostate cancer detection and provide insights for further research and improvement of the models.

Despite being an abundant marine organism in Indonesia, black sea cucumber is still underutilised due to its slightly bitter taste. Previous studies have hinted at the potential of black sea cucumber as an anti-cancer agent. However, specific identification of bioactive compounds that can interact with cancer proteins is still lacking. In the same place, cancer ranks third as Indonesia's leading cause of death. Therefore, this study aims to identify potential anti-cancer compounds from black sea cucumbers using a comprehensive in silico drug discovery approach. This research uses machine learning, molecular docking, and ADMET analysis to identify bioactive compounds that specifically interact with cancer proteins. A combination of the Cascade Deep Forest algorithm and ECFP-AAIndex1 feature combination proved to be the most effective in predicting these interactions. Through molecular docking validation, four bioactive compounds with strong binding affinity were identified: Afimoxifene, Danazol, Taxifolin, and Terfenadine. ADMET analysis highlighted Taxifolin as the most promising candidate, as it passed most ADMET parameters. Further wet laboratory studies are required to confirm the effects and potential of these compounds as anti-cancer agents. This study builds a foundation for future investigations into alternative cancer treatments using abundant natural resources.

In this work, an IoT system with edge computing capability is proposed facilitating postoperative surveillance of patients who have undergone knee surgery. The main objective is to reliably identify whether a set of orthopedic rehabilitation exercises are executed correctly, which is critical since, it is often necessary to supervise patients during the rehabilitation period so as to avoid injuries or long recovery periods. The proposed system leverages Internet of Things (IoT) paradigm, in combination with Deep Learning and Edge Computing to classify the extend-flex movement of one’s knee via embedded Machine Learning (ML) classification algorithms. The contribution of the proposed work is multilayered. Furthermore as an outcome of this work a dataset of labeled knee movements is freely available on https://www.kaggle.com/datasets/billskarm/knee-range-of-motion to the research community. It also provides real time movement detection with an accuracy reaching 100%, which is achieved with a ML model trained to fit a low cost, off the shelf, Bluetooth Low Energy platform. The proposed Edge Computing approach allows predictions to be performed on-device rather than solely relying on a cloud service. This yields critical benefits in terms of wireless bandwidth and power conservation, drastically enhancing device autonomy, while delivering reduced event detection latency. In particular, the “on device” implementation is able to yield a drastic 99,9% wireless data transfer reduction, a critical 39% prediction delay reduction and a valuable 17% increase of prediction event. Finally, enhanced privacy comprises another significant benefit from the implemented Edge Computing ML model as sensitive data can be processed on-site and only events or predictions are shared with the medical personnel.

The dominance of social media has added to the channels of bullying to perpetrators. Unfortunately, cyberbullying (CB) is the most prevalent phenomenon in today’s cyber world and is a severe threat to the mental and physical health of citizens. This opens the need to develop a robust system to prevent bullying content from online forums, blogs, and social media platforms to manage the impact in our society. Several machine learning (ML) algorithms have been proposed for this purpose. However, their performances are not consistent due to high class imbalance issue and generalisation. In recent years, large language models (LLM) like BERT and RoBERTa have achieved state of the art (SOTA) results in several natural language processing (NLP) tasks. Unfortunately, the LLMs have not been applied extensively. In our paper, we explored the use of these models for cyberbullying (CB) detection. We have prepared a new dataset (D2) from existing studies (Formspring and Twitter). Our experimental results for dataset D1 and D2 showed RoBERTa outperformed other models.

Software governance is a management structure that guides projects in terms of their accountability and responsibility. Prime motivation of this approach is to improve the skillset of the team members through software governance policies and increase the overall success rate of the software projects. The scope of skill development is across the pillars of governance, such as structure, people, and information. Primary focus of this paper is on the skillset development of the project team members through educational policies in software governance. As part of the governance process, educational policies are defined for the skillset development of project team members. The JIRA dataset was used to determine the skillset development of the team members. Machine learning techniques, such as J48, Random Forest, Decision Table, Logistics, and Naïve Bayes, were used in the JIRA dataset. These machine learning techniques were processed using WEKA open-source software. Based on these results, it was concluded that the J48 algorithm can be applied to multiple projects/programs to monitor and track the skill development process. Machine learning model such as J48 is required to use this model at an organizational level. The skillset development of project team members should be aligned with IT governance and educational policies. Overall upskilling and reskilling strategies are provided to demonstrate the impact of skillset development through software governance.

A high performance Driver State Monitoring (DSM) application for the detection of driver drowsiness is presented in this paper. It relies on the usage of an Ensemble of Regression Trees (ERTs) machine learning method that aligns 68 facial landmarks. Special focus is given on the acceleration of the frame processing using reconfigurable hardware. Reducing the frame processing latency saves time that can be used to apply frame-to-frame facial shape coherency rules. False face detection and false shape estimations can be ignored for higher robustness and accuracy in the operation of the DSM application without reducing the frame processing rate that can reach 65 frames per second. The sensitivity and precision in yawning recognition can reach 93% and 97%, respectively. The implementation of the employed DSM algorithm in reconfigurable hardware is challenging since the kernel arguments require large data transfers and the degree of data reuse in the computational kernel is low. Due to this, unconventional hardware acceleration techniques have been employed that can also be useful for the acceleration of several other applications.

This paper presents the development of an underwater crack detection system for structural integrity assessment of submerged structures, like offshore oil and gas installations, underwater pipelines, underwater foundations for bridges, dams etc. Focus is on use of machine learning based approaches. First a detailed literature review of state of the current methods for underwater surface crack detection is presented highlighting challenges and opportunities. An overview of image augmentation approach for creation of underwater optical effects is also presented. Experimental results using standard network based machine learning approach, used for surface crack detection in onshore environment, is presented. Series of Test cases are presented where existing networks performance are improved using augmented images for underwater conditions. The experimental results demonstrate the effectiveness and accuracy of the proposed system in detecting cracks in underwater structures. The system has the potential to improve the safety and reliability of underwater structures and prevent catastrophic failures.

This paper presents a cascade predictive control structure based on the field-oriented control (FOC) in the dq rotor reference frame for the synchronous reluctance machine (SynRM). The constant d-axis current control strategy was used and thus, the electromagnetic torque was directly controlled by the q-axis current. Because the model of the two axes currents from the inner loop is a coupled non-linear multivariable one, to non-interaction linear control the two currents, their decoupling was achieved through feedforward components. Following the decoupling, two independent monovariable linear systems resulted for the two currents dynamics that were controlled using model predictive control (MPC) algorithms, considering their ability to automatically handle the state bounds. The most important bounds for SynRM are the limits imposed on currents and voltages, which in the dq plane correspond to a circular limit. To avoid computational effort, linear limitations were adopted through polygonal approximations, resulting in rectangular regions in the dq plane. For the outer loop that controls the angular speed with a constrained MPC algorithm, as plant was considered the q-axis current closed loop dynamics and the torque linear equation. To evaluate the performances of the proposed cascade predictive control structure, a simulation study using MPC controllers versus PI ones was conducted.

This study aims to focus on using the Volterra series and machine learning for forecasting random and chaotic wind speed regimes, since the calm weather is mostly noticed at the local site, making the dataset selection difficult. A novel method is proposed to predict Volterra kernels up to the third order, using a forward-back propagation neural network with 12-month measurements at Fujairah site (UAE). Both daily and monthly wind speed data sets are investigated for forecasting. The three dominant hourly and daily kernels are extracted for each day and each month. Predicted future Volterra kernels are estimated from past values using both statistical analysis and individual neuro networks for each of the Volterra kernel coefficients. Due to the random nature of wind speed at the local site, a two-layer with 4 neurons per layer neuro network is used to locate the most variable and intense speed during 8-hours in the day. Forecasted wind speed is determined with errors arising from different sources such as the utilization of only 3rd-order Volterra kernels and the difficulty of machine training of the employed shallow network. Nevertheless, this work depicts a useful algorithm to forecast chaotic and random wind speed regimes. Computational time is a trade of the complexity of Volterra mathematical analysis.

Structural integrity is a crucial aspect of engineering components, particularly in the field of additive manufacturing (AM). Surface roughness is a vital parameter that significantly influences the structural integrity of additively manufactured parts. In this study, we present a comprehensive investigation into the relationship between surface roughness and structural integrity of Polyactic Acid (PLA) specimens produced through additive manufacturing. This research work focuses on the prediction of surface roughness of Additive Manufactured Polyactic Acid (PLA) specimens using eight different supervised machine learning regression-based algorithms. For the first time, Explainable AI techniques are employed to enhance the interpretability of the machine learning models. The eight algorithms used in this study are Support Vector Regression, Random Forest, XG Boost, Ada Boost, Catboost, Decision Tree, Extra Tree regressor, and Gradient Boosting regressor. The study analyzes the performance of these algorithms to predict the surface roughness of PLA specimens, while also investigating the impact of individual input parameters through Explainable AI methods. The experimental results indicate that the XG Boost algorithm outperforms the other algorithms with the highest coefficient of determination value of 0.9634. This value demonstrates that the XG Boost algorithm provides the most accurate predictions for surface roughness compared to other algorithms. The study also provides a comparative analysis of the performance of all the algorithms used in this study, along with insights derived from Explainable AI techniques.

The makeup of human microbiota has been linked to a number of autoimmune disorders. Recent developments in whole metagenome sequencing and 16S rRNA sequencing technology have considerably aided research into the microbiome and its relationship to disease. Due to the inherent high dimensionality and complexity of data generated by high-throughput platforms, conventional bioinformatics techniques could only provide an inadequate explanation for the most relevant changes and seldom provide correct predictions. Machine learning, on the other hand, is a subset of artificial intelligence applications that enable the untangling of high-dimensional systems and intricate knots in correlation by learning complex patterns and improving automatically from training data without being explicitly programmed. Machine learning is increasingly being utilized to research the influence of microbes on the onset of illness and other clinical features since computer power has increased dramatically in the last few decades. In this review paper, we focused on emerging methodological approaches of supervised machine learning algorithms for identification of autoimmune disorders utilizing metagenomics data, as well as the potential benefits and limitations of machine learning models in clinical applications.

The exponential growth of digital media content has introduced new challenges in managing and classifying internet traffic. Digital media traffic is composed of various applications such as video, audio, social media, and search, and its data structure is complex, incorporating a vast array of features. The classification of traffic data is a crucial aspect of internet traffic management and network security, and it forms the basis for several scenarios, including content distribution, advertising recommendations, and data analysis. Traditional classification methods rely mainly on deep packet inspection and port-based techniques, which have become increasingly ineffective due to the rapid evolution of network traffic. To address this issue, this study proposes a machine learning-based traffic classification method aimed at enhancing the accuracy and efficiency of digital media traffic classification to meet the current needs of traffic management and network security. The paper also analyzes and evaluates the classification effect and prediction capability of various algorithms under different training set sizes to validate the feasibility and effectiveness of the proposed method. The result demonstrates that the neural network algorithm has superior classification and prediction capabilities compared to the decision tree and support vector machine algorithms. Furthermore, our proposed method achieves the highest accuracy of 96.88% with a large training sample of 40,000 data streams, proving its superiority in handling high-dimensional data and complex datasets. The research results are significant for the development of digital media traffic classification and prediction methods and are expected to be applied in practical scenarios.

Background and Purpose: Open-source Artificial intelligence (AI) applications are fast transforming access to information and allow students to prepare assignments and offer quite accurate responses to a wide range of exam questions which are routinely used in assessments of students across the board including undergraduate dental students. This study aims to evaluate the performance ChatGPT, an AI-based application, on a wide range of dental assessments and discusses the implications for undergraduate dental education. Methods: This was an exploratory study investigating the accuracy of ChatGPT to attempt a range of recognized assessments in undergraduate dental curricula. ChatGPT was used to attempt ten items based on each of the five commonly used question formats including single-best answer (SBA) multiple-choice questions (MCQs); short answer questions (SAQs); short essay questions (SEQs); True/False questions and fill in the blanks items. In addition, ChatGPT was used to generate reflective reports based on multisource feedback (MSF); research methodology; critical appraisal of the literature. Results: ChatGPT application provided accurate responses to majority of knowledge-based assessments based on MCQs, SAQs, SEQs, Tue/False and fill in the blanks items. However, it was only able to answer text-based questions and did not allow processing of questions based on images. Responses generated to written assignments were also of good quality apart from those for critical appraisal of literature. Word count was the key limitation observed in outputs by ChatGPT as it was only able to produce reports limited to approximately 650 words. Conclusion: Notwithstanding their current limitations, AI-based applications have the potential to revolutionize virtual learning. Instead of treating it as a threat, dental educators need to adapt teaching and assessments in dental education to the benefits of the learners whilst mitigating against dishonest use of AI-based applications.

The constant increase in volume and wide variety of available Internet of Things (IoT) devices leads to highly diverse software and hardware stacks, which opens new avenues for exploiting previously unknown vulnerabilities. The ensuing risks are amplified by the inherent IoT resource constraints both in terms of performance and energy expenditure. At the same time, IoT devices often times generate or collect sensitive, real-time data used in critical application scenarios (e.g. health monitoring, transportation, smart energy, etc.). All these factors combined make IoT networks a primary target and potential victims for malicious actors. In this paper, we present a brief overview of existing attacks and defense strategies against urban IoT networks. The goal of this work is twofold: First, it presents a summary of some of the common attack vectors and the corresponding solutions available in the research literature. Then, the paper lays out a theoretical plan and a corresponding pipeline of steps (i.e. development and implementation process) for the design and application of the solutions encountered throughout the course of the research efforts. The end goal of following this plan is the deployment of the proposed IoT security measures in a real-world urban IoT infrastructure.

Caries is a prevalent oral disease that primarily affects children and teenagers. Advances in ma-chine learning have caught the attention of scientists working with decision support systems to predict early tooth decay. Current research has developed machine learning algorithm for caries classification and reached high accuracy especially in ML for image data. Unfortunately, most studies on dental caries only focus on classification and prediction tasks, meanwhile dental carries prevention is more important. Therefore, this study aims to design an efficient feature for decision support system machine learning based that can identify various risk factors that cause dental caries and its prevention. The data used in the research work was obtained from the 2018 Korean Children's Oral Health Survey, which totaled nine datasets. The experimental results show that combining the mRMR and GINI Feature Importance methods when training with the GBDT model achieved the optimum performance of 95%, 93%, 99%, and 88% for accuracy, F1 score, precision, and recall, respectively. So, the proposed method has provided effective predictive model for dental caries prediction.

Aim: This study aimed to evaluate the sociodemographic, behavioral, and biological profile and its relationship with the emergence of chronic non-communicable diseases in riverside populations in the Xingu region, Pará, Brazil. Methods: Characteristics related to health indicators and which risk factors are considered most important were also analyzed. This is a cross-sectional, exploratory, and descriptive study. The sample consisted of riverside people over 18 years of both sexes. The sample size (n.86) was calculated with a confidence level of 95% and a sample error of 5%. To divide the groups, the K-means clustering algorithm was adopted through an unsupervised method and the values were expressed as median. For continuous and categorical data, the Mann-Whitney and Chi-square tests were used, respectively, and the significance level was set at p<5%. To classify the degree of importance of each variable, the multi-layer perceptron algorithm was applied. Results: Based on this information, the sample was divided into two groups: the group with low or no education, with bad habits and worse health conditions, and the group with opposite characteristics. The risk factors considered for cardiovascular diseases and diabetes in the groups were low education (p < 0.001), sedentary lifestyle (p < 0.01), smoking, alcoholism, body mass index (p < 0.05) and the waist-hip ratio, with values above the expected, in both groups. Conclusions: The factors considered important to have a good health condition or not were the educational and social conditions of these communities and a part of the riverside population was considered healthier than the other.

In this work, we adapt the fine-tuning algorithm of the Naïve Bayesian (FTNB) classifier to make it more suitable for imbalanced datasets. In particular, we boost misclassified instance probability terms by an amount that is disproportional to the harmonic mean of actual and predicted classes. The intuition is that discriminative attributes when the instance is misclassified would have small probability term pair values in both the actual class due to data scarcity and the predicted class due to weak correlation. Conversely, if both values are relatively high, then the attribute has good data coverage (support) and it should not be a cause for misclassification. Since the harmonic average is dominated by the smaller value and we have an imbalanced dataset, we should enact a large update if both or either term probabilities of actual and predicted classes are small. We used several benchmark datasets (60 different balanced and imbalanced datasets) to determine if the poor performance of the NB classifier is due to the scarcity of data and compared the performance of the proposed algorithm with NB, original FTNB, and other relatively new SOTA Ensemble Imbalanced Classifiers. Our empirical results reveal that the new proposed algorithm significantly outperforms all other classifiers.

The national-scale evaluation and modelling of the impact of agricultural management and cli-mate change on soils, crop growth, and the environment require soil information at a spatial res-olution addressing individual agricultural fields. This manuscript presents a data science ap-proach which agglomerates the soil parameter space into a limited number of functional soil pro-cess units (SPUs) which may be used to run agricultural process models. In fact, two unsupervised classification methods were developed to generate a multivariate 3D data product consisting of SPUs, each being defined by a multivariate parameter distribution along the depth profile from 0 to 100 cm. The two methods account for differences in variable types and distributions and in-volve genetic algorithm optimization to identify those SPUs with the lowest internal variability and maximum inter-unit difference with regards to both, their soil characteristics and landscape setting. The high potential of the methods was demonstrated by applying them to the agricultural German soil landscape. The resulting data product consists of twenty SPUs. It has a 100 m raster resolution in the 2D mapping space, and its resolution along the depth profile is 1 cm. It includes the soil properties texture, stone content, bulk density, hydromorphic properties, total organic carbon content, and pH.

This paper investigates if Corporate Social Responsibility (CSR) reports published by a selected group of Nordic companies are aligned with the Global Reporting Initiative (GRI) standards. To achieve this goal, several natural language processing, and text mining techniques were implemented and tested. We extracted strings, corpus, and hybrid semantic similarities from the reports and evaluated the models through the intrinsic assessment methodology. A quantitative ranking score based on index matching was developed to complement the semantic valuation. The final results show that Latent Semantic Analysis (LSA) and Global Vectors for Word Representation (GloVE) are the best methods for our study. Our findings will open the door to the automatic evaluation of sustainability reports which could have a strong impact on the environment.

Precipitation type is a key parameter used for better retrieval of precipitation characteristics as well as to understand the cloud-convection-precipitation coupling processes. Ice crystals and water droplets inherently exhibit different characteristics in different precipitation regimes (e.g., convection, stratiform), which reflect on satellite remote sensing measurements that help us distinguish them. The Global Precipitation Measurement (GPM) Core Observatory’s Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR) together provide ample information on global precipitation characteristics. As an active sensor, DPR provides an accurate precipitation type assignment, while passive sensors like GMI are traditionally only used for empirical understanding of precipitation regimes. Using collocated precipitation type flags from DPR as the “truth”, this paper employs machine learning (ML) models to train and test the predictability and accuracy of using passive GMI-only observations together with ancillary information from reanalysis and GMI surface emissivity retrieval products. Out of six ML models, four simple ones (Support Vector Machine, Neural Network, Random Forest, and Gradient Boosting) and the 1-D convolutional neural network (CNN) model are identified to produce 90% - 94% prediction accuracy globally for 5 types of precipitation (convective, stratiform, mixture, no precipitation, and other precipitation), which is much more robust than previous similar effort. One novelty of this work is to introduce data augmentation (subsampling and bootstrapping) to handle extremely unbalanced samples in each category. Careful evaluation of Impact matrices demonstrate that polarization difference (PD) and surface emissivity at high-frequency channels dominate the decision process, which are consistent with the physical understanding of polarized microwave radiative transfer over different surface types, as well as in snow and liquid clouds with different microphysical properties. Furthermore, the view-angle dependency artifact that DPR precipitation flag bears with does not propagate into the conical-viewing GMI retrievals. This work provides a new and promising way for future physics-based ML retrieval algorithm development.

Many dedicated designs for real-time operations provide functionality on fixed-sized operators, but where speed, scalability, and flexibility are required, extensive research is demanded. Dedicated designs can provide real-time processing for many applications. This paper presents an FPGA-based design of a general image processor. The proposed design is based on a fixed-point representation of binary numbers. The proposed design provides a mechanism to manage matrices on-chip along with matrix arithmetic. The matrices are represented with simple identifiers and microinstruction that assist in the computation of many operations which are useful for solving complex problems. The design was successfully implemented and tested using VHDL language. The proposed design is an efficient architecture as a standalone processor with all embedding computational resources necessary for an embedded image processing application.

Deep learning techniques, such as convolutional neural networks (CNN), generative adversarial networks (GAN), and graph neural networks (GNN), have over the past decade changed the ac-curacy of prediction in many diverse fields. In recent years, the application of deep learning tech-niques in computer vision tasks in pathology demonstrated extraordinary potential in assisting clinicians, automating diagnosis, and reducing costs for patients. Formerly unknown pathologi-cal evidence, such as morphological features related to specific biomarkers, copy number varia-tions, and other molecular features, were also able to be captured by deep learning models. In this paper, we review popular deep learning methods and some recent publications about their appli-cations in pathology.

The global spread of COVID-19 led the World Health Organization to declare a pandemic on 11 March 2020. To decelerate this spread, countries have taken strict measures that affected the lifestyle and economy. Various studies have been focused on the identification of COVID-19 impact to mental health of children and adolescents via traditional statistical approaches. However, a machine learning methodology must be developed to explain the main factors that contribute to the change of mood state of children and adolescents during the first lockdown. Therefore, to this study an explainable machine learning pipeline is presented focusing on children and adolescents in Greece, where a strict lockdown was imposed. The target group consists of children and adolescents, recruited from children and adolescent mental health services, who present mental health problems diagnosed before the pandemic. The proposed methodology imposes: (i) data collection via questionnaires; (ii) a clustering process to identify the groups of subjects with amelioration, deterioration and stability to their mood state; (iii) a feature selection process to identify the most informative features that contribute to mood state prediction; (iv) a decision-making process based on an experimental evaluation among classifiers; (v) calibration of the best performing model and (v) a post-hoc interpretation of the features’ impact on the best performing model. The results showed that a blend of heterogeneous features from almost all feature categories is necessary to increase our understanding regarding the effect of COVID-19 pandemic on the mood state of children and adolescents.

This paper provides a methodology for the production of synthetic images for training neural networks to recognise shapes and objects. There are many scenarios in which it is difficult, expensive and even dangerous to produce a set of images that is satisfactory for the training of a neural network. The development of 3D modelling software has nowadays reached such a level of realism and ease of use that it seemed natural to explore this innovative path and to give an answer regarding the reliability of this method that bases the training of the neural network on synthetic images. The results obtained in the two proposed use cases, that of the recognition of a pictorial style and that of the recognition of migrants at sea, leads us to support the validity of the approach, provided that the work is conducted in a very scrupulous and rigorous manner, exploiting the full potential of the modelling software. The code produced, which automatically generates the transformations necessary for the data augmentation of each image, and the generation of random environmental conditions in the case of Blender and Unity3D software, is available under the GPL licence on GitHub. The results obtained lead us to affirm that through the good practices presented in the article, we have defined a simple, reliable, economic and safe method to feed the training phase of a neural network dedicated to the recognition of objects and features, to be applied to various contexts.

Private vehicle travel is the most basic mode of transportation, and the effective control of the real-world fuel consumption rate of light-duty vehicles plays a vital role in promoting sustainable economic development as well as achieving a green low-carbon society. Therefore, the impact factors of individual carbon emission must be elucidated. This study builds five different models to estimate real-world fuel consumption rate of light-duty vehicles in China. The results reveal that the Light Gradient Boosting Machine (LightGBM) model performs better than the linear regression, Naïve Bayes regression, Neural Network regression, and Decision Tree regression models, with mean absolute error of 0.911 L/100 km, mean absolute percentage error of 10.4%, mean square error of 1.536, and R squared (R2) of 0.642. This study also assesses a large number of factors, from which three most important factors are extracted, namely, reference fuel consumption rate value, engine power and light-duty vehicle brand. Furthermore, a comparative analysis reveals that the vehicle factors with greater impact on real-world fuel consumption rate are vehicle brand, engine power, and engine displacement. Average air pressure, average temperature, and sunshine time are the three most important climate factors.

Cytosine (C) to uracil (U) RNA editing is one of the most important post-transcriptional processes, however exploring C-to-U editing events efficiently within the crop mitochondrial genome remains a challenge. An improving predictive RNA editor for crop mitochondrial genomes, iPReditor-CMG, was proposed, which was based on SVM, three common crop mitochondrial genomes and self-sequenced tobacco mitochondrial ATPase. After multi-combination feature extracting, high-dimension feature screening and multi-test independent predicting, the results showed that the average accuracy of intraspecific prediction was 0.85, and the highest value even up to 0.91, which outperformed the previous reference models. While the prediction accuracies were 0.78 between dicotyledons and no more than 0.56 between dicotyledons and monocotyledons, implying a possible similarity in C-to-U editing mechanisms among close relatives. The best model was finally identified with an independent test accuracy of 0.91 and an area under the curve of 0.88, and further suggested that five unreported feature sequences TGACA, ACAAC, GTAGA, CCGTT and TAACA were closely associated with the editing phenomenon. Multiple evaluation findings supported that the iPReditor-CMG could be effectively applied to predict crop mitochondrial editing sites, which may contribute to insight into their recognition mechanisms and even other post-transcriptional events in crop mitochondria.

The load of power system changes with the development of economy, short-term load forecasting play a very important role in dispatching and management of power system. In this paper, the Ant Lion Optimizer (ALO) is introduced to improve the input weights and hidden-layer Matrix of extreme learning machine (ELM), after the parameters of ELM are optimized by ALO, then input nodes, hidden layer nodes and output nodes are determined, so a load forecasting model based on ALO-ELM combined algorithm is established. The proposed method is illustrated based on the historical load data of a city in China. The results show that the average absolute error of short-term load demand predicted by ALO-ELM model is 1.41, while that predicted by ELM is 4.34, the proposed ALO-ELM algorithm is superior to the ELM and meet the requirements of engineering accuracy, which proves the effectiveness of proposed method.

Egyptian cotton is one of the most important commodities to the Egyptian economy and is renowned globally for its quality, which is currently graded by manual inspection. This has several drawbacks including significant labour requirement, low inspection efficiency, and influence from inspection conditions such as light and human subjectivity. This current work uses a low-cost colour vision system, combined with machine learning to predict the cotton lint grade of the cultivars Giza 86, 97, 90, 94 and 96. Unsupervised and supervised machine learning approaches were explored and compared. Three different supervised learning algorithms were evaluated: linear discriminant analysis, decision trees and ensemble modelling. The highest accuracy models (77.3-98.2%) used an ensemble modelling technique to classify samples within the Egyptian cotton grades: Fully Good, Good, Fully Good Fair, Good Fair and Fully Fair. The unsupervised learning technique k-means showed that human error is more likely to occur when classifying lint belonging to the higher quality grades and underlined the need for an intelligent system to replace manual inspection.

Diabetics or Diabetic Mellitus is a metabolic disorder of blood sugar levels in the human body. It is a major non-communicable disease and involved many serious health risk issues. This disease is rapidly increasing in India. It is a chronic condition and it occurs when a body doesn't produce enough insulin hormone to control the blood sugar level. In this study, different variables have been analyzed that cause the diabetics, and different machine learning algorithms are used to predict whether an unknown sample is diabetes or not. For this purpose, PIMA diabetic detection for Female patients was used. Here 10 different classification model is used for prediction. Finally, the detailed performance analysis of the different variables of the PIMA dataset and also the classification model are discussed.

The present study aims to develop a risk-based approach to find optimal solutions for life extension management for offshore wind farms based on Markowitz’s modern portfolio theory, adapted from finance. The developed risk-based approach assumes that the offshore wind turbines (OWT) can be considered as cash-producing tangible assets providing positive return from the initial investment (capital) with a given risk attaining the targeted (expected) return. In this regard, the present study performs a techno-economic life extension analysis within the scope of the multi-objective optimisation problem. The first objective is to maximise the return from the overall wind assets, while the latter aims to minimise the risk associated with obtaining the return. In formulating the multi-dimensional optimisation problem, the life-extension assessment considers the results of a detailed structural integrity analysis, free-cash-flow analysis, and probability of project failure, local and global economic constraints. Further, the risk is identified as the variance from the expected mean of return on investment. The risk-return diagram is utilised to classify the OWTs of different classes using an unsupervised machine learning algorithm. The optimal portfolios for the various required rate of return are recommended for different stages of life extension.

We propose a chemical language processing model to predict polymers’ glass transition temperature (Tg) through a polymer language (SMILES, Simplified Molecular Input Line Entry System) embedding and recurrent neural network. This model only receives the SMILES strings of polymer’s repeat units as inputs and considers the SMILES strings as sequential data at the character level. Using this method, there is no need to calculate any additional molecular descriptors or fingerprints of polymers, and thereby, being very computationally efficient and simple. More importantly, it avoids the difficulties to generate molecular descriptors for repeat units containing polymerization point `*’. Results show that the trained model demonstrates reasonable prediction accuracy on unseen polymer’s Tg. Besides, this model is further applied for high-throughput screening on an unlabeled polymer database to identify high-temperature polymers that are desired for applications in extreme environments. Our work demonstrates that the SMILES strings of polymer’s repeat units can be used as an effective feature representation to develop a chemical language processing model for predictions of Tg. The framework of this model is general and can be used to construct structure-property relationships for other polymer’s properties.

SARS-CoV-2 infection produces a response of the innate immune system causing oxidative stress and a strong inflammatory reaction termed ‘cytokine storm’ that is one of the leading causes of death. Paraoxonase-1 (PON1) protects against oxidative stress by hydrolyzing lipoperoxides. Alterations in PON1 activity have been associated with pro-inflammatory mediators such as the chemokine (C-C motif) ligand 2 (CCL2) and the glycoprotein galectin-3. We aimed to investigate the alterations in the circulating levels of PON1, CCL2 and galectin-3 in 126 patients with COVID-19 and their interactions with clinical variables and analytical parameters. A machine learning approach was used to identify predictive markers of the disease. For comparisons, we recruited 45 COVID-19 negative patients and 50 healthy individuals. Our approach identified a synergy between oxidative stress, inflammation and fibrogenesis in positive patients that is not observed in negative patients. PON1 activity was the parameter with the greatest power to discriminate between patients who were either positive or negative for COVID-19, while their levels of CCL2 and galectin-3 were similar. We suggest that the measurement of serum PON1 activity may be a useful marker for the diagnosis of COVID-19.

The monitoring of surface-water quality followed by water-quality modeling and analysis is essential for generating effective strategies in water-resource management. However, worldwide, particularly in developing countries, water-quality studies are limited due to the lack of a complete and reliable dataset of surface-water-quality variables. In this context, several statistical and machine-learning models were assessed for imputing water-quality data at six monitoring stations located in the Santa Lucía Chico river (Uruguay), a mixed lotic and lentic river system. The challenge of this study is represented by the high percentage of missing data (between 50% and 70%) and the high temporal and spatial variability that characterizes the water-quality variables. The competing algorithms implemented belonged to both univariate and multivariate imputation methods (inverse distance weighting (IDW), Random Forest Regressor (RFR), Ridge (R), Bayesian Ridge (BR), AdaBoost (AB), Hubber Regressor (HR), Support Vector Regressor (SVR), and K-nearest neighbors Regressor (KNNR)). According to the results, more than 76% of the imputation outcomes are considered satisfactory (NSE > 0.45). The imputation performance shows better results at the monitoring stations located inside the reservoir than the ones positioned along the mainstream. IDW was the most chosen model for data imputation.

Artificial Intelligence and especially machine learning have noticed rapid advancement on image processing operations. However, its involvement in the architectural design is still in its initial stages compared to other disciplines. Therefore, this paper addresses the issues of developing an integrated bottom up digital design approach and details a research framework for the incorporation of Deep Convolutional Generative Adversarial Network (GAN) for early stage design exploration and generation of intricate and complex alternative facade designs for urban infill. This paper proposes a novel building facade design by merging two neighboring building’s architecture style, size, scale, openings, as reference to create a new building design in the same neighborhood for urban infill. This newly produced building contains the outline, style and shape of the parent buildings. A 2D urban infill building design is generated as a picture where 1) neighboring buildings are imported as a reference using mobile phone and 2)iFACADE decode their spatial adjacency. It is depicted the iFACADE will be useful for designers in the early design stage to generate new façades depending on existing buildings in a short time that will save time and energy. Besides, building owners can use iFACADE to show their architects their preferred architecture facade by mixing two building styles and generating a new building. Therefore, it is depicted that iFACADE can become a communication platform in the early design stages between architects and owners. Initial results properly define a heuristic function for generating abstract design facade elements and sufficiently illustrate the desired functionality of our developed prototype.

Extra virgin olive oil (EVOO) is the highest quality of olive oil and is characterized by highly beneficial nutritional properties. The large increase in both consumption and fraud, for example through adulteration, creates new challenges and an increasing demand for developing new quality assessment methodologies that are easier and cheaper to perform. As of today, the determination of olive oil quality is performed by producers through chemical analysis and organoleptic evaluation. The chemical analysis requires the advanced equipment and chemical knowledge of certified laboratories, and has therefore a limited accessibility. In this work a minimalist, portable and low-cost sensor is presented, which can perform olive oil quality assessment using fluorescence spectroscopy. The potential of the proposed technology is explored by analyzing several olive oils of different quality levels, EVOO, virgin olive oil (VOO), and lampante olive oil (LOO). The spectral data were analyzed using a large number of machine learning methods, including artificial neural networks. The analysis performed in this work demonstrates the possibility of performing classification of olive oil in the three mentioned classes with an accuracy of 100%. These results confirm that this minimalist low-cost sensor has the potential of substituting expensive and complex chemical analysis.

The relationship between the street environment and the health, education, mobility, and criminal behaviors of its citizens has long been investigated by economists, sociologists and urban planners. Home buyers were found to pay a premium for better street appearance. Prior studies considering streetscapes mainly focus on objective measures such as the number of nearby trees, the tree canopy area, or the view index of physical features such as greenery, sky or building. However, subjective perceptions may have complex or subtle relationships to physical features, individual physical features or simply summing them up do not capture people’s comprehensive perception. In contrast, this study proposed a new approach for the urban-scale application to quantify both subjectively and objectively measured streetscape scores for six important perception qualities, namely Greenness, Walkability, Safety, Imageability, Enclosure, and Complexity. Built on prior quantitative studies in urban design quality and emerging applications in deep learning and open source street view imagery for urban perceptions, we integrated existing frameworks to (1) effectively collect and evaluate both subjectively and objectively- measured perceptions; (2) investigate the coherence and divergence in ML-predicted subjective scores and formula-derived objective scores; and (3) compare their effects in affecting house prices taking Shanghai as a case study using a large-scale dataset on home transactions. The results implied: first, the percentage increase in sales price attributable to street scores is significant for both subjective and objective measurements. In general subjective scores explained more variance over structural attributes and objective scores in hedonic price model. Particularly, objective Greenness, subjective Safety and Imageability scores positively affected house prices. Second, for Greenness and Imageability scores, the subjective and objective measures exhibited opposite signs in affecting house prices, which implied that there might be mechanisms related to the psychological, social-demographical characteristics of street users that have not been fully incorporated by objective measures that taking view indices or recombination of them. In addition, certain objective measure might outperform subjective counterpart when the connotation of the perception is self-evident and not complicated, for example the Greenness. For those concepts were not familiar to the average person, subjective framework exhibits better performance. This is the first study comprehensively expanding hedonic price method with both subjectively and objectively measured streetscape qualities. It suggested that city authorities could levy a street environment tax to compensate the public budget invested in street environment where developers secured benefits from a price premium. This study enriches our understanding of the economic values of the subjective and objective measures street qualities. It sheds light on promising future study areas where the coherence and divergence of the two measurements should be further stressed.

In recent years, since the propagation channel characteristics have been effectively used for applications such as motion sensing, position detection, etc. A great deal of attention is attracted to channel sounding methods easy to utilize using low-cost devices. This paper presents a device-free indoor location estimation method using spatio-temporal features of radio propagation channels using the 2.4-GHz band 3-by-3 MIMO channel sounder developed using commodity wireless LANs. The measurement results demonstrated a reasonable performance of the proposed method with small number of antennas.

The ongoing COVID-19 global pandemic is affecting every facet of human lives (e.g., public health, education, economy, transportation, and the environment). This novel pandemic and citywide implemented lockdown measures are affecting virus transmission, people’s travel patterns, and air quality. Many studies have been conducted to predict the COVID-19 diffusion, assess the impacts of the pandemic on human mobility and air quality, and assess the impacts of lockdown measures on viral spread with a range of Machine Learning (ML) techniques. This review study aims to analyze results from past research to understand the interactions among the COVID-19 pandemic, lockdown measures, human mobility, and air quality. The critical review of prior studies indicates that urban form, people's socioeconomic and physical conditions, social cohesion, and social distancing measures significantly affect human mobility and COVID-19 transmission. During the COVID-19 pandemic, many people are inclined to use private transportation for necessary travel purposes to mitigate coronavirus-related health problems. This review study also noticed that COVID-19 related lockdown measures significantly improve air quality by reducing the concentration of air pollutants, which in turn improves the COVID-19 situation by reducing respiratory-related sickness and deaths of the people. It is argued that ML is a powerful, effective, and robust analytic paradigm to handle complex and wicked problems such as a global pandemic. This study also discusses policy implications, which will be helpful for policymakers to take prompt actions to moderate the severity of the pandemic and improve urban environments by adopting data-driven analytic methods.

This is an article or technical note which is intended to provides an insight journey of Machine Learning Systems (MLS) testing, its evolution, current paradigm and future work. Machine Learning Models, used in critical applications such as healthcare industry[1], Automobile [2], [3] and Air Traffic control, Share Trading etc., and failure of ML Model can lead to severe consequences in terms of loss of life or property. To remediate this, developers, scientists, and ML community around the world, must build a highly reliable test architecture for critical ML application. At the very foundation layer, any test model must satisfy the core testing attributes such as test properties and its components. This attribute comes from the software engineering [5], [6], but the same cannot be applied in as-is form to the ML testing and we will tell you “why”.

This paper presents an effective heart disease prediction model through detecting the anomalies, also known as outliers, in healthcare data using the unsupervised K-means clustering algorithm. Most existing approaches for detecting anomalies are based on constructing profiles of normal instances. However, such techniques require an adequate number of normal profiles to justify those models. Our proposed model first evaluates an \textit{optimal} value of K using Silhouette method. Next, it intends to locate anomalies that are far from a certain threshold distance with respect to their clusters. Finally, the five most popular classification techniques such as K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), and Logistic Regression (LR) are applied to build the resultant prediction model. The effectiveness of the proposed methodology is justified using a benchmark dataset of heart disease.

Tail-biting convolutional codes extend the classical zero-termination convolutional codes: Both encoding schemes force the equality of start and end states, but under the tail-biting each state is a valid termination. This paper proposes a machine-learning approach to improve the state-of-the-art decoding of tail-biting codes, focusing on the widely employed short length regime as in the LTE standard. This standard also includes a CRC code. First, we parameterize the circular Viterbi algorithm, a baseline decoder that exploits the circular nature of the underlying trellis. An ensemble combines multiple such weighted decoders, each decoder specializes in decoding words from a specific region of the channel words' distribution. A region corresponds to a subset of termination states; the ensemble covers the entire states space. A non-learnable gating satisfies two goals: it filters easily decoded words and mitigates the overhead of executing multiple weighted decoders. The CRC criterion is employed to choose only a subset of experts for decoding purpose. Our method achieves FER improvement of up to 0.75dB over the CVA in the waterfall region for multiple code lengths, adding negligible computational complexity compared to the circular Viterbi algorithm in high SNRs.

In Lagrangian dynamics, the detection of coherent clusters can help understand the organization of transport by identifying regions with coherent trajectory patterns. Many clustering algorithms, however, rely on user-input parameters, requiring a priori knowledge about the flow and making the outcome subjective. Building on the conventional spectral clustering method of Hadjighasem et al (2016), a new parameter-free spectral clustering approach is developed that automatically identifies parameters and does not require any user-input choices. A noise-based metric for quantifying the coherence of the resulting coherent clusters is also introduced. The parameter-free spectral clustering is applied to two benchmark analytical flows, the Bickley Jet and the asymmetric Duffing oscillator, and to a realistic, numerically-generated oceanic coastal flow. In the latter case, the identified model-based clusters are tested using observed trajectories of real drifters. In all examples, our approach succeeded in performing the partition of the domain into coherent clusters with minimal inter-cluster similarity and maximum intra-cluster similarity. For the coastal flow, the resulting coherent clusters are qualitatively similar over the same phase of the tide on different days and even different years, whereas coherent clusters for the opposite tidal phase are qualitatively different.

ClinVar is a web platform that stores around 774k curated entries, which allows exploring genetic variants and their associations with complex phenotypes. A partial set of ClinVar’s genetic associations were reported with conflict of interpretation or uncertain clinical impact significance, which currently challenges clinicians and geneticists. Here, we evaluate the performance of data pre-processing methods combined with classical prediction methods, such as Naive Bayes, Random Forest, and Support Vector Machine to build a meta-prediction model aiming to improve genetic pathogenicity interpretation. Models were trained with ClinVar data (September 2020), and genetic variants were annotated with eight functional impact predictors catalogued with SnpEff/SnpSift (v4.3). A 10-fold cross-validation strategy was performed for evaluation by accuracy, F1-Score, Receiver Operating Characteristic, Area Under Curve. The best meta-prediction model raises by combining one-hot encoding with tree-based classifiers as Random Forest, which shows Area Under Curve ≥ 0,93. We predict pathogenicity for 109k genetic variants, which were found labeled as uncertain significance or conflict of interpretation. Additionally, we implemented AmazonForest (https://www.lghm.ufpa.br/amazonforest), a web tool to query data for a set of 5k variants that were predicted with high pathogenic probability (RFprob >= 0.9).

This paper presents a multi-year research study conducted at the University of Toledo, aimed at improving human-machine teaming (HMT) methods and technologies. With the incorporation of artificial intelligence (AI) into 21st-century machines, collaboration between humans and machines has become highly complicated for real-time applications. The penetration of intelligent and synthetic assistants (IA/SA) in virtually every field has opened a path for the new area of HMT. When it comes to crucial tasks such as patient treatment and care, defense, and industrial production, the use of non-standardized HMT technologies may pose a risk to human lives as well as up to billions of taxpayer dollars. A thorough literature survey revealed that there are not many established standards or benchmarks for HMT. In this paper, we aim to address the lack of HMT design and development standards. We propose a method to design an HMT based on a generalized architecture. This design includes the development of an intelligent collaborative system and human team. Followed by processes and metrics to test and validate the proposed model, we present a novel human-in-the-loop (HIL) simulation method using two controlled HMT scenarios; first, emergency care provider (ECP) training, and second, patient treatment by an experienced medic. Both scenarios include humans processing visual data and performing actions that represent real-world applications while responding to a Voice-Based Synthetic Assistant (VBSA) as a collaborator that keeps track of actions. As part of these HMT simulation studies, the impact of each parameter related to machines, humans, and HMT (such as errors made by humans and by the VBSA) is presented from the perspective of performance, rules, roles, and operation limitations. In addition, based on the analysis of results from two case studies, we presented guidelines for designing future HMT. Our conclusions indicate that the proposed HIL can be used to perform standardization studies in the pursuit of developing techniques for benchmarking HMT that can be used in critical situations.

The fact is quite transparent that almost everybody around the world is using android apps. Half of the population of this planet is associated with messaging, social media, gaming, and browsers. This online marketplace provides free and paid access to users. On the Google Play store, users are encouraged to download countless of applications belonging to predefined categories. In this research paper, we have scrapped thousands of users reviews and app ratings. We have scrapped 148 apps’ reviews from 14 categories. We have collected 506259 reviews from Google play store and subsequently checked the semantics of reviews about some applications form users to determine whether reviews are positive, negative, or neutral. We have evaluated the results by using different machine learning algorithms like Naïve Bayes, Random Forest, and Logistic Regression algorithm. we have calculated Term Frequency (TF) and Inverse Document Frequency (IDF) with different parameters like accuracy, precision, recall, and F1 and compared the statistical result of these algorithms. We have visualized these statistical results in the form of a bar chart. In this paper, the analysis of each algorithm is performed one by one, and the results have been compared. Eventually, We've discovered that Logistic Regression is the best algorithm for a review-analysis of all Google play store. We have proved that Logistic Regression gets the speed of precision, accuracy, recall, and F1 in both after preprocessing and data collection of this dataset.

In order to design efficient and sustainable office spaces and to automate lighting, heating and air circulation in these facilities, solving the challenge of occupancy prediction is crucial. In office spaces where large areas need to be observed, multiple sensors must be used for full coverage. In these cases, it is normally important to keep the costs low, but also to make sure that the privacy of the people who use such environments are preserved. Low-cost and low-resolution heat (thermal) sensors can be very useful to build solutions that address these concerns. However, they are extremely sensitive to noise artifacts which might be caused by heat prints of the people who left the space or by other objects which are either using electricity or exposed to sunlight. There are some earlier solutions for occupancy prediction that employ low-resolution heat sensors, however, they have not addressed nor compensate for such heat artifacts. Therefore, in this paper, we present a low-cost and low-energy consuming smart space implementation to predict the number of people in the environment based on whether their activity is static or dynamic in time. We use a low-resolution (8×8) and non-intrusive heat sensor to collect data from an actual meeting room. We propose two novel workflows to predict the occupancy; one based on computer vision and one based on machine learning. Besides comparing the advantages and disadvantages of these different workflows, we use several state-of-the-art explainability methods in order to provide a detailed analysis of the algorithm parameters and how the image properties influence the resulting performance. Furthermore, we analyze noise resources which affect the heat sensor data. We hope that our analysis brings light into understanding how to handle very low-resolution heat images in these environments. The presented workflows could be used in various domains and applications other than smart offices, where occupancy prediction is essential, e.g., for elderly care.

Research on Traffic Light Recognition (TLR) has grown in recent years, primarily driven by the growing interest in autonomous vehicles development. Machine Learning algorithms have been widely used to that purpose. Mainstream approaches, however, require large amount of data to properly work, and as a consequence, a lot of computational resources. In this paper we propose the use of Expert Instruction (IE) as a mechanism to reduce the amount of data required to provide accurate ML models for TLR. Given an image of the exterior scene taken from the inside of the vehicle, we stand the hypothesis that the picture of a traffic light is more likely to appear in the central and upper regions of the image. Frequency Maps of traffic light location were thus constructed to confirm this hypothesis. The frequency maps are the result of a manual effort of human experts in annotating each image with the coordinates of the region where the traffic light appears. Results show that EI increased the accuracy obtained by the classification algorithm in two different image datasets by at least 15%. Evaluation rates achieved by the inclusion of EI were also higher in further experiments, including traffic light detection followed by classification by the trained algorithm. The inclusion of EI in the PCANet achieved a precision of 83% and recall of 73% against 75.3% and 51.1%, respectively, of its counterpart. We finally presents a prototype of a TLR Device with that expert model embedded to assist drivers. The TLR uses a smartphone as a camera and processing unit. To show the feasibility of the apparatus, a dataset was obtained in real time usage and tested in an Adaptive Background Suppression Filter (AdaBSF) and Support Vector Machines (SVMs) algorithm to detect and recognize traffic lights. Results show precision of 100% and recall of 65%.

Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant C and the kernel function parameter $\mathsf{\gamma }$—are investigated. The changes associated with adding white-noise and pink-noise on these two parameters along with adding different sources of movement variations (i.e., localized muscle fatigue and mixed activities) are further discussed. The results indicate that the SVM algorithm is capable of keeping high overall accuracy by adjusting the two parameters for dynamic as well as static activities, and may be applied as a tool for automatically identifying dynamic and static activities of daily life in the older adults.

For the generation of representative volume elements a statistical description of the relevant parameters is necessary. These parameters usually describe the geometric structure of a single grain. Commonly, parameters like area, aspect ratio and slope of the grain relative to the rolling direction are applied. However, usually simple distribution functions like log normal or gamma distribution are used. Yet, these do not take the interdependencies between the microstructural parameters into account. To fully describe any metallic microstructure though, these interdependencies between the singular parameters need to be accounted for. To accomplish this representation, a machine learning approach was applied in this study. By implementing a Wasserstein generative adversarial network, the distribution, as well as the interdependencies could accurately be described. A validation scheme was applied to verify the excellent match between microstructure input data and synthetically generated output data.

The presented research faces the problem of early detection of sepsis for patients in the Intensive Care Unit. The PhysioNet/Computing in Cardiology Challenge 2019 facilitated the development of automated, open-source algorithms for the early detection of sepsis from clinical data. A labeled clinical records dataset for training and verification of the algorithms was provided by the challenge organizers. However, a relatively small number of records with sepsis, supported by Sepsis-3 clinical criteria, led to highly unbalanced dataset (only 2% records with sepsis label). A high number of unbalanced data records is a great challenge for machine learning model training and is not suitable for training classical classifiers. To address these issues, a number of various models were investigated. A solution including feature selection and data balancing techniques was proposed in this paper. In addition, several performance metrics were investigated. Results show, that for successful prediction, a particular model having few or more predictors based on the length of stay in the Intensive Care Unit should be applied.

We present a general process for the halting problem, valid regardless of the time and space computational complexity of the decision problem. It can be interpreted as the maximization of entropy for the utility function of a given Shannon-Kolmogorov-Bernoulli process. Applications to non-polynomials problems are given. The new interpretation of information rate proposed in this work is a method that models the solution space boundaries of any decision problem (and non polynomial problems in general) as a communication channel by means of Information Theory. We described a sort method that order objects using the intrinsic information content distribution for the elements of a constrained solution space - modeled as messages transmitted through any communication systems. The limits of the search space are defined by the Kolmogorov-Chaitin complexity of the sequences encoded as Shannon-Bernoulli strings. We conclude with a discussion about the implications for general decision problems in Turing machines.

The hybridization of machine learning methods with soft computing techniques is an essential approach to improve the performance of the prediction models. Hybrid machine learning models, particularly, have gained popularity in the advancement of the high-performance control systems. Higher accuracy and better performance for prediction models of exergy destruction and energy consumption used in the control circuit of heating, ventilation, and air conditioning (HVAC) systems can be highly economical in the industrial scale to save energy. This research proposes two hybrid models of adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO), and adaptive neuro-fuzzy inference system-genetic algorithm (ANFIS-GA) for HVAC. The results are further compared with the single ANFIS model. The ANFIS-PSO model with the RMSE of 0.0065, MAE of 0.0028, and R² equal to 0.9999, with a minimum deviation of 0.0691 (KJ/s), outperforms the ANFIS-GA and single ANFIS models.

A 'little brother' of pain, itch is an unpleasant sensation that creates a specific urge to scratch. To date, various machine-learning based image classifiers (MBICs) have been proposed for quantitative analysis of itch-induced scratch behaviour of laboratory animals in an automated, non-invasive, inexpensive and real-time manner. In spite of MBICs' advantages, the overall performances (accuracy, sensitivity and specificity) of current MBIC approaches remains inconsistent, with their values varying from ~50% to ~99%, for which the reasons underlying have yet to be investigated further, both computationally and experimentally. To look into the variation of the performance of MBICs in automated detection of itch-induced scratch, this article focuses on the experimental data recording step, and reports here for the first time that MBICs' overall performance is inextricably linked to the sharpness of experimentally recorded video of laboratory animal scratch behaviour. This article furthermore demonstrates for the first time that a linearly correlated relationship exists between video sharpness and overall performance (accuracy and specificity, but not sensitivity) of MBICs, and highlight the primary role of experimental data recording in rapid, accurate and consistent quantitative assessment of laboratory animal itch.

An increase in the use of smartphones has laid to the use of the internet and social media platforms. The most commonly used social media platforms are Twitter, Facebook, WhatsApp and Instagram. People are sharing their personal experiences, reviews, feedbacks on the web. The information which is available on the web is unstructured and enormous. Hence, there is a huge scope of research on understanding the sentiment of the data available on the web. Sentiment Analysis (SA) can be carried out on the reviews, feedbacks, discussions available on the web. There has been extensive research carried out on SA in the English language, but data on the web also contains different other languages which should be analyzed. This paper aims to analyze, review and discuss the approaches, algorithms, challenges faced by the researchers while carrying out the SA on Indigenous languages.

The upsurge of Generative Adversarial Networks (GANs) in the previous five years has led to advancements in unsupervised data manipulation, sourced feature translation, and precise input-output synthesis through a competitive optimization of the discriminator and generator networks. More specifically, the recent rise of cycle-consistent GANs enables style transfers from a discrete source (input A) to target domain (input B) by preprocessing object features for a multi-discriminative adversarial network. Traditionally, cyclical adversarial networks have been exploited for unpaired image-to-image translation and domain adaptation by determining mapped relationships between an input A graphic and an input B graphic. However, this integral mechanism of domain adaptation can be applied to the complex acoustical features of human speech. Although well-established datasets, such as the 2018 Voice Conversion Challenge repository, paved way for female-male voice transformation, cycle-GANs have rarely been re-engineered for voices outside the datasets. More critically, cycle-GANs have massive potential to extract surface-level and hidden feature to distort an input A source into a texturally unrelated target voice. By preprocessing, compressing, and packaging unique acoustical voice properties, CycleGANs can learn to decompose speech signals and implement new translation models while preserving emotion, the intent of words, rhythm, and accents. Due to the potential of CycleGAN’s autoencoder in realistic unsupervised voice-voice conversion/feature adaptation, the researchers raise the ethical implications of controlling source input A to manipulate target voice B, particularly in cases of defamation and sabotage of target B’s words. This paper analyzes the potential of cycle-consistent GANs in deceptive voice-voice conversion by manipulating interview excerpts of political candidates.

We first review the literature on machine behavior which may contain the interaction between machines, and then discuss the methodology and the related case involving collaboration between robots to instantiate the concept with AI empowerment. The navigation case aims to a security application with autonomous control and roadside agent embedded in a mobile edge computing structure. In the studied navigation based on the artificial potential field method, robots need to use position information to calculate the moving direction frequently. In the case of high motion speed as well as GPS positioning error, the path trajectory may show a sharp change of direction. In order to mitigate the trajectory oscillation, this paper proposes a path planning design where training process and motion direction prediction are integrated by using artificial neural network. The auxiliary navigation agent near multiple obstacles can first extract the past movement information of the robot and then determines whether there is a serious path jitter event. Computer simulation analysis shows that the combination of autonomous control and cooperative behavior can effectively reduce the path jitter so as to achieve a fast and safe path planning.

Additive manufacturing (AM) is an attractive technology for manufacturing industry due to flexibility in design and functionality, but inconsistency in quality is one of the major limitations that does not allow utilizing this technology for production of end-use parts. Prediction of mechanical properties can be one of the possible ways to improve the repeatability of the results. The part placement, part orientation, and STL model properties (number of mesh triangles, surface, and volume) are used to predict tensile modulus, nominal stress and elongation at break for polyamide 2200 (also known as PA12). EOS P395 polymer powder bed fusion system was used to fabricate 217 specimens in two identical builds (434 specimens in total). Prediction is performed for XYZ, XZY, ZYX, and Angle orientations separately, and all orientations together. The different non-linear models based on machine learning methods have higher prediction accuracy compared with linear regression models. Linear regression models have prediction accuracy higher than 80% only for Tensile Modulus and Elongation at break in Angle orientation. Since orientation-based modeling has low prediction accuracy due to a small number of data points and lack of information about material properties, these models need to be improved in the future based on additional experimental work.

The violation of privacy, others people or personal, is a very current problem, which concerns not only on the web but also in private life. In the years 1990 it was expected that nowadays, that any routine operation was carried out "manually", and it would be performed through mobile phones or personal computers. The problem pertains the distribution network that allows to share and bring together information and as result the network becomes unsafe, if subjected to attacks. Nowaday we put personal information on web because otherwise we are seen as “weak”. This work aims to measure and analyze how much information are shared by users of a pre-established social network and it is carried out through a set of algorithms techniques of machine learning.

This paper evaluates the feasibility of applying Massive MIMO to tackle the uplink mixed-service communication problem. Under the assumption of an available physical narrowband shared channel (PNSCH), devised to exclusively consume data traffic from Machine Type Communications (MTC) devices, the capacity (i:e:, number of connected devices) of MTC networks and, in turn, that of the whole system, can be increased by clustering such devices and letting each cluster share the same time-frequency physical resource blocks. Following this research line, we study the possibility of employing sub-optimal linear detectors to the problem and present a simple and practical channel estimator that works without previous knowledge of the large-scale channel coefficients. Our simulation results suggest that the proposed channel estimator performs asymptotically as well as the MMSE estimator with respect to the number of antennas and the uplink transmission power. Furthermore, the results also indicate that, as the number of antennas is made progressively larger, the performance of sub-optimal linear detection methods approaches the perfect interference-cancellation bound. The findings presented in this paper shed light on and motivate for new and exciting research lines towards a better understanding of the use of massive MIMO in MTC networks.

Bike sharing systems are a key element of a smart city as they have the potential for reducing pollutant emissions and traffic congestion thus substantially improving citizens’ quality of life. In these systems, bicycles are made available for shared use to individuals on a very short-term basis. They are rented in a station and returned in any other station with free docks. However, to achieve a satisfactory user experience, all the stations in the system must be neither overloaded nor empty. The occupancy level of the stations can be constantly monitored through IoT-based services. The goal of this work is to analyze occupancy level data acquired from real systems to discover situations of dock overload in multiple stations which could lead to service disruption. The proposed methodology relies on a pattern mining approach. A new pattern type, called Occupancy Monitoring Pattern (OMPs), is proposed to characterize situations of dock overload in multiple stations. Since stations are geo-referenced and their occupancy levels are periodically monitored, OMPs can be filtered and evaluated by considering also the spatial and temporal correlation of the acquired measurements. The results achieved on real Smart City data highlight the potential of these techniques in supporting domain experts in maintenance activities, such as periodic re-balancing of the occupancy levels of the stations, as well as in improving the user experience, such as suggesting alternative stations in the neighborhood.

This study aimed to ascertain the applicability of a machine learning approach to the description of residential mobility patterns of households in the Seoul metropolitan region (SMR). The spatial range and temporal scope of the empirical study were set to 2015 to review the most recent residential mobility patterns in the SMR. The analysis data used in this study involve the microdata of Internal Migration Statistics provided by the Microdata Integrated Service of Statistics Korea. We analysed the residential relocation distance of households in the SMR by using machine learning techniques such as ordinary least squares regression and decision tree regression. The results of this study showed that a decision tree model can be more advantageous than ordinary least squares regression in terms of the explanatory power and estimation of moving distance. A large number of residential movements are mainly related to the accessibility to employment markets and some household characteristics. The shortest movements occur when households with two or more members move into densely populated districts. In contrast, job-based residential movements have relatively longer distance. Furthermore, we derived knowledge on residential relocation distance, which can provide significant information on the urban management of metropolitan residential districts and the construction of reasonable housing policies.

Many agonists for the estrogen receptor are known to disrupt endocrine functioning. We have developed a computational model that predicts agonists for the estrogen receptor ligand-binding domain in an assay system. Our model was entered into the Tox21 Data Challenge 2014, a computational toxicology competition organized by the National Center for Advancing Translational Sciences. This competition aims to find high-performance predictive models for various adverse-outcome pathways, including the estrogen receptor. Our predictive model, which is based on the random forest method, delivered the best performance in its competition category. In the current study, the predictive performance of the random forest models was improved by strictly adjusting the hyperparameters to avoid overfitting. The random forest models were optimized from 4,000 descriptors simultaneously applied to 10,000 activity assay results for the estrogen receptor ligand-binding domain, which have been measured and compiled by Tox21. At this time, our model delivers the highest predictive power on estrogen receptor agonists in the world. Furthermore, analysis of the optimized model revealed some important features of the agonists, such as the number of hydroxyl groups in the molecules.

Background: This research examines how market knowledge and artificial intelligence (AI) interact in different market designs such as business-to-business (B2B) settings while taking emerging technologies and the changing digitalization landscape into account. Objective: The main goal is to understand how AI affects market knowledge in different market designs such as business-to-business (B2B) contexts, taking into account language barriers, practical difficulties, and the revolutionary effects on decision-making and customer interactions. Result: They underscore the transformative potential of artificial intelligence (AI) by highlighting how it shapes market knowledge, encourages customized approaches, and improves marketing efficacy in the business-to-business (B2B) space. Conclusion: In order to create a path for responsible AI integration in B2B marketing, the study concludes with recommendations for standardized terminology related to AI, practical insights into implementation challenges, and ethical issues.

The globe and more particularly the economically developed regions of the world are currently in the era of the fourth Industrial revolution (4IR). Conversely; the economically developing regions in the world and more particularly the African continent have not yet even fully passed through the Third Industrial Revolution (3IR) wave and its economy is still heavily dependent on the agricultural field. On the other hand, the state of global food insecurity is worsening on an annual basis thanks to the exponential growth of the global human population which continuously heightens the food demand in both quantity and quality. This justifies the significance of the focus on digitizing agricultural practices to improve the farm yield to meet up with the steep food demand and stabilize the economy of the African continent and countries like India whose economy is mainly dependent on Agriculture. The tools we have at our disposal to utilize in the digitization of farming practices include space technology and Global Navigation and Satellite System (GNSS) in particular, Machine learning (ML), precision agriculture and communication systems such as the Internet of Things (IoT) and Information And Communication Technologies (ICT). The most pressing challenges in the farming field include the monitoring of diseases, pests, weeds and nutrient deficiencies in the crops as early detection translates to swift and timely correction actions and hence more yield at the end of a farming cycle. Vast opportunities in the field of precision agriculture still exist that can amount to further research studies such as the lack of real-time monitoring and real-time corrective action focus.

Large Language Models (LLMs) have proved significant proficiency when comes to code generation especially in Structured Query Language (SQL) for databases and recent successful Text-to-SQL method involves fine-tuning pre-trained LLMs for SQL generation tasks. Transforming natural language text into SQL queries, has been attempted to solve with various learning techniques including Few-shot learning[1], fine tuning. In this paper we propose Supervised fine-tuning (SFT) as a better alternative for learning technique for text-to-SQL generation task using Code-Llama that pushes state of art accuracy on spider test suite to 89.6% on dev set which represent first instance of surpassing the earlier best-in-class with 5.5% higher score and 86.8% of exact match accuracy on dev set. Furthermore, we demonstrate that properly prompted LLM along with SFT provides far fewer hallucinations and much more robust LLM that can be used as a general tool for any text-to-SQL generation use case.

(1) Background: Efficient detection and rectification of defects or post-processing manufacturing conditions such as sharp edges and burrs on metal components are crucial for quality control in precision manufacturing industries. (2) Methods: This paper describes a lab-scale integrated system for real-time and automated metal edge image detection using a customized YOLOv5 ma-chine vision algorithm. The YOLOv5 algorithm and model were developed and embedded in the NVIDIA Jetson Nano microprocessor. The YOLOv5 model can detect three different image types for classification i.e., normal, sharp, and burrs edge on aluminum workpiece blocks. An integrated system connects the NVIDIA Jetson Nano with an embedded YOLOv5 model to a Mitsubishi Electric Melfa RV-2F-1D1-S15 manipulator to perform selective automated chamfer-ing and grinding when certain condition defects are detected. (3) Results: The model demonstrates durable performance, achieving a mean average precision of 0.886 across defect classes with minimal misclassifications. The Mitsubishi Electric Melfa RV-2F-1D1-S15 manipulator received input from the machine vision system and performed an automated chamfering and grinding process accordingly; (4) Conclusions: By integrating camera, embedded deep learning in the microprocessor and manipulator, automated chamfering and grinding process in metal component shapes can be efficiently rectified. This tailored solution promises to improve productivity and consistency in metal manufacturing and remanufacturing.

Maritime transport has adapted to recent political and economic shifts by addressing stringent pollution reduction requirements, redrawing transport routes for safety, reducing onboard technical incidents, managing data security risks and transitioning to autonomous vessels. The integration of AI-based technologies for fault diagnosis and decision-making, coupled with proper personnel training, has become essential for optimal ship operation, with AI-assisted predictive maintenance emerging as the key strategy to ensure onboard systems function within desired parameters and enhance equipment availability. This study proposes a machine-learning algorithm for evaluating onboard systems performance by analyzing sensor data and mapping it to fault patterns to estimate functional states, validated through tests on a seawater cooling system from an oil tanker, demonstrating operational efficiency and reliability. The immediate advantage is reduced time for fault diagnosis by rapidly assessing the system using operational data, the algorithm enabling real-time monitoring and fault data management to minimize efforts as part of predictive maintenance assisted by machine learning tools.

It is critical to keep abreast of emerging dangers in the constantly changing field of cyber security. As 2024 approaches, numerous technological developments and trends are changing how businesses handle cyber security. This article explores the cutting edge of cyber security developments that are expected to rule in 2024. The field of cyber security is undergoing a revolution, from the adoption of Zero Trust Architecture (ZTA) as the new paradigm to the integration of AI and ML for real-time threat detection. Solutions for Extended Detection and Response (XDR) are becoming the mainstay for thorough threat detection and response in a variety of contexts, including networks, endpoints, and cloud environments. The use of biometric authentication techniques is growing in popularity because they provide better access control and identity verification. Concurrently, Deception Technologies are altering defensive tactics by using fictitious assets to trick and identify attackers at an early stage. Quantum-resistant cryptography, which aims to protect sensitive data from future quantum attacks, is on the horizon given the threat posed by quantum computing. Furthermore, the growing number of linked devices emphasizes how critical it is to strengthen IoT security protocols. Moreover, as long as companies are using cloud computing, they must make sure that strong cloud security protocols are in place. Simultaneously, the emergence of containerization technologies demands a targeted strategy for containerized environment security. Organizations must adopt these practices and strengthen their defenses against new threats as the cyber security landscape changes. Businesses may confidently and resiliently traverse the complex cybersecurity landscape of 2024 by remaining aware and implementing proactive security measures.

By 2024, artificial intelligence (AI) will have had a revolutionary impact on cyber security, putting the sector at a critical turning point. This article explores the significant influence AI has had on cyber security procedures, outlining the progress made as well as the difficulties faced. artificial intelligence (AI) has transformed threat detection and response systems, allowing for proactive security tactics against a constantly changing array of cyber threats. By utilizing neural networks and machine learning algorithms, artificial intelligence (AI) systems can predict possible security breaches, detect unusual trends, and automatically reduce risks in real-time. These capabilities are unparalleled. Nevertheless, despite these impressive advancements, worries about data privacy, adversarial attacks, and ethical ramifications remain significant. Moreover, the never-ending game of cat and mouse between cyber attackers and defenders continues, requiring constant innovation and adaptability in cyber security strategies driven by Artificial Intelligence. In addition to providing insights into its implications for protecting digital assets and bolstering resilience against cyber threats in the changing cyber security scenario of 2024, this paper summarizes the current state of the artificial intelligence revolution in cyber security.

Due to its flexibility, cloud computing has become essential in modern operational schemes. However, the effective management of cloud resources to ensure cost-effectiveness and maintain high performance presents significant challenges. The pay-as-you-go pricing model, while convenient, can lead to escalated expenses and hinder long-term planning. Consequently, FinOps advocates proactive management strategies, with resource usage prediction emerging as a crucial optimization category. In this research, we introduce the multi-time series forecasting system (MSFS), a novel approach for data-driven resource optimization alongside the hybrid ensemble anomaly detection algorithm (HEADA). Our method prioritizes the concept-centric approach, focusing on factors such as prediction uncertainty, interpretability and domain-specific measures. Furthermore, we introduce the similarity-based time-series grouping (STG) method as a core component of MSFS for optimizing multi-time series forecasting, ensuring its scalability with the rapid growth of the cloud environment. The experiments performed demonstrate that our group-specific forecasting model (GSFM) approach enabled MSFS to achieve a significant cost reduction of up to 44%.

In recent years, due to the detrimental impact of traditional gas-liquid refrigerants on the environment, the pursuit of solid-state refrigeration technology based on elastocaloric effect, as a promising sustainable alternative, has been promoted. However, the traditional trial-and-error method is inefficient and difficult to meet the material application requirements. In this work, machine learning (ML) was employed to accelerate the development of NiTi-based shape memory alloy (SMA) with excellent elastocaloric effect. By means of active learning, we identified 9 novel NiTi-based SMAs in four iterations, the phase transformation-induced entropy change (ΔS) of which are as high as over 90 J/kg·K-1. Moreover, the model exhibits good interpretability, allowing us to understand the relevance between the target and features from the aspect of mechanism.

Diabetic Foot Ulcer (DFU) is a severe complication of diabetes mellitus (DM), often leading to hospitalization and non-traumatic amputations in the United States. It is particularly prevalent among individuals of Hispanic descent, with a prevalence rate exceeding 10% [1, 2]. The target of this study is to identify markers of severity of DFU. This work follows a strategy that uses first Electronic Health Records and machine learning to identify risk factors reported as measurements of blood tests, such as cholesterol, blood sugar, and specific proteins in a cohort of thousands of patients. After this RNA risk factors are predicted using samples of bulk and single-cell gene expression across patients with different levels of DFU severity. We found the Albu-min/Creatinine Ratio (ACR) test as a key factor in severe DFUs. We also found a cluster of cells that harbor high expression of Apolipoprotein E protein (APOE) and it is significant in non-healing DFU cases. Overall, the study shows how the use of hundreds of thousands of EHR can reduce and inform the search space of molecules in a few samples of bulk-single cell transcriptomics and identify molecular markers of severity of DFU.

This qualitative research explores the impact of Artificial Intelligence (AI) on Customer Relationship Management (CRM), providing a comprehensive analysis of how AI technologies are transforming CRM practices. AI integration in CRM has revolutionized customer interactions, enabling enhanced personalization, efficiency, and customer engagement. By leveraging machine learning, natural language processing, and predictive analytics, organizations can create highly tailored customer experiences, automate routine tasks, and anticipate future customer needs with precision. The study identifies the significant benefits of AI, including improved customer insights, faster response times, and proactive engagement strategies. However, it also highlights the challenges associated with AI adoption in CRM, such as data privacy and security concerns, algorithmic bias, high implementation costs, and resistance to change. The findings emphasize the need for robust data protection measures, strategies to mitigate biases, significant investments in technology and training, and effective change management to overcome these challenges. The thematic analysis reveals key areas where AI is making an impact, including customer service, customer segmentation, and personalized marketing strategies. Additionally, the study discusses the organizational implications of AI adoption, such as enhanced decision-making, increased productivity, and the necessity for staff reskilling. Future directions for AI in CRM suggest further advancements in AI algorithms, greater integration with other business systems, enhanced customer experiences, and a focus on ethical AI practices. This research underscores the transformative potential of AI in CRM and provides insights for organizations aiming to leverage AI for improved customer relationship management.

Emergency departments (EDs) are critical in urgent care, where accurate triage optimizes patient flow and resource allocation. Manual triage faces challenges due to increasing volume and com-plexity. This study compares logistic regression, gradient boosting, neural network, and random forest models in predicting Emergency Severity Index (ESI) triage levels for non-traumatic pa-tients at Lampang Hospital's ED. Using data from January 1, 2023, to April 30, 2024, we ana-lyzed 45,245 complete records. The gradient boosting model achieved the highest accuracy (0.81), significantly outperforming logistic regression (accuracy 0.64). Pain scale, sex, and mean arterial pressure were key predictors. This is the first comparison of these models for ESI triage in a Thai hospital, highlighting their potential to improve triage accuracy and efficiency. Implementing these models could enhance patient outcomes and resource management in ED.

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

This paper presents machine learning methods for approximate solutions of reaction-diffusion equations with multivalued interaction functions. This approach addresses the challenge of finding all possible solutions for such equations, which often lack uniqueness. The proposed method utilizes physics-informed neural networks (PINNs) to approximate generalized solutions.

A multicenter retrospective analysis of 408 patients diagnosed with onychomycosis and attended at three tertiary care Spanish hospitals was conducted to assess the effectiveness and safety of a combined treatment with ciclopirox 8% hydroxypropyl chitosan (CPX 8% HPCH) nail lacquer and oral antifungal agents (terbinafine, itraconazole, and fluconazole). Data were extracted and analyzed using the EHRead® technology based on natural language processing and machine learning and using SNOMED CT terminology. The median age was 51 years with a ratio male: female close to 1. Repeated nail trauma was the most common risk factor (7.1%). More than half of patients (56.6%) underwent fungal culture. Terbinafine was used in 67.7% of patients, itraconazole in 20.8%, and fluconazole in 11.5%. Treatment synchronicity distribution revealed that 59.1% of patients started concomitant treatment with CPX 8% HPCH and oral antifungals, 27.9% had initial oral antifungals, and 13% had initial nail lacquer. The response to treatment (positive response 15.7%, presumed positive 59.8%) was unrelated to treatment synchronicity or type of antifungal agent. Erythema (5.6%), diarrhea (4.9%) and fever (4.2%) were the most frequently registered potential adverse events. The present findings provide valuable insights for physicians paving the way for better management of patients with onychomycosis.

Background: Low back pain (LBP) is a major cause of disability globally, and the diagnosis of LBP is challenging for clinicians. Objective: This study aimed to assess the accuracy level of arti-ficial intelligence as a Clinical Decision Support System (CDSS) called Therapa compared to MRI in predicting lumbar disc herniated patients. Methods: One hundred low back pain patients aged ≥18 years old were included in the study. The study was conducted in 3 stages, at first, a case se-ries was conducted by matching MRI and Therapha diagnosis for 10 patients. Subsequently, Delphi methodology was employed to establish a clinical consensus. Lastly, to determine the ac-curacy of the newly developed software, a cross-sectional study was undertaken, involving 100 patients. Results: The software showed a significant diagnostic accuracy with the area under the curve in the ROC analysis determined as 0.84 with a sensitivity of 88% and a specificity of 80%. Conclusion: The study’s findings revealed that CDSS using Therapha has a reasonable level of ef-ficacy, and this can be utilized clinically to acquire faster and more accurate screening of patients with lumbar disc herniation.

The world health organisation (WHO) revealed approximately 280 million people in the world suffer from depression. Yet, existing studies on early-stage depression detection using machine learning (ML) techniques are limited. Prior studies have applied a single stand-alone algorithm which are unable to deal with data complexities, prone to overfitting and limited in generalisation. To this end, our paper examined the performance of several ML algorithms for early-stage depression detection using two benchmark social media datasets (D1 and D2). More specifically, we incorporated sentiment indicator to improve our model performance. Our experimental results showed that sentence bidirectional encoder representations from transformers (SBERT) numerical vectors fitted into stacking ensemble model achieved comparable F1 scores of 69% in dataset (D1) and 76% in dataset (D2). Our findings suggest that utilising sentiment indicators as additional feature for depression detection yields an improved model performance and thus, we recommend the development of depressive term corpus for future work.

Liposomal amphotericin B (L-AmB) has become the cornerstone for the treatment of severe invasive fungal infections. However, renal toxicity may be an associated risk. Our aim was to evaluate the incidence of acute kidney injury (AKI) in critically ill patients receiving L-AmB for more than 48 hours. Clinical, demographic and laboratory variables were obtained retrospectively from electronic health record. Follow-up of the patients was for 7 days after L-AmB infusion. Patients were analyzed globally and differentiated between patients with "low" and "high" risk of developing AKI with machine learning techniques. Sixty-seven patients were included: 61 years, 67% male, with mean SOFA score of 4 (3-6.5) and a crude intensive care unit mortality of 34.3%. No significant variations in serum creatinine were observed during the observation period, except for the decrease in the high-risk subgroup. Overall, 26.8% of patients developed AKI-I, while 25% and 13% of patients at low and high risk of AKI developed AKI-I, respectively. The random forest models performed in overall and low-risk subgroup identified several major contributor’s factors to the development of AKI other than L-AmB administration. The development of AKI is multifactorial and the administration of L-AmB appears to be safe in this group of patients.

This paper presents an innovative approach to dimensionality reduction and feature extraction in high-dimensional datasets, with a specific application focus on wood surface defect detection. The proposed framework integrates sparse modeling techniques, particularly Lasso and proximal gradient methods, into a comprehensive pipeline for efficient and interpretable feature selection. Leveraging pre-trained models such as VGG19 and incorporating anomaly detection methods like Isolation Forest and Local Outlier Factor, our methodology addresses the challenge of extracting meaningful features from complex datasets. Evaluation metrics such as accuracy and F1 score, alongside visualizations, are employed to assess the performance of the sparse modeling techniques. Through this work, we aim to advance the understanding and application of sparse modeling in machine learning, particularly in the context of wood surface defect detection.

The acquisition of vegetation coverage information is crucial for crop field management, and utilizing visible light spectrum vegetation indices to extract vegetation coverage information is a commonly used method. However, most visible light spectrum vegetation indices do not fully consider the relationships between the red, green, and blue bands during their construction, making it difficult to ensure accurate extraction of coverage information throughout the crop's entire growth cycle. To rapidly and accurately obtain potato vegetation coverage information, drones were used in this study to obtain high-resolution digital orthoimages of potato growth stages. Based on the differences in the grayscale values of potato plants, soil, shadows, and drip irrigation belts, this study presents a combination index of blue and green bands (BGCI) and a combination index of red and green bands (RGCI). The vegetation index intersection method was used with 10 vegetation information indices to extract vegetation coverage, and the differences in extraction accuracy were compared with those of the maximum entropy method and bimodal histogram method. Based on the high-precision fraction vegetation coverage (FVC) extraction results, the Pearson correlation coefficient method and random forest feature selection were used to screen 10 vegetation and 24 texture features, and the top 6 vegetation indices most strongly correlated with the FVC were selected for potato growth stage FVC estimation and accuracy verification. A high-precision potato vegetation coverage estimation model was successfully established. This study revealed that during the potato tuber formation and expansion stages, the BGCI combined with the vegetation index intersection method achieved the highest vegetation coverage extraction accuracy, with overall accuracies of 99.61% and 98.84%, respectively. The RGCI combined with the vegetation index intersection method achieved the highest accuracy, 98.63%, during the maturation stage. For the potato vegetation coverage estimation models, the model based on the BGCI achieved the highest estimation accuracy (R2=0.9116, RMSE=5.7903), and the RGCI also achieved good accuracy in terms of vegetation coverage estimation (R2=0.8987, RMSE=5.8633). In the generality verification of the models, the R2 values of the FVC estimation models based on the BGCI and RGCI were both greater than 0.94. A potato vegetation coverage estimation model was constructed based on two new vegetation information indices, demonstrating good accuracy and universality.

A suitable waste management strategy is crucial for a sustainable and efficient circular economy in the construction sector, and requires precise data on the volume of demolition waste (DW) gen-erated. Therefore, we developed an optimal machine learning (ML) model to forecast the quantity of recycling and landfill waste based on the characteristics of DW. A dataset comprising infor-mation on the characteristics of 150 buildings, demolition equipment utilized, and volume of five waste types generated (i.e., recyclable mineral, recyclable combustible, landfill specified, and landfill mix waste, and recyclable minerals) was constructed. ML models were developed to predict the quantities of such waste. Artificial neural network, decision tree, gradient boosting machine, k-nearest neighbors, linear regression, random forest (RF), and support vector regression were applied, and optimal models were derived via hyperparameter tuning. The RF model demonstrated superior performance. In both validation and test phases, the “recyclable mineral waste” and “recyclable combustible waste” models achieved accuracies of of 0.987 and 0.972, re-spectively. The “recyclable metals” and “landfill specified waste” models achieved accuracies of 0.953 and 0.858 or higher, respectively. Moreover, the “landfill mix waste” model exhibited an accuracy of 0.984 or higher. SHapley Additive exPlanations analysis highlighted floor area as the primary input variable influencing model performance. The type of equipment employed in demolition emerged as another crucial input variable impacting the volume of recycling and landfill wastes generated. The developed model can provide precise data on waste management, thereby facilitating the decision-making process for industry professionals.

This research project aimed to address the growing concern about methane emissions from seaweed by developing a Convolutional Neural Network (CNN) model capable of accurately predicting these emissions. The study used PANDAS to read and analyze the dataset, incorporating statistical measures like mean, median, and standard deviation to understand the dataset. The CNN model was trained using the ReLU activation function and mean absolute error as the loss function. Model performance was evaluated through MAPE graphs, comparing the mean absolute percentage error (MAPE) between training and validation sets and between true and predicted emissions, and analyzing trends in yearly greenhouse gas emissions. The results demonstrated that the CNN model achieved a high level of accuracy in predicting methane emissions, with a low MAPE between the expected and actual values. This approach should enhance our understanding of methane emissions from Sargassum, contributing to more accurate environmental impact assessments and effective mitigation strategies.

Photovoltaic modules should pass series of tests and examinations, verifying the electrical and thermal characteristics of the module, before being released to the market, examining the properties of the modules should undergo appropriate durability and efficiency tests under real outdoor conditions with exposure to climatic conditions and under laboratory conditions. The performance of photovoltaic cells depends on many factors, such as solar irradiance, module operating temperature, installation location, weather conditions and module shading. In this paper, three selected photovoltaic modules were examined using a large-scale steady-state solar simulator. The current-voltage (I-V) characteristics of the photovoltaic modules were experimentally tested and analyzed. The next step was to implement a three-layer artificial neural network model (MLP). The experimental data obtained in the previous step coupled with output data from MLP were then used in global sensitivity analysis (GSA). Experiments carried out on a large-scale stationary solar simulator showed differences between the values declared by the manufacturer and the values obtained from measurements of PV modules. The first module tested achieved the maximum power point greater than that specified by the manufacturer, while the other two showed power drops; it was 85-87% for the second module, and 95-98% for the third, respectively. The performed global sensitivity analysis (GSA) for the MLP model showed that the parameters: eff (22.9) and Voc/V (14.19) have the largest effect on the power-voltage relationship, while U (7.29) has the smallest effect. The usefulness of machine learning (ML) methods in the comparative analysis of PV modules has been proved.

Cloud data centers play a vital role in modern computing infrastructure, offering scalable resources for diverse applications. However, managing costs and resources efficiently in these centers has become a crucial concern due to the exponential growth of cloud computing. User applications exhibit complex behavior, leading to fluctuations in system performance and increased power usage. To tackle these obstacles, we propose the consolidation of virtual machines (VMs) to optimize resource utilization and reduce operational costs. This paper introduces a novel algorithm, leveraging the Modified Artificial Feeding Birds Algorithm (ModAFBA), to manage cost optimally in cloud data centers by reducing power usage while meeting service level agreements (SLAs). The ModAFBA algorithm, inspired by bird foraging behavior, dynamically adjusts solutions based on environmental changes. Comprehensive experimental evaluation demonstrates the superiority of the proposed algorithm in terms of power usage, migration count, and SLA compliance. Through this research, we aim to advance cost management and resource optimization in cloud data centers, providing a valuable tool for cloud service providers to enhance infrastructure efficiency and achieve cost savings.

This study investigates the impact of real-time emotional feedback on the quality of teamwork conducted over videoconferencing. We developed a framework that provides real-time feedback through a virtual mirror based on facial and voice emotion recognition. In an experiment with 28 teams (84 participants), teams collaborated over Zoom to set up a virtual Mars station using custom simulation software. Participants were divided into 14 experimental teams which were shown the virtual mirror, and 14 control teams without it. Team performance was measured by the improvement in the Mars simulation output quality. Our analysis using correlation, multilevel regression, and machine learning revealed that fewer interruptions but an increasing number over time correlated with higher performance. Higher vocal arousal and happiness also enhanced performance. We confirmed that female presence in teams boosts performance. SHAP values indicated that high variability in happiness, head movement, and positive facial valence—an “emotional rollercoaster”—positively predicted team performance. The experimental group outperformed the control group, suggesting that virtual mirroring improves virtual teamwork and that interrupting each other more, while speaking less, leads to better results.

Machine learning and computer vision have proven to be valuable tools for farmers to streamline their resource utilization to lead to more sustainable and efficient agricultural production. These techniques have been applied to strawberry cultivation in the past with limited success. To build on this past work, in this study two separate sets of strawberry images, along with their associated diseases, were collected and subjected to, resizing, and augmentation. Subsequently, a combined dataset consisting of 9 classes was utilized to fine-tune three distinct pre-trained models: Vision Transformer (ViT), MobileNetV2, and ResNet18. To address the imbalanced class distribution in the dataset, each class was assigned weights to ensure nearly equal impact during the training process. To enhance the outcomes, new images were generated by removing backgrounds, reducing noise, and flipping them. The performances of ViT, MobileNetV2, and ResNet18 were compared after being selected. Customization specific to the task was applied to all three algorithms, and their performances were assessed. Throughout this experiment, none of the layers were frozen, ensuring all layers remained active during training. Attention heads were incorporated into the first 5 and last 5 layers of MobileNetV2 and ResNet18, while the architecture of ViT was modified. The results indicated accuracy factors of 98.4%, 98.1%, and 97.9% for ViT, MobileNetV2, and ResNet18, respectively. Despite the data being imbalanced, the precision, which indicates the proportion of correctly identified positive instances among all predicted positive instances, approached nearly 99% with the ViT. MobileNetV2 and ResNet18 demonstrated similar results. Overall, the analysis revealed that the Vision Transformer model exhibited superior performance in strawberry ripeness and disease classification. The inclusion of attention heads in the early layers of ResNet18 and MobileNet18, along with the inherent attention mechanism in ViT, improved the accuracy of image identification. These findings offer the potential for farmers to enhance strawberry cultivation through passive camera monitoring alone, promoting the health and well-being of the population.

This study assesses the predictive performance of six machine learning models in forecasting tumor dynamics within three months following Gamma Knife Radiosurgery (GKRS) in 77 brain metastasis (BM) patients. The analysis meticulously evaluates each model before and after hyperparameter tuning, utilizing accuracy, AUC and other metrics derived from confusion matrices. Initial findings highlighted that XGBoost significantly outperformed other models with an accuracy of 0.95 and an AUC of 0.95 before tuning. Post-tuning, the Support Vector Machine (SVM) demonstrated the most substantial improvement, achieving an accuracy of 0.98 and an AUC of 0.98. Conversely, XGBoost showed a decline in performance after tuning, indicating potential overfitting. The study also explores feature importance across models, noting that features like “control at one year”, “age of the patient”, and “beam on time for volume V1 treated”, were consistently influential across various models, albeit their impacts were interpreted differently depending on the model’s underlying mechanics. This comprehensive evaluation not only underscores the importance of model selection and hyperparameter tuning but also highlights the practical implications in medical diagnostic scenarios, where the accuracy of positive predictions can be crucial.

In this study, I will present some documentary evidence that Carlos Drummond de Andrade’s poem “A Máquina do Mundo” [The Machine of the World] (1949) was based on prior reflections contained in a forgotten newspaper article entitled “Poesia e Pesadelo” [Poetry and Nightmare] (1949). In that article, Drummond critically analyzed some of the theses of Stephen Spender's essay “What Is Modern in Modern Poetry” (1948), which had just been published in The Tiger's Eye magazine. As soon as he read Spender's text, Drummond wrote “Poetry and Nightmare” and began to elaborate a narrative poem whose protagonist would be a machine. All the documentary evidence indicates that Drummond took Spender's lessons as touchstones for the elaboration of the narrative and allegorical structure of “The Machine of the World.” According to the interpretation I am proposing here, Drummond, in writing the machine poem, put into practice at least six lessons drawn from Spender’s essay, namely: 1) the modern poet must represent the tension between the external machinelike landscape and the landscape of the inner world of the creative imagination; 2) he must also represent the machinelike, ugly, and urban world around him in a beautiful and poetic way; 3) he must avoid the misconceptions of excessive utopian visionarism, as well as creeping realism; 4) he must reject the imperative of political engagement, whose principle of action states that poetry has a transformative mission in society; 5) he must free himself from the obsession with free verse as the only form of modern poetry and start using the traditional fixed forms; 6) he must understand machines as poetic symbols of human passions, and not just as exotic objects foreign to human reality. My conclusion is that, after reading Spender’s essay, Drummond changed his aesthetic and philosophical understanding of what defines the essence of modern poetry, both in terms of form and content.

YouTube is a very popular social media platform in today’s digital landscape. The primary focus of this study is to explore the underlying sentiment in user comments on food-related videos on YouTube, specifically within two pivotal food product categories: Plant-Based and Hedonic. Our research involved labelling comments using sentiment lexicons such as TextBlob and VADER. Furthermore, the sentiments of the comments were classified using advanced Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), Multinomial Naive Bayes, Random Forest, Logistic Regression, and XGBoost. The evaluation of these models encompassed key macro average metrics, including accuracy, precision, recall, and f1-score. Results from VADER showcased a high accuracy level, with SVM achieving 93% accuracy in the plant-based dataset and 96% in the hedonic dataset. In addition to sentiment analysis, we delved into user interactions within the two datasets, measuring crucial metrics such as views, likes, comments, and engagement rate. The findings illuminate significantly higher levels of views, likes, and comments in the hedonic food dataset, but the plant-based dataset maintains a superior overall engagement rate.

Digital machine intelligence has evolved from its inception in the form of computation of numbers to AI, which is centered around performing cognitive tasks that humans can perform, such as predictive reasoning or complex calculations. The state of the art includes tasks that are easily described by a list of formal, mathematical rules or a sequence of event-driven actions such as modeling, simulation, business workflows, interaction with devices, etc., and also tasks that are easy to do “intuitively”, but are hard to describe formally or as a sequence of event-driven actions such as recognizing spoken words or faces. While these tasks are impressive, they fall short in applying common sense reasoning to new situations, filling in information gaps, or understanding and applying unspoken rules or norms. Human intelligence uses both associative memory and event-driven transaction history to make sense of what they are observing fast enough to do something about it while they are still observing it. In addition to this cognitive ability, all bio-logical systems exhibit autopoiesis and self-regulation. In this paper, we demonstrate how machine intelligence can be enhanced to include both associative memory and event-driven transaction history to create a new class of knowledge-based assistants to augment human intelligence. The digital assistants use global knowledge derived from the Large Language Models to bridge the knowledge gap between various participants interacting with each other. We use the general theory of information and schema-based knowledge representation to create the memory and history of various transactions involved in the interactions.

This paper explores the capability of various machine learning algorithms, including Random Forests and advanced gradient boosting techniques such as XGBoost, LightGBM, and CatBoost, to predict customer churn in the telecommunications sector. For this analysis, a dataset available to the public was employed. The performance of these algorithms was assessed using recognized metrics, including Accuracy, Precision, Recall, F1-score, and the Receiver Operating Characteristic Area Under Curve (ROC AUC). These metrics were evaluated at different phases: subsequent to data preprocessing and feature selection; following the application of SMOTE and ADASYN sampling methods; and after conducting hyperparameter tuning on the data that had been adjusted by SMOTE and ADASYN. The outcomes underscore the notable efficiency of upsampling techniques such as SMOTE and ADASYN in addressing the imbalance inherent in customer churn prediction. Notably, the application of random grid search for hyperparameter optimization did not significantly alter the results, which remained comparatively unchanged. The algorithms' performance post- ADASYN application marginally surpassed that observed after SMOTE application. Remarkably, LightGBM achieved an exceptional F1-score of 89% and an ROC AUC of 95% subsequent to the ADASYN sampling technique. This underlines the effectiveness of advanced boosting algorithms and upsampling methods like SMOTE and ADASYN in navigating the complexities of imbalanced datasets and intricate feature interdependencies.

This paper explores if plants are capable of responding to human movement by changes in their electrical signals. Towards that goal we conducted a series of experiments, where humans over a period of 6 months were performing different types of eurythmic gestures in proximity of garden plants, namely salad, basil, and tomatoes. To measure plant perception, we used the plant spikerbox, a device that measures changes in voltage differentials of plants between roots and leaves. Using machine learning, we find that the voltage differentials over time of the plant predict if (a) eurythmy has been performed, and (b) which kind of eurythmy gestures has been performed. We also find that the signals are different based on the species of the plant. In other words, the perception of a salad, tomato, or basil might differ just as perception of different species of animals differ. This opens new ways of studying plant ecosystems, while also paving the way to use plants as biosensors for analyzing human movement.

In this paper major machine learning (ML) tools and the most important applications developed elsewhere for numerical weather and climate modeling systems (NWCMS) are reviewed. NWCMSs are briefly introduced. The most important papers published in this field in recent years are reviewed. The advantages and limitations of the ML approach in applications to NWCMS are briefly discussed. Currently, this field is experiencing explosive growth. Several important papers are published every week. Thus, this paper should be considered a simple introduction to the problem.

Efficient triaging and referral assessment are critical in ensuring prompt medical intervention in the Community Health Care (CHC) system. However, the existing triaging systems in many Community Health Services are an intensive, time-consuming process and often lack accuracy, particularly for various symptoms that might represent heart failure or other health-threatening conditions. There is a noticeable limit of research papers describing AI technologies for triaging patients. This paper proposes a novel quantitative data-driven approach using machine learning (ML) modelling to improve the community clinical triaging process. Furthermore, this study aims to employ the feature selection process and machine learning power to reduce the triaging process’s waiting time and increase accuracy in clinical decision-making. The model was trained on medical records of “Heart Failure” patients’ dataset, which included demographics, past medical history, vital signs, medications, and clinical symptoms. A comparative study was conducted using the list of machine learning algorithms where XGBoost demonstrated the best performance among other ML models. The triage levels of 2,35,982 patients achieved an accuracy of 99.94%, precision of 0.9986, recall of 0.9958, and f1-score of 0.9972 within 0.059 seconds. The proposed diagnostic model can be implemented for the CHC decision system and be developed further for other medical conditions.

Merkel cell carcinoma (MCC) is a rare, highly aggressive skin cancer characterized by two etiologies: the first one, observed in most cases, is attributed to Merkel cell polyomavirus (MCPyV), a DNA tumor virus, while the second one is due to ultraviolet (UV) radiation exposure. Currently, there are limited diagnostic, prognostic, and therapeutic biomarkers available for MCC. MicroRNAs (miRNAs) are small RNA molecules that do not code for proteins but regulate various cellular processes, including cell cycle progression, proliferation, differentiation, and apoptosis. The dysregulation of miRNAs has been associated with a wide range of cancers since they can act as either tumor suppressors or oncogenes. This paper aims to collect, summarize and re-organize the recent discoveries concerning miRNAs, whose dysregulation in the pathogenesis and progression of MCC is slowly emerging. Furthermore, the potential clinical applications of miRNAs as diagnostic biomarkers, possible differentiation between MCPyV-related and unrelated MCC, predictors of response to therapy and prognostics are also discussed. From this review, it emerges that miRNAs may have, in the near future, the potential to become clinically significant in improving the diagnostic accuracy, prognostic evaluation, and therapeutic options of MCCs. Finally, the new perspectives on the poorly explored field of artificial intelligence in MCC are provided.

The utilization of selective laser melting (SLM) for high entropy alloys (HEAs) holds significant promise in commercial applications, and substantial experimental research efforts have been directed toward this domain. To take advantage of the reported experimental data of SLM manufactured (SLM-ed) HEAs and reduce unnecessary experimentation, this study incorporates machine learning (ML) techniques for the phase and tensile properties prediction of SLM-ed HEAs, thus presenting a novel avenue for accelerating the discovery of new SLM-ed HEAs. Through the adjustment of material descriptors and machine learning models, a model has been developed with an impressive accuracy of 93.8% in distinguishing between face-centered cubic (FCC), body-centered cubic (BCC), and dual-phase (FCC+BCC). Additionally, optimized models have been devised for the prediction of tensile properties, namely ultimate tensile strength (UTS), yield strength (YS), and elongation (δ), achieving noteworthy MAPEs (mean absolute percentage errors) of 10.33%, 8.55%, and 28.48%, respectively. Furthermore, several HEAs were fabricated using SLM, and the experimental outcomes exhibited a favorable alignment with the predicted results. These efforts carry significant implications in advancing the utilization of SLM for HEAs.

The storm 'Daniel' has caused the most severe flood phenomenon that Greece has ever experienced, with thousands of hectares of farmland submerged for days. This led to sediment deposition in the inundated areas, which significantly altered the chemical properties of the soil, as revealed by extensive soil sampling and laboratory analysis. Causal relationships between soil chemical properties and sediment deposition were extracted using the DirectLiNGAM algorithm. Results of causality analysis showed that the sediment deposition affected the CaCO3 concentration in the soil. Also, causal relationships were identified between CaCO3 and available phosphorus (P-Olsen), as well as between sediment deposit depth and available manganese. The quantified relationships between the soil variables were then used to generate data using a Multiple Linear Perceptron (MLP) regressor for various levels of deposit depth (0, 5, 10, 15, 20, 25 and 30 cm). Then linear regression equations were fitted across the different levels of deposit depth to determine the effect of deposit depth on CaCO3, P, and Mn. The results show that there is a linear slope of 0.12, -0.16, and 0.13, for CaCO3, P, and Mn with deposit depth, respectively. Statistical analysis indicates that corn growing in soils with sediment over 10 cm requires a 31.8% increase in P rate to prevent yield decline. Additional notifications regarding cropping strategies in the near future are also discussed.

Using the potent tools of information geometry (IG), the current research analyses the information geometrics of the five-dimensional HTBFC Manifold. By enabling a unique revolutionary analysis for the performance of Human-Trust Based Feedback Control (HTBFC), the integration of information geometry into HTBFC facilitates the relativistic analysis of the five-dimensional HTBFC manifold. Both the two- and three-dimensional manifold subcases are investigated. This work provides a ground-breaking approach to IG unification of Human- Machine Interactions (HMIs). Some potential applications of HTBFC to robotics are presented.Conclusions are provided, along with some emerging open problems and recommendations for future research.