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Flotation deinking is one of the most widely used techniques for the separation of ink particles from cellulose fibers during the process of paper recycling. It is a complex process influenced by a variety of factors, and is difficult to represent and usually results in models that are inconvenient to implement and/or interpret. In this paper, a comprehensive study of several machine learning methods for the prediction of flotation deinking performance is carried out, including support vector regression, regression tree ensembles (random forests and boosting) and Gaussian process regression. The prediction relies on the development of a limited dataset that assumes representative data samples obtained under a variety of laboratory conditions, including different reagents, pH values and flotation residence times. The results obtained in this paper confirm that the machine learning methods enable the accurate prediction of flotation deinking performance even when the dataset used for training the model is limited, thus enabling the determination of optimal conditions for the paper recycling process, with only minimal costs and effort. Considering the low complexity of the Gaussian process regression compared to the aforementioned ensemble models, it should be emphasized that the Gaussian process regression gave the best performance in estimating fiber recovery (R2 = 97.77%) and a reasonable performance in estimating the toner recovery (R2 = 86.31%). [ABSTRACT FROM AUTHOR]

The document discusses the importance of vibration monitoring in diagnosing faults in rotating machinery, emphasizing the role of signal processing and pattern analysis. Vibration monitoring involves measuring mechanical oscillations in machinery components to detect faults early. Signal processing techniques are used to extract meaningful information from raw vibration data, while pattern analysis techniques help diagnose specific faults. The integration of vibration monitoring with signal processing and pattern analysis offers benefits such as early fault detection, improved reliability, and data-driven maintenance decisions. Challenges include the complexity of machinery, variability of operating conditions, and the gap between research advancements and industrial adoption. The document also highlights the use of benchmark datasets for research and the application of machine learning in fault detection and machinery health monitoring. [Extracted from the article]

Sitting posture recognition systems have gained significant attention due to their potential applications in various domains, including healthcare, ergonomics, and human-computer interaction. This paper presents a comprehensive literature review and analysis of existing sitting posture recognition systems. Through an extensive examination of relevant research articles and conference papers, we identify and analyze the underlying technologies, methodologies, datasets, performance metrics, and applications associated with these systems. The review encompasses both traditional methods, such as vision-based approaches and sensor-based techniques, as well as emerging technologies such as machine learning and deep learning algorithms. Additionally, we examine the challenges, constraints, and future trends in the field of sitting posture recognition systems. Researchers, practitioners, and policymakers who want to comprehend the most recent developments and latest trends in sitting posture recognition technology will find great value in this study. [ABSTRACT FROM AUTHOR]

This review analyzes the most influential artificial intelligence (AI) studies in health and life sciences from the past three years, delineating the evolving role of AI in these fields. We identified and analyzed the top 50 cited articles on AI in biomedicine, revealing significant trends and thematic categorizations, including Drug Development, Real-World Clinical Implementation, and Ethical and Regulatory Aspects, among others. Our findings highlight a predominant focus on AIs application in clinical settings, particularly in diagnostics, telemedicine, and medical education, accelerated by the COVID-19 pandemic. The emergence of AlphaFold marked a pivotal moment in protein structure prediction, catalyzing a cascade of related research and signifying a broader shift towards AI-driven approaches in biological research. The review underscores AIs pivotal role in disease subtyping and patient stratification, facilitating a transition towards more personalized medicine strategies. Furthermore, it illustrates AIs impact on biology, particularly in parsing complex genomic and proteomic data, enhancing our capabilities to disentangle complex, interconnected molecular processes. As AI continues to permeate the health and life sciences, balancing its rapid technological advancements with ethical stewardship and regulatory vigilance will be crucial for its sustainable and effective integration into healthcare and research. [ABSTRACT FROM AUTHOR]

Walking robots have been widely applied in complex terrains due to their good terrain adaptability and trafficability. However, in some environments (such as disaster relief, field navigation, etc.), although a single strategy can adapt to various environments, it is unable to strike a balance between speed and stability. Existing control schemes like model predictive control (MPC) and traditional incremental control can manage certain environments. However, they often cannot balance speed and stability well. These methods usually rely on a single strategy and lack adaptability for dynamic adjustment to different terrains. To address this limitation, this paper proposes an innovative double-layer reinforcement learning algorithm. This algorithm combines Deep Double Q-Network (DDQN) and Proximal Policy Optimization (PPO), leveraging their complementary strengths to achieve both fast adaptation and high stability in complex terrains. This algorithm utilizes terrain information and the robot's state as observations, determines the walking speed command of the quadruped robot Unitree Go1 through DDQN, and dynamically adjusts the current walking speed in complex terrains based on the robot action control system of PPO. The speed command serves as a crucial link between the robot's perception and movement, guiding how fast the robot should walk depending on the environment and its internal state. By using DDQN, the algorithm ensures that the robot can set an appropriate speed based on what it observes, such as changes in terrain or obstacles. PPO then executes this speed, allowing the robot to navigate in real time over difficult or uneven surfaces, ensuring smooth and stable movement. Then, the proposed model is verified in detail in Isaac Gym. Wecompare the distances walked by the robot using six different control methods within 10 s. The experimental results indicate that the method proposed in this paper demonstrates excellent speed adjustment ability in complex terrains. On the designed test route, the quadruped robot Unitree Go1 can not only maintain a high walking speed but also maintain a high degree of stability when switching between different terrains. Ouralgorithm helps the robot walk 25.5 m in 10 s, outperforming other methods. [ABSTRACT FROM AUTHOR]

This document discusses the recent advancements in decision support systems (DSSs) for disease detection and diagnosis. The combination of biomedical studies and information technology has led to the development of accessible and accurate solutions that can improve patient survival rates. The document highlights several research papers that cover a wide range of topics, including multiple sclerosis detection, neurodegenerative disease detection, breast lesion classification, COVID-19 mortality prediction, melanoma diagnosis, and prediction of second primary skin cancer. The adoption of efficient DSSs can aid clinical assessment, reduce misdiagnosis, and facilitate evidence-based decision-making. However, challenges such as validation, training, and user interface design need to be addressed for widespread application of DSSs in clinical practice. The document concludes by emphasizing the importance of future studies and developments in overcoming limitations and expanding the use of DSSs in different contexts. [Extracted from the article]

Raman spectroscopy (RS) is a promising tool for cancer diagnosis. In particular, in the last years several studies have demonstrated how the diagnostic performances of RS can be significantly improved by employing machine learning (ML) algorithms for the interpretation of Raman-based data. Recently, it has been demonstrated that RS can perform an accurate classification of chondrosarcoma tissues. Chondrosarcoma is a cancer of bones, that can occur in the soft tissues near the bones. It is normally characterized by three different malignant degrees and a benign counterpart, knows as enchondroma. In line with these findings, in this paper, we exploited ML algorithms to distinguish, as well as possible, between the three grades of chondrosarcoma and to distinguish between chondrosarcoma and enchondroma. We obtained a high level of accuracy of classification by analyzing a dataset composed of a relatively small number of Raman spectra, collected in a previous study by one of the authors of this paper. Such spectra were acquired from micrometric tissue sections with a confocal Raman microscope. We tested the classification performances of a support vector machine (SVM) and a random forest classifier (RFC), as representatives of ML algorithms, and two versions of the multi-layer perceptron (MLPC) as representatives of deep learning (DL). These models, especially RFC and MLPC, showed excellent classification performances, with accuracy reaching 99.7%. This outcome makes the aforementioned models a promising route for future improvements of diagnostic devices focused on detecting cancerous bone tissues. Alongside the diagnostic purpose, the aforementioned approach allowed us to identify characteristic molecules, i.e., amino acids, nucleic acids, and bioapatites, relevant for obtaining the final diagnostic response, through the use of a tool named by us Raman Band Identification (RBI). The method to evaluate RBI is the most important contribution of this paper, because RBI could represent a relevant parameter for the identification of biochemical processes on the basis of the tumor progression and associated malignant degree. In turn, the spectral bands highlighted by RBI could provide precious indicators in an attempt to restrict the spectral acquisition to specific Raman bands. This last objective could help to reduce the amount of experimental data needed to obtain an accurate final grading outcome, with a consequent reduction in the computational cost. [ABSTRACT FROM AUTHOR]

Fall risk assessment is becoming an important concern, with the realization that falls, and more importantly fainting occurrences, in most cases require immediate medical attention and can pose huge health risks, as well as financial and social burdens. The development of an accurate inertial sensor-based fall risk assessment tool combined with machine learning algorithms could significantly advance healthcare. This research aims to investigate the development of a machine learning approach for falling and fainting detection, using wearable sensors with an emphasis on forward falls. In the current paper we address the problem of the lack of inertial time-series data to differentiate the forward fall event from normal activities, which are difficult to obtain from real subjects. To solve this problem, we proposed a forward dynamics method to generate necessary training data using the OpenSim software, version 4.5. To develop a model as close to the real world as possible, anthropometric data taken from the literature was used. The raw X and Y axes acceleration data was generated using OpenSim software, and ML fall prediction methods were trained. The machine learning (ML) accuracy was validated by testing with data acquired from six unique volunteers, considering the forward fall type. [ABSTRACT FROM AUTHOR]

The evolving landscape of cyber threats necessitates continuous advancements in defensive strategies. This paper explores the potential of artificial intelligence (AI) as an emerging tool to enhance cybersecurity. While AI holds widespread applications across information technology, its integration within cybersecurity remains a recent development. We offer a comprehensive review of current AI applications in this domain, focusing particularly on their preventative capabilities against prevalent threats like phishing, social engineering, ransomware, and malware. To illustrate these concepts, the paper presents a case study showcasing a specific AI application in a cybersecurity context. This case study addresses a critical gap in securing communication within resource-constrained Internet of Things (IoT) networks using the IEEE 802.15.4 standard. We discussed the advantages and limitations of employing PN sequence encryption for this purpose. [ABSTRACT FROM AUTHOR]

Soccer is evolving into a science rather than just a sport, driven by intense competition between professional teams. This transformation requires efforts beyond physical training, including strategic planning, data analysis, and advanced metrics. Coaches and teams increasingly use sophisticated methods and data-driven insights to enhance decision-making. Analyzing team performance is crucial to prepare players and coaches, enabling targeted training and strategic adjustments. Expected goals (xG) analysis plays a key role in assessing team and individual player performance, providing nuanced insights into on-field actions and opportunities. This approach allows coaches to optimize tactics and lineup choices beyond traditional scorelines. However, relying solely on xG might not provide a full picture of player performance, as a higher xG does not always translate into more goals due to the intricacies and variabilities of in-game situations. This paper seeks to refine performance assessments by incorporating predictions for both expected goals (xG) and actual goals (aG). Using this new model, we consider a wider variety of factors to provide a more comprehensive evaluation of players and teams. Another major focus of our study is to present a method for selecting and categorizing players based on their predicted xG and aG performance. Additionally, this paper discusses expected goals and actual goals for each individual game; consequently, we use expected goals per game (xGg) and actual goals per game (aGg) to reflect them. Moreover, we employ regression machine learning models, particularly ridge regression, which demonstrates strong performance in forecasting xGg and aGg, outperforming other models in our comparative assessment. Ridge regression's ability to handle overlapping and correlated variables makes it an ideal choice for our analysis. This approach improves prediction accuracy and provides actionable insights for coaches and analysts to optimize team performance. By using constructed features from various methods in the dataset, we improve our model's performance by as much as 12%. These features offer a more detailed understanding of player performance in specific leagues and roles, improving the model's accuracy from 83% to nearly 95%, as indicated by the R-squared metric. Furthermore, our research introduces a player selection methodology based on their predicted xG and aG, as determined by our proposed model. According to our model's classification, we categorize top players into two groups: efficient scorers and consistent performers. These precise forecasts can guide strategic decisions, player selection, and training approaches, ultimately enhancing team performance and success. [ABSTRACT FROM AUTHOR]

Accessibility in online platforms is a critical concern in our increasingly digital world, where information and services are predominantly accessed through the Internet. The purpose of this systematic review is to provide a comprehensive overview of the current state of the art in online accessibility technologies, and it is focused on key tools such as sign language recognition, speech-to-text, text-to-speech, and voice recognition. Despite advancements in digital inclusivity, numerous technical limitations persist, which limit the accessibility of online content for individuals with disabilities. Our findings indicate that while speech and voice technologies have achieved good accuracies and low word error rates, further research is needed to improve the accuracy and usability of sign language recognition systems, especially for continuous sign language recognition, as they have low accuracy. In this review, we analyzed research articles and publications from well-known databases, including Google Scholar, Elsevier, IEEE Xplore, and Springer. In order to ensure a high standard of quality, we applied the PRISMA 2020 and PEDro methodologies to quantitatively and qualitatively filter the thousands of articles provided by these databases, and we selected only studies that were related to our study. Key areas of investigation included the performance and accuracy of sign language interfaces, speech-to-text, text-to-speech, and speech recognition applications and the compatibility of these technologies with different platforms and devices. This review also explores the role of emerging technologies such as artificial intelligence (AI) and machine learning (ML) in enhancing accessibility and personalizing user experiences. Through a critical analysis of current solutions and a discussion of existing gaps, this paper offers insights into potential improvements and future directions for creating more accessible online environments. The findings might be valuable to researchers and developers dedicated to promoting digital inclusivity and equality. [ABSTRACT FROM AUTHOR]

Theano, TensorFlow, Keras, Torch, PyTorch, and other software frameworks have remarkably stimulated the popularity of deep learning (DL). Apart from all the good they achieve, the danger of such frameworks is that they unintentionally spur a black-box attitude. Some practitioners play around with building blocks offered by frameworks and rely on them, having a superficial understanding of the internal mechanics. This paper constitutes a concise tutorial that elucidates the flows of signals and gradients in deep neural networks, enabling readers to successfully implement a deep network from scratch. By "from scratch", we mean with access to a programming language and numerical libraries but without any components that hide DL computations underneath. To achieve this goal, the following five topics need to be well understood: (1) automatic differentiation, (2) the initialization of weights, (3) learning algorithms, (4) regularization, and (5) the organization of computations. We cover all of these topics in the paper. From a tutorial perspective, the key contributions include the following: (a) proposition of R and S operators for tensors—rashape and stack, respectively—that facilitate algebraic notation of computations involved in convolutional, pooling, and flattening layers; (b) a Python project named hmdl ("home-made deep learning"); and (c) consistent notation across all mathematical contexts involved. The hmdl project serves as a practical example of implementation and a reference. It was built using NumPy and Numba modules with JIT and CUDA amenities applied. In the experimental section, we compare hmdl implementation to Keras (backed with TensorFlow). Finally, we point out the consistency of the two in terms of convergence and accuracy, and we observe the superiority of the latter in terms of efficiency. [ABSTRACT FROM AUTHOR]

This paper presents a significant advancement in information security risk assessment by introducing a flexible and comprehensive model. The research integrates established standards, expert knowledge, machine learning, and ontological modeling to create a multifaceted approach for understanding and managing information security risks. The combination of standards and expert insights forms a robust foundation, ensuring a holistic grasp of the intricate risk landscape. The use of cluster analysis, specifically applying k-means on information security standards, expands the data-driven approach, uncovering patterns not discernible through traditional methods. The integration of machine learning algorithms in the creation of information security risk dendrogram demonstrates effective computational techniques for enhanced risk discovery. The introduction of a heat map as a visualization tool adds innovation, facilitating an intuitive understanding of risk interconnections and prioritization for decision makers. Additionally, a thesaurus optimizes risk descriptions, ensuring comprehensiveness and relevance despite evolving terminologies in the dynamic field of information security. The development of an ontological model for structured risk classification is a significant stride forward, offering an effective means of categorizing information security risks based on ontological relationships. These collective innovations enhance understanding and management of information security risks, paving the way for more effective approaches in the ever-evolving technological landscape. [ABSTRACT FROM AUTHOR]

Landslides are pervasive geological hazards that pose significant risks to human life, property, and the environment. Understanding landslide susceptibility is crucial for predicting and mitigating these disasters. This article advocates for a comprehensive review by systematically compiling and analyzing 146 relevant studies up to 2024. It assesses current progress and limitations and offers guidance for future research. This paper provides a comprehensive overview of the diverse challenges encountered by machine learning models in landslide susceptibility assessment, encompassing aspects such as model selection, the formulation of evaluation index systems, model interpretability, and spatial heterogeneity. The construction of an evaluation index system, which serves as the foundational data for the model, profoundly influences its accuracy. This study extensively investigates the selection of evaluation factors and the identification of positive and negative samples, proposing valuable methodologies. Furthermore, this paper briefly deliberates and compares classical machine learning models, offering valuable insights for model selection. Additionally, it delves into discussions concerning model interpretability and spatial heterogeneity issues. These research findings promise to enhance the precision of landslide susceptibility assessments and furnish effective strategies for risk management. [ABSTRACT FROM AUTHOR]

Every part of society relies on energy systems due to the growing population and the constant demand for energy. Because of the high energy demands of transportation, industry, and daily life, energy systems are crucial to every part of society. Electrical transmission lines are a crucial component of the electrical power system. Therefore, in order to determine the power system's protection plan and increase its reliability, it is critical to foresee and classify fault types. With this motivation, the main goal of this paper is to design a deep network model to classify faults in transmission lines based on real, generated, and publicly available datasets. A deep learning architecture that was based on a one-dimensional convolutional neural network (CNN) was utilized in this study. Accuracy, specificity, recall, precision, F1 score, ROC curves, and AUC were employed as performance criteria for the suggested model. Not only synthetic but also real data were used in this study. It has been seen that the created model can be used successfully for both real data and synthetic data. In order to measure the robustness of the network, it was tested with three different datasets consisting of real, generated, and publicly available datasets. In the paper, 1D CNN, one of the machine learning methods, was used on three different power systems, and it was observed that the machine learning method was successful in all three power systems. [ABSTRACT FROM AUTHOR]

Federated learning, as a novel distributed machine learning mode, enables the training of machine learning models on multiple devices while ensuring data privacy. However, the existence of single-point-of-failure bottlenecks, malicious threats, scalability of federated learning implementation, and lack of incentive mechanisms have seriously hindered the development of federated learning technology. In recent years, as a distributed ledger, blockchain has the characteristics of decentralization, tamper-proof, transparency, security, etc., which can solve the issues encountered in the above-mentioned federated learning. Particularly, the integration of federated learning and blockchain leads to a new paradigm, called blockchain-based federated learning (BFL), which has been successfully applied in many application scenarios. This paper aims to provide a comprehensive review of recent efforts on blockchain-based federated learning. More concretely, we propose and design a taxonomy of blockchain-based federated learning models, along with providing a comprehensive summary of the state of the art. Various applications of federated learning based on blockchain are introduced. Finally, we expand on current trends and provide new perspectives pertaining to this new and exciting development in the field. [ABSTRACT FROM AUTHOR]

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. This paper presents a novel approach to predictive maintenance in the maritime industry, leveraging Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance fault detection and maintenance planning for naval systems. Traditional maintenance strategies, such as corrective and preventive maintenance, are increasingly ineffective in meeting the high safety and efficiency standards required by maritime operations. The proposed model integrates AI-driven methods to process operational data from shipboard systems, enabling more accurate fault diagnosis and early identification of system failures. By analyzing historical operational data, ML algorithms identify patterns and estimate the functional states, helping prevent unplanned failures and costly downtime. This approach is critical in environments where technical failures are a leading cause of incidents, as demonstrated by the high rate of machinery-related accidents in maritime operations. Our study highlights the growing importance of AI and ML in predictive maintenance and offers a practical tool for improving operational safety and efficiency in the naval industry. The paper discusses the development of a fault detection approach, evaluates its performance on real shipboard data-through tests on a seawater cooling system from an oil tanker and concludes with insights into the broader implications of AI-driven maintenance in the maritime sector. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to develop a reinforcement agent capable of effectively exploiting a specific vulnerability. Automating pentesting can reduce the cost and time of the operation. While there are existing tools like Metasploit Pro that offer automated exploitation capabilities, they often require significant execution times and resources due to their reliance on exhaustive payload testing. In this paper, we have created a deep reinforcement agent specifically configured to exploit a targeted vulnerability. Through a training phase, the agent learns and stores payloads along with their corresponding reward values in a neural network. When encountering a specific combination of a target operating system and vulnerability, the agent utilizes its neural network to determine the optimal exploitation options. The novelty of this work lies in employing Deep Reinforcement Learning in vulnerability exploitation analysis. To evaluate our proposed methodology, we conducted training and testing on the Metasploitable platform. The training phase of the reinforcement agent was conducted on two use cases: the first one has one vulnerability, and the second one has four vulnerabilities. Our approach successfully achieved the attacker's primary objective of establishing a reverse shell with a maximum accuracy of 96.6% and 73.6% for use cases one and two, respectively. [ABSTRACT FROM AUTHOR]

The lack of reliable negative samples is an important factor limiting the quality of machine learning-based debris flow susceptibility mapping (DFSM). The purpose of this paper is to propose multiple negative-sample acquisition strategies for DFSM considering different sample representation forms. The sample representation forms mainly include a single grid, multi-grid, and watershed unit, and the negative-sample acquisition strategies are based on support vector machine (SVM), spy technique, and isolation forest (IF) methods, respectively. These three strategies can assign a value to all the samples based on different assumptions, and reliable, negative samples can be generated from samples with values below a predefined threshold. Combining different sample representation forms with negative sample acquisition strategies, nine datasets were then involved in random forest (RF) modeling. The receiver operating characteristic (ROC) curves and related statistical results were used to evaluate the models. The results show that the strategy based on the spy technique is suitable for multiple datasets, while the IF-based strategy is well-adapted to the watershed unit datasets. This study can provide more options for improving the quality of datasets in DFSM, which can further improve the performance of machine learning models. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative method for enhancing time series data preprocessing by integrating a cycling layer into a self-attention mechanism. Traditional approaches often fail to capture the cyclical patterns inherent to time series data, which affects the predictive model accuracy. The proposed method aims to improve models' ability to identify and leverage these cyclical patterns, as demonstrated using the Jena Climate dataset from the Max Planck Institute for Biogeochemistry. Empirical results show that the proposed method enhances forecast accuracy and speeds up model fitting compared to the conventional techniques. This paper contributes to the field of time series analysis by providing a more effective preprocessing approach. [ABSTRACT FROM AUTHOR]

Due to the rising prevalence of polypharmacy, pharmacists face more challenges in ensuring patient safety and optimizing medication management. This paper introduces PharmiTech, a Clinical Decision Support System that leverages Artificial Intelligence (AI) to tackle the growing need for efficient tools to assist pharmacists. The primary focus of the tool is to identify possible herb-drug interactions and instances of prescription drug abuse, combining an expert knowledge base with a supervised classification model and providing user-friendly alerts to pharmacists. To demonstrate the capabilities of the developed tool, this paper presents its functionalities through a case study involving simulated scenarios using de-identified information to maintain the confidentiality of real patients' personal data. Tested in Portuguese pharmacies, PharmiTech enhances pharmaceutical care, safeguards patient data, and aids pharmacists in informed decision-making, making it a valuable resource for healthcare professionals. [ABSTRACT FROM AUTHOR]

The agricultural sector is undergoing a transformative paradigm shift with the integration of advanced technologies, particularly artificial intelligence (AI), to enhance data analysis techniques and streamline decision-making processes. This paper delves into the integration of advanced technologies in agriculture, focusing specifically on optimizing data analysis through artificial intelligence (AI) to strengthen decision-making processes in farming. We present a novel AI-powered model that leverages historical agricultural datasets, utilizing a comprehensive array of established machine learning algorithms to enhance the prediction and classification of agricultural data. This work provides tailored algorithm recommendations, bypassing the need to deploy and fine-tune numerous algorithms. We approximate the accuracy of suitable algorithms, highlighting those with the highest precision, thus saving time by leveraging pre-trained AI models on historical agricultural data. Our method involves three phases: collecting diverse agricultural datasets, applying multiple classifiers, and documenting their accuracy. This information is stored in a CSV file, which is then used by AI classifiers to predict the accuracy of new, unseen datasets. By evaluating feature information and various data segmentations, we recommend the configuration that achieves the highest accuracy. This approach eliminates the need for exhaustive algorithm reruns, relying on pre-trained models to estimate outcomes based on dataset characteristics. Our experimentation spans various configurations, including different training–testing splits and feature sets across multiple dataset sizes, meticulously evaluated through key performance metrics such as accuracy, precision, recall, and F-measure. The experimental results underscore the efficiency of our model, with significant improvements in predictive accuracy and resource utilization, demonstrated through comparative performance analysis against traditional methods. This paper highlights the superiority of the proposed model in its ability to systematically determine the most effective algorithm for specific agricultural data types, thus optimizing computational resources and improving the scalability of smart farming solutions. The results reveal that the proposed system can accurately predict a near-optimal machine learning algorithm and data structure for crop data with an accuracy of 89.38%, 87.61%, and 84.27% for decision tree, random forest, and random tree algorithms, respectively. [ABSTRACT FROM AUTHOR]

Featured Application: The importance of generative adversarial networks (GANs) in economics is growing and is driven by successes in other fields. Many economic problems could benefit from GANs, although few studies exist and progress is needed. This paper argues for the use of GANs as a novel and effective tool in economics. An important issue is the need for large datasets, where traditional techniques fall short due to a multiplicity of problems, making the use of GANs very useful in this task. Generative adversarial networks (GANs) have become a recent and rapidly developing research topic in machine learning. Since their inception in 2014, a significant number of variants have been proposed to address various topics across many fields, and they have particularly excelled not only in image and language processing but also in the medical and data science domains. In this paper, we aim to highlight the significance of and advancements that these GAN models can introduce in the field of Business Economics, where they have yet to be fully developed. To this end, a review of the literature of GANs is presented in general together with a more specific review in the field of Business Economics, for which only a few papers can be found. Furthermore, the most relevant papers are analysed in order to provide approaches for the opportunity to research GANs in the field of Business Economics. [ABSTRACT FROM AUTHOR]

In this paper, a prediction model of the coupling coefficient based on a multi-module neural network (MMNN) is developed to quickly optimize the installation position of the roof antenna of the 5G-Railway (5G-R) communication system, so as to improve the anti-interference performance of the roof antenna. Firstly, a simulation model of the coupling coefficient between the pantograph arcing and the roof antenna (a monopole antenna operating frequency of 2 GHz) was established to construct the dataset. It is also verified that the influence of the electromagnetic interference (EMI) of pantograph arcing can be significantly reduced by predicting the new installation position (minimum coupling coefficient), and the installation position optimization of roof antenna can be realized. Then, the mind evolutionary algorithm of the back propagation neural network (MEA-BP) algorithm and particle swarm optimization—extreme learning machine (PSO-ELM) algorithm were adopted, respectively. The extreme learning machine algorithm constructed a different prediction model. And, by setting the integrated strategy of piecewise prediction, the prediction results are optimized and the accuracy of the prediction model based on the MMNN is further improved. Finally, the prediction model is proven to be able to replace the complicated electromagnetic simulation work accurately and efficiently by a variety of prediction performance indices, which provides an effective prediction method for the rapid optimization of the installation position of the roof antenna. [ABSTRACT FROM AUTHOR]

Two types of grounding systems are recommended for use in the international standard IEC 62305-3, Part 3: Physical damage to structures and life hazard. One of these is a radial-based grounding system (type-A), which is used in soil resistivities of up to 3000 Ω m and is considered in this paper. It is a well-known fact that during lightning strikes, only a part of the grounding wire contributes to dissipating the lightning current into the surrounding soil. This effective part of the grounding system depends on several features, such as soil resistivity, burial depth, and rise time of the dissipated lightning current. The effect of all of these features on the effective length of the type-A grounding system is explored in this paper. A suitable supervised machine learning regression model is developed, which will enable readers to accurately approximate the effective length of the type-A grounding system for realistic values of input features. The trained model in the paper yielded an R 2 value of 0.99998 on the test set. In addition, two simple mathematical formulas are also provided, which produce similar but less accurate results ( R 2 values of 0.989883 and 0.998557, respectively). [ABSTRACT FROM AUTHOR]

In the field of risk assessment, the traditional econometric models are generally used to assess credit risk. And with the introduction of the "dual-carbon" goals to promote the development of a low-carbon economy, the scale of green credit in China has rapidly expanded. But with the advent of the big data era, due to the poor interpretability of a traditional single machine learning model, it is difficult to capture nonlinear relationships, and there are shortcomings in prediction accuracy and robustness. This paper selects the adjusted ensemble learning model based on the homogeneous and heterogeneous factors for user default prediction, which can efficiently process large quantities of high-dimensional data. This article adjusts each model to adapt to the task and innovatively compares various models. In this paper, the missing value filling method, feature selection, and ensemble model are studied and discussed, and the optimal ensemble model is obtained. When comparing the predictions of single models and ensemble models, the accuracy, sensitivity, specificity, F1-Score, Kappa, and MCC of Categorical Features Gradient Boosting (CatBoost) and Random undersampling Boosting (RUSBoost) all reach 100%. The experimental results prove that the algorithm based on adjusted homogeneous and heterogeneous ensemble learning can predict the user default efficiently and accurately. This paper also provides some references for establishing a risk assessment index system. [ABSTRACT FROM AUTHOR]

Featured Application: The potential application of the work is automatic ML-based quality control and defect detection systems within Industry 4.0/5.0 paradigm. This paper presents the use of noncontact ultrasound for the nondestructive detection of defects in two plastic plates made of polyamide (PA6) and polyethylene (PE). The aim of the study was to: (1) assess the presence of defects as well as their size, type, and orientation based on the amplitudes of Lamb ultrasonic waves measured in plates made of polyamide (PA6) and polyethylene (PE) due to their homogeneous internal structure, which mainly determined the selection of such model materials for testing; and (2) verify the possibilities of building automatic quality control and defect detection systems based on ML based on the results of the above-mentioned studies within the Industry 4.0/5.0 paradigm. Tests were conducted on plates with generated synthetic defects resembling defects found in real materials such as delamination and cracking at the edge of the plate and a crack (discontinuity) in the center of the plate. Defect sizes ranged from 1 mm to 15 mm. Probes at 30 kHz were used to excite Lamb waves in the slab material. This method is sensitive to the slightest changes in material integrity. A significant decrease in signal amplitude was observed, even for defects of a few millimeters in length. In addition to traditional methods, machine learning (ML) was used for the analysis, allowing an initial assessment of the method's potential for building cyber-physical systems and digital twins. By training ML models on ultrasonic data, algorithms can distinguish subtle differences between signals reflected from normal and defective areas of the material. Defect types such as voids, cracks, or weak bonds often produce distinct acoustic signatures, which ML models can learn to recognize with high accuracy. Using techniques like feature extraction, ML can process these high-dimensional ultrasonic datasets, identifying patterns that human inspectors might overlook. Furthermore, ML models are adaptable, allowing the same trained algorithms to work on various material batches or panel types with minimal retraining. This combination of automation and precision significantly enhances the reliability and efficiency of quality control in industrial manufacturing settings. The achieved accuracy results, 0.9431 in classification and 0.9721 in prediction, are comparable to or better than the AI-based quality control results in other noninvasive methods of flat surface defect detection, and in the presented ultrasonic method, they are the first described in this way. This approach demonstrates the novelty and contribution of artificial intelligence (AI) methods and tools, significantly extending and automating existing applications of traditional methods. The susceptibility to augmentation by AI/ML may represent an important new property of traditional methods crucial to assessing their suitability for future Industry 4.0/5.0 applications. [ABSTRACT FROM AUTHOR]

The detection of the human mental fatigue state holds immense significance due to its direct impact on work efficiency, specifically in system operation control. Numerous approaches have been proposed to address the challenge of fatigue detection, aiming to identify signs of fatigue and alert the individual. This paper introduces an approach to human mental fatigue assessment based on the application of machine learning techniques to the video of a working operator. For validation purposes, the approach was applied to a dataset, "Human Fatigue Assessment Based on Video Data" (HFAVD) integrating video data with features computed by using our computer vision deep learning models. The incorporated features encompass head movements represented by Euler angles (roll, pitch, and yaw), vital signs (blood pressure, heart rate, oxygen saturation, and respiratory rate), and eye and mouth states (blinking and yawning). The integration of these features eliminates the need for the manual calculation or detection of these parameters, and it obviates the requirement for sensors and external devices, which are commonly employed in existing datasets. The main objective of our work is to advance research in fatigue detection, particularly in work and academic settings. For this reason, we conducted a series of experiments by utilizing machine learning techniques to analyze the dataset and assess the fatigue state based on the features predicted by our models. The results reveal that the random forest technique consistently achieved the highest accuracy and F1-score across all experiments, predominantly exceeding 90%. These findings suggest that random forest is a highly promising technique for this task and prove the strong connection and association among the predicted features used to annotate the videos and the state of fatigue. [ABSTRACT FROM AUTHOR]

Featured Application: This work can potentially be applied to industrial belt conveyors of any type. The tested system can be used for real-time monitoring in order to identify and prevent overloads, misalignments, growing damage to the belt in the early stages, and other trends that may cause failure. This paper is devoted to the real-time monitoring of close transportation devices, namely, belt conveyors. It presents a novel measurement system based on the linear strain gauges placed on the tail pulley surface. These gauges enable the monitoring and continuous collection and processing of data related to the process. An initial assessment of the machine learning application to the load identification was made. Among the tested algorithms that utilized machine learning, some exhibited a classification accuracy as high as 100% when identifying the load placed on the moving belt. Similarly, identification of the preset damage was possible using machine learning algorithms, demonstrating the feasibility of the system for fault diagnosis and predictive maintenance. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive analysis of the plastic injection molding process through the integration of data acquisition technologies and classification models. In collaboration with a company specializing in plastic injection, data were extracted directly from the machine during a specific period at the beginning of a shift change. These data were subjected to exploratory analysis to identify correlations between important variables, such as injection time, cycle time, and mold pressures. Additionally, classification models, including Random Forest and Logistic Regression, were constructed to predict and classify the process state based on these variables. The model results demonstrated high predictive performance, with 99.5% accuracy for Random Forest and 97% for Logistic Regression. These results provide a strong foundation for the early identification of potential problems and informed decision making to improve the efficiency of the plastic injection molding process. This study contributes to the advancement of the integration of intelligent technologies in industrial process optimization, aligned with the principles of Industry 4.0. [ABSTRACT FROM AUTHOR]

This paper introduces a classification model that detects and classifies argumentative behaviors between two individuals by utilizing a machine learning application, based on the MediaPipe Holistic model. The approach involves the distinction between two different classes based on the behavior of two individuals, argumentative and non-argumentative behaviors, corresponding to verbal argumentative behavior. By using a dataset extracted from video frames of hand gestures, body stance and facial expression, and by using their corresponding landmarks, three different classification models were trained and evaluated. The results indicate that Random Forest Classifier outperformed the other two by classifying argumentative behaviors with 68.07% accuracy and non-argumentative behaviors with 94.18% accuracy, correspondingly. Thus, there is future scope for advancing this classification model to a prediction model, with the aim of predicting aggressive behavior in patients suffering with dementia before their onset. [ABSTRACT FROM AUTHOR]

Xerostomia, commonly known as dry mouth, is characterized by reduced salivary secretion, which can lead to various oral health issues and discomfort. In this paper, we propose a novel, non-invasive method for predicting xerostomia through the analysis of tongue images. To predict salivary gland secretion from tongue images, we collected images from patients who visited the hospital with complaints of dry mouth and measured their saliva secretion. Features were extracted from these tongue images, and correlation analysis was performed using machine learning techniques to assess the relationship between the extracted features and measured saliva secretion. We obtained tongue images and saliva secretion measurements from 176 patients. Images were cropped to 100 × 100 pixels, resulting in 462 features. The dataset was divided into training and test sets, consisting of 160 and 16 samples, respectively. The correlation coefficients for the training and test datasets were 0.9496 and 0.9415, respectively, while the correlation coefficient for the entire dataset was 0.9482. The estimated linear equation was y = 0.9244x + 2.1664. This study aimed to predict salivary gland secretion based on tongue images. By extracting features from color images and employing a neural network machine learning model, we estimated salivary gland secretion. With a sufficiently large dataset of tongue images, further advancements in regression analysis using deep learning techniques could enhance the accuracy of these predictions. [ABSTRACT FROM AUTHOR]

This article investigates the application of eXtreme gradient boosting (XGBoost) and hybrid metaheuristics optimisation techniques to predict the axial load bearing capacity of fully grouted rock bolting systems. For this purpose, a comprehensive dataset of 72 pull-out tests was built, considering various influential parameters such as three water-to-grout (W/G) ratios, five ranges of curing time (CT), three different grout admixtures with two different fly ash (FA) contents, and two different diameter confinements (DCs). Additionally, to find out the effect of the mechanical behaviour of grouts on the performance of fully grouted rock bolting systems, seventy-two uniaxial compression strength (UCS) samples were cast and tested simultaneously with pull-out samples. The UCS samples were prepared with the same details as the pull-out samples to avoid any inconsistency. The results highlight that peak load values generally increase with longer curing times, lower W/G, and higher UCS and DC values. The main novelty of this paper lies in its data-driven approach, using various XGBoost models. This method offers a time-, cost-, and labour-efficient alternative to traditional experimental methods for predicting rock bolt performance. For this purpose, after building the dataset and dividing it randomly into two training and testing datasets, five different XGBoost models were developed: a standalone XGBoost model and four hybrid models incorporating Harris hawk optimisation (HHO), the jellyfish search optimiser (JSO), the dragonfly algorithm (DA), and the firefly algorithm (FA). These models were subsequently evaluated for their ability to predict peak load values. The results demonstrate that all models effectively predicted peak load values, but the XGBoost-JSO hybrid model demonstrated superior performance, achieving the highest R-squared coefficients of 0.987 and 0.988 for the training and testing datasets, respectively. Sensitivity analysis revealed that UCS values were the most influential parameter, while FA content had the least impact on the maximum peak load values of fully cementitious grouted rock bolts. [ABSTRACT FROM AUTHOR]

Suicide is a global public health problem that takes hundreds of thousands of lives each year. The key to effective suicide prevention is early detection of suicidal ideations and timely intervention. However, several factors hinder traditional suicide risk screening methods. Primarily, the social stigma associated with suicide presents a challenge to suicidal ideation detection, as existing methods require patients to explicitly communicate their suicidal propensities. In contrast, progressively more at-risk people choose online platforms—such as Reddit—as their preferred avenues for sharing their suicidal experiences and seeking emotional support. As a result, these online platforms have become an unobtrusive source of user-generated textual data that can be used to detect suicidality with supervised machine learning and natural language processing techniques. In this paper, we proposed a suicidal ideation detection approach that combines textual and psycholinguistic features extracted from the Reddit forum. Subsequently, we selected the most informative features using the Boruta algorithm and employed four classifiers: logistic regression, naïve Bayes, support vector machines, and random forest. The naïve Bayes models trained with the combination of term frequency-inverse document frequency (TF-IDF) and National Research Council (NRC) features demonstrated the highest performance, obtaining a F1 score of 70.99%. Our experimental results illustrate that a combination of textual and psycholinguistic features yields better classification performance compared to using those features separately. [ABSTRACT FROM AUTHOR]

Due to the expansion of Artificial Intelligence (AI), especially Machine Learning (ML), it is more common to face confidentiality regulations about using sensitive data in learning models generally hosted in cloud environments. Confidentiality regulations such as HIPAA and GDPR seek to guarantee the confidentiality and privacy of personal information. Input and output data of a learning model may include sensitive data that must be protected. Adversaries could intercept and exploit this data to infer more sensitive data or even to determine the structure of the prediction model. To guarantee data privacy, one option could be encrypting data and making inferences over encrypted data. This strategy would be challenging for learning models that now must receive encrypted data, make inferences over encrypted data, and deliver encrypted data. To address this issue, this paper presents a privacy-preserving machine learning approach using Fully Homomorphic Encryption (FHE) for a model that predicts risk levels of suffering a traffic accident. Despite the limitations of experimenting with FHE on machine learning models using a low-performance computer, limitations that are undoubtedly overcome by using high-performance computational infrastructure, we built some encrypted models. Among the encrypted models based on Decision Trees, Random Forests, XGBoost, and Fully Connected Neural Networks (FCNN), the model based on FCNN reached the highest accuracy (80.1%) for the lowest inference time (8.476 s). [ABSTRACT FROM AUTHOR]

Hyperspectral imaging (HSI) is one of the non-destructive quality assessment methods providing both spatial and spectral information. HSI in food quality and safety can detect the presence of contaminants, adulterants, and quality attributes, such as moisture, ripeness, and microbial spoilage, in a non-destructive manner by analyzing spectral signatures of food components in a wide range of wavelengths with speed and accuracy. However, analyzing HSI data can be quite complicated and time consuming, in addition to needing some special expertise. Artificial intelligence (AI) has shown immense promise in HSI for the assessment of food quality because it is so powerful at coping with irrelevant information, extracting key features, and building calibration models. This review has shown various machine learning (ML) approaches applied to HSI for quality and safety control of foods. It covers the basic concepts of HSI, advanced preprocessing methods, and strategies for wavelength selection and machine learning methods. The application of HSI to AI increases the speed with which food safety and quality can be inspected. This happens through automation in contaminant detection, classification, and prediction of food quality attributes. So, it can enable decisions in real-time by reducing human error at food inspection. This paper outlines their benefits, challenges, and potential improvements while again assessing the validity and practical usability of HSI technologies in developing reliable calibration models for food quality and safety monitoring. The review concludes that HSI integrated with state-of-the-art AI techniques has good potential to significantly improve the assessment of food quality and safety, and that various ML algorithms have their strengths, and contexts in which they are best applied. [ABSTRACT FROM AUTHOR]

This paper analyzes time series forecasting methods applied to thermal systems in Brazil, specifically focusing on diesel consumption as a key determinant. Recognizing the critical role of thermal systems in ensuring energy stability, especially during low rain seasons, this study employs bagged, boosted, and stacked ensemble learning methods for time series forecasting focusing on exploring consumption patterns and trends. By leveraging historical data, the research aims to predict future diesel consumption within Brazil's thermal energy sector. Based on the bagged ensemble learning approach a mean absolute percentage error of 0.089% and a coefficient of determination of 0.9752 were achieved (average considering 50 experiments), showing it to be a promising model for the short-time forecasting of thermal dispatch for the electric power generation system. The bagged model results were better than for boosted and stacked ensemble learning methods, long short-term memory networks, and adaptive neuro-fuzzy inference systems. Since the thermal dispatch in Brazil is closely related to energy prices, the predictions presented here are an interesting way of planning and decision-making for energy power systems. [ABSTRACT FROM AUTHOR]

Studies on probabilistic demand forecasting remain relatively limited compared with point forecasting, despite it being especially valuable for operational and planning purposes. This paper demonstrates different applications of probabilistic forecasting in demand response, together with Physical-Based Load Models. The first application shows how to determine the percentage of uncertainty potentially covered by demand response throughout a day, whereas the second application deals with obtaining the number of consumers that would be needed in demand response actions for a desired percentage of coverage. Finally, in the third application, a specific hourly strategy is proposed for demand response policies. These applications facilitate the aggregator's energy purchasing in the market and the planning of subsequent consumption adjustments through demand response to minimize deviations from the purchased energy. The approach is illustrated using hourly demand data from a small Spanish city, and two machine learning methods were used to produce a set of probabilistic forecasts and compare results: Linear Quantile Regression and a Quantile Regression Forest. Main results show a significant reduction in deviations from the purchased energy after applying the proposed strategy, especially in the case of the forecasting method being less accurate (40.95% and 33.62% of reduction, respectively). [ABSTRACT FROM AUTHOR]

There has been very limited research conducted to predict rental prices in the German real estate market using an AI-based approach. From a general perspective, conventional approaches struggle to handle large amounts of data and fail to consider the numerous elements that affect rental prices. The absence of sophisticated, data-driven analytical tools further complicates this situation, impeding stakeholders, such as tenants, landlords, real estate agents, and the government, from obtaining the accurate insights necessary for making well-informed decisions in this area. This paper applies novel machine learning (ML) approaches, including ensemble techniques, neural networks, linear regression (LR), and tree-based algorithms, specifically designed for forecasting rental prices in Munich. To ensure accuracy and reliability, the performance of these models is evaluated using the R2 score and root mean squared error (RMSE). The study provides two feature sets for model comparison, selected by particle swarm optimisation (PSO) and CatBoost. These two feature selection methods identify significant variables based on different mechanisms, such as seeking the optimal solution with an objective function and converting categorical features into target statistics (TSs) to address high-dimensional issues. These methods are ideal for this German dataset, which contains 49 features. Testing the performance of 10 ML algorithms on two sets helps validate the robustness and efficacy of the AI-based approach utilising the PyTorch framework. The findings illustrate that ML models combined with PyTorch-based neural networks (PNNs) demonstrate high accuracy compared to standalone ML models, regardless of feature changes. The improved performance indicates that utilising the PyTorch framework for predictive tasks is advantageous, as evidenced by a statistical significance test in terms of both R2 and RMSE (p-values < 0.001). The integration results display outstanding accuracy, averaging 90% across both feature sets. Particularly, the XGB model, which exhibited the lowest performance among all models in both sets, significantly improved from 0.8903 to 0.9097 in set 1 and from 0.8717 to 0.9022 in set 2 after being combined with the PNN. These results showcase the efficacy of using the PyTorch framework, enhancing the precision and reliability of the ML models in predicting the dynamic real estate market. Given that this study applies two feature sets and demonstrates consistent performance across sets with varying characteristics, the methodology may be applied to other locations. By offering accurate projections, it aids investors, renters, property managers, and regulators in facilitating better decision-making in the real estate sector. [ABSTRACT FROM AUTHOR]

With the deepening of oilfield development, logging data proliferate, and their complexity makes manual stratigraphic division both difficult and time-consuming. Aimed at the current network model widely used to solve the problem of stratigraphic delineation, which has problems such as not considering the multi-scale features of logging curves and insufficient accuracy, the YOLOv8x target detection algorithm in deep learning is utilized to detect the target strata, which has the ability to characterize the multi-scale features and can improve the efficiency and accuracy of the division. In order to better localize and identify targets, this paper proposes a new stratigraphic automatic division method, YOLOv8x-CAMDP, which introduces a CA (Coordinate Attention) mechanism module into the original YOLOv8x model to improve the model's ability to identify stratigraphic interval boundaries. In addition, the CIOU loss function in the original YOLOv8x network model was replaced using the MDPIOU loss function to effectively improve the accuracy and efficiency of bounding box regression. Based on the logging data from the Xing 10 area pure oil zone, a thorough comparison of the YOLOv8x-CAMDP and YOLOv8x models' training results is presented. The YOLOv8x-CAMDP model achieves a mean Average Precision (mAP) value of 98.7%, outperforming the YOLOv8x model by one percentage point. Moreover, the YOLOv8x-CAMDP model demonstrates greater precision in boundary division for each stratigraphic interval. The application of the YOLOv8x-CAMDP model to project implementation achieved significant results in stratigraphic division, reduced workload, and optimized manual division. These results not only confirm the practical value of the YOLOv8x-CAMDP model but also demonstrate the prospect and potential of its wide application. [ABSTRACT FROM AUTHOR]

Nowadays, mobile–mobile interaction is becoming a fundamental methodology for human–human networking services since mobile devices are the most common interfacing equipment for recent smart services such as food delivery, e-commerce, ride-hailing, etc. Unlike legacy ways of human interaction, on-site and in-person mutual recognition between a service provider and a client in mobile–mobile interaction is not trivial. This is because of not only the avoidance of face-to-face communication due to safety and health concerns but also the difficulty of matching up the online user using mobiles with the real person in the physical world. So, a novel mutual recognition scheme for mobile–mobile interaction is highly necessary. This paper comes up with a novel cyber-physical secure communication scheme relying on the digital twin paradigm. The proposed scheme designs the digital twin networking architecture on which real-world users form digital twins as their own online abstraction, and the digital twins authenticate each other for a smart service interaction. Thus, inter-twin communication (ITC) could support secure mutual recognition in mobile–mobile interaction. Such cyber-physical authentication (CPA) with the ITC is built on the dynamic BLE beaconing scheme with accurate proximity detection and dynamic identifier (ID) allocation. To achieve high accuracy in proximity detection, the proposed scheme is conducted using a wide variety of data pre-processing algorithms, machine learning technologies, and ensemble techniques. A location-dependent ID exploited in the CPA is dynamically generated by the physical user for their own digital twin per each mobile service. [ABSTRACT FROM AUTHOR]

In this paper, we present an analysis of the effectiveness of various machine learning algorithms in classifying astronomical objects using data from the third release (DR3) of the Gaia space mission. The dataset used includes spectral information from the satellite's red and blue spectrophotometers. The primary goal is to achieve reliable classification with high confidence for symbiotic stars, planetary nebulae, and red giants. Symbiotic stars are binary systems formed by a high-temperature star (a white dwarf in most cases) and an evolved star (Mira type or red giant star); their spectra varies between the typical for these objects (depending on the orbital phase of the object) and present emission lines similar to those observed in PN spectra, which is the reason for this first selection. Several classification algorithms are evaluated, including Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Networks (ANN), Gradient Boosting (GB), and Naive Bayes classifier. The evaluation is based on different metrics such as Precision, Recall, F1-Score, and the Kappa index. The study confirms the effectiveness of classifying the mentioned stars using only their spectral information. The models trained with Artificial Neural Networks and Random Forest demonstrated superior performance, surpassing an accuracy rate of 94.67%. [ABSTRACT FROM AUTHOR]

TextNetTopics is a novel topic modeling-based topic selection approach that finds highly ranked discriminative topics for training text classification models, where a topic is a set of semantically related words. However, it suffers from several limitations, including the retention of redundant or irrelevant features within topics, a computationally intensive topic-scoring mechanism, and a lack of explicit semantic modeling. In order to address these shortcomings, this paper proposes fasTNT, an enhanced version of TextNetTopics grounded in the Grouping–Scoring–Modeling approach. FasTNT aims to improve the topic selection process by preserving only informative features within topics, reforming LDA topics using fastText word embeddings, and introducing an efficient scoring method that considers topic interactions using Random Forest feature importance. Experimental results on four diverse datasets demonstrate that fasTNT outperforms the original TextNetTopics method in classification performance and feature reduction. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive evaluation of the previously introduced multidisciplinary model to quantify human uncertainty (MMtQHU) within a realistic 5G-enabled cyber–physical–social systems (CPSS) environment. The MMtQHU, which integrates human, social, and environmental factors into CPSS modeling, is applied to the Ingolstadt traffic scenario (InTAS), a detailed urban simulation reflecting high-traffic conditions. By modeling unpredictable driver behaviors, such as deviations from optimal routes, the study assesses the model's effectiveness in managing human-induced uncertainties in vehicle-for-hire (VFH) applications. The evaluation shows that human uncertainty significantly impacts 5G network resource allocation and traffic dynamics. A comparative analysis of traditional resource allocation methods reveals their limitations in handling the dynamic nature of human behavior. These findings underscore the necessity for advanced, adaptive strategies, potentially leveraging artificial intelligence and machine learning to enhance the resilience and efficiency of 5G networks in CPSS environments. The study offers valuable insights for future advancements in robust and adaptive 5G infrastructure by highlighting the critical role of integrating human behavior into CPSS models. [ABSTRACT FROM AUTHOR]

Current research on game difficulty prediction mainly uses heuristic functions or physiological signals. The former does not consider user data, while the latter easily causes interference to the user. This paper proposes a difficulty prediction method based on multiple facial cues and game performance. Specifically, we first utilize various computer vision methods to detect players' facial expressions, gaze directions, and head poses. Then, we build a dataset by combining these three kinds of data and game performance as inputs, with the subjective difficulty ratings as labels. Finally, we compare the performance of several machine learning methods on this dataset using two classification tasks. The experimental results showed that the multilayer perceptron classifier (abbreviated as MLP) achieved the highest performance on these tasks, and its accuracy increased with the increase in input feature dimensions. These results demonstrate the effectiveness of our method. The proposed method could assist in improving game design and user experience. [ABSTRACT FROM AUTHOR]

This paper introduces a novel method for emotion classification within virtual reality (VR) environments, which integrates biosignal processing with advanced machine learning techniques. It focuses on the processing and analysis of electrocardiography (ECG) and galvanic skin response (GSR) signals, which are established indicators of emotional states. To develop a predictive model for emotion classification, we extracted key features, i.e., heart rate variability (HRV), morphological characteristics, and Hjorth parameters. We refined the dataset using a feature selection process based on statistical techniques to optimize it for machine learning applications. The model achieved an accuracy of 97.78% in classifying emotional states, demonstrating that by accurately identifying and responding to user emotions in real time, VR systems can become more immersive, personalized, and emotionally resonant. Ultimately, the potential applications of this method are extensive, spanning various fields. Emotion recognition in education would allow further implementation of adapted learning environments through responding to the current emotional states of students, thereby fostering improved engagement and learning outcomes. The capability for emotion recognition could be used by virtual systems in psychotherapy to provide more personalized and effective therapy through dynamic adjustments of the therapeutic content. Similarly, in the entertainment domain, this approach could be extended to provide the user with a choice regarding emotional preferences for experiences. These applications highlight the revolutionary potential of emotion recognition technology in improving the human-centric nature of digital experiences. [ABSTRACT FROM AUTHOR]

This paper is concerned with an end-to-end electrocardiogram (ECG) signal transformation from a continuous-wave (CW) radar signal using a specialized deep learning model. For this purpose, the presented deep learning model is designed using convolutional neural networks (CNNs) and bidirectional long short-term memory (Bi-LSTM) with a maximum-overlap discrete wavelet transform (MODWT) layer and an adaptive neuro-fuzzy network (ANFN) layer. The proposed method has the advantage of developing existing deep networks and machine learning to reconstruct signals through CW radars to acquire ECG biological information in a non-contact manner. The fully connected (FC) layer of the CNN is replaced by an ANFN layer suitable for resolving black boxes and handling complex nonlinear data. The MODWT layer is activated via discrete wavelet transform frequency decomposition with maximum-overlap to extract ECG-related frequency components from radar signals to generate essential information. In order to evaluate the performance of the proposed model, we use a dataset of clinically recorded vital signs with a synchronized reference sensor signal measured simultaneously. As a result of the experiment, the performance is evaluated by the mean squared error (MSE) between the measured and reconstructed ECG signals. The experimental results reveal that the proposed model shows good performance in comparison to the existing deep learning model. From the performance comparison, we confirm that the ANFN layer preserves the nonlinearity of information received from the model by replacing the fully connected layer used in the conventional deep learning model. [ABSTRACT FROM AUTHOR]

In the fields of machine learning and artificial intelligence, the processing of time-series data has been a continuous concern and a significant algorithm for intelligent applications. Traditional deep-learning-based methods seem to have reached performance ceilings in certain specific areas, such as online character recognition. This paper proposes an algorithmic framework to break this deadlock by classifying time-series data by evaluating the similarities among handwriting samples using multidimensional Dynamic Time Warping (mDTW) distances. A simplified hierarchical clustering algorithm is employed as a classifier for character recognition. Moreover, this work achieves joint modeling with current mainstream temporal models, enabling the mDTW model to integrate modeling results from methods like RNN or Transformer, therefore further enhancing the accuracy of related algorithms. A series of experiments were conducted on a public database, and the results indicate that our method overcomes the bottleneck of current deep-learning-based methods in the field of online handwriting character recognition. More importantly, compared to deep -learning-based methods, the proposed method has a simpler structure and higher interpretability. Experimental results demonstrate that our proposed method outperforms existing state-of-the-art models in handwriting character recognition, achieving a top-1 accuracy of 98.5% and a top-3 accuracy of 99.3%, thus confirming its effectiveness in overcoming the limitations of traditional deep-learning models in temporal sequence processing. [ABSTRACT FROM AUTHOR]

The use of generative learning models in natural language processing (NLP) has significantly contributed to the advancement of natural language applications, such as sentimental analysis, topic modeling, text classification, chatbots, and spam filtering. With a large amount of text generated each day from different sources, such as web-pages, blogs, emails, social media, and articles, one of the most common tasks in NLP is the classification of a text corpus. This is important in many institutions for planning, decision-making, and creating archives of their projects. Many algorithms exist to automate text classification tasks but the most intriguing of them is that which also learns these tasks automatically. In this study, we present a new model to infer and learn from data using probabilistic logic and apply it to text classification. This model, called GenCo, is a multi-input single-output (MISO) learning model that uses a collaboration of partial classifications to generate the desired output. It provides a heterogeneity measure to explain its classification results and enables a reduction in the curse of dimensionality in text classification. Experiments with the model were carried out on the Twitter US Airline dataset, the Conference Paper dataset, and the SMS Spam dataset, outperforming baseline models with 98.40%, 89.90%, and 99.26% accuracy, respectively. [ABSTRACT FROM AUTHOR]

The current advancements in the field of machine learning can have an important application in agriculture and global food security. Machine learning has considerable potential in establishing knowledge-based farming systems. One of the main challenges of data-driven agriculture is to minimize food waste and establish more sustainable farming systems. The prediction of the right harvest time is one of the ways to obtain the mentioned goals. This paper describes multiple machine learning algorithms that are used to predict peach firmness. By accurately predicting peach firmness based on various peach measurement data, a more precise harvest time can be obtained. The evaluation of nature-inspired metaheuristic optimization algorithms in enhancing machine learning model accuracy is the primary objective of this paper. The possibility of improving the peach firmness prediction accuracy of regression tree models using various metaheuristic optimization techniques implemented in GA and metaheuristicOpt R packages is studied. The RMSE on test data of the default regression tree model is 1.722285, while the regression tree model optimized using the gray wolf optimization algorithm scored the lowest RMSE of 1.570924. The obtained results show that it is possible to improve the peach firmness prediction accuracy of the regression tree model by 8.8% using the described method. [ABSTRACT FROM AUTHOR]