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Understanding the intellectual structure of geosciences is crucial for identifying emerging topics, which can guide future research and funding strategies. This study employs Latent Dirichlet Allocation (LDA) topic modeling and co-word network analysis to explore the intellectual landscape of highly cited geoscientific papers. Through empirical analysis, the study identifies 15 research areas, including climate change, geological processes, environmental impacts, and advancements in data analysis and remote sensing. These findings highlight the prominent role of big data analysis and machine learning methods across various geoscientific domains. Additionally, a notable gap has been identified in the integration of these computational methods with research on organic chemistry and formation processes, suggesting a potential direction for future exploration and innovation. [ABSTRACT FROM AUTHOR]

This paper reflects the research to design and application the complex method to evaluate the quality of the paper written in the IMRaD format. As a field we choose Russian segment of the financial technologies area since we had interest to use multilingual language model. The method covers paper's relevance to the chosen field and internal topics, the actuality, relation between parts, text borrowing, and experiment part. The clustering and classifying algorithms, and language machine learning model were used. The experimental part based on corpus of Russian papers shows the deviation of method's recommendations from expert opinion not more than 20%. The results may be interested not only reviewers but student as authors of futures papers to avoid common mistakes during paper's writing. [ABSTRACT FROM AUTHOR]

Pre-evaluating scientific paper quality aids in alleviating peer review pressure and fostering scientific advancement. Although prior studies have identified numerous quality-related features, their effectiveness and representativeness of paper content remain to be comprehensively investigated. Addressing this issue, we propose a content-based interpretable method for pre-evaluating the quality of scientific papers. Firstly, we define quality attributes of computer science (CS) papers as integrity , clarity , novelty , and significance , based on peer review criteria from 11 top-tier CS conferences. We formulate the problem as two classification tasks: Accepted/Disputed/Rejected (ADR) and Accepted/Rejected (AR). Subsequently, we construct fine-grained features from metadata and knowledge entity networks, including text structure, readability, references, citations, semantic novelty, and network structure. We empirically evaluate our method using the ICLR paper dataset, achieving optimal performance with the Random Forest model, yielding F1 scores of 0.715 and 0.762 for the two tasks, respectively. Through feature analysis and case studies employing SHAP interpretable methods, we demonstrate that the proposed features enhance the performance of machine learning models in scientific paper quality evaluation, offering interpretable evidence for model decisions. • Define four criteria for quality evaluation of scientific papers: integrity, clarity, novelty, and significance. • Propose a framework for quality evaluation of scientific papers based on coarse features and knowledge entity network. • An effective algorithm for measuring the novelty and significance of scientific papers based on knowledge entity networks. • Create and release a rigorous dataset, which could serve as the gold standard for quality evaluation of scientific papers. • Conduct extensive experiments to validate the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

Automatic data processing represents the future for the development of any system, especially in scientific research. In this paper, we describe one of the automatic classification methods applied to scientific research as a supervised learning task. Throughout the process, we identify the main features that are used as keys to play a significant role in terms of predicting the new rank under the supervised learning setup. First, we propose an overview of the work that has been realized in ranking scientific research papers. Second, we evaluate and compare some of state-of-the-art for the classification by supervised learning, semi-supervised learning and non-supervised learning. During the preliminary tests, we have obtained good results for performance on realistic corpus then we have compared performance metrics, such as NDCG, MAP, GMAP, F-Measure, Precision and Recall in order to define the influential features in our work. [ABSTRACT FROM AUTHOR]

The article provides a quantitative and qualitative description of machine learning research in India using measures such as growth rate, global publication share, national-level share of international collaborative papers, citations per paper, and highly cited papers. The data for the study was sourced from Scopus database covering 12-year period 2006-17. The study finds that the United States is the world leader in the machine learning research, followed by China, United Kingdom, Germany, and India. India registered 52.46% annual growth in the subject, more than twice that of the world (22.03%). India's citation impact was 5.34 citations per paper and its national share of highly cited papers was 0.68% (27 papers). The leading Indian research organizations in machine learning research include Vellore Institute of Technology, Anna University, Chennai, Indian Institute of Technology, New Delhi, Indian Institute of Science (IIS), Bangalore, and so on. The top 15 journals that accounted for 31.46% of India's output include International Journal of Machine Learning and Cybernetics (with 82 papers), International Journal of Applied Engineering Research (77 papers), Journal of Machine Learning Research (46 papers), Neurocomputing (44 papers), and so on. [ABSTRACT FROM AUTHOR]

Copyright of Informatica (03505596) is the property of Slovene Society Informatika and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the realm of network intrusion detection using machine learning, feature selection aims for computational efficiency, enhanced performance, and model interpretability, preventing overfitting and optimizing data visualization. This paper proposes a filtering method for feature selection, which optimizes information quantity and linear correlation between resultant features. The method identifies Pareto dominant pairs of informative and correlated features, constructs a graph, and selects key features based on betweenness centrality in its connected components. The proposal yields a more concise and informative dataset representation. Experimental results, using three diverse datasets, demonstrate that the proposal achieves more than 95% accuracy in classifying network attacks with just 14% of the total number features in original datasets. [ABSTRACT FROM AUTHOR]

Since its inception, the choice modelling field has been dominated by theory-driven modelling approaches. Machine learning offers an alternative data-driven approach for modelling choice behaviour and is increasingly drawing interest in our field. Cross-pollination of machine learning models, techniques and practices could help overcome problems and limitations encountered in the current theory-driven modelling paradigm, such as subjective labour-intensive search processes for model selection, and the inability to work with text and image data. However, despite the potential benefits of using the advances of machine learning to improve choice modelling practices, the choice modelling field has been hesitant to embrace machine learning. This discussion paper aims to consolidate knowledge on the use of machine learning models, techniques and practices for choice modelling, and discuss their potential. Thereby, we hope not only to make the case that further integration of machine learning in choice modelling is beneficial, but also to further facilitate it. To this end, we clarify the similarities and differences between the two modelling paradigms; we review the use of machine learning for choice modelling; and we explore areas of opportunities for embracing machine learning models and techniques to improve our practices. To conclude this discussion paper, we put forward a set of research questions which must be addressed to better understand if and how machine learning can benefit choice modelling. • Clarifies the similarities and differences between theory and data-driven paradigms. • Reviews the use of machine learning for choice modelling. • Explores opportunities for embracing machine learning to benefit choice modelling. • Puts forward research agenda. [ABSTRACT FROM AUTHOR]

Technical terms are vital elements for understanding the techniques used in academic research papers, and in this paper, we use focused technical terms to explore technical trends in the research literature. The major purpose of this work is to understand the relationship between techniques and research topics to better explore technical trends. We define this new text mining issue and apply machine learning algorithms for solving this problem by (1) recognizing focused technical terms from research papers; (2) classifying these terms into predefined technology categories; (3) analyzing the evolution of technical trends. The dataset consists of 656 papers collected from well-known conferences on ACM. The experimental results indicate that our proposed methods can effectively explore interesting evolutionary technical trends in various research topics. [ABSTRACT FROM AUTHOR]

Abstract: Two players of Rock-Paper-Scissors are modeled as adaptive agents which use a reinforcement learning algorithm and exhibit chaotic behavior in terms of trajectories of probability in mixed strategies space. This paper demonstrates that an external super-agent can exploit the behavior of the other players to predict favorable moments to play against one of the other players the symbol suggested by a sub-optimal strategy. This third agent does not affect the learning process of the other two players, whose only goal is to beat each other. The choice of the best moment to play is based on a threshold associated with the Local Lyapunov Exponent or the Entropy, each computed by using the time series of symbols played by one of the other players. A method for automatically adapting such a threshold is presented and evaluated. The results show that these techniques can be used effectively by a super-agent in a game involving adaptive agents that exhibit collective chaotic behavior. [Copyright &y& Elsevier]

In tutorial format, this paper explores the intricate relationship between Artificial Intelligence (AI) and managerial decision-making, emphasizing the alignment problem--a critical challenge in ensuring AI systems align with human values and ethical standards. It defines AI and examines various methods, including machine learning algorithms, natural language processing, recommendation systems, sentiment analysis, data visualization, anomaly detection, expert systems, neural networks, and deep learning. The alignment problem is dissected into key aspects such as defining goals, value misalignment, robustness and safety, long-term consequences, and interpreting human preferences. The types of AI most affected by the alignment problem, including deep learning, reinforcement learning, generative adversarial networks, evolutionary algorithms, and open-ended learning systems, are highlighted. Practical examples illustrate how biases and misalignments manifest in business processes like recruitment, credit scoring, and automated trading. The paper also categorizes managerial decisions and discusses how AI methods support these decisions while addressing alignment issues. This document underscores the importance of aligning AI systems with human values to ensure ethical and effective outcomes in business environments. This paper is intended to be a road map of possible research topics on the effect of AI on Business Decision Making through the lens of the Alignment Problem. [ABSTRACT FROM AUTHOR]

This paper presents the basic quantum reinforcement learning theory and its applications to various engineering problems. With the advances in quantum computing and deep learning technologies, various research works have focused on quantum deep learning and quantum machine learning. In this paper, quantum neural network (QNN)‐based reinforcement learning (RL) models are discussed and introduced. Moreover, the pros of the QNN‐based RL algorithms and models, such as fast training, high scalability, and efficient learning parameter utilization, are presented along with various research results. In addition, one of the well‐known multi‐agent extensions of QNN‐based RL models, the quantum centralized‐critic and multiple‐actor network, is also discussed and its applications to multi‐agent cooperation and coordination are introduced. Finally, the applications and future research directions are introduced and discussed in terms of federated learning, split learning, autonomous control, and quantum deep learning software testing. [ABSTRACT FROM AUTHOR]

Copyright of Informatica (03505596) is the property of Slovene Society Informatika and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Distributed Denial of Service (DDoS) attacks pose a significant threat to online services by flooding targets with unusually high volumes of traffic or data, disrupting services and causing financial and reputational damage. This paper explores the workings and impact of DDoS attacks, with a variety of methods used by attackers to exploit vulnerabilities in the target infrastructure. To address these risks, this paper advocates the application of Machine Learning (ML) techniques. ML allows computers to learn patterns from data and make decisions autonomously and offers a proactive defense against DDoS attacks. Using UNSW-NB15 Dataset, this study evaluates the performance of eight ML algorithms Logistic Regression, KNN, SVM, Random Forest, Decision Tree, LSTM, MLP, and GRU, the evaluation of this study is based on Accuracy, Precision, Recall, and F1 Score metrics. The results show that Random Forest is the most effective algorithm, showing superior performance across all metrics with 97.68% accuracy. This explains the potential of Machine Learning, specifically the Random Forest algorithm. can be considered to improve security and reduce the risk posed by DDoS attacks. [ABSTRACT FROM AUTHOR]

Air is very beneficial and crucial for every living creature on earth, hence it is very important to protect the air quality in order to avoid diseases. However, due to the increase in population, some human activities have been causing detrimental damage to the air quality. Some people have realized the potential impact of this problem and have done studies on predicting future air pollution. Some implemented machine learning models such as random forest regression, support vector machine, etc, while others have utilized deep learning models in their research.. This study will implement five different models, specifically linear regression, ridge regression, random forest regression, and multilayer perceptron regression. There will be two datasets used in this paper which will be merged and processed. There will be three key evaluation metrics being used in this paper namely root mean squared error, mean absolute error, and r-squared. The results from all of the models have concluded that more optimization and factors are needed in order to boost the final result of this study. [ABSTRACT FROM AUTHOR]

Skin diseases are growing all over the world. Many times, these diseases are malignant too. Detection of the type of skin disease is highly critical for designing the best treatment procedure. In this paper, we proposed a computer aided diagnosis (CAD) approach to identify the type of skin disease. Our research paper introduces an important improvement in dermatological diagnostics by introducing artificial intelligence technology known as Convolutional Neural Network (CNN). In our approach, we have utilized a convolutional neural network-based model. We have used a swin transformer to detect the type of skin disease in ISIC 2019 data set. We have analyzed how this technology pushes the traditional methods to accurately analyze skin lesions and identifying complex patterns that may have been overlooked. With the proposed approach, the accuracy of detecting skin lesion type can be achieved up to 73%. We also address ethical concerns and envision a future where AI integrates into dermatological practice. Overall, our work not only enhances diagnostic accuracy but also contributes to a future where technology and healthcare come together to improve patients' health. [ABSTRACT FROM AUTHOR]

Purpose: This paper presents the Edge Load Management and Optimization through Pseudoflow Prediction (ELMOPP) algorithm, which aims to solve problems detailed in previous algorithms; through machine learning with nested long short-term memory (NLSTM) modules and graph theory, the algorithm attempts to predict the near future using past data and traffic patterns to inform its real-time decisions and better mitigate traffic by predicting future traffic flow based on past flow and using those predictions to both maximize present traffic flow and decrease future traffic congestion. Design/methodology/approach: ELMOPP was tested against the ITLC and OAF traffic management algorithms using a simulation modeled after the one presented in the ITLC paper, a single-intersection simulation. Findings: The collected data supports the conclusion that ELMOPP statistically significantly outperforms both algorithms in throughput rate, a measure of how many vehicles are able to exit inroads every second. Originality/value: Furthermore, while ITLC and OAF require the use of GPS transponders and GPS, speed sensors and radio, respectively, ELMOPP only uses traffic light camera footage, something that is almost always readily available in contrast to GPS and speed sensors. [ABSTRACT FROM AUTHOR]

By combining machine learning AI model with traditional credit risk measurement model, this paper aims to more accurately measure the credit default risk of real estate enterprises. Through professional credit management knowledge, the integration of these two models can fill the gaps of traditional models outside of financial data, and use the research experience of traditional models in the financial field to provide guidance for machine learning. This paper focuses on the key issues of capital intensity, exploration of Supply Chain Finance Growth and Credit Risk Control within the Real Estate Sector. By combining qualitative and quantitative methods. Through literature research and empirical analysis, this paper develops a supply chain finance credit risk assessment index system tailored for small and medium-sized enterprises operating in China's real estate sector. The index system comprises 23 risk assessment indicators, and comprehensively considers many factors such as corporate financial status and market environment. Then, a credit risk assessment model for small and medium-sized real estate enterprises is developed from a supply chain finance perspective, utilizing genetic algorithm optimization for Support Vector Machine (GA-SVM)., and the advantages of this model compared with the traditional model in accuracy and generalization ability are verified through comparative analysis of experiments. The research results can help reduce the information asymmetry in the real estate industry, enhance the credit rating evaluation process for small and medium-sized enterprises, and furnish references to aid commercial banks in credit and credit risk management within the real estate supply chain finance sector.which has important theoretical and practical value. [ABSTRACT FROM AUTHOR]

This paper explores the application of artificial intelligence technology in ensuring the security of digital currencies. With the increasing popularity of digital currencies, ensuring their security has become a crucial concern. AI techniques, such as machine learning and deep learning, can be employed to identify and prevent security threats in digital currency systems. Additionally, it investigates how AI enhances security measures and risk control on digital currency trading platforms, offering insights into real-time risk alert systems. Therefore, through empirical research in four distinct sections, this paper continuously refines and revises the theoretical model proposed in this project. The ultimate goal is to provide new theoretical tools for the management of financial stability in the process of Digital Currency replacing M0. this project gives the research content of the four parts, which include public perception, delivery path, underlying technology and virtual currency bubble, we hope that this research can offer valuable insights to relevant field managers. [ABSTRACT FROM AUTHOR]

The traditional electrical control system has the problem of insufficient function control. This paper designs the engineering electrical control system through the learning algorithm based on fuzzy neural network. The control strategy design module combines fuzzy reasoning with neural network to design intelligent control strategy. Develop a variety of control principles and strategies that can meet the needs and characteristics of the system. The user interface module assists users to monitor and adjust the system, and has the functions of real-time data display and setting control parameters. Fuzzy neural network modeling module combines fuzzy logic with neural network to model the system. In this paper, the sliding window technology is used to collect and process data in real time. When the running time is 1 hour, the system output value is 100.2 and the output fluctuation value is 0.6; When the running time is 2 hours, the system output value is 99.8 and the output fluctuation value is 0.7. The learning algorithm based on fuzzy neural network has little fluctuation and stable performance. [ABSTRACT FROM AUTHOR]

In the realm of education, the pursuit of effective learning outcomes often faces the challenge of limited resources. This paper explores the intersection of maximizing learning outcomes and minimizing costs through a cost-effective approach to mobile learning (m-learning). Recognizing the ubiquitous presence of mobile devices and their potential to revolutionize learning, this study explores strategies that leverage the capabilities of mobile technology while being mindful of budgetary constraints. By leveraging the flexibility and accessibility offered by m-learning platforms, educators can design engaging and interactive learning experiences that cater to diverse learners needs. The paper investigates various cost-effective methodologies, including utilizing open-source software, repurposing existing resources, and adopting collaborative learning environments. Furthermore, it examines the role of scalable technologies and adaptive learning algorithms in optimizing resource allocation and personalizing learning experiences. Through case studies and empirical analysis, this research illustrates how institutions and educators can achieve significant cost savings without compromising the quality of educational delivery. Ultimately, the findings highlight the transformative potential of a cost-effective approach to m-learning in enhancing learning outcomes and widening access to education. [ABSTRACT FROM AUTHOR]

In light of the increasing sophistication and frequency of mobile attacks, there is a growing demand for advanced intelligent techniques capable of offering comprehensive mobile attack detection and prevention. This paper aims to critically evaluate the landscape of mobile security, outlining the evolution of mobile attack vectors and pinpointing the deficiencies in traditional security methods. The text embarks on a journey to understand the connection between machine learning (ML) and its promising applications in enhancing mobile security. First, we outline the current state of mobile attacks and the traditional methods used for their detection, emphasizing the clear limitations and the necessity for an innovative approach. Following this, we will elucidate the fundamentals of ML and its implications in cybersecurity, exploring the benefits it can provide to mobile attack detection frameworks. We delve into discussing various ML algorithms, such as decision trees, random forests, and support vector machines, highlighting their effectiveness and the metrics used to evaluate ML models in security tasks. Moreover, the paper sheds light on novel approaches such as semi-supervised and unsupervised learning in anomaly detection, as well as the applications of transfer learning in security. Addressing the pressing challenges faced in artificial intelligence (AI)-driven mobile attack detection, we delve deep into the intricacies of data collection, labeling, and the prevailing issues of imbalance and overfitting. Furthermore, we explore contemporary adversarial attacks and defenses, scrutinizing the real-world adaptability of AI models and the pivotal role of human-AI collaboration in enhancing attack detection mechanisms. [ABSTRACT FROM AUTHOR]

The impact of air pollution on public health is substantial, and accurate long-term predictions of air quality are crucial for early warning systems to address this issue. Air quality prediction has drawn significant attention, bridging environmental science, statistics, and computer science. This paper presents a comprehensive review of the current research status and advances in air quality prediction methods. Deep learning, a novel machine learning approach, has demonstrated remarkable proficiency in identifying complex, nonlinear patterns in air quality data, yet its application in air quality prediction is still relatively nascent. This paper also conducts a systematic analysis and summarizes how cutting-edge deep learning models are applied in air quality prediction. Initially, the historical evolution of air quality prediction methods and datasets is presented. This is followed by an examination of conventional air quality prediction techniques. A thorough comparative analysis of progress made with both traditional and deep learning-based prediction methods is provided. This review particularly focuses on three aspects: temporal modeling, spatiotemporal modeling, and attention mechanisms. Finally, emerging trends in the field of air quality prediction are identified and discussed. [ABSTRACT FROM AUTHOR]

Breast Cancer (BC) is a significant cause in women and is the leading cause of death worldwide. The analysis and diagnosis of breast cancer using medical imaging analysis is a promising research area that facilitates the decision-making of different diseases. This paper provides advanced research papers that mainly define breast cancer detection (BCD) using machine learning (ML) in medical imaging. The research articles are tabulated with their advantages, disadvantages, machine learning methods, the dataset used in their experiments, and the performance metrics obtained from their experimental results. Some research challenges and future research suggestions in breast cancer detection using ML in medical imaging are discussed. This paper has more potential and is helpful for beginners in this research field. [ABSTRACT FROM AUTHOR]

Blockchain is an emerging technology with Big data, Artificial Intelligence, and Machine Learning. It disrupted industries such as health, education, manufacturing, and banking. However, the increasing popularity of Blockchain ex- poses the scalability issues of major public blockchain platforms (e.g., Bitcoin and Ethereum) and dramatically affects its development. The scalability problem manifests in terms of Low throughput, high transaction latency, and massive energy consumption. Several reviews and studies cover these factors and their potential solutions, yet these studies need to highlight more information through actual application to natural systems or projects. This study investigates all relevant papers on current research solutions for public blockchain scalability issues. The scope of this paper is to explore the implementation of different state-of-the-art scalability solutions to natural systems and projects while simultaneously highlighting the results. This study discusses the methods and techniques used and the challenges encountered that have yet to future researchers must explore. [ABSTRACT FROM AUTHOR]

In tutorial format, this paper explores the intricate relationship between Artificial Intelligence (AI) and managerial decision-making, emphasizing the alignment problem--a critical challenge in ensuring AI systems align with human values and ethical standards. It defines AI and examines various methods, including machine learning algorithms, natural language processing, recommendation systems, sentiment analysis, data visualization, anomaly detection, expert systems, neural networks, and deep learning. The alignment problem is dissected into key aspects such as defining goals, value misalignment, robustness and safety, long-term consequences, and interpreting human preferences. The types of AI most affected by the alignment problem, including deep learning, reinforcement learning, generative adversarial networks, evolutionary algorithms, and open-ended learning systems, are highlighted. Practical examples illustrate how biases and misalignments manifest in business processes like recruitment, credit scoring, and automated trading. The paper also categorizes managerial decisions and discusses how AI methods support these decisions while addressing alignment issues. This document underscores the importance of aligning AI systems with human values to ensure ethical and effective outcomes in business environments. This paper is intended to be a road map of possible research topics on the effect of AI on Business Decision Making through the lens of the Alignment Problem. [ABSTRACT FROM AUTHOR]

The growing Internet of Things (IoT) landscape requires robust security; traditional rule-based systems are insufficient, driving the integration of machine learning (ML) for effective intrusion detection. This paper provides an inclusive overview of research efforts focused on harnessing ML methodologies to fortify intrusion detection within IoT. Tailored feature extraction techniques are pivotal for achieving high detection accuracy while minimizing false positives. The study employs the IoT23 dataset from Kaggle and incorporates four optimization algorithms – Particle Swarm Optimizer, Whale-Pearson optimization algorithm, Harris-Hawks Optimizer, and Support Vector Machine with Particle Swarm optimization algorithm (SVM-PSO) – for feature extraction and selection. A comparison with ML algorithms such as logistic regression, decision tree and naïve Bayes classifier highlights Harris-Hawks Optimizer as the most effective. Furthermore, ensemble methods, particularly the fusion of random forest with HHO optimization, yield an impressive accuracy of 99.97%, surpassing AdaBoost and XGBoost approaches. This paper underscores the application of diverse ensemble learning techniques to enhance intrusion detection precision and efficiency within the intricate IoT landscape, effectively tackling the challenges posed by its complex and ever-changing nature. [ABSTRACT FROM AUTHOR]

Copyright of Rudarsko-Geolosko-Naftni Zbornik is the property of Faculty of Mining, Geology & Petroleum Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

JSON Web Token (JWT) is a simple, compact way to share claims in a space-constrained environment. JWT is part of the interoperable JSON-based identity suite. Many libraries that provide JWT-based authentication and authorization exist. While the JWT standard is secure, some implementations still need to be made. This research paper delves into a comprehensive analysis of the prominent Python libraries utilized for JWT authentication. By meticulously examining these libraries, we aim to provide an in-depth understanding of their features and capabilities. Our investigation encompasses an enumeration of the distinct signing algorithms that are supported by each of these JWT Python libraries. To ensure the robustness and security of these libraries, we employ a multifaceted approach that utilizes various statistical application Security Testing (SAST) tools. These tools play a pivotal role in our assessment by not only evaluating the adherence of the codebase to the PEP8 standard but also by meticulously scanning for common security vulnerabilities and bugs that could potentially compromise the integrity of the authentication process. Our research goes beyond mere identification; we meticulously analyze each warning generated by the SAST tools, emphasizing those warnings that hold the most tremendous significance regarding potential security risks. Furthermore, our investigation extends to gauging the popularity and adoption of each library. To achieve this, we leverage GitHub statistics and harness the power of the Sourcegraph code search utility. By delving into these metrics, we gain a comprehensive view of the community's engagement, usage trends, and overall traction of each library. In summary, this paper thoroughly explores the landscape of JWT authentication in Python, encompassing library evaluation, security assessment, warning analysis, and popularity metrics. [ABSTRACT FROM AUTHOR]

Emotion AI deals with the sentiments of human beings at various domains. It is named emotion AI as using the capabilities of AI the emotions of humans would be interpreted and analysed. The objective of this paper is to learn from the experience of patients towards the healthcare facilities by studying and analysing the sentiments of the patients using machine learning. This paper focus on training the machine for reading the reviews given by patients who have used various healthcare facilities. The machine will be trained to understand the polarity for each healthcare facility in terms of cleanliness, availability of doctors, interaction of doctors with patients etc. The code is implemented in python with various libraries required for machine Learning. The code is able to extract the polarity and is able to handle the emotions of the patients for questions answered in the dataset by patients. The paper would contribute and help the patients decides which healthcare facility they shall choose based on the experiences of various other patients. For this the goodness score for every healthcare facility is calculated and implemented using Machine learning. It's the contribution of artificial Intelligence and machine learning for healthcare Domain. [ABSTRACT FROM AUTHOR]

This paper explores the synergies between collective intelligence and edge computing, presenting a novel paradigm that harnesses decentralized processing power for collaborative problem-solving. Edge devices, such as sensors and IoT devices, collect spatial data in real time from their local environments. They can incorporate machine learning algorithms to analyze spatial data, enabling quicker and more context-aware decision-making. Spatial clustering, a pivotal strategy in IoT edge computing, is examined to optimize localized data processing, enhance resource efficiency, and enable real-time analytics in decentralized environments. By leveraging the physical proximity of devices, spatial clustering contributes to the effectiveness and sustainability of IoT deployments at the edge. The paper introduces an innovative approach to adaptive spatial clustering by adopting swarm intelligence, drawing inspiration from the collective behavior of a flock of birds. Building upon the classical flock model of Reynolds, our extended model incorporates movement in a multi-dimensional space and introduces different types of birds. In this context, the birds serve as agents for discovering points with specific characteristics in a multidimensional space. The integration of swarm intelligence into spatial clustering presents a promising avenue for addressing the challenges of decentralized processing in edge computing environments, paving the way for more efficient and responsive IoT deployments. [ABSTRACT FROM AUTHOR]

This paper presents a novel framework for enhancing privacy in Vehicular Ad Hoc Networks (VANETs) by integrating homomorphic encryption with machine learning applications. VANETs, essential for Intelligent Transport Systems (ITS), face significant challenges in privacy and security due to their highly dynamic and heterogeneous nature. Our framework addresses these challenges by employing a simplified but effective machine learning algorithm, the K-nearest neighbors (KNN), to ensure the security and privacy of the network. The flexibility of the framework allows for the incorporation of other machine learning algorithms, enhancing its adaptability and efficiency in various VANET scenarios. Key to this framework is the use of homomorphic encryption (HE), a cryptographic technique that enables computations on encrypted data without the need for decryption. This feature preserves data confidentiality and allows for secure third-party computations. Our paper discusses the evolution and types of homomorphic encryption, emphasizing the importance of Fully Homomorphic Encryption (FHE) for its ability to evaluate complex polynomial functions. The paper also highlights the different domains of cybersecurity concerns in VANETs, including in-vehicle systems, ad-hoc and infrastructure networks, and data analysis. The proposed framework aims to mitigate these vulnerabilities, particularly focusing on preventing common attacks like DoS and location tracking. A significant advantage of our approach is its general nature, making it applicable to various privacy issues in VANETs. We propose the potential integration of homomorphic encryption with other privacy-preserving techniques, such as differential privacy or secure multi-party computation, to enhance computation times while ensuring robust privacy protection. [ABSTRACT FROM AUTHOR]

Diabetes mellitus remains a global health challenge, requiring innovative solutions for effective disease management. This paper offers a thorough analysis of diabetes technologies, highlighting their various roles in diabetes care. Through a thorough review of the literature and analysis of emerging trends, we explore the multifaceted impact of technology on diabetes care. We investigate the key role of continuous glucose monitoring systems, insulin pumps and smart insulin pens in achieving optimal glycaemic control. The paper also evaluates the integration of artificial intelligence and machine learning algorithms in predictive modelling for early detection of glucose fluctuations, ultimately preventing diabetes-related complications. Additionally, studies the potential of telemedicine and mobile applications in enhancing patient engagement and self-management. Moreover, the review covers advancements in closed-loop insulin delivery systems, offering insights into their clinical effectiveness and potential to revolutionize diabetes care. Ethical and privacy considerations related to the use of patient data in these technologies are discussed, emphasizing the importance of striking a balance between technological innovation and patient security. This paper's evidence synthesis underscores the increasing influence of diabetes technologies on patient outcomes, quality of life, and healthcare systems. It underscores the need for multidisciplinary collaboration between healthcare professionals, researchers and technology developers to ensure the seamless integration and accessibility of these tools to patients living with diabetes. This study serves as a valuable resource for clinicians, researchers, and policymakers, providing a comprehensive view of evolving diabetes technologies and their potential in the field. [ABSTRACT FROM AUTHOR]

The rapid growth of e-commerce platforms has emphasized the significance of personalized recommendation systems in enhancing user engagement and satisfaction. This research paper presents the development and evaluation of an innovative Product Recommendation System that leverages advanced Artificial Intelligence (AI) techniques to provide tailored product suggestions. The primary objective is to create a user-centric experience by integrating an AI assistant, enabling natural and interactive interactions. Through a comprehensive survey conducted to understand customer behaviour while purchasing product using AI, the study aims to assess the system's effectiveness in delivering accurate recommendations and providing a seamless purchasing experience. The paper contributes to the field by showcasing the practical implementation of AI-driven recommendation systems, highlighting their potential to transform e-commerce interactions. [ABSTRACT FROM AUTHOR]

The increasing exploitation of Artificial Intelligence (AI) technologies has enabled the design of user interfaces in a way that integrating artificial intelligence capabilities has become crucial in the modern digital landscape. Exploring the main features and best practices for designing user interfaces for Web applications, which effectively support and leverage AI functionalities, is currently one of the relevant topics in this context. This research work discusses the fundamental principles of user interface (UI) design, and the challenges posed by the integration of AI into web applications. It emphasizes the need to strike a balance between the AI advanced capabilities and the users' ability to understand and control the system. Furthermore, the paper highlights the importance of creating intuitive and engaging UI designs that empower users to interact with AI-driven features effortlessly. The study presents a comprehensive analysis of various UI design techniques specifically tailored for AI-enabled web applications user interfaces. Additionally, the paper explores the incorporation of AI-driven recommendation systems, personalized interfaces, and adaptive designs, which dynamically adapt to users' preferences and behavior. To validate the proposed user interface design principles, the study presents a proposal for a guidelines structure that promotes empirical evaluations through user studies and usability testing. Results collected via a survey based on measuring the effectiveness and user satisfaction of AI-enabled Web interfaces. User interfaces in real-life scenarios are presented and provides information on the impact of UI design decisions on user interaction and overall experience. The outcomes of this research work contribute to a deeper understanding of UI design for AI-supported Web applications user interfaces and offer practical guidelines for designers and developers. By embracing the suggested principles, organizations and designers can create Web interfaces that effectively harness the power of AI while prioritizing user-centricity, accessibility, and ethical considerations. [ABSTRACT FROM AUTHOR]

This paper acknowledges the need for a user-centric solution that helps with emotional regulation and stress management in children with ADHD. The paper presents a unique and comprehensive solution that integrates Reinforcement Learning (RL) algorithms to enhance user experience and aid children with ADHD to regulate their emotions and behaviours through a reward-based system. Through careful analysis of existing literature, and user requirements assessment, a comprehensive framework that integrates machine learning algorithms, physical and digital solution components through a user-centric design approach has been proposed. The core objective is to design and develop a sensory regulation system specifically tailored to the requirements of children with ADHD. Through the development of an engaging and impactful sensory regulation system, children can experience social and academic aspects of school positively while also having the opportunity to expand their social circle through inclusive play environments and ultimately improving their daily experiences. This paper aims to address the imminent need for emotional regulation and stress management tools catering to children with ADHD. By incorporating Reinforcement Learning (RL) algorithms with a reward-based interaction, this paper aims to solve critical challenges faced by children with ADHD, like emotional regulation difficulties, stress management, poor social skills, and academic performance issues so that they can lead more holistic lives. [ABSTRACT FROM AUTHOR]

Fault prediction is the process of using data analysis and machine learning models to anticipate potential defects or faults in the software system. Using only the base machine learning models for software fault prediction leads to limited performance, difficulty in handling non-linear relationships and imbalanced data, inadequate feature representation, and limited complexity handling. Hence, in order to overcome these challenges, this paper proposes a new technique for the selection of classifiers that forms a heterogeneous ensemble. The main goal is to remove or trim out the classifiers that show poor performance compared to the other base classifiers, which can result into a more effective ensemble and can produce better results. The algorithm proposed in this paper finds a set of classifiers that can perform better than using all the classifiers. The challenge that was faced was how to identify the poor-performing classifiers. This challenge is dealt with by performing an experiment using different threshold values to choose the trimmed set of classifiers. For evaluation of the proposed model, 8 different benchmark software fault datasets were used, which are taken from PROMISE and the Apache repository, and AUC is used as the performance measure. The results obtained after the experimental analysis demonstrate the effectiveness of our algorithm compared to the traditional approaches, which used all the base classifiers. There is a significant increase in the AUC values for 6 datasets out of 8, while using the average of probabilities and majority voting, it was seen that there is improvement in 7 out of 8 datasets used. The best-performing dataset by using the average of probabilities is ARC, where the AUC values increase from 0.6505 to 0.694, and while using majority voting, the best-performing dataset is XALAN, where the AUC values increase from 0.5455 to 0.679. From this, it can be seen that the proposed ensemble approach achieved higher AUC values for the tested datasets when compared to the base machine learning classifiers. [ABSTRACT FROM AUTHOR]

Missing data imputation is a critical task in the data pre-processing stage to ensure the quality, stability, and reliability of machine learning models. If missing values are imputed incorrectly, it can result in erroneous predictions and inconsistent model performance. Traditional imputation methods often struggle with complex data patterns having non-linearity and uncertainties. By integrating soft clustering principles, the proposed work provides a flexible framework for imputing missing data by taking into account the underlying inherent data pattern. To address the missing data challenges, this paper presents a novel imputation technique called Radial Basis Kernel Intuitionistic Fuzzy Ⅽ-Means Imputation (KIFCMI), which builds upon the standard Intuitionistic Fuzzy Ⅽ-Means (IFCM) technique. KIFCMI explores the application of centroid-based imputation using IFCM by incorporating the RBF kernel-induced metric in the data space as a replacement for the original Euclidean norm metric. By employing a kernel function, KIFCMI enables the clustering of data that lacks linear separability within the original space, enabling the formation of homogeneous clusters in a space of higher dimensionality. The effectiveness of the proposed imputation technique is validated on ten diverse real-world datasets obtained from the Public Library UCI with 10% and 20% missing data. The comparative analyses of the proposed technique KIFCMI, is carried out with three conventional techniques, namely fuzzy c-means imputation (FCMI), kernelized fuzzy c-means imputation (KFCMI), and intuitionistic fuzzy c-means imputation (IFCMI). The experimental results using two performance measures, namely RMSE and MAE, showcase the robustness and versatility of the proposed technique across other imputation outcomes. This research paper contributes to the evolving landscape of missing data imputation, offering insights into the practical applications of fuzzy clustering techniques. [ABSTRACT FROM AUTHOR]

Air writing is a unique method that allows individuals to freely write or draw in the air without the need for sensor devices. One of the significant applications is aimed at addressing the challenges faced by dyslexic children, who often struggle with reading, writing, and spelling. Currently, there is a lack of tailored applications to guide dyslexic children through their learning process. This paper presents a groundbreaking solution designed to assist dyslexic individuals in mastering the art of writing alphabets, digits, and words. Using various hand gestures, children can effortlessly write any character, digit, or word on a virtual canvas and memorize them easily through the hand movements. This approach not only makes learning enjoyable but also boosts a child's confidence. Additionally, the application allows children to unleash their imagination by creating doodles. While existing air writing methods have limitations, such as reliance on devices and restricting content to digits and characters within predefined boundaries. These limitations have been overcome in our proposed approach. Leveraging afordable technologies, our system caters to the needs of dyslexic learners and serves as an interactive tool to enhance their writing skills. It also provides a means of communication for the deaf and mute community. Utilizing cutting-edge technologies such as machine learning, deep learning, computer vision, and web development, we have developed a practical application that seamlessly tracks finger movements and gestures, capturing trajectories and images for classification. Our model exhibits remarkable performance, achieving a training accuracy rate of 98.57% for digit recognition, 98.80% for character recognition, and 97.09% for doodle recognition. This paper contributes valuable insights into the development of an effective and engaging tool to improve learning skills for students, particularly dyslexic children, while also addressing communication barriers for the deaf and mute community. Our approach opens up new possibilities for inclusive and accessible learning experiences. [ABSTRACT FROM AUTHOR]

Fake news generation and propagation is a major challenge of the digital age, resulting in various social impacts namely bandwagon, validity, echo chamber effects, deceiving the public with spams, misinformation, malicious content and many more. The widespread proliferation of fake news not only fosters misinformation but also undermines the credibility of news sources. The veracity of the information is a major concern at all the stages of generation, publication, and propagation. To comprehend the critical need for addressing this pervasive problem, this research paper presents a framework for automatic detection of fake news using a knowledge-based approach. An automatic fact checking mechanism is applied using concepts of Information Retrieval (IR), Natural Language Processing (NLP) and Graph theory. The knowledge base is generated using Twitter dataset, which basically contains four attributes: Subject-Predicate-Object (SPO) triplet, SPO sentiment polarity, SPO occurrence, and topic modeling. These attributes serve as pivotal indicators for the development of a knowledge base, subsequently employed to detect prevalent patterns and traits linked to deceptive or false information. We have employed Named Entity Recognition (NER) model to extract SPO triples and Latent Dirichlet Allocation (LDA) for topic modeling, thereby contributing to knowledge base generation. To evaluate the efficacy and efficiency of our proposed model, we utilize deep learning algorithms like RNN, GRU, LSTM, GPT-3 and BERT Transformer providing an acceptable level of accuracy. This research paper delivers valuable insights into addressing the proliferation of fake news on Twitter, employing data-driven approaches and advanced deep learning algorithms. [ABSTRACT FROM AUTHOR]

A critical aspect of software engineering is Software fault prediction which aims to identify and prevent errors in software systems before their release which can cause failures or issues for its users. Various techniques and tools have been developed to detect software faults, including static code analysis, dynamic testing, and machine learning-based approaches. In past few years, the world has seen a growing interest in the use of ML models for predicting software faults, as they can effectively analyse high dimensional datasets and detect complex patterns which are difficult for human experts to detect. However, developing accurate and reliable software fault detection models requires careful selection of data, feature engineering, and model evaluation. This purpose of this paper is to present a comprehensive analysis of potential applications and future research directions in the field of software fault detection. The study emphasizes the importance of identifying and addressing software faults to ensure the reliability and efficiency of software systems. Additionally, the paper outlines various approaches and techniques that can be employed for effective software fault detection. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is now affecting all aspects of our social lives. Without always knowing it, we interact daily with intelligent systems. They serve us invisibly. At least that's the goal we assign to them: to make our lives better, task by task. Artificial intelligence has the potential to make biology education more engaging, personalized, and effective by providing students with interactive simulations, personalized learning experiences, and other tools that help them understand complex biological concepts. In this paper, we discuss the integration of AI into the virtual classroom, which significantly enhances student learning experiences in various ways. The study shows that an effective integration of technology into the virtual classroom requires a thoughtful approach that aligns with educational goals and the specific needs of students. In fact, interactive simulations can help make biology more engaging and memorable for students. Besides, personalized learning AI algorithms can help biology students receive a more tailored and effective learning experience, helping them to better understand the course material and develop a deeper appreciation for the natural world. In this work, we will discuss the use of AI to enhance interactive simulation-based cellular processes, with additional application in anatomy, physiology, and ecology teaching. Moreover, this paper discusses how AI could be used to analyze student data and propose personalized learning using adaptive assessments, content recommendations, and data sciences. This paper illustrates examples of AI algorithms that could be useful for teaching biology. [ABSTRACT FROM AUTHOR]

Modern e-businesses heavily rely on advanced data analytics for product recommendations. However, there are still untapped opportunities to enhance user interfaces. Currently, online stores offer a single-page version to all customers, overlooking individual characteristics. This paper aims to identify the essential components and present a framework for enabling multiple e-commerce user interfaces. It also seeks to address challenges associated with personalized e-commerce user interfaces. The methodology includes detailing the framework for serving diverse e-commerce user interfaces and presenting pilot implementation results. Key components, particularly the role of algorithms in personalizing the user experience, are outlined. The results demonstrate promising outcomes for the implementation of the pilot solution, which caters to various e-commerce user interfaces. User characteristics support multivariant websites, with algorithms facilitating continuous learning. Newly proposed metrics effectively measure changes in user behavior resulting from different interface deployments. This paper underscores the central role of personalized e-commerce user interfaces in optimizing online store efficiency. The framework, supported by machine learning algorithms, showcases the feasibility and benefits of different page versions. The identified components, challenges, and proposed metrics contribute to a comprehensive solution and set the stage for further development of personalized e-commerce interfaces. [ABSTRACT FROM AUTHOR]

This paper bibliometrically examines the integration of artificial intelligence (AI) technologies into decisionmaking in management contexts. Using advanced algorithms and machine learning techniques, AI provides transformative capabilities for analyzing complex data sets, forecasting trends, and optimizing decision outcomes. Through a comprehensive literature review and case studies, this paper explores the diverse applications of AI in management decision-making, from strategic planning and resource allocation to risk management and operational efficiency. In addition, the bibliometric analysis discusses the implications of AI adoption, including ethical considerations, the dynamics of organizational change, and the role of human judgment along with AI-based perspectives. By synthesizing current research findings and practical insights, this paper provides a nuanced understanding of how AI can be effectively leveraged to improve management decision-making processes. [ABSTRACT FROM AUTHOR]

Copyright of Informatica (03505596) is the property of Slovene Society Informatika and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Informatica (03505596) is the property of Slovene Society Informatika and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

High-frequency quantitative trading in the emerging digital currency market poses unique challenges due to the lack of established methods for extracting trading information. This paper proposes a deep evolutionary reinforcement learning (DERL) model that combines deep reinforcement learning with evolutionary strategies to address these challenges. Reinforcement learning is applied to data cleaning and factor extraction from a high-frequency, microscopic view-point to quantitatively explain the supply and demand imbalance and to create trading strategies. In order to determine whether the algorithm can successfully extract the significant hidden features in the factors when faced with large and complex high-frequency factors, this paper trains the agent in reinforcement learning using three different learning algorithms, including Q-learning, evolutionary strategies, and policy gradient. The experimental dataset, which contains data on sharp up, sharp down, and continuous oscillation situations, was chosen to test Bitcoin in January-February, September, and November of 2022. According to the experimental results, the evolutionary strategies algorithm achieved returns of 59.18%, 25.14%, and 22.72%, respectively. The results demonstrate that deep reinforcement learning based on the evolutionary strategies outperforms Q-learning and policy gradient concerning risk resistance and return capability. The proposed approach offers a robust and adaptive solution for high-frequency trading in the digital currency market, contributing to the development of effective quantitative trading strategies. [ABSTRACT FROM AUTHOR]

In an era where the demand for efficient and practical machine learning (ML) solutions on resource-constrained devices is evergrowing, the realm of tiny machine learning (TinyML) emerges as a promising frontier. Motivated by the need for lightweight, low-power models that can be deployed on edge devices, this research paper presents an innovative TinyML model tailored to recognize Arabic hand gestures executed in mid-air. With a primary emphasis on the precise classification of Arabic numbers through these expressive hand movements, the paper unveils a comprehensive dataflow architecture. This intricate architecture processes accelerometer and gyroscope data to derive exact 2D gesture coordinates, a fundamental component of the recognition process. The cornerstone of the proposed model is the integration of Convolutional Neural Networks (CNNs), elucidating their exceptional role in achieving an impressive 93.8% accuracy rate in the classification of diverse Arabic Numbers gestures. This remarkable level of precision underscores the model's efficacy and resilience, rendering it an ideal candidate for real-time deployment in various gesture recognition scenarios. [ABSTRACT FROM AUTHOR]

Energy consumption forecasting plays a pivotal role in modern resource management and sustainable development. This paper presents a concise overview of state-of-the-art techniques and methodologies employed in the field of energy consumption forecasting, with a particular emphasis on the application of Machine Learning (ML) models. The paper surveys recent advancements, addresses key challenges, and identifies promising directions for future research in this critical domain. By examining the current landscape of energy consumption forecasting through the lens of machine learning, this review aims to offer researchers and practitioners valuable insights and guidance for enhancing the accuracy and efficiency of energy consumption pattern prediction. [ABSTRACT FROM AUTHOR]