Machine Learning Processing Research Articles

AI and Big Data approaches to addressing the opioid crisis: a scoping review protocol

IntroductionThis paper outlines the steps necessary to assess the latest developments in artificial intelligence (AI) as well as Big Data technologies and their relevance to the opioid crisis. Fatal opioid...

AI Unleashed: Pioneering Trends and Future Directions in Artificial Intelligence

Artificial Intelligence (AI) expeditiously transmutes from a specialized area of study to a key component of contemporary technology, propelling breakthroughs in a wide range of industries. AI Unleashed, Pioneering Trends and Future Directions in Artificial Intelligence is a study examining the current developments influencing the field's progress and future course. This study explores essential fields, including autonomous systems, machine learning, and natural language processing, showcasing new developments and present uses. It also considers AI's ethical and societal ramifications, including issues with prejudice, privacy, and the necessity of robust governance systems. Exploring the confluence of artificial intelligence (AI) with other cutting-edge technologies, such as quantum computing and the Internet of Things (IoT), highlights the potential for unparalleled capabilities. With a perspective beyond the future, this overview highlights the significant obstacles and possibilities that will shape artificial intelligence (AI), from improving human-machine interaction to expanding general intelligence. This assessment offers insights into the cutting-edge trends propelling AI forward and the future paths that will mold the next wave of AI innovation through an extensive examination.

The Role of Artificial Intelligence in Criminal Justice

This study explores the transformative role of artificial intelligence (AI) in the criminal justice system, examining its applications, benefits, and potential challenges. AI technologies, including machine learning, predictive analytics, and natural language processing, are increasingly integrated into various facets of criminal justice, from law enforcement and legal processes to corrections and rehabilitation. Through a comprehensive literature review, this research analyzes how AI enhances the efficiency and effectiveness of criminal justice operations. Key findings reveal that AI can significantly improve crime prediction and prevention, aid in evidence analysis, streamline administrative tasks, and support decision-making processes. For instance, predictive policing models using AI can identify crime hotspots and allocate resources more effectively, while AI-driven tools can assist in analyzing large volumes of legal documents and evidence. However, the study also highlights critical concerns related to bias, fairness, transparency, and ethical implications. There is a growing need for frameworks that ensure AI applications in criminal justice are transparent, accountable, and aligned with ethical standards to prevent discrimination and protect civil liberties. The role of AI in criminal justice presents a dual-edged sword, offering significant advancements while posing substantial risks if not properly managed. This study provides a balanced perspective, offering insights for policymakers, practitioners, and researchers on leveraging AI for a more just and efficient criminal justice system. Future research directions are proposed to address the ethical challenges and to develop robust regulatory frameworks for AI in criminal justice.

Application of Artificial Intelligence to Improve Production Process Efficiency in Manufacturing Industry

The rapid advancement of Artificial Intelligence (AI) has profoundly impacted the manufacturing industry, offering transformative potential to enhance production process efficiency. This paper presents a systematic literature review of AI applications in the manufacturing sector, focusing on key AI technologies such as machine learning, robotics, predictive analytics, and natural language processing. The review highlights how these technologies have improved quality control, resource management, and overall operational performance. However, the adoption of AI also presents challenges, including significant investment costs, the need for a skilled workforce, and concerns over data security and privacy. Despite these challenges, the integration of AI in manufacturing presents numerous opportunities for future research and innovation, particularly in the areas of sustainable manufacturing and the convergence of AI with other emerging technologies. This study concludes that while AI offers substantial benefits for production efficiency, its successful implementation requires careful strategic planning and investment in both technology and human resources.

Identifying patterns and mechanisms of AI integration in blockchain for e-voting network security

The study focuses on the enhancement of e-voting blockchain network security through the integration of artificial intelligence. The critical problem addressed is the existing limitations in real-time threat detection and anomaly detection within blockchain transactions. These limitations can compromise the integrity and security of blockchain networks, making them vulnerable to attacks and fraudulent activities. The core results of the research include the development and implementation of sophisticated AI algorithms designed to enhance the monitoring of blockchain transactions and the auditing of smart contracts. These AI-driven advancements introduce unique features, such as the capability to detect and respond to security threats and anomalies in real-time. This significantly strengthens and optimizes the security frameworks of blockchain systems in e-voting. These results are explained by the strategic application of machine learning and natural language processing methodologies. By employing these advanced AI techniques, the study has achieved more accurate and efficient threat detection, thereby addressing the security challenges previously mentioned. The practical applications of these findings are extensive and diverse. Enhanced security mechanisms can be utilized in financial transactions, supply chain management, and decentralized applications, providing a robust framework for improved blockchain-based e-voting security. In conclusion, integrating AI into blockchain security mechanisms addresses current limitations in threat detection and offers a scalable and effective solution for future security challenges

Using Machine Learning and Natural Language Processing for Unveiling Similarities between Microbial Data

Microbiota analysis can provide valuable insights in various fields, including diet and nutrition, understanding health and disease, and in environmental contexts, such as understanding the role of microorganisms in different ecosystems. Based on the results, we can provide targeted therapies, personalized medicine, or detect environmental contaminants. In our research, we examined the gut microbiota of 16 animal taxa, including humans, as well as the microbiota of cattle and pig manure, where we focused on 16S rRNA V3-V4 hypervariable regions. Analyzing these regions is common in microbiome studies but can be challenging since the results are high-dimensional. Thus, we utilized machine learning techniques and demonstrated their applicability in processing microbial sequence data. Moreover, we showed that techniques commonly employed in natural language processing can be adapted for analyzing microbial text vectors. We obtained the latter through frequency analyses and utilized the proposed hierarchical clustering method over them. All steps in this study were gathered in a proposed microbial sequence data processing pipeline. The results demonstrate that we not only found similarities between samples but also sorted groups’ samples into semantically related clusters. We also tested our method against other known algorithms like the Kmeans and Spectral Clustering algorithms using clustering evaluation metrics. The results demonstrate the superiority of the proposed method over them. Moreover, the proposed microbial sequence data pipeline can be utilized for different types of microbiota, such as oral, gut, and skin, demonstrating its reusability and robustness.

Enhancing Car Segmentation for Thailand's Expressway Industry With an Automated Hybrid Machine Learning Framework

Car segmentation on Thailand's expressways poses challenges for traditional models due to unique characteristics, often resulting in predictive inaccuracies. Manual data analytics in this field is time-consuming and human centric. This research introduces an Automated Hybrid Machine Learning (AHML) framework leveraging advancements in AutoML, tailored for personalized customer segmentation in Thailand's expressway industry. The framework streamlines and automates the machine learning process, aiming to expedite model construction while enhancing performance. By employing clustering as an initial step followed by the Random Forest classifier as a hybrid classification approach, significant performance improvements are achieved compared to existing methods. Specifically, the model outperforms by 9.15% and 12.84% in both clusters, respectively. This research highlights the potential of the framework to address complex segmentation challenges and advance personalized customer targeting.

Feasibility of Using Wavelet Analysis and Machine Learning Method in Technical Diagnosis of Car Seats

This paper presents the results of preliminary research aimed at developing a method for rapid, noncontact diagnostics of the electric drive of car seats. The method is based on the analysis of acoustic signals produced during the operation of the drive. Pattern recognition and machine learning processes were used in the diagnosis. A method of feature extraction (diagnostic symptoms) using wavelet decomposition of acoustic signals was developed. The discriminative properties of a set of diagnostic symptoms were tested using the “Classification Learner” application available in MATLAB. The obtained results confirmed the usefulness of the developed method for the technical diagnostics of car seats.

Suicidal ideation detection on social media using machine learning: A review

Suicide remains a critical global health issue, with over 800,000 deaths annually and profound impacts on individuals and their communities. The increasing role of social media platforms in expressing personal struggles has highlighted the potential of these platforms for early detection of suicidal tendencies. Advances in machine learning and natural language processing (NLP) have revolutionized this detection process, allowing for more accurate identification of suicidal ideation in social media posts. This review examines the integration of machine learning algorithms in suicide detection, including the use of multi-layered neural networks for analyzing textual, audio, and visual data. It explores various machine learning models and their performance in predicting suicide attempts, with a focus on their accuracy, precision, and recall. The review also discusses applications such as real-time monitoring systems and emergency response mechanisms, emphasizing how these technologies can enhance early intervention and support. By surveying current research and identifying challenges and future directions, this review aims to provide a comprehensive understanding of how machine learning can significantly contribute to suicide prevention efforts through effective social media monitoring.

AI integration in supply chain and operations management: Enhancing efficiency and resilience

Artificial intelligence (AI) has become a transformative force in supply chain and operations management, offering significant enhancements in efficiency and resilience. This paper examines the integration of AI technologies such as machine learning, predictive analytics, and real-time data processing in demand forecasting, inventory management, logistics, and risk mitigation. By analyzing diverse data sources, AI improves demand forecasting accuracy, reduces inventory costs, optimizes logistics routes, and enhances supply chain visibility. Case studies and data-driven insights demonstrate how AI-driven systems enable companies to adapt to market dynamics, prevent disruptions, and achieve substantial cost savings. The findings suggest that embracing AI is essential for businesses aiming to optimize their supply chain operations and build robust, resilient frameworks capable of withstanding future challenges.

ROLE OF ARTIFICIAL INTELLIGENCE FOR ADAPTIVE LEARNING ENVIRONMENTS IN HIGHER EDUCATION BY 2030

This study focuses on the ability of Artificial Intelligence (AI) to redesign learning experience of higher education by making learning adaptable by the year 2030. Machine learning and natural language processing afford the possibility of developing adaptive learning environment for students. The research also focuses on AI's present and future uses in the learning & issues of realizing those uses. The purposive sampling technique selected 5 faculty members from different universities. A semi-structured interview guide was developed to get data from the participants. Data was analyzed thematically by facilitation of NVivo 14. The potential of AI for enhancing personalized tasks, automated tasks related to administration, and creating interactive learning experiences. The concerns of data confidentiality and ethical considerations were also addressed. By analyzing the improvement of adaptive learning technologies, the study presents views of how AI can improve educational outcomes. Therefore, the findings also emphasize the diverse implications of equalizing technological innovation with keeping important human fundamentals in education while highlighting the justice and inclusivity.

Evaluation of a Natural Language Processing Approach to Identify Diagnostic Errors and Analysis of Safety Learning System Case Review Data: Retrospective Cohort Study.

Diagnostic errors are an underappreciated cause of preventable mortality in hospitals and pose a risk for severe patient harm and increase hospital length of stay. This study aims to explore the potential of machine learning and natural language processing techniques in improving diagnostic safety surveillance. We conducted a rigorous evaluation of the feasibility and potential to use electronic health records clinical notes and existing case review data. Safety Learning System case review data from 1 large health system composed of 10 hospitals in the mid-Atlantic region of the United States from February 2016 to September 2021 were analyzed. The case review outcome included opportunities for improvement including diagnostic opportunities for improvement. To supplement case review data, electronic health record clinical notes were extracted and analyzed. A simple logistic regression model along with 3 forms of logistic regression models (ie, Least Absolute Shrinkage and Selection Operator, Ridge, and Elastic Net) with regularization functions was trained on this data to compare classification performances in classifying patients who experienced diagnostic errors during hospitalization. Further, statistical tests were conducted to find significant differences between female and male patients who experienced diagnostic errors. In total, 126 (7.4%) patients (of 1704) had been identified by case reviewers as having experienced at least 1 diagnostic error. Patients who had experienced diagnostic error were grouped by sex: 59 (7.1%) of the 830 women and 67 (7.7%) of the 874 men. Among the patients who experienced a diagnostic error, female patients were older (median 72, IQR 66-80 vs median 67, IQR 57-76; P=.02), had higher rates of being admitted through general or internal medicine (69.5% vs 47.8%; P=.01), lower rates of cardiovascular-related admitted diagnosis (11.9% vs 28.4%; P=.02), and lower rates of being admitted through neurology department (2.3% vs 13.4%; P=.04). The Ridge model achieved the highest area under the receiver operating characteristic curve (0.885), specificity (0.797), positive predictive value (PPV; 0.24), and F1-score (0.369) in classifying patients who were at higher risk of diagnostic errors among hospitalized patients. Our findings demonstrate that natural language processing can be a potential solution to more effectively identifying and selecting potential diagnostic error cases for review and therefore reducing the case review burden.

From EHR to Machine Learning: A Preliminary Report on an Ingestion Pipeline Based on JSON-LD.

In this paper, we present the preliminary experiments for the development of an ingestion mechanism to move data from Electronic Health Records to machine learning processes, based on the concept of Linked Data and the JSON-LD format.

Enhancing Agile Story Point Estimation: Integrating Deep Learning, Machine Learning, and Natural Language Processing with SBERT and Gradient Boosted Trees

Advances in software engineering, particularly in Agile software development (ASD), demand innovative approaches to effort estimation due to the volatility in Agile environments. Recent trends have made the automation of story point (SP) estimation increasingly relevant, with significant potential for enhancing accuracy. This study introduces a novel model for software effort estimation (SEE) utilizing a deep learning (DL)-based sentence-BERT (SBERT) model for feature extraction combined with advanced gradient-boosted tree (GBT) algorithms. A comprehensive evaluation shows that the proposed model outperforms standard SEE and state-of-the-art models, demonstrating a mean absolute error (MAE) of 2.15 and a median absolute error (MdAE) of 1.85, representing a 12% improvement over the baseline model and an 18% improvement over the best-performing state-of-the-art model. The standardized accuracy (SA) is 93%, which is 7% higher than the next best model, across a dataset of 31,960 issues from 26 open-source Agile projects. This study contributes to software engineering by offering a more accurate and reliable decision support system for estimating project efforts.

Use of Patterns of Service Utilization and Hierarchical Survival Analysis in Planning and Providing Care for Overdose Patients and Predicting the Time-to-Second Overdose

Individuals from a variety of backgrounds are affected by the opioid crisis. To provide optimal care for individuals at risk of opioid overdose and prevent subsequent overdoses, a more targeted response that goes beyond the traditional taxonomical diagnosis approach to care management needs to be adopted. In previous works, Graph Machine Learning and Natural Language Processing methods were used to model the products for planning and evaluating the treatment of patients with complex issues. This study proposes a methodology of partitioning patients in the opioid overdose cohort into various communities based on their patterns of service utilization (PSUs) across the continuum of care using graph community detection and applying survival analysis to predict time-to-second overdose for each of the communities. The results demonstrated that the overdose cohort is not homogeneous with respect to the determinants of risk. Moreover, the risk for subsequent overdose was quantified: there is a 51% higher chance of experiencing a second overdose for a high-risk community compared to a low-risk community. The proposed method can inform a more efficient treatment heterogeneity approach for a cohort made of diverse individuals, such as the opioid overdose cohort. It can also guide targeted support for patients at risk of subsequent overdoses.

Spatiotemporal discoordination of brain spontaneous activity in major depressive disorder

BackgroundMajor depressive disorder (MDD) is a widespread mental health issue, impacting spatial and temporal aspects of brain activity. The neural mechanisms behind MDD remain unclear. To address this gap, we introduce a novel measure, spatiotemporal topology (SPT), capturing both the hierarchy and dynamic attributes of brain activity in depressive disorder patients. MethodsWe analyzed fMRI data from 285 MDD inpatients and 141 healthy controls (HC). SPT was assessed by coupling brain gradient measurement and time delay estimation. A nested machine learning process distinguished between MDD and HC using SPT. Person's correlation tested the link between SPT's and symptom severity, and another machine learning method predicted the gap between patients' chronological and brain age. ResultsSPT demonstrated significant differences between patients and healthy controls (F = 2.944, p < 0.001). Machine learning approaches revealed SPT's ability to discriminate between patients and healthy controls (Accuracy = 0.65, Sensitivity = 0.67, Specificity = 0.64). Moreover, SPT correlated with the severity of depression symptom (r = 0.32. pFDR = 0.045) and predicted the gap between patients' chronological age and brain age (r = 0.756, p < 0.001). LimitationsEvaluation of brain dynamics was constrained by MRI temporal resolution. ConclusionsOur study introduces SPT as a promising metric to characterize the spatiotemporal signature of brain function, providing insights into deviant brain activity associated with depressive disorders and advancing our understanding of their psychopathological mechanisms.

Fundamentals and Applications Related to Data Science, Machine Learning, and Statistical Processing

Fundamentals and Applications Related to Data Science, Machine Learning, and Statistical Processing

Hybridizing flower pollination algorithm with particle swarm optimization for enhancing the performance of IPv6 intrusion detection system

Internet Protocol version 6 (IPv6) is the most recent iteration of IP, designed to accommodate hundreds of thousands of devices with unique IP addresses. IPv6 introduces new features, such as the Neighbor Discovery Protocol (NDP) and Address Auto-configuration Scheme. For its effective operation, IPv6 relies on several protocols, including ICMPv6, which carries significant responsibilities. Similar to IPv4, IPv6 is susceptible to various attacks, including newer types like DDoS attacks executed via ICMPv6 messages, posing serious security and financial threats. Consequently, an Intrusion Detection System (IDS) is essential to safeguard against these attacks. IDS continuously evolve to incorporate features that can accurately detect such threats. However, feature selection strategies, particularly bio-inspired algorithms, often yield incorrect subsets of features. During machine learning processes, these inaccuracies impede the detection accuracy of DDoS attacks using ICMPv6. Many Optimization Search Algorithms become trapped in local minima and fail to consider multi-objective approaches, resulting in suboptimal feature selection. To address this, optimizing a bio-inspired algorithm within an IPv6 network has been proposed. Specifically, hybridizing the MFPA algorithm with the PSO algorithm is suggested to enhance detection accuracy. The selected features are used to train the dataset with a Support Vector Machine (SVM) classifier. The proposed approach is evaluated using the ICMPv6 dataset on various attacks, demonstrating superior classification accuracy of 97.99 %. It also reduced the number of features from 19 to 8, showcasing its efficiency.

Examining air pollution exposure dynamics in disadvantaged communities through high-resolution mapping.

This study bridges gaps in air pollution research by examining exposure dynamics in disadvantaged communities. Using cutting-edge machine learning and massive data processing, we produced high-resolution (100 meters) daily air pollution maps for nitrogen dioxide (NO2), fine particulate matter (PM2.5), and ozone (O3) across California for 2012-2019. Our findings revealed opposite spatial patterns of NO2 and PM2.5 to that of O3. We also identified consistent, higher pollutant exposure for disadvantaged communities from 2012 to 2019, although the most disadvantaged communities saw the largest NO2 and PM2.5 reductions and the advantaged neighborhoods experienced greatest rising O3 concentrations. Further, day-to-day exposure variations decreased for NO2 and O3. The disparity in NO2 exposure decreased, while it persisted for O3. In addition, PM2.5 showed increased day-to-day variations across all communities due to the increase in wildfire frequency and intensity, particularly affecting advantaged suburban and rural communities.

Potentiality and Apprehensions of Artificial Intelligence in Education: Perspectives of Education Staff

The present study aimed to assess the potentiality and apprehensions of artificial intelligence (AI) in education. It also investigated the challenges of AI integration into education from the teachers' perspectives. A cross-sectional study design was adopted. Through random sampling, a total of 63 members of faculty were recruited from Kuwait University. An online questionnaire was administered to the study participants. The data was analyzed through SPSS version 26, using descriptive statistics, t-tests, and ANOVA. The results showed that there was a remarkably high consensus about the potentiality of AI for education. The teachers’ readiness to adopt AI was low. Data analysis, machine learning, and natural language processing were the most important aspects of linking education and AI. The participants highlighted that for the empowerment of students, AI system use cases, evaluation of the intelligence of AI systems, and identification of the technical limitations of AI systems were crucial. Greater were challenges and difficulties in using AI such as the lack of availability of suitable educational materials, unavailability of required expertise in the field, and the complexity of the subject. However, no statistical difference attributed to gender, academic degree, and academic department in terms of facing challenges was found.