Advanced Machine Learning Techniques Research Articles

Machine learning insights into scapular stabilization for alleviating shoulder pain in college students

Abstract Non-specific shoulder pain is a common musculoskeletal condition, especially among college students, and it can have a negative impact on the patient’s life. Therapists have used scapular stabilization exercises (SSE) to enhance scapular control and mobility. This study investigates the prediction of the impact of scapular stability exercises in treating non-specific shoulder pain, leveraging advanced machine learning techniques for comprehensive evaluation and analysis. Using a diverse range of regression models, including Gamma Regressor, Tweedie Regressor, Poisson Regressor, and others, the study examines the relationship between the effectiveness of various exercises and their impact on shoulder pain management. Furthermore, the study employs optimization techniques, such as Hyperopt, scikit-optimize, optunity, GPyOpt, and Optuna, to fine-tune the exercise protocols for optimal outcomes. The results reveal that scapular stabilization exercises, when optimized using machine learning algorithms, significantly contribute to reducing shoulder pain in college students. Among the optimization techniques, scikit-optimize demonstrated the best performance, resulting in a mean squared error of 0.0085, a mean absolute error of 0.0712, and an impressive R2 score of 0.8501. This indicates that the scikit-optimize approach yielded the most accurate predictions and effectively captured the relationship between the exercises and shoulder pain management. The findings highlight the critical role of scapular stabilization exercise interventions in ameliorating non-specific shoulder pain and underscore the potential of machine learning techniques in optimizing therapeutic strategies for musculoskeletal health management. The utilization of scikit-optimize, in particular, showcases its effectiveness in fine-tuning the exercise protocols for optimal outcomes. The study’s results serve as a crucial stepping stone in developing personalized rehabilitation programs for non-specific shoulder pain, emphasizing the importance of integrating machine learning methodologies in the assessment and treatment of musculoskeletal disorders among college students.

Enhancing water security through advanced modeling: integrating deep learning and a novel metaheuristic optimization algorithm for accurate pan evaporation prediction

ABSTRACT This study evaluates and enhances machine learning models for predicting pan evaporation under diverse climatic conditions. Five fundamental machine learning models were employed and tested across four different stations. Subsequent comparisons were made with advanced techniques, including long short-term memory (LSTM) networks. An innovative approach was introduced, combining LSTM with Binary Al-Biruni Earth Radius (BER–LSTM). This hybrid method was benchmarked against other optimization techniques. The BER–LSTM model consistently outperformed other models across all stations and time scales, achieving up to a 97.54% improvement in root mean square error (RMSE) compared to standard LSTM on daily time scales. Compared to simpler models like Multilayer Perceptron and Support Vector Regressor, BER–LSTM showed even more substantial improvements, with up to a 99.03% reduction in RMSE. The BER–LSTM model demonstrates superior predictive capabilities for pan evaporation across varied climatic conditions, offering significant improvements over both traditional and advanced machine learning techniques. This approach shows promise for enhancing evaporation forecasting in diverse environmental contexts.

Causality-Driven Feature Selection for Calibrating Low-Cost Airborne Particulate Sensors Using Machine Learning

With escalating global environmental challenges and worsening air quality, there is an urgent need for enhanced environmental monitoring capabilities. Low-cost sensor networks are emerging as a vital solution, enabling widespread and affordable deployment at fine spatial resolutions. In this context, machine learning for the calibration of low-cost sensors is particularly valuable. However, traditional machine learning models often lack interpretability and generalizability when applied to complex, dynamic environmental data. To address this, we propose a causal feature selection approach based on convergent cross mapping within the machine learning pipeline to build more robustly calibrated sensor networks. This approach is applied in the calibration of a low-cost optical particle counter OPC-N3, effectively reproducing the measurements of PM1 and PM2.5 as recorded by research-grade spectrometers. We evaluated the predictive performance and generalizability of these causally optimized models, observing improvements in both while reducing the number of input features, thus adhering to the Occam’s razor principle. For the PM1 calibration model, the proposed feature selection reduced the mean squared error on the test set by 43.2% compared to the model with all input features, while the SHAP value-based selection only achieved a reduction of 29.6%. Similarly, for the PM2.5 model, the proposed feature selection led to a 33.2% reduction in the mean squared error, outperforming the 30.2% reduction achieved by the SHAP value-based selection. By integrating sensors with advanced machine learning techniques, this approach advances urban air quality monitoring, fostering a deeper scientific understanding of microenvironments. Beyond the current test cases, this feature selection method holds potential for broader applications in other environmental monitoring applications, contributing to the development of interpretable and robust environmental models.

Impact of data bias on machine learning for crystal compound synthesizability predictions

Abstract Machine learning models are susceptible to being misled by biases in training data that emphasize incidental correlations over
the intended learning task. In this study, we demonstrate the impact of data bias on the performance of a machine learning
model designed to predict the likelihood of synthesizability of crystal compounds. The model performs a binary classification on
labeled crystal samples. Despite using the same architecture for the machine learning model, we showcase how the model’s
learning and prediction behavior differs once trained on distinct data. We use two data sets for illustration: a mixed-source data
set that integrates experimental and computational crystal samples and a single-source data set consisting of data exclusively
from one computational database. We present simple procedures to detect data bias and to evaluate its effect on the model’s
performance and generalization. This study reveals how inconsistent, unbalanced data can propagate bias, undermining real world
applicability even for advanced machine learning techniques.

Advancing Additive Manufacturing Through Machine Learning Techniques: A State-of-the-Art Review

In the fourth industrial revolution, artificial intelligence and machine learning (ML) have increasingly been applied to manufacturing, particularly additive manufacturing (AM), to enhance processes and production. This study provides a comprehensive review of the state-of-the-art achievements in this domain, highlighting not only the widely discussed supervised learning but also the emerging applications of semi-supervised learning and reinforcement learning. These advanced ML techniques have recently gained significant attention for their potential to further optimize and automate AM processes. The review aims to offer insights into various ML technologies employed in current research projects and to promote the diverse applications of ML in AM. By exploring the latest advancements and trends, this study seeks to foster a deeper understanding of ML’s transformative role in AM, paving the way for future innovations and improvements in manufacturing practices.

The role of generative AI in cyber security

<p>In the ever-evolving landscape of cyber threats, the integration of Artificial Intelligence (AI) has become popular into safeguarding digital assets and sensitive information for organisations throughout the world. This evolution of technology has given rise to a proliferation of cyber threats, necessitating robust cybersecurity measures. Traditional approaches to cybersecurity often struggle to keep pace with these rapidly evolving threats. To address this challenge, Generative Artificial Intelligence (Generative AI) has emerged as a transformative sentinel. Generative AI leverages advanced machine learning techniques to autonomously generate data, text, and solutions, and it holds the potential to revolutionize cybersecurity by enhancing threat detection, incident response, and security decision-making processes. We explore here the pivotal role that Generative AI plays in the realm of cybersecurity, delving into its core concepts, applications, and its potential to shape the future of digital security.</p>

More than words: the role of personality in shaping the timeliness of online reviews

Purpose The purpose of this paper is to investigate the potential influence of the big five personality traits − extraversion, neuroticism, agreeableness, openness and conscientiousness − on the time taken by travelers to submit online reviews after their hotel stay. Design/methodology/approach The study analyzed 83,235 TripAdvisor reviews from 415 hotels in six major US tourism cities using random forest algorithms and Poisson regression. The research investigated the influence of the big five personality traits on the time taken by travelers to submit online reviews post-hotel stay, merging personality psychology with consumer behavior research through a combination of machine learning and statistical analysis. Findings The findings reveal significant correlations between certain personality traits and the time taken to post online hotel reviews. Extraversion, neuroticism and agreeableness were found to be negatively correlated with response time, suggesting that individuals scoring higher in these traits tend to submit their reviews more quickly. Conversely, openness exhibited a positive correlation, indicating that those with higher levels of openness tend to delay their feedback. Conscientiousness showed no significant correlation with response time. Originality/value This study represents a novel approach to understanding the relationship between personality traits and online review behavior in the hospitality industry. By leveraging advanced machine learning techniques, such as random forest algorithms, alongside traditional statistical methods like Poisson regression, this research offers a unique perspective on the influence of personality on consumer behavior. The innovative application of these technologies to a large data set of TripAdvisor reviews provides fresh insights that can inform the development of personalized customer engagement strategies. The findings contribute to the growing body of literature on the intersection of personality psychology, consumer behavior and hospitality management in the digital age.

Exploring machine learning algorithms in sickle cell disease patient data: A systematic review.

This systematic review explores the application of machine learning (ML) algorithms in sickle cell disease (SCD), focusing on diagnosis and several clinical characteristics, such as early detection of organ failure, identification of drug dosage, and classification of pain intensity. A comprehensive analysis of recent studies reveals promising results in using ML techniques for diagnosing and monitoring SCD. The review covers various ML algorithms, including Multilayer Perceptron, Support Vector Machine, Random Forest, Logistic Regression, Long short-term memory, Extreme Learning Machines, Convolutional Neural Networks, and Transfer Learning methods. Despite significant advances, challenges such as limited dataset sizes, interpretability concerns, and risks of overfitting are identified in studies. Future research directions entail addressing these limitations by harnessing larger and more representative datasets, enhancing model interpretability, and exploring advanced ML techniques like deep learning. Overall, this review underscores the transformative potential of ML in increasing the diagnosis, monitoring and define prognosis of sickle cell disease while also highlighting the need for further investigation in the field.

Neuroimage-Based Stroke Identification: A Machine Learning Approach

Stroke diagnosis is a time-critical process that requires rapid and accurate identification to ensure timely treatment. This study proposes a machine learning-based diagnostic model for stroke identification using neuro images. Early identification and timely intervention are critical to improving outcomes for stroke patients, but current diagnostic techniques, such as CT and MRI scans, often require time-consuming expert analysis. These delays can limit the effectiveness of treatment, particularly in acute cases where every minute counts. The problem lies in the need for faster, more reliable diagnostic tools that can analyze neuroimaging data with high accuracy and minimal human intervention. Machine learning, specifically deep learning, offers a promising solution to address this gap by automating the process of stroke detection. We employed a comprehensive approach, utilizing Inceptionv3, MobileNet, Convolutional Neural Network (CNN) algorithms to analyze neuroimages and predict stroke occurrence. This research proposes a machine learning-based diagnostic model for stroke identification using neuroimages, leveraging the power of Convolutional Neural Networks (CNN), with Inception V3 and MobileNet architectures. Inception V3, known for its ability to capture intricate image features through deep convolutional layers, and MobileNet, optimized for efficiency and speed, were employed to process large datasets of brain scans. The model was trained on these neuroimaging datasets to distinguish between healthy brain tissues and those affected by stroke. The combination of these two architectures allows for both detailed analysis and fast processing, making the model adaptable to clinical settings. The results showed that the model achieved a high accuracy rate in stroke identification, demonstrating its potential to assist healthcare professionals in diagnosing stroke faster and more accurately. By integrating this machine learning model into existing diagnostic workflows, it could significantly reduce the time to diagnosis, enabling earlier treatment and ultimately improving patient outcomes. Our model has the potential to enhance patient outcomes and reduce the economic burden of stroke. By leveraging the power of these advanced machine learning techniques, the model aims to enhance the efficiency and accuracy of stroke diagnosis compared to traditional methods

Enhancing Customer Satisfaction

Customer satisfaction is crucial for the sustained success of e-commerce platforms, necessitating effective Customer Relationship Management (CRM) strategies to bolster relationships and overall performance. While existing research provides insights into factors like payment methods, shipping times, and customer demographics that influence satisfaction, significant gaps remain in fully understanding these dynamics, particularly within specific contexts such as Olist, a prominent Brazilian online marketplace. This study aims to address these gaps by leveraging advanced machine learning techniques on a comprehensive dataset comprising 100,000 orders spanning 2016 to 2018. Through meticulous analysis, which includes rigorous feature selection and the application of predictive models like logistic regression, decision trees, and random forests, we pinpoint crucial determinants of customer satisfaction. Our findings underscore the pivotal role of operational efficiencies in shaping customer experiences and offer actionable recommendations for enhancing e-commerce performance. By bridging these research gaps, this study not only contributes to the existing body of knowledge but also informs practical strategies for optimizing customer satisfaction and guides future research endeavors in the realm of e-commerce and CRM.

Innovative Approach to Detecting Autism Spectrum Disorder Using Explainable Features and Smart Web Application

Autism Spectrum Disorder (ASD) is a complex developmental condition marked by challenges in social interaction, communication, and behavior, often involving restricted interests and repetitive actions. The diversity in symptoms and skill profiles across individuals creates a diagnostic landscape that requires a multifaceted approach for accurate understanding and intervention. This study employed advanced machine-learning techniques to enhance the accuracy and reliability of ASD diagnosis. We used a standard dataset comprising 1054 patient samples and 20 variables. The research methodology involved rigorous preprocessing, including selecting key variables through data mining (DM) visualization techniques including Chi-Square tests, analysis of variance, and correlation analysis, along with outlier removal to ensure robust model performance. The proposed DM and logistic regression (LR) with Shapley Additive exPlanations (DMLRS) model achieved the highest accuracy at 99%, outperforming state-of-the-art methods. eXplainable artificial intelligence was incorporated using Shapley Additive exPlanations to enhance interpretability. The model was compared with other approaches, including XGBoost, Deep Models with Residual Connections and Ensemble (DMRCE), and fast lightweight automated machine learning systems. Each method was fine-tuned, and performance was verified using k-fold cross-validation. In addition, a real-time web application was developed that integrates the DMLRS model with the Django framework for ASD diagnosis. This app represents a significant advancement in medical informatics, offering a practical, user-friendly, and innovative solution for early detection and diagnosis.

Leveraging Machine Learning for Cybersecurity: Techniques, Challenges, and Future Directions

This study aims to explore the application of machine learning (ML) in enhancing cybersecurity measures, focusing specifically on intrusion detection, malware detection, fraud detection, and anomaly detection systems. A systematic review of 743 scholarly papers was conducted, from which 115 were selected for in-depth analysis. The review process involved evaluating advancements in ML techniques within the context of cybersecurity. The findings reveal that ML-driven systems significantly enhance the automation of security processes, improve the recognition of novel threats, and reduce human error in cyber threat management. However, challenges such as adversarial attacks and the need for high-quality model training pose significant barriers to the broader adoption of ML in cybersecurity. The study discusses the implications of these findings, emphasizing the necessity for developing robust and adaptive ML models that can withstand adversarial threats while improving integration across various cybersecurity applications. The insights gained from this research provide a comprehensive overview of the potential benefits and challenges of implementing ML in cybersecurity, highlighting the need for continuous innovation in threat detection mechanisms. This review acknowledges limitations such as the predominance of literature from Western contexts, potentially overlooking insights from other regions. Furthermore, the complexity of implementing ML systems in dynamic cyber environments remains a critical challenge. Future research should concentrate on refining ML algorithms to enhance resilience against adversarial threats, exploring the integration of emerging technologies for improved cybersecurity, and addressing gaps in the existing literature regarding the life cycle of ML models in real-world applications.

Pseudo Replay-based Class Continual Learning for Online New Category Anomaly Detection in Advanced Manufacturing

The incorporation of advanced sensors and machine learning techniques has enabled modern manufacturing enterprises to perform data-driven classification-based anomaly detection based on the sensor data collected in manufacturing processes. However, one critical challenge is that newly presented defect category may manifest as the manufacturing process continues, resulting in monitoring performance deterioration of previously trained machine learning models. Hence, there is an increasing need for empowering machine learning models to learn continually. Among all continual learning methods, memory-based continual learning has the best performance but faces the constraints of data storage capacity. To address this issue, this paper develops a novel pseudo replay-based continual learning framework by integrating class incremental learning and oversampling-based data generation. Without storing all the data, the developed framework could generate high-quality data representing previous classes to train machine learning model incrementally when new category anomaly occurs. In addition, it could even enhance the monitoring performance since it also effectively improves the data quality. The effectiveness of the proposed framework is validated in three cases studies, which leverages supervised classification problem for anomaly detection. The experimental results show that the developed method is very promising in detecting novel anomaly while maintaining a good performance on the previous task and brings up more flexibility in model architecture.

Enhancing machine learning recommendation systems with CNN architectures: Applications and innovations in computer vision and speech recognition

Abstract. This paper provides an in-depth analysis of enhancing machine learning recommendation systems using Convolutional Neural Network (CNN) architectures, with a focus on their applications in computer vision and speech recognition. Traditional recommendation systems often struggle with scalability and the complexity of high-dimensional data. By integrating CNNs, these systems can utilize advanced feature extraction and representation learning to more effectively process and analyze diverse data sources. Our study demonstrates significant improvements in recommendation accuracy and personalization through CNN-enhanced systems. We discuss the architecture, design principles, and advantages of CNNs, supported by case studies across various domains. Our findings illustrate the potential of CNNs to revolutionize recommendation systems by addressing existing limitations and offering innovative solutions for real-time, high-quality recommendations. This research emphasizes the importance of advanced machine learning techniques in creating robust and scalable recommendation systems, paving the way for future advancements and applications in multiple fields.

Integrating computer technology and policy management for sustainable smart city development

Abstract. This paper presents the development and application of a machine learning-based Environmental, Social, and Governance (ESG) performance evaluation model. The model quantifies and assesses the ESG performance of companies, enabling them to identify strengths and weaknesses in their sustainability efforts. By leveraging advanced machine learning techniques, the model offers accurate and comprehensive insights into ESG performance, guiding companies in formulating effective improvement strategies. The study involves detailed data collection and preprocessing, model construction and training, and rigorous evaluation metrics to ensure reliability and effectiveness. Additionally, the paper discusses practical applications of the model in strategic decision-making, resource allocation, and enhancing stakeholder engagement. The findings highlight the importance of integrating technology with ESG considerations to drive sustainable business practices. This research provides a robust framework for ESG performance assessment and sets the stage for future advancements in this critical area.

An Efficient Network Intrusion Detection and Classification System using Machine Learning

In today's digital landscape, network security is of paramount importance, with intrusion detection systems (IDS) playing a crucial role in protecting sensitive data from malicious attacks. Traditional IDS, often reliant on signature-based methods, struggle with high false positive rates, difficulty in adapting to novel threats, and significant computational demands. This paper explores the development of an efficient network intrusion detection and classification system utilizing machine learning techniques to address these challenges. By leveraging datasets such as NSL-KDD and UNSW-NB15, our study employs a combination of supervised learning algorithms, including Support Vector Machines (SVM), Random Forests, and Neural Networks, alongside comprehensive data preprocessing and feature engineering strategies. The evaluation of our models through metrics like accuracy, precision, recall, and ROC-AUC demonstrates a marked improvement in detection capabilities and computational efficiency. Our findings suggest that machine learning-based IDS can significantly enhance network security by reducing false positives and adapting to emerging threats more effectively than traditional systems. This research not only underscores the potential of advanced machine learning techniques in IDS but also provides a robust framework for future developments in the field. In the rapidly evolving landscape of cybersecurity, effective network intrusion detection and classification systems are critical for safeguarding sensitive data and maintaining operational integrity. This paper presents a novel approach utilizing machine learning techniques to enhance the efficiency and accuracy of intrusion detection systems (IDS). By employing a combination of supervised and unsupervised learning algorithms, our system can identify and classify both known and unknown threats in real-time. We leverage advanced feature selection methods to optimize the performance of our models, ensuring high detection rates with minimal false positives. Our experimental results, validated on benchmark datasets, demonstrate significant improvements in detection accuracy and processing speed compared to traditional IDS solutions. The proposed system not only strengthens network defenses but also provides a scalable and adaptive framework for future cybersecurity challenges..

Advanced Machine Learning Techniques for Precise hourly Air Quality Index (AQI) Prediction in Azamgarh, India

Advanced Machine Learning Techniques for Precise hourly Air Quality Index (AQI) Prediction in Azamgarh, India

An Optimization Framework for Waste Treatment Center Site Selection Considering Nighttime Light Remote Sensing Data and Waste Production Fluctuations

As urbanization accelerates, the management of urban solid waste poses increasingly intricate challenges. Traditional urban metrics, such as GDP and per capita consumption rates, have become inadequate for accurately reflecting the realities of waste generation; moreover, the linear correlation between these metrics and waste production is progressively diminishing. Consequently, this study introduces a novel methodology leveraging nighttime light remote sensing data to enhance the precision of urban solid waste production forecasts. By processing remote sensing data to mitigate noise and integrating it with conventional urban datasets, an innovative index system and predictive model were developed. Using Beijing as a case study, the gradient boosting regression algorithm yielded a prediction accuracy of 92%. Furthermore, in light of the substantial costs associated with waste recovery route planning and site selection for treatment facilities, this research further devised a location and distribution framework for waste treatment centers based on high-precision predictions of waste production while employing multi-objective evolutionary algorithms (MOEAs) alongside the non-dominated sorting genetic algorithm II (NSGA-II) for optimization. Distinct from prior studies, this study suggests that service point waste quantities are not fixed values but rather adhere to a normal distribution within specified ranges and thus provides a more realistic simulation of fluctuations in waste production while enhancing both the robustness and predictive accuracy of the model. In conclusion, by incorporating nighttime light remote sensing data along with advanced machine learning techniques, this study markedly improves forecasting accuracy for waste production while offering effective optimization strategies for site selection and recovery route planning—thereby establishing a robust data foundation aimed at refining urban solid waste management systems.

Machine learning-based clustering analysis of the banking green credit for the renewable energy industry

Credit policies for clean and renewable energy businesses play a crucial role in supporting carbon neutrality efforts to combat climate change. Clustering the credit capacity of these companies to prioritize lending is essential given the limited capital available. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are two robust machine learning algorithms for addressing complex clustering problems. Additionally, hyperparameter selection within these models is effectively enhanced through the support of a robust heuristic optimization algorithm, Particle Swarm Optimization (PSO). To leverage the strength of these advanced machine learning techniques, this paper aims to develop SVM and ANN models, optimized with the PSO, for the clustering problem of green credit capacity in the renewable energy industry. The results show low Mean Square Error (MSE) values for both models, indicating high clustering accuracy. The credit capabilities of wind energy, clean fuel, and biomass pellet companies are illustrated in quadrant charts, providing stakeholders with a clear view to adjust their credit strategies. This helps ensure the efficient operation of banking green credit policies.

Bouncing back to the surface: Factors determining SME recovery

ABSTRACT Limited attention has been devoted to the key determinants of survival in situations of severe financial distress for small and medium-sized enterprises (SMEs). Against this backdrop, drawing from turnaround management literature, we develop hypotheses about SMEs investigating the importance of prior financial blockages, financial indicators, personnel characteristics, and the dynamics between SMEs and their personnel on recovery probability. By relying on advanced machine learning techniques, we analyze and select key predictors of recovery likelihood for SMEs in situations of severe distress. The results provide empirical support for our hypotheses, showing the significant roles of prior bank account blockages, creditworthiness, mean employee age, firing ratio, and the share of permanent work contracts in influencing recovery odds. The implications extend knowledge on SME turnaround strategies and offer practical suggestions for managers and policymakers in tailoring interventions.