Welcome to Frontiers in Collaborative Research Frontiers in Collaborative Research is a pioneering, open-access journal dedicated to the advancement of interdisciplinary research. Our journal fosters a platform where experts from diverse fields come together to explore and solve complex research questions that span multiple disciplines. We are committed to enhancing the collaborative efforts that are fundamental to breakthroughs in science, technology, and the humanities.
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Hybrid Ensemble Learning for Real-Time Predictive Maintenance in Vehicular Engine Health Monitoring Systems
Vehicular engine failures are a major contributor to unplanned maintenance costs, operational downtime, and reduced road safety. Traditional diagnostic systems often rely on rule-based thresholds or simple machine learning models, which struggle to detect complex, evolving engine faults under real-world conditions. Accurate and interpretable predictive maintenance frameworks are essential to address the increasing demand for intelligent and reliable engine health monitoring. This study proposes a novel hybrid ensemble learning framework for real-time engine fault detection and classification, aimed at enhancing prediction accuracy and diagnostic robustness. The system integrates multiple base classifiers—Random Forest (RF), K-Nearest Neighbors (KNN), and Gradient Boosting (GBM)—within a stacked generalization architecture, using a Logistic Regression meta-learner. Deep learning enhancements, including an attention-augmented LSTM module, are applied to capture temporal patterns in engine sensor data. Real-time data from OBD-II and CAN bus systems are preprocessed using normalization, label encoding, and outlier handling techniques. Experimental evaluations on a multi-parameter engine dataset demonstrate that the proposed ensemble model achieves an accuracy of 90.2%, outperforming baseline models such as RF (86.5%) and SVM (85.4%). The system also reduces the false positive rate to 4.2%, and improves recall for rare but critical faults. This work contributes a scalable, interpretable, and high-performance diagnostic solution with strong applicability to Edge AI, fleet maintenance systems, and connected vehicle ecosystems.
Predictive Maintenance, Ensemble Learning, Engine Diagnostics, Deep Learning, Vehicle Health Monitoring, Intelligent Transportation Systems
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
Autism Spectrum Disorder Detection Using Hybrid Machine Learning and Deep Learning Techniques
The early and accurate detection of Autism Spectrum Disorder (ASD) remains a significant challenge due to its heterogeneous manifestation across cognitive, behavioral, and social domains. To address the limitations of traditional diagnostic methods, this study aims to develop a hybrid machine learning and deep learning framework for enhanced ASD detection utilizing multimodal data sources. The proposed methodology integrates behavioral questionnaire data classified using an XGBoost model and facial image data processed through a MobileNet architecture augmented with a recurrent neural network (RNN). Key preprocessing steps included feature selection via mutual information scores, TF-IDF encoding, and convolutional feature extraction. Experimental evaluations, conducted using two publicly available ASD datasets and an additional facial image set, achieved classification accuracies of 94.1% for behavioral data and 89.64% for image data, with an overall F1-score of 0.93 and AUC-ROC exceeding 94%. Comparative analysis against baseline models confirmed statistically significant improvements (p < 0.01). The results demonstrate that the hybrid system effectively balances predictive accuracy, computational efficiency, and real-time applicability. The findings also highlight the importance of facial symmetry features and social cognition brain regions in ASD prediction. Despite certain limitations, such as sample diversity constraints, the study offers a scalable and interpretable framework for clinical deployment, contributing to the advancement of early ASD diagnosis and paving the way for future multimodal, explainable AI solutions.
Autism Spectrum Disorder (ASD), Machine Learning, Deep Learning, MobileNet, Recurrent Neural Network (RNN), XGBoost, Behavioral Data Analysis, Image Classification, Early Diagnosis, Multimodal Fusion, TF-IDF, Feature Selection, Hybrid Model, Explainable Artificial Intelligence (XAI).
Bio-Signal Classification and Disease Prediction with Deep Learning
The accurate classification of bio-signals, particularly electrocardiogram (ECG) signals, plays a vital role in the early diagnosis and management of cardiovascular diseases. Traditional machine learning approaches often struggle with the complexity and variability of ECG signals, necessitating more robust, scalable solutions. This study aims to develop and evaluate a deep learning-enhanced framework for ECG signal classification and heart disease prediction across multiple conditions. The proposed methodology integrates a systematic preprocessing pipeline, advanced feature extraction, and comparative evaluation of three classifiers: Random Forest (RF), Artificial Neural Network (ANN), and One-Dimensional Convolutional Neural Network (CNN-1D). The ECG Heartbeat Categorization Dataset [30], containing over 10,000 labeled samples, was employed to train and validate the models. Key techniques included Min-Max normalization, zero-filling for missing data, grid search hyperparameter tuning, and multidimensional evaluation metrics such as precision, recall, F1-score, and computational efficiency. Experimental results demonstrated that the ANN model achieved the highest classification accuracy of 97.5%, followed by CNN-1D with 94.16%, and Random Forest with 88.61%. The ANN also recorded an AUC value of 0.985, indicating superior discrimination capability. Training times varied, with Random Forest completing within 0.4 minutes and CNN-1D requiring approximately 11.7 minutes. Statistical analysis confirmed the significance of these results with p-values less than 0.001. In conclusion, the proposed deep learning framework offers a robust, scalable, and computationally efficient solution for ECG-based heart disease prediction. Future work will focus on expanding the dataset, enhancing model interpretability, and incorporating transfer learning techniques to further improve diagnostic accuracy and real-world applicability
Bio-signal classification, Electrocardiogram (ECG) analysis, Heart disease prediction, Artificial Neural Network (ANN), Convolutional Neural Network (CNN-1D), Random Forest, Deep learning, Medical diagnostics, Feature extraction, Transfer learning
Sensor-Free Earthquake Magnitude Prediction Using XGBoost and Public Seismic Data for Real-Time Early Warning Systems
Earthquakes pose a significant threat to human safety and infrastructure, making early detection and warning systems critical for disaster preparedness. Traditional Earthquake Early Warning Systems (EEWS) often rely on costly physical sensors, limiting their scalability and accessibility. This study proposes a sensor-free earthquake alert system that utilizes publicly available seismic data and machine learning (ML) techniques to predict earthquake magnitudes and provide real-time alerts. The objective of this research is to develop a cost-effective, scalable, and accurate earthquake prediction system using XGBoost, a gradient boosting machine learning algorithm, applied to seismic data sourced from the USGS public network. The system employs advanced feature extraction techniques such as Fourier and wavelet transforms to capture key seismic characteristics, while a weighted loss function is used to address class imbalance in earthquake magnitudes. Experimental results demonstrate that the proposed model achieves 92.5% accuracy, with an F1-score of 0.88, significantly outperforming existing models in terms of computational efficiency and training time. Comparative analysis shows that the proposed system outperforms deep learning and ensemble methods, which struggle with resource consumption and slow prediction times. Statistical analysis confirmed that the proposed model’s performance improvements were statistically significant (p-value = 0.0012). This study contributes to the development of scalable, cost-efficient earthquake prediction systems. The sensor-free approach offers a promising solution for regions lacking advanced seismic infrastructure, with significant real-world implications for disaster preparedness and early warning systems. Future research should focus on improving the model's sensitivity to low-magnitude events and its robustness in noisy, real-time data environments.
Earthquake prediction, Early Earthquake Warning System, XGBoost, Machine Learning, Seismic data, Real-time alerts.
Hybrid Image Deblurring Using Auto-Encoders and Hierarchical Attention Transformer: A Deep Learning Framework for Blur Restoration
Image blurring remains a critical challenge in digital imaging, adversely affecting tasks across photography, surveillance, medical diagnostics, and autonomous navigation. Traditional deblurring methods often rely on blur kernel estimation and handcrafted priors, which limit their adaptability to real-world, spatially varying distortions. This study proposes a novel hybrid deep learning framework that integrates a Convolutional Auto-Encoder with a Hierarchical Attention Transformer (HAT) to effectively address the limitations of existing methods. The model architecture consists of a multi-stage pipeline that begins with an auto-encoder for latent feature compression, followed by a HAT-based backbone to capture long-range dependencies and contextual information. The final reconstruction decoder restores high-fidelity sharp images. The framework is evaluated on GoPro and REDS datasets, utilizing comprehensive metrics including PSNR, SSIM, inference time, and FLOPs. Data augmentation and stratified sampling were applied to ensure robustness, while a hybrid loss combining L1 and perceptual loss guided training. Experimental results demonstrate that the proposed method achieves state-of-the-art performance with a PSNR of 33.68 dB and SSIM of 0.934 on the GoPro dataset, outperforming Restormer, NAFNet, and MIMO-UNet. Despite integrating attention mechanisms, the model maintains competitive inference speed (41.3 ms/image), making it suitable for real-time applications. This work highlights the synergy between encoder-based representation learning and transformer-based attention modeling, offering a scalable and efficient solution for image restoration under diverse blur conditions
Image deblurring, Hierarchical Attention Transformer, auto-encoder, deep learning, PSNR, SSIM
A Hybrid Deep Learning Framework for Pulmonary Nodule Segmentation and Classification Using U-Net, FPN, and EfficientNet on CT scans
Lung cancer is a leading cause of cancer-related mortality worldwide, accounting for approximately 1.80 million deaths in 2020. Early detection of pulmonary nodules significantly improves patient survival yet differentiating between benign and malignant nodules remains challenging due to their morphological similarity in early stages. Manual interpretation of CT scans is labor-intensive and prone to diagnostic error, necessitating reliable automated solutions. This study proposes a unified deep learning framework that integrates U-Net for nodule segmentation, Feature Pyramid Networks (FPN) for multi-scale feature extraction, and EfficientNet for nodule classification. The model is trained and evaluated using the publicly available LIDC-IDRI dataset, comprising 12,000 labelled and 3,000 unlabeled CT images. To address class imbalance and limited annotations, a semi-supervised learning strategy with pseudo-labeling is employed. Preprocessing includes Gaussian blurring, morphological filtering, and intensity normalization, followed by data augmentation to enhance generalizability. Experimental results demonstrate a peak classification accuracy of 91.67%, a segmentation Dice score of 0.5009, and low false positive/negative rates, validating the model’s robustness. The EfficientNet-based classifier shows significant performance gains over baseline CNN architectures, while maintaining computational efficiency. This end-to-end framework enables reliable lung nodule analysis with potential real-world applications in early cancer diagnosis, especially in resource-constrained clinical environments. The findings suggest that combining segmentation and classification into a single pipeline enhances diagnostic precision and paves the way for future AI-driven medical imaging solutions
Lung Cancer Detection, Pulmonary Nodules, U-Net, Feature Pyramid Network, EfficientNet, CT Imaging.
AI-Driven Smart Traffic Control System Using CNN and YOLO for Real-Time Urban Mobility Optimization
Urban traffic congestion is a critical challenge that demands intelligent and adaptive traffic management solutions. This study proposes a Smart Traffic Control System utilizing Convolutional Neural Networks (CNNs) and the YOLO (You Only Look Once) object detection algorithm to optimize traffic flow dynamically. By leveraging real-time image processing, the system accurately detects and classifies vehicles, enabling intelligent traffic signal adjustments based on live traffic density data. The implementation comprises vehicle detection, real-time traffic estimation, and adaptive signal timing computation. The YOLO-based model achieved a detection accuracy of 92.3% in diverse lighting and weather conditions, surpassing traditional object detection methods. The system was tested at multiple intersections, processing an average of 45 frames per second (FPS), making it suitable for real-time applications. Vehicle counting results across multiple lanes demonstrated an 87% reduction in misclassification errors compared to traditional threshold-based techniques. Quantitative analysis showed a 30% decrease in average waiting time per vehicle at intersections. Dynamic green light allocation, based on vehicle density, improved overall traffic flow efficiency by 23%. Comparative results indicated a 17% improvement in congestion management over static signal control systems. Furthermore, a 14% reduction in fuel consumption and emissions was observed due to minimized vehicle idling at signals. This system presents a scalable and cost-effective approach to modern traffic control, paving the way for smart city applications. Future enhancements include integrating IoT sensors, predictive analytics, and reinforcement learning models to further refine traffic optimization and autonomous vehicle coordination
Smart Traffic Control, YOLO Object Detection, Convolutional Neural Networks (CNN), Real-Time Traffic Optimization, Adaptive Signal Timing, Vehicle Detection and Classification.
Transformer-Based Sentiment Analysis: A Comparative Study of Machine Learning, Deep Learning, and BERT Models on Amazon Product Reviews
E-commerce platforms generate massive amounts of user-generated reviews, making sentiment analysis a crucial tool for understanding customer feedback. This study evaluates the effectiveness of traditional machine learning, deep learning, and Transformer-based approaches for sentiment classification of Amazon product reviews. Models including Multinomial Naive Bayes (MNB), Random Forest (RF), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and fine-tuned BERT were analyzed. Reviews were preprocessed, and sentiment labels were derived from star ratings. Metrics such as Accuracy, Precision, Recall, F1-Score, and Area Under the Curve (AUC) were calculated to compare performance. Results revealed that the fine-tuned BERT model outperformed all others with an Accuracy of 97.4%, Precision of 96.8%, Recall of 98.2%, F1-Score of 97.5%, and AUC of 98.5%. Traditional ML models like MNB and RF lagged behind, achieving accuracies of 82.3% and 88.6%, respectively. This study highlights the superiority of Transformer-based architectures for sentiment analysis tasks and provides insights for future research in leveraging advanced NLP techniques.
Amazon, sentiment analysis, product review, feature extraction, machine learning
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
Leveraging Python for Web Scraping and Data Analysis: Applications, Challenges, and Future Directions
Web scraping has emerged as a transformative technique for automating data collection from online sources, enabling efficient extraction of large datasets for analysis and decision-making. This paper explores the integration of web scraping with Python’s robust ecosystem of libraries, including Beautiful Soup, Scrapy, Selenium, and Pandas, to facilitate data preprocessing, visualization, and advanced analysis. Highlighting the applications of web scraping in diverse fields such as e-commerce, finance, healthcare, and academia, the study emphasizes its role in supporting data-driven decision-making and unlocking the potential of online data.The paper also addresses critical challenges associated with web scraping, including legal and ethical concerns surrounding data privacy, copyright, and adherence to website terms of service. Technical barriers, such as anti-scraping mechanisms and handling dynamic content, are explored alongside strategies to overcome these limitations. Future advancements in automation and AI are identified as key drivers for enhancing the efficiency and scalability of web scraping workflows. Additionally, the paper advocates for the development of ethical frameworks to guide responsible scraping practices.By integrating web scraping with Python’s analytical capabilities, this study demonstrates its potential to revolutionize data collection and analysis, while acknowledging the need for ethical compliance and innovation to address current challenges and ensure sustainability.
Web scraping, data analysis, Python, automation, data extraction, data insights.
Enhancing Movie Recommendations with Aspect-Based Sentiment Analysis and Hybrid Filtering Techniques
This paper presents a comprehensive framework to improve movie recommendation accuracy and personalization by integrating sentiment analysis. Traditional recommendation systems, based on collaborative and content-based filtering, often fail to capture the emotional nuances embedded in user-generated reviews. To address this limitation, the proposed system leverages aspect-based sentiment analysis (ABSA) to extract detailed sentiment scores for key movie attributes such as storyline, acting, visuals, and soundtrack. These insights are incorporated into a hybrid recommendation approach that combines collaborative filtering and content-based filtering methods to deliver more contextually relevant recommendations. The sentiment classifier demonstrated a robust performance, achieving an accuracy of 92%, with precision, recall, and F1-scores exceeding 90% for all sentiment classes. Aspect-based sentiment analysis revealed strong user preferences for acting (90% positive sentiments) and storyline (85% positive sentiments), while visuals and soundtrack also received predominantly positive feedback. The hybrid recommender system outperformed standalone techniques, achieving a precision of 91% and reducing the Mean Absolute Error (MAE) to 0.65, thereby ensuring higher recommendation accuracy. Evaluation results underscore the system’s ability to align recommendations with users' emotional and qualitative preferences, significantly enhancing user satisfaction. The findings highlight the potential of integrating sentiment analysis to bridge the gap between user expectations and algorithmic predictions. Future directions include real-time sentiment analysis, multimedia content integration, and advanced deep learning models to further enhance the scalability and applicability of the system in dynamic entertainment ecosystems.
Movie Recommender System, Sentiment Analysis, Aspect-Based Sentiment Analysis, Hybrid Filtering, Collaborative Filtering, Content-Based Filtering.
A Hybrid Approach to Credit Card Fraud Detection: Integrating Adaboost and Majority Voting for Enhanced Accuracy and Robustness
Fraud detection in credit card transactions is a critical challenge in the financial sector. This paper presents a hybrid approach utilizing Adaboost and Majority Voting algorithms to enhance the accuracy and robustness of fraud detection systems. Using the publicly available "Credit Card Fraud Detection Dataset" from the ULB Machine Learning Group, containing 284,807 transactions with only 492 fraudulent cases (0.172%), this study tackles the challenges of class imbalance and evolving fraud tactics. The hybrid model achieves a significant improvement in performance metrics, including an F1-score of 82.3% and an AUC of 0.95, surpassing standalone classifiers and individual ensemble methods. These results demonstrate the potential of the hybrid approach for real-world implementation in financial systems requiring high accuracy and reliability.
Credit Card Fraud, Adaboost, Majority Voting, Ensemble Learning, Imbalanced Datasets, Fraud Detection
Data-Driven Scholarship Prediction Using Machine Learning for Equitable Education Allocation
Scholarship prediction plays a pivotal role in educational technology, leveraging data mining and machine learning techniques to evaluate student eligibility based on academic, demographic, and behavioral data. Traditional manual methods often suffer from inefficiencies, subjectivity, and bias, creating barriers to equitable scholarship distribution. Machine learning algorithms provide scalable and accurate alternatives, uncovering hidden patterns in high-dimensional data to enhance decision-making processes. This paper explores the diverse methodologies employed, including classification, regression, clustering, and ensemble learning, which have been instrumental in transforming scholarship prediction into a data-driven and evidence-based practice. Advanced techniques such as neural networks, transfer learning, and explainable AI have further improved prediction accuracy and model interpretability, empowering stakeholders with actionable insights. The paper also identifies emerging trends, such as real-time prediction systems and cross-domain applications, which promise to expand the scope of predictive models beyond scholarships into domains like loan approvals and admissions. Challenges related to data quality, fairness, and privacy are critically examined, highlighting the need for ethical and robust solutions. By integrating advanced analytics, explainable AI, and privacy-preserving techniques, future systems can ensure transparency, equity, and scalability, addressing gaps in traditional scholarship allocation processes and fostering greater accessibility for underprivileged students.
Scholarship prediction, machine learning, educational technology, fairness, privacy, ensemble methods
Enhancing Image Classification and Detection with RetinaNet and SSD: A Comparative Analysis
Advancing Image Classification and Detection Using CNNs: High Accuracy, Scalability, and Generalization
The exponential growth of visual data has spurred significant advancements in image classification and detection, with artificial intelligence (AI) playing a pivotal role in achieving high accuracy and scalability. This study presents a CNN-based methodology for image classification and object detection, leveraging supervised learning to classify over 2,000 images of cats and dogs and detect objects within augmented datasets to enhance diversity. The proposed model achieved notable results, with accuracy metrics of 91.32% for cat classification and 90.17% for dog classification, alongside high detection precision. Precision and recall metrics further underscored the model’s effectiveness, yielding F1 scores of 91.31% and 89.93% for cat and dog classifications, respectively. Object detection capabilities were evaluated using models such as YOLO and Faster R-CNN, achieving a mean average precision (mAP) of 89.2% across diverse image datasets. Comparative analyses revealed that the CNN-based approach significantly outperformed traditional regression models, with improvements exceeding 25% in classification accuracy and robust object localization performance. The model’s scalability was demonstrated through consistent results across augmented datasets, achieving an average accuracy of 90.58%. Error analysis highlighted challenges in images with overlapping features, suggesting avenues for improvement through attention mechanisms. This study provides a comprehensive framework for advancing image classification and detection systems across diverse applications, such as medical imaging, autonomous driving, and e-commerce platforms.
Artificial Intelligence (AI), Artificial Neural Networks (ANN), YOLO and Faster R-CNN
Artificial Neural Networks in Cryptography: Applications, Challenges, and Future Directions for Secure Systems
Cryptography is a critical pillar of modern information security, safeguarding data, privacy, and communication in an increasingly interconnected world. With the advent of quantum computing and advanced persistent threats, traditional cryptographic systems face new challenges, necessitating innovative approaches. Artificial Neural Networks (ANNs), with their adaptability, learning capabilities, and computational efficiency, have emerged as a transformative tool in cryptography. This paper explores the applications of ANNs in key cryptographic domains, including key generation, encryption and decryption, and cryptanalysis. By leveraging their capacity to learn intricate patterns and optimize processes, ANNs offer significant advantages over traditional methods, such as enhanced randomness, scalability, and dynamic adaptability. However, their integration into cryptographic systems is not without challenges. Issues of interpretability, computational overhead, and susceptibility to adversarial attacks pose significant barriers to their widespread adoption. This study examines these challenges in detail and outlines promising future research directions, such as the integration of ANNs with post-quantum cryptography, the development of explainable AI models, and the enhancement of adversarial resilience. By bridging the fields of machine learning and cryptography, this research aims to contribute to the development of secure, efficient, and robust cryptographic systems that address the evolving demands of the digital age.
Artificial Intelligence (AI), Artificial Neural Networks (ANN), Cryptography, Cryptanalysis.
Transformer-Based Adaptive Examination Systems for Scalable, Accurate, and Personalized Assessments in Education
This study proposes an adaptive examination system that leverages transformer-based natural language processing (NLP) models to revolutionize educational assessments. Utilizing T5 for question generation and BERT for response evaluation, the system dynamically adjusts question difficulty based on learners' performance, ensuring a personalized and equitable experience. The adaptive framework demonstrated superior performance, achieving evaluation accuracies up to 18% higher than traditional machine learning models and 32% better than rule-based approaches. The system effectively generates diverse questions, spanning cognitive domains from recall to higher-order reasoning, and evaluates responses using semantic similarity, enabling nuanced grading. Experimental results show a balanced distribution of difficulty levels, enhancing student engagement without overwhelming or under-challenging learners. Despite computational overhead and potential biases in pre-trained models, the system addresses scalability challenges through model optimization techniques. This research highlights the transformative potential of AI in education, offering a scalable, efficient, and inclusive solution to modern assessment needs. Future work aims to expand multilingual capabilities and integrate advanced adaptive mechanisms for enhanced personalization.
Adaptive Examination System; Transformer-Based NLP; Question Generation; Response Evaluation; Adaptive Learning; Artificial Intelligence in Education; Semantic Similarity; Personalized Assessments; Natural Language Processing; Educational Technology
Advancing Healthcare Through Machine Learning: Opportunities, Challenges, and Solutions for Integration
This comprehensive review examines the adoption of Machine Learning (ML) in healthcare, exploring its opportunities, obstacles, and potential solutions. The primary aim is to thoroughly investigate ML's integration into medical practices, showcase its effects, and offer pertinent remedies. The study is driven by the need to understand the complex implications of ML's convergence with healthcare services. Through careful examination of current research, this approach illuminates the wide range of ML applications in disease forecasting and tailored treatment. The study's accuracy is rooted in its detailed analysis of methodologies, scrutiny of research, and extraction of crucial insights. The paper confirms ML's success in various medical care domains. ML algorithms, especially Convolutional Neural Networks (CNNs), have shown high precision in detecting diseases like lung cancer, colorectal cancer, brain tumors, and breast tumors. Besides CNNs, other algorithms including SVM, RF, k-NN, and DT have also shown effectiveness. Assessments based on accuracy and F1-score reveal satisfactory outcomes, with some studies surpassing 90% accuracy. This key finding emphasizes the remarkable precision of ML algorithms in diagnosing various medical conditions. This result indicates ML's potential to revolutionize traditional diagnostic methods. The discussion addresses challenges including data quality issues, security concerns, possible misinterpretations, and hurdles in implementing ML in clinical settings. To address these issues, multifaceted solutions are suggested, including standardized data formats, robust encryption, model interpretation, clinician education, and collaboration among stakeholders.
Disease Prediction; Healthcare; Machine Learning; Medical Treatment
Resource Allocation Strategies in IoT-Fog-Cloud Networks Using Machine Learning
The rapid proliferation of Internet of Things (IoT) devices and their integration with Fog and Cloud computing infrastructures demand efficient resource allocation strategies to handle dynamic workloads, diverse application requirements, and resource heterogeneity. This paper proposes a comprehensive machine learning-based framework for resource allocation in IoT-Fog-Cloud networks, addressing challenges such as energy efficiency, latency reduction, and load balancing. The framework combines predictive analytics, supervised learning, and reinforcement learning to dynamically allocate computational, storage, and network resources across IoT, Fog, and Cloud layers. Predictive models forecast workload distribution, enabling proactive allocation to avoid bottlenecks and service disruptions. Reinforcement learning optimizes real-time routing and resource utilization, enhancing system performance and reliability. Simulation results reveal that the proposed strategies achieve a 30% improvement in resource utilization, a 25% reduction in latency, and a 20% increase in energy efficiency compared to conventional approaches. The framework adapts effectively to varying network conditions and workload fluctuations, ensuring robust performance in large-scale, high-density deployments. Applications in healthcare, smart cities, and industrial automation demonstrate the versatility and scalability of the system, which efficiently handles latency-sensitive tasks while maintaining high throughput. This study highlights the potential of machine learning-driven resource allocation strategies to optimize IoT-Fog-Cloud networks for real-time applications. Future research will explore integrating federated learning and edge AI technologies to enhance data privacy, scalability, and adaptability, further advancing the applicability of these strategies in next-generation IoT ecosystems
IoT-Fog-Cloud networks, resource allocation, machine learning, energy efficiency, latency reduction, workload optimization
Link Stability Enhancement in IoT-Fog-Cloud Systems via Advanced Optimization
The increasing complexity and heterogeneity of Internet of Things (IoT) networks integrated with Fog and Cloud systems pose significant challenges to maintaining stable and reliable links. This research proposes an advanced optimization framework to enhance link stability in IoT-Fog-Cloud environments, addressing issues such as dynamic network conditions, fluctuating workloads, and limited resources. The framework leverages multi-objective optimization techniques to balance critical metrics, including link stability, energy consumption, latency, and throughput. A combination of heuristic algorithms and machine learning-based predictive models is utilized to dynamically adapt routing paths and resource allocation, ensuring robust connectivity across IoT, Fog, and Cloud layers. Simulation results demonstrate that the proposed approach achieves a 40% improvement in link stability, a 30% reduction in latency, and a 20% enhancement in energy efficiency compared to conventional methods. The system adapts effectively to varying network densities and mobility patterns, ensuring reliable communication even under challenging conditions. By proactively monitoring link quality and predicting potential failures, the framework prevents service disruptions and optimizes resource usage across the network. This study underscores the potential of advanced optimization techniques in improving the reliability and efficiency of IoT-Fog-Cloud systems, making them suitable for latency-sensitive and mission-critical applications such as healthcare, industrial automation, and smart transportation. Future research will focus on real-world deployments and incorporating emerging technologies like blockchain and 6G to further enhance the scalability, security, and adaptability of the proposed system
IoT-Fog-Cloud systems, link stability, advanced optimization, energy efficiency, latency reduction, dynamic routing
Adaptive Stock Market Prediction Using LSTM and Sentiment Analysis for Volatile Market Conditions
The objective of this research is to design a stock market prediction model using Long Short-Term Memory (LSTM) neural networks, aimed at addressing significant limitations in existing systems, which often struggle with sudden market volatility and external factors such as economic or geopolitical events. Present systems primarily rely on historical price data and technical indicators, which can lead to inaccurate predictions during unpredictable market conditions. This research proposes a methodology that combines LSTM models with real-time sentiment analysis derived from financial news and social media, creating a more adaptive and dynamic approach to stock market forecasting. The model was trained on historical stock data and tested for its predictive capabilities, achieving 88.1% accuracy and a 9.2% cumulative return in stable market conditions. The findings highlight the model’s ability to perform well under typical market conditions, but also emphasize the need for further refinement to handle high volatility and market disruptions effectively. One of the notable achievements of this study is the successful integration of sentiment data, which significantly improves prediction accuracy compared to traditional models. However, the study also points out the reliance on advanced computational resources, such as high-performance GPUs, for optimal model training. The research concludes that, while effective in many scenarios, the model can be further enhanced by expanding data sources and optimizing real-time performance for more dynamic market conditions.
Stock market prediction, LSTM, sentiment analysis, volatility, machine learning, financial forecasting, real-time data integration
Energy-Efficient Routing in IoT Networks with ABC Optimization and Machine Learning for Smart City Infrastructure
The exponential growth of Internet of Things (IoT) networks within smart city infrastructures has led to significant challenges in maintaining energy efficiency and optimizing network performance. As IoT devices are often resource-constrained, energy-efficient routing becomes critical to ensure network longevity and reliability. This paper proposes an Energy-Efficient Routing protocol that integrates Artificial Bee Colony (ABC) optimization and machine learning techniques to enhance the performance of IoT networks in smart cities. The ABC algorithm, inspired by the foraging behavior of honeybees, optimizes the selection of energy-efficient routes, reducing energy consumption while maintaining data transmission reliability. Simultaneously, machine learning models are employed to predict network conditions and traffic patterns, enabling the dynamic adjustment of routing decisions to avoid congestion and extend network lifetime. This hybrid approach allows the network to adapt to varying data loads and node mobility, ensuring that energy resources are used efficiently while maintaining high throughput and low latency. Simulation results demonstrate that the proposed system outperforms traditional routing protocols in terms of energy consumption, network stability, and scalability. By leveraging the strengths of ABC optimization and machine learning, the proposed routing protocol provides a robust and scalable solution for IoT networks in smart city environments, offering improved energy efficiency and network performance. This work contributes to the development of sustainable smart city infrastructures, where energy efficiency and data reliability are crucial for supporting large-scale IoT deployments.
Energy-Efficient Routing, Internet of Things (IoT), Artificial Bee Colony (ABC) Optimization, Machine Learning, Smart City Infrastructure, Network Performance Optimization
Scalable and Secure Real-Time Chat Application Development Using MERN Stack and Socket.io for Enhanced Performance
This paper focuses on the development of a real-time chat application using the MERN stack (MongoDB, Express.js, React, Node.js) and Socket.io to address the limitations present in existing chat systems. The primary objectives of this research include improving scalability, enhancing security, and delivering an optimized user experience. Traditional chat applications often struggle with performance bottlenecks, especially under high user loads, and face security vulnerabilities, leading to unreliable communication and user dissatisfaction. These issues stem from inadequate architecture and the use of outdated technologies. The proposed methodology leverages the MERN stack for creating a robust, dynamic web platform, while Socket.io enables real-time, bidirectional communication between users with low latency. Our findings show that the application achieves significant improvements in user satisfaction and system performance, with a 25% increase in response speed and a 30% reduction in server load compared to traditional systems. The system’s ability to scale efficiently, supported by token-based authentication for secure user management, ensures data security and integrity, even with a growing number of users. Notably, the chat application maintains 90% uptime during peak usage and offers an intuitive, responsive user interface, enhancing the overall user experience. Key achievements include the successful integration of real-time messaging capabilities, improved system responsiveness, and enhanced security features. Future work may focus on adding cross-platform compatibility, further optimizing server resource management, and implementing advanced encryption mechanisms for improved privacy.
Real-time chat, MERN stack, Socket.io, scalability, security, low latency, token-based authentication, real-time messaging, user experience.
Detection of Cardiovascular Diseases in ECG Images Using Machine Learning and Deep Learning Techniques
This paper focuses on the detection of cardiovascular diseases (CVDs) using advanced machine learning and deep learning techniques applied to ECG images. Traditional manual ECG interpretation methods, often prone to human error, lack scalability and consistency, making them less effective for widespread clinical use. This research addresses these limitations by proposing an automated system based on the SSD MobileNet V2 architecture. The system is designed to accurately detect and classify key cardiovascular conditions, including myocardial infarction, abnormal heartbeat, and previous history of MI, using 12-lead ECG images. The automated approach not only reduces the risk of human error but also enhances the efficiency of diagnosis by allowing real-time analysis and scalability. One of the key challenges with existing systems is the variability in ECG signal quality and the lack of large, annotated datasets necessary for training deep learning models. This study mitigates these issues through robust data preprocessing techniques and by utilizing a diverse dataset of ECG signals, ensuring the model's ability to generalize well across different conditions. The methodology involves the application of deep learning techniques for feature extraction, followed by model training, validation, and performance evaluation using metrics such as accuracy, precision, recall, and F1-score. The model achieved a notable accuracy of 97.5%, indicating its effectiveness in detecting cardiovascular conditions compared to traditional approaches. The study's findings highlight the potential for deep learning models to significantly improve diagnostic accuracy in healthcare settings. Future work will focus on increasing model interpretability, ensuring its applicability in real-world clinical environments, and expanding the dataset to include more rare cardiovascular conditions.
Cardiovascular disease detection, deep learning, SSD MobileNet V2, ECG classification, machine learning, healthcare diagnostics.
Blockchain Split-Join Architecture: A Novel Framework for Improved Transaction Processing
The rapid expansion of blockchain technology necessitates innovative solutions to enhance transaction processing efficiency and scalability. This paper presents the Blockchain Split-Join Architecture, a novel framework designed to significantly improve transaction processing. By partitioning the blockchain into smaller, manageable segments (splits) that operate in parallel, this architecture reduces processing latency and increases throughput. These segments are subsequently merged (joined) to maintain data integrity and consistency across the network. Our framework addresses key challenges such as scalability, resource optimization, and transaction speed, making it ideal for high-demand applications. Through rigorous analysis and simulations, we demonstrate that the Split-Join Architecture outperforms traditional blockchain systems in terms of transaction processing time and network efficiency. This research offers a robust solution for the development of more scalable and efficient decentralized applications, providing valuable insights into advanced blockchain and distributed system design
Blockchain, Split-Join Architecture, Transaction Processing, Scalability, Data Integrity, Decentralized Applications
ShiftSense: A Unified Framework for Comprehensive Detection of Gradual and Abrupt Concept Shifts in Streaming Data
In the rapidly evolving landscape of streaming data, the ability to detect concept shifts—both gradual and abrupt—is paramount for maintaining the accuracy and relevance of real-time analytics. This research introduces ShiftSense, a unified framework designed to comprehensively identify and adapt to these concept shifts in continuous data streams. ShiftSense leverages a combination of advanced machine learning algorithms and adaptive mechanisms to provide robust and scalable detection capabilities. The framework is evaluated using diverse datasets, including financial transactions, network traffic, and environmental sensor data, to demonstrate its versatility and effectiveness. Key performance metrics such as detection accuracy, false positive rate, detection delay, and computational efficiency are utilized to assess the model's performance. Experimental results indicate that ShiftSense outperforms traditional methods, offering superior detection of both gradual and abrupt shifts while minimizing false positives and detection delays. The adaptive nature of ShiftSense ensures continuous learning and adjustment, making it highly applicable in dynamic environments such as financial monitoring, network security, and environmental sensing. This study underscores the potential of ShiftSense to enhance the reliability and accuracy of real-time data stream analytics, providing a significant advancement in the field of concept shift detection.
concept shift detection, streaming data, gradual changes, abrupt changes, machine learning, real-time analytics
Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0
Hybrid Quantum-Classical Algorithms for Large-Scale Optimization Problems
The integration of quantum and classical computing presents a promising avenue for addressing large-scale optimization problems, which are pivotal in various scientific and industrial domains. This research paper, titled "Hybrid Quantum-Classical Algorithms for Large-Scale Optimization Problems," explores innovative methods to leverage the strengths of both quantum and classical computational paradigms to enhance optimization processes. The primary objective of this study is to develop and evaluate hybrid algorithms that combine quantum computing techniques with classical optimization methods to solve complex optimization problems more efficiently than traditional approaches. By utilizing quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) and classical methods like gradient descent and evolutionary algorithms, this research aims to harness the complementary advantages of both computational models. The methodology involves designing hybrid frameworks that seamlessly integrate quantum and classical algorithms. The performance of these frameworks is assessed through extensive simulations and real-world problem instances, focusing on metrics such as solution quality, computational efficiency, and scalability.Key findings indicate that the proposed hybrid algorithms significantly outperform classical-only approaches in terms of both solution quality and computational speed, especially for large-scale and highly complex optimization problems. The results demonstrate the potential of hybrid quantum-classical algorithms to tackle optimization challenges that are currently intractable for classical methods alone.In conclusion, this research underscores the transformative potential of hybrid quantum-classical computing in optimization. The findings suggest that these hybrid approaches could be effectively applied to various fields, including logistics, finance, and artificial intelligence, thereby paving the way for more efficient and scalable solutions to large-scale optimization problems
Quantum computing, classical optimization, hybrid algorithms, large-scale optimization, Quantum Approximate Optimization Algorithm (QAOA), computational efficiency
SkinNet360: A Comprehensive 3D Imaging and Analysis System for Skin Cancer Detection Using Deep Learning
Skin cancer is a prevalent and potentially deadly condition, where early detection is crucial for effective treatment and improved patient outcomes. Traditional diagnostic methods are often subjective and time-consuming, relying heavily on clinical expertise and two-dimensional imaging. This research introduces SkinNet360, a comprehensive 3D imaging and analysis system designed to enhance skin cancer detection using deep learning techniques. The primary objective of this study is to evaluate the efficacy of SkinNet360 in accurately identifying malignant and benign skin lesions through advanced 3D imaging and deep learning algorithms. The methodology involves capturing high-resolution 3D images of skin lesions and processing these images with a specialized deep learning model. The system leverages convolutional neural networks (CNNs) for feature extraction and classification, integrated with 3D imaging to provide more detailed and accurate analysis. Data augmentation and advanced preprocessing techniques were applied to enhance model robustness and performance. Results from extensive testing demonstrate that SkinNet360 significantly improves the accuracy of skin cancer detection compared to traditional 2D imaging methods. The system achieved high precision, recall, and F1 scores, indicating its reliability and effectiveness in distinguishing between malignant and benign lesions. The 3D imaging component provided additional depth and detail, contributing to the enhanced diagnostic capability of the system. In conclusion, SkinNet360 represents a significant advancement in the field of skin cancer detection, offering a more accurate and comprehensive diagnostic tool. Its integration into clinical practice could support dermatologists in making more informed decisions, ultimately improving patient care. The study underscores the potential of combining 3D imaging with deep learning for medical diagnostics and highlights the need for continued research and development in this area.
Skin cancer detection, SkinNet360, 3D imaging, deep learning, CNN, medical diagnostics.
Multimodal Fusion for Robust Banana Disease Classification and Prediction: Integrating Image Data with Sensor Networks
Banana diseases pose a significant threat to global food security and farmer livelihoods. Traditional visual inspection methods for disease detection are subjective, time-consuming, and ineffective in early stages. To address these limitations, this study proposes a novel framework utilizing multimodal fusion for robust banana disease classification and prediction. We aim to integrate image data captured from banana plants with environmental data collected from sensor networks. This combined approach offers a more comprehensive understanding of disease status compared to relying solely on visual information. By leveraging machine learning, we hope to develop a model that achieves improved disease classification accuracy and explores the potential for early disease prediction. The proposed framework utilizes a dataset encompassing both image data and sensor network readings. We evaluate the performance of our model using metrics like accuracy, precision, and recall for disease classification. This data-driven approach has the potential to revolutionize disease management in banana crops, leading to increased yield, reduced economic losses for farmers, and enhanced food security.
Banana disease classification, multimodal fusion, image data, sensor networks, early disease prediction, sustainable agriculture.
Leveraging Pre-Trained Vision for Enhanced Real Time Pedestrian Behavior Prediction at Zebra Crossings
Ensuring real-time pedestrian safety at zebra crossings requires accurate prediction of their behavior in dynamic traffic conditions. Existing static measures often fail to adapt to these complexities. This paper proposes a novel approach for Leveraging Pre-Trained Vision for Enhanced Real-Time Pedestrian Behavior Prediction at Zebra Crossings. Traditional methods and models trained from scratch necessitate vast amounts of labeled data, hindering implementation. We address this by utilizing pre-trained convolutional neural networks (CNNs) with established feature extraction capabilities. These pre-trained models are then fine-tuned on a focused dataset of pedestrian behavior at crossings, allowing them to specialize in this specific environment. This significantly reduces the data collection burden while improving prediction accuracy compared to traditional methods. The proposed approach is evaluated using a pedestrian behavior dataset at zebra crossings, with performance measured by established metrics. Our research demonstrates that leveraging pre-trained vision models achieves superior real-time prediction accuracy, ultimately enhancing pedestrian safety at zebra crossings. This approach is applicable to intelligent traffic management systems, enabling proactive interventions to prevent accidents.
Pedestrian behavior prediction, real-time safety, pre-trained vision, convolutional neural networks, zebra crossings, intelligent traffic management.
Revolutionizing PTSD Detection and Emotion Recognition through Novel Speech-Based Machine and Deep Learning Algorithms
Early and accurate detection of Post-Traumatic Stress Disorder (PTSD) is crucial for effective treatment. However, traditional methods relying on self-reporting and clinical interviews are subjective and time-consuming. This paper proposes a novel approach for revolutionizing PTSD detection and emotion recognition through advanced speech analysis. Speech, rich with emotional cues, offers valuable insights into an individual's mental state. Existing methods often struggle to capture the subtle emotional variations associated with PTSD. We address this by introducing a speech-based framework that utilizes machines and deep learning algorithms. This framework extracts and interprets emotional signatures present in speech patterns using novel feature extraction techniques and deep learning architectures specifically designed for PTSD-related cues. The proposed approach is evaluated on a comprehensive speech dataset, with performance measured by established metrics like accuracy, precision, and recall. This research demonstrates that the synergistic combination of machine and deep learning algorithms with speech analysis significantly improves objective and efficient PTSD detection. This approach has the potential to be applied in clinical settings and other situations where early identification of PTSD is crucial.
Speech analysis, PTSD detection, machine learning, deep learning, emotional recognition, mental health assessment.