International Journal of Data Informatics and Intelligent Computing
Edge-Enabled Pattern Recognition Architecture for Energy-Efficient Intelligent Computing in Next-Generation Wearable Health Devices
Wearable health devices have moved from step-counting toys to genuine medical instruments, but the gap between what they can sense and what they can decide on-device is still wide. Most clinical-grade pattern recognition still happens in the cloud, which costs latency, privacy, and battery life. In this paper, we propose NeuroPulse-Edge, a lightweight on-device architecture that combines spiking neurons with a spike-only attention block to deliver real-time anomaly detection on wearable biosignals at a sub-2 mW power budget. The system encodes ECG, PPG, accelerometer, and skin-temperature streams into spike trains through a delta-modulation front end, runs them through a stack of leaky integrate-and-fire neurons, a spike convolution block, and a binary spiking-attention block, and ends in a small INT8 classifier. The attention block is the key piece — it preserves the long-range temporal sensitivity that arrhythmia recognition needs, but does it with logical AND-OR and popcount instead of floating-point multiply-accumulates, so it costs almost nothing to run on a microcontroller. We evaluate the system on four 2025–2026 wearable benchmarks (CACHET-CADB, WildPPG, the 2025 multimodal stress dataset, and a 2026 long-term smartwatch arrhythmia release) against five strong baselines. NeuroPulse-Edge reaches 92.1% accuracy and a 0.921 macro F1 on a five-class arrhythmia task, draws 1.17 mW continuously on an ARM Cortex-M4 testbench, and answers in 14 ms per inference — an 8.2× power reduction and a 4.4× latency reduction over the strongest deep-learning baseline at the same accuracy, with every gain confirmed at p < 0.01 (Wilcoxon signed-rank with Holm–Bonferroni correction). Embedded analyses establish a surrogate-gradient convergence bound and a spike-sparsity energy bound for the architecture. The findings indicate that spike-based attention is a practical building block for clinical-grade wearable AI.
Copyright (c) 2026 Anirudha Gaikwad, Atit Gaikwad, Dheeraj Lokhande https://creativecommons.org/licenses/by-sa/4.0
Hybrid Quantum-Inspired Intelligent Computing Model for Real-Time Optimization of Massive Heterogeneous Climate Datasets
The volume of climate observations is now growing faster than classical optimizers can process it. With petabyte-scale reanalysis archives and multi-source satellite streams in routine operation, the training budget of large climate models has become a genuine bottleneck for real-time forecasting. In this paper, we propose a hybrid quantum-inspired intelligent computing model (HQI-Opt) for fast and energy-efficient optimization of deep forecasting networks trained on heterogeneous climate tensors. The method combines a classical gradient branch, driven by a parameter-shift-style analytic estimator over a latitude-weighted loss, with a quantum-inspired branch that encodes the parameter state into an Ising/QUBO representation and applies a quantum rotation-gate mixing step modulated by an adiabatic schedule. A training loop with three concurrent branches is developed and evaluated on an ERA5 subset following the WeatherBench 2 protocol. The method is compared against six widely used optimizers: SGD with momentum, Adam, AdamW, LAMB, Lion, and Sophia. Across 69 variables and four lead times, HQI-Opt reaches the target latitude-weighted loss in approximately 43% fewer epochs; the training energy per run falls from 29.8 to 17.6 kWh, the associated carbon footprint is reduced by 41%, and the RMSE at the 3-day lead on geopotential height at 500 hPa improves from 140.8 to 137.3 m²/s². A convergence analysis is provided showing that the iterates approach a stationary point of the latitude-weighted loss as the annealing schedule decays. The results indicate that a classically simulated quantum-inspired update step, with no quantum hardware in the loop, is already a viable building block for real-time climate informatics pipelines.
Copyright (c) 2026 Anirudha Gaikwad, Atit Gaikwad, Shardul Singh Chauhan https://creativecommons.org/licenses/by-sa/4.0
Development and Evaluation of Life-Threatening Statement Detection Models for Social Media Platforms Using Supervised Machine Learning Algorithms
In today’s digital age, threats to life have become alarmingly common, infiltrating everything from social media platforms to personal communications. About 11.3% of people experienced suicidal ideation over the past year, 5.5% of adults in the United States reported serious suicidal thoughts in the past year, and 10-14% of children and adolescents experienced cyberbullying globally. This study developed machine learning models to detect life-threatening statements across different model categories by employing hyperparameter tuning and Adam optimization. Six models were built using RF, SVM, FFNN, LSTM, pre-trained BERT, and T5, and evaluated using benefit and cost measures and confusion matrices. The outcome indicates that the six models performed well, but the FFNN model outperformed all other models by achieving an accuracy of 98.86%, precision of 98.86%, MCC of 97.72%, Recall of 98.86%, MAE of 0.0113, and RMSE of 0.1067. It also achieved the highest number of accurately classified instances and the lowest number of misclassified instances, which demonstrates that the FFNN model exhibits robustness and precision, making it highly reliable for detecting life-threatening statements compared to other models. Having achieved a very high accuracy, the method still lacks an in-depth understanding of contextual information. Therefore, further studies may focus on incorporating contextual information for more accurate classification of life-threatening statements and integrating the developed model into a real-time detection system.
Copyright (c) 2026 Ifeoluwa Michael Olaniyi, Jide Ebenezer Taiwo Akinsola, Maryann Gold Azodo, Fathia Oluwadamilola Onipede, Emmanuel Ajayi Olajubu, Ganiyu Adesola Aderounmu https://creativecommons.org/licenses/by-sa/4.0
Optimized Large-Scale Data Analytics Using Advanced Computational Methods in Cloud Computing Frameworks
In today's data-driven world, the exponential growth of information from diverse sources necessitates robust, scalable, and efficient computational solutions. Large-scale data analytics is pivotal in extracting valuable insights from massive datasets, facilitating informed decision-making across industries. This research proposes an advanced computational framework that integrates Deep Learning (DL) techniques to optimize large-scale data analytics in cloud computing environments. The proposed system leverages the Adaptive Satin Bowerbird Optimizer-driven Dynamic Capsule Network (ASB-Dynamic-CapsNet) to automate workload distribution while minimizing execution time and resource utilization. The data collection process involves gathering large-scale datasets from multiple sources, including cloud platforms, Internet of Things (IoT) devices, transactional systems, social media, and enterprise databases. Preprocessing steps like handling missing values and standardizing data ensure data quality and consistency. The framework is evaluated using key performance metrics like Central Processing Unit (CPU) utilization (86%), Random Access Memory (RAM) utilization (68%), and training accuracy (95.9%). Experimental results demonstrate that the ASB-Dynamic-CapsNet model outperforms alternative scheduling approaches and significantly improves the performance of CPU, RAM, and training accuracy compared to traditional algorithms. Overall, the findings highlight the efficacy of DL-driven scheduling in optimizing cloud-based data analytics, providing a scalable and efficient solution for high-volume workloads in modern computing environments.
Copyright (c) 2026 Aditya Gupta, Sai Kiran Oruganti https://creativecommons.org/licenses/by-sa/4.0
Recruitment of research lecturers in Madagascar by a multicriteria group decision support system
The recruitment of permanent Lecturers for higher education is a major challenge for all universities. Given the absence of defined hiring criteria, this article will suggest some and support the recruitment committees at the Ministry of Higher Education in Madagascar. Three methods will be implemented in a decision support system. Each recruitment committee member will assess the importance of each criterion. The Rank Sum weight method is used to determine the weightings of the criteria. The Combined Compromise for Ideal Solution (CoCoFISo) method ranks the candidates. The Minimum of rank (MIRA) method will aggregate the several rankings of candidates. Three fundamental criteria were selected: experience in university teaching, time elapsed since obtaining the teacher's opinion, and number of research publications. These criteria were assessed according to their importance by six members of the recruitment committee. The weights assigned to the criteria vary from one criterion to another and from one decision-maker to another. Applying these weights to the CoCoFISo method resulted in six rankings of the candidates. The rankings of the candidates varied from one decision-maker to another, except for the candidate in first place and the candidate in last place. The MIRA method successfully merged these six rankings into a final ranking for each candidate. The Kendall rank correlation coefficient between the MIRA method and the six rankings obtained using the CoCoFISo method varies between 0.80 and 0.97, thus confirming the validity of the rankings. This new technique will solve the challenge of hiring temporary lecturers as permanent lecturers in Madagascar universities, thanks to the transparency of the process, and will assist the Ministry of Higher Education in its decision-making.
Copyright (c) 2026 Rolin Gabriel Rasoanaivo, Joseph Alphonse Tata, Hantamalala Nirinarivelo https://creativecommons.org/licenses/by-sa/4.0
A Data-Informatics-Oriented CNN-Based Intelligent Model for Handwritten Mathematical Symbol Recognition
Handwritten mathematical symbol recognition remains a challenging problem due to variations in writing styles, stroke structures, and visual similarities among symbols, which often reduce classification accuracy. This study proposes a data-informatics-oriented convolutional neural network (CNN) model for robust recognition of handwritten mathematical symbols. The research adopts a supervised experimental design using a balanced dataset consisting of six mathematical symbol classes. A systematic preprocessing pipeline including image resizing, normalization, and structured dataset partitioning is implemented to ensure data consistency and improve feature learning. The CNN model is implemented in MATLAB and optimized using stochastic gradient descent with momentum. Model performance is evaluated using confusion matrix–based metrics, including accuracy, precision, recall, and F1-score, along with computational time analysis. Experimental results demonstrate stable performance across multiple experimental runs, achieving an average accuracy of 97.08%, precision of 97.10%, recall of 97.08%, and F1-score of 97.07%. Confusion matrix analysis indicates that most handwritten symbols are correctly classified, with only minor misclassifications occurring among visually similar operators. These results confirm the effectiveness of integrating data informatics principles with CNN-based feature learning for handwritten mathematical symbol recognition. The proposed framework provides a reliable foundation for intelligent systems supporting digital education, automated assessment, and mathematical document digitization.
Copyright (c) 2026 Muhammad Athoillah, Rani Kurnia Putri, Fenny Fitriani, Prayogo https://creativecommons.org/licenses/by-sa/4.0
Integrating Federated Transfer Learning for Secure Multi-Tenant Data Management in Decentralized Cloud Infrastructures
Ensuring secure multi-tenant data management while enabling cross-domain knowledge sharing is challenging in decentralized cloud infrastructures. Traditional centralized learning methods pose risks like data leakage and non-compliance with privacy regulations. To address these concerns, this research integrates Federated Transfer Learning (FTL) for secure and efficient multi-tenant data management. The proposed approach employs Federated Learning (FL) to enable collaborative model training while keeping raw data localized, preserving privacy. Additionally, BERT-based transfer learning improves knowledge sharing by adapting pre-trained models to tenant-specific tasks, enhancing efficiency and reducing computational overhead. A tenant-aware security mechanism dynamically assesses trust levels to ensure secure workload allocation and mitigate risks. Furthermore, a decentralized aggregation strategy enhances data privacy and prevents single-point failures, improving system robustness. The framework was evaluated using real-world datasets, assessing privacy, adaptability, communication overhead, and computational efficiency. Experimental results demonstrate that FTL-driven decentralized architectures achieve low latency (6ms), high throughput (850000 requests/sec at 10000 tenants), better utilization of resources (83%), and good security compliance (9.5/10). Consistency models also come with a 50% overhead for strong consistency, which shows the trade-offs involved in ensuring data consistency. The results confirm the proposed model as a scalable, efficient, and privacy-preserving solution for multi-tenant cloud environments.
Multi-Agent AI Systems for Autonomous and Context-Aware Data Orchestration in Hybrid Cloud Platforms
Hybrid cloud platforms face challenges in data orchestration due to dynamic resource allocation and workload changes. The framework uses multiple reinforcement learning agents equipped with context-awareness to autonomously manage data orchestration tasks. This investigation aims to develop an artificial intelligence (AI)-based data orchestration model using a Flexible Binary Spider Wasp Algorithm-enriched Double Deep Q-Network with Markov Decision Process (FBSWA-DDQN-MDP) to autonomously manage and optimize data placement, migration, and processing in hybrid cloud platforms. Data is collected from simulated hybrid cloud environments with varying workloads and resource availability. To ensure the dataset is prepared for modeling tasks, it has been preprocessed to eliminate missing values, normalize continuous features, and encode categorical variables. Principal Component Analysis (PCA) was used for feature extraction to improve computational efficiency. Using Python, simulations showed that the FBSWA-DDQN-MDP model outperformed traditional techniques with average energy consumption (AEC) (17.0 J), and average renting charge (ARC) (0.0006 cost) obtained at 1.8 Weight ω₂, normalized reward mean and standard deviation (SD) (0.97±0.01) values achieved at 1800 no of training episodes with adaptive response times under dynamic workloads. The proposed multi-agent AI system significantly improves data orchestration in hybrid cloud environments.
Wavelet-Based Intelligent Framework for Network Traffic Anomaly Detection in IoT Embedded Systems
The rapid growth of the Internet of Things (IoT) and embedded systems has increased the vulnerability of network infrastructures to cyber-attacks, necessitating efficient real-time anomaly detection methods. In this study, we introduce a primary IoT network traffic dataset generated through controlled simulation of normal and malicious behaviors. Both time-domain features, including packet size, inter-arrival time, protocol type, and TCP flags, and frequency-domain features, such as spectral entropy and band energy derived via wavelet transform, were extracted to capture comprehensive traffic characteristics. These features were used to train a hybrid deep learning model, the Adaptive Differential Evolution Weighted Deep Belief Network (ADE-WDBN), which combines deep hierarchical feature learning with evolutionary weight optimization to enhance detection accuracy and computational efficiency. Experimental results demonstrate that ADE-WDBN outperforms traditional machine learning models and conventional deep learning approaches, achieving an accuracy of 98.37%, precision of 97.65%, recall of 98.02%, and F1-score of 97.83%. The low variability in performance across cross-validation folds indicates the model's robustness and generalizability. This research contributes a novel IoT traffic dataset and a cost-effective, adaptive anomaly detection framework capable of detecting subtle anomalies.
Copyright (c) 2026 Praveen Gujjar, Raghavendra M Devadas, Nikitha K https://creativecommons.org/licenses/by-sa/4.0
Optimized Fault-Tolerant Placement of Microservices in Distributed Fog Networks
We propose a fault-tolerance aware placement algorithm for microservices in distributed fog environments that combines a reactive Dynamic Connections Load Balancer (DCLB) with a proactive, fault-threshold driven migration and backup-predetermination mechanism. The DCLB places services only on healthy nodes by using active-connection-based load metrics, while the proactive module preassigns backup nodes for critical services and triggers rapid migration when a node crosses a fault threshold. We evaluate the approach using extensive iFogSim simulations on a smart-home management microservice composition under controlled failure injections. This paper presents a fault–tolerance–aware microservice placement strategy that integrates health-aware primary placement with proactive backup node predetermination. Experimental results show that the strategy effectively limits service disruption, achieving an average service downtime of less than 30% of the total simulation time across failure scenarios. System reliability remains consistently high, with values exceeding 0.85 in single-failure cases and remaining above 0.75 under multiple failures. Resource utilization across fog nodes remains balanced, with moderate memory usage and stable energy consumption. These results demonstrate that proactive, health-aware placement improves service continuity and resilience in fog-based microservice deployments.
Copyright (c) 2025 Shally Gupta, Nanhay Singh https://creativecommons.org/licenses/by-sa/4.0
Convolutional Neural Network-Based Classification of Skin Lesions Using Dermoscopic Images
Skin cancer is one of the most common and deadliest diseases globally, in which early detection may help improve patient survival rates. In this paper, an automatic skin cancer classification framework based on deep learning using dermoscopic images is presented. Various convolutional neural network (CNN) models were trained and tested on an annotated skin lesion dataset, including MobileNet, EfficientNetB0, VGG16, ConvNet, and ResNet50. Metrics of the models were calculated by using Micro-averaged metrics to assess the general effectiveness for all the classes. ResNet50 obtained the best performance against all tested models with a micro-average accuracy of 97.75%, precision of 97.79%, recall of 97.75%, and F1 score of 97.76%. Our results indicate that the model enables accurate, consistent, and balanced classification of different skin lesion categories, including actinic keratoses, basal cell carcinoma, benign Keratosis-like lesions, dermatofibroma, and melanoma. For real-world utilization, the top performer, the ResNet50 model, was implemented in a Streamlit-based web application, which is designed to automatically predict skin diseases in dermoscopic images that were uploaded. Experimental results show that deep residual learning is effective for improving the classification performance of skin lesions, and it can become an assistive decision-making tool for dermatologists in early diagnosis and clinics.
Copyright (c) 2026 Sujatha Krishna, Osamah Ibrahim Khalaf https://creativecommons.org/licenses/by-sa/4.0
AI-Based Optimal Energy Harvesting to Reduce Electromagnetic Radiation from GSM and WiFi at ATBU Yalwa Campus
The tuning of LC impedance matching networks for Wireless Power Transfer (WPT) systems and the optimisation of Microstrip Patch Antennas (MPAs) are thoroughly examined in this work. A workable approach that achieves dependable convergence without requiring intricate mathematical modelling is put forth. This study examined the health hazards linked to rising mobile phone use and the positioning of mobile base stations in Nigeria's most populated regions. At the Abubakar Tafawa Balewa University (ATBU) Yelwa campus, measurements of power density, electric field strength, and magnetic field strength were made using TM-195 meter technology and OpenSignal software. Within 16.48 meters of the main coverage area, power density values were found to exceed the permitted limit of 0.001 W/m² by up to 4.81 times, posing serious health and safety risks to the local population. Additionally, it was shown that scavenging low-density RF signals from 0.5 to 3 GHz is feasible. At a resonant frequency of 1.8 GHz, optimised rectenna designs achieved peak voltages of 1.2 V and peak currents of 120 μA. The measurement setup indicates that the rectifier achieves 69.4% efficiency at +10 dBm, 20% from +5 dBm to +13 dBm, with output voltages ranging from 27.9 mV to 104.1 mV. Harvesting energy in the form of power to supply loads of the required rating allowed for a 99.88% power reduction, reducing 7,583.55 µW from the TM-195's maximum of 5810 µW to a simulated 306.38 µW. By lowering exposure to electromagnetic radiation, this optimisation improved energy efficiency and preserved operational efficacy while lowering possible health risks.
Copyright (c) 2025 Ishaku T. Balami, Okpo U. Okereke, Ejike C. Anene, Hassan Aliyu https://creativecommons.org/licenses/by-sa/4.0
Orchestrating Data Leaks in Multi Cloud: Projects in Controlled Environments Approach
Hybrid cloud environments offer organizations essential flexibility and scalability by integrating private and public infrastructure, but this complexity introduces the core research problem: a greatly expanded attack surface and inconsistent application of security controls, leading to critical vulnerabilities and data exposure risks. The study employed a mixed-methods design, analyzing extensive secondary data through quantitative findings combined with qualitative thematic analysis of security reports. The key results quantified the main contributors to data leakage, finding misconfigured APIs as the leading threat (35%), followed by endpoint vulnerabilities (30%), and encryption failures (25%). While efficient AES-256 encryption is common, the research noted critical challenges in key management and the continued use of outdated protocols. Furthermore, inadequate use of secure API management frameworks was highlighted as significantly increasing the risk of unauthorized access. The implications of these findings led to the development of a project management framework that addresses these gaps. This framework integrates cloud-specific solutions—including end-to-end encryption, secure API configurations, automated compliance monitoring, and advanced endpoint detection and response (EDR) tools—within a PRINCE2 method. This proposed multi-layer approach provides proactive risk management, effectively bridging architectural gaps and improving the overall security posture against technical and compliance issues across the hybrid environment.
Copyright (c) 2025 Rachel John Robinson https://creativecommons.org/licenses/by-sa/4.0
Machine learning approaches using correlation filters for heart failure diagnosis: a comparative study of supervised techniques
This study addressed how machine learning could be used to detect factors that influenced the probability of survival of patients with heart failure, based on a database with 12 attributes collected from 299 different patients. Along with applying correlation filters, to obtain attributes that may be more important in a certain way for the disease, further assisting in new forms of treatment, and helping to reduce costs for diagnosis. In this study, we evaluated the accuracy of eight data mining algorithms for predicting heart disease using the heart failure dataset. We implement a methodology that includes 100 simulations with 10 correlation filter variations to ensure reliable and robust results. Among the eight classification algorithms, Support Vector Machine and Random Forest provided the best accuracy (84.18%). Considering the averages for all correlation filter variations, the Random Forest algorithm had the highest average (80.07%), and the Probabilistic Neural Network had the worst performance (69.43%). Analysis of other evaluation metrics revealed that our approach using a Multilayer Perceptron with a correlation filter (0.10) was the best alternative with 83.50% accuracy. Therefore, the diagnosis of cardiac insuﬀiciency required only four attributes: creatinine phosphokinase, serum sodium, sex, and hospitalization time. This streamlined approach not only saved time and resources but also enhanced diagnostic eﬀiciency, unlike previous works that use all base attributes for classification. Our findings suggest that data mining techniques can be a useful tool for predicting heart disease, and the proposed method.
Copyright (c) 2025 Vitória S. Souza, Danielli A. Lima https://creativecommons.org/licenses/by-sa/4.0
Deep Reinforcement Learning for Robust USVs Navigation in Diverse Environmental Scenarios
Collision avoidance is essential for the safe operation of unmanned surface vehicles (USVs) in marine environments. While existing studies have addressed USV collaboration and navigation, they often overlook environmental challenges. In this research, we develop a novel deep reinforcement learning model and train USVs to navigate safely under different situations, such as rain, wind, and their combined factors that can disrupt control and increase collision risk. We apply Generative Adversarial Imitation Learning (GAIL) and Proximal Policy Optimization (PPO) to improve the agent's performance using expert demonstrations. The experimental results demonstrate that the proposed framework significantly outperforms the baseline across multiple metrics: the maximum episode length increased from 15 to 350 steps, the cultivated reward improved by 158%, the extrinsic reward increased by 175%, and the number of collisions decreased by 75-78% across all environmental conditions. Moreover, the policy loss stabilized after 1.1 million training steps, confirming efficient convergence. These results show that our model performs better than the baseline model in mean reward, episode length, value estimates, and collision reduction. In the end, some concluding remarks and future directions are added by the authors.
Copyright (c) 2025 Rahim Ullah, Wajid Ali, Usman Ghanni https://creativecommons.org/licenses/by-sa/4.0
Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning Technologies
Machine learning (ML) frameworks rely heavily on pseudorandom number generators (PRNGs) for tasks such as data shuffling, weight initialization, dropout, and optimization. Yet, the statistical quality and reproducibility of these generators—particularly when integrated into frameworks like PyTorch, TensorFlow, and NumPy—are underexplored. In this paper, we compare the statistical quality of PRNGs used in ML frameworks (Mersenne Twister, PCG, and Philox) against their original C implementations. Using the rigorous TestU01 BigCrush test suite, we evaluate 896 independent random streams for each generator. Our results challenge claims of statistical robustness, revealing that even generators labelled "crush-resistant" (e.g., PCG, Philox) may fail certain statistical tests. Surprisingly, we can observe some differences in failure profiles between the native and framework-integrated versions of the same algorithm, highlighting some implementation differences that may exist. Mersenne Twister implementation in Pytorch and Numpy does not have the exact same failure profile as the original implementation in C. In addition, this is also the case for the TensorFlow implementation of Philox.
Copyright (c) 2025 Benjamin Antunes https://creativecommons.org/licenses/by-sa/4.0
Novel Deep Learning Framework for Automated Access Control in Cloud Computing Environments
The rapid adoption of Cloud Computing (CC) has significantly increased the complexity of managing secure and efficient access control due to the dynamic nature of cloud environments. Traditional methods, such as static rule-based systems or Machine Learning (ML) models, often fail to adapt to evolving user behavior and the emerging threats. To address these challenges, a novel Malleable Clouded-Leopard tuned Deep Recurrent Neural Network (MCL-DRNN) is designed for automated, real-time access control and resource allocation in CC environments. This framework utilizes cloud access logs, user behavior metrics, device identifiers, geolocation, time-stamps, and environmental variables. Preprocessing includes data cleaning, normalization, and dimensionality reduction using Principal Component Analysis (PCA) to retain critical patterns. The DRNN model is optimized using the MCL method, enabling the system to anticipate complex access patterns and detect deviations from normal behavior. By leveraging the recurrent structure of the DRNN, the system identifies subtle, persistent abnormalities indicative of potential security concerns. Performance evaluations demonstrate that the MCL-DRNN achieves 95% precision, 96% recall, and 95.6% F1-Score, outperforming traditional approaches. The intelligent, adaptive system provides a robust, self-optimizing solution for enhanced cloud security, capable of adjusting to rapidly changing cloud environments and mitigating unwanted access.
Copyright (c) 2025 Aditya Gupta, Sai Kiran Oruganti https://creativecommons.org/licenses/by-sa/4.0
On the Improvement of E-Commerce Based Recommender Systems
Recommender systems are essential for improving user experience in e-commerce by providing personalized product suggestions. However, traditional systems often struggle with the cold start problem, particularly for new users with minimal interaction history. This research proposes a Monte Carlo-based pagerank hybrid recommender system (MCPRHRec), which integrates Monte Carlo-based PageRank into the Multi-source Category Extended Historical Sequential Recommendation (MCE-HSPRec) System to enhance recommendation accuracy. By constructing a user-category graph, the system generates enriched user profiles and effectively mitigates the user cold start issue. Experimental results on an e-commerce dataset demonstrate significant improvements in recommendation performance, achieving a 97.09% increase in F1-score, 99.08% improvement in recall, 95.19% enhancement in precision, and 94.45% advancement in accuracy compared to existing methods. The proposed approach offers a scalable and efficient solution for personalized recommendations, providing a valuable contribution to the field of recommender systems.
Copyright (c) 2025 Abba Almu, Halimatu Abubakar Ainu https://creativecommons.org/licenses/by-sa/4.0
A Real-Time Smart Door Access Control System Using Haar Cascade Classifier and Embedded Vision
Traditional access control methods, such as mechanical keys and PIN-based systems, are ever more vulnerable to theft, copying, and unauthorized access, while high-fidelity biometric systems require intensive computing resources not suitable for low-power embedded systems. This research addresses the difficulty of achieving a real-time contactless face recognition system that compromises between security, performance, and expense in the resource-constrained embedded IoT setting. We show the design and test of an intelligent door access system using a Haar Cascade classifier on the ESP32-CAM module with cloud analytics for enhanced control, monitoring, and data logging. The system is developed to perform well on resource-constrained hardware while providing secure and remote access control. Experimental trials under varying light conditions, facial angles, and image resolutions show that the system detects faces with 97% accuracy, with a mean detection time of 151.25 milliseconds and CPU utilization of 40.5%. By adapting conventional machine learning for embedded vision, this project bridges the gap between high-fidelity biometric security and real-world IoT deployment in practice, offering an affordable and scalable solution for office and home access control in contemporary times.
Copyright (c) 2025 Chukwuemeka Obasi, Kasim Mohammed Tahir https://creativecommons.org/licenses/by-sa/4.0
Malicious URL detection using machine learning techniques
With numerous new websites being created every day, it's getting increasingly challenging to tell which ones are safe and which could be dangerous. These websites frequently gather sensitive user data that may be hacked in the absence of proper cybersecurity safeguards, such as the effective identification and categorization of dangerous URLs. In order to improve cybersecurity, this study attempts to create models based on machine learning algorithms for the effective detection and categorization of harmful URLs. In this regard, our proposal uses decision trees, logistic regression, support vector machines, and Naive Bayes to reliably categorize dangerous URLs. To improve classification efficiency, we have integrated hyper-parameter tuning using the Grid Search technique, optimizing model performance for more accurate and reliable results. The results demonstrate the effectiveness of Naive Bayes in achieving high accuracy (91.9%) and reliable performance in detecting malicious URLs. Implementation as a web service of the study provides evidence of the practicality and natural fit into more generalized security frameworks. Ultimately, our approach significantly enhances the detection of unsafe URLs, offering a robust solution to address the growing challenges in cybersecurity.
Copyright (c) 2025 Mohamed Cherradi, Hajar El Mahajer https://creativecommons.org/licenses/by-sa/4.0
An Improved Web-Based Weather Information Retrieval Application
The Improved Web-Based Weather Information Retrieval Application was implemented to address Nigeria's challenges in weather information retrieval, including limited data collection and accessibility issues for diverse linguistic groups. The system integrates advanced forecasting models, real-time updates, caching mechanisms, and multilingual support for Hausa, Yoruba, and Igbo, ensuring inclusivity and accessibility, particularly for rural users. With a user-friendly interface, the application caters for users with varying technical expertise and supports critical sectors like agriculture and disaster management. The results of the system evaluation revealed that the existing system took a time of 1.774s at the speed of 8.719s to retrieve relevant weather information, while the proposed system took a lesser time of 0.753s at a faster speed of 3.929s to retrieve relevant weather information. This result shows significant improvements over the existing system, which lacked caching mechanisms and multilingual support, resulting in slower data retrieval and limited accessibility. It also demonstrated its effectiveness in enhancing decision-making, climate resilience, and disaster preparedness.
Copyright (c) 2025 Abba Almu, Abdulkudus Yunusa https://creativecommons.org/licenses/by-sa/4.0
Artificial Intelligence integrated framework for stability of functions in persistent homology
Explaining the spatial properties in point set topological spaces is a tough task. Experts in TDA have sought to discover if it is possible to find a strong intuition about the geometry and topology in big datasets, easily seen when dealing with all of them at the same time. This point also notes that the estimates will stay useful if we can detect whether the constants remain stable as the data changes, for instance, the Hausdorff distance function when the data exhibits noise, or even when a little noise is added to the point-cloud datapoints. This could happen if these properties are considered in topologically invariant compact subsets of X, which requires very stringent and restrictive assumptions to obtain well-defined shapes that can be drawn from the data in the compact subsets. The aim of this study is to outline factors that make functions in persistent homology stable. The results show that factors like triangularability affect stability of functions. Moreover, we have given an integrated artificial intelligence (AI) framework for stability of functions, to trace the accuracy levels of algorithms in cyber-threat identification and cyber-threat attacks using Persistent Homology.
Copyright (c) 2025 Allan Onyango, Benard Okelo, Priscah Omoke https://creativecommons.org/licenses/by-sa/4.0
Evaluation Metrics and Optimization Strategies for Routing Protocols in Resource-Constrained Wireless Sensor Networks
Wireless Sensor Networks (WSNs) are indispensable for current applications, such as smart cities, industrial automation and environment monitoring. However, the performance of these networks is heavily dependent on the routing protocols used, especially given the strict limitations on energy, memory, and processing power in sensor nodes. This study presents a detailed evaluation of routing protocols designed for resource-constrained WSN nodes with a focus on energy efficiency, computation overhead, communication performance, scalability and security. A comparable study for the most popular routing protocols such as LEACH, AODV, DSDV, PEGASIS, and GPSR was performed to highlight the compatibility of such protocols with variety of WSN applications. Various optimization techniques to enhance the efficiency of the protocol are also introduced based on adaptive duty cycles, hierarchical clustering, hybrid routing paradigms, and lightweight security mechanisms. The vision and insights of this paper are to offer a good and well-organized ground for enabling and refining the selection of routing protocols for WSN implementation in the complex world of reality.
Copyright (c) 2025 Ahmed A Al-Healy, Qutaiba Ibrahim Ali https://creativecommons.org/licenses/by-sa/4.0
Deep Learning based Seasonality and Trend Detection in Sales Forecasting
Sales forecasting is essential for business planning, as it aids inventory management, marketing, and decision-making. Deep Learning combined with time-series analysis boosts prediction accuracy by capturing intricate temporal patterns. Precise sales forecasting remains difficult because of trends, seasonality, and noise. Previous techniques have issues with feature extraction and sequential dependencies, resulting in suboptimal efficiency. This study aims to develop a Hybrid Deep Learning (HDL) technique that combines the benefits of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to improve sales prediction accuracy. The primary emphasis is on combining feature extraction and temporal sequence learning to address the shortcomings of conventional methods. The proposed HDL framework prepares a sales dataset for time-series evaluation using a structured workflow that includes data exploration, preprocessing, and aggregation. To better comprehend the fundamental patterns, seasonal decomposition and autocorrelation analyses are used. The sliding window method is used to produce sequential data, which is then split into training and testing sets. Three predictive models—CNN, LSTM, and a hybrid CNN-LSTM—are built and trained using hyperparameter tuning. The models are evaluated using performance metrics such as root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Experimental results demonstrate that the proposed HDL surpasses CNN and LSTM with the lowest RMSE (2171.38), MAE (1219.79), and MAPE (538.18). The HDL technique combines CNN and LSTM to enhance sales prediction accuracy by capturing patterns and seasonality for better demand prediction and business evaluation.
Copyright (c) 2025 Karthikeyan Venkateshwaran https://creativecommons.org/licenses/by-sa/4.0
Fine-Tuned CNNs with Self-Attention Mechanism for Enhanced Facial Expression Recognition
The growing need for facial emotion recognition in various domains, particularly in online education, has driven advancements in Artificial Intelligence (AI) and computer vision. Facial expressions are a vital source of nonverbal communication as they convey a wide range of emotions through subtle changes in facial features. Recent developments in Deep Learning (DL) and Convolutional Neural Networks (CNNs) have opened new avenues for analyzing and interpreting human emotions. This study proposes a novel CNN-based real-time facial expression recognition (FER) framework tailored for online education systems. The framework incorporates dynamic region attention and self-attention mechanisms, enabling the model to focus on key facial regions that vary in importance depending on emotional context. The proposed model is fine-tuned to enhance its capability to identify facial expressions in various situations by integrating these methods with transfer learning. Experimental results demonstrate that the model achieves an accuracy of 83% using FER 2013, surpassing traditional static image-based techniques. This study proposes to bridge the gap in facial expression observation in online education, facilitating educators with valuable visions into pupil sentiments to advance learning consequences.
Copyright (c) 2025 Rabika Khalid, Atta Ur Rahman, Sania Ali , Bibi Saqia https://creativecommons.org/licenses/by-sa/4.0
Remote Health Parameter Monitoring Using Internet of Things: An Edge-Cloud Centric Integration for Real-time Reporting
Monitoring human health has become a phenomenon that integrates cutting-edge technology, which is capable of provisioning updated and sufficient data information to support human well-being in general. A key component of healthy living is preventing disease and health issues in general. The development of Internet of Things (IoT) technology has greatly improved a number of industries, including healthcare. In this study, a unique four-layered architecture for a Remote Health Parameter Monitoring (RHPM) system that uses sensors to monitor blood oxygen concentration (SpO2), body temperature (BT), and heart rate (HR) is presented. The system incorporates edge and cloud computing technologies. Data preprocessing at the edge is performed using an Arduino ESP8266 board and transmitted to cloud servers via the Message Queuing Telemetry Transport protocol for real-time processing and visualization. The testing of the system returned very high accuracy, yielding Mean Absolute Percentage Error (MAPE) values of 2.32%, 2.94%, and 3.43% for BT, SpO2, and HR, respectively. Another metric of evaluation was the R-squared value, which yielded 98% for BT and 97% for both SpO2 and HR, respectively. This paper integrates Support Vector Machine models, which enhances its predictive capability and achieves a cross-validation accuracy of 94.7%. The result indicated that the RHPM system is able to improve the well-being of the patient through early detection and informed preventive health management. Health institutions can tap into the real-time characteristics of the health parameters being monitored in fueling medical decision support systems for improved customer satisfaction and the delivery of modern healthcare solutions.
Copyright (c) 2025 Chukwuemeka Obasi, Braimoh Ikharo https://creativecommons.org/licenses/by-sa/4.0
Knowledge Discovery for Patient Survival in a Clinical Discharge Dataset Using Causal Graph Ontological Framework
Knowledge mining from clinical datasets is a critical task in healthcare as well as other fields. While the existing methods, such as randomized controlled trials (RCT) and other automatic machine extraction, have been helpful, they have become increasingly insufficient to keep pace with time, and robust models are required for clinical decisions. In this paper, we present a new method to address this challenge by using the Causal graph ontological model. Our study used a semi-structured textual clinical discharge dataset from the Statewide Planning and Research Cooperative System (SPARCS) to design and validate the patient survival rate assumptions from the dataset. We extracted the clinical information and organized it according to medically relevant fields for decision-making (Diseases, confounders, treatment, and the survival rate). The initial assumptions model was validated using the conditional independent test (CIT) criteria. The outputs of the LocalTest validation showed that the conceptual assumptions of the causal graph hold since the Pearson correlation coefficient ranges between -1 and 1, the p-value was (>0.05), and the confidence intervals of 95% and 25% were satisfied. Furthermore, we used Shapley values to perform sensitivity analysis on the features. Our analysis showed that two variables, such as gender and diseases, contributed little to the survival rate prediction. Our study concludes that the combination of causal graph ontological framework and sensitivity analysis to discover knowledge from the clinical text could help improve the quality of clinical decisions in the text, remove bias in the assumption in medical applications, and serve as a premise for modelling causal data for natural Language machine learning predictions.
Copyright (c) 2025 Omachi Okolo, B.Y Baha, M.D Philemon https://creativecommons.org/licenses/by-sa/4.0
An Effective Machine Learning Approach for Explosive Trace Detection
Globally, the proliferation of explosives and terrorist attacks has caused significant harm to public areas and heightened security concerns. The majority of public places, such as trains, airports, and government buildings, are being targeted, endangering people's lives and property. These target sites must be shielded against terrorist attacks and explosives without putting human security workers in jeopardy. Animals have been used as one of various techniques to try and tackle the aforementioned issue. It has been demonstrated that machine learning models, however, offer superior results. Large volumes of data are necessary for machine learning models to be accurate, but certain specialized training methods have drawbacks of their own because they can be difficult to get. It is now essential to create systems that are highly adaptable to real-time data. This work focuses on the essence of deploying an Artificial intelligence model for effective explosive trace detection. The model used was adapted from deep learning technology trained with a large explosive trace data set that was collected from a sensor network. The dataset was converted to 2D data using serial data to an image generator. The model was developed to classify explosive gas based on the concentration of Carbon (C), Hydrogen (H), Oxygen (O), and Nitrogen (N) gases and was able to classify the gas combinations as either explosive or not. The adaptation of CNN was tested and validated using 10% of the explosive trace dataset with an accuracy of 98.2%, and an AUC of 1 was recorded. The result shows that the deep learning concept is a useful tool in explosive trace detection.
Copyright (c) 2025 Monday F. Ohemu, Ambrose A. Azeta, Ibrahim A. Adeyanju, Chukwuemeka C. Obasi https://creativecommons.org/licenses/by-sa/4.0
A Comprehensive Study on Digital Watermarking for Security Threats and Research Directions
With the growing popularity of the Internet of Things (IoT) and the proliferation of AI-powered smart devices, a vast amount of digital data is being stored and shared on public platforms such as LinkedIn, Facebook, Twitter, Flickr, and other social media websites. Among these, digital images are the most commonly used medium for data sharing. Recent reports indicate that Google Photos stores over a billion images per week. Additionally, digital images play a crucial role in various applications, including social media, smart healthcare, intelligent transportation systems, industrial automation, robotics, the film industry, legal systems, news and insurance industries, and business sectors. However, the misuse of these images raises significant concerns regarding privacy and security. Identity theft, a growing issue in the 21st century, is a primary contributor to fraud in India and other countries. Thus, ensuring the security of digital images is a critical challenge that needs to be addressed. Currently, watermarking algorithms offer the most cost-effective solution for securing digital images. Watermarking involves embedding hidden marks within digital content to enhance its security. The key benefits of watermarking include: 1. Reducing bandwidth and storage demands, 2. Preventing copyright infringement and ownership disputes, 3. Protecting against tampering, and 4. Serving as an authentication keyword. Watermarking has gained significant traction in various fields, including cybersecurity and e-governance; given the growing importance of digital watermarking from a security perspective, this study aims to provide a comprehensive analysis of watermarking techniques and their evaluation methods across different applications. The findings of this extensive study are presented in this paper. Social media and healthcare. The primary objective of watermarking research is to enhance robustness, watermark capacity, and invisibility—an intricate trade-off among these factors. However, many existing watermarking techniques lack adequate security measures. Various approaches are available for both implementing and evaluating digital watermarking algorithms.
Copyright (c) 2025 Sambhaji Marutirao Shedole, Santhi V https://creativecommons.org/licenses/by-sa/4.0
Ensemble Deep learning model using panoramic radiographs and clinical variables for osteoporosis disease detection
Worldwide, a large number of people suffer from the bone disease osteoporosis. Accurate diagnosis and classification are essential for managing and preventing many disorders. In order to classify bone density images into two categories—normal and osteoporotic—this study suggests a hybrid model that combines a multiclass Support Vector Machine (MSVM) with a Deep Convolutional Neural Network (DCNN). The bone density pictures are subjected to feature extraction by the DCNN, and the information is then classified into two categories using the MSVM. The National Health and Nutrition Examination Survey (NHANES) database's dataset of bone density photos was used to train and evaluate the suggested hybrid model. According to the results, the ensemble model performs better than the most advanced methods available today in terms of F1 score, sensitivity, accuracy, and specificity. According to our research, osteoporosis may be efficiently classified by the DCNN and MSVM ensemble model, which can help with the diagnosis and treatment of various bone disorders. The proposed model gives better performance in terms of accuracy of 0.8913 and specificity of 0.9123 when compared to other models. Thus, a deep-learning diagnostic network applied to lumbar spine radiographs could facilitate screening for osteoporosis.
Copyright (c) 2025 T Ramesh, V Santhi https://creativecommons.org/licenses/by-sa/4.0