Results (36)
Search Parameters:
Keyword: ModelsBengali Emotion Classification from Social Media Text Using Deep Learning and Transformer‑Based Models with Explainability
by Mujtahid Alam, Shuhena Salam Aonty, Sha Newaz Mahmud, Nahid Riaz Swachha and Ahmed Talal Wazih
Journal of Engineering Research and Sciences, Volume 5, Issue 6, Page # 39-55, 2026; DOI: 10.55708/js0506004
Abstract: Bengali emotion classification remains challenging due to limited annotated resources, informal social media language, and the lack of comprehensive evaluations of modern transformer architectures. This study presents a unified framework for six‑class Bengali emotion classification using a corpus of 5,401 manually annotated social media comments. We systematically compare recurrent neural networks, transformer‑based models, and hybrid… Read More
(This article belongs to the Special Issue on SP8 (Special Issue on Digital and Engineering Transformations in Science and Technology (SI-DETST-26)) and the Section Artificial Intelligence – Computer Science (AIC))
Binary Image Classification with CNNs, Transfer Learning and Classical Models
by Nikolaos Vasileios Oikonomou, Dimitrios Vasileios Oikonomou, Sofia Panagiota Chaliasou and Nikolaos Rigas
Journal of Engineering Research and Sciences, Volume 5, Issue 1, Page # 66-75, 2026; DOI: 10.55708/js0501006
Abstract: This study presents a comprehensive comparative analysis of binary face classification utilizing Deep Learning and traditional Machine Learning approaches. We evaluate three distinct modeling strategies: (1) End-to-end Convolutional Neural Networks (CNNs), including a baseline TensorFlow model and an optimized PyTorch architecture; (2) Hybrid CNN-MLP networks; and (3) Feature extraction via a pre-trained ResNet50 coupled with… Read More
(This article belongs to the Special Issue on Special Issue on Multidisciplinary Sciences and Advanced Technology 2025 and the Section Artificial Intelligence – Computer Science (AIC))
Comparative Analysis of Supervised Machine Learning Models for PCOS Prediction Using Clinical Data
by Ranyah Taha, Huda Zain El Abdin and Tala Musleh
Journal of Engineering Research and Sciences, Volume 4, Issue 6, Page # 16-26, 2025; DOI: 10.55708/js0406003
Abstract: Polycystic Ovary Syndrome (PCOS) is a prevalent hormonal disorder affecting women of reproductive age, commonly resulting in irregular menstrual cycles, elevated androgen levels, and the presence of polycystic ovaries. It is a major cause of infertility and is often linked with metabolic complications such as insulin resistance and obesity. Symptoms vary and may include acne,… Read More
(This article belongs to the Section Artificial Intelligence – Computer Science (AIC))
Enhancing Python Code Embeddings: Fusion of Code2vec with Large Language Models
by Long H. Ngo and Jonathan Rivalan
Journal of Engineering Research and Sciences, Volume 4, Issue 1, Page # 1-7, 2025; DOI: 10.55708/js0401001
Abstract: Automated code comprehension has recently become integral to software development. Neural networks, widely employed in natural language processing tasks, can capture the semantic meanings of language by representing it in vector form. Although programming code differs from natural language, we hypothesize that neural models can learn both the syntactic and semantic attributes inherent in code.… Read More
(This article belongs to the Special Issue on Special Issue on Multidisciplinary Sciences and Advanced Technology 2024 and the Section Software Engineering – Computer Science (SEC))
Using Artificial Intelligence Models to Predict the Wind Power to be fed into the Grid
by Sambalaye Diop, Papa Silly Traore, Mamadou Lamine Ndiaye and Issa Zerbo
Journal of Engineering Research and Sciences, Volume 3, Issue 6, Page # 1-9, 2024; DOI: 10.55708/js0306001
Abstract: The Taïba Ndiaye wind farm, connected to the SENELEC grid, plays a key role in offsetting shortfalls in electricity consumption, with an installed capacity of 158.7 MW. Moreover, as an intermittent power station, its production is highly dependent on the environmental conditions in the region. Bad weather can disrupt the electricity network, requiring forecasting methods… Read More
(This article belongs to the Special Issue on Special Issue on Computing, Engineering and Sciences 2023-24 and the Section Electrical Engineering (ELE))
Early Warning for Maritime Storm Formation Using Temporal Autoencoder-Based Anomaly Detection
by Snehashish Srivastava, Haiping Xu, Donghui Yan and Ramprasad Balasubramanian
Journal of Engineering Research and Sciences, Volume 5, Issue 5, Page # 19-39, 2026; DOI: 10.55708/js0505003
Abstract: Storms remain a serious hazard at sea, exposing vessels to rapidly changing conditions that endanger human life and result in substantial economic losses. Satellite-based detection methods are widely used but require significant computational resources and depend on land-to-sea communication links, which may become unreliable during severe weather. Machine learning approaches offer strong potential for early… Read More
(This article belongs to the Special Issue on SP9 (Special Issue on Multidisciplinary Sciences & Advanced Technology (SI-MSAT 2026)) and the Section Artificial Intelligence – Computer Science (AIC))
Explainable AI for SSD Failure Prediction: Using LIME and SHAP for Transparency
by Saurav Kant Kumar
Journal of Engineering Research and Sciences, Volume 5, Issue 4, Page # 1-16, 2026; DOI: 10.55708/js0504001
Abstract: Artificial Intelligence (AI) has become increasingly crucial for modern data centers for automating tasks ranging from anomaly detection to predictive maintenance. Nevertheless, a significant limitation of underlying machine learning (ML) models is their “black box” nature. This lack of transparency limits trust among stakeholders who require visibility into model decisions. We address this lack of… Read More
(This article belongs to the Section Artificial Intelligence – Computer Science (AIC))
An Analytical Examination of Predictive Denial Pattern Recognition in Healthcare Claims Utilizing Real-Time Power BI Analytics for Revenue Enhancement
by Nida Fatima and Amir Ghazanfer
Journal of Engineering Research and Sciences, Volume 5, Issue 3, Page # 27-32, 2026; DOI: 10.55708/js0503004
Abstract: This article looks at the growing problems in the healthcare revenue cycle, especially the big money losses that come from claim rejections. It emphasizes the need for predictive, real-time analytics to diminish avoidable rejections and improve overall operational efficiency. The novelty of this study lies in the operational integration of a machine-learning–based denial prediction model… Read More
(This article belongs to the Section Health Care Sciences and Services (HCS))
AI-Driven Data Lake Optimization: Integrating Quality Monitoring with Intelligent Physical Design Decisions
by Sowjanya Deva and Surya Narayana Reddy Chintacunta
Journal of Engineering Research and Sciences, Volume 5, Issue 3, Page # 1-13, 2026; DOI: 10.55708/js0503001
Abstract: Cloud data lakes require continuous optimization across multiple dimensions: physical design (partitioning, compression), query execution, and data quality assurance. This paper presents AIDALOS (AI-Driven Autonomous Data Lake Optimization System), a framework that integrates quality monitoring with physical optimization decisions. The system uses reinforcement learning to adapt monitoring intensity and trigger physical design changes based on… Read More
(This article belongs to the Section Artificial Intelligence – Computer Science (AIC))
Demographic Stereotype Elicitation in LLMs through Personality and Dark Triad Trait Attribution
by Nikolaos Vasileios Oikonomou, Ioannis Palaiokrassas, Dimitrios Vasileios Oikonomou, Sofia Panagiota Chaliasou and Nikolaos Rigas
Journal of Engineering Research and Sciences, Volume 5, Issue 1, Page # 46-65, 2026; DOI: 10.55708/js0501005
Abstract: This study investigates how Large Language Models (LLMs), specifically Meta LLaMA-3.1-8B-Instruct, implicitly attribute personality and Dark Triad traits to demographic personas. By prompting the model with 660 synthetic identity descriptors (constructed from balanced combinations of gender, race, religion, and region) and standardized psychometric questionnaires, we extract Likert-scale responses and compute aggregated Big Five (EACNO) and… Read More
(This article belongs to the Special Issue on Special Issue on Multidisciplinary Sciences and Advanced Technology 2025 and the Section Artificial Intelligence – Computer Science (AIC))
Model Uncertainty Quantification: A Post Hoc Calibration Approach for Heart Disease Prediction
by Peter Adebayo Odesola, Adewale Alex Adegoke and Idris Babalola
Journal of Engineering Research and Sciences, Volume 4, Issue 12, Page # 25-54, 2025; DOI: 10.55708/js0412003
Abstract: We investigated whether post-hoc calibration improves the trustworthiness of heart-disease risk predictions beyond discrimination metrics. Using a Kaggle heart-disease dataset (n = 1,025), we created a stratified 70/30 train-test split and evaluated six classifiers, Logistic Regression, Support Vector Machine, k-Nearest Neighbors, Naive Bayes, Random Forest, and XGBoost. Discrimination was quantified by stratified 5-fold cross-validation with… Read More
(This article belongs to the Section Artificial Intelligence – Computer Science (AIC))
A Vendor-Agnostic Multi-Cloud Integration Framework Using Boomi and SAP BTP
by Padmanabhan Venkiteela
Journal of Engineering Research and Sciences, Volume 4, Issue 12, Page # 1-14, 2025; DOI: 10.55708/js0412001
Abstract: The shift toward multi-cloud strategies has made a vendor-agnostic integration framework indispensable for seamlessly orchestrating workflows across heterogeneous platforms. Modern enterprises increasingly rely on a mix of cloud ecosystems leveraging Amazon Web Services (AWS) for elasticity, Google Cloud Platform (GCP) for advanced AI/ML capabilities, Azure Cloud and Oracle Cloud Infrastructure (OCI) for critical enterprise workloads… Read More
(This article belongs to the Section Information Systems – Computer Science (ISC))
Energy-Optimized Smart Transformers for Renewable-Rich Grids
by Sunday Omini Oboma and Edward Lambart
Journal of Engineering Research and Sciences, Volume 4, Issue 10, Page # 21-28, 2025; DOI: 10.55708/js0410003
Abstract: The accelerating and unrestrained use of energy globally raises serious concerns for the future of the planet, primarily due to the environmental devastation caused by fossil fuels. Achieving high energy efficiency in both fuel-driven and renewable energy systems is crucial for future energy optimization. Clean energy production is one of the most effective strategies to… Read More
(This article belongs to the Section Energy and Fuels (ENF))
Magnetic AI Explainability: Retrofit Agents for Post-Hoc Transparency in Deployed Machine-Learning Systems
by Maikel Leon
Journal of Engineering Research and Sciences, Volume 4, Issue 8, Page # 31-40, 2025; DOI: 10.55708/js0408004
Abstract: Artificial intelligence already influences credit allocation, medical diagnosis, and staff recruitment, yet most deployed models remain opaque to decision makers, regulators, and the citizens they affect. A new wave of transparency mandates across multiple jurisdictions will soon require organizations to justify automated decisions without disrupting tightly coupled production pipelines that have evolved over the years.… Read More
(This article belongs to the Special Issue on Special Issue on Multidisciplinary Sciences and Advanced Technology 2025 and the Section Artificial Intelligence – Computer Science (AIC))
Cloud ERP vs. On-Premise QAD ERP: A Cost-Benefit Analysis for Mid-Sized Manufacturers
by Ravi Jaiswal
Journal of Engineering Research and Sciences, Volume 4, Issue 7, Page # 1-14, 2025; DOI: 10.55708/js0407001
Abstract: For mid-sized manufacturing firms, the Enterprise Resource Planning (ERP) system plays a crucial role in streamlining operations and enabling strategic growth. Both adopting cloud-based ERP solutions and continuing to use On-Premise applications like QAD are important decisions as digital transformation increases. In this study, we conduct a retrospective case study of the cost-benefit analysis of… Read More
(This article belongs to the Section Information Systems – Computer Science (ISC))
Fire Type Classification in the USA Using Supervised Machine Learning Techniques
by Ranyah Taha, Fuad Musleh and Abdel Rahman Musleh
Journal of Engineering Research and Sciences, Volume 4, Issue 6, Page # 1-8, 2025; DOI: 10.55708/js0406001
Abstract: Wildfires are a growing global concern, causing widespread environmental, economic, and health impacts. In the USA, fire incidents have become more frequent and intense due to factors such as climate change, prolonged droughts, and human activities. Machine learning plays a vital role in predicting and classifying fires by analyzing vast satellite and environmental datasets with… Read More
(This article belongs to the Special Issue on Special Issue on Computing, Engineering and Sciences 2024-25 and the Section Remote Sensing (RMS))
Water Potability Prediction Using Neural Networks
by Ranyah Taha, Fuad Musleh and Abdel Rahman Musleh
Journal of Engineering Research and Sciences, Volume 4, Issue 5, Page # 1-9, 2025; DOI: 10.55708/js0405001
Abstract: The crucial need for maintaining specific water potability levels depending on the sector of utilization, this is becoming increasingly challenging due to the increased pollution. It is therefore important to have fast and reliable water potability assessment techniques. A subset of Machine Learning (ML); being Deep Learning (DL), can be utilized to develop models capable… Read More
(This article belongs to the Special Issue on Special Issue on Computing, Engineering and Sciences 2024-25 and the Section Artificial Intelligence – Computer Science (AIC))
Analysis of 5G Business Model Components for Mobile Network Operators in Sub-Saharan Africa
by Laurence Banda
Journal of Engineering Research and Sciences, Volume 4, Issue 2, Page # 1-10, 2025; DOI: 10.55708/js0402001
Abstract: The fierce race among mobile network operators (MNOs) to roll out fifth-generation (5G) networks has intensified. One of the potential markets where 5G deployment has increased tremendously is Sub-Saharan Africa. This is primarily due to rapid economic growth and the new opportunities that 5G networks and the associated technologies are expected to offer through sustainable… Read More
(This article belongs to the Section Operations Research and Management Science (ORM))
Smart Vehicle Safety System Using Arduino: An Experimental Study in Bahrain’s Driving Conditions
by Youmna Rabie Farag and Bintu Jasson
Journal of Engineering Research and Sciences, Volume 3, Issue 11, Page # 74-80, 2024; DOI: 10.55708/js0311006
Abstract: The high rate of vehicle accidents is increasingly linked to drivers failing to maintain adequate safety distances between their vehicles and those in front. This issue is exacerbated by varying weather conditions such as rain, sandstorms, and fog. To mitigate this problem, we propose an Arduino-based intelligent system designed to assist drivers in maintaining safe… Read More
(This article belongs to the Section Automation and Control Systems (ACS))
Evaluation of Equivalent Aacceleration Factors of Repairable Systems in a Fleet: a Process-Average-Based Approach
by Renyan Jiang, Kunpeng Zhang, Xia Xu and Yu Cao
Journal of Engineering Research and Sciences, Volume 3, Issue 10, Page # 44-54, 2024; DOI: 10.55708/js0310005
Abstract: Research on repairable systems in a fleet is mainly concerned with modelling of the failure times using point processes. One important issue is to quantitatively evaluate the heterogeneity among systems, which is usually analyzed using frailty models. Recently, a fleet heterogeneity evaluation method is proposed in the literature. This method describes the heterogeneity with the… Read More
(This article belongs to the Special Issue on Special Issue on Multidisciplinary Sciences and Advanced Technology 2024 and the Section Operations Research and Management Science (ORM))
Conceptual Business Model Framework for AI-based Private 5G-IoT Networks
by Laurence Banda
Journal of Engineering Research and Sciences, Volume 3, Issue 10, Page # 13-20, 2024; DOI: 10.55708/js0310002
Abstract: The fusion of fifth generation (5G) networks, Internet of Things (IoT) and artificial intelligence (AI), referred to as intelligent connectivity by most industry experts, can be seen as a crucial success factor for sustainable digitalization. Until recently, research into these key triad technologies has been conducted in isolation. One of the promising applications of intelligent… Read More
(This article belongs to the Special Issue on Special Issue on Multidisciplinary Sciences and Advanced Technology 2024 and the Section Telecommunications (TEL))
An Integrated Approach to Manage Imbalanced Datasets using PCA with Neural Networks
by Swarup Kumar Mondal and Anindya Sen
Journal of Engineering Research and Sciences, Volume 3, Issue 10, Page # 1-12, 2024; DOI: 10.55708/js0310001
Abstract: Imbalanced dataset handling in real time is one of the most challenging tasks in predictive modelling. This work handles the critical issues arising in imbalanced dataset with implementation of artificial neural network and deep neural network architecture. The usual machine learning algorithms fails to achieve desired throughput with certain input circumstances due to mismatched class… Read More
(This article belongs to the Special Issue on Special Issue on Multidisciplinary Sciences and Advanced Technology 2024 and the Section Artificial Intelligence – Computer Science (AIC))
BIM and Risk Management: A Review of Strategies for Identifying, Analysing and Mitigating Project Risks
by Muhammad Numan
Journal of Engineering Research and Sciences, Volume 3, Issue 1, Page # 20-26, 2024; DOI: 10.55708/js0301004
Abstract: Construction projects involve numerous risks that can impact cost, schedule, quality, safety, and sustainability. Effective risk management is critical for project success. Building Information Modelling (BIM) offers capabilities that can transform risk management across the project lifecycle. This paper provides a systematic review of how BIM can aid core risk management processes including identification, analysis,… Read More
(This article belongs to the Section Civil Engineering (CVE))
NNR Artificial Intelligence Model in Azure for Bearing Prediction and Analysis
by Henry Ogbemudia Omoregbee, Mabel Usunobun Olanipekun and Bright Aghogho Edward
Journal of Engineering Research and Sciences, Volume 2, Issue 6, Page # 1-9, 2023; DOI: 10.55708/js0206001
Abstract: Neural Network regression (NNR) is considered more effective as compared to multiple neural networks model readily available in Azure to evaluate the Remaining Useful Life (RUL) of bearing in this work because it performs better than other models when used and was demonstrated as a non-programing technique for analyzing enormous data without the use of… Read More
(This article belongs to the Special Issue on Special Issue on Multidisciplinary Sciences and Advanced Technology 2023 and the Section Artificial Intelligence – Computer Science (AIC))
Orthogonal Polynomials in the Problems of Digital Information Processing
by Yaroslav Pyanylo, Valentyna Sobko, Halyna Pyanylo and Oksana Pyanylo
Journal of Engineering Research and Sciences, Volume 2, Issue 5, Page # 1-9, 2023; DOI: 10.55708/js0205001
Abstract: The paper examines spectral methods based on classical orthogonal polynomials for solving problems of digital information processing. Based on Jacobi polynomials, signal approximation methods are built to identify objects in the natural environment. Based on Chebyshev-Laguerre polynomials, methods of filtering multiplicative signal noises in linear filter models are proposed. Numerical experiments on model problems were… Read More
(This article belongs to the Special Issue on Special Issue on Computing, Engineering and Sciences 2023-23 and the Section Mathematics (MAT))