Iqra Journal of Engineering and Computing


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ISSN: 3105-4528 (print) ISSN: 3105-451X (online)

About the Journal

The Iqra Journal of Engineering and Computing (IJEC) is a peer-reviewed, open-access journal published by the Faculty of Engineering Sciences and Technology at IQRA University. IJEC aims to be a leading bi-annual scientific publication that is freely accessible online, focusing on innovative research that addresses contemporary issues in engineering and computing. The journal seeks to publish original research articles that span a broad spectrum of topics in engineering and computing, including but not limited to novel approaches, algorithms, applications, and theoretical advancements that contribute to societal development. IJEC is committed to providing immediate open access to all accepted articles upon publication, ensuring that research is available to a global audience. Each published paper is licensed under Creative Commons and preserved in perpetuity on our platform. The journal prioritizes high-quality academic rigor through a Double-Blind Peer Review process, ensuring that only original and impactful contributions are considered for publication. Submissions are welcomed from researchers worldwide, with a focus on studies that address real-world challenges through theoretical innovation and practical applications. All manuscripts submitted to IJEC will undergo originality verification through an plagiarism checking tool.

Article Processing Charges: IJEC does not impose any article processing, editorial processing, or submission fees.
The editorial board expresses deep gratitude to the IQRA University for its unwavering support in sustaining the journal's operations.

The scope of a journal like this can cover a wide range of topics, including but not limited to:
Computer Science: Algorithms, Artificial Intelligence, Machine Learning, Data Science, and Cybersecurity.
Electrical and Electronics Engineering: Circuit design, communication systems, robotics, and embedded systems.
Software Engineering: Software development, programming languages, and software architecture.
Information Technology: Networking, databases, IT infrastructure, cloud computing, and IoT.

This selection covers a broad and relevant range of topics in the fields of software engineering, computer science, AI, cyber security, information security, and electrical engineering. We encourage authors to refer to the Committee on Publication Ethics' International Standards for Authors and to review the publication ethics guidelines provided by COPE. Additionally, please consult the guidelines on good publication practices and the Code of Conduct established by the Committee on Publication Ethics (COPE).

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A Comparative Review of Transformer Architectures: Evolution, Efficiency, Applications, and Future Directions 0

Transformer architectures have become a central foundation of modern artificial intelligence because of their scalability, parallel computation, and representation learning across language, vision, code, and multimodal domains. Since the original Transformer, the architecture has evolved into encoder-only, decoder-only, encoder-decoder, efficient attention, vision, multimodal, sparse Mixture-of-Experts, open-weight, state-space, recurrent, and hybrid model families. This review presents a structured and benchmark-informed comparison of Transformer architectures using a defined literature selection protocol, inclusion and exclusion criteria, model-family coding dimensions, and source-verification rules. The paper compares major architectures with respect to training objective, attention or sequence-modeling mechanism, computational complexity, scalability, context length, modality support, [...]

2026
By Israr Ali,Ali Ahmed Siddiqui,Aarij Mahmood Hussaan
Keywords: Transformer, self-attention, BERT, GPT, large language models, multimodal learning, Mixture-of-Experts, efficient attention, Vision Transformer, state-space models, benchmark evaluation, long-context reasoning, open-weight models
Performance Evaluation of Machine Learning for Cross-Lingual Ham and Spam Detection 0

This paper presents a multilingual spam email classification framework capable of processing both German and English texts using classical machine learning models combined with a translation-enhanced pipeline. The system integrates TF-IDF vectorization, Logistic Regression, Multinomial Naive Bayes, and a probability-calibrated Linear Support Vector Classifier (SVC). A key contribution is the incorporation of an automatic German-to-English translation layer using MarianMT, allowing cross-lingual evaluation and robustness analysis. Multiple models are trained on a manually curated dataset of 3,790 emails, achieving up to 98.55% accuracy and 0.9860 AUC-ROC on the evaluated dataset. A Streamlit-based application is implemented for real-time inference, [...]

2026
By Kashif Iqbal1,SikandarAli,MustafaHaiderAli,Muhammad Hammad, Faraz Ali, Raheema Agha
Keywords: Spam classification, TF-IDF, Logistic Regression, Na ??ve Bayes, Calibrated Linear SVC, Calibrated Classifier CV, machine translation, MarianMT, multilingual NLP, Streamlit, Deep Learning, BERT
Myoelectric Control based on Machine Learning of a Low-Cost 7-DOF Transhumeral Prosthetic Arm through TinyML 0

Upper-limb amputations are a significant source of disability, especially in resource-limited environments where expensive commercial-grade prosthetics (often costing over USD 70,000) are unaffordable. This paper describes the design, optimisation and pilot testing of a 7-degree of freedom (DOF) transhumeral motorised prosthesis controlled by TinyML-based myoelectric decoding. The gesture-recognition accuracy (F1-score) of 95.5% across five able-bodied participants (gesture-level accuracy: approximately 92.3% for the most difficult gesture) with an end-to-end response time of 100ms is achieved using an affordable ESP32 microcontroller and a Cost-Complexity Pruned (CCP) Random Forest (RF) classifier. This paper outlines the hardware-software co-design, mathematical formulation of [...]

2026
By Misbah Anwer, Muhammad Fahad, Anusha Hasan, Abdul Karim Hasan, Falak Shah, Rafia Khan
Keywords: sEMG, TinyML, Prosthetics, Edge Computing, Random Forest, ESP32, Incremental Learning, Z-Transform, Signal Processing, 7-DOF
From Vulnerability Severity to Security Debt: An Evidence-Driven PrioritizationModelforOWASP2025WebApplicationRisksinLow-Resource Healthcare and SME Systems 0

Web application security reports are usually ordered by technical severity but low-resourced organizations want to know which vulnerability will become the most expensive security debt to repay? This paper presents SECURE-DEBT a model used to rank the 2025 OWASP web application risks to prioritize the remediation of web applications in resource-constrained healthcare-style organizations and small to medium enterprises (SMEs)? SECURE-DEBT considers the unresolved vulnerabilities as security debt thus incurring future risk based on evidence, exposure, sensitive information, exploitability, operational impact and time considerations for a fix? The study follows an empirically-driven, framework-based conceptual research article? The scoring considers mapping to [...]

2026
By Muhammad Shahzad Khadim
Keywords: SecurityDebt;WebApplicationSecurity;OWASP2025;Vulnerability Prioritization; Penetration Testi
Solar-Powered Variable Frequency Drive (VFD) Based Flour Grinding System 0

Pakistan agricultural sector forms the backbone of its economy, with wheat being the staple crop, processed predominantly through small-scale flour grinders (Aata Chakkis). Alongside wheat, other grains such as maize (corn), barley, millet, rice, and chickpeas are also ground in these mills for daily consumption. However, these mills consume significant electricity, increasing operational costs and pressuring Pakistans fragile power grid. With rising power costs and frequent outages, mill owners are adopting solar-powered solutions. This study explores how Variable Frequency Drives (VFDs) can optimize the efficiency of solar-powered flour grinders by providing grain-specific motor speed control, reducing electricity consumption, and maintaining [...]

2026
By Muhammad Zain, Muhammad Ahmad, Muhammad Abdullah, Hamza Ahmad Raza, Abdul Basit Taj, Misbah Sattar
Keywords: VFD, Induction Motor, Solar-Powered System, Flour Grinding Machine, Chakkis
The Impact of Dust on the Performance and Efficiency of Solar Panels 0

Dust accumulation on solar panels, commonly referred to as soiling, significantly reduces the energy conversion performance of Photo Voltaic (PV) systems. Dust deposition on the panel surface obstructs sunlight from reaching the cells, and combined with elevated panel temperatures and deterioration of glass optical properties, this results in substantial efficiency losses. Previous studies have reported efficiency reductions ranging from 5% to 40%, depending on dust characteristics, particle size, and environmental conditions. Such losses not only reduce total energy output but also cause considerable economic impact, particularly for large-scale solar power plants. To mitigate these effects, the scientific community has actively [...]

2025
By Jameel Ghaffar
Keywords: The Impact of Dust on the Performance and Efficiency of Solar Panels
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