MIST International Journal of Science and Technology (MIJST)

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A Neural Network Based Software Defect Prediction Approach Using SMOTE and Noise Filtering-CLNI
(Research and Development Wing, MIST, 2025-12-30) Ashfaque, Ahmmed Bin; Sattar, Abdus; Jahan, Hosney; Akhtaruzzaman, M.; Nur, Fernaz Narin
Software defects can cause significant loss and system failures in software development life cycle. Software Defect Prediction (SDP) is a vital step for ensuring the quality of software. Till now, a number of machine learning models have been proposed to predict potential defects and make the software more reliable. However, SDP models suffer from the problem of imbalanced dataset, resulting in poor prediction accuracy. To mitigate this, issue several data balancing techniques, i.e., over sampling, under sampling etc. have been proposed to balance the dataset. In some cases, the data balancing methods may further introduce noisy and mislabeled samples in the dataset. To deal with these issues, in this paper, we propose a neural network based approach that combines the oversampling technique Synthetic Minority Oversampling Technique (SMOTE) with the noise filtering technique Class Level Noise Identification (CLNI). Here, we applied three different CLNI methods which are Edited Nearest Neighbor (ENN), Repeated ENN (RENN) and All-KNN. Our aim is to make the dataset clean, balanced and efficient by combining SMOTE with CLNI. In addition, we applied a number of feature selection methods to identify the most important features, further contributing towards achieving better prediction accuracy. To evaluate the effectiveness of the proposed model, we conduct experiments on several benchmark datasets (MC1, PC1, PC2, PC3 and PC4) obtained from NASA MDP and (ML, LC and JDT) AEEEM repository. The experimental results have been evaluated and compared in terms of accuracy, precision, recall and AUC-ROC curve. The experimental results demonstrated that our proposed approach has achieved up to 98% accuracy and outperformed state-of- the-art approaches.
Decentralized LRM System Architecture with Biometric Authentication and Digital Certificate Verification through Blockchain Technology
(Research and Development Wing, MIST, 2025-12-30) Mosharrof, Shakil; Nizami, Farhan Nasif; Mohtasim, Mahdi; Adib, Mahdi; Akhtaruzzaman, M.; Islam, Md Shofiqul; Rahman, Muhammad Towfiqur; Jahan, Hosney
Managing land records is a fundamental duty of a government, ensuring the accuracy, consistency, integrity of ownership, and reliable transaction of data. Conventional paper-based or centralized digital technology-based land record systems are unable to hold the system trust, efficiency, and consistency, thus mostly demonstrate errors, fraud, and corruptions. On the other hand, blockchain technology presents a transformative solution. It offers transparent, tamper-proof, secure, and reliable approach for Land Record Management (LRM) system. In this study a blockchaindriven LRM system architecture with distributed ledger technology is presented. The proposed strategy enhances security and trust by ensuring transparency, acceptance, and accountability. The study also designs the smart-contracts algorithm in detail that facilitates land registration, ownership transfers, verification, and streamlining processes. The proposed architecture ensures automated functionalities with little human intervention, uplifting the system security. Moreover, the proposed blockchain architecture integrates finger-print biometric authentication that boosts the system strength in terms of security through identity verification. This mitigates the risks of errors, fraud, illegal modification, and unauthorized access. This article outlines a blockchain-based LRM framework and verified through implementation and testing, reflecting the potentiality of viable adoption of this advanced technology.