International Journal of Information Technology & Computer Engineering

Survey on whale optimization algorithm: from fundamental principles to modern adaptations and applications

Wed, 04 Feb 2026 00:00:00 +0000

The Whale Optimization Algorithm (WOA), inspired by the bubble-net hunting behavior of humpback whales, has emerged as one of the most influential swarm intelligence algorithms for solving complex optimization problems. Since its introduction, WOA has attracted substantial research attention due to its balance between exploration and exploitation, simplicity of implementation, and competitive performance across diverse problem domains. This survey provides a comprehensive analysis of WOA, encompassing its fundamental principles, mathematical formulation, variants, hybridizations, and extensive real-world applications. A systematic taxonomy is presented to classify WOA modifications into adaptive, chaotic, hybrid, discrete, multi-objective, and intelligent-learning-based frameworks. The review also highlights WOA’s successful integration into fields such as structural engineering, energy systems, machine learning, bioinformatics, and emerging intelligent technologies including IoT and cloud computing. Comparative benchmarking and statistical analyses demonstrate the superiority of enhanced WOA variants over traditional metaheuristics in terms of convergence rate, stability, and accuracy. Finally, the paper identifies existing challenges and outlines promising research directions, including theoretical convergence proofs, parameter self-adaptation, large-scale optimization, and hybridization with deep learning and quantum paradigms. This work provides a consolidated and critical overview of WOA’s evolution, serving as a valuable reference for researchers and practitioners in the field of metaheuristic optimization.

Security capabilities of blockchain technology on iot-based payment system

Maimunatu Ya’u Ibrahim, Kabiru Ibrahim Musa, Aminu Ahmad — Fri, 20 Feb 2026 00:00:00 +0000

This study examines at how blockchain can improve the security of digital banking systems based on the Internet of Things. These systems are vulnerable because of their centralized structures and limited device capability. 404 bank account holders with IT and blockchain expertise participated in the poll, which evaluated their opinions on how well blockchain works to improve data security, integrity, and traceability. The findings show that there is broad consensus that blockchain improves security and trust by enabling decentralization, encryption, and unchangeable records. The study concluded that using blockchain technology is essential for designing scalable and secure digital payment systems in dynamic marketplaces.

Esp32 based wearable wrist device for monitoring and managing risks in patients with congenital insensitivity to pain with anhidrosis (cipa sense-band)

Thu, 26 Mar 2026 00:00:00 +0000

People with Congenital Insensitivity to Pain with Anhidrosis (CIPA) face dangerous health conditions because they cannot feel pain and they cannot feel when their body temperature reaches dangerous levels. The study aims to develop and evaluate a wrist device called the CIPA Sense-Band which functions as a continuous monitoring system for risk assessment. The system uses an ESP32 microcontroller together with body temperature and heart rate and movement and pressure sensors to detect hazardous conditions. The system immediately activates vibration alerts together with caregiver text messages whenever it detects abnormal readings to enable rapid response. We tested the device in controlled and simulated settings to see how accurate, responsive, and comfortable it was. The overall accuracy of injury detection was 86.7%, and the accuracy of impact detection was 96.7%. Heart rate monitoring achieved 92.5% accuracy when compared to a medical reference device while most users reported they could comfortably wear it throughout the day. The CIPA Sense-Band functions effectively as an early risk detection tool which helps caregivers to improve their monitoring of potential threats. The research shows that an affordable wearable device exists which can improve safety for people with CIPA, but researchers need to enhance temperature sensing and conduct field trials.

Software defect prediction using ensemble machine learning on open-source code repositories

Dr. Ruwaida Mohammed Yas — Thu, 02 Apr 2026 00:00:00 +0000

Software defect prediction is an important software quality assurance exercise which helps the development teams to allocate the testing software resource effectively and identify the modules that are prone to the risk of fault before the software is introduced to the market. Single-classifier methods that are traditional are usually affected by the bias-variance trade-offs, and poor cross-heterogeneous-codebase generalization. This paper provides a Postulation of ensemble machine learning structure involving the incorporation of Random Forest, XGBoost, Support Vector Machine (SVM), and LightGBM to be able to be base learner in a stacking meta-ensemble architecture in order to take defect prediction of open-source software projects details. The models used in the proposed framework are based on the Chidamber-Kemerer (CK) object-oriented measures of feature engineering of six open-source projects, namely Camel, Jedit, Xerces, Ant, Log4j, and Lucene. In order to overcome the class disparity that exists in sets of defects, Synthetic Minority Oversampling Technique (SMOTE) is used during preprocessing. A logistic regression meta-learner thereof is a combination of the probability output of the four base classifiers and in this way the stacking ensemble is able to identify a wide range of decision boundaries and accurately reduce prediction error. Strategic 10-fold cross-validation experimental validation with proposed ensemble model on benchmark PROMISE and NASA MDP datasets show that the ensemble model has an accuracy of 94.3 and 93.1 as well as the recall of 92.7 and F1-score of 92.9 and an AUC-ROC of 0.97. These scores are the improvements of 3.3 to 10.6 percentages points as compared to single classifiers. The Wilcoxon signed-rank tests are found to have no statistical significance (p < 0.05). The paper also compares the cross-project transferability, ranking of the feature importance, and states that the measures of complexity and coupling are the most pertinent ones as far as detecting the defects are concerned. The results indicate that ensemble stacking is practically viable and a strengthened and broad applicability of the method in managing the quality of software on a large scale in industries.

Blockchain-integrated IoT framework for tamper-proof healthcare data management in smart hospitals

Dr. Mayur R. Bhoyar — Tue, 07 Apr 2026 00:00:00 +0000

The rapid expansion of IoT in smart hospitals enables continuous patient monitoring, automated diagnosis and real-time clinical decision support. However, centralized healthcare data systems remain vulnerable to unauthorized data modification, single points of failure and poor audit transparency threatening patient safety and regulatory compliance. This paper proposes a Block-chain-IoT (BC-IoT) framework built on a three-tier hierarchical architecture. The first tier connects heterogeneous medical devices (ECG monitors, glucose sensors, infusion pumps, pulse oximeters) through a lightweight IoT sensor layer. The second tier applies AI-based anomaly detection at edge computing nodes. The third tier employs a dual-block-chain approach, combining the IOTA Tangle protocol for feeless micro-transactions with a permissioned Hyperledger Fabric network for enterprise-grade data governance. Medical data is encrypted using AES-256 and TLS 1.3, screened for anomalies at edge nodes and stored on an immutable distributed ledger. Smart contracts enforce role-based access control, ensuring only authorized personnel can access or modify patient records. The Inter Planetary File System (IPFS) handles decentralized storage of large medical files, with cryptographic content identifiers stored on-chain for full traceability. Evaluation on a simulated smart hospital testbed with 350 IoT nodes across five department’s demonstrated strong results: 42ms transaction latency, 1,250 transactions per second, a 99.7% tamper detection rate and a 99.98% data integrity score outperforming existing block-chain-IoT healthcare systems across all key metrics. The BC-IoT framework offers a scalable, energy-efficient and standards-compliant solution for securing digital health infrastructure.