International Journal of Electrical and Computer Engineering Systems

An Overview of Cybersecurity: Key Issues and Emerging Solutions

Medha Wyawahare — 2026-02-19

In an age where digital interconnectivity permeates every aspect of daily life, cyber threats have grown more advanced, and as a result, they pose very dangerous threats to individuals, enterprises, and governments all the same. This review offers a systematic synthesis of cyber threats, new attack surfaces, and new defense techniques, with emphasis on the convergence of artificial intelligence (AI) and domain-specific issues within cloud, IoT, and mobile networks. Upcoming new technologies like quantum and 5G further present risks that require further new developments in cryptography and solutions in network security. In addition to providing an overview of current work, this paper makes an original contribution by presenting a comparison of prominent methods and studies, divided by defense strategy, domain, and performance measures. The approach for the study focuses more on the requirement of technical innovation to be blended with frameworks that are ethical and regulatory in nature, addressing complex and dynamic threats in the nature of cybersecurity. Recommendations for further research in the future include quantum-resistant algorithms, improved AI models that can be used for more effective cybersecurity, and creation of ethical standards in the digital defense of the resources of the nation to do it more robustly and responsibly.

Trends and Networks in the Application of MCDM Methods in Computer Science: Analysis of the Web of Science Database

Ana Veljić — 2026-02-17

This paper presents a comprehensive bibliometric analysis of scientific output in the field of Multi-Criteria Decision-Making (MCDM) within the context of computer science, focusing on the period from 2019 to mid-2025. Using data from the Web of Science Core Collection, a total of 302 relevant papers were identified based on criteria such as publication year, language, document type, open access status, and research area. The analysis covers publication dynamics, the most influential authors and institutions, thematic directions, and collaboration structures. Special emphasis is placed on citation analysis of the most impactful works, as well as the visualization of co-authorship networks using the PRISMA methodology and tools such as RStudio and Biblioshiny. The results reveal a growing trend in publications, high activity by certain authors (e.g., Akram M and Liu Y), and strong collaboration within research clusters. Dominant topics include decision-making models, alternative selection, aggregation operators, and priority evaluation. This study provides insights into the structure and dynamics of the scientific community engaged in MCDM methods in computer science and may serve as a guide for future researchers, practitioners, and scientific development strategies.

Metaheuristic Optimization for Deep Learning in Plant Disease Detection: A Hybrid Approach

Aqeel Majeed Breesam — 2026-02-06

This study investigates metaheuristic hyperparameter optimization for deep learning–based plant disease detection across two datasets: Dataset A (1,530 images; three classes: Healthy, Powdery, Rust) and a large multi-crop corpus evaluated in a binary Healthy/Diseased setting with an 80/20 training–validation split. A hybrid optimizer is proposed that interleaves Dragonfly Algorithm (DA) for population-wide exploration with Firefly Algorithm (FA) for elite intensification (DA–FLA), and is applied to five pretrained CNN backbones (DenseNet, VGG19, InceptionV3, MobileNet, Xception). All models are trained under an identical 50-epoch protocol. On Dataset A, DenseNet provides the strongest baseline (accuracy/macro-F1 = 0.9733/0.9735), which rises to 0.9800/0.9800 with DA–FLA tuning. On the large-scale binary corpus, Xception and DenseNet perform competitively (≈0.9846 macro-F1 and 0.9838 macro-F1, respectively), while the optimized Xception attains 0.9924 accuracy and 0.9913 macro-F1. A one-way ANOVA with Tukey HSD confirms significant performance differences (p < 0.001), with optimized Xception outperforming all comparators. The hybrid search introduces modest training overhead but leaves inference cost essentially unchanged. Results demonstrate that balancing global exploration with local exploitation yields reproducible, statistically supported gains, advancing accurate and efficient plant disease diagnostics suitable for mobile/edge deployment and supporting early intervention and sustainable farming practices.

Application of multi-algorithm approach for lung cancer prediction

Zulkifli Zulkifli — 2026-02-10

Lung cancer is one of the leading causes of cancer-related mortality worldwide, with most cases diagnosed at an advanced stage. Accurate and cost-effective early detection remains a major challenge due to the heterogeneity of imaging and histopathological features. Therefore, this study aimed to develop diagnostic software for lung cancer prediction using a multi- algorithm method. Patient data, including 16 clinical and lifestyle variables, were processed and analyzed with five machine learning algorithms, namely Neural Network (NN), Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Random Forest (RF), and Naïve Bayes (NB). Model performance was evaluated based on accuracy, precision, recall, and F1-score. The results showed that RF, NB, SVM, and NN achieved perfect predictive performance (100% across all metrics), while k-NN obtained slightly lower but still high performance (99%). These findings signified that multi-algorithm predictive modeling could provide robust diagnostic support for lung cancer detection. The proposed software offered potential as an accessible, low-cost decision-support tool to assist clinicians in early diagnosis and improve patient outcomes.

Filtering Microstrip Patch Antenna Design Using Coupling Matrix Approach for ISM Applications

Kharmana Ramazan Ahmad — 2026-02-16

This paper presents a novel design approach for a filtering microstrip patch antenna inspired by a bandpass filter (BPF). It is based on the coupling matrix approach, where the magnitudes of the matrix elements are utilized to extract the physical dimensions. Two rectangular microstrip patch resonators (RMPRs) are directly coupled via an air-gap to realize a second-order BPF and a filtering microstrip patch antenna. For the BPF, a 50-Ω microstrip feedline is employed at the input/output ports and extended into the center of the RMPRs to ensure strong impedance matching within the passband of interest. For the filtering antenna, the output feedline port is removed, and the second RMPR is modified to obtain the required radiation quality factor and provide radiation within the passband frequency range. To validate the proposed approach, both designs are fabricated and experimentally tested, showing excellent agreement with the simulation results. The measured 10-dB fractional bandwidth (FBW), passband peak gain, and total efficiency are 4.05%, 6.0 dBi, and 74.0%, respectively. These results demonstrate that the proposed designs offer a compact size, high gain, and high efficiency, making them promising candidates for ISM band applications.

Area and Power Optimized Architecture of Sample Rate Converter for IoT Gateway Applications

Swetha Pinjerla — 2026-02-16

Nowadays, the Internet of things plays a major role in society for various applications such as medical diagnostics, telecommunications, agriculture, mobile computing, broadcasting, video surveillance etc. In Internet of Things (IoT) networks, several sensors with different data rates should be integrated to perform overall control or monitoring processes.High-speed data transmission technologies should be needed to communicate with IoT servers or storage. Generally, a gateway device is used to integrate low-data rate devices and IoT interfaces. Field Programmable Gate Array Logic (FPGA) can be utilized to implement high-speed and low-power gateway. The paper suggests a design of an FPGA-based IoT gateway architecture, which allows multi-protocol communications and an effective way of controlling sample rates. The design provides RF transceivers, protocol specific modules, and dynamic Sample Rate (SR) Selector to support smooth synchronization of data between diverse IoT devices. Clock generation and control blocks guarantee adaptive frequency assignment and upsampling and downsampling CIC filtering-based units ensure good signal conditioning. Experimental analysis shows that the presented method creates the low root mean square error (RMSE): 1.2 percent (downlink) and 1.4 percent (uplink), and high signal to noise ratios (SNR): 26.3 dB (downlink) and 24.8 dB (uplink) in 45 nm CMOS technology, resulting in better results than conventional 180 nm implementations. The Application-Specific Integrated Circuit (ASIC) implementation achieved a compact core area, reducing from 2.3 μm2 at 180 nm to 0.3 μm2 at 45 nm, demonstrating significant area efficiency with technology scaling. The results affirm that the architecture can provide reliable and high-quality data transfers of next-generation IoT gateways.