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Computer Science and Information Technologies
Published by Institute of Advanced Engineering and Science
ISSN : 2722323X     EISSN : 27223221     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Computer Science and Information Technologies ISSN 2722-323X, e-ISSN 2722-3221 is an open access, peer-reviewed international journal that publish original research article, review papers, short communications that will have an immediate impact on the ongoing research in all areas of Computer Science/Informatics, Electronics, Communication and Information Technologies. Papers for publication in the journal are selected through rigorous peer review, to ensure originality, timeliness, relevance, and readability. The journal is published four-monthly (March, July and November).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 191 Documents
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QuishingShield: on-device multi-modal detection of quick response phishing Shabour Banda; Maronge Musara; Mainford Mutandavari
Computer Science and Information Technologies Vol 7, No 3: November 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i3.p271-290

Abstract

Quick response (QR) code phishing (quishing) attacks take advantage of user trust in QR physical and digital media, while currently available protection mechanisms only detect a single dimension signal and are unable to detect cross-modal deception. This paper introduces QuishingShield, an on-device quishing detection system based on multi-modal deep learning which preserves privacy on mobile platforms. Cross-modal attention fusion is used to combine visual poster features, optical character recognition (OCR) recognized surrounding text and recognized uniform resource locator (URL) structure, as well as network reputation signals in a system. A teacher model is trained on 205,488 real-world QR code poster samples from 45 countries and 23 languages, and the knowledge is distilled into a compact model for the student model, in order to be deployed in mobile applications. The student has an accuracy of 95.55% and a recall of 98.18% for a held-out test set, with an inference latency of 25.34 ms on mobile devices, all of which are at or below the deployment targets. Robustness of 95.00% when tested adversarially on four sets of attacks. The multi-modal fusion approach enhances performance by 5.17-11.07 percentage points over the unimodal baseline approaches (p0.001). QuishingShield, to our best knowledge, is the first validated multi-modal quishing detection system satisfying accuracy, speed, size and privacy requirement for mobile deployment.
A comparative review of modern large language model paradigms: GPT-4, BERT, Gemini, and DeepSeek Kavish Sanghvi; Aparna S. Sharma; Surbhi Hooda
Computer Science and Information Technologies Vol 7, No 3: November 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i3.p404-418

Abstract

This review provides comparative analysis of GPT-4, BERT (bidirectional encoder representations from transformers), Gemini, and DeepSeek large language models (LLM), focusing architectures, training methodologies, and real-world applications. The primary research question is: How do these models differ in design, strengths, limitations, and potential areas for enhancement? By addressing this question, the study aims to provide insights into the trade-offs and future directions for optimizing LLM performance and deployment. The analysis reveals GPT-4 excels in natural language generation and complex reasoning, supporting up to 128K tokens with moderate latency and higher costs making it effective for conversational artificial intelligence (AI). BERT excels bidirectional contextual understanding with smaller computational overhead and broad open-source adoption, effective for text classification. Gemini demonstrates superior multimodal integration processing text, image, audio, and code with context lengths up to 1M tokens, enabling cross-domain adaptability. DeepSeek excels in specialized domains like finance and programming, is optimized for efficiency and supports extended context windows exceeding 200K tokens. However, all models face challenges related to computational cost, hallucinations, and ethical concerns, necessitating further improvements. Despite advancements, LLMs continue to grapple with issues such as data bias, model interpretability, and responsible AI deployment. Future research should focus on hybrid model approaches, domain-specific fine-tuning, and transparency to mitigate risks while maximizing the transformative potential of LLMs in real-world applications.
Hematological profiling of malaria-induced anemia using deep learning Vanrose Panashe Nyamangodo; Wellington Makondo; Simbai Zindove
Computer Science and Information Technologies Vol 7, No 3: November 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i3.p304-313

Abstract

However, malaria-induced anemia (MIA) still persists to be one of the global health challenges with many cases of illness and fatalities mainly in pregnant women and children. Diagnosis of malaria and associated hematologic diseases such as anemia is traditionally carried out through examination of blood smear. However, such techniques require expertise, take long periods, and there are high chances of inter-observer variability. Here, an automatic system based on deep learning for detection of Plasmodium parasite and estimation of anemia is introduced. The proposed system uses convolutional neural network (CNN) branch for image analysis and multi-layer perceptron (MLP) branch for analyzing clinical data, thereby using their combination in multi-label classification. The advantage of such technique is the capability of the model to diagnose complex hematological signs and give probabilistic scores. This system was found to be accurate, precise and reliable.
Time series forecasting: a comparative analysis of ARIMA, LSTM, and TFT models with missing data handling Maryam Hosseini; Mohamad Forouzanfar
Computer Science and Information Technologies Vol 7, No 3: November 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i3.p291-303

Abstract

Time series forecasting plays a critical role in finance, healthcare, and energy applications, where accurate predictions support decision-making and operational efficiency. Traditional approaches such as autoregressive integrated moving average (ARIMA) perform well for linear patterns but often struggle with nonlinear and complex temporal dependencies found in real-world data. Although deep learning methods such as long short-term memory (LSTM) networks and Transformer-based models have shown promise, comprehensive evaluations across multiple domains and under missing-data conditions remain limited. This study compares ARIMA, LSTM, and temporal fusion transformer (TFT) models across three applications: stock price forecasting using SP 500 data, heart-rate prediction from electrocardiogram (ECG)-derived signals, and electricity demand forecasting using Pennsylvania–New Jersey–Maryland (PJM) power grid data. To evaluate robustness under realistic conditions, varying levels of missingness were introduced using missing completely at random (MCAR) and missing at random (MAR) mechanisms. Missing values were handled using forward fill, linear interpolation, and k-nearest neighbors (k-NN) imputation. Results show that TFT consistently achieved superior forecasting performance and demonstrated greater robustness to increasing missingness, while k-NN generally provided the most effective imputation performance across datasets.
Early Escherichia coli prediction in broiler chickens Nicole Chimwamafuku; Brian Mupini
Computer Science and Information Technologies Vol 7, No 3: November 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i3.p314-324

Abstract

Poultry farming remains an important contributor to global food security and commercial livestock production. However, infectious diseases such as Escherichia coli (E. coli) cause mortality, poor feed efficiency, reduced growth performance, and economic losses in broiler production systems. This study proposes a checkpoint-based multimodal transformer-convolutional neural networks (CNN) framework for early flock-level E. coli infection risk prediction using environmental, production, behavioural, and visual poultry data. Flock monitoring records collected from 2022 to 2025 were structured across six production checkpoints: day 3, day 7, day 14, day 21, day 28, and day 31. After long-format conversion, approximately 90,000 temporal observations were used for transformer modelling, with 72,000 records for training and 18,000 for testing. The CNN component evaluated 249 poultry images across healthy, low-risk, medium-risk, high-risk, and non-broiler classes. The transformer model achieved 99.96% accuracy, while the CNN model achieved 95.58% accuracy. The integrated dashboard generated flock risk scores, contributing factors, alerts, gradient-weighted class activation mapping (Grad-CAM) explanations, and veterinary advisory recommendations, demonstrating the potential of multimodal artificial intelligence (AI) for proactive poultry health monitoring.
Detection and translation of logic gate images into Boolean functions Muhammad Shidqii Taqiyyuddin; Muhammad Subali
Computer Science and Information Technologies Vol 7, No 3: November 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i3.p346-352

Abstract

Artificial intelligence (AI) has become an essential tool in solving complex problems effectively and efficiently. In digital electronics, translating logic gate circuits into Boolean functions can be challenging, especially for more complex structures. This study presents the design of a detection and translation system for logic gate images into Boolean functions using the You Only Look Once (YOLOv5) object detection model. A dataset of 800 images was collected using a smartphone camera under varied lighting conditions and preprocessing to ensure robustness. The dataset was divided into 70% for training, 20% for validation, and 10% for testing. Training was conducted using YOLOv5s with batch size 32, 100 epochs, and pre-trained weights. The trained model achieved strong results with an overall mean average precision (mAP@0.5) of 0.922, precision of 0.98, and recall of 0.984. The confusion matrix confirmed accurate detection across all classes, with minimal misclassification. Furthermore, the translator system successfully converted recognized objects into correct Boolean expressions, with results validated in multiple test cases. This demonstrates that the proposed system can reliably automate circuit image-to-Boolean translation, bridging image recognition and symbolic computation.
Optimizing deep learning models for plant leaf disease classification using nature-inspired algorithms Avinesh Culloo; Avinash Bhunjun; Geerish Suddul
Computer Science and Information Technologies Vol 7, No 3: November 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i3.p369-376

Abstract

Plant diseases greatly affect agricultural production, especially in developing countries, where prompt diagnosis can be quite challenging due to the limited availability of experts in real-time. Deep learning techniques for image analysis is gaining popularity and are increasingly considered an alternative to traditional manual inspection of plants. This research presents the evaluation of plant leaf disease detection system based on a convolutional neural network (CNN) optimized with different nature-inspired algorithms. The backbone model is based on the EfficientNet-B0 pretrained on ImageNet. Therefore, transfer learning is used to adapt the model to an updated PlantVillage dataset. Experiments have been conducted with multiple nature-inspired algorithms to improve generalisation and training efficiency of the prediction model. Different data preparation techniques have been carefully applied to the dataset, creating a unified approach to ensure consistency in the preprocessing pipeline for the training, validation, and testing phases. Our experiments indicate that application of the grey wolf optimizer (GWO) for tuning key hyperparameters of the model, including dropout, learning rates, and weight decay produced the best results, with and accuracy around 99.45%.
AI health assistant combining transformers and XGBoost for multilingual care Shamiso Simango; Mainford Mutandavari
Computer Science and Information Technologies Vol 7, No 3: November 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i3.p353-368

Abstract

Limited healthcare access, shortages of healthcare professionals, and linguistic diversity continue to impede timely symptom assessment and healthcare delivery in low-resource settings such as Zimbabwe. Existing virtual health assistant (VHAs) are frequently cloud-dependent, English-centric, and lack interpretable decision-making, limiting their effectiveness in bandwidth-constrained and privacy-sensitive environments. This study proposes CIMAS HealthMate, a hybrid multilingual VHA that integrates transformer-based natural language processing (NLP) with an explainable extreme gradient boosting (XGBoost) decision model to provide accurate and transparent symptom triage. The framework employs the no language left behind (NLLB) model for offline English–Shona translation, bidirectional encoder representations from transformers (BERT)-based models for intent classification and medical entity recognition, and XGBoost for structured triage recommendation. The system was evaluated using a multilingual symptom corpus and an anonymized electronic health record-style dataset comprising approximately 23,000 patient records. Experimental results achieved translation accuracies of 76.5% for Shona-to-English and 82.2% for English-to-Shona, symptom extraction accuracy of 86.6%, and end-to-end triage accuracy of 93.3% with an F1-score of 93.3%. These findings demonstrate that the proposed hybrid architecture effectively combines multilingual language understanding, interpretable machine learning, and offline deployment to deliver reliable and privacy-preserving triage support. The proposed approach provides a scalable and practical solution for improving equitable digital healthcare services in multilingual, resource-constrained environments.
A hierarchical mixed-effects modeling framework with Weibull reliability characterization for spatiotemporal throughput variability in cellular networks Victor Dela Gordon; Amevi Acakpovi
Computer Science and Information Technologies Vol 7, No 3: November 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i3.p241-255

Abstract

Reliable cellular throughput is essential for ensuring consistent user experience in modern mobile networks, yet it exhibits significant variability across spatial and operational conditions due to propagation effects, interference, and network congestion. This study proposes a hierarchical mixed-effects modeling framework integrated with Weibull-based reliability analysis to characterize spatiotemporal throughput variability in real-world operating conditions. The analysis is based on a large-scale dataset comprising over 77,000 field measurements collected across multiple university campus locations in Ghana, enabling cross-layer evaluation of network performance using key indicators, including reference signal received power (RSRP), reference signal received quality (RSRQ), and round-trip time (RTT). Analysis results indicate that all modeled predictors significantly influence throughput performance, with signal quality emerging as the dominant factor, alongside notable spatial heterogeneity. The model explains 18.9% of variability using fixed effects and 45.1% when spatial effects are included. Reliability analysis indicates that the probability of achieving 5 Mbps and 10 Mbps is 39.7% and 22.5%, respectively. These findings demonstrate that the proposed framework effectively captures throughput variability, spatial heterogeneity, and probabilistic service reliability in operational cellular environments, providing a practical analytical framework for reliability-aware cellular network optimization and performance evaluation.
A literature review of recurrent neural network approaches to malaria outbreak prediction in Sub-Saharan Africa Sophia Tembure; Wellington S. Manjoro
Computer Science and Information Technologies Vol 7, No 3: November 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i3.p377-393

Abstract

Malaria remains a significant public health issue in Sub-Saharan Africa. Predictive modelling is increasingly viewed as a way to move from counting cases reactively to preparing proactively for outbreaks. This literature review examines peer-reviewed research published from 1997 to 2025. It covers recurrent neural networks (RNNs) methods, attention mechanisms, multi-source data integration, and health-system informatics relevant to malaria prediction in low- and middle-income countries. A thorough search through Google Scholar, IEEE Xplore, ScienceDirect, PubMed, and SpringerLink identified 187 candidate papers. After reviewing titles and abstracts, 42 papers met the inclusion criteria. The review organizes the selected studies into six main categories: climate-disease ecology, statistical forecasting, machine learning (ML) baselines, RNNs, attention mechanisms, and health-system data integration. A comparative matrix highlights the similarities and differences in methods across twenty representative studies. The review points out four persistent gaps: the lack of attention-augmented RNNs for malaria forecasting in Southern Africa, limited integration of health-facility infrastructure features with climate predictors, inadequate handling of missing data in African satellite-derived climate series, and the absence of operational dashboards that make model outputs usable for district health officers without statistical training. Future research should focus on cross-country transferability studies, using graph neural networks to capture spatial spillover, and real-time integration with national surveillance systems like district health information software 2 (DHIS2).

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