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31,456 Article Results

A hierarchical mixed-effects modeling framework with Weibull reliability characterization for spatiotemporal throughput variability in cellular networks

10.11591/csit.v7i3.p241-255
Victor Dela Gordon , Amevi Acakpovi
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.
Volume: 7
Issue: 3
Page: 241-255
Publish at: 2026-11-01

Optimizing deep learning models for plant leaf disease classification using nature-inspired algorithms

10.11591/csit.v7i3.p369-376
Avinesh Culloo , Avinash Bhunjun , Geerish Suddul
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%.
Volume: 7
Issue: 3
Page: 369-376
Publish at: 2026-11-01

A literature review of recurrent neural network approaches to malaria outbreak prediction in Sub-Saharan Africa

10.11591/csit.v7i3.p377-393
Sophia Tembure , Wellington S. Manjoro
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).
Volume: 7
Issue: 3
Page: 377-393
Publish at: 2026-11-01

Detection and translation of logic gate images into Boolean functions

10.11591/csit.v7i3.p346-352
Muhammad Shidqii Taqiyyuddin , Muhammad Subali
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.
Volume: 7
Issue: 3
Page: 346-352
Publish at: 2026-11-01

A network-based security information system for safeguarding computer-based test platforms in organizational environments

10.11591/csit.v7i3.p325-336
Ajani Dele , Owolabi Abdulhakim Adewale , Inaya Adesuwa
Computer-based testing (CBT) platforms have transformed education and certification by enabling scalable, efficient, and accessible examinations. However, these systems face significant cybersecurity risks, including unauthorized access, denial-of-service (DoS) attacks, and digital cheating, which threaten fairness and reliability. This study proposes a network-based security information system (NBSIS) designed specifically for CBT environments. The framework integrates layered defense, including pfSense firewalls (FW), Snort intrusion detection, Splunk security information and event management (SIEM), and artificial intelligence (AI)-powered analytics, into a unified architecture. A human-centered dashboard ensures usability for non-technical exam administrators, providing real-time alerts and intuitive controls. Validation through simulated attack scenarios demonstrated strong resilience, with high detection accuracy, reduced false positives, and rapid response times. Comparative analysis against intrusion detection system (IDS)-only and SIEM-only systems confirmed superior performance. The findings highlight NBSIS as a robust, scalable, and adaptive solution that safeguards exam integrity while remaining practical for diverse organizational contexts. This research contributes to computer science by advancing secure architecture, applying AI-driven anomaly detection, and integrating human-computer interaction principles into cybersecurity for education.
Volume: 7
Issue: 3
Page: 325-336
Publish at: 2026-11-01

Image processing and computer vision on low-cost microcontrollers: a case study on water meter reading

10.11591/ijres.v15.i3.pp629-644
Sohrab Jalali , Filiz Ersöz , Babak Taeb
Despite the worldwide move towards smart meters, mechanical water meters are still used extensively in residential and municipal networks. Upgrading them with computer vision avoids costly infrastructure replacement, yet most approaches send data to cloud servers for processing. This paper introduces an end-to-end on-device solution for recognizing water meter digits on the ESP32-CAM board, which costs below $15 and features only 520 KB SRAM. A MobileNetV2-based digit classifier is quantized to INT8. Three firmware improvements extend the inference loop: i) a temporal check confirming a reading only when three consecutive detections match, ii) on-device anomaly detection without server support, and iii) adaptive sampling between 3 s during water usage and 60 s when idle. A Flask-based internet of things (IoT) dashboard named MeterWatch provides live monitoring. The model achieves 2 ms latency and 235.6 KB peak memory. Ona553-imagetest subset from a custom 6,155-image dataset with 13,470 digit annotations it reaches 99.60% cell-level accuracy including background, 96.89% digit-only accuracy and 87.70% exact five-digit reading accuracy. To the best of our knowledge, this is the first system combining on-device INT8 deployment, water meter digit recognition and intelligent firmware behaviour on the ESP32-CAM. Evaluations used a single meter brand under fixed flash illumination.
Volume: 15
Issue: 3
Page: 629-644
Publish at: 2026-11-01

Smart lock system with internet of things using facial recognition for home access

10.11591/ijres.v15.i3.pp746-755
Patrick Ramos , José Echenique , Ricardo Yauri
Security in urban residential environments is a significant challenge in regions with high crime rates and unauthorized access. This has led to the integration of internet of things (IoT) technologies and biometric authentication mechanisms into access control systems. Research has described smart lock solutions that require significant hardware resources or computationally complex recognition algorithms. This research proposes the implementation of a low-cost distributed architecture for facial recognition, lock control, monitoring, and supervision using mobile devices. The proposed architecture performs facial recognition using a histogram-oriented gradient (HOG) model executed on a desktop processing node, while an ESP32 microcontroller activates the lock. Remote monitoring is achieved through a communication framework using Flask libraries, tunneling with Ngrok, and a mobile application. Experimental validation was performed in a controlled environment, achieving 96.2% accuracy, and an F1-score of 95.9%. Furthermore, the control subsystem achieved an activation time of 0.78 seconds. The results validate the integration of biometric authentication, IoT control, and remote monitoring. However, limitations were identified in low-light or spoofing environments.
Volume: 15
Issue: 3
Page: 746-755
Publish at: 2026-11-01

Deployment-aware acoustic anomaly detection on an embedded microcontroller

10.11591/ijres.v15.i3.pp645-655
Truong Minh Duc , Nguyen Ngoc Minh , Truong Cao Dung
Industrial acoustic anomaly detection is often optimized in off-device benchmarks, while the consistency of exported thresholded decisions on embedded microcontrollers remains less studied. This paper presents a bounded embedded consistency-evaluation case study for an internet of things (IoT)-class acoustic monitoring pipeline on the public MIMII fan 0 dB subset. The pipeline uses a 40×63×1 log-mel representation, compact convolutional autoencoder (CAE), INT8 TFLite deployment, and latent-space Mahalanobis scoring. The raw Mahalanobis rule defines the on-device decision; a 2-out-of-5 temporal rule is retained only for offline ordered-subset analysis. For fan/id 06/0 dB, repeated stratified evaluation yielded average area under the curve (AUC) 0.96 and F1 0.82. Supporting all-ID evaluation over id 00, id 02, id 04, and id 06 showed the raw Mahalanobis path had the strongest aggregate metrics among tested scoring paths. Stored-sample replay on ESP32-S3 used a fixed 20-sample id 06 batch with generated C audio headers through the canonical DSP path. Both reference-input and digital signal processing (DSP)-path checks produced 20/20 raw-decision agreement with the off-device reference. The results support bounded reference-device consistency for the evaluated batch and provide a reference-device basis for future embedded, IoT-edge, and block-level reconfigurable-system studies, but not cross-ID robustness, streaming, latency, or energy behavior.
Volume: 15
Issue: 3
Page: 645-655
Publish at: 2026-11-01

Throughput bottleneck shift in an embedded Wi-Fi gateway

10.11591/ijres.v15.i3.pp837-847
Zhao Jianan , Yuanbin Mu
An ESP32 software access point (SoftAP) often serves as the sole transmission control protocol (TCP) gateway in field deployments without routers; however, prior benchmarks varied only one factor at a time. We varied two factors that differ in practice: TCP connection count (3, 5, and 10) and per-packet payload size (10, 64, and 256 bytes), yielding 9 configurations. Each was tested at increasing send frequencies, with each frequency repeated 3 times, for a total of 246 60-second runs. Two-way analysis of variance (ANOVA) on the 30 Hz data confirms that both factors and their interaction are significant (all p < 0.001). With 10 connections and 10-byte payloads, the gateway sustained 3,237 packets per second (pps) with zero loss; switching to 256-byte payloads dropped the safe rate below 946 pps and pushed central processing unit (CPU) utilization past 100%. In our experiments, system constraints shift from per-frame media access control (MAC) overhead to channel-bandwidth saturation as payload size increases. We present a stability-region chart that helps designers understand the limits of system stability, optimize system performance, and choose the appropriate connection count and message size.
Volume: 15
Issue: 3
Page: 837-847
Publish at: 2026-11-01

QuishingShield: on-device multi-modal detection of quick response phishing

10.11591/csit.v7i3.p271-290
Shabour Banda , Maronge Musara , Mainford Mutandavari
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.
Volume: 7
Issue: 3
Page: 271-290
Publish at: 2026-11-01

Heterogeneous internet of things device interoperability using fog-based adaptive semantic mechanism

10.11591/ijres.v15.i3.pp819-836
Devamekalai Nagasundaram , Selvakumar Manickam , Mahmood A. Al-Shareeda , Huda Mohammed Alsayednoor , Ali A. Yassin , Shankar Karuppayah
With the rapid growth of heterogeneous internet of things (IoT) ecosystems, insufficient semantic interoperability caused by non-uniform communication protocols, data formats, and semantic representations has become a core challenge in the field. Existing cloud-based, static ontology schemes have defects including high latency, large overhead, and poor adaptability. This paper proposes the dynamic semantic fog (DSF) framework, which performs semantic processing at the fog layer to achieve real-time interoperability. The framework integrates four core modules: workload offloading, resource description framework (RDF) generation, semantic representation, and adaptive ontology expansion. It supports dynamic access to newly added IoT devices. Tests were completed using iFogSim in a smart city traffic scenario. Compared with traditional cloud solutions, the framework reduces latency, energy consumption, and network usage rate by 51.7%, 41.3%, and 55.6% respectively, and can support dynamic semantic interoperability for heterogeneous IoT environments.
Volume: 15
Issue: 3
Page: 819-836
Publish at: 2026-11-01

Energy-efficient clustering optimization for heterogeneous wireless sensor networks with K-nearest neighbors algorithm

10.11591/ijres.v15.i3.pp563-571
Adil Hilmani , Yassine Sabri , Abderrahim Maizate , Siham Aouad , Fouad Ayoub
Wireless sensor networks (WSNs) consist of autonomous sensing nodes whose operation relies on limited energy resources. To extend the operational lifetime of such systems, optimizing power consumption is essential. Clustering plays a key role in enhancing network stability and improving communication efficiency. This work proposes an improved clustering strategy based on the K-nearest neighbors (KNN) algorithm to reduce transmission distances in heterogeneous networks. By integrating this approach into hierarchical protocols such as Department of Electrical and Electronic Engineering (DEEC), Electrical Engineering Center (EEC), Center for Advanced Materials and Processes (CAMP), the method refines both node selection and cluster organization. Simulation results obtained using network simulator 2 (NS2) demonstrate that the modified protocols significantly reduce energy dissipation while increasing overall network lifetime. This combined approach improves system efficiency and provides a robust solution for sustainable energy management in WSNs, meeting the requirements of highly demanding applications.
Volume: 15
Issue: 3
Page: 563-571
Publish at: 2026-11-01

Hybrid geostatistical and machine learning for gold grade estimation

10.11591/csit.v7i3.p394-403
Tanyaradzwa Miriam Mtetwa , Monika Gondo
The evaluation of ore grade is a basic part of the process of mine planning, production scheduling and resource evaluation. The nugget effect and irregular mineralisation that occurs in some greenstone belt deposits on the Zimbabwean Archean causes problems in estimating accurately. The traditional geostatistical models like Ordinary Kriging (OK) produce smoothed estimates which have systematic underestimation in high-grade areas and overestimation in low-grade areas, thus affecting the deposit selectivity. In addition, machine learning (ML) techniques can capture the more complex nonlinear grade relationships, but they are unable to model spatial continuity and can be unrealistic in predicting grades. The hybrid OK-Gradient Boosting (GB) model was designed and evaluated with the drill-hole data of a gold mine in Zimbabwe. The spatial baseline was produced using OK and GB was trained to correct the systematic residual errors. The hybrid model was found to have the maximum overall explanatory power (R²=0.8795), competitive prediction accuracy and increased spatial realism. A spatial prediction map and extraction priority zone classification was created to aid operational mine planning.
Volume: 7
Issue: 3
Page: 394-403
Publish at: 2026-11-01

Risk-integrated contractor allocation in Zimbabwe’s timber value chain

10.11591/csit.v7i3.p337-345
Tavengwa Norman , Brian Mupini
In Zimbabwe, the commercial forestry industry is reliant on a large proportion of outsourced harvest and milling contractors whereas existing enterprise resource planning (ERP) systems record completed transactions instead of predicting contractor failure before assigning activities. We introduce the dynamic resource allocation framework (DRAF), a risk-integrated decision-support framework that supplements the probability of contractor failure derived from XGBoost to a non-dominated sorting genetic algorithm II (NSGA-II) multi-objective optimizer. It divides contractor-block-mill combinations, minimizes cost and expected delay, and maximizes risk-adjusted timber recovery and operational reliability. We created the solution on an 828,789-record virtual ERP dataset and calibrated it to Manicaland forestry conditions and tested with statistical, heuristic and risk-free optimization baselines. Extreme gradient boosting (XGBoost) obtained a holdout receiver operating characteristic area under the curve (ROC-AUC) of 0.965, recall of 0.999, and F1 of 0.867, showing an improvement of 0.365 over logistic regression. The optimizer developed 64 complete Pareto solutions to balanced and high-recovery scenarios and uncovered a constraint-feasibility boundary for a more conservative low-risk scenario. The satisfaction score of the 30-practitioner stakeholder assessment was 4.19 out of 5.0. The results demonstrate that embedding predictive risk into an optimization objective can optimize forestry allocation decisions and suggest that some real ERP validation is required before such measures will be broadly implemented.
Volume: 7
Issue: 3
Page: 337-345
Publish at: 2026-11-01

Convolutional neural network and long short-term memory forecasting and variational autoencoder anomaly detection in 4G cellular networks

10.11591/csit.v7i3.p256-270
Ruvarashe C. Hove , Eng Mainford Mutandavari
Spectrum monitoring in cellular networks with limited resources remains predominantly reactive because capacity planning and failure detection occur after network congestion, while spectrum monitoring data are rarely available for reproducible research. Existing studies address short-term traffic forecasting and anomaly detection as separate tasks and largely depend on densely sampled operator telemetry data that are inaccessible to academia and regulators in emerging markets. This study integrates both tasks into a unified deep-learning pipeline for a 4G cellular environment. The Zimbabwe spectrum dataset contains 315,247 hourly measurements collected from 13 cell sites, three operators, and five frequency bands, annotated with four operator-defined anomaly classes representing 2.03% of all measurements. A hybrid one-dimensional (1-D) convolutional neural network-long short-term memory (CNN–LSTM) model uses a 72-hour traffic window to forecast the next six hours of aggregate network traffic, while a variational autoencoder (VAE) trained exclusively on normal records detects anomalies when reconstruction error exceeds the 99th-percentile validation threshold. On the held-out test set, the model achieved a mean absolute error (MAE) of 158.97 GB, root mean square error (RMSE) of 230.41 GB, and mean absolute percentage error (MAPE) of 52.93%, outperforming a seasonal-naive baseline (MAE=216.43 GB, MAPE=66.77%, p0.001, Diebold-Mariano test). The study contributes a publicly available localized synthetic dataset, a reproducible end-to-end forecasting and anomaly detection baseline, and a per-class anomaly analysis.
Volume: 7
Issue: 3
Page: 256-270
Publish at: 2026-11-01
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