Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

31,456 Article Results

Recent advances and emerging trends in smart parking technologies from sensor based to next generation parking system: a review

10.11591/ijres.v15.i3.pp756-768
Arif Shaikh Farid , Shubham Shukla
Rapid urbanization, growing vehicle ownership, and limited parking infrastructure have made parking allocation a major challenge in modern cities. To address this issue, numerous smart parking systems (SPSs) have been proposed to improve parking efficiency, reduce congestion, and enhance user convenience. This paper presents a comprehensive review and comparative analysis of SPSs in terms of technological approaches, sensing methods, networking technologies, computational models, user interfaces, and service capabilities. It also addresses an important research gap by evaluating the suitability of different SPS approaches under diverse environmental conditions and highlighting their key advantages and limitations. The review emphasizes the growing role of internet of things (IoT)-based, multi-approach architectures in enabling real-time monitoring, reservation, payment integration, and vehicle guidance for smart city applications. Furthermore, it discusses the potential of integrating reconfigurable systems with IoT to improve adaptability, scalability, and responsiveness through dynamic resource allocation and efficient local decision-making. Despite these benefits, challenges related to interoperability, security, energy management, design complexity, and deployment cost remain significant. Future SPSs are expected to incorporate artificial intelligence, predictive analytics, dynamic pricing, autonomous vehicle support, and digital twin technologies to achieve more adaptive, intelligent, and sustainable parking management.
Volume: 15
Issue: 3
Page: 756-768
Publish at: 2026-11-01

Software simulation of magnetohydrodynamic cooling flow in curved porous fusion reactor channels

10.11591/ijres.v15.i3.pp656-670
Hassan Mohammad Hassan , Liqaa Zeki Hummady
This paper presents a numerical simulation of the hydrodynamic magnetoelectric cooling flow in a curved porous reactor channel. The research aims to evaluate the interrelated effects of channel curvature, porous resistivity, magnetic damping, and heat transfer on the hydrodynamic and thermal behavior of the candidate reactor chillers. The mass, momentum, pressure gradient, and energy equations were formulated in a curvature-compatible coordinate system. Porous resistivity was represented using the Darcy-Brinkman-Forchheimer model, while the magnetic interaction was incorporated through a quasi-static magnetohydrodynamic (MHD) approximation. The resulting interrelated equations were solved in MATLAB R2025b using the finite-difference fractionation method and iterative solution. Four chillers were examined within the same geometric and numerical framework: water, helium, Pb-17Li, and a Pb-17Li+AlN nanofluid. The results show that curvature and porous resistivity modify the velocity distribution and increase the pressure gradient requirements, while magnetic damping contributes to the stability of the conductive flows by inhibiting secondary motion. The thermal results indicate that the Pb-17Li+AlN nanofluid improves heat transfer behavior compared to base coolants, despite its high hydraulic resistance. The proposed simulation framework could support future internet of thing (IoT)-enabled integrated reactor cooling monitoring systems.
Volume: 15
Issue: 3
Page: 656-670
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

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

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

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

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 low cost intelligent sorting mechanism using infrared and colour sensor with servo driven robotic arm

10.11591/ijres.v15.i3.pp683-692
Khajan Segaran , Khalil Azha Mohd Annuar , Sanjoy Kumar Debnath , Mohd Razali Mohamad Sapiee
In the era of industrial automation, traditional manual sorting remains a bottleneck characterized by high operational costs and reduced throughput. This research presents an integrated automated sorting system comprising a robotic arm and a conveyor belt mechanism designed to streamline object classification. The system utilizes an infrared (IR) sensor for object detection and a colour sensor for classification based on predetermined criteria. Upon detection, the robotic arm executes precise picking motions to sort objects by colour, significantly minimizing human intervention. Experimental results demonstrate the system's high reliability and efficiency. While the IR sensor consistently detected objects within a 5 cm range, the integration of a physical stopper proved essential for stabilizing object positioning. Initial inaccuracies in colour detection were resolved through iterative frequency-to-red, green, blue (RGB) mapping, eventually achieving 100% accuracy for red, green, and blue targets. Performance tuning revealed that a 1 s servo delay optimized the balance between mechanical stability and speed, yielding an average cycle time of 6.66 seconds. With a 95% overall success rate, this system offers a cost-effective automation solution tailored for small and medium-sized enterprises (SMEs) and highlights the scalability of servo-based robotic sorting in manufacturing and recycling sectors.
Volume: 15
Issue: 3
Page: 683-692
Publish at: 2026-11-01

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

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

Enhancing network performance in IoT-WSNs through federated deep reinforcement learning-based intelligent routing

10.11591/ijres.v15.i3.pp609-618
Neerukonda Lakshmi Kalyani , Shailaja Muthyala , Sravanam Sreeja , Koneti Varalakshmi , Zarapala Sunitha Bai , Mani Mohan Dupaty
By making it possible to collect and monitor data in real time, the installation of wireless sensor networks (WSNs) in the era of the internet of things (IoT) has brought about a transformation in a number of different industries. On the other hand, efficient data routing continues to be a significant challenge because of the dynamic nature of WSNs and the limitations of electronic devices connected to the IoT. This research presents an intelligent data routing technique that is based on federated deep reinforcement learning (FDRL), with the intention of optimizing data transmission in WSNs that are enabled by the IoT. In order to increase the efficiency of data routing, decrease latency, and extend the lifetime of the network, the model that has been recommended makes use of the collaborative learning skills of federated learning (FL) and the decision-making features of deep reinforcement learning (DRL). This is done in order to achieve these goals. It has been demonstrated through extensive simulations that the FDRL-based routing approach is superior to the methods that were previously utilized in terms of energy efficiency, the accuracy of data transfer, and the responsiveness to changes in the network. As a consequence of this, it is a solution that is conceivable for future applications including the IoT.
Volume: 15
Issue: 3
Page: 609-618
Publish at: 2026-11-01

A review of intelligent task scheduling models for fog computing in large-scale internet of things networks

10.11591/ijres.v15.i3.pp777-795
Sababathy Palanisamy , Erum Ashraf , Selvakumar Manickam , Huda Mohammed Alsayednoor , Mahmood A. Al-Shareeda , Shankar Karuppayah
The rapid growth of internet of things (IoT) applications has generated an urgent demand for efficient task scheduling mechanisms that feature low latency, scalability, and resource awareness. While fog computing can meet this demand by extending computing and storage resources to the vicinity of data sources, it still confronts four core challenges: resource heterogeneity, dynamic workloads, mobility, and security constraints. To address these issues, this study conducts a systematic review of intelligent task scheduling for fog-enabled IoT systems. It classifies the included eligible studies into five scheduling paradigms, and innovatively proposes a cross-domain framework that integrates three types of awareness, including priority awareness. The subsequent work of this study will sort out the core strengths and limitations of existing relevant methods. Finally, open challenges and emerging research directions, including edge artificial intelligence (AI), federated learning, blockchain-assisted scheduling, and hardware-accelerated implementations are discussed. The findings provide useful guidance for developing scalable, secure, and adaptive scheduling frameworks for next-generation fog-enabled IoT systems.
Volume: 15
Issue: 3
Page: 777-795
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
1 2 3
...
2097 2098
Show 5 of 2098

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration