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31,456 Article Results

Boosting stroke prediction with ensemble learning on imbalanced healthcare data

10.11591/ijeecs.v38.i2.pp1137-1148
Outmane Labaybi , Mohamed Bennani Taj , Khalid El Fahssi , Said El Garouani , Mohamed Lamrini , Mohamed El Far
Detecting strokes at the early day is crucial for preventing health issues and potentially saving lives. Predicting strokes accurately can be challenging, especially when working with unbalanced healthcare datasets. In this article, we suggest a thorough method combining machine learning (ML) algorithms and ensemble learning techniques to improve the accuracy of predicting strokes. Our approach includes using preprocessing methods for tackling imbalanced data, feature engineering for extracting key information, and utilizing different ML algorithms such as random forests (RF), decision trees (DT), and gradient boosting (GBoost) classifiers. Through the utilization of ensemble learning, we amalgamate the advantages of various models in order to generate stronger and more reliable predictions. By conducting thorough tests and assessments on a variety of datasets, we demonstrate the efficacy of our approach in addressing the imbalanced stroke datasets and greatly enhances prediction accuracy. We conducted comprehensive testing and validation to ensure the reliability and applicability of our method, improving the accuracy of stroke prediction and supporting healthcare planning and resource allocation strategies.
Volume: 38
Issue: 2
Page: 1137-1148
Publish at: 2025-05-01

Detection of COVID-19 based on cough sound and accompanying symptom using LightGBM algorithm

10.11591/ijeecs.v38.i2.pp940-949
Wiharto Wiharto , Annas Abdurrahman , Umi Salamah
Coronavirus disease 19 (COVID-19) is an infectious disease whose diagnosis is carried out using antigen-antibody tests and reverse transcription polymerase chain reaction (RT-PCR). Apart from these two methods, several alternative early detection methods using machine learning have been developed. However, it still has limitations in accessibility, is invasive, and its implementation involves many parties, which could potentially even increase the risk of spreading COVID-19. Therefore, this research aims to develop an alternative early detection method that is non-invasive by utilizing the LightGBM algorithm to detect COVID-19 based on the results of feature extraction from cough sounds and accompanying symptoms that can be identified independently. This research uses cough sound samples and symptom data from the Coswara dataset, and cough sound’s features were extracted using the log mel-spectrogram, mel frequency cepstrum coefficient (MFCC), chroma, zero crossing rate (ZCR), and root mean square (RMS) methods. Next, the cough sound features are combined with symptom data to train the LightGBM. The model trained using cough sound features and patient symptoms obtained the best performance with 95.61% accuracy, 93.33% area under curve (AUC), 88.74% sensitivity, 97.91% specificity, 93.17% positive prediction value (PPV), and 96.33% negative prediction value (NPV). It can be concluded that the trained model has excellent classification capabilities based on the AUC values obtained.
Volume: 38
Issue: 2
Page: 940-949
Publish at: 2025-05-01

High-efficiency multimode charging interface for Li-Ion battery with renewable energy sources in 180 nm CMOS

10.11591/ijeecs.v38.i2.pp744-754
Hajjar Mamouni , Karim El Khadiri , Anass El Affar , Mohammed Ouazzani Jamil , Hassan Qjidaa
The high-efficiency multi-source Lithium-Ion battery charger with multiple renewable energy sources described in the present paper is based on supply voltage management and a variable current source. The goal of charging the battery in a constant current (CC) mode and controlling the supply voltage of the charging circuit are both made achievable using a variable current source, which may improve the battery charger’s energy efficiency. The battery must be charged with a degraded current by switching from the CC state for the constant voltage (CV) state to prevent harming the Li-Ion battery. The Cadence Virtuoso simulator was utilized to obtain simulation results for the charging circuit, which is constructed in 0.18 μm CMOS technology. The simulation results obtained using the Cadence Virtuoso simulator, provide a holding current trickle charge (TC) of approximately 250 mA, a maximum charging current (LC) of approximately 1.3 A and a maximum battery voltage of 4.2 V, and takes only 29 minutes to charge.
Volume: 38
Issue: 2
Page: 744-754
Publish at: 2025-05-01

A GRU-based approach for botnet detection using deep learning technique

10.11591/ijeecs.v38.i2.pp1098-1105
Suchetha G. , Pushpalatha K.
The increasing volume of network traffic data exchanged among interconnected devices on the internet of things (IoT) poses a significant challenge for conventional intrusion detection systems (IDS), especially in the face of evolving and unpredictable security threats. It is crucial to develop adaptive and effective IDS for IoT to mitigate false alarms and ensure high detection accuracy, particularly with the surge in botnet attacks. These attacks have the potential to turn seemingly harmless devices into zombies, generating malicious traffic that disrupts network operations. This paper introduces a novel approach to IoT intrusion detection, leveraging machine learning techniques and the extensive UNSW-NB15 dataset. Our primary focus lies in designing, implementing, and evaluating machine learning (ML) models, including K-nearest neighbors (KNN), random forest (RF), long short-term memory (LSTM), and gated recurrent unit (GRU), against prevalent botnet attacks. The successful testing against prominent Bot- net attacks using a dedicated dataset further validates its potential for enhancing intrusion detection accuracy in dynamic and evolving IoT landscapes.
Volume: 38
Issue: 2
Page: 1098-1105
Publish at: 2025-05-01

S-commerce: competition drives action through small medium enterprise top management

10.11591/ijeecs.v38.i2.pp1042-1050
Erwin Sutomo , Nur Shamsiah Abdul Rahman , Awanis Romli
This study investigates the factors influencing the continued use of S-commerce in small and medium enterprises (SMEs), focusing on the roles of top management (TM) support, competitive pressure (CP), facilitating conditions, and service quality. Data were collected from 341 SME owners and analyzed using SEM. Data was analyzed with SmartPLS using a two-step approach. The findings indicate that TM support significantly impacts the continued use of S-commerce by influencing facilitating conditions and service quality while CP affects TM behavior and usage continuity. However, the findings reveal that operational factors, such as infrastructure and service quality, play a more critical role in sustaining S-commerce engagement than external pressures. Facilitating conditions, in particular, were found to have a strong influence on service quality and platform engagement, underscoring the importance of technical and organizational resources. The study extends prior research by highlighting the interplay between internal and external drivers in fostering the continuous use of S-commerce, offering practical insights for SMEs and future research directions.
Volume: 38
Issue: 2
Page: 1042-1050
Publish at: 2025-05-01

An efficient load balance using virtual machine migration hybrid optimization technique in cloud computing

10.11591/ijeecs.v38.i2.pp1265-1272
Saravanan Madderi Sivalingam , Pavan Kumar Prathapagiri
Cloud computing is becoming increasingly important to developers and companies because to the rapid development of information technology and the wide availability of internet applications. Every information technology industry has a significant role for cloud computing. Numerous multinational technology businesses, like Google, Microsoft, and Facebook, have established data centers across the world to offer processing and storage capabilities. Customers can submit their jobs to cloud centers directly. Reducing overall power usage is the primary goal, which was overlooked in the early stages of cloud development. Using gene expression programming (GEP), symbolic regression models of virtual machines (VMs) are developed using measured VM loads and the corresponding resource parameters. In order to minimize resource use, multidimensional resource load balancing of all the physical machines within the cloud computing platform is the aim of this analysis. The VMH loads estimated and the genetic algorithm that considers the current and the future loads of VMHs and decides an optimal VM-VMH for migrating VMs and performing load-balance. Hence, an efficient load balance using virtual machine migration hybrid optimization technique (HOT) in cloud computing shows better results in terms of accuracy, energy consumption, migration cost.
Volume: 38
Issue: 2
Page: 1265-1272
Publish at: 2025-05-01

A variant of particle swarm optimization in cloud computing environment for scheduling workflow applications

10.11591/ijeecs.v38.i2.pp1392-1401
Ashish Tripathi , Rajnesh Singh , Suveg Moudgil , Pragati Gupta , Nitin Sondhi , Tarun Kumar , Arun Pratap Srivastava
Cloud computing offers on-demand access to shared resources, with user costs based on resource usage and execution time. To attract users, cloud providers need efficient schedulers that minimize these costs. Achieving cost minimization is challenging due to the need to consider both execution and data transfer costs. Existing scheduling techniques often fail to balance these costs effectively. This study proposes a variant of the particle swarm optimization algorithm (VPSO) for scheduling workflow applications in a cloud computing environment. The approach aims to reduce both execution and communication costs. We compared VPSO with several PSO variants, including Inertia-weighted PSO, gaussian disturbed particle swarm optimization (GDPSO), dynamic-PSO, and dynamic adaptive particle swarm optimization with self-supervised learning (DAPSO-SSL). Results indicate that VPSO generally offers significant cost reductions and efficient workload distribution across resources, although there are specific scenarios where other algorithms perform better. VPSO provides a robust and cost-effective solution for cloud workflow scheduling, enhancing task-resource mapping and reducing costs compared to existing methods. Future research will explore further enhancements and additional PSO variants to optimize cloud resource management.
Volume: 38
Issue: 2
Page: 1392-1401
Publish at: 2025-05-01

Artificial intelligence approaches for cardiovascular disease prediction: a systematic review

10.11591/ijeecs.v38.i2.pp1208-1218
Jasim Faraj Hammadi , Aliza Binti Abdul Latif , Zaihisma Binti Che Cob
Cardiovascular disease (CVD) remains a top global cause of mortality, highlighting the critical need for precise prediction models to improve patient outcomes and optimize healthcare resource allocation. Accurate prediction of CVD is paramount for early diagnosis and reducing mortality rates. Achieving efficient CVD detection and prediction requires a deep understanding of health history and the underlying causes of heart disease. Harnessing the power of data analytics proves advantageous in leveraging vast datasets to make informed predictions, aiding healthcare clinics in disease prognosis. By consistently maintaining comprehensive patient-related data, healthcare providers can anticipate the emergence of potential diseases. Our study conducts a meticulous comparative analysis of CVD prediction methods, focusing on various artificial intelligence (AI) algorithms, particularly classification and predictive algorithms. Scrutinizing approximately sixty papers on cardiovascular disease through the prism of AI techniques, this study carefully assesses the selected literature, uncovering gaps in existing research. The outcomes of this study are expected to empower medical practitioners in proactively predicting potential heart threats and facilitating the implementation of preventive measures.
Volume: 38
Issue: 2
Page: 1208-1218
Publish at: 2025-05-01

Retraction Notice: Quay crane assignment in container terminals using a genetic algorithm

10.11591/csit.v6i1.p40-47
Aidi Sanaa , Torbi Imane , Mazouzi Mohamed
Notice of Retraction: A. Sanaa, T. Imane, and M. Mohamed, "Quay crane assignment in container terminals using a genetic algorithm," Computer Science and Information Technologies, vol. 6, no. 1, 2023, pp. 40-47, doi: 10.11591/csit.v6i1.p40-47." This article has been determined to have contravened IAES publication principles following a thorough and thoughtful review of its content by a duly established expert committee, following the report of Hizia Amani and Rachid Chaib. In particular, the content of the following source was copied without proper attribution in this article: H. Amani, L. Bouyaya, R. Chaib, F. Z. Djekrif, and M. Aizi, "Optimization of Quay Crane Scheduling Problem at the Port of Algeria," in I. Kabashkin, I. Yatskiv, and O. Prentkovskis, Eds. Cham: Springer International Publishing, 2023, pp. 232–241, doi: 10.1007/978-3-031-26655-3_21. Consequently, IAES has removed the content of this article from this online system. The authors concurred with the decision to retract when it was communicated. Additionally, the authors requested that the article be removed.The article is not suitable for research or citation. We apologize for any inconvenience this may have caused.
Volume: 6
Issue: 1
Page: 40-47
Publish at: 2025-04-05

A longitudinal network analysis of research trends and policy implications: southernmost of Thailand case study

10.11591/ijece.v15i2.pp2109-2127
Komgrit Rumdon , Kamonthip Longha , Nawapon Kaewsuwan , Wannisa Matcha
The government funds research projects to address problems, advance knowledge, and support national development. Data repositories are often used to store this research information; however, such information is not optimally used when making the decision. This is particularly important, especially in the areas that require extensive effort and budget allocation to drive development, such as the southernmost provinces of Thailand. This area has been in a violent situation for more than twenty years, leading to poor education, economic challenges, and many more. This study aims to analyze the trends in research topics on these provinces over 30 years (1982–2020) using epistemic network analysis (ENA) on data from the Southern Border Provinces Research Database (SOREDA). Key findings showed a prolonged focus on “education” and “Islamic studies,” reflecting steady government support but raising concerns about its effectiveness. Another important point was that conflict management research arose in response to the surge in violence in 2004 and prolonged existing. The current trending research focused on local–based capital and how it is used to drive society and the economy, such as through tourism. These highlight evolving priorities in addressing the region's challenges and opportunities.
Volume: 15
Issue: 2
Page: 2109-2127
Publish at: 2025-04-01

Enhancing plant disease detection using machine learning approaches for improved agricultural productivity

10.11591/ijece.v15i2.pp2081-2088
Ganga Piska , Swarali Janaskar , Ojas Chandgadkar , Paras Bhawsar , Pinki Prakash Vishwakarma
India's agricultural sector faces persistent challenges due to the prevalence of plant diseases, which severely impact crop quality and productivity, exacerbating the ongoing food supply crisis. Traditional methods of diagnosing plant diseases are often time-consuming, labor-intensive, and prone to inaccuracies, making it difficult for farmers to implement timely interventions. To address these issues, a forward-looking strategy utilizing artificial intelligence (AI) and machine learning (ML) has been proposed, aiming to revolutionize disease detection and management in agriculture. This involves the development of a comprehensive novel dataset named Leafsnap, which is uniquely sourced directly from real-world agricultural environments. This dataset ensures the authenticity and relevance of the data, reflecting the actual conditions faced by farmers. Leafsnap serves as a foundation for training advanced algorithmic models designed to identify patterns and symptoms indicative of various leaf diseases. The proposed system leverages a combination of cutting-edge AI and ML techniques, including convolutional neural networks (CNN), random forest (RF), support vector machines (SVM), and extreme gradient boosting (XGBoost) and logistic regression (LR). By integrating these advanced computational techniques into agricultural practices, the system aims to provide farmers with an efficient, reliable, and scalable solution for disease management. The ultimate goal is to foster agricultural sustainability by minimizing crop losses due to disease, thereby bolstering food security and supporting the livelihoods of farmers across India.
Volume: 15
Issue: 2
Page: 2081-2088
Publish at: 2025-04-01

D-RAKE compression for enhanced internet of things data management in air quality monitoring

10.11591/ijece.v15i2.pp2468-2478
Kartika Sari , Rahmi Hidayati
This study addresses the issue of air pollution in Pontianak, marked by high levels of pollutant particles and chemical compounds that cause respiratory health risks. The research involves essential air quality monitoring using various sensors for temperature, humidity (DHT22), O2 (MQ-135), CO (MQ-7), CO2 (MG-811), and dust (GP2Y1010AU0F), collected real-time, leading to a notable increase in data volume. Due to limitations in internet of things (IoT) devices, there is a need for integration between cloud and IoT through data transmission to reduce the communication time and memory usage. The escalation in sensor data volume requires a lossless compression technique to ensure efficient storage without sacrificing crucial information. Compression plays a vital role in overcoming complex storage challenges, facilitating real-time data access for monitoring, and contributing to sustainable efforts to improve air quality in Pontianak. This research applies the D-RAKE compression method based on basic counting procedures with minimal memory requirements, cost-effective, low-speed microcontrollers commonly used in IoT devices. Despite its simplicity, simulation results indicate that the D-RAKE algorithm outperforms well-established compression methods such as gzip, bzip2, and rar, particularly for data sequences with sparse elements. Moreover, when applied to real- world data, D-RAKE achieves superior compression ratios compared to IoT-focused compression techniques.
Volume: 15
Issue: 2
Page: 2468-2478
Publish at: 2025-04-01

Multi-robot architecture based on hybridized blockchain model

10.11591/ijece.v15i2.pp1511-1520
Rahul Harish Kumar , Gopalakrishnan Muthu Subramanian , Sahana Bailuguttu
Multi-robot systems (MRS) are groups of robots that coordinate to complete a given task. In communication-based systems, the integrity of the information shared between robots becomes highly important as any security threat due to a malicious node in the system can cause a chain reaction to compromise the entire system. This paper proposes a hybridized blockchain model-based architecture (HBMA) built on robot operating system (ROS) which offers a semi-decentralized environment into which any communication-based algorithm can be plugged in. A security monitoring system is also provided with the architecture that identifies and shuts down malicious robots while also sending out alerts about the threat. This architecture is used to create secured, coupled approaches to localization of multi-robots and multi-robot path planning. This approach is validated on both physical robots and simulations run on ROS.
Volume: 15
Issue: 2
Page: 1511-1520
Publish at: 2025-04-01

Cost-effective IoT-based automated vehicle headlight control system: design and implementation

10.11591/ijict.v14i1.pp325-333
Momotaz Begum , Nayeem Ullah , Mehedi Hasan Shuvo , Towhidul Islam , Thofazzol Hossen , Jia Uddin
The current world would be difficult without vehicles, which offer vital advantages for social connectivity, mobility, and technical advancement. Though motor vehicles provide benefits to passenger transportation, they also present certain challenges in their use. A major issue is nighttime traffic accidents caused by headlamps from automobiles traveling in reverse directions, that's why there is a high probability of accidents due to the glare on the driver's eyes. The phrase "Troxler effect" refers to an unexpected glare that a motorist recognizes. In this paper, we will provide an optimal solution to this challenge/Troxler effect. The primary objective of this paper is to design an internet of things (IoT)-based smart headlight control model. Our system introduced a cost-effective vehicle’s headlights controlled by light detection. According to this paper, a vehicle’s headlights are automatically rotated down when the sensor detects lights from the opposite direction of the vehicle headlights. We tried to reduce the road accident rate with our proposed system. This type of technology will prove useful in the motor vehicle sector and offer an innovative approach that ensures driver safety as well as increasing economic development.
Volume: 14
Issue: 1
Page: 325-333
Publish at: 2025-04-01

Optimized control and simulation of a grid-integrated photovoltaic and fuel cell hybrid power system

10.11591/ijece.v15i2.pp1411-1423
Amal Satif , Laamari Hlou , Rachid Elgouri
In this study, we propose an advanced power control strategy tailored for a grid-connected photovoltaic-hydrogen hybrid system aimed at ensuring reliable energy supply to consumers. This hybrid system integrates photovoltaic (PV) panels as the primary energy source supplemented by hydrogen fuel production through electrolysis of surplus PV-generated electricity. To optimize system flexibility and efficiency, we employ several advanced control techniques. A novel maximum power point tracking (MPPT) method, enhanced with a proportional-integral (PI) controller, surpasses conventional perturb and observe (P&O) techniques to maximize PV power output. For precise control of the three-phase photovoltaic inverter, we utilize a space vector pulse width modulation (SVPWM) algorithm. Synchronization with the utility grid is ensured by a phase-locked loop (PLL), maintaining phase coherence between the inverter output and grid supply. The primary goal is to develop a comprehensive and efficient control strategy for grid-connected hybrid systems, addressing both energy generation and storage challenges. MATLAB simulations validate the system's performance, demonstrating high energy conversion efficiency and robust control across varying conditions. This study underscores the potential of hybrid PV-hydrogen systems to provide sustainable and resilient energy solutions for future grid integration.
Volume: 15
Issue: 2
Page: 1411-1423
Publish at: 2025-04-01
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