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25,002 Article Results

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

Performance of rocket data communication system using wire rope isolator on sounding rocket RX

10.11591/ijeecs.v38.i2.pp783-793
Kandi Rahardiyanti , Shandi Prio Laksono , Khaula Nurul Hakim , Yuniarto Wimbo Nugroho , Andreas Prasetya Adi , Salman Salman , Kurdianto Kurdianto
The rocket experiment (RX) ballistic rocket requires a reliable data communication system capable of withstanding intense vibrations and shocks during flight. This study investigates the application of wire rope isolators (WRI) to damper mechanical disturbances and protect the rocket's communication system. Installation of WRI position and direction in this experiment with compression position. A series of vibration tests were conducted using 4 WRI installed in the rocket’s 30 kg data communication compartment, vibration test results frequency between 4 Hz and 1500 Hz with acceleration of 8.37 g to 20.37 g, higher "g" readings on the test object sensor compared to vibration machine readings are usually caused by phenomena such as resonance, differences in dynamic response, non-linear behavior, sensor placement location, and swing effects when the vibration machine oscillates. This is a natural mechanical response to external vibrations during testing. While the results of flight tests rocket RX has an acceleration of 8 g to 9.3 g. The results showed that the WRI dampers are effective in protecting the data communication system and ensuring the uninterrupted transmission of flight data to the ground control station (GCS).
Volume: 38
Issue: 2
Page: 783-793
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

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

Implementation of innovative deep learning techniques in smart power systems

10.11591/ijeecs.v38.i2.pp723-731
Odugu Rama Devi , Pavan Kumar Kolluru , Nagul Shaik , Kamparapu V. V. Satya Trinadh Naidu , Chunduri Mohan , Pottasiri Chandra Mohana Rai , Lakshmi Bhukya
The integration of deep learning techniques into smart power systems has gained significant attention due to their potential to optimize energy management, enhance grid reliability, and enable efficient utilization of renewable energy sources. This research article explores the enhanced application of deep learning techniques in smart power systems. It provides an overview of the key challenges faced by traditional power systems and presents various deep learning methodologies that can address these challenges. The results showed that the root mean square errors (RMSE) for the weekend power load forecast were 18.4 for the random forest and 18.2 for the long short-term memory (LSTM) algorithm, while 28.6 was predicted by the support vector machine (SVM) algorithm. The authors' approach provides the most accurate forecast (15.7). After being validated using real-world load data, this technique provides reliable power load predictions even when load oscillations are present. The article also discusses recent advancements, future research directions, and potential benefits of utilizing deep learning techniques in smart power systems.
Volume: 38
Issue: 2
Page: 723-731
Publish at: 2025-05-01

An efficient hardware implementation of number theoretic transform for CRYSTALS-Kyber post-quantum cryptography

10.11591/ijeecs.v38.i2.pp732-743
Trang Hoang , Tu Dinh Anh Duong , Thinh Quang Do
CRYSTALS-Kyber was chosen to be the standardized key encapsulation mechanisms (KEMs) out of the finalists in the third round of the National Institute of Standards and Technology (NIST) post-quantum cryptography (PQC) standardization program. Since the number theoretic transform (NTT) was used to reduce the computational complexity of polynomial multiplication, it has always been a crucial arithmetic component in CRYSTALS-Kyber design. In this paper, a simple and efficient architecture for NTT is presented where we easily archived the functionality of polynomial multiplication with efficient computation time. Only 857 Look-Up Tables and 744 flip-flops were utilized in our NTT design, which consisted of two processing elements (PEs) and two butterfly cores within each PE.
Volume: 38
Issue: 2
Page: 732-743
Publish at: 2025-05-01

Optimizing cloud tasks scheduling based on the hybridization of darts game hypothesis and beluga whale optimization technique

10.11591/ijeecs.v38.i2.pp1195-1207
Manish Chhabra , Rajesh E.
This paper presents the hybridization of two metaheuristic algorithms which belongs to different categories, for optimizing the tasks scheduling in cloud environment. Hybridization of a game-based metaheuristic algorithm namely, darts game optimizer (DGO), with a swarm-based metaheuristic algorithm namely, beluga whale optimization (BWO), yields to the evolution of a new algorithm known as “hybrid darts game hypothesis – beluga whale optimization” (hybrid DGH-BWO) algorithm. Task scheduling optimization in cloud environment is a critical process and is determined as a non-deterministic polynomial (NP)-hard problem. Metaheuristic techniques are high-level optimization algorithms, designed to solve a wide range of complex, optimization problems. In the hybridization of DGO and BWO metaheuristic algorithms, expedition and convergence capabilities of both algorithms are combined together, and this enhances the chances of finding the higher-quality solutions compared to using a single algorithm alone. Other benefits of the proposed algorithm: increased overall efficiency, as “hybrid DGH-BWO” algorithm can exploit the complementary strengths of both DGO and BWO algorithms to converge to optimal solutions more quickly. Wide range of diversity is also introduced in the search space and this helps in avoiding getting trapped in local optima.
Volume: 38
Issue: 2
Page: 1195-1207
Publish at: 2025-05-01

Assessment of cloud-free normalized difference vegetation index data for land monitoring in Indonesia

10.11591/ijeecs.v38.i2.pp845-853
Ahmad Luthfi Hadiyanto , Sukristiyanti Sukristiyanti , Arif Hidayat , Indri Pratiwi
Continuous land monitoring in Indonesia using optical remote sensing satellites is difficult due to frequent clouds. Therefore, we studied the feasibility of monthly land monitoring during the second half of 2019, using moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data from Terra and Aqua satellites. We divide the Indonesian area into seven regions (Sumatra, Java, Kalimantan, Sulawesi, Nusa Tenggara, Maluku, and Papua) and examine NDVI data for each of the regions. We also calculated the cloud occurrence percentage every hour using Himawari-8 data to compare cloud conditions at different acquisition times. This research shows that Terra satellite provides more cloud-free pixels than Aqua while combining data from both significantly increase the cloud-free NDVI pixels. Monthly monitoring is feasible in most regions because the cloudy areas are less than 10%. However, in Sumatra, the cloudy area was more than 10% in October 2019. We need to include further data processing to improve the feasibility of continuous monitoring in Sumatra. This research concludes that monthly monitoring is still feasible in Indonesia, although some data require further processing. The use of additional data from other satellites in the monitoring can be an option for further research.
Volume: 38
Issue: 2
Page: 845-853
Publish at: 2025-05-01

A novel mobile application for personality assessment based on the five-factor model and graphology

10.11591/ijeecs.v38.i2.pp915-927
Ahmed Remaida , Zineb Sabri , Benyoussef Abdellaoui , Chakir Fri , Yassine Lakhchaf , Younès El Bouzekri El Idrissi , Mohammed Amine Lafraxo , Aniss Moumen
With the rising interest over the last decade, automated graphology has emerged as a promising filed of research, providing new insights on personality traits prediction on the basis of handwriting analysis. Although, few practical solutions to automate the extraction of handwriting features and personality prediction exist in the literature. This work aims to contribute to closing the gap in automated handwriting personality prediction by proposing a novel mobile application that uses robust feature extraction and machine learning models to predict big five personality traits. Our findings, based on high correlations between handwriting characteristics and personality traits, revealed convincing links. Notably, extraversion and extraversion have strong correlations with top margin feature, whereas agreeableness is expressed through line spacing. These findings emphasize the ability of automated graphology to properly interpret individual personalities. The proposed system achieved exceptional accuracy by using well known machine learning classifiers. The testing accuracy exceeded 92% in binary classification and 87% in multi-class case scenario, proving the adaptability and dependability of the system’s architecture. Our Android app promises to provide users with unprecedented insights into their personalities, establishing a robust tool for psychological assessment and self-discovery.
Volume: 38
Issue: 2
Page: 915-927
Publish at: 2025-05-01

Enhanced vegetation encroachment detection along power transmission corridors using random forest algorithm

10.11591/ijeecs.v38.i2.pp1376-1382
Deepa Somasundaram , Nivetha Sivaraj , Shalinirajan Shalinirajan , Santhi Karuppiah , Sudha Rajendran
Vegetation encroachment along power transmission corridors poses significant risks to infrastructure safety and reliability, necessitating effective monitoring and management strategies. This study introduces an innovative methodology for detecting vegetation encroachment using a combination of manual and automatic processes integrated with the random forest algorithm. The issue of vegetation encroachment is critical as it can lead to power interruptions and safety hazards if not addressed promptly. The objective of this research is to develop a scalable and cost-effective solution for vegetation management in power infrastructure maintenance. The methodology involves manual patch extraction and labeling to ensure the accuracy of the training dataset, combined with automatic feature extraction techniques to capture relevant information from satellite imagery. Leveraging the random forest algorithm, the model constructs an ensemble of decision trees based on the extracted features, achieving robust classification accuracy. Findings from this study demonstrate that the proposed approach enables consistent and timely identification of vegetation encroachment in new satellite imagery. Stored model parameters facilitate efficient testing, enhancing the system's ability to provide proactive interventions. This scalable solution significantly reduces reliance on manual labor and offers a cost-effective method for continuous monitoring, ultimately contributing to the resilience and safety of power transmission infrastructure.
Volume: 38
Issue: 2
Page: 1376-1382
Publish at: 2025-05-01

Enhancing SDN security using ensemble-based machine learning approach for DDoS attack detection

10.11591/ijeecs.v38.i2.pp1073-1085
Abdinasir Hirsi , Lukman Audah , Adeb Salh , Mohammed A. Alhartomi , Salman Ahmed
Software-defined networking (SDN) is a groundbreaking technology that transforms traditional network frameworks by separating the control plane from the data plane, thereby enabling flexible and efficient network management. Despite its advantages, SDN introduces vulnerabilities, particularly distributed denial of service (DDoS) attacks. Existing studies have used single, hybrid, and ensemble machine learning (ML) techniques to address attacks, often relying on generated datasets that cannot be tested because of accessibility issues. A major contribution of this study is the creation of a novel, publicly accessible dataset, and benchmarking the proposed approach against existing public datasets to demonstrate its effectiveness. This paper proposes a novel approach that combines ensemble learning models with principal component analysis (PCA) for feature selection. The integration of ensemble learning models enhances predictive performance by leveraging multiple algorithms to improve accuracy and robustness. The results showed that the ensemble of random forests (ENRF) model achieved the highest performance across all metrics with 100% accuracy, precision, recall, and F1-score. This study provides a comprehensive solution to the limitations of existing models by offering superior performance, as evidenced by the comparative analysis, establishing this approach as the best among the evaluated models.
Volume: 38
Issue: 2
Page: 1073-1085
Publish at: 2025-05-01

Trends in machine learning for predicting personality disorder: a bibliometric analysis

10.11591/ijeecs.v38.i2.pp1299-1307
Heni Sulistiani , Admi Syarif , Warsito Warsito , Khairun Nisa Berawi
Over the last decade, research on artificial intelligence (AI) in the medical field has increased. However, unlike other disciplines, AI in personality disorders is still in the minority. For this reason, we conduct a map research using bibliometric and build a visualization map using VOSviewer in AI to predict personality disorders. We conducted a literature review using the systematic literature review (SLR) method, consisting of three stages: planning, implementation, and reporting. The evaluation involved 22 scientific articles on AI in predicting personality disorders indexed by Scopus Quartile Q1–Q4 from the Google Scholar database during the last five years, from 2018–2023. In the meantime, the results of bibliometric analysis have led to the discovery of information about the most productive publishers, the evolution of scientific articles, and the quantity of citations. In addition, VOSviewer’s visualization of the most frequently occurring terms in abstracts and titles has made it easier for researchers to find novel and infrequently studied subjects in AI on personality disorders.
Volume: 38
Issue: 2
Page: 1299-1307
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

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

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
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