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25,016 Article Results

A deep learning approach to detect DDoS flooding attacks on SDN controller

10.11591/ijeecs.v38.i2.pp1245-1255
Abdullah Ahmed Bahashwan , Mohammed Anbar , Selvakumar Manickam , Taief Alaa Al-Amiedy , Iznan H. Hasbullah
Software-defined networking (SDN), integrated into technologies like internet of things (IoT), cloud computing, and big data, is a key component of the fourth industrial revolution. However, its deployment introduces security challenges that can undermine its effectiveness. This highlights the urgent need for security-focused SDN solutions, driving advancements in SDN technology. The absence of inherent security countermeasures in the SDN controller makes it vulnerable to distributed denial of service (DDoS) attacks, which pose a significant and pervasive threat. These attacks specifically target the controller, disrupting services for legitimate users and depleting its resources, including bandwidth, memory, and processing power. This research aims to develop an effective deep learning (DL) approach to detect such attacks, ensuring the availability, integrity, and consistency of SDN network functions. The proposed DL detection approach achieves 98.068% accuracy, 98.085% precision, 98.067% recall, 98.057% F1-score, 1.34% false positive rate (FPR), and 1.713% detection time.
Volume: 38
Issue: 2
Page: 1245-1255
Publish at: 2025-05-01

A novel approach for generating physiological interpretations through machine learning

10.11591/ijeecs.v38.i2.pp1339-1352
Md. Jahirul Islam , Md. Nasim Adnan , Md. Moradul Siddique , Romana Rahman Ema , Md. Alam Hossain , Syed Md. Galib
Predicting blood glucose trends and implementing suitable interventions are crucial for managing diabetes. Modern sensor technologies enable the collection of continuous glucose monitoring (CGM) data along with diet and activity records. However, machine learning (ML) techniques are often used for glucose level predictions without explicit physiological interpretation. This study introduces a method to extract physiological insights from ML-based glucose forecasts using constrained programming. A feed-forward neural network (FFNN) is trained for glucose prediction using CGM data, diet, and activity logs. Additionally, a physiological model of glucose dynamics is optimized in tandem with FFNN forecasts using sequential quadratic programming and individualized constraints. Comparisons between the constrained response and ML predictions show higher root mean square error (RMSE) in certain intervals for the constrained approach. Nevertheless, Clarke error grid (CEG) analysis indicates acceptable accuracy for the constrained method. This combined approach merges the generalization capabilities of ML with physiological insights through constrained optimization.
Volume: 38
Issue: 2
Page: 1339-1352
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

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

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

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

Face recognition based on landmark and support vector machine

10.11591/ijeecs.v38.i2.pp1289-1298
Hassan Afifi , Abdallah Marhraoui Hsaini , Mostafa Merras , Aziz Bouazi , Idriss Chana
Nowadays, the fast development of face recognition technologies used in fields such as security and video surveillance, gives us many theories and algorithms, a view of these algorithms provides us with an idea of their performance and limitations. In this paper, we will develop a new face recognition approach using the face estimation landmark algorithm to detect faces in real-time videos. Then, we use a pre-trained neural network to extract the 128 facial features of each face detected in the database images and register each vector of 128 values with the corresponding person’s name. Then, we form the linear support vector machine (SVM) classifier to recognize faces. Extensive experiments on real and generated data are presented to demonstrate the quality of the proposed method in terms of accuracy, reliability, and speed.
Volume: 38
Issue: 2
Page: 1289-1298
Publish at: 2025-05-01

Fuzzy based energy efficient cluster head selection with balanced clusters formation in wireless sensor networks

10.11591/ijeecs.v38.i2.pp928-939
Maruthi Hanumanthappa Chandrappa , Poornima Govindaswamy
The importance of energy conservation presents a considerable challenge in wireless sensor networks (WSNs), where the sensor nodes (SNs) that constitute the network depend on battery power. Recharging the batteries of SNs in the field is challenging. The clustering technique is a commonly employed method for attaining energy efficiency. In this article, we are proposing a fuzzy-based energy efficient cluster head (CH) selection with the balanced cluster formation (FEECH-BCF) technique. It is a hybrid of the k-means algorithm, low energy adaptive clustering hierarchy- uniform size cluster (LEACH-USC) technique, and fuzzy logic technique. To create the clusters, the k-means approach is employed. The idea of LEACH-USC is used for load balancing to produce clusters with uniform size by assigning member nodes (MNs) from larger clusters to smaller clusters. Optimized CHs are selected using fuzzy based CH selection technique. The k-means algorithm is simple and quick to set up, assigning the membership of SNs to the next best cluster based on centroid locations of clusters reduces intra-cluster distance among clusters, and with the help of fuzzy logic, optimized CHs will be selected. The proposed algorithm performs exceptionally well in attaining uniform energy consumption amongst clusters and extends the network’s lifetime to a greater extent.
Volume: 38
Issue: 2
Page: 928-939
Publish at: 2025-05-01

Novel intelligent trust computation for securing internet-of-things using probability based artificial intelligence

10.11591/ijeecs.v38.i2.pp988-996
Nasreen Fathima , Mysore Shantharaj Sunitha Patel , Kiran Basavegowda
With rising demands of smart appliances with normal locations transforming themselves in smart cities, internet-of-things (IoT) encounters various evolving security challenges. The frequently adopted encryption-based approaches have its own limitation of identifying dynamic threats while artificial intelligence (AI) based methodologies are found to address this gap and yet they too have shortcomings. This manuscript presents an intelligent trust computational scheme by harnessing probability-based modelling and AI-scheme for monitoring the dynamic malicious behavior of an unknown adversaries. The study contributes towards a novel AI-model using reinforcement learning towards leveraging decision making for confirming the presence of unknown adversaries. The benchmarked study shows that proposed system offers significant improvement when compared to existing AI-models and other cryptographic schemes with respect to delay, throughput, detection accuracy, execution duration.
Volume: 38
Issue: 2
Page: 988-996
Publish at: 2025-05-01

A hybrid learning model to detect cardiovascular disease from electrocardiogram

10.11591/ijeecs.v38.i2.pp1086-1097
G. V. Rajya Lakshmi , S. Krishna Rao , K. Venkata Rao
Cardiovascular diseases (CVDs) continue to be the world’s most significant cause of morbidity and mortality. This paper introduces a unique hybrid learning model for CVD detection using advanced deep learning (DL) methods. The proposed method combines the potent feature extraction powers of the EfficientNet pre-trained model with attention mechanisms and graph convolutional networks (GCNs) for improved performance. First, rich representations from cardiovascular electrocardiogram (ECG) data extract using the EfficientNet architecture as a feature extractor. Using a large dataset of cardiovascular ECG images, you can fine-tune the pre-trained EfficientNet model with Pipeline to make it more suitable for disease identification. Including attention techniques that allow the network to focus on informative regions within the input, ECG images enhanced the model’s discriminative capacity. The model can attend to the salient areas selectively linked with CVD path physiology through dynamic attention processes. More accurate predictions result from this attention-based refining, strengthening the model’s ability to identify significant patterns suggestive of cardiovascular problems. GCN aims to link the natural structure in cardiovascular data. It can efficiently capture complex interactions and dependencies among various data pieces by expressing medical data as graphs, where nodes correspond to image regions, and edges imply spatial connections. Combining GCN into the proposed hybrid learning architecture facilitates extracting contextual information from local and global sources, augmenting the model’s accuracy.
Volume: 38
Issue: 2
Page: 1086-1097
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
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