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

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

Chebyshev distance-embedded twin support vector machine for skewed classification problems

10.11591/ijeecs.v38.i2.pp1383-1391
Sai Lakshmi Balasubramanian , Gajendran Ganesan
Support vector machine (SVM) is a pivotal classification algorithm, and its evolutionary counterpart, the twin SVM (TWSVM), has gained acclaim for its advanced generalization capabilities, particularly in handling imbalanced data. TWSVMs achieve swift training by explicitly exploring a pair of non-parallel hyperplanes, yet selecting numerical values for hyperparameters poses a challenge due to the uncertainty introduced by random preferences. This paper presents a novel approach, the Chebyshev distance-based TWSVM, specifically designed for hyperparameter tuning in imbalanced binary classification. This innovative model mitigates the uncertainty of hyperparameter selection by leveraging Chebyshev distance, thereby enhancing the generalization capabilities of the TWSVM. To evaluate its efficacy, computational tests were conducted on publicly accessible real-world benchmark datasets across various domains, including non-linear cases. The results demonstrate that the Chebyshev distance-based TWSVM outperforms several existing methods, achieving superior performance with reduced computational time and setting a new benchmark in the field.
Volume: 38
Issue: 2
Page: 1383-1391
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

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

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

Intrusion detection in clustering wireless network by applying extreme learning machine with deep neural network algorithm

10.11591/ijeecs.v38.i2.pp887-896
Palaniraj Rajidurai Parvathy , Satheeshkumar Sekar , Bharat Tidke , Rudraraju Leela Jyothi , Venugopal Sujatha , Madappa Shanmugathai , Subbiah Murugan
Nowadays, intrusion detection systems (IDSs) have growingly come to be considered as an important method owing to their possible to expand into a key factor, which is crucial for the security of wireless networks. In wireless network, when there is a thousand times more traffic, the effectiveness of normal IDS to identify hostile network intrusions is decreased by an average factor. This is because of the exponential growth in network traffic. This is due to the decreased number of possibilities to discover the intrusions. This is because there are fewer opportunities to see possible risks. We intend an extreme learning machine with deep neural network (DNN) algorithm-based intrusion detection in clustering (EIDC) wireless network. The main objective of this article is to detect the intrusion efficiently and minimize the false alarm rate. This mechanism utilizes the extreme learning machine (ELM) with a deep neural network algorithm for optimizing the weights of input and hidden node biases to deduce the network output weights. Simulation outcomes illustrate that the EIDC mechanism not only assures a better accuracy for detection, considerably minimizes an intrusion detection time, and shortens the false alarm rate.
Volume: 38
Issue: 2
Page: 887-896
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 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 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

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

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

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

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

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

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
1 2 3
...
2097 2098
Show 310 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