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

Strengthening resilience against cyberattacks in Moroccan Universities through AHP, TOPSIS, and ITIL v4

10.11591/ijeecs.v37.i3.pp1999-2008
Abdelilah Chahid , Souad Ahriz , Kamal El Guemmat , Khalifa Mansouri
This study addresses the complex challenges of digital transformation in higher education by enhancing IT governance to combat cyber threats in Moroccan universities. By adopting a hybrid multi-criteria decision-making (MCDM) framework, the research combines the analytic hierarchy process (AHP) and the TOPSIS method to evaluate fourteen IT governance criteria, categorized into structural, procedural, and relational dimensions. Using TOPSIS, the study identifies the most relevant SVC services from the ITIL v4 value chain for each category, with the aim of developing an optimized strategic approach against cyberattacks. The input from ten academic experts was crucial in prioritizing these services. The results show that SVC services A5 and A2 are fundamental for optimizing the resources of structural and procedural mechanisms, while A4 and A2 play a key role in relational mechanisms. This strategic alignment enhances the resilience of Moroccan universities to cyber threats by ensuring a more efficient allocation of security resources and providing a robust defense against potential attacks.
Volume: 37
Issue: 3
Page: 1999-2008
Publish at: 2025-03-01

Marine scientific workflow execution resource provisioner in edge-cloud platform

10.11591/ijeecs.v37.i3.pp1607-1615
Sheetal Bagali , Pradeep Kumar Siddana , Poongothai C , Kavita Avinash Patil , Vani Bengaluru Purushothama , Ashwini Kumari P
Workload application for deadline-intensive marine science, usually represented by directed acyclic graphs (DAGs), consists of interconnected operations that communicate huge quantities of information and operate on cutting-edge computational platforms. Massive communications of information among jobs running on separate computing servers, nonetheless, might come with substantial processing time, power consumption, and financial expenses. Therefore, there is room for further study of exchanging certain communications for computing to lower total interaction expenses. In addressing research this work introduces an effective resource provisioner for deadline-intensive scientific workload executer (ERP-DISWE) to reduce the makespan, consumption of energy, and cost of edge-computing platforms. The performance of ERP-DISWE is validated with the current resource provisioner using the sipht marine workload and shows superior performance concerning makespan, energy, and cost.
Volume: 37
Issue: 3
Page: 1607-1615
Publish at: 2025-03-01

Analysis of similarity index between iThenticate and Ouriginal plagiarism detection software: a comprehensive study

10.11591/ijeecs.v37.i3.pp2096-2104
Md. Hamidur Rahman , Bayissa Leta Danno , Dessalegn Wase Mola , Muhammad Shahidul Islam , Syed Murtaza Hussain Andrabi , Md. Nasim Reza , Dhananjoy Shaw
Intellectual property plagiarism is increasingly prominent in contemporary society, involving the unethical practice of claiming someone else's ideas, words, or creative works without proper acknowledgment. This study aimed to compare the performance of iThenticate and Ouriginal plagiarism detection software by analyzing their similarity index. Twenty original manuscripts (N=20) were examined for content similarity, with each manuscript analyzed first with Ouriginal and then with iThenticate. The focus was on comparing the two tools based on matched sources, word matches, and overall similarity index percentage. Data analysis using SPSS v26 included descriptive statistics, an independent t-test, correlation, and ranking of the similarity percentages, with significance set at p<0.05. The results indicated no significant differences in matching sources, matching words, or similarity index (p>0.05) between iThenticate and Ouriginal. A strong positive correlation (r=.758, p<.000) was observed between the similarity indices of the two software programs. The analysis of the low similarity range (≤10%) also revealed no statistical significant difference (p>.05). However, the mean similarity percentage detected by iThenticate was higher at 11.40%, compared to 6.85% for Ouriginal. Based on the findings, both iThenticate and Ouriginal demonstrated comparable effectiveness in detecting plagiarism, highlighting their importance in curbing academic dishonesty and protecting intellectual property rights.
Volume: 37
Issue: 3
Page: 2096-2104
Publish at: 2025-03-01

A novel hybrid model for sentiment analysis in MOOC forums with hybrid word and character-level neural networks

10.11591/ijeecs.v37.i3.pp1758-1771
Mohammed Jebbari , Mohamed Amine Ouassil , Mouaad Errami , Rabia Rachidi , Soufiane Hamida , Bouchaib Cherradi , Abdelhadi Raihani
Sentiment analysis is crucial, in the field of natural language processing (NLP). Has applications in different areas. This study focuses on analyzing sentiments in massive open online course (MOOC) forums highlighting its importance in understanding how users interact and shaping educational strategies. The study presents a novel hybrid neural network model specifically tailored for sentiment analysis in MOOC forums. This innovative model combines word level and character level embeddings to handle the linguistic expressions commonly found in this context. The model architecture integrates bidirectional long short-term memory (BiLSTM) layers for word level embeddings and convolutional neural networks (CNNs) for character level embeddings aiming to harness the strengths of both types of embeddings for a view of the linguistic used in MOOC forum posts. Notably this model achieves an accuracy rate of 93.11% showcasing its effectiveness, in sentiment analysis within MOOC forums. This research contributes to sentiment analysis within the context of online education.
Volume: 37
Issue: 3
Page: 1758-1771
Publish at: 2025-03-01

A case study on the causal of electric two-wheelers traffic accidents in Yangzhou, China

10.11591/ijeecs.v37.i3.pp1918-1926
Wang Ruijie , Suzi Iryanti Fadilah , Azizul Rahman Mohd Shariff , Khuzairi Mohd Zaini , Pantea Keikhosrokiani , Wang Ning
In recent years, electric two-wheelers are a very popular means of transportation in China and are loved by the people. And, because driving electric two-wheelers does not require a driver's license or insurance, electric two-wheelers have become the preferred mode of transportation for the people in China. However, while electric two-wheelers bring convenience to people, they also bring hidden dangers to traffic safety. Nearly 200,000 traffic accidents occur in China every year, of which electric two-wheelers account for 60%. In addition, the trend of traffic accidents is increasing year by year, which is seriously threatening the safety of people's lives and property. It is urgent to prevent the traffic accidents of electric two-wheelers and maintain the personal safety of citizens. Therefore, this study aims to determine which factors contribute to traffic accidents of electric two-wheelers and to explore the importance of their influence on traffic accidents. The sample data for this study will be collected from the people of Yangzhou City, Jiangsu Province, China by distributing questionnaires. The data from the completed questionnaires will also be analyzed using SPSS analysis tool.
Volume: 37
Issue: 3
Page: 1918-1926
Publish at: 2025-03-01

Improving energy indicators of pulse converters

10.11591/ijeecs.v37.i3.pp1442-1451
Sarfaroz Dovudov , Mikhail Dunaev , Anastasia Zhiravetska , Saidjon Tavarov , Rustam Yunusov , Murodbek Safaraliev
All rectifier circuits are divided into single-phase and three-phase, according to the number of phases of the supply network, single-cycle and two-cycle. Voltage conversion, which can vary in both frequency and amplitude, is carried out by two series-connected converters-a rectifier (AC/DC converter) and an inverter (DC/AC converter). Using simulation techniques in the MATLAB-based Simulink environment, the blocks used were taken from the sim power system/Simscape library. Models of semiconductor converters with pulse-width modulation based on one power thyristor switch and a semiconductor converter with pulse-frequency modulation based on four power thyristor switches have been developed. Experiments prove the correctness of the models. The results of a study of the developed models of semiconductor converters with pulse-width and pulse-frequency modulation are presented. The static and dynamic characteristics of pulsed semiconductor converters are presented. Analysis of the static characteristics of pulse converter circuits showed that the rigidity of the output characteristics of converters with pulse-frequency modulation is higher than that of converters with pulse-width modulation. The results of assessing the efficiency of pulsed semiconductor converters based on the analysis of static output characteristics allow us to conclude that the efficiency of a semiconductor converter with pulse-frequency modulation is more than one percent higher than that of a semiconductor converter with pulse-width modulation.
Volume: 37
Issue: 3
Page: 1442-1451
Publish at: 2025-03-01

Forecasting virtual machine resource utilization in cloud computing: a hybrid artificial intelligence approach

10.11591/ijeecs.v37.i3.pp1887-1898
Rim Doukha , Abderrahmane Ez-Zahout , Aristide Ndayikengurukiye
Cloud computing has transformed the management of IT infrastructures by providing scalable, flexible, and cost-effective solutions. However, efficient resource management in cloud environments remains a significant challenge, as over-provisioning or under-provisioning of resources can lead to unnecessary costs or degraded performance. Accurate forecasting of virtual machine (VM) resource utilization is crucial for optimizing resource allocation, reducing operational expenses, and ensuring compliance with service level agreements (SLAs). This study aims to address these challenges by developing a hybrid forecasting model that combines the strengths of auto regressive integrated moving average (ARIMA), linear regression (LR), and long short-term memory (LSTM) techniques. By integrating these methods, our model provides more accurate predictions and better adaptability to various workload patterns, helping cloud service providers and users to make informed decisions about resource allocation, ultimately reducing costs. The data was collected from multiple EC2 instances and processed using amazon web services (AWS) Glue with Spark. The experimental results demonstrate that the hybrid model outperforms individual models such as ARIMA, LR, and LSTM in terms of accuracy for forecasting memory, CPU, and disk utilization, offering a more effective solution for managing cloud resources efficiently.
Volume: 37
Issue: 3
Page: 1887-1898
Publish at: 2025-03-01

Conception of speech emotion recognition methods: a review

10.11591/ijeecs.v37.i3.pp1856-1864
Abdelkader Benzirar , Mohamed Hamidi , Mouncef Filali Bouami
In recent years, speech emotion recognition (SER) has emerged as a pivotal tool for understanding and enhancing human-computer interaction (HCI), thus garnering significant attention from researchers due to its diverse range of applications. However, SER systems encounter numerous challenges, particularly concerning the selection of appropriate features and classifiers for emotion recognition. This paper provides a concise survey of the field of speech emotion recognition, elucidating its classification algorithms and various feature extraction techniques across multiple languages. Additionally, it explores the limitations and weaknesses inherent in speech emotion recognition systems. Furthermore, the paper endeavors to categorize recent research endeavors in Arabic speech emotion recognition, employing diverse modeling approaches and extraction methods.
Volume: 37
Issue: 3
Page: 1856-1864
Publish at: 2025-03-01

Clustering and routing using spiral exploration mechanism with honey badger optimization in wireless sensor network

10.11591/ijeecs.v37.i3.pp1734-1743
Anitha Chikkanayakanahalli Lokesh Kumar , Subhash Kamble , Sanjay Kumar Naazre Vittal Rao
Wireless sensor network (WSN) contains a huge number of spatially distributed sensor nodes that are connected by wireless to monitor and record information from the environment. The WSN nodes are battery-powered, thus reducing energy after a certain period which affects the network lifetime. To overcome this issue, this research proposed a spiral exploration mechanism with honey badger optimization (SEM-HBO) for cluster head (CH) and route path selection in WSN. The objective of this research is to reduce energy consumption and enhance network lifespan in WSN. The distance, communication cost, residual energy and cluster density are considered as fitness functions for selecting CH and route path in WSN. Through the SEM-HBO search behavior, it explores different routes and recognizes best one for reducing energy consumption and delays thereby enhancing network lifetime. The SEM-HBO performance is calculated based on packet delivery ratio (PDR), delay, energy consumption (EC), network lifetime (NL), and throughput for 100-500 nodes. The SEM-HBO performance is efficient and it achieves 99.62% and 99.59% of PDR for 100 and 200 nodes when compared to harmony search algorithm and competitive swarm optimization (HSA-CSO).
Volume: 37
Issue: 3
Page: 1734-1743
Publish at: 2025-03-01

RNN Diabetic framework for identifying diabetic eye diseases

10.11591/ijeecs.v37.i3.pp1830-1844
Arwa Albelaihi , Dina M. Ibrahim
Many areas of image identification and classification for medical imaging diagnostics have greatly benefited from deep learning (DL). Diabetic retinopathy (DR) will become the most common cause of blindness worldwide, making diabetes a major threat to public health. This research proposes an automated identification system using deep recurrent neural networks (RNNs) to identify and classify four categories of diabetic eye diseases: DR, cataract, glaucoma, and diabetic macular edema (DME). We use three different model architectures based on RNN and their types, we called our proposed system RNN Diabetic framework. These models are combined with one of the commonly used architectures that support sufficient accuracy and speed for the model which is residual network (ResNet)152V2. The three model architectures are RNN+ResNet152V2, gated recurrent unit (GRU)+ResNet152V2, and bidirectional GRU (Bi-GRU)+ResNet152V2. The proposed models were assessed as collected datasets: DIARETDB0, DIARETDB1, messidor, HEI-MED, ocular, and retina. A full analysis and evaluation of these three deep RNN architectures are presented. The experiments showed that the Bi-GRU+ResNet152V2 model worked better than the other two proposed models. In addition, we compare these three proposed models with the previous studies and find that the proposed Bi-GRU+ResNet152V2 model achieves the highest results with accuracy equal to 99.8%, 98.1% sensitivity, 98.6% specificity, 99.8% precision, 99.8% F1 score, and 99.8% areas under the curve (AUC).
Volume: 37
Issue: 3
Page: 1830-1844
Publish at: 2025-03-01

Analysis of FinFET based SRAM cells with improved performance parameters

10.11591/ijeecs.v37.i3.pp1464-1475
Deepak Garg , Rupali Singh , Devendra Kumar Sharma
To resist the advanced process variation and enable ultra-low power operation, static random-access memory (SRAM) undergoes an expansion stage. The most common type of memory is SRAM, which occupy more than 60% of the chip area. All memories have occupied more than 80% of the circuit area in that day’s micro devices, and this trend is expected to continue. This paper develops into the deployment of SRAM using FinFET technology for implementation, with a primary objective of mitigating critical memory parameters, including parameters named as power dissipation, data retention and noise voltage. In this article, multiple simulations are carried out among conventional SRAM cells and FinFET based SRAM cells (6T, 7T, and 8T) utilizing the Cadence Virtuoso tool with a 45nm technology node. In modern era, FinFET is gaining increased preference over CMOS for high controllability of short-channel effects and flexible adjustment of threshold voltage (Vth) through the presence of a double gate. The thinner width of FinFET (Wfin) shows less degradation in performance in compared to thicker width of FET. To improve the circuit performance, the key factors like area, power and delay should be reduced. In the proposed SRAM cell using FinFET, power dissipation is lowered by 17% data retention voltage is reduced by 7% and noise voltage abridged up to 35% as compared to conventional SRAM cell.
Volume: 37
Issue: 3
Page: 1464-1475
Publish at: 2025-03-01

Investigations related to effect of mass density of metal oxide on specific capacitance of asymmetrical supercapacitor

10.11591/ijeecs.v37.i3.pp1435-1441
Mugdha Bhajekar , Ankur Karandikar , Sarang Joshi , Shalaka Chaphekar , P. B. Karandikar
Various types of batteries and fuel cells are widely used electrical energy storage devices in most of the applications. Supercapacitors have also become increasingly popular for energy storage in a few applications. Recently the need to develop better energy storage device has resulted in the development of asymmetrical supercapacitors (ASCs). Modeling of this device can be promising to obtain the desired characteristics to meet the need of continuous power and pulse current. For this, both positive and negative electrodes are important components for ASCs. The effect of mass density of metal oxide in positive electrode of this device is hardly researched. Component-based modeling for positive electrode is presented in the paper. Positive electrode parameters are being analyzed with the three most significant parameters affecting specific capacitance. The parameters viz. loading of materials on the current collector, percentage metal oxide, and mass density of metal oxide of the positive electrode are correlated with specific capacitance by using statistical methods to avoid the use of costly electrochemical methods. The major contribution of the presented research work is the identification of – mass density of metal oxide used in positive electrode plays the most significant role in deciding the value of the specific capacitance of this device.
Volume: 37
Issue: 3
Page: 1435-1441
Publish at: 2025-03-01

Linguistic feature selection for personality trait identification from textual data

10.11591/ijeecs.v37.i3.pp1976-1984
Angad Singh , Priti Maheshwary , Nitin Kumar Mishra , Timothy Malche
Personality identification is a common and central problem in text processing. Sensing personality is helpful for various purposes; for example, estimating users' personalities before providing them with any service is necessary. Individuality is essential in a person's nature in every outlook, for instance, in text writing. But, this remains a core challenge because of the low accuracy achieved. The proposed study solves this problem and presents a big five trait identification technique from text data, which applies a feature selection method to increase accuracy. This technique is called linguistic feature selection for personality trait identification (LFSPTI). This technique first finds features based on mutual information (MI), F-statistic, principal component analysis (PCA), and chi-square, then uses the genetic algorithm (GA) to select high-ranked features from all feature subsets. These four parameters provide various forms of the dataset. The experimental results exhibit that the LFSPTI method enhances the classification accuracy against the best of the competing methods by 1.18%, 0.83%, 1.61%., 1.15%, 1.82%, and 1.39% for extraversion (EXT), neuroticism (NEU), agreeableness (AGR), conscientiousness (CON), openness (OPN), and mean overall personality traits, respectively.
Volume: 37
Issue: 3
Page: 1976-1984
Publish at: 2025-03-01

Unveiling perceptions: aspect-based sentiment analysis of Malaysia’s e-hailing reviews

10.11591/ijeecs.v37.i3.pp2077-2086
Khyrina Airin Fariza Abu Samah , Mohamad Azan Amit , Siti Salwa Salleh , Wannoraini Abdul Latif , Nurul ‘Azwa Kamarudin , Shahadan Saad , Mohd Nor Hajar Hasrol Jono
The growing demand for e-hailing services in Malaysia leads to increased competition among more than 20 licensed e-hailing service providers. Consumer satisfaction is a crucial factor influencing variables in the business organization, and understanding consumers’ perceptions is vital for service improvement. Reviews on e-hailing services are unstructured data and massive, making comparisons difficult. Thus, this study aims to classify Malaysia’s e-hailing service reviews from Google Play Store and X using latent Dirichlet allocation (LDA) and support vector machine (SVM). Aspect-based sentiment analysis (ABSA) was performed using a two-staged method, applying LDA for aspect category detection and SVM for aspect sentiment classification separately. Fare, availability, comfort, time, and convenience are five predetermined aspect categories in this study. The LDA for English and Malay achieved a perplexity of -7.31 and -7.49, respectively. Besides, the accuracy scores of SVM for English and Malay are 86.32% and 62.97%, respectively.
Volume: 37
Issue: 3
Page: 2077-2086
Publish at: 2025-03-01

Adaptive dung beetle optimization-based agile perturb and observe technique for energy management system

10.11591/ijpeds.v16.i1.pp546-554
Shweta Sengar , Aniket Kumar
Energy storage system (ESS) plays a significant role in maximizing the use of renewable energies to ensure a balance between power generation and demand. ESS assists in maintaining grid stability by providing backup power during fluctuations or outages and smoothing out the variability of renewable energy source (RES). However, EMS fails to effectively balance dynamic interactions due to the unpredictable nature of renewable energy sources (RES) which results in a suboptimal performance. This research proposes an adaptive T-distribution dung beetle optimization-based agile perturb and observe technique (ADBO-APO) for EMS. Photovoltaic (PV) module, battery, and wind turbine are the three sources utilized to establish an effective EMS in a grid-connected system. The ADBO is applied to manage the switching between battery storage and wind turbines. The APO is utilized for triggering the bidirectional DC-DC switch to obtain stable power from wind, PV, and battery. APO enhances EMS by involving perturbation levels for optimal power extraction. It improves the stability and efficiency across variable energy sources. The proposed ADBO-APO achieves a superior average index of 1.2598×104 when compared to the existing method, levy flight quasi oppositional based learning smell agent optimization (LFQOBL-SAO).
Volume: 16
Issue: 1
Page: 546-554
Publish at: 2025-03-01
1 2 3
...
2097 2098
Show 350 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