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31,456 Article Results

Evolving strategies in anti-phishing: an in-depth analysis of detection techniques and future research directions

10.11591/ijeecs.v37.i3.pp1726-1733
Preeti Preeti , Priti Sharma
Phishing attacks are a major digital threat, impacting individuals and organizations globally. This review paper examines evolving anti-phishing strategies by analyzing five key techniques: URL blacklists, visual similarity detection, heuristic methods, machine learning models, and deep learning techniques. Each technique is evaluated for its mechanisms, unique features, and challenges. A systematic literature survey (SLR) is conducted to compare these methods; effectiveness. The paper highlights significant research challenges and suggests future directions, emphasizing the integration of artificial intelligence and behavioral analytics to combat evolving phishing tactics, this study aims to advance understanding and inspire more effective anti-phishing solutions.
Volume: 37
Issue: 3
Page: 1726-1733
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

A review based on sentimental analysis for Hindi language

10.11591/ijeecs.v37.i3.pp1815-1829
Vaibhav Prakash Vasani , Asha Ambhaikar
The 'Mother Tongue' of India is considered to be Hindi. The demand for Hindi content rises as the Indian population grows. Hindi is typically used when Indians express their opinions about something. This generates data in Hindi that may later be analyzed. Sentiment analysis (SA) is a category for one of the analyses. SA examines how a speaker or author's emotions, sentiments, and attitudes are expressed in a particular text. SA, occasionally referred to as "Opinion Mining," is a sort of contextual mining that uses algorithmic recognition and categorization to detect the viewpoints expressed in a text and determine whether they are positive, negative, or neutral. Word polarity, that SA gives, enables us to assess the text's impact and decide if it is good or negative. It is possible to implement SA using a variety of methods. There was a lot of study on SA in the English language, but not as much for Hindi. In this essay, SA studies regarding the Hindi language are reviewed and analyzed.
Volume: 37
Issue: 3
Page: 1815-1829
Publish at: 2025-03-01

Predicting student status using machine learning by analyzing classroom behaviors with X-API data

10.11591/ijeecs.v37.i3.pp2069-2076
Abdelamine Elouafi , Ilyas Tammouch , Souad Eddarouich , Raja Touahni
We explore the emergence and growing significance of educational data mining, a field dedicated to extracting valuable insights from vast datasets gathered from diverse educational environments. Utilizing the experience API (XAPI) and the Kalboard 360 online learning platform, our research presents a novel behaviorally based student performance model that evaluates the influence of student interactions on academic results. We create reliable models for precisely projecting academic success by utilizing machine learning techniques including logistic regression, k-nearest neighbors (KNNs), support vector machines (SVM), decision trees, random forests (RF), and XGBoost. The outcomes show a notable increase in categorization accuracy. Through the personalization of instruction, formative assessment support, and proactive identification of each student's unique needs to maximize their learning experience, this approach holds the potential to improve educational processes.
Volume: 37
Issue: 3
Page: 2069-2076
Publish at: 2025-03-01

Optimizing renewable energy potential in island areas based on analytical hierarchy process

10.11591/ijaas.v14.i1.pp218-226
Arnawan Hasibuan , Muhammad Sayuti , Widyana Verawaty Siregar , Ferdy Hidayatullah , Muhammad Daud , Azmi Azmi , Azman Azman , Rizky Almunadiansyah , Fahrian Roid
The decreasing availability of fossil fuels and their contribution to environmental degradation and emissions of harmful gases has encouraged a shift to renewable energy sources. This change aims to produce electricity without using fossil fuels. Banyak Islands, a new tourist destination in Aceh Singkil, Indonesia, is experiencing an increase in visitors, increasing electricity demand currently supplied by diesel power plants. Therefore, this research aims to examine the possibilities of wind and solar power plants and assess their energy output. The four factors used to determine the potential of renewable energy are technology, economics, environment, and social politics, with 13 sub-criteria. Analytical hierarchy process techniques prioritize potential alternative energy sources such as wind and solar power facilities. Research findings show that environmental criteria have a higher value (0.3455) than financial criteria (0.2861), next is technological criteria (0.1734), then socio-political criteria (0.1603). Based on the results of the requirements and sub-requirements, wind power generation is the most effective and efficient alternative energy used in the Banyak Islands region (0.549), followed by solar power generation (0.451). This data shows that the area is prone to medium to high wind speeds, specifically in geographic locations facing the Indian Ocean which is located offshore.
Volume: 14
Issue: 1
Page: 218-226
Publish at: 2025-03-01

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

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

Improving unmanned armoured mobile robot navigation accuracy using Kalman filter

10.11591/ijeecs.v37.i3.pp1569-1579
Tanya Porang , Dechrit Maneetham , Padma Nyoman Crisnapati
Thailand's remarkable economic progress has earned it recognition as a development success story, but terrorism remains a significant threat, particularly in the southern provinces. To effectively combat terrorism, intelligent systems like military robots are being increasingly utilized worldwide, including the development of vision-based robots and unmanned military equipment. The Thai government aligns with this trend by aiming to manufacture military robots, including armored mobile robot (AMR) equipped with cameras for peacekeeping and surveillance purposes. The development of an automatic tank simulation robot control system integrates global position system (GPS) technology, digital compasses, and lidar sensors to enhance efficiency, directional control, and obstacle avoidance. The study investigates the implementation of Kalman filters to enhance the precision of navigation in AMRs used in military contexts. The suggested system combines GPS, Lidar sensors, and digital compasses, utilizing advanced sensor fusion algorithms to improve directional control and obstacle avoidance. The system's usefulness is demonstrated through field tests and simulations, especially in complicated contexts where conventional approaches may face difficulties. The findings help to the progress of military robotics by providing a strong answer for improving navigation accuracy in real-world situations.
Volume: 37
Issue: 3
Page: 1569-1579
Publish at: 2025-03-01

A new deep learning model based on convolutional neural network and residual blocks for driver drowsiness detection

10.11591/ijeecs.v37.i3.pp1692-1701
Abdelfettah Soultana , Faouzia Benabbou , Nawal Sael , Soukaina Bohsissin
Recognizing the pivotal importance of monitoring driver inattention in the quest to minimize accidents and enhance safety and security, it is essential to highlight the inherent danger posed by drowsiness-a specific form of inattention that can significantly contribute to accidents. To address this issue, several propositions involving artificial intelligence have been put forth to effectively monitor and identify instances of driver drowsiness. However, challenges persist in the form of real-time processing constraints, the intricate nature of model parameters, and the response time of the model. The proposed methodology focuses on using convolutional neural networks (CNNs) and Residual blocks as robust and effective deep-learning models for the real-time detection of driver drowsiness. The integration of CNNs and Residual blocks enhances the model's precision, striking a well-balanced synergy between computational efficiency and performance. Notably, this approach demonstrated an impressive accuracy of 96.09% along with a recall, f1-score, and precision all at 96% when evaluated on the publicly available eye_dataset. Furthermore, the runtime of the developed model is a mere 70 ms. To further validate the efficiency of our proposed model, we conducted a comparative analysis with various pre-trained residual neural networks, including ResNet152, ResNet50, ResNet50v2, ResNet101, and ResNet101v2.
Volume: 37
Issue: 3
Page: 1692-1701
Publish at: 2025-03-01

Communication induced checkpointing based fault tolerance mechanism using deep-learning in IoT applications

10.11591/ijeecs.v37.i3.pp1785-1796
Sowjanya Lakshmi A , Vanipriya Ch
Internet of things (IoT) is increasingly used in diverse environments such as healthcare, industry and agriculture. They carry a risk of adverse effects if they make decisions based on faulty information. Software faults, especially transient faults are a primary contributor to deficient decision-making. The existing fault tolerant mechanisms often suffer from checkpoint overheads as checkpoints are placed in all the nodes. This paper describes a novel communication induced checkpointing based fault tolerance mechanism (CIC-FTM) designed to efficiently recover from transient faults, while minimizing useless and forced checkpoints. Long short-term memory (LSTM) based deep learning algorithm is used in our approach to predict fault occurrences and strategically place checkpoints. The proposed method also in turn improve system reliability and performance. Experimental results demonstrate the effectiveness of proposed CIC-FTM in IoT environment by minimizing the practicable operating time for checkpointing and back propagation, compared to traditional fault-tolerance mechanisms.
Volume: 37
Issue: 3
Page: 1785-1796
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

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

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

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

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
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