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29,922 Article Results

Phishing URL prediction – two-phase model using logistic regression and finite state automata

10.11591/ijeecs.v40.i1.pp356-365
Nisha T N , Dhanya Pramod
The human factor in security is more important when they become the carriers of attacks on enterprises. Phishing attacks can be classified as insider attacks when the employees unintentionally participate in the attack propagation. Since complete user training is a myth, enterprises must implement detection tools for phishing attacks on their network perimeters. This research discusses a two-phase model for phishing URL detection, in which the first phase identifies the properties of URLs that detect phishing and their relative weight using logistic regression. The second phase checks the probability of a new URL being categorized as phishing using the knowledge achieved during the first phase using the dynamically created Finite state machines. The model defines a malicious score (MS), which can be used to check any URL in real-time to identify whether it is phishing or not. The model described in this work has been experimented with different benchmarking datasets to verify the performance. The model provided a decent result in classifying a URL as phishing or naive. The malicious score (MS) defined by this model can be used to evaluate any URL and can be used as a filtering mechanism for end-point phishing URL detection. The key contribution is towards developing a two-phase model which evaluates the URL with the help of self-crafted features without reliance on a feature set. This accommodates the model's hyper-competitive phishing URL detection area in cyber security.
Volume: 40
Issue: 1
Page: 356-365
Publish at: 2025-10-01

Voltage stability investigation: enabling large-scale renewable energy integration in Tamil Nadu’s grid

10.11591/ijeecs.v40.i1.pp47-56
Chelladurai Chandarahasan , Edwin Sheeba Percis
The integration of renewable energy sources (RES) such as wind and solar, characterized by their inherent intermittency and variability, poses notable challenges to the stability and reliability of power systems. This research addresses these challenges by conducting a detailed load flow, voltage sensitivity and stability analysis at a significant extra high voltage (EHV) pooling station under high-RES penetration. Employing advanced simulation methodologies, the study evaluates the effects of RES integration on voltage profiles and the system’s capacity to sustain equilibrium under steady-state operations. The findings highlight that substantial RES integration induces considerable voltage fluctuations and reactive power disparities. To counter these effects, static var compensators (SVCs) were deployed, demonstrating their efficacy in enhancing voltage stability and providing essential reactive power support. The study confirms the pivotal role of SVCs in alleviating voltage sensitivity to RES fluctuations, thereby promoting a more stable and reliable power supply. This research underscores the importance of strategic reactive power management devices in enabling a seamless transition towards a renewable-dominant energy landscape.
Volume: 40
Issue: 1
Page: 47-56
Publish at: 2025-10-01

Systematic review: the application of ChatGPT on Arabic language text processing

10.11591/ijece.v15i5.pp4837-4847
Ali Mousa AlSbou , Fadzli Syed Abdullah , Ashanira Mat Deris
Over 420 million people speak Arabic, and it is the official language of 22 countries. Its complex morphology and dialectal diversity present unique challenges for natural language processing (NLP) models like ChatGPT. This systematic review investigates the application of ChatGPT in Arabic language text processing, examining its potential uses, accuracy, and limitations. Covering literature published between 2021 and 2024, this review synthesizes findings from 21 articles, addressing four key research questions: ChatGPT’s applications in Arabic text processing, its performance in terms of accuracy and reliability, the challenges and limitations encountered, and future directions to enhance its utilization. Results indicate that ChatGPT has potential in several applications, including educational tools, machine translation, text generation, and sentiment analysis. Despite current limitations, ChatGPT's potential in Arabic text processing is promising. While it shows high accuracy in structured tasks, it struggles with dialectal variations and cultural nuances, especially in complex text types. Primary limitations include a lack of high-quality Arabic datasets, difficulty handling dialects, and a need for more nuanced contextual understanding. Future research should focus on improving data quality, expanding dialectal coverage, fine-tuning models for specific linguistic tasks, and integrating AI with human teaching methods. Addressing these areas will enhance ChatGPT's accuracy and reliability for Arabic NLP.
Volume: 15
Issue: 5
Page: 4837-4847
Publish at: 2025-10-01

A multi-path routing protocol for IoT-based sensor networks

10.11591/ijeecs.v40.i1.pp225-235
Udaya Suriya Rajkumar Dhamodharan , Krishna Prasad Karani , Saranya Pichandi , Kavitha Palani , Sathiyaraj Rajendran
Internet of things (IoT) based sensors are to link a big number of low-cost and power-integrated devices in a reliable manner. Numerous military and adventurous applications are regulated by communication among IoT sensors. The multi-path routing protocol (MRP) approach presented in this research to enhance secure routing in IoT sensors is significant. This technique makes use of data transfer routing and the relationships between network components. It finds the most efficient route between the nodes that minimizes communication overhead and is both reliable and economical in terms of shortest duration. The particle swarm optimization (PSO) technique is used to find the shortest path that is most cost-effective. To reach the target node, end-to-end data transmission must transit via intermediary nodes, which are provided by the routing path node history. The optimal path is chosen by MRP from PSO, and it traces the path to identify the intermediate nodes. In the unlikely event of a crisis, MRP offers the most affordable backup route for data transfer. When compared to earlier techniques, the outcomes of these current approaches enhance network efficiency, balance energy consumption among nodes, and routing attacks.
Volume: 40
Issue: 1
Page: 225-235
Publish at: 2025-10-01

Technical-economic analysis for ON/OFF GRID solar photovoltaic system design

10.11591/ijeecs.v40.i1.pp93-107
Walter Naranjo Lourido , Oscar Mauricio Niño Archila , Daniel Eduardo Guarin Preciado , Omar Yesid Beltrán Gutierrez , Javier Eduardo Martínez Baquero
This manuscript presents a detailed techno-economic analysis of a hybrid solar photovoltaic (PV) system designed to operate in both grid-connected (ON GRID) and stand-alone (OFF GRID) modes. The study focuses on the Leonardo Da Vinci academic building at Universidad de Los Llanos, located in Villavicencio, Colombia, in the tropical Orinoquía region. Using local solar irradiance, temperature data, and real load profiles from the facility, the system was modeled to assess performance under true operating conditions. A key part of the system design involved a detailed shadow analysis to identify potential obstructions and optimize solar access. This step significantly improved the accuracy of energy yield predictions and contributed to long-term system reliability. Additionally, regression-based methods were used to determine optimal panel tilt angles and refine system sizing based on peak sun hours. Both ON GRID and OFF GRID configurations were evaluated in terms of energy output, levelized cost of electricity (LCOE), net present value (NPV), and internal rate of return (IRR). Results show that ON GRID systems are financially advantageous in urban environments with net metering, while OFF GRID systems are critical for ensuring energy autonomy in remote or underserved areas. The findings provide practical insights for the deployment of hybrid PV systems in institutional settings across equatorial regions.
Volume: 40
Issue: 1
Page: 93-107
Publish at: 2025-10-01

A memory improved proportionate affine projection algorithm for sparse system identification

10.11591/ijece.v15i5.pp4605-4619
Senthil Murugan Boopalan , Sarojini Raju , Krithiga Sukumaran , Manimegalai Munisamy , Kalphana Ilangovan , Sudha Ramachandran , Janani Munisamy , Bharathiraja Ramamoorthi , Sakthivel Pichaikaran
For cluster sparse system identification, it is known that the cluster sparse improved proportionate affine projection algorithm (CS-IPAPA) outperforms the standard IPAPA. However, since CS-IPAPA does not retain past proportionate factors, its performance can be further improved. In this paper, a modification to CS-IPAPA is proposed by utilizing the past instant proportionate elements based on its projection order. Steady-state performance of the proposed memory cluster sparse improved proportionate affine projection algorithm (MCS-IPAPA) is studied by deriving the condition for mean stability. Different simulation setups show that the proposed algorithm outperforms different versions of IPAPA in terms of convergence rate, normalized misalignment (NM) and tracking, for different types of inputs like colored noise, white noise, and speech signal. By incorporating past proportionate factors, the proposed MCS-IPAPA significantly reduces computational complexity for higher projection orders.
Volume: 15
Issue: 5
Page: 4605-4619
Publish at: 2025-10-01

A centroid-based algorithm for measuring and tracking vehicle speed from a monocular camera using the YOLOv8 object detector

10.11591/ijeecs.v40.i1.pp437-449
Pankaj Kumar Gautam , Sanjeev Kumar
Accurate real-time vehicle speed measurement is crucial for enhancing road safety and advance intelligent transportation systems (ITS). This paper proposes a centroid-based tracking algorithm that integrates YOLOv8, a state-of-the-art object detector, with DeepSORT for robust multi-object tracking. By leveraging YOLOv8’s anchor-free detection and DeepSORT’s appearance-based association, the proposed method effectively mitigates occlusions and minimizes identity switches. Evaluations on the VS13 benchmark dataset reveal a 2–5% improvement in measurement accuracy as compared to existing solutions, while maintaining real-time performance at 30 FPS. The method demonstrates consistent reliability across different vehicle models, speeds, and lighting conditions, underscoring its adaptability to real-world traffic scenarios. Moreover, larger bounding boxes enhance tracking stability, reducing false detections. Overall, the approach’s low computational overhead and high accuracy position it as a practical solution for ITS applications in constrained environments.
Volume: 40
Issue: 1
Page: 437-449
Publish at: 2025-10-01

A comprehensive analysis of smartphones and tablets in graphic design and digital art

10.11591/ijeecs.v40.i1.pp146-155
Jirawat Sookkaew , Nakharet Chaikaew , Nakarin Chaikaew
This paper discusses how smartphones and tablets have changed creativity and graphic design. These portable tools and easy apps have transformed the creative process, allowing artists, designers, and students to create high-quality work anywhere. Mobile design apps promote creativity, accessibility, and skill development across broad user groups, according to the study. Unlike desktop tools, it addresses key constraints. Mobile apps sometimes struggle with smaller screens, restricted processing power, and reduced capabilities for complicated tasks like multi-layer editing and advanced graphics. These restrictions may inhibit expert designers working on complex, precise designs. Even Nevertheless, mobile technology like larger screens, stylus support, and cloud-based solutions are making mobile devices more feasible for creative work. The findings emphasise the relevance of integrating mobile technology into education and professional workflows and its complementarity to desktop solutions for resource-intensive jobs. In the developing digital landscape, our dual-platform approach maximizes creativity and flexibility.
Volume: 40
Issue: 1
Page: 146-155
Publish at: 2025-10-01

A hybrid intelligent model for prediction of coronary artery diseases using TabNet and multiclass SVM

10.11591/ijeecs.v40.i1.pp156-163
Niveditha Honnemadu Rudreshgowda , Balakrishna Kempegowda , Anitha Sammilan
Cardiovascular disease is one of the significant fatality-causing diseases in this era by affecting the heart and blood vessels. Cardio diseases are classified into coronary heart disease (CHD), heart failure, valve disease, and arrhythmias. Medical diagnosis of heart disease and treating the patient is a challenging process, where early detection can lead to decreased fatality. In this research, hybrid model-based prediction of CHD detection is developed by TabNet and multiclass support vector machine (SVM). We created our datasets for experimentation by visiting the hospitals in the Mysore and Mandya regions of Karnataka, India. Datasets consist of 16 features; the features are pre-processed to normalize, encode, and handle missing values to extract the aggregate features using TabNet, and the multiclass SVM model is trained to classify the disease based on the classes. The proposed hybrid model prediction performance was evaluated using various metrics such as accuracy, recall, precision, and F1-score.
Volume: 40
Issue: 1
Page: 156-163
Publish at: 2025-10-01

Autoencoder-based Gaussian mixture model for diagnosing early onset of diabetic retinopathy

10.11591/ijeecs.v40.i1.pp164-172
Priyanka Sreenivas , Kavita V. Horadi , Kalpa Rajashekar
The current study presents a simplified yet innovative solution towards effective early diagnosis of diabetic retinopathy (DR) that leads to irreversible blindness. A review of current literature shows a considerable number of machine learning and deep learning approaches have been presented; however, there are significant issues with the early detection of DR. Hence, the proposed study deploys a novel architecture using an autoencoder that extracts a hidden representation of retinal images while binary classification is carried out using a Gaussian mixture model. The prime contribution is the joint integration of deep learning with statistical modelling towards efficient feature extraction and anomaly detection, supporting early determination of DR. The study outcome shows a proposed system to significantly exhibit 96.5% accuracy, 94.2% sensitivity, and 98.3% specificity on two standard benchmarked datasets in comparison to existing models frequently used for the diagnosis of DR.
Volume: 40
Issue: 1
Page: 164-172
Publish at: 2025-10-01

Comparison of long short-term memory and deep neural network optimized neural networks for maximum power tracking of wind turbines

10.11591/ijece.v15i5.pp4454-4464
Ezzitouni Jarmouni , Ahmed Mouhsen , Mohamed Lamhamdi , Ennajih Elmehdi , Naoual Ajedioui , En-Naoui Ilias
In wind energy conversion systems, maximum power point tracking (MPPT) performance is crucial, as it is directly related to wind speed variability and the characteristics of the equipment used. Maximum power point tracking controllers are essential for optimizing the efficiency of wind power generation. This paper presents the development of three distinct approaches to maximum power point tracking: the classical perturb and observe (P&O) method, and two other techniques based on artificial intelligence, namely long short-term memory (LSTM) networks and deep neural networks (DNNs). Rather than focusing solely on the development of an intelligent neural network-based maximum power point tracking model, our work emphasizes the design of a deep neural network controller with an optimized architecture and a reduced number of layers and neurons per layer, thereby simplifying its implementation in embedded process control units while maintaining high maximum power point tracking performance. The results obtained show that our optimized deep neural network model identifies the point of maximum power more effectively than other techniques, demonstrating remarkable performance in terms of response time, accuracy, and the quality of the generated power.
Volume: 15
Issue: 5
Page: 4454-4464
Publish at: 2025-10-01

A hybrid extreme learning machine and sine cosine algorithm model for accurate electricity price forecasting

10.11591/ijece.v15i5.pp4366-4375
Udaiyakumar Sambathkumar , Sangeetha Shanmugam , Kannayeram Ganapathiya Pillai
Electricity demand is continually rising due to the advancement of new technology, the switch to greener energy, and the popularity of electric vehicles over conventional ones. The proliferation of businesses in the generation and distribution sectors has increased competition in the electricity market. Forecasting electricity prices enables consumers to control their monthly electricity bills and consumer-owned distributed generation by knowing the forecasted hourly price. For demand management, generation scheduling, and bidding price quotations, electricity price forecasting is crucial for buyers, generation businesses, and bidders alike. Electricity price data is highly nonlinear and affected by numerous factors because of which EPF models are more complex, highly volatile and slow in convergence. A range of neural network models, training algorithms, and hybrid systems comprising two or more models have been suggested for precise and efficient electricity price forecasting by researchers over the decade. This study involves the development of a hybrid neural network model with two intelligent algorithms sine cosine algorithm (SCA) and extreme learning machine (ELM) to predict electricity price for a particular duration. The newly developed network model is trained and tested with real-time Indian electricity price data from the year 2022. The selected annual price data set is divided into three different sets to explore seasonal variations and all the sets are given as the input to the model for training and testing to obtain the effective price forecasting.
Volume: 15
Issue: 5
Page: 4366-4375
Publish at: 2025-10-01

Sentiment analysis of YouTube videos comments for children using machine learning and deep learning

10.11591/ijeecs.v40.i1.pp397-410
Amal Alrehaili , Abdullah Alsaeedi , Wael M.S. Yafooz
Nowadays, online connectivity is increasing with the rapid growth of the world wide web. Consequently, content shared across numerous platforms varies in appropriateness. it is necessary to ensure the suitability of the content since children are among the consumers of online content. A lot of children watch videos on YouTube these days, and such platforms can contain useful content. However, such videos can also have a negative impact on children. The suitability of these videos can be determined through sentiment analysis to refine the content for children on YouTube, by classifying the posted comments as either positive or negative. Therefore, this study utilizes natural language processing methods, machine learning classifiers (MLCs) and deep learning models (DLMs) to detect and classify negative user comments using the proposed dataset. Different MLCs such as random forest (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), decision tree (DT), K-nearest neighbour (KNN), AdaBoost, and support vector machine (SVM) have been used. Additionally, DLMs were also used such as artificial neural network (ANN), convolutional neural network (CNN) and long short-term memory (LSTM). Overall, the experimental results showed that the LR, RF, AdaBoost, ANN and LSTM classifiers outperformed all the other classifiers in terms of accuracy.
Volume: 40
Issue: 1
Page: 397-410
Publish at: 2025-10-01

DigiScope: IoT-enhanced deep learning for skin cancer prognosis

10.11591/ijeecs.v40.i1.pp202-215
Aymane Edder , Fatima-Ezzahraa Ben-Bouazza , Oumaima Manchadi , Idriss Tafala , Bassma Jioudi
In dermatology, early identification and intervention are crucial for optimizing patient outcomes in skin cancer care. Recent technological advances, particularly in the internet of things (IoT), have led to significant growth in telemedicine. This study introduces a cutting-edge system that proactively predicts the emergence of skin cancer by combining deep learning algorithms, IoT devices, and sophisticated medical imaging techniques. The experimental setup leverages a high-resolution mobile camera for dermoscopy, associated with a cloud-integrated machine learning framework. The proposed algorithm comprehensively examines lesion characteristics, Utilizing color, texture, and shape characteristics to evaluate the probability of malignancy. Subsequently, a cloud-hosted machine learning model analyzes and scrutinizes the collected data, yielding a thorough diagnostic evaluation. Initial results reveal that this system achieves an impressive predictive accuracy rate exceeding 97.6%, enabling swift and efficient skin cancer detection. These promising findings emphasize the potential for rapid, efficient, and proactive diagnosis, significantly improving patient prognosis and reinforcing the value of telemedicine in contemporary healthcare.
Volume: 40
Issue: 1
Page: 202-215
Publish at: 2025-10-01

An intelligent system for job recommendation based on semantic analysis of candidate's resume

10.11591/ijeecs.v40.i1.pp450-460
Hardik Jain , Aparna Joshi , Deepali Naik , Rupali Gangarde , Ranjit Koragoankar , Yash Khapke , Varad Kulkarni
The contemporary job market presents significant obstacles to effectively aligning proficient candidates with pertinent employment prospects. The conventional methods of resume screening and job matching frequently require substantial manual effort and are susceptible to subjective biases, resulting in recruiting decisions that are frequently suboptimal. The present study proposes the development of an intelligent job recommendation system that utilises semantic analysis of candidates' resumes and job descriptions sourced from several job portals. The objective of the proposed intelligent system is to enhance and streamline the recruiting process through the automated extraction and analysis of pertinent skills from resumes and job descriptions, utilising natural language processing (NLP) and machine learning (ML) techniques. In addition, web scraping techniques were used to collect job advertisements from several job portals. The developed model exhibits the ability to recommend the most suitable job prospects by computing similarity metrics, such as Euclidean distance, between skill clusters identified in a job advertisement and a specified candidate's resume. The implemented model achieves an accuracy rate of 98.92%. It is anticipated that the integration of an intelligent job recommendation system will augment the recruitment procedure for both job seekers and employers.
Volume: 40
Issue: 1
Page: 450-460
Publish at: 2025-10-01
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