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31,456 Article Results

RNN-driven integration of spatial, temporal, features for Indian sign language recognition and video captioning

10.11591/ijeecs.v38.i2.pp821-829
Ajay Manohar Pol , Shrinivas A. Patil
This paper presents a novel model that integrates spatial features from residual blocks and temporal features from FFT, alongside a sophisticated RNN architecture comprising BiLSTM, gated recurrent units (GRU) layers, and multi-head attention. Achieving nearly 99% accuracy on both WLASL and INCLUDE datasets, this model outperforms standard CNN pretrained models in feature extraction. Notably, the BiLSTM and GRU combination proves superior to other combinations such as LSTM and GRU. The BLEU score analysis further validates the model's efficacy, with scores of 0.51 and 0.54 on the WLASL and INCLUDE datasets, respectively. These results affirm the model's proficiency in capturing intricate spatial and temporal nuances inherent in sign language gestures, enhancing accessibility and communication for the deaf and hard-of-hearing communities. The comparison highlights the superiority of this paper's proposed model over standard approaches, emphasizing the significance of the integrated architecture. Continued refinement and optimization hold promise for further augmenting the model's performance and applicability in real-world scenarios, contributing to inclusive communication environments.
Volume: 38
Issue: 2
Page: 821-829
Publish at: 2025-05-01

End-user software engineering approach: improve spreadsheets capabilities using Python-based user-defined functions

10.11591/ijeecs.v38.i2.pp1024-1032
Tamer Bahgat Elserwy , Tarek Aly , Basma E. El-Demerdash
End-user computing enables non-developers to handle data and applications, boosting collaboration and productivity. Spreadsheets are a key example of end-user programming environments that are extensively utilized in business for data analysis. However, the functionalities of Excel have limitations compared to specialized programming languages. This study aims to address this shortcoming by proposing a prototype that integrates Python's features into Excel via standalone desktop Python-based user-defined functions (UDFs). This method mitigates potential latency concerns linked to cloud-based solutions. This study employs Excel-DNA (dynamic network access) and IronPython; Excel-DNA facilitates the creation of custom Python functions that integrate smoothly with Excel's calculation engine, while IronPython allows these Python UDFs to run directly within Excel. Core components include C# and visual studio tools office (VSTO) add-ins, which enable communication between Python and Excel. This approach grants users the chance to execute Python UDFs for various tasks such as mathematical computations and predictions — all within the familiar Excel environment. The prototype showcases seamless integration, enabling users to invoke Python-based UDFs just like built in Excel functions. This study enhances the capabilities of spreadsheets by harnessing Python's strengths within Excel. Future work may focus on expanding the Python UDF library and examining user experiences with this innovative approach to data analysis.
Volume: 38
Issue: 2
Page: 1024-1032
Publish at: 2025-05-01

Enhancing SDN security using ensemble-based machine learning approach for DDoS attack detection

10.11591/ijeecs.v38.i2.pp1073-1085
Abdinasir Hirsi , Lukman Audah , Adeb Salh , Mohammed A. Alhartomi , Salman Ahmed
Software-defined networking (SDN) is a groundbreaking technology that transforms traditional network frameworks by separating the control plane from the data plane, thereby enabling flexible and efficient network management. Despite its advantages, SDN introduces vulnerabilities, particularly distributed denial of service (DDoS) attacks. Existing studies have used single, hybrid, and ensemble machine learning (ML) techniques to address attacks, often relying on generated datasets that cannot be tested because of accessibility issues. A major contribution of this study is the creation of a novel, publicly accessible dataset, and benchmarking the proposed approach against existing public datasets to demonstrate its effectiveness. This paper proposes a novel approach that combines ensemble learning models with principal component analysis (PCA) for feature selection. The integration of ensemble learning models enhances predictive performance by leveraging multiple algorithms to improve accuracy and robustness. The results showed that the ensemble of random forests (ENRF) model achieved the highest performance across all metrics with 100% accuracy, precision, recall, and F1-score. This study provides a comprehensive solution to the limitations of existing models by offering superior performance, as evidenced by the comparative analysis, establishing this approach as the best among the evaluated models.
Volume: 38
Issue: 2
Page: 1073-1085
Publish at: 2025-05-01

Enhanced vegetation encroachment detection along power transmission corridors using random forest algorithm

10.11591/ijeecs.v38.i2.pp1376-1382
Deepa Somasundaram , Nivetha Sivaraj , Shalinirajan Shalinirajan , Santhi Karuppiah , Sudha Rajendran
Vegetation encroachment along power transmission corridors poses significant risks to infrastructure safety and reliability, necessitating effective monitoring and management strategies. This study introduces an innovative methodology for detecting vegetation encroachment using a combination of manual and automatic processes integrated with the random forest algorithm. The issue of vegetation encroachment is critical as it can lead to power interruptions and safety hazards if not addressed promptly. The objective of this research is to develop a scalable and cost-effective solution for vegetation management in power infrastructure maintenance. The methodology involves manual patch extraction and labeling to ensure the accuracy of the training dataset, combined with automatic feature extraction techniques to capture relevant information from satellite imagery. Leveraging the random forest algorithm, the model constructs an ensemble of decision trees based on the extracted features, achieving robust classification accuracy. Findings from this study demonstrate that the proposed approach enables consistent and timely identification of vegetation encroachment in new satellite imagery. Stored model parameters facilitate efficient testing, enhancing the system's ability to provide proactive interventions. This scalable solution significantly reduces reliance on manual labor and offers a cost-effective method for continuous monitoring, ultimately contributing to the resilience and safety of power transmission infrastructure.
Volume: 38
Issue: 2
Page: 1376-1382
Publish at: 2025-05-01

An efficient load balance using virtual machine migration hybrid optimization technique in cloud computing

10.11591/ijeecs.v38.i2.pp1265-1272
Saravanan Madderi Sivalingam , Pavan Kumar Prathapagiri
Cloud computing is becoming increasingly important to developers and companies because to the rapid development of information technology and the wide availability of internet applications. Every information technology industry has a significant role for cloud computing. Numerous multinational technology businesses, like Google, Microsoft, and Facebook, have established data centers across the world to offer processing and storage capabilities. Customers can submit their jobs to cloud centers directly. Reducing overall power usage is the primary goal, which was overlooked in the early stages of cloud development. Using gene expression programming (GEP), symbolic regression models of virtual machines (VMs) are developed using measured VM loads and the corresponding resource parameters. In order to minimize resource use, multidimensional resource load balancing of all the physical machines within the cloud computing platform is the aim of this analysis. The VMH loads estimated and the genetic algorithm that considers the current and the future loads of VMHs and decides an optimal VM-VMH for migrating VMs and performing load-balance. Hence, an efficient load balance using virtual machine migration hybrid optimization technique (HOT) in cloud computing shows better results in terms of accuracy, energy consumption, migration cost.
Volume: 38
Issue: 2
Page: 1265-1272
Publish at: 2025-05-01

A novel mobile application for personality assessment based on the five-factor model and graphology

10.11591/ijeecs.v38.i2.pp915-927
Ahmed Remaida , Zineb Sabri , Benyoussef Abdellaoui , Chakir Fri , Yassine Lakhchaf , Younès El Bouzekri El Idrissi , Mohammed Amine Lafraxo , Aniss Moumen
With the rising interest over the last decade, automated graphology has emerged as a promising filed of research, providing new insights on personality traits prediction on the basis of handwriting analysis. Although, few practical solutions to automate the extraction of handwriting features and personality prediction exist in the literature. This work aims to contribute to closing the gap in automated handwriting personality prediction by proposing a novel mobile application that uses robust feature extraction and machine learning models to predict big five personality traits. Our findings, based on high correlations between handwriting characteristics and personality traits, revealed convincing links. Notably, extraversion and extraversion have strong correlations with top margin feature, whereas agreeableness is expressed through line spacing. These findings emphasize the ability of automated graphology to properly interpret individual personalities. The proposed system achieved exceptional accuracy by using well known machine learning classifiers. The testing accuracy exceeded 92% in binary classification and 87% in multi-class case scenario, proving the adaptability and dependability of the system’s architecture. Our Android app promises to provide users with unprecedented insights into their personalities, establishing a robust tool for psychological assessment and self-discovery.
Volume: 38
Issue: 2
Page: 915-927
Publish at: 2025-05-01

A tag-based recommender system for tourism using collaborative filtering

10.11591/ijeecs.v38.i2.pp960-974
Afef Selmi , Maryah Alawadh , Raghad Alotaibi , Shrefah Alharbi
Recommender systems have garnered significant attention from researchers due to their potential for delivering personalized recommendations in light of the vast amount of information available online. These systems have found applications in various domains, including financial services, movies, and research articles. Their implementation in the tourism industry is particularly promising. Travelers often face the daunting task of selecting the right tourist attractions from a plethora of options, which can consume considerable time and energy. By leveraging personalized recommendation technologies, it is possible to provide highly accurate travel suggestions tailored to individual preferences. This study proposes the development of a customized recommendation system (RS) aimed at assisting travelers in the Qassim region of the Kingdom of Saudi Arabia. By using this region as a case study, the proposed RS consists of two main modules: a user registration and login module and a recommendation technique and tag module. The system will capture users’ interests and prompt them to select from various options, subsequently presenting them with tailored recommendations based on their preferences. This approach aims to enhance the travel experience by offering relevant suggestions that align with the interests of each traveler.
Volume: 38
Issue: 2
Page: 960-974
Publish at: 2025-05-01

Accurate segmentation of fruit based on deep learning

10.11591/ijeecs.v38.i2.pp1331-1338
Esraa Abu Elsoud , Omar Alidmat , Suhaila Abuowaida , Esraa Alhenawi , Nawaf Alshdaifat , Ahmad Aburomman , Huah Yong Chan
In the last few years, deep learning has exhibited its efficacy and capacity in the field of computer vision owing to its exceptional precision and widespread acceptance. The primary objective of this study is to investigate an improved approach for segmentation in the context of various fruit categories. Despite the utilization of deep learning, the current segmentation techniques for various fruit items exhibit subpar performance. The proposed enhanced multiple fruit segmentation algorithm has the following main steps: 1) modifying the size of the filter, 2) the process of optimizing the ResNet-101 block involves selecting the most suitable count of repetitions. The multiple fruit dataset is split 80% in the training stage and 20% in the testing stage. These images were utilized to train a deep learning (DL) based algorithm, which aims to identify multiple fruit items within images accurately. The proposed algorithm has a lower training time compared to the other algorithms. The thresholds exhibit greater values compared to the thresholds of state-of-the-art algorithms.
Volume: 38
Issue: 2
Page: 1331-1338
Publish at: 2025-05-01

The integration of metaverse technology in healthcare: a comprehensive review and future research directions

10.11591/ijeecs.v38.i2.pp975-987
Rita Roy , Tarinmoy Das , Dimitrios Alexios Karras
The impact of using the metaverse in healthcare is investigated in this research work. Emerging technologies are essential to enhancing medical consultants’ care, especially in developing countries like India. The study filters and reviews the pertinent literature using the scientific procedures and rationales for systematic literature reviews (SPAR-4-SLR) methodology. The initial search yielded 180 articles. Forty-four articles were considered for the study after screening the papers in light of the research questions and relevant literature. The theory-context-characteristics-methodology (TCCM) framework is used in this study to assess future metaverse research trends. This study also used the context, intervention, mechanism, and outcome (CIMO) logic for planning and decision-making. This study examines the development of metaverse research over the past ten years and supports research findings published in peer-reviewed journals. Based on the TCCM framework, recommendations have been made for additional research.
Volume: 38
Issue: 2
Page: 975-987
Publish at: 2025-05-01

Enhancing mobility with customized prosthetic designs driven by genetic algorithms

10.11591/ijeecs.v38.i2.pp876-886
Senthil Kumar Seeni , Ganadamoole Madhava Harshitha , Anantha Raman Rathinam , Nagaiyanallur Lakshminarayanan Venkatara , Venkatesan Sasirekha , Bharat Tidke , Subbiah Murugan
Using genetic algorithms, this research intends to usher in a new era of prosthetic design that is redefining mobility. Through repeated evolutionary processes influenced by natural selection, the goal is to optimize prosthetic design parameters including material composition, structure, and control systems. The objective is to create prosthetic limbs that are more personalized to each user's requirements, improving their efficiency, comfort, and functioning via the application of genetic algorithms. The goal of this study is to show that the suggested strategy may improve mobility and user happiness more than standard ways by simulating and testing prosthetic devices in real-world settings. The end goal is to create conditions for a new age of prosthetic technology, where amputees' quality of life is greatly enhanced by devices that are individually designed to meet their biomechanical needs. The impact of prosthetic design and individual patient factors patient dataset derived from a random 5-sample with the following characteristics: ages 32–68, weight 65–90, height 155–180, crossover rate 0.6–0.9, mutation rate 0.05–0.2, population size 70–120, generations 30–60.
Volume: 38
Issue: 2
Page: 876-886
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

Textual and numerical data fusion for depression detection: a machine learning framework

10.11591/ijeecs.v38.i2.pp1231-1244
Mohammad Tarek Aziz , Tanjim Mahmud , Md Faisal Bin Abdul Aziz , Md Abu Bakar Siddick , Md. Maskat Sharif , Mohammad Shahadat Hossain , Karl Andersson
Depression, a widespread mood disorder, significantly affects global mental health. To mitigate the risk of recurrence, early detection is crucial. This study explores socioeconomic factors contributing to depression and proposes a novel machine learning (ML)-based framework for its detection. We develop a tailored questionnaire to collect textual and numerical data, followed by rigorous feature selection using methods like backward removal and Pearson’s chi-squared test. A variety of ML algorithms, including random forest (RF), support vector machine (SVM), and logistic regression (LR), are employed to create a predictive classifier. The RF model achieves the highest accuracy of 96.85%, highlighting its effectiveness in identifying depression risk factors. This research advances depression detection by integrating socioeconomic analysis with ML, offering a robust tool for enhancing predictive accuracy and enabling proactive mental health interventions.
Volume: 38
Issue: 2
Page: 1231-1244
Publish at: 2025-05-01

A review of the impacts of linked open data on cross-domain recommender systems for individual and groups

10.11591/ijeecs.v38.i2.pp1181-1194
Yui Chee Xuan , Rosmamalmi Mat Nawi , Shahrul Azman Mohd Noah
As users' viewpoints on information searching change from information seeking to information receiving, new search paradigms are continuously emerging. Utilizing a recommender system (RS) is one of the modern ways to get information. The RS has succeeded in various traditional domains, including tourism, health, and books. However, some scenarios are more suitable to recommend to a group of users than an individual, such as listening to music at the same place and group traveling. The limited and incomplete number of user-item ratings triggers the challenges of the group and individual RSs. The data sparsity problem emerges because of this incompleteness. The quality of recommendations offered to individuals and groups suffers when there is data sparsity. Using knowledge gained from a source domain, cross-domain RSs can enhance recommendations in target domain. Cross-domain and linked open data approaches are two ways to increase recommendation systems' performance. The impacts of the two aforementioned approaches on individual and group RSs have been discussed. Furthermore, we highlighted various domains employed in cross-domain RSs for individuals and groups, examined diverse methodologies and algorithms, outlined current issues, and suggested future directions for cross-domain RSs research for groups leveraging linked open data technology.
Volume: 38
Issue: 2
Page: 1181-1194
Publish at: 2025-05-01

Investigation on TiO2/graphene as resistance-based gas sensor for volatile organic compound gases detection

10.11591/ijeecs.v38.i2.pp774-782
Siti Amaniah Mohd Chachuli , Muhammad Haziq Nor Azmi , Omer Coban , Nur Hazahsha Shamsudin
Volatile organic compound (VOC) gases are usually produced from industrial activities. Short-term exposure to VOC gases can cause dizziness, headaches, nausea, and throat irritation. Years to a long time exposure to VOC gases can cause cancer and system damage in the human body. With the growth of gas sensor technology, a resistance-based gas sensor based on various structures of resistance-based gas sensors using Titanium dioxide/graphene (TiO2/graphene) were investigated as a sensing material for detecting volatile organic compound gases, which are acetone and ethanol. The TiO2/graphene gas sensor was deposited on a Kapton film using a screen printing technique. All TiO2/graphene gas sensors were exposed to acetone and ethanol at room operating temperature. The results revealed that the highest response values to acetone and ethanol were produced by T99_G1_2 and T98_G2_1, respectively. It can be concluded that design 1 generated the most consistent response to acetone, while design 2 generated the most consistent response to ethanol.
Volume: 38
Issue: 2
Page: 774-782
Publish at: 2025-05-01

Exploring parents’ perceptions of sex education pedagogy in Moroccan schools using an association rules mining-based algorithm

10.11591/ijeecs.v38.i2.pp1124-1136
Chaymae Ben Azza , Sara El Hamdani , Mohamed Taj Bennani , Khalid El Fahssi , Mohamed Lamrini , Mohamed Elfar
Sex education is vital for promoting healthy relationships and preventing sexual exploitation by teaching boundaries, consent and abuse recognition. Customized strategies are needed for children, balancing age-appropriate content with parental and community perspectives. Our study assessed Moroccan parents’ views on sex education’s adoption in schools. Conducted in Taza city, the survey targeted 1946 parents of students over 7 years old. Using association rule mining (ARM), we analyzed their responses. Therefore, Apriori algorithm was implemented to discover strong association rules within parents’ selected responses. Results showed that 74.53% of parents aged 19-30 support sexual education, citing its absence as a factor in child abuse. Meanwhile, 60.48% of those aged 31-59 with university education believe psychological disorders contribute to assaults. While some fathers (32.48%) and some mothers (67.52%) support sexual education, others don’t, but all agree on restricting children’s internet use until age 16 to avoid harmful content. These findings can inform comparative studies, aid decision-makers and enhance AI-based EdTech systems by offering insights into sex education perceptions.
Volume: 38
Issue: 2
Page: 1124-1136
Publish at: 2025-05-01
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