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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

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

An embedded system for the classification of sleep disorders using ECG signals

10.11591/ijeecs.v38.i2.pp767-773
Lavu Venkata Rajani Kumari , Babishamili Daravath , Yarlagadda Padma Sai
Sleep apnea (SA) is a well-known sleep disorder. It predominantly appears due to lack of oxygen in humans. Identifying SA at an early stage can help early diagnosis. The primary motto of our research is to identify SA using electrocardiogram (ECG) signals. Here, three classes are considered for classification. One is normal (N), and the other two are SA classes obstructive sleep apnea (OA) and central sleep apnea (CA). ECG signals are accumulated for MIT-BIH polysomnographic dataset. The ECG data divided into ECG segments and labelled using annotation file. The proposed deep long short-term memory (LSTM) model is then trained using ECG segments and further tested. The model is then finetuned and optimized to obtain the best accuracy. An accuracy of 98.51% is obtained. In addition, performance measures like precision, sensitivity, specificity, F-score are also evaluated. The model is then deployed on NVIDIA’s Jetson nano board to build a prototype. Our model is effective, promising and outperformed existing state of art techniques.
Volume: 38
Issue: 2
Page: 767-773
Publish at: 2025-05-01

Deep learning-based cryptanalysis in recovering the secret key and plaintext on lightweight cryptography

10.11591/ijeecs.v38.i2.pp1115-1123
Yulia Fatma , Muhammad Akmal Remli , Mohd Saberi Mohamad , Januar Al Amien
The development of machine learning (ML) technologies provide a new development direction for cryptanalysis. Several ML research in the field of cryptanalysis was carried out to identify the cryptographic algorithm used, find out the secret key, and even recover the secret message The first objective of this study is to see how much influence optimization and activation function have on the multi-layer perceptron (MLP) model in performing cryptanalysis. The second research objective, which is to compare the performance of cryptanalysis in recovering keys and the plaintext. Several experiments have been carried out, the observed parameters found that the use of the rectified linear unit (ReLU) activation function and the ADAM optimizer improves the performance of deep learning (DL)-based cryptanalysis as evidenced by a significantly smaller error rate. DL-based cryptanalysis works more effectively in recovering keys than recovering plaintext. DL-based cryptanalysis managed to recover the keys with an average loss of 0.007, an average of 49 epochs, and an average time of 0.178 minutes.
Volume: 38
Issue: 2
Page: 1115-1123
Publish at: 2025-05-01

Credit card fraud detection using CNN and LSTM

10.11591/ijeecs.v38.i2.pp1402-1410
Nishant Upadhyay , Nidhi Bansal , Divya Rastogi , Rekha Chaturvedi , Mohammad Asim , Suraj Malik , Khel Prakash Jayant , Abhay Kumar Vajpayee
Credit card fraud is an evolving problem with the fraudsters developing new technologies to perform fraud. Fraudsters have found diverse ways to make a fraud transaction to the card holder. Thus, detecting suspicious behavior of a card is critical for preventing fraudulent transactions to happen. Artificial intelligence techniques, in particular deep learning algorithms can tackle these credit card fraud attacks by identifying patterns that predict transactions as fraud or legitimate. One-dimensional convolutional neural network (1D CNN) and long short-term memory (LSTM) both performs well on the sequential data especially on transactions data, yet there are not many studies done on combining these two algorithms to make an effective fraud detection approach. However, the dataset is highly imbalanced containing only 492 fraud transaction out of two lacs transactions. In this experimental study, firstly datasets will get prepared by using different sampling techniques along with their hybrid techniques secondly, observing the performance of individual CNN and LSTM on the datasets, finally on those datasets in which CNN and LSTM are performing well, by implementing ensemble on those data. The performance of the ensembles is observed using the performance metrics namely accuracy, F1-score, precision and recall. In the proposed experimental study, getting the F1-score of 99.96% and 99.89% in ensemble: early fusion and ensemble: late fusion respectively.
Volume: 38
Issue: 2
Page: 1402-1410
Publish at: 2025-05-01

Optimizing photovoltaic system performance through MPPT synergetic adaptive control

10.11591/ijeecs.v38.i2.pp808-820
Kamel Hadjadj , Hadjira Attoui
This paper investigates enhancement of energy conversion through the implementation of new MPPT control strategy based on synergetic adaptive control (SAC) for a photovoltaic system. The architecture of this system encompasses a photovoltaic module, a DC-DC boost converter, a resistive load, and an MPPT controller. The controller amalgamates two distinct methodologies: the initial algorithm deduces the peak power current through a perturbation and observation (P&O) method, which serves as the reference point for the subsequent algorithm founded on synergetic adaptive control. The parameters for the latter are refined through the particle swarm optimization (PSO) technique This innovative method is employed to ascertain the optimal power output across varying weather conditions, aiming to enhance power transmission performance irrespective of meteorological variations. The efficacy of this strategy was affirmed through a comparative study with the conventional P&O method using MATLAB/Simulink simulations, which verified the superior performance of the proposed algorithm.
Volume: 38
Issue: 2
Page: 808-820
Publish at: 2025-05-01

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

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

Word embedding for contextual similarity using cosine similarity

10.11591/ijeecs.v38.i2.pp1170-1180
Yessy Asri , Dwina Kuswardani , Amanda Atika Sari , Atikah Rifdah Ansyari
Perspectives on technology often have similarities in certain contexts, such as information systems and informatics engineering. The source of opinion data comes from the Quora application, with a retrieval limit of the last 5 years. This research aims to implement Indo-bidirectional encoder representations from transformers (BERT), a variant of the BERT model optimized for Indonesian language, in the context of information system (IS) and information technology (IT) topic classification with 414 original data, which, after being augmented using the synonym replacement method, The generated data becomes 828. Data augmentation aims to evaluate the performance of models by using synonyms and rearranging text while maintaining meaning and structure. The approach used is to label the opinion text based on the cosine similarity calculation of the embedding token from the IndoBERT model. Then, the IndoBERT model is applied to classify the reviews. The experimental results show that the approach of using IndoBERT to classify SI and IT topics based on contextual similarity achieves 90% accuracy based on the confusion matrix. These positive results show the great potential of using transformer-based language models, such as IndoBERT, to support the analysis of comments and related topics in Indonesian.
Volume: 38
Issue: 2
Page: 1170-1180
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

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

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

Temperature-dependent based optimal reactive power dispatch by chaotic equilibrium optimization algorithm

10.11591/ijeecs.v38.i2.pp698-712
Minh Trung Dao , Ngoc Dieu Vo
The optimal reactive power dispatch (ORPD) problem is considered as an important aspect in power system operation of the reactive power, which is vital to maintain network voltage within desirable limit for system reliability. In conventional ORPD problem, the resistance of components in power systems is considered to be independent to their temperature variations. Actually, there is a correlation between the branch resistance and temperature, thus the temperature should be taken into account when performing power flow analysis to improve the accuracy in the calculation of the power flow and power loss on branches. This paper proposes a new chaotic equilibrium optimization (CEO) method to solve the temperature-dependent based optimal reactive power dispatch (TDORPD) problem in power systems by optimizing the reactive power loss and voltage deviation. The proposed CEO algorithm is implemented for the conventional ORPD and TDORPD problems on the benchmark IEEE 30 bus testing network. Moreover, the effects of temperature variations on the considered TDORPD problem are also considered. The obtained results have demonstrated a better performance of the proposed CEO algorithm compared to the original EO and other methods in the literature review for the problem in terms of the solution quality, which confirms its efficacy to effectively resolve the ORPD and TDORPD problem.
Volume: 38
Issue: 2
Page: 698-712
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
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