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31,456 Article Results

A three-phase model to keyword detection in Arabic corpora

10.11591/ijeecs.v37.i1.pp206-213
Driss Namly , Karim Bouzoubaa , Ridouane Tachicart
The exponential growth of Arabic text data in recent years has created an urgent demand for sophisticated keyword detection techniques that are specifically tailored to the nuances of the Arabic language. This study addresses the critical need for efficient tools capable of swiftly and accurately identifying keywords within a collection of Arabic documents, particularly when analyzing multiple documents in a corpus. To meet this challenge, we present a novel corpus specifically designed for keyword detection in Arabic texts, along with an innovative approach that integrates three distinct candidate keyword lists: a frequency-based list, a vector space model list, and a machine learning-based list. This hybrid methodology leverages the strengths of each technique, enabling a more comprehensive and effective keyword identification process. We conducted extensive experimental validation to assess the performance and computational efficiency of our proposed pipeline. The results demonstrate that our approach consistently achieves robust performance across a variety of domains, with evaluation metrics indicating F1-scores that consistently surpass 91%. Overall, this study contributes to the advancement of automated keyword detection in Arabic, paving the way for enhanced information retrieval and text analysis capabilities.
Volume: 37
Issue: 1
Page: 206-213
Publish at: 2025-01-01

Bit-rate aware effective inter-layer motion prediction using multi-loop encoding structure

10.11591/ijeecs.v37.i1.pp569-579
Sandeep Gowdra Siddaramappa , Gowdra Shivanandappa Mamatha
Recently, there has been a notable increase in the use of video content on the internet, leading for the creation of improved codecs like versatile-video-coding (VVC) and high-efficiency video-coding (HEVC). It is important to note that these video coding techniques continue to demonstrate quality degradation and the presence of noise throughout the decoded frames. A number of deep-learning (DL) algorithm-based network structures have been developed by experts to tackle this problem; nevertheless, because many of these solutions use in-loop filtration, extra bits must be sent among the encoding and decoding layers. Moreover, because they used fewer reference frames, they were unable to extract significant features by taking advantage from the temporal connection between frames. Hence, this paper introduces inter-layer motion prediction aware multi-loop video coding (ILMPA-MLVC) techniques. The ILMPA-MLVC first designs an multi-loop adaptive encoder (MLAE) architecture to enhance inter-layer motion prediction and optimization process; second, this work designs multi-loop probabilistic-bitrate aware compression (MLPBAC) model to attain improved bitrate efficiency with minimal overhead; the training of ILMPA-MLVC is done through novel distortion loss function using UVG dataset; the result shows the proposed ILMPA-MLVC attain improved peak-singal-to-noise-ratio (PSNR) and structural similarity (SSIM) performance in comparison with existing video coding techniques.
Volume: 37
Issue: 1
Page: 569-579
Publish at: 2025-01-01

An enhanced predictive modelling framework for highly accurate non-alcoholic fatty liver disease forecasting

10.11591/ijeecs.v37.i1.pp214-222
Nidhi Arora , Shilpa Srivastava , Aprna Tripathi , Varuna Gupta
Non-alcoholic fatty liver disease (NAFLD) is a chronic medical ailment characterized by accumulation of excessive fat in the liver of non-alcoholic patients. In absence of any early visible indications, application of machine learning based predictive techniques for early prediction of NAFLD are quite beneficial. The objective of this paper is to present a complete framework for guided development of varied predictive machine learning models and predict NAFLD disease with high accuracy. The framework employs step–by-step data quality enhancement to medical data such as cleaning, normalization, data upscaling using SMOTE (for handling class imbalances) and correlation analysis-based feature selection to predict NAFLD with high accuracy using only clinically recorded identifiers. Comprehensive comparative analysis of prediction results of seven machine learning predictive models is done using unprocessed as well as quality enhanced data. As per the observed results, XGBoost, random forest and neural network machine learning models reported significantly higher accuracies with improved ‘AUC’ and ‘ROC’ values using preprocessed data in contrast to unprocessed data. The prediction results are also assessed on various quality metrics such as ‘accuracy’, ‘f1-score’, ‘precision’, and ‘recall’ significantly support the need for presented methodologies for qualitative NAFLD prediction modelling.
Volume: 37
Issue: 1
Page: 214-222
Publish at: 2025-01-01

Information system success model: continuous intention on users’ perception of e-learning satisfaction

10.11591/ijeecs.v37.i1.pp389-397
Rina Fiati , Widowati Widowati , Dinar Mutiara Kusumo Nugraheni
The information systems success strategy contributes to understanding of digitalization. This research aims to evaluate user satisfaction with the e-learning system continuously. The research method is hybrid, combining constructs of a unified theory of acceptance and use of technology, the technology acceptance model, and service quality (SVQ). Data collection was conducted through the distribution of questionnaires targeting instructors. Data analysis utilized structural equation modeling with partial least squares. This method was used to test the measurement model with factor loadings and average variance extracted (AVE) above 0.5. Meanwhile, validity testing on cross-loading had indicator values for each variable higher than other variables, with composite reliability above 0.7. These results were supported by hypothesis testing, which indicated that website quality positively influences user satisfaction, leading to sustained intention. The original sample obtained a value of 0.633; mean of 0.624; standard deviation of 0.105, and a p-value below 0.01. Additionally, user subjective norms have a strong relationship between sustained intention and system appropriateness, of 0.763 in using e-learning.
Volume: 37
Issue: 1
Page: 389-397
Publish at: 2025-01-01

Innovative virtual reality solutions for technical training in heavy construction equipment repair and maintenance

10.11591/ijeecs.v37.i1.pp627-635
Wirawan Istiono , Andhika Nugraha Wira Pratama
The construction industry is significantly impacted by heavy construction equipment, including bulldozers, excavators, and vehicles. This equipment speeds up building, moves supplies, and builds infrastructure. Using heavy construction equipment correctly can boost productivity and shorten project timelines. Due to their complexity and scale, this equipment must be maintained and repaired. Poor maintenance and repair of heavy construction equipment can reduce performance, damage, and even cause accidents. Due to these problems, this study focuses on the design and development of a simulation training application to enhance the technical skills of workers in maintaining and repairing heavy construction equipment using virtual reality (VR) technology, the development of this application will be carried out using Unreal Engine 5 and thereafter tested and implemented at PT Menara Indonesia or M-Knows Consulting, Indonesia. At the end of this study, the design and development of a VR training simulation application for heavy equipment repair has been successfully completed. After testing the VR application and conducting user acceptance tests, it was concluded that the created VR application greatly assists M-Knows Consulting in training workers to perform maintenance and repair on heavy equipment, with a user acceptance rate of 84%.
Volume: 37
Issue: 1
Page: 627-635
Publish at: 2025-01-01

Optimization signal writing with machine learning assisted control

10.11591/ijeecs.v37.i1.pp90-100
Chaweng Sapapporn , Soontaree Seangsri , Sorada Khaengkarn , Jiraphon Srisertpol
The high-precision signal writing machine, experiencing a 0.1% failure rate due to discrete fourier transform (DFT) of position error signal (PES) exceeding control limits, can be improved with an appropriate controller gain. This paper combines machine learning (ML) classification and controller optimization to determine the suitable gain for the hard disk drive (HDD) signal writing process. The result from machine classification has a high potential for position error improvement, distinguishing them from those with obvious degradation. The identified machine classes with high potential for signal write quality improvement undergo controller optimization using a genetic algorithm (GA). The objective function considers gain crossover frequency, phase margin, and PES DFT at low frequencies. Experimental results demonstrate that the new controller gain enhances signal write quality of class 0 and class 3 by 14.68% and 17.18%, respectively, leading to a reduced failure rate down to 0.05%.
Volume: 37
Issue: 1
Page: 90-100
Publish at: 2025-01-01

Predicting student performance using Moodle data and machine learning with feature importance

10.11591/ijeecs.v37.i1.pp223-231
Jamal Kay Rogers , Tamara Cher Mercado , Remelyn Cheng
Despite the growing technological advancement in education, poor academic performance of students remains challenging for educational institutions worldwide. The study aimed to predict students’ academic performance through modular object-oriented dynamic learning environment (Moodle) data and tree-based machine learning algorithms with feature importance. While previous studies aimed at increasing model performance, this study trained a model with multiple data sets and generic features for improved generalizability. Through a comparative analysis of random forest (RF), XGBoost, and C5.0 decision tree (DT) algorithms, the trained RF model emerged as the best model, achieving a good ROC-AUC score of 0.77 and 0.73 in training and testing sets, respectively. The feature importance aspect of the study identified the submission actions as the most crucial predictor of student performance while the delete actions as the least. The Moodle data used in the study was limited to 2-degree programs from the University of Southeastern Philippines (USeP). The 22 courses still resulted in a small sample size of 1,007. Future research should broaden its focus to increase generalizability. Overall, the findings highlight the potential of machine learning techniques to inform intervention strategies and enhance student support mechanisms in online education settings, contributing to the intersection of data science and education literature.
Volume: 37
Issue: 1
Page: 223-231
Publish at: 2025-01-01

AI in Moroccan education: evaluating student acceptance using machine learning classification models

10.11591/ijeecs.v37.i1.pp452-462
Khoual Mohamed , Elkaimbillah Zineb , Mcharfi Zineb , El Asri Bouchra
Personalized learning is becoming a reality in education thanks to the rise of AI. This study investigates the possibilities of AI within the realm of education, focusing on the individualization of the learning experience. The research is based on the responses of 395 students from various faculties in Morocco. The questionnaire aimed to assess the students’ opinions of AI, their level of knowledge, their previous experiences, and their perception of the application of AI within educational settings. Employing classification techniques such as decision trees (DT), multilayer perceptron (MLP), and random forests (RF), our aim was to predict the receptivity of AI in education. The findings highlight significant differences in how Moroccan students perceive AI, identifying key factors such as familiarity with the technology, ethical concerns, and perception of its potential impact on the learning experience. Classification models showed varied performance in anticipating these attitudes. This study highlights the critical importance of understanding students’ perspectives on AI in education. These findings offer crucial insights for education policymakers as well as designers of educational technology solutions in Morocco. The findings can be used as a guide to adapt the incorporation of AI into the education sector with discernment, taking into account students’ perceptions and preferences.
Volume: 37
Issue: 1
Page: 452-462
Publish at: 2025-01-01

LMD-based fault detection scheme for TCSC compensated wind integrated transmission lines

10.11591/ijeecs.v37.i1.pp26-34
Saritha Market , Seenivasan Swaminathan , Ravindranath Gurram
In this paper, a fast fault detection scheme is presented to detect the faults in thyristor-controlled series capacitor (TCSC) compensated transmission line connected with the large wind farms to export the electrical power to grid. The proposed logic utilizes the current information at the relay location and processes through the local mean decomposition technique to extract the magnitude features of the current. Cumulative sum of these features are computed for each phase currents to detect the faults in the transmission lines and further to classify the faulty phase in the system. The residual component of the current is used to detect the ground involvement in the faulty phase. The proposed method is tested during variety of faults by changing the nature of the fault using the fault parameters. Furthermore, the impact of the TCSC is also investigated along with the dynamic changes of the WF and their influence on the protection scheme. All the simulations are performed in MALTAB-Simulink software.
Volume: 37
Issue: 1
Page: 26-34
Publish at: 2025-01-01

Characterization of A2G UAV communication channels under rician fading conditions

10.11591/ijeecs.v37.i1.pp143-153
Yomi Guno , Trio Adiono , Joko Suryana , Fadjar Rahino Triputra , Asyaraf Hidayat , Siti Vivi Octaviany
The variation in the k-factor value significantly influences the performance of unmanned aerial vehicle (UAV) air-to-ground point-to-point line of sight (A2G PTP LOS) communications over a Rician channel at 1,800 MHz using quadrature phase shift keying (QPSK) modulation and orthogonal frequency division multiplexing (OFDM) techniques. The research emphasizes the impact of the k-factor, which quantifies the dominance of the line-of-sight component over multipath scattering. The variation in the k-factor significantly influences UAV A2G PTP LOS communication performance for the empirical model (EM), as it involves precise measurements of the received power level in dBm from UAV to ground control station (GCS) across varying distances and altitudes. We introduce a method to compute the k-factor by assessing the ratio of the line-of-sight signal power to the multipath signal power, thereby enhancing channel modeling accuracy. Empirical analysis shows a strong correlation between bit error rate (BER) and signal-to-noise ratio (SNR) with differing k-factor values; a higher k-factor of 16.3 markedly improves performance, virtually eliminating errors at a 10 dB SNR, while a lower k-factor of 2.39 still shows significant errors at a 30 dB SNR. These results highlight the necessity of optimizing the k-factor in UAV A2G PTP LOS systems to ensure stable and reliable communication under diverse operational conditions.
Volume: 37
Issue: 1
Page: 143-153
Publish at: 2025-01-01

Field-level sugarcane yield estimation utilizing Sentinel-2 time-series and machine learning

10.11591/ijeecs.v37.i1.pp475-487
Rekha B. U. , Veena V. Desai , Suresh Kuri , Pratijnya S Ajawan , Sunil Kumar Jha , V. C. Patil
This work focused on developing a methodology for using machine learning (ML) approaches to establish a pre-harvest yield prediction model for sugarcane at field level by integrating time-series remote sensing imagery data with ML techniques. Ground truth agro data and thirty-one spectral vegetation indices were extracted from Sentinel-2 imagery and were considered for yield modeling. A two-level feature selection technique was used to determine the most significant variables that best correlated with sugarcane yield to predict yield in advance. Seven ML algorithms, including those based on regularization, decision trees, and ensemble methods like boosting, were used to predict yield. The approach achieved the highest R2 score of 0.73 and the lowest root mean squared error (RMSE) of 13.45 t/ha with random forest (RF) among the seven ML models tested. Furthermore, all feature selection procedures identified normalized difference red edge (NDRE), red edge chlorophyll index (RECI), and ratio vegetation index (RVI) as major yield-driving variables. The experiments during feature selection demonstrated the potential of red edge spectral bands in development of a reliable sugarcane-yield prediction approach. The RF model obtained using the proposed methodology outperforms the two baseline models developed using NDVI and GNDVI indices, with an improved RMSE of 16-18%.
Volume: 37
Issue: 1
Page: 475-487
Publish at: 2025-01-01

A framework for dynamic monitoring of distributed systems featuring adaptive security

10.11591/ijeecs.v37.i1.pp660-669
Sudhakar Periyasamy , Prabu Kaliyaperumal , Abinaya Alagarsamy , Thenmozhi Elumalai , Tamilarasi Karuppiah
Distributed systems play a crucial role in today’s information-based society, enabling seamless communication among governmental, industrial, social, and non-governmental institutions. As information becomes increasingly complex, the software industry is highly concerned about the heterogeneity and dynamicity of distributed systems. It is common for various types of information and services to be disseminated on different sites, especially in web 2.0. Since ‘information’ has become a prime tool for organizations to achieve their vision and mission, a high level of quality of service (QoS) is mandatory to disseminate and access information and services over remote sites, despite an unsecure communication system. These systems are expected to have security mechanisms in place, render services within an acceptable response time, dynamically adapt to environmental requirements, and secure key information. This research article proposes a framework for evaluating and determining a threshold up to which distributed systems can collect data to adapt to the environment. The study also proposes a dynamic security metric to determine the level of security disturbance caused by the monitoring system for adaptation and the measures to be implemented. Additionally, the paper details the role of the monitoring system in safeguarding the adaptive distributed system and proposes an adaptive monitoring system that can modify its functionality as per the environment.
Volume: 37
Issue: 1
Page: 660-669
Publish at: 2025-01-01

Backstepping approach for the control of the double-fed asynchronous generator in a wind power system

10.11591/ijeecs.v37.i1.pp78-89
Mbarek Chahboun , Mohcine Abouyaakoub , Ali Ait Ali , Aziz El Mrabet , Hicham Hihi , Hassan Ouabi , Youssef El Bid
This paper aims to model and control the dual-fed asynchronous generator (DFIG). The modeling and vector control were simulated using MATLAB, followed by the application of the Backstepping control strategy. A comparative study between two DFIG control strategies, fuzzy logic control (FLC) and Back-stepping control, was conducted. The results for the Backstepping approach are discussed and compared with FLC, highlighting that the Backstepping technique addresses robustness issues regarding variations in operating conditions and internal parameters. Both control strategies are applied to a wind turbine system, and the simulation results and robustness tests are analyzed.
Volume: 37
Issue: 1
Page: 78-89
Publish at: 2025-01-01

A new intensity-modulated radiation therapy with deep learning heart rate prediction framework for smart health monitoring

10.11591/ijeecs.v37.i1.pp300-314
Saravanan Madderi Sivalingam , Syed Thisin
This research paper monitors the patient’s health using sensor data, cloud, and big data Hadoop tools and used to predict heart attack and related results were discussed in detail. The integration of big data, and wearable sensors in pervasive computing has significantly enhanced healthcare services. This proposal focuses on developing an advanced healthcare monitoring system tailored for tracking the activities of elderly individuals. The wearable sensors are placed on humans at a right angle, left arm, right arm, and chest to collect the data. The large data are split into smaller segments using the map and reduce process of big data Hadoop tools. The intensity-modulated radiation therapy (IMRT) approach is used for the mapping phase and deep convolutional neural network (DCNN), deep belief network (DBN), and long short-term memory (LSTM) and proposed deep learning heart rate prediction (DLHRP) algorithms are used for the combiner/reduce phase. The reduction process combines similar segments of data to predict identical classes to predict the severity of human conditions. The proposed IMRT-DLHRP system has improved performance of 96.34% accuracy compared with 84.25%, 89.47%, and 91.58% compared to DCNN, DBN, and LSTM respectively, therefore proposed framework has significant improvement over existing approaches.
Volume: 37
Issue: 1
Page: 300-314
Publish at: 2025-01-01

Seasonal meat stock demand used comparison of performance smoothing-average forecasting

10.11591/ijeecs.v37.i1.pp425-433
Tundo Tundo , Shoffan Saifullah , Tio Dharmawan , Junaidi Junaidi , Elmi Devia
Seasonal patterns significantly influence the demand for beef stock, especially in rural areas that rely on natural feed. Accurate forecasting is essential for managing this demand due to beef's status as a government-regulated nutritional commodity. Food production, consumption, and income levels affect the demand for beef stocks. This research aims to identify the most precise forecasting method for predicting future beef stock needs. We evaluated multiple techniques, including single exponential smoothing (SES), double exponential smoothing (DES), single moving average (SMA), and double moving average (DMA), using the mean absolute percentage error (MAPE) metric, focusing specifically on beef supplies in Pemalang. The results indicated that the DMA method achieved the highest accuracy with a MAPE value of 5.993% at the 4th -order parameter. Additionally, increasing the data volume improved forecasting accuracy, demonstrating the effectiveness of the DMA method for beef stock prediction.
Volume: 37
Issue: 1
Page: 425-433
Publish at: 2025-01-01
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