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New qualitative perspective in students’ English presentation skills in China-developing a student-based module

10.11591/ijere.v14i6.33708
Lifang Sun , Hanita Hanim Ismail , Azlina Abdul Aziz
Since English is the world’s lingua franca, English learners need to master communication skills to succeed in their respective fields. However, Chinese college students face the problem of separation between learning and using what they learned in the traditional English classrooms. This study aims to explore the university students’ needs of English presentation learning. The research questions are: i) What are the students’ language needs to improve an English presentation? ii) What are the skills needed when doing an English presentation? and iii) What are the students’ preferences in English presentation class? The researchers conducted focus-group interviews (FGI) which were participated by 30 students and semi-structured interview for five teachers to understand the students’ real needs and preferences in the process of learning English speaking. Three themes were generated by axial coding from the interview data: i) English language needs; ii) presentation skills’ needs; and iii) students’ preferences.The findings can help the teacher design the English-speaking class more effective and have adjustments according to students’ real productions using production-oriented approach in English presentation teaching.
Volume: 14
Issue: 6
Page: 5174-5186
Publish at: 2025-12-01

Data transmission technologies for the development of a drilling rig control and diagnostic system

10.11591/ijece.v15i6.pp5506-5514
Irina Rastvorova , Sergei Trufanov
This article examines telecommunication technologies used in automatic control and diagnostics systems and discusses key aspects of using telecommunication solutions for monitoring and controlling the operation processes of the electrical complex of a drilling rig, including remote access, data transmission and real-time information analysis. It provides a comprehensive overview of such communication technologies as Bluetooth, Wi-Fi, ZigBee, global system for mobile communication (GSM), RS-232, RS-422, RS-485, universal serial bus (USB), Ethernet, narrowband internet of things (NB-IoT), long range wide area network (LoRaWAN), and power line communication (PLC). Technologies that will be most effective for use in control and diagnostics systems of a drilling rig complex are proposed. The possibility of using machine learning to process a large amount of data obtained during the drilling process to optimize the controlled drilling parameters is investigated.
Volume: 15
Issue: 6
Page: 5506-5514
Publish at: 2025-12-01

Low-power and reduced delay in inverter and universal logic gates using Hvt-FinFET technology

10.11591/ijece.v15i6.pp5193-5204
Veerappa Chikkagoudar , G. Indumathi
The rapid scaling of conventional complementary metal–oxide– semiconductor (CMOS) metal–oxide–semiconductor field-effect transistors (MOSFETs) led to significantly increasing power dissipation, delay, and short channel effects (SCEs). Fin field-effect transistor (FinFET) technology is a better alternative to MOSFETs with superior electrostatic control, low power, and reduced leakage current. FinFETs have been chosen for their efficiency in overcoming these issues. This work focuses on the design of high-threshold voltage fin field-effect transistor (Hvt-FinFET) 18 nm technology-based inverter with optimized parameters and implementing universal gates NAND and NOR in Cadence Virtuoso tool. These three gates are basic building blocks for any complex digital system design. The results demonstrate significant improvement in power and reduced propagation delay in comparison with conventional CMOS technology. The Hvt-FinFET inverter obtained power dissipation and delay reduction of 13.63% and 33.33%, respectively. Power and delay optimization of 29.10% and 11.8% have been obtained in the NAND gate and 31.28% and 29.08% in the NOR gate when compared to conventional CMOS circuits. The results demonstrate significant improvements in power savings, reduced propagation delay, and superior energy efficiency, validating the effectiveness of Hvt-FinFET technology for next-generation very large scale integration (VLSI) applications.
Volume: 15
Issue: 6
Page: 5193-5204
Publish at: 2025-12-01

Impact of outlier detection techniques on time-series forecasting accuracy for multi-country energy demand prediction

10.11591/ijece.v15i6.pp5067-5079
Shreyas Karnick , Sanjay Lakshminarayanan , Madhu Palati , Prakash R
Accurate energy demand prediction is crucial for efficient grid management and resource optimization, particularly across multiple countries with varying consumption patterns. However, real-world energy demand data often contains outliers that can distort forecasting accuracy. This study evaluates the impact of five outlier detection techniques—Z-Score, density- based spatial clustering of applications with noise (DBSCAN), isolation forest (IF), local outlier factor (LOF), and one-class support vector machine (SVM)—on the performance of three time-series forecasting models: long short-term memory (LSTM) networks, convolutional neural network (CNN) Autoencoders, and LSTM with attention mechanisms. The models are tested using energy demand data from four European countries— Germany, France, Spain, and Italy—derived from real-time consumption records. A comparative analysis based on root mean squared error (RMSE) demonstrates that incorporating outlier detection significantly enhances model robustness, reducing forecasting errors caused by anomalous data. The findings emphasize the importance of selecting appropriate outlier detection strategies to improve the accuracy and reliability of energy demand forecasting. This research provides valuable insights into the trade-offs involved in outlier removal, with implications for policy and operational practices in energy management.
Volume: 15
Issue: 6
Page: 5067-5079
Publish at: 2025-12-01

Geometrical determination of the focal point of parabolic solar concentrators

10.11591/ijece.v15i6.pp5055-5066
Bekzod Maxmudov , Sherzod A. Korabayev , Nosir Yu. Sharibaev , Abror Abdulkhaev , Xulkarxon Mahmudova , Sh A. Mahsudov
Parabolic solar concentrators play a crucial role in harnessing solar energy by focusing sunlight onto a single focal point, enhancing efficiency in solar thermal applications. However, accurately determining the focal point remains a significant challenge, affecting energy efficiency, stability, and operational costs. This study presents a novel approach to determining the focal point of parabolic solar concentrators using two distinct geometric and mathematical methods. The first method applies standard parabolic equations to derive the focal point, while the second method introduces a geometric approach based on the properties of straight-line tangents and angular measurements. Experimental validation was conducted by comparing the proposed method against laser-based focal point determination. The results demonstrate that the proposed method enhances heat collection efficiency and stability, leading to improved energy output. The findings of this study contribute to optimizing solar concentrator designs, reducing energy losses, and promoting sustainable energy applications.
Volume: 15
Issue: 6
Page: 5055-5066
Publish at: 2025-12-01

Robotic product-based manipulation in simulated environment

10.11591/ijece.v15i6.pp5894-5903
Juan Camilo Guacheta-Alba , Anny Astrid Espitia-Cubillos , Robinson Jimenez-Moreno
Before deploying algorithms in industrial settings, it is essential to validate them in virtual environments to anticipate real-world performance, identify potential limitations, and guide necessary optimizations. This study presents the development and integration of artificial intelligence algorithms for detecting labels and container formats of cleaning products using computer vision, enabling robotic manipulation via a UR5 arm. Label identification is performed using the speeded-up robust features (SURF) algorithm, ensuring robustness to scale and orientation changes. For container recognition, multiple methods were explored: edge detection using Sobel and Canny filters, Hopfield networks trained on filtered images, 2D cross-correlation, and finally, a you only look once (YOLO) deep learning model. Among these, the custom-trained YOLO detector provided the highest accuracy. For robotic control, smooth joint trajectories were computed using polynomial interpolation, allowing the UR5 robot to execute pick-and-place operations. The entire process was validated in the CoppeliaSim simulation environment, where the robot successfully identified, classified, and manipulated products, demonstrating the feasibility of the proposed pipeline for future applications in semi-structured industrial contexts.
Volume: 15
Issue: 6
Page: 5894-5903
Publish at: 2025-12-01

Optimal design, decoding, and minimum distance analysis of Goppa codes using heuristic method

10.11591/ijece.v15i6.pp5411-5421
Bouchaib Aylaj , Said Nouh , Mostafa Belkasmi
Error-correcting codes are crucial to ensure data reliability in communication systems often affected by transmission noise. Building on previous successful applications of our heuristic method degenerate quantum simulated annealing (DQSA) to Bose–Chaudhuri–Hocquenghem (BCH) and quadratic residue (QR) codes. This paper proposes two algorithms designed to address two coding problems for Goppa codes. DQSA-dmin computes the minimum distance (dmin) while DQSA-Dec, serves as a hard decoder optimized for additive white gaussian noise (AWGN) channels. We validate DQSA-dmin comparing its computed minimum distances with theoretical estimates for algebraically constructed Goppa codes, showing accuracy and efficiency. DQSA-dmin further used to find the optimal Goppa codes that reach the lower bound of dmin for linear codes known in the literature and stored in Marcus Grassl's online database. Indeed, we discovered 12 Goppa codes reaching this lower bound. For DQSA-Dec, experimental results show that it obtains a bit error rate (BER) of 10-5 when SNR=7.5 for codes with lengths less than 65, which is very interesting for a hard decoder. Additionally, a comparison with the Paterson algebraic decoder specific to this code family shows that DQSA-Dec outperforms it with a 0.6 dB coding gain at BER=10-4. These findings highlight the effectiveness of DQSA-based algorithms in designing and decoding Goppa codes.
Volume: 15
Issue: 6
Page: 5411-5421
Publish at: 2025-12-01

The impact of work concerns on teaching effectiveness: evidence from Chinese private universities

10.11591/ijere.v14i6.35367
Liang Mingyu , Mohd Khairuddin Abdullah , Connie Shin
Understanding how young teachers cope with work concerns is crucial for improving teaching quality in Chinese private higher education. This study investigates the relationship between different stages of such concerns and teacher effectiveness of young lecturers in private universities. These lecturers often face workload pressure andlack of career supports, which may influence their effectiveness and professional development. This research involved 416 full-time lecturers under the age of 40 from Shandong Province. The sample was determined using Krejcie and Morgan’s formula and selected through a multi-stage sampling method. Private universities were stratified into four categories, one university from each category was purposively selected, and participants were randomly sampled. Data were gatheredthrough a structured questionnaire adapted from the stages of concern (SoC) and the school teacher effectiveness questionnaire (STEQ). Pearson correlation, multiple regression, and structural equation modeling (SEM) were conducted for analysis. The results show that task concerns and impact concerns significantly influenced teacher effectiveness across instructional planning and strategies, assessment, and learning environment. In contrast, self-concerns showed weaker influence. These findings suggest that work concerns reflect not only stress but also deeper professional motivation, pointing to the need for more purposeful supports to increase teacher effectiveness and career growth.
Volume: 14
Issue: 6
Page: 4604-4613
Publish at: 2025-12-01

Professional training of future primary school teachers in the context of Kazakh ethnopsychology and ethnopedagogy

10.11591/ijere.v14i6.32273
Serikkhan Zhuzeyev , Manat Zhailauova
Within the realm of primary education, the educational content and structure for the present youth generation are aligned with state regulations and rooted in the national values and traditions of the people. National education content should be integrated into primary school programs, with a focus on nurturing it through the ethnopsychological features and ethnopedagogical traditions of the nation. With that in mind, this study aims to enhance the ethnopedagogical and ethnopsychological professional qualifications of primary school teachers with focus on their ability to incorporate Kazakh national spiritual values and traditions in educational process. Research methodology includes theoretical instruments, consisting of analytical, and synthetic techniques, as well as pedagogical experiment, conducted via elective course “developmental and pedagogical psychology” among two groups of third-year university students from the department of primary education. To evaluate the effectiveness of the teacher training techniques, proposed in this study, we conducted and compared results of preliminary and final assessment of students’ professional competence. The research outcomes show the beneficial nature of training techniques and include an analysis of the content of the training carried out. These research findings can be applied in the further development of the fields of pedagogy and primary school methodology.
Volume: 14
Issue: 6
Page: 4700-4710
Publish at: 2025-12-01

Exploring feature selection method for microarray classification

10.11591/ijece.v15i6.pp5584-5593
Muhammad Zaky Hakim Akmal , Devi Fitrianah
Effectively selecting features from high-dimensional microarray data is essential for accurate cancer detection. This study explores the pivotal role of feature selection in improving the accuracy of classifying microarray data for ovarian cancer detection. Utilizing machine learning techniques and microarray technology, the research aims to identify subtle gene expression patterns that indicate ovarian cancer. The research explores the utilization of principal component analysis (PCA) for dimensionality reduction and compares the effectiveness of feature selection techniques such as artificial bee colony (ABC) and sequential forward floating selection (SFFS). The dataset used in this study comprises of 15154 genes, 253 instances, and 2 classes related to ovarian cancer. Through a comprehensive analysis, the study aims to optimize the classification process and improve the early detection of ovarian cancer. Moreover, the study presents the classification accuracy results obtained by PCA, ABC, and SFFS. While PCA achieved an accuracy of 96% and SFFS yielded a classification accuracy of 98%, ABC demonstrated the highest classification accuracy of 100%. These findings underscore the effectiveness of ABC as the preferred choice for feature selection in improving the classification accuracy of ovarian cancer detection using microarray data.
Volume: 15
Issue: 6
Page: 5584-5593
Publish at: 2025-12-01

Modified differential evolution algorithm to finding optimal solution for AC transmission expansion planning problem

10.11591/ijece.v15i6.pp5045-5054
Thanh Long Duong , Nguyen Duc Huy Bui
The transmission expansion planning (TEP) problem primarily aims to determine the appropriate number and location of additional lines required to meet the increasing power demand at the lowest possible investment cost while meeting the operation constraints. Most of the research in the past solved the TEP problem using the direct current (DC) model instead of the alternating current (AC) model because of its non-linear and non-convex nature. In order to improve the effectiveness of solving the AC transmission expansion planning (ACTEP) problem, a modified version of the differential evolution (DE) is proposed in this paper. The main idea of the modification is to limit the randomness of the mutation process by focusing on the first, second, and third-best individuals. To prove the effectiveness of the suggested method, the ACTEP problem considering fuel costs is solved in the Graver 6 bus system and the IEEE 24 bus system. Moreover, the result of each system is compared to the original DE algorithm and state-of-the-art methods such as the one-to-one-based optimizer (OOBO), the artificial hummingbird algorithm (AHA), the dandelion optimizer (DO), the tuna swarm optimization (TSO), and the chaos game optimization (CGO). The results show that the proposed algorithm is more effective than the original DE algorithm by 1.86% in solving the ACTEP problem.
Volume: 15
Issue: 6
Page: 5045-5054
Publish at: 2025-12-01

Machine learning model for accurate prediction of coronary artery disease by incorporating error reduction methodologies

10.11591/ijece.v15i6.pp5655-5666
Santhosh Gupta Dogiparthi , Jayanthi K. , Ajith Ananthakrishna Pillai , K. Nakkeeran
Coronary artery disease (CAD) remains a leading cause of mortality worldwide, with an especially high burden in developing countries such as India. In light of increasing patient loads and limited medical resources, there is an urgent need for accurate and reliable diagnostic support systems. This study introduces a machine learning (ML) framework that aims to enhance CAD prediction accuracy by specifically addressing the reduction of false negatives (FN), which are critical in medical diagnostics. Utilizing a stacked ensemble model comprising five base classifiers and a meta-classifier, the framework integrates cost-sensitive learning, classification threshold tuning, engineered features, and manual weighting strategies. The model was developed using a clinically acquired dataset from the Jawaharlal Institute of postgraduate medical education and research (JIPMER), consisting of 428 patient records with 36 original features. Evaluation metrics show that the proposed model achieved an accuracy of 92.19%, sensitivity of 98%, and an F1-score of 95.15%. These improvements are significant in a clinical context, potentially reducing missed diagnoses and improving patient outcomes. The model is intended for deployment in cardiology outpatient settings and demonstrates a scalable, adaptable approach to medical diagnostics.
Volume: 15
Issue: 6
Page: 5655-5666
Publish at: 2025-12-01

A new algorithm for quality-of-service improvement in mobile ad hoc networks

10.11591/ijece.v15i6.pp5466-5483
Hanafy M. Ali , Adel F. El-Kabbany , Yahia B. Hassan
The quality of service (QoS) in mobile ad hoc networks (MANETs) plays a crucial role in optimizing overall network resource utilization. MANET routing protocols, fundamental to QoS, demand adaptive and swift solutions for efficient path searching. In this context, our paper introduces a novel algorithm based on MANETs, employing a hybrid approach that combines ant colony optimization (ACO) with hybrid multipath quality of service ant (HMQAnt) routing protocols. Our algorithm emphasizes bandwidth optimization as a pivotal factor for providing effective paths. By incorporating bandwidth as a significant parameter in the MANETs algorithm, we aim to enhance its overall properties. The proposed routing protocol, focusing on bandwidth optimization, is anticipated to improve the delivery of total network traffic. Evaluation of the algorithm's performance is conducted through QoS metrics, which are overhead, end-to-end delay, and jitter, throughputs, utilizing a MATLAB simulator. Simulation results indicate that our proposed routing protocol holds a distinct advantage compared to ad hoc on-demand distance vector (AODV), destination- sequenced distance (DSDV), dynamic source routing (DSR), and hybrid ant colony optimization-based (ACO) routing protocol called (ANTMANET) algorithms.
Volume: 15
Issue: 6
Page: 5466-5483
Publish at: 2025-12-01

Devanagari optical character recognition of printed text

10.11591/ijece.v15i6.pp5914-5923
Malathi P. , Chandrakanth G. Pujari
Hundreds of native languages and scripts are making their way on digital platform to sustain in multiple data formats. Optical character recognition (OCR) is one such dimension where the low resource languages are yet to find their stability. Devanagari OCR is one such low resource script problem to be dealt with, though it is the fourth widely used global script. Recent works carried on OCR have focused on word level approach and face challenges of spiraling complexity as language alphabet set size crosses hundreds. Most of these OCR works are done in constrained environment, with huge datasets and large computational resources. As a result, effective benchmark evaluation of the works against one another on defined metrics is scarce. Aim here is to explore character level Devanagari OCR with printed text images as input. Pattern recognition (PR) principles for diacritic classification and convolutional neural network (CNN) for base character classification are used. word error rate (WER) of 24.47% is attained. However, the training dataset complexity is reduced by 4.35 times. The ten multi class models, training time range from 45 minutes to 2.5 hours. Further the models can be trained in parallel to complete the training process in 3-4 hours. Thus, the approach used for text classification facilitates the Devanagari OCR solution to be offered in off-the-shelf computing devices.
Volume: 15
Issue: 6
Page: 5914-5923
Publish at: 2025-12-01

Electric load forecasting using ARIMA model for time series data

10.11591/ijict.v14i3.pp830-836
Balasubramanian Belshanth , Haran Prasad , Thirumalaivasal Devanathan Sudhakar
Any country's economic progress is heavily reliant on its power infrastructure, network, and availability, as energy has become an essential component of daily living in today's globe. Electricity's distinctive quality is that it cannot be stored in huge quantities, which explains why global demand for home and commercial electricity has grown at an astonishing rate. On the other hand, electricity costs have varied in recent years, and there is insufficient electricity output to meet global and local demand. The solution is a series of case studies designed to forecast future residential and commercial electricity demand so that power producers, transformers, distributors, and suppliers may efficiently plan and encourage energy savings for consumers. However, load prognosticasting has been one of the most difficult issues confronting the energy business since the inception of electricity. This study covers a new one–dimensional approach algorithm that is essential for the creation of a short–term load prognosticasting module for distribution system design and operation. It has numerous operations, including energy purchase, generation, and infrastructure construction. We have numerous time series forecasting methods of which autoregressive integrated moving average (ARIMA) outperforms the others. The auto–regressive integrated moving average model, or ARIMA, outperforms all other techniques for load forecasting.
Volume: 14
Issue: 3
Page: 830-836
Publish at: 2025-12-01
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