Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

31,456 Article Results

Cognitive restructuring as a panacea for maladaptive behaviors among primary school children

10.11591/ijere.v14i1.29393
Nonso Ngozika Bisong , Blessing Agbo Ntamu , Veronica Akwenabuaye Undelikwo
This study investigated the efficacy of cognitive restructuring as a panacea for maladaptive behaviors among primary school children in Calabar Municipal, Cross River State, Nigeria. The study was a quasi-experimental study using a pre-test-post-test experimental design. Data were collected from pupils in primary four. Data was collected over twelve weeks. The mean and standard deviation of the pre-and post-test scores were calculated and analysis of covariance (ANCOVA) was used to determine the differences in the two scores. There were 39 students recruited for the study. The study found that maladaptive behaviors such as aggression and disobedience were pervasive among primary school pupils in Calabar. Notably, the findings indicated that cognitive restructuring had a statistically significant impact on reducing aggressive behavior, though its influence on disobedient behavior was not as pronounced. The paper concluded that cognitive restructuring is an effective intervention strategy for addressing maladaptive behaviors among primary school pupils. The intervention achieved its intended goal of improving children’s behavior and provided a framework for more sustainable behavior management strategies.
Volume: 14
Issue: 1
Page: 398-405
Publish at: 2025-02-01

Machine learning for mental health: predicting transitions from addiction to illness

10.11591/ijai.v14.i1.pp385-396
Ali Alkhazraji , Fatima Alsafi , Mohamed Dbouk , Zein Al Abidin Ibrahim , Ihab Sbeity
The increasing prevalence of infection-causing diseases due to environmental factors and lifestyle choices has strained the healthcare system, necessitating advanced techniques to save lives. Disease prediction plays a crucial role in identifying individuals at risk, enabling early treatment, and benefiting governments and health insurance providers. The collaboration between biomedicine and data science, particularly artificial intelligence and machine learning, has led to significant advancements in this field. However, researchers face challenges related to data availability and quality. Clinical and hospital data, crucial for accurate predictions, are often confidential and not freely accessible. Moreover, healthcare data is predominantly unstructured, requiring extensive cleaning, preprocessing, and labeling. This study aims to predict the likelihood of patients transitioning to mental illness by monitoring addiction conditions and constructing treatment protocols, with the goal of modifying these protocols accordingly. We focus on predicting such transformations to illuminate the underlying factors behind shifts in mental health. To achieve this objective, data from an Iraqi hospital has been collected and analyzed yielding promising results. 
Volume: 14
Issue: 1
Page: 385-396
Publish at: 2025-02-01

Measuring Vietnamese-speaking English as a foreign language students’ socio-emotional skills

10.11591/ijere.v14i1.30099
Do Minh Hung , Le Thanh Nguyet Anh , Vo Phan Thu Ngan , Pham Van Tac , Bui Thanh Tinh
Socio-emotional skills are crucial in learning processes and academic performances, but research in this field among college students, especially among Vietnamese-speaking students majoring in English as a foreign language (EFL) is still rare. Thus, the study attempts to fill this gap. As the first necessary part of a larger research project, the present study measured the target population’s socio-emotional skills via a 30-item questionnaire scale made up of two core components (the self and the others) embracing five subcomponents (self-awareness, self-regulation, self-utilization, empathy, and social skills). The sample group of 615 EFL majors from a university in Vietnam was surveyed. Statistic survey results show that the group appeared to reach a high level of socio-emotional skills in general. In addition, there was no significant gap between two core components, but five subcomponents stood out in a descending magnitude line of self-awareness>self-utilization, empathy>social skills>self-regulation. These significant findings provide constructive guidance needed for our research team to project instructional action plans in the subsequent phases. It also provokes further research on similar strands within Vietnam and beyond.
Volume: 14
Issue: 1
Page: 739-748
Publish at: 2025-02-01

The level of career indecision of senior high school students based on gender

10.11591/ijere.v14i1.27891
Edy Cahya Saputra , Edi Purwanta , Budi Astuti
The purpose of this study was to determine the level of adolescent career indecision based on gender differences between male and female adolescents. This study used a quantitative descriptive method. The sampling technique employed a random sampling technique. The sample of this research was 361 students of class XII (12th grade) from 24 senior high schools in Kulon Progo Regency, Indonesia. The number of respondents based on gender consisted of 295 female and 66 male students. Data collection technique had been carried out using the career decision making difficulties questionnaire (CDDQ) in the 2022/2023 academic year through quantitative descriptive techniques and independent sample t-test. The independent sample t-test obtained the results of the Sig. less than 0.05 (0.031<0.05). Meanwhile, the results of descriptive analysis show that the level of career indecision of students who fall into the salient category is 26.44% of female students and 16.67% of male students. Thus, it can be concluded that there were respondents, both female and male students, who were included in the level of career indecision in the salient category or serious category that required assistance by a counselor to overcome its problem.
Volume: 14
Issue: 1
Page: 171-178
Publish at: 2025-02-01

Factors that contribute to the sustainability of graduate education in Malaysian research-based universities

10.11591/ijere.v14i1.28023
Mohd Fathi Sariman , Zaidatun Tasir , Noor Hazarina Hashim
Today, research is a crucial agenda of universities, and graduate education plays an important role in producing research, publications, and innovation. Thus, the quality of graduate education among Malaysian research universities must be enhanced by exploring the factors that contribute to the sustainability of graduate education. This is done systematically based on relevant literatures and experts’ opinions in graduate education. Therefore, the objective of this study is to identify factors that contribute to the sustainability of graduate education among Malaysian research-based universities (RUs). Findings demonstrate that the factors are governance of graduate education, quality of supervision, quality of programs, quality of students, research facilities, research ecosystem, and financial assistance.
Volume: 14
Issue: 1
Page: 17-27
Publish at: 2025-02-01

Flooding distributed denial of service detection in software-defined networking using k-means and naïve Bayes

10.11591/ijece.v15i1.pp817-826
Hicham Yzzogh , Hafssa Benaboud
Software-defined networking (SDN) is a network architecture that enables the separation of the control plane and data plane, facilitating centralized management of the network. While centralized control offers numerous benefits, it also comes with certain drawbacks. Flooding distributed denial of service (DDoS) attacks pose a significant threat in SDN environments. These attacks involve overwhelming a target system with a large volume of packets, aiming to disrupt its functionality. In this paper, we propose a new approach for detecting DDoS attacks based on multiple k-means models and the naive Bayes algorithm. Our methodology involves training multiple k-means models to cluster each data point within every column of the dataset, where each column represents a feature. This process results in a new dataset with the same shape, containing only clusters, except the column containing the target variable (labels). These clusters are then used as input by naïve Bayes to perform binary classification. We assessed our approach using the InSDN and CIC-DDoS2017 datasets. The results underscore the impressive accuracy of our model, achieving 99.9839% on the InSDN dataset and 99.7030% on the CIC-DDoS2017 dataset. This performance was achieved by optimizing the desired number of clusters.
Volume: 15
Issue: 1
Page: 817-826
Publish at: 2025-02-01

Optimizing seismic sequence clustering with rapid cube-based spatiotemporal approach

10.11591/ijai.v14.i1.pp737-748
Silviya Hasana , Wina Permana Sari , Rojali Rojali , Devi Fitrianah
Due to their extensive volume and range of features, seismic data is regarded as highly complex data. Earthquakes that typically composed of foreshocks, mainshocks, and aftershocks, exhibit a unique sensitivity to temporal dimension, a characteristic that differs them from other natural hazards. Foreshocks and aftershocks that emanate from a similar epicenter, often display temporal patterns that contribute significantly to determining a sequence. This study introduces a density cube-based approach to cluster spatiotemporal seismic data. It addresses spatial irregularities observed in earthquake clusters and incorporates temporal aspects, acknowledging that seismic events originating from a similar epicenter could occur in separate time frames. We achieved the highest Silhouette score of 0.935 in daily-based clustering and 0.782 in weekly-based clustering. Notably, our analysis reveals a trend where weekly clustering lambda λ tend to be lower (λ=0.01) than in daily clustering (λ=0.1, λ=0.5), thus emphasizing the significance of temporal granularity where daily clustering requires higher λ to capture rapid fluctuations, while weekly clustering benefits from lower λ to cover broader trends. These findings enhance the understanding of the nuanced interplay of temporal dynamics in seismic sequence analysis.
Volume: 14
Issue: 1
Page: 737-748
Publish at: 2025-02-01

Factors influencing students’ intention to enroll at private higher education institution

10.11591/ijere.v14i1.29711
Ace Somantri , Zaid Zaid , Katon Pratondo , Abdullah Qiqi Asmara
Intense competition exists amid the rapid growth of universities worldwide and in Indonesia. Often, private higher education is the victim of defeat from competition. Because competitive pressures such as these will undoubtedly lead to reduced revenues, universities are encouraged to increase their student numbers to increase revenues. According to the theory of planned behavior, the intention to carry out certain actions is an essential prerequisite for the strategy’s success. This research examines the factors influencing students to enroll in private, Muhammadiyah contexts, higher education institutions. By involving 572 respondents and using partial least squares structural equation modeling (PLS-SEM) analysis, this research shows that among the factors that influence students’ intention in enrolling in Muhammadiyah Higher Education Institutions are higher education institution image and student characteristics, where both have a positive and significant influence with values (β=0.409; p-value=0.000) and (β=0.461; p-value=0.001).
Volume: 14
Issue: 1
Page: 289-296
Publish at: 2025-02-01

Personality types persistency, occupational consistency, and occupational satisfaction of graduates

10.11591/ijere.v14i1.31471
Ann Gathigia Waruita , Ciriaka Muriithi Gitonga , Edwin Benson Atitwa
Personality type affects career path and can determine an individual’s job satisfaction or dissatisfaction after graduation. In Kenya, high graduate unemployment has forced many to seek jobs unrelated to their qualifications or personalities. The purpose of the current study was to examine persistency of Holland’s personality types, mediating effect of consistency of career choices, and the degree of occupational satisfaction, informed by Holland’s theory. Longitudinal cohort research design was adopted, to access participants involved in a study conducted in 2012, from which 76 participants were accessed and provided required data. Data was collected using Holland’s self-directed search 4th edition questionnaire and an interview schedule. Spearman’s rank correlation was used to determine correlation between persistency of personality types and occupational satisfaction. Logistic regression was used to check the mediating effects of consistency on the relationship between personality types and occupational satisfaction. Results of the study indicate that there was a positive significant relationship between persistency of personality types and occupational satisfaction at p<0.05; mediating effect of consistency on the relationship between personality types and occupational satisfaction was statistically significant at (β=0.254, p<0.05). This study highlights the importance of persistency of personality types and consistency of career choices in ensuring occupational satisfaction.
Volume: 14
Issue: 1
Page: 319-331
Publish at: 2025-02-01

Imitation of the human upper limb by convolutional neural networks

10.11591/ijai.v14.i1.pp193-203
Paula Useche Murillo , Robinson Jimenez Moreno , Javier Eduardo Martinez Baquero
The paper outlines the development of an algorithm focused on imitating movements of a human arm and replicating strokes generated by the user's hand within a working environment. The algorithm was crafted to discern the position of either the user's left or right arm, tracking each section (fingers, wrist, elbow, and shoulder) through a detection and tracking system. These movements are then replicated onto a virtual arm, simulating the actions of a cutting tool, generating strokes as it moves. Convolutional neural networks (CNNs) were employed to detect and classify each arm section, while geometric analysis determined the rotation angles of each joint, facilitating the virtual robot's motion. The stroke replication program achieved an 84.2% accuracy in stroke execution, gauged by the closure of the polygon, distance between initial and final drawing points, and generated noise, which was under 10%, with a 99% probability of drawing a closed polygon. A Fast region-based convolutional neural network (Fast R-CNN) network detected each arm section with 60.2% accuracy, producing detection boxes with precision ranging from 17% to 59%. Any recognition shortcomings were addressed through mathematical estimation of missing points and noise filters, resulting in a 90.4% imitation rate of human upper limb movement.
Volume: 14
Issue: 1
Page: 193-203
Publish at: 2025-02-01

Performance of 5G and Wi-Fi 6 coexistence: spectrum sharing based on optimized duty cycle

10.11591/ijece.v15i1.pp386-400
Asmaa Helmy Zaid , Fayez Wanis Zaki , Hala Bahy-Eldeen Nafea
Smart mobile device usage is increasing rapidly; hence, cellular operators face the challenge of spectrum resource shortage. To address this issue, researchers have explored several approaches to achieving a highly efficient utilization of wireless communication network resources. One promising solution lies in the fair coexistence of 5G/Wi-Fi 6 in the unlicensed 5 GHz band. This research investigates a duty cycle mechanism to perform fair spectrum sharing between these two wireless technologies, intending to optimize performance metrics such as throughput, capacity, bit error rate (BER), and latency. The results of this study demonstrate a significant improvement in system performance when employing the proposed coexistence method compared to using 5G alone in a single cell. Specifically, a 40% increase in throughput and a 14% improvement in capacity are reported. Moreover, for a single cell using Wi-Fi 6 only, the BER was reduced by 19%, and the latency was less than one millisecond. Additionally, the duty cycle mechanism reported here is used to prioritize call services, with the blocking probability for voice-over internet protocol (VoIP) and video stream calls being improved. Furthermore, the adaptive bandwidth reservation reduced the blocking probability of video calls from 21.8% to 0.9% compared to the fixed method; no VoIP calls were blocked.
Volume: 15
Issue: 1
Page: 386-400
Publish at: 2025-02-01

High body temperature detection solution through touchless machine for health monitoring

10.11591/ijai.v14.i1.pp166-171
Siddharth Swami , Lalit Mohan Joshi , Mohammed Ismail Iqbal , Meera Sharma , Amar Jeet Rawat , Sameer Dev Sharma , Rajesh Singh
The demand for reliable health monitoring systems has surged in today's health-conscious society. Body temperature monitoring is crucial for preserving health and preventing infectious disease outbreaks. In this study an Arduino uno hardware board with a touchless temperature sensor is proposed to detect elevated body temperature, indicating fever and early signs of illness. The system prioritizes real-time health surveillance, accessibility, and usability, blending seamlessly with normal life. Arduino's versatility allows the system to function covertly, uphold privacy and autonomy, and foster wellbeing. The goal is to highlight the system's ability to function covertly, uphold privacy and autonomy, and foster wellbeing. This technology exemplifies the synergy between personal wellness and contemporary technologies, offering a useful and adaptable fever detection solution for various contexts, including homes and public areas.
Volume: 14
Issue: 1
Page: 166-171
Publish at: 2025-02-01

Performance analysis of a neuromodel for breast histopathology decision support system

10.11591/ijai.v14.i1.pp102-108
Adedayo Olukayode Ojo , Adetoro Mayowa Sola , Florence Omolara Ojo , Moses Onibonoje Oluwafemi
Breast cancer detection and diagnosis are crucial in reducing mortality rates among women globally. This research article explores an artificial intelligence technique for early breast cancer detection, aiding doctors in making informed decisions for improved patient management. The study employs histopathological analysis of breast tissue microscopically to detect abnormalities, with the aim of categorizing normal tissue, benign lesions, in situ carcinoma, and invasive carcinoma. The proposed technique utilizes an artificial neural network trained using the resilient backpropagation algorithm (RP_ANN). The study further compares the observed performance with those of three other algorithms, including gradient descent algorithm (GDA_ANN), Levenberg-Marquardt algorithm (LM_ANN), and layer sensitivity-based (LSB_ANN) algorithm based on various evaluation metrics. RP_ANN and LSB_ANN demonstrated superior performance, with high validation and training variance accounted for (VAF) and low root mean squared error (RMSE). The results underscore the potential of deep learning-based algorithms for improving breast cancer detection, promising better patient outcomes and enhanced diagnostic accuracy.
Volume: 14
Issue: 1
Page: 102-108
Publish at: 2025-02-01

Overcoming imbalanced rice seed germination classification: enhancing accuracy for effective seedling identification

10.11591/ijai.v14.i1.pp62-72
Muhlasah Novitasari Mara , Sidiq Syamsul Hidayat , Farika Tono Putri , Dwi Rahmawati , Sri Wahyuni , Muhamad Cahyo Ardi Prabowo , Noer Ni'mat Syamsu Kabir , Ragil Tri Indra
This study aimed to automatically classify rice seedling germination on day seven using image analysis. The categories included normal, abnormal, and dead seeds. Due to the rarity of abnormal seedlings, capturing their images resulted in imbalanced data. To address this, abnormal categories were combined into a single class. We compared logistic regression, random forest, and deep learning models (VGG19, VGG16, Alex Net) for classification. Surprisingly, logistic regression achieved the highest accuracy (93.89%) and F1-scores (0.96 normal, 0.81 abnormal) despite the imbalanced data and complex task. The effectiveness of logistic regression for rice seedling classification with imbalanced data has been demonstrated in this novel research. Historically, deep learning models dominate image recognition, but our findings suggest simpler models can excel in specific scenarios, especially with limited data availability. This highlights the importance of selecting models based on data characteristics. The urgency for this research stems from the need for efficient and accurate rice seedling evaluation. Improved classification can enhance agricultural practices and optimize resource allocation.
Volume: 14
Issue: 1
Page: 62-72
Publish at: 2025-02-01

Communication competence model: how to train ability to say what you really mean

10.11591/ijere.v14i1.29806
Nataliia Glushanytsia , Tetyana Tarnavska , Nadiia Chernukha , Zoriana Krupnyk , Dmytro Kostenko
Business is becoming increasingly multinational. Non-native language communication is a background activity for many jobs and a challenge for those whose first language is not English. The problem is that a non-native language activity distracts attention, increases the risk of misunderstanding, and reduces the effectiveness of professional communication. The article aims to present a Foreign Language Communicative Competence model that is a way to solve the problem and enables fluent, errorless communication that supports professional activity. The main question of the research is what learning conditions, methods and strategies, approaches, and technologies provide the development of foreign language communication competence. We used questionnaires, interviews, psychological diagnostic techniques, observations, and a pedagogical experiment in the research. The pedagogical experiments occurred at the National Aviation University in the 2021 to 2022 academic year. Two groups of second-year students majoring in “Aviation Maintenance” were involved. The experiment outcomes show the enhanced level of students’ foreign language communication competence, motivation, and engagement in learning. The developed model contributes to the ability to concentrate on the job and make quick decisions under the influence of psychological factors like time pressure, stress, or noise while speaking a foreign language.
Volume: 14
Issue: 1
Page: 708-719
Publish at: 2025-02-01
1 2 3
...
2097 2098
Show 363 of 2098

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration