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

Weighted nearest neighbors and radius oversampling for imbalanced data classification

10.11591/ijai.v14.i1.pp416-427
Gede Angga Pradipta , Putu Desiana Wulaning Ayu , Made Liandana , Dandy Pramana Hostiadi
The challenges associated with high-dimensional and imbalanced datasets were observed to often lead to a degradation in the performance of classical machine learning algorithms. In the case of high dimensional data, not all features contribute significantly and are considered relevant to the performance of the model. Therefore, this study introduced a novel method called feature weighted variance analysis-nearest neighbors (WFVANN) which was developed on the foundation of k-nearest neighbors (KNN). The process involved modifying the calculation of the Euclidean distance by fully considering the relevance and contribution levels of features based on their Fvalue. WFVANN at the algorithmic level processing and radius-synthetic minority oversampling technique (R-SMOTE) at the data level processing used as the oversampling method later became the proposed model to solve the aforementioned issues. Moreover, extensive experiments were conducted on two distinct types of data including the high-dimensional and imbalanced by comparing WFVANN with the state-of-art KNN-based and synthetic minority oversampling technique (SMOTE)-based methods. The results showed that the proposed method had the highest accuracy, precision, recall, and F1-measure values across the majority of test datasets and outperformed the other methods.
Volume: 14
Issue: 1
Page: 416-427
Publish at: 2025-02-01

Blended learning in mathematic: the fusion of GeoGebra and Edmodo for enhanced problem-solving abilities

10.11591/ijere.v14i1.27713
Anton Nasrullah , Mimih Aminah , Umalihayati Umalihayati , Kurniati Rahmadani , Sri Adi Widodo , Mohamad Husni
This research investigates the effectiveness of problem-based mathematics learning through blended learning with the help of Edmodo and GeoGebra in improving problem-solving skills in mathematics learning. The primary motivation of this research is to develop a learning approach that can overcome challenges in solving mathematical problems. The method used in this study used a quantitative approach, and a quasi-experimental pre-test and post-test control group design was applied. This study involved 72 students in two semesters from a university in Banten, Indonesia, with 36 students in the experimental class (11 male and 25 female) and 36 in the control class (10 male and 26 female). Data was collected through problem-solving tests before and after the learning intervention—data analysis using paired t-tests and independent t-tests. The group using Edmodo-GeoGebra (BL-UEG) and the group Edmodo without GeoGebra (BL-EWG) experienced an increase in the moderate category. In conclusion, problem-based mathematics learning through blended learning with the help of Edmodo and GeoGebra can be an effective alternative learning model for improving students’ problem-solving abilities in mathematics. Therefore, this approach deserves further exploration in learning settings.
Volume: 14
Issue: 1
Page: 423-432
Publish at: 2025-02-01

Influence of digital leadership on Malaysian public university branding: professionals’ identity as mediator

10.11591/ijere.v14i1.28286
Wan Rosni Wan Yakob , Yahya Don
The purpose of this study was to identify the influence of digital leadership on the professional identity and branding of public university education programs in Malaysia. A quantitative method was chosen in this study by using simple random sampling. The study population was 13,804 people, while the study sample was 476 students of education programs at Universiti Utara Malaysia (UUM), Universiti Tun Hussein Onn Malaysia (UTHM), Universiti Putra Malaysia (UPM) and Universiti Malaysia Sabah (UMS) involved. Data was analyzed using IBM-SPSS-Amos version 24. The results of the analysis show the digital leadership has an impact on professional identity and branding of public university education programs in Malaysia. In fact, professional identity are also partial mediators between the relationship between digital leadership and branding. The findings of this study are perceived to have the potential to impact the variable models of the study and represent one of the early research endeavors in this field, serving as a foundation for future studies. In addition, the results of this study are expected to provide new input to university management, stakeholders and investors to be used as a guide during the digital transformation process carried out on campus.
Volume: 14
Issue: 1
Page: 124-132
Publish at: 2025-02-01

A systematic analysis on machine learning classifiers with data pre-processing to detect anti-pattern from source code

10.11591/ijai.v14.i1.pp376-384
Nazneen Akhter , Afrina Khatun , Md. Sazzadur Rahman , A. S. M. Sanwar Hosen , Mohammad Shahidul Islam
Automatic detection of anti-patterns from source code can reduce software maintenance costs massively. Nowadays, machine learning approaches are very commonly used to identify anti-patterns. Hence, it is very crucial to choose a classifier that can be useful for detecting anti-patterns. This work aims to help practitioners to choose a suitable classifier to detect anti-patterns. In this paper, we highlight 16 classifiers in four different categories to detect anti-patterns. Furthermore, the performance of these classifiers is identified with the data pre-processing (DPP) to detect four commonly occurring anti-patterns from the three commonly used open-source Java projects’ source code. The accuracy of Dagging classifiers is 98.4%. Kernel logistic regression (KLR) also performs well i.e., 97%. In the case of time complexity, naive Bayes (NB), decision trees (DT), support vector machines (SVM), library for support vector machines (LibSVM), logistic, and LightGBM (LB) have less time complexity to build a model in all the projects.
Volume: 14
Issue: 1
Page: 376-384
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

Hybrid intrusion detection model for hierarchical wireless sensor network using federated learning

10.11591/ijai.v14.i1.pp492-499
Sathishkumar Mani , Parasuram Chandrasekaran Kishoreraja , Christeena Joseph , Reji Manoharan , Prasannavenkatesan Theerthagiri
The applications of wireless sensor networks are vast and popular in today’s technology world. These networks consist of small, independent sensors that are capable of measuring various physical quantities. Deployment of wireless sensor networks increased due to immense applications which are susceptible to different types of attacks in an unprotected and open region. Intrusion detection systems (IDS) play a vital part in any secured environment for any network. IDS using federated learning have the potential to achieve better classification accuracy. Usually, all the data is stored in centralized server in order to communicate between the systems. On the other hand, federated learning is a distributed learning technique that does not transfer data but trains models locally and transfers the parameters to the centralized server. The proposed research uses a hybrid IDS for wireless sensor networks using federating learning. The detection takes place in real-time through detailed analysis of attacks at different levels in a decentralized manner. Hybrid IDS are designed for node level, cluster level and the base station where federated learning acts as a client and aggregated server.
Volume: 14
Issue: 1
Page: 492-499
Publish at: 2025-02-01

Evaluating machine learning models for predictive analytics of liver disease detection using healthcare big data

10.11591/ijece.v15i1.pp1162-1174
Osama Mohareb Khaled , Ahmed Zakareia Elsherif , Ahmed Salama , Mostafa Herajy , Elsayed Elsedimy
Liver diseases rank among the most prevalent health issues globally, causing significant morbidity and mortality. Early detection of liver diseases allows for timely intervention, which can prevent the progression of such diseases to more severe stages such as cirrhosis or liver cancer. To this end, many machine learning models have been previously developed to early predict liver diseases among potential patients. However, each model has its accuracy and performance limitations. In this paper, we present a comprehensive comparison of three different machine learning models that can be employed to enhance the prediction and management of liver diseases. We utilize a big data set of 32,000 records to evaluate the performance of each model. First, we implement a preprocessing technique to rectify missing or corrupt data in liver disease datasets, ensuring data integrity. Afterwards, we compare the performance of three machine models: k-nearest neighbors (KNN), gaussian naive Bayes (Gaussian NB) and random forest (RF). We concluded that the RF algorithm demonstrates superior performance in our evaluation, excelling in both predictive accuracy and the ability to classify patients accurately regarding the presence of liver disease. Our results show that RF outperforms other models based on several performance metrics including accuracy: 97.3%, precision: 97%, recall: 96%, and f1-score: 95%.
Volume: 15
Issue: 1
Page: 1162-1174
Publish at: 2025-02-01

Families’ involvement at schools: the perspective of the psychosocial duos

10.11591/ijere.v14i1.28290
Andrea Precht Gandarillas , Yasna Anabalón Anabalón
This article aims to investigate the perspective on family involvement of social workers and school psychologists. Based on a qualitative study, we analyzed a corpus of four interviews with psychosocial duos from state-subsidized public schools. We analyzed these interviews by conducting a thematic analysis. The results show that for these professionals, family involvement would ensure the educability of students in a framework of individual parental responsibility, understanding the efforts of schools as a support to the work of parents and guardians. The consequences and relationships of these perspectives for the work with school communities in the framework of public education are discussed.
Volume: 14
Issue: 1
Page: 433-441
Publish at: 2025-02-01

Machine learning-assisted decision support in industrial manufacturing: a case study on injection molding machine selection

10.11591/ijai.v14.i1.pp270-285
Faouzi Tayalati , Soulaimane Idiri , Ikhlass Boukrouh , Abdellah Azmani , Monir Azman
Selecting the right injection molding machine for new products remains a challenging task that significantly influences the profitability and flexibility of companies. The conventional approach involves performing theoretical calculations for clamping force, conducting mechanical validations of the mold, and carrying out real trials for new parts. This approach is time-consuming, costly, and requires a high level of expertise to ensure the optimal machine choice. This study explores the use of machine learning (ML) methods for efficient machine selection based on product, material, and mold criteria. Six supervised learning techniques were tested on a dataset comprising 70 plastic parts and five machines. Evaluation metrics like F1-score, recall, precision, and accuracy were used to compare models. The results indicate that ML can provide guidance for predicting machine selection, with a preference for the random forest (RF), decision tree (DT), and support vector machine (SVM) models. The most favorable outcome is demonstrated by the RF model, displaying an accuracy of 93%. In this manner, these findings may be helpful for injection molding businesses that are considering the significance of using classification algorithms in their manufacturing process. 
Volume: 14
Issue: 1
Page: 270-285
Publish at: 2025-02-01

The crucial role of artificial intelligence in addressing climate change

10.11591/ijai.v14.i1.pp1-11
Laberiano Andrade-Arenas , Domingo Hernández Celis , Cesar Yactayo-Arias
Addressing climate change is one of the fundamental priorities at a global level, given its significant impact on both the environment and society. This systematic literature review explores the role of artificial intelligence (AI) in addressing climate change. It identified applications, contributions to predicting extreme events, techniques used, ethical challenges, and associated biases. The rapid systematic literature review (RSL) was conducted using databases such as Scopus, Dimensions, directory of open access journals (DOAJ), and IEEE Xplore. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement was used to ensure the completeness and transparency of the analysis. 40 articles were selected that were published between 2018 and 2023 and addressed AI in climate change. The findings show that AI is being used to predict and mitigate extreme climate events, estimate the greenhouse effect, and predict temperatures. In addition, innovative techniques such as hybrid machine learning models, convolutional neural networks, artificial neural networks, support vector machines, and logistic regression. In conclusion, AI offers a promising approach to addressing climate change, with transformative potential in predicting and mitigating its effects. However, continuous ethical considerations are required to guarantee its conscientious and efficient utilization.
Volume: 14
Issue: 1
Page: 1-11
Publish at: 2025-02-01

Arabic fake news detection using hybrid contextual features

10.11591/ijece.v15i1.pp836-845
Hussain Mohammed Turki , Essam Al Daoud , Ghassan Samara , Raed Alazaidah , Mais Haj Qasem , Mohammad Aljaidi , Suhaila Abuowaida , Nawaf Alshdaifat
Technology has advanced and social media users have grown dramatically in the last decade. Because social media makes information easily accessible, some people or organizations distribute false news for political or commercial gain. This news may influence elections and attitudes. Even though English fake news is widely detected and limited, Arabic fake news is hard to recognize owing to a lack of study and data collection. Wara Arabic bidirectional encoder representations from transformers (WaraBERT), a hybrid feature extraction approach, combines word level tokenization with two Arabic bidirectional encoder representations from transformers (AraBERT) variants to provide varied features. The study also discusses eliminating stopwords, punctuations, and tanween markings from Arabic data. This study employed two datasets. The first, Arabic fake news dataset (AFND), has 606,912 records. Second dataset Arabic news (AraNews) has 123,219 entries. WaraBERT-V1 obtained 93.83% AFND accuracy using the bidirectional long short-term memory (BiLSTM) model. However, the WaraBERT-V2 technique obtained 81.25%, improving detection accuracy above previous researchers for the AraNews dataset. These findings show that WaraBERT outperforms word list techniques (WLT), term frequency-inverse document frequency (TF-IDF), and AraBERT on both datasets.
Volume: 15
Issue: 1
Page: 836-845
Publish at: 2025-02-01

Machine learning for the detection of soil pH, macronutrients, and micronutrients with crop and fertilizer recommendations

10.11591/ijai.v14.i1.pp439-446
John Joshua Montañez , Jeffrey Sarmiento
The study aims to determine the levels of soil parameters such as soil pH, macronutrients, and micronutrients. After determining said parameters, the system appropriately recommends crops and fertilizers suitable for the soil samples. For soil pH and macronutrient levels, i.e., nitrogen, phosphorus, and potassium, these parameters can be detected using the soil test kit. Meanwhile, for soil micronutrients, i.e., copper, iron, and zinc, there is a need for the development of appropriate assays for colorimetric processes that can be done for the appropriate determination of said micronutrients. Comparison of available machine learning such as support vector machine algorithm, naïve Bayes algorithms, and K-nearest neighbor algorithm is a must to determine the well-fit algorithm that is considered fast and has high predictive power in classification and regression. The outputs of the colorimetric and spectrometric processes are the inputs in the machine learning activities intended for crop and fertilizer recommendation.
Volume: 14
Issue: 1
Page: 439-446
Publish at: 2025-02-01

Collaborative praxis learning to empower rural area: an action research

10.11591/ijere.v14i1.27716
Abdul Aziz , Ahmad Barizi , Syuhadak Syuhadak , Kususanto Ditto Prihadi , Hamim Hamim , Eliya Nur khayati , Syaiful Mustofa
The emergence of collaborative learning praxis among adult learners has gained prominence, fostering lifelong practical learning. This approach empowers adults to define their learning goals based on interests and needs, facilitating contextual information sharing, knowledge acquisition, and skill enhancement. Such an approach promotes innovation and creativity, strengthening market competitiveness. This study focuses on collaborative learning’s impact in the village of Sukodono, East Java, Indonesia, post-COVID-19. Collaborative learning facilitated skill development, innovation, and market influence. Action research was employed to restore economic well-being. Themes of healthy environment preservation, community self-help, and market power were explored. The villagers’ adoption of biogas technology, digital platforms for product promotion, and focus on high-demand products showcased the efficacy of collaborative learning. This praxis enhances economic conditions and knowledge acquisition, enabling sustainable community development. Further quantitative studies are recommended to assess long-term impacts.
Volume: 14
Issue: 1
Page: 749-757
Publish at: 2025-02-01

Assessment for learning practices among Malay language teachers in Malaysia National Type Chinese School

10.11591/ijere.v14i1.31404
Siti Aishah Wan Oya , Zaimuariffudin Shukri Nordin
This study investigates the issues Malay language teachers in Malaysia National Types Chinese School (SJKC) regarding the inconsistent application assessment for learning (AfL) practices, the persistence of traditional teachercentered, and the challenge of aligning AfL practices with curriculum standards and assessment criteria. These issues have a critical impact on the effectiveness of assessment, teaching, and learning. Given AfL’s potential to address educational challenges and enhance student outcomes, this study is highly relevant. To achieve this goal, a qualitative case study design approach, six participants were selected using the following criteria in purposive sampling, using data gathered from in-depth semi-structured interviews, informal conversations, lesson plans and assessment methods. Thematic analysis conducted using Atlas.ti, revealed three main themes: knowledge of AfL, AfL practice, and obstacles and challenges. The results show that most teachers primarily use belief AfL to improve student outcomes, but there are significant differences between beliefs and practices. Moreover, lack of knowledge which poses challenges due to shifts in curriculum standards and assessment administration in school. Given the results, suggestions embedding AfL principle into AfL practices to develop an objective framework for AfL and tracking second language learners’ language proficiency and progress, improving learning outcomes and language acquisition.
Volume: 14
Issue: 1
Page: 365-372
Publish at: 2025-02-01
1 2 3
...
2097 2098
Show 362 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