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

30,411 Article Results

A survey of missing data imputation techniques: statistical methods, machine learning models, and GAN-based approaches

10.11591/ijai.v14.i4.pp2876-2888
Rifaa Sadegh , Ahmed Mohameden , Mohamed Lemine Salihi , Mohamedade Farouk Nanne
Efficiently addressing missing data is critical in data analysis across diverse domains. This study evaluates traditional statistical, machine learning, and generative adversarial network (GAN)-based imputation methods, emphasizing their strengths, limitations, and applicability to different data types and missing data mechanisms (missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR)). GAN-based models, including generative adversarial imputation network (GAIN), view imputation generative adversarial network (VIGAN), and SolarGAN, are highlighted for their adaptability and effectiveness in handling complex datasets, such as images and time series. Despite challenges like computational demands, GANs outperform conventional methods in capturing non-linear dependencies. Future work includes optimizing GAN architectures for broader data types and exploring hybrid models to enhance imputation accuracy and scalability in real-world applications.
Volume: 14
Issue: 4
Page: 2876-2888
Publish at: 2025-08-01

Using the ResNet-50 pre-trained model to improve the classification output of a non-image kidney stone dataset

10.11591/ijai.v14.i4.pp3182-3191
Kazeem Oyebode , Anne Ngozi Odoh
Kidney stone detection based on urine samples seems to be a cost-effective way of detecting the formation of stones. Urine features are usually collected from patients to determine if there is a likelihood of kidney stone formation. There are existing machine learning models that can be used to classify if a stone exists in the kidney, such as the support vector machine (SVM) and deep learning (DL) models. We propose a DL network that works with a pre-trained (ResNet-50) model, making non-image urine features work with an image-based pre-trained model (ResNet-50). Six urine features collected from patients are projected onto 172,800 neurons. This output is then reshaped into a 240 by 240 by 3 tensors. The reshaped output serves as the input to the ResNet-50. The output of this is then sent into a binary classifier to determine if a kidney stone exists or not. The proposed model is benchmarked against the SVM, XGBoost, and two variants of DL networks, and it shows improved performance using the AUC-ROC, Accuracy and F1-score metrics. We demonstrate that combining non-image urine features with an image-based pre-trained model improves classification outcomes, highlighting the potential of integrating heterogeneous data sources for enhanced predictive accuracy.
Volume: 14
Issue: 4
Page: 3182-3191
Publish at: 2025-08-01

Influence of playing online video games on Filipino college students’ confidence in speaking English

10.11591/ijere.v14i4.32842
Allan Jay Esteban , Kiwan Sung
Online video games that require players to communicate in English provide opportunities for students to practice their language skills and overcome their fear of speaking in English. Unfortunately, the literature reveals an existing gap in investigating how such games can influence students’ confidence in speaking English, especially in the Philippine context. Therefore, this study surveyed 148 Filipino college English-as-a-second language (ESL) students to examine differences in their perceived confidence in speaking English depending on learner variables such as gender, time spent online gaming (TSOG), number of games played (NOGP), self-rated speaking proficiency (SRSP), and game interactivity.Using independent t-tests and one-way analysis of variance (ANOVA) analyses, results revealed statistically significant differences in the development of communication skills in English (DCSE) depending on the TSOG, willingness to communicate (WTC) in English depending on the NOGP, and enhancement of communication skills in English, active participation in class, and reduced anxiety in using English (RAUE) depending on the SRSP. This exploratory study indicates that online video games can be valuable tools in increasing English speaking confidence among Filipino college students. Further research is posited to understand the extent to which online games influence ESL learners’ speaking confidence in different educational and cultural contexts.
Volume: 14
Issue: 4
Page: 2555-2564
Publish at: 2025-08-01

Investigation on low-performance tuned-regressor of inhibitory concentration targeting the SARS-CoV-2 polyprotein 1ab

10.11591/ijai.v14.i4.pp3003-3013
Daniel Febrian Sengkey , Angelina Stevany Regina Masengi , Alwin Melkie Sambul , Trina Ekawati Tallei , Sherwin Reinaldo Unsratdianto Sompie
Hyperparameter tuning is a key optimization strategy in machine learning (ML), often used with GridSearchCV to find optimal hyperparameter combinations. This study aimed to predict the half-maximal inhibitory concentration (IC50) of small molecules targeting the SARS-CoV-2 replicase polyprotein 1ab (pp1ab) by optimizing three ML algorithms: histogram gradient boosting regressor (HGBR), light gradient boosting regressor (LGBR), and random forest regressor (RFR). Bioactivity data, including duplicates, were processed using three approaches: untreated, aggregation of quantitative bioactivity, and duplicate removal. Molecular features were encoded using twelve types of molecular fingerprints. To optimize the models, hyperparameter tuning with GridSearchCV was applied across a broad parameter space. The results showed that the performance of the models was inconsistent, despite comprehensive hyperparameter tuning. Further analysis showed that the distribution of Murcko fragments was uneven between the training and testing datasets. Key fragments were underrepresented in the testing phase, leading to a mismatch in model predictions. The study demonstrates that hyperparameter tuning alone may not be sufficient to achieve high predictive performance when the distribution of molecular fragments is unbalanced between training and testing datasets. Ensuring fragment diversity across datasets is crucial for improving model reliability in drug discovery applications.
Volume: 14
Issue: 4
Page: 3003-3013
Publish at: 2025-08-01

Graduate competencies as predictors of the pre-service English teachers’ work-readiness

10.11591/ijere.v14i4.32609
Cristie Ann L. Jaca , Wilson D. Castañeda Jr.
This study examines the competencies that predict work readiness among the pioneering cohort of English graduates from a newly implemented curriculum. By evaluating graduates’ perceived competencies and work-readiness over the curriculum’s initial four years, the research addresses its relevance with the current demands of the teaching profession. Employing a descriptive-survey quantitative research design and regression analysis, the study evaluated all 43 graduates of bachelor of secondary education (BSEd) major in English. Respondents were assessed on work readiness and core competencies in content, pedagogy, and essential 21st-century skills, including 4Cs (communication, collaboration, critical thinking, and creativity). Findings revealed that, while graduates demonstrate confidence in pedagogy, creativity, and collaboration, there remains a need for a more balanced skill set, particularly in content knowledge and critical thinking. Regression analysis identified that pedagogy, communication, and critical thinking are key predictors of work-readiness, highlighting that these skills significantly enhance graduates’ preparedness for a dynamic workplace. These insights emphasize the need to refine program objectives to support future cohorts better, underscoring the importance of analyzing graduate competencies to improve curriculum design. This study contributes to adapting educational programs and preparing future English teaching professionals to meet the complexities of modern education and evolving workforce demands.
Volume: 14
Issue: 4
Page: 3211-3223
Publish at: 2025-08-01

Hope and grit: the driving forces behind student-athletes’ motivation in sports and academics

10.11591/ijere.v14i4.32916
Jet C. Longakit , Joseph Lobo , Teejay Panganiban , Jay Mark D. Sinag , Elvie F. Celestial , Janice D. Ballera
While the challenges student-athletes face in balancing academics and sports have garnered increasing attention, few studies have explored how factors like hope and grit contribute to this balance and enhance motivation. This study investigated the relationship between hope, grit, and motivation of academic and sports of student-athletes. A total of 247 student-athletes of Mindanao State University-Iligan Institute of Technology (MSU-IIT) answered a set of questionnaires assessing grit, dispositional hope, and student-athletes motivation towards sports and academics with the age range of 18-24 years old. Ethical approval was gained from the participants. The results showed that factors of hope (agency and pathways) and grit (consistency of interest or COI and perseverance of effort or POE) were significant predictors of motivation in sports and academics. This suggests that student-athletes with higher levels of hope and grit demonstrate increased motivation in both domains. These results could offer a more valuable insights for coaches, educators, and sports psychologist striving to support the long-term success and wellbeing of student-athletes.
Volume: 14
Issue: 4
Page: 2574-2583
Publish at: 2025-08-01

Performance analysis and comparison of machine learning algorithms for predicting heart disease

10.11591/ijai.v14.i4.pp2849-2863
Neha Bhadu , Jaswinder Singh
Heart disease (HD) is a serious medical condition that has an enormous effect on people's quality of life. Early as well as accurate identification is crucial for preventing and treating HD. Traditional methods of diagnosis may not always be reliable. Non-intrusive methods like machine learning (ML) are proficient in distinguishing between patients with HD and those in good health. The prime objective of this study is to find a robust ML technique that can accurately detect the presence of HD. For this purpose, several ML algorithms were chosen based on the relevant literature studied. For this investigation, two different heart datasets the Cleveland and Statlog datasets were downloaded from Kaggle. The analysis was carried out utilizing the Waikato environment for knowledge analysis (WEKA) 3.9.6 software. To assess how well various algorithms predicted HD, the study employed a variety of performance evaluation metrics and error rates. The findings showed that for both the datasets radio frequency is a better option for predicting HD with an accuracy and receiver operating characteristic (ROC) values of 94% and 0.984 for the Cleveland dataset and 90% and 0.975 for the Statlog dataset. This work may aid researchers in creating early HD detection models and assist medical practitioners in identifying HD.
Volume: 14
Issue: 4
Page: 2849-2863
Publish at: 2025-08-01

A systematic literature review of computational thinking study in physics learning

10.11591/ijere.v14i4.29632
Riwayani Riwayani , Edi Istiyono , Supahar Supahar , Riki Perdana , Jumadi Jumadi , Soeharto Soeharto
This systematic review aims to summarize the technological resources for teaching and learning about computational thinking (CT) in physics and provide suggestions to conduct new studies in future research. A total of 22 academic articles on CT in physical learning were reviewed from 2012 to 2022. The number of research participants was 3,269, with details of 2,752 college students, 439 high school students, 32 junior high school students, 20 elementary students, 21 teachers, and five librarians. This study confirmed that research on CT in physical learning has been dominated by two countries, the United States and Indonesia. Over the past 10 years, there has been an increase in physics courses focusing on topics in kinematics, force and motion, and electricity. The common method practices are quantitative and qualitative, with some developing learning. The implications of this research can inform education experts, educators, and technologists interested in the CT environment and technological development in physics learning. Computational skills in physics have the potential to improve cognitive, affective, and psychomotor outcomes, including students’ thinking abilities. Students can benefit from their experience learning physics using the concept of CT because they can solve technology-based problems and develop various competencies needed in learning physics.
Volume: 14
Issue: 4
Page: 2698-2709
Publish at: 2025-08-01

Urban incident detection based on hybrid convolutional neural networks and bidirectional long short-term memory

10.11591/ijai.v14.i4.pp3153-3159
Meryem Ayou , Jaouad Boumhidi
Real-time incident detection is a major challenge in urban roads. This paper proposes an innovative hybrid method for incident detection, combining convolutional neural networks (CNN) and bidirectional-long short-term memory (Bi-LSTM). CNN extracts complex spatial features from raw data, while Bi-LSTMs are used for incident detection by capturing long-term temporal dependencies present in data. The proposed algorithm is evaluated using simulated data from the open-source software simulation of urban mobility (SUMO). This combination improves incident detection's accuracy and robustness by exploiting spatial and temporal information. Experimental results show that our hybrid approach outperforms the support vector machine (SVM), random forest (RF), and Bi-LSTM algorithms, with a substantial decrease in false positives and the speed of detecting urgent situations.
Volume: 14
Issue: 4
Page: 3153-3159
Publish at: 2025-08-01

Domain-specific knowledge and context in large language models: challenges, concerns, and solutions

10.11591/ijai.v14.i4.pp2568-2578
Kiran Mayee Adavala , Om Adavala
Large language models (LLMs) are ubiquitous today with major usage in the fields of industry, research, and academia. LLMs involve unsupervised learning with large natural language data, obtained mostly from the internet. There are several challenges that arise because of these data sources. One such challenge is with respect to domain-specific knowledge and context. This paper deals with the major challenges faced by LLMs due to data sources, such as, lack of domain expertise, understanding specialized terminology, contextual understanding, data bias, and the limitations of transfer learning. This paper also discusses some solutions for the mitigation of these challenges such as pre-training LLMs on domain-specific corpora, expert annotations, improving transformer models with enhanced attention mechanisms, memory-augmented models, context-aware loss functions, balanced datasets, and the use of knowledge distillation techniques.
Volume: 14
Issue: 4
Page: 2568-2578
Publish at: 2025-08-01

Synthesizing strategies and innovations in combating land degradation: a global perspective on sustainability and resilience

10.11591/ijai.v14.i4.pp3133-3142
Gangamma Hediyalad , Ashoka Kukkuvada , Govardhan Hegde Kota
This paper presents a comprehensive examination of land degradation, a critical environmental challenge with far-reaching implications for agricultural productivity, ecosystem sustainability, and socio-economic stability worldwide. With the backdrop of escalating human population pressures and the exacerbating impact of climate change. It delves into the causes and consequences of soil erosion, desertification, salinization, and biodiversity loss, highlighting the interplay between natural processes and anthropogenic activities. Through a detailed review of literature spanning various remediation technologies, conservation practices, and policy frameworks, the paper critically assesses the effectiveness of current land management approaches, including the utilization of biosurfactants, remote sensing technologies, and agroforestry systems. Furthermore, it identifies significant research gaps and future directions, emphasizing the need for quantitative assessments, exploration of socio-economic impacts, and evaluation of restoration techniques. By offering evidence-based recommendations for policymakers and practitioners, this paper contributes to the global dialogue on sustainable land management and aims to catalyze action towards halting the advance of land degradation, ensuring food security, and preserving biodiversity for future generations. This work not only advances our understanding of land degradation challenges but also outlines a path forward for research, policy, and practice in the pursuit of environmental sustainability and resilience.
Volume: 14
Issue: 4
Page: 3133-3142
Publish at: 2025-08-01

Improving firewall performance using hybrid of optimization algorithms and decision trees classifier

10.11591/ijai.v14.i4.pp2839-2848
Mosleh M. Abualhaj , Ahmad Adel Abu-Shareha , Sumaya Nabil Al-Khatib , Adeeb M. Alsaaidah , Mohammed Anbar
One of the primary concerns of governments, corporations, and even individual users is their level of online protection. This is because a large number of attacks target their primary assets. A firewall is a critical tool that almost every organization uses to protect its assets. However, firewalls become less reliable when they deal with large amounts of data. One method for reducing the amount of data and enhancing firewall performance is feature selection. The main aim of this study is to enhance the firewall's performance by proposing a new feature selection method. The proposed feature selection method combines the strengths of Harris Hawks optimization (HHO) and whale optimization algorithm (WOA). Experiments were performed utilizing the NSL-KDD dataset to measure the effectiveness of the proposed method. The experiments employed the decision trees (DTs) as a machine classifier. The experimental results show that the achieved accuracy is 98.46% when using HHO/WOA for feature selection and DT for classification, outperforming the HHO and WOA when used separately for feature selection. The study's findings offer insightful information for researchers and practitioners looking to improve firewall effectiveness and efficiency in defending internet connections against changing threats.
Volume: 14
Issue: 4
Page: 2839-2848
Publish at: 2025-08-01

Federated deep learning intrusion detection system on software defined-network based internet of things

10.11591/ijai.v14.i4.pp3109-3120
Heba Dhirar , Ali H. Hamad
The internet of things (IoT) and software-defined networks (SDN) play a significant role in enhancing efficiency and productivity. However, they encounter possible risks. Artificial intelligence (AI) has recently been employed in intrusion detection systems (IDSs), serving as an important instrument for improving security. Nevertheless, the necessity to store data on a centralized server poses a potential threat. Federated learning (FL) addresses this problem by training models locally. In this work, a network intrusion detection system (NIDS) is implemented on multi-controller SDN-based IoT networks. The interplanetary file system (IPFS) FL has been employed to share and train deep learning (DL) models. Several clients participated in the training process using custom generated dataset IoT-SDN by training the model locally and sharing the parameters in an encrypted format, improving the overall effectiveness, safety, and security of the network. The model has successfully identified several types of attacks, including distributed denial of service (DDoS), denial of service (DoS), botnet, brute force, exploitation, malware, probe, web-based, spoofing, recon, and achieving an accuracy of 99.89% and a loss of 0.005.
Volume: 14
Issue: 4
Page: 3109-3120
Publish at: 2025-08-01

Music genre classification using Inception-ResNet architecture

10.11591/ijai.v14.i4.pp3300-3310
Fauzan Valdera , Ajib Setyo Arifin
Music genres help categorize music but lack strict boundaries, emerging from interactions among public, marketing, history, and culture. With Spotify hosting over 80 million tracks, organizing digital music is challenging due to the sheer volume and diversity. Automating music genre classification aids in managing this vast array and attracting customers. Recently, convolutional neural networks (CNNs) have been used for their ability to extract hierarchical features from images, applicable to music through spectrograms. This study introduces the Inception-ResNet architecture for music genre classification, significantly improving performance with 94.10% accuracy, precision of 94.19%, recall of 94.10%, F1-score of 94.08%, and 149,418 parameters on the GTZAN dataset, showcasing its potential in efficiently managing and categorizing large music databases.
Volume: 14
Issue: 4
Page: 3300-3310
Publish at: 2025-08-01

Insights from the vision-mission statements of Philippine and other ASEAN universities: a K-means clustering analysis

10.11591/ijai.v14.i4.pp3386-3394
Julius Ceazar G. Tolentino , John Paul P. Miranda
This study analyzed the vision and mission statements (VMS) of 117 Philippine state universities and colleges (SUCs) and compared them with 330 other ASEAN universities to identify thematic trends and institutional priorities. Using web scraping and K-means clustering, the study identified thematic clusters in VMS. Thematic trends through word frequency and collocation analyses provided further insights and a comparative analysis examined differences between Philippine SUCs and other ASEAN universities. Philippine SUCs’ vision statements formed three clusters: global competitiveness, premier recognition, and regional leadership in science and technology. Mission statements clustered into: mandated functions, global innovation, and advancement in the sciences. Philippine SUCs emphasized institutional prestige, workforce development, and sustainability while other ASEAN universities focus more on knowledge creation, student empowerment, and internationalization. Philippine SUCs aligned their VMS with national development and global ranking metrics and prioritizes institutional recognition and economic contributions more than the other ASEAN universities. Future studies should expand to more private institutions and international comparisons to assess broader higher education trends.
Volume: 14
Issue: 4
Page: 3386-3394
Publish at: 2025-08-01
1 2 3
...
2027 2028
Show 187 of 2028

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