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From family to classroom: mediating roles in promoting social and emotional learning among early adolescents

10.11591/ijere.v14i6.35535
Pantipa Thiamta , Suntonrapot Damrongpanit
This research aims to examine the influence of authoritative parenting (PAREN), cooperative learning (COOP), school environment (ENVI), positive classroom climate (CLASS), and extrovert personality (EXTRO) on social and emotional learning (SEL), as well as analyze the complexity of mediating variable roles linking these factors. The sample consisted of 684 lower secondary school students from the upper northern region of Thailand. Questionnaires were used for data collection, and analysis was conducted using partial least square structural equation modeling (PLS-SEM) technique. Research findings revealed complex structures among factors collectively explaining 67.37% of SEL variance. PAREN emerged as the most powerful driving force followed by school factors, namely COOP and CLASS, which demonstrated strong interconnection while ENVI showed only indirect influence through EXTRO. Furthermore, CLASS and EXTRO functioned as significant mediating variables between classroom factors and SEL. However, EXTRO did not play a mediating role in the relationship between parenting and SEL, reflecting that family influence remains the primary factor determining SEL development in Thai youth.
Volume: 14
Issue: 6
Page: 4916-4927
Publish at: 2025-12-01

Enhanced integration of renewable energy and smart grid efficiency with data-driven solar forecasting employing PCA and machine learning

10.11591/ijpeds.v16.i4.pp2645-2654
Jayashree Kathirvel , Pushpa Sreenivasan , M. Vanitha , Soni Mohammed , T. Sathish Kumar , I. Arul Doss Adaikalam
A significant obstacle to preserving grid stability and incorporating renewable energy into smart grids is variations in solar irradiation. To improve solar power management's dependability, this research proposes a short-term solar forecasting framework powered by AI. Multiple machine learning models, such as long short-term memory (LSTM), random forest (RF), gradient boosting (GB), AdaBoost, neural networks (NN), K-Nearest neighbor (KNN), and linear regression (LR), are integrated into the suggested system, which also uses principal component analysis (PCA) for dimensionality reduction. The Abiod Sid Cheikh station in Algeria (2019-2021) provided real-world data for the model's validation. With a two-hour-ahead RMSE of 0.557 kW/m², AdaBoost had the most accuracy, whereas LR had the lowest, at 0.510 kW/m². In addition to increasing computing efficiency, PCA preserved 99.3% of the data volatility. In addition to increasing computing efficiency, PCA preserved 99.3% of the data volatility. These findings highlight the efficiency of hybrid AI models based on PCA for accurate forecasting, which is crucial for smart grid stability.
Volume: 16
Issue: 4
Page: 2645-2654
Publish at: 2025-12-01

Design of low-power, high-speed approximate 4:2 compressors for efficient partial product reduction in multipliers

10.11591/ijra.v14i4.pp459-467
Jabez Daniel Vincent David Michael , Anusha Gorantla , Ahilan Appathurai , Dinesh Ramachandran
Partial product reduction becomes the main task in the multiplication process. Therefore, the partial product stages of multipliers are reduced with the usage of compressors, by using compressors in the multiplier. Using compressors in the multiplier circuit significantly impacts multiplier performance. Approximate compressors are crucial for achieving better design metrics in parallel multipliers. This paper proposes to create various new approximate 4:2 compressor circuits. A trade-off is made between the performance and accuracy of this approximate circuit design approach. The proposed designs have been implemented using XOR-XNOR gates with a 2-to-1 multiplexer, and also XOR-XNOR gates with transmission gates. All these circuits have been simulated using Cadence in different technological nodes. Compared with the existing technique, the proposed 4:2 approximation compressor provides 51.4% power reduction and 26.45% delay reduction for 45 nm equipment.
Volume: 14
Issue: 4
Page: 459-467
Publish at: 2025-12-01

Digital literacy and cybersecurity in higher education: the unseen power of academic librarians

10.11591/ijere.v14i6.34916
Mohammad Fazli Baharuddin , Abdurrahman Jalil , Zahari Mohd Amin , Fadhilnor Rahmad , Shamila Mohamed Shuhidan
The increasing reliance on digital technologies in higher education has amplified the need for students to develop digital literacy and cybersecurity awareness. However, many undergraduate students lack the competencies required for responsible and secure digital engagement, posing significant risks in the digital landscape. Academic librarians, as key facilitators of information literacy, are uniquely positioned to address these challenges, yet their roles in promoting digital literacy and cybersecurity awareness remain underexplored. The study addresses the following key issues: how do academic librarians play their roles on undergraduate students’ digital literacy and cyber security awareness; what are the challenges related to library initiatives; and, perhaps most importantly, what are the strategies do librarians employ to improve it? Using a qualitative research methodology, data were collected through interviews with six academic librarians and analyzed using thematic analysis. The findings reveal that academic librarians play critical roles in fostering digital literacy and cybersecurity by teaching information literacy, promoting ethical online behavior, and enhancing students’ digital safety practices. Challenges identified include limited resources, diverse digital skill levels among students, and difficulties in maintaining student engagement. Librarians address these issues through strategies such as faculty collaboration, integrating digital literacy programs, employing interactive learning tools, and pursuing continuous professional development. This research offers actionable insights for integrating digital literacy and cybersecurity initiatives into library services, improving librarian training, and enhancing the sustainability and visibility of academic libraries within higher education institutions.
Volume: 14
Issue: 6
Page: 4404-4417
Publish at: 2025-12-01

Comparative evaluation of PVGIS, PVsyst, and SAM models for predicting solar power output in equatorial tropical climates

10.11591/ijeecs.v40.i3.pp1221-1231
Fabian Alonso Lara Vargas , Miguel Angel Ortiz Padilla , Alvaro Torres Amaya , Carlos Vargas Salgado
Accurate evaluation of energy production in photovoltaic (PV) systems is critical for renewable projects, especially in tropical climates where environmental factors such as temperature significantly affect performance. Although commercial simulation tools exist (photovoltaic geographic information system (PVGIS), PVsyst, and system advisor model (SAM)), previous studies have identified notable deviations between their predictions and actual data, particularly in tropical climates. Moreover, these investigations are usually limited to short periods (one year) and do not systematically compare multiple tools under interannual conditions. This study evaluates the accuracy of PVGIS, PVsyst, and SAM in predicting the energy production of a PV installation in a tropical equatorial climate for 24 months to identify the most suitable tool for this context. Monthly energy production data were collected from a PV plant in Monteria, Colombia, equipped with 240 modules and two 36 kW inverters. Simulations were performed using the most recent PVGIS, PVsyst, and SAM versions. Accuracy was evaluated using metrics such as root mean square error (RMSE) and mean absolute error (MAE). SAM showed the highest accuracy, with an overall RMSE of 1,993.71 kWh and MAE of 1,615.87 kWh, followed by PVGIS (RMSE: 2,076.65 kWh, MAE: 1,830.84 kWh) and PVsyst (RMSE: 3,546.18 kWh, MAE: 3,250.17 kWh). The results highlight that SAM provides estimates closer to the real data and less dispersion than other tools. This study contributes to the renewable energy field by systematically comparing simulation tools in an understudied tropical context. The findings emphasize the importance of selecting appropriate software according to the specific environmental conditions of the project, thus optimizing the design and efficiency of PV systems in tropical regions.
Volume: 40
Issue: 3
Page: 1221-1231
Publish at: 2025-12-01

Low-speed scalar control of induction motor by fuzzy logic

10.11591/ijai.v14.i6.pp4623-4635
Alfonso Alejandro Sevilla-Hidalgo , Rossy Uscamaita-Quispetupa , Julio Cesar Herrera-Levano , Limberg Walter Utrilla Mego , Roger Jesus Coaquira-Castillo
Efforts have continually been directed toward optimizing processes in various fields, and the application in induction motors is no exception. Scalar control V/f offers a straightforward method to regulate the speed of a three-phase induction motor (TIM). However, it faces challenges at low speeds or proportionally at low frequencies, often failing to operate below 20% of its rated speed. This control typically pairs with a PI controller (PIC) for closed loop speed regulation, but its limited control range hinders performance at low speeds. Although intelligent methods have been developed to improve scalar V/f control, attention is often focused on high speeds, while control at low speeds is overlooked. This paper presents the simulation of a fuzzy controller (FC) with a Mamdani-type structure designed to achieve effective low-speed control of a TIM using the V/f scalar control technique. The results not only show improvements in overshoot and settling time but also reveal that the FC can control speeds as low as 6.06% of the rated speed, and it ensures a starting current below the designed motor current under load. Comparative analysis indicates that the FC outperforms the PIC in low-speed control, and it provides an optimal inrush current across different low speeds.
Volume: 14
Issue: 6
Page: 4623-4635
Publish at: 2025-12-01

Fine-tuning multilingual transformers for Hinglish sentiment analysis: a comparative evaluation with BiLSTM

10.11591/ijai.v14.i6.pp4684-4693
Jyoti S. Verma , Jaimin N. Undavia
Growing trend of code-mixing in languages, in the form of Hinglish, greatly tests the skills of conventional sentiment analysis tools. The research contributes a fine-tuned multilingual transformer model built exclusively for classifying sentiment of Hinglish customer reviews. Drawing from pre trained BERT-base-multilingual-case architecture, the model gets transformed with the process of fine-tuning the same on synthetically prepared and balanced dataset simulating positive, negative, and neutral sentiments. Sophisticated methods like focal loss for addressing the class imbalance and mixed precision training for maximization of computational effectiveness are embedded within the training process. Experimental results suggest that the proposed method significantly captures the fine-grained linguistic patterns of code-mixed text, improving sentiment classification accuracy. The results show promising potential for enhancing customer feedback analysis in e-commerce, social media monitoring, and customer support use cases, where it is crucial to comprehend the sentiment behind code-mixed reviews.
Volume: 14
Issue: 6
Page: 4684-4693
Publish at: 2025-12-01

A merchant analytics framework for revenue forecasting and financial stress detection using transaction data

10.11591/ijai.v14.i6.pp4848-4864
Yara Harb , Wissam Baaklini , Nadine Abbas , Seifedine Kadry
By processing payments and providing specialized financial services, acquiring banks are essential for merchants’ operations. To forecast 30-day revenue trajectories, identify seasonal demand patterns, and identify early indicators of financial stress, this paper presents a scalable merchant analytics framework that benefits from transactional data. The framework captures multi-level seasonalities using Prophet time series model, allowing dynamic product offerings like revenue-based loans. Proactive risk management is supported offerings like revenue-based loans. Proactive risk management is supported. by a new stress-flagging mechanism that identifies merchants at risk based on deviations in revenue trends. The framework achieved a median 30-day mean absolute percentage error (MAPE) of 56.51% after the validation on a dataset with 130,350 transactions from 460 merchants in a volatile economic environment. The model demonstrated significant practical utility in identifying financial distress and segmenting merchant behavior, despite its moderate predictive precision, which is common challenge in high-variance merchant datasets. Model outputs are converted into decision-support visualizations along with an interactive dashboard.
Volume: 14
Issue: 6
Page: 4848-4864
Publish at: 2025-12-01

Enhancing academic conferences with AI: defining the role of the human AI editor

10.11591/ijai.v14.i6.pp4484-4493
Esteban Galan-Cubillo , Emilio Saez-Soro
Academic conferences serve as key platforms for knowledge exchange, yet they face challenges in managing large volumes of content efficiently while maintaining academic rigor. To address these challenges, this study introduces and evaluates the "AI editor": a novel human expert role who, using tools like ChatGPT, supervises, refines, and structures artificial intelligence (AI)-generated content in real time. Through a mixed-methods approach, we examine the role of AI in enhancing content creation and engagement. This approach included the experimental deployment of the AI editor in three sustainability-focused European academic conferences (in Spain and UK) and formative workshops with 127 university students from the same countries. While AI-assisted tools improve efficiency, concerns persist regarding traceability, reliability, and ethical oversight. Our findings indicate that AI by itself cannot guarantee scholarly integrity; continuous human oversight is indispensable. The AI editor ensures coherence, quality control, and compliance with academic standards, addressing a critical gap in AI adoption within research environments. This study contributes to the discourse on responsible AI use in academia by proposing a structured framework for its integration into conferences, balancing automation with human oversight. Moreover, it highlights the growing need for digital intelligence that enables researchers to interact ethically and effectively with AI and other digital technologies, fostering responsible and informed academic innovation.
Volume: 14
Issue: 6
Page: 4484-4493
Publish at: 2025-12-01

Predicting the severity of road traffic accidents Morocco: a supervised machine learning approach

10.11591/ijai.v14.i6.pp4461-4473
Halima Drissi Touzani , Sanaa Faquir , Ali Yahyaouy
Early prediction of road accidents fatality and injuries severity is one of the important subjects to road safety emphasizing the critical need to prevent serious consequences to reduce injuries and fatalities. This study uses real road accidents data set in Morocco. It represents the intersection between road safety and data science, aiming to employ machine learning techniques to provide valuable insights in accident’s severity prevention. The purpose of this paper is to study road accidents data in the country and combine results from statistical methods, spatial analysis, and machine learning models to determine which factors will mostly contribute to increase the accident’ severity in the country. A comparison of results obtained was also conducted in this paper using different metrics to evaluate the effectiveness of each method and determine the most important factors that contribute to increase the fatality or injuries severity in the specific context of accidents. The best prediction model was then injected into a proposed algorithm where more intelligent techniques are included to be implemented in a car engine to perform an early detection of severe accidents and therefore preventing crashes from happening.
Volume: 14
Issue: 6
Page: 4461-4473
Publish at: 2025-12-01

A smart grid fault detection using neuro-fuzzy deep learning algorithm

10.11591/ijai.v14.i6.pp5096-5105
Etienne Francois Mouckomey , Jacques Bikai , Camille Franklin Mbey , Alexandre Teplaira Boum , Felix Ghislain Yem Souhe , Vinny Junior Foba Kakeu
This paper proposes a novel data analysis framework that integrates deep learning with a binary neuro-fuzzy algorithm to address the problem of fault localization in smart power grids. In the first stage, a long short-term memory (LSTM) network is employed to train data samples collected from smart meters. The resulting learned features are subsequently utilized by an adaptive neuro-fuzzy inference system (ANFIS) for accurate fault detection and classification. Through this intelligent hybrid approach, multi-phase faults can be efficiently identified using a limited amount of data. The proposed method distinguishes itself by its capacity to rapidly train and test large datasets while maintaining high computational efficiency. To evaluate the performance of the model, an advanced simulation of the IEEE 123-node test feeder is conducted. The robustness and effectiveness of the proposed framework are validated using multiple performance metrics, including precision, recall, accuracy, F1-score, computational complexity, and the ROC curve. The results demonstrate that the proposed deep learning–based model significantly outperforms existing approaches in the literature, achieving a fault detection and classification precision of 99.99%.
Volume: 14
Issue: 6
Page: 5096-5105
Publish at: 2025-12-01

Classification algorithm with artificial intelligence for the diagnostic process of obstructive sleep apnea

10.11591/ijai.v14.i6.pp4520-4532
Jehil Ventura-Tecco , Jesús Fajardo-Avalos , Michael Cabanillas-Carbonell
Obstructive sleep apnea (OSA) is a disease that affects millions of people worldwide, and a large proportion of them remain undiagnosed due to the high cost of polysomnography (PSG) tests. For this reason, it is crucial to develop affordable diagnostic tools to facilitate early detection of this condition. This study aims to analyze how an artificial intelligence (AI) based classification algorithm impacts the diagnostic process of OSA in Lima, Peru. The algorithm was developed following the Kanban methodology, which guaranteed an efficient and transparent follow-up during the development cycle, which is key in the medical context where software quality and traceability are fundamental. A decision tree (DT) was used for diagnosis and classification, employing a training dataset provided by the National Sleep Research Resource (NSRR), from which six relevant attributes were selected for analysis. The research results indicated that, although the improvement in clinical diagnostic accuracy was minimal at 10.81%, positive results were obtained in other aspects: diagnostic time was significantly reduced by 28.17%, and the number of tests required decreased by 24.07%.
Volume: 14
Issue: 6
Page: 4520-4532
Publish at: 2025-12-01

Metaheuristic optimization for sarcasm detection in social media with embedding and padding techniques

10.11591/ijai.v14.i6.pp5027-5037
Geeta Sahu , Manoj Hudnunkar
Sarcasm is a sophisticated mode of expression that allows speakers to express their opinions subtly. Stakeholders provide unstructured messages with extended phrases, making it difficult for computers and people to understand. This research aims to develop a sarcasm detection method to identify words in phrases as sarcastic or non-sarcastic from text, utilizing natural language processing appliances. The first step is pre-processing, when the padding and embedding are performed. Zero padding and end padding are used for the padding. At the same time, different embedding techniques, such as word2vec, Glove, and BERT, are used. Following pre-processing, the features are extracted from the pre-processed data, including "information gain, chi-square, mutual information, and symmetrical uncertainty-based features." Then, a hybrid optimization technique known as clan-updated grey wolf optimization (CU-GWO) is used for optimized features and weight selection. An ensemble technique was applied to extract optimal features. The classifiers in the proposed suggested ensemble technique with deep convolution neural network (DCNN). DCNN offers fine weight tuning and detection results.The performance analysis and its impact on the proposed model for sarcasm detection are classified with good accuracy into sarcastic and non-sarcastic categories. The results are also compared with against those of the GloVe and BERT techniques.
Volume: 14
Issue: 6
Page: 5027-5037
Publish at: 2025-12-01

Environmental and psychological influences on adolescents’ self-concept: teacher-student relationship as a moderator

10.11591/ijere.v14i6.34518
Ting Chen , Jamalsafri Saibon
Adolescence is a critical stage for the development of self-concept and psychological resilience. However, the impact of environmental and psychological factors on adolescents’ self-concept through psychological resilience has not been fully explored. Meanwhile, the discussion on whether the teacher-student relationship moderates the relationship between psychological resilience and self-concept is relatively rare. Based on cognitive-behavioral and social learning theories, this study collected data from 404 Chinese adolescents through a questionnaire survey. It employed partial least squares structural equation modeling (SEM) to test the hypotheses. The study found that environmental and psychological factors significantly influence adolescents’ psychological resilience, and psychological resilience mediates the relationship between environmental and psychological factors and self-concept. Moreover, the teacher-student relationship moderates psychological resilience and self-concept, particularly the positive teacher-student relationship, significantly promoting adolescents’ self-concept. This research highlights the critical influence of psychological resilience and teacher-student relationships in shaping adolescents’ self-concept. It provides empirical support for educational practice, highlighting the key role of environment, psychological factors, and good teacher-student relationships in adolescents’ mental health and self-concept development.
Volume: 14
Issue: 6
Page: 4528-4539
Publish at: 2025-12-01

A two-step intelligent framework for gene expression-based cancer diagnosis

10.11591/ijai.v14.i6.pp4731-4738
Sara Haddou Bouazza , Jihad Haddou Bouazza
DNA microarray technology has advanced cancer diagnosis by enabling large-scale gene expression analysis, yet challenges remain in selecting relevant genes and achieving accurate classification. This study introduces two novel methods: the three-stage gene selection (3SGS) method and the statistics classifier (SC). By eliminating redundant, noisy, and less informative genes, the 3SGS method effectively lowers the dimensionality of gene expression data, while the SC classifier uses statistical measures of gene expression to classify samples with high accuracy and speed. Evaluated on leukemia, prostate cancer, and colon cancer datasets, the 3SGS method effectively identified minimal yet informative gene subsets, achieving 100% accuracy for leukemia, 99.3% for prostate cancer, and 97% for colon cancer. The SC classifier consistently outperformed traditional models in both accuracy and computational efficiency, completing predictions in under 2 seconds per dataset. Compared to conventional classifiers, it requires no parameter tuning and performs reliably even with small gene sets. While promising, future work should address multiclass classification and clinical validation to broaden the framework’s applicability. Together, these methods offer a precise and rapid cancer classification framework, supporting early diagnosis and personalized treatment strategies across diverse cancer types.
Volume: 14
Issue: 6
Page: 4731-4738
Publish at: 2025-12-01
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