Journal title Journal of Applied Data Sciences Initials JADS Abbreviation J. Appl. Data Sci. Online ISSN 2723-6471 Frequency 4 issues per year DOI doi.org/10.47738/jads Editor-in-chief Prof. Dr. Ibrahiem M. M. El Emary, (ID Scopus: 15135499400), King Abdulaziz University, Kingdom of Saudi Arabia Collaboration Computer Science and Systems Information Technology, King Abdulaziz University, Kingdom of Saudi Arabia. Publisher Bright Publisher Citation Analysis Scopus | Web of Science | Google Scholar Established in 2020, the Journal of Applied Data Science (JADS) aims to advance and promote data science methods, computing, and applications in all scientific fields where knowledge and insights are to be extracted from data. The journal publishes research works on the full spectrum of the data science including statistics, computer science, and domain applications. The topics can be about any aspect of the life cycle (collecting, processing, analyzing, communicating, etc.) of data science projects from any field that involves understanding and making effective use of data. The emphasis is on applications, case studies, statistical methods, computational tools, and reviews of data science. Finally, accepted and published papers will be freely accessed in this website and the following abstracting & indexing databases: Scopus Dimensions Google Scholar Index Copernicus GARUDA: Digital Reference Garba by Ristekdikti - Indonesia Indonesia OneSearch Crossref Search ROAD: the Directory of Open Access scholarly Resources OAI Address Journal of Applied Data Sciences has OAI address: https://bright-journal.org/Journal/index.php/JADS/oai Before submission, You have to make sure that your paper is prepared using the JADS paper Template, has been carefully proofread and polished, and conformed to the author guidelines. Online Submissions Already have a Username/Password for JADS? GO TO LOGIN Need a Username/Password? GO TO REGISTRATION Registration and login are required to submit items online and to check the status of current submissions. 7 days First Time Decision 93 days Review Time 100 days Submission to Accepted 32 days Acceptance to Publication
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Assimilate Grid Search and ANOVA Algorithms into KNN to Enhance Network Intrusion Detection Systems
The recent progress of operational network intrusion detection systems (NIDS) has become increasingly essential. Herein, a fruitful attempt to introduce an innovative NIDS methodology that integrates the grid search optimization algorithm and ANOVA techniques with the K nearest neighbor (KNN) algorithm to analyze both spatial and temporal characteristics of data for network traffic. We employ the UNSW-NB15 benchmark dataset, which presents various patterns and a notable imbalance between the training and testing data, with 257674 samples. Therefore, the Synthetic Minority Oversampling Technique has been used since this method is effective in handling imbalanced datasets. Further, to handle the overfitting issue the K folds cross-validation method has been applied. The feature sets within the dataset are meticulously selected using ANOVA mechanisms. Subsequently, the KNN classifier is fine-tuned through hyperparameter tuning using the grid search algorithm. This tuning process includes adjusting the number of K neighbors and evaluating various distance metrics such as 'euclidean', 'manhattan', and 'minkowski'. Herein, all attack types in the dataset were labeled as either 1 for abnormal instances or 0 for normal instances. Our model excels in binary classification by harnessing the strengths of these integrated techniques. By conducting extensive experiments and benchmarking against cutting-edge machine learning and deep learning models, the effectiveness and advantages of our proposed approach are thoroughly demonstrated. Achieving an impressive performance of 99.1%. Also, several performance metrics have been applied to assess the proposed model's efficiency.
Sentiment and Emotion Classification Model Using Hybrid Textual and Numerical Features: A Case Study of Mental Health Counseling
Mental health issues among individuals, particularly in counseling contexts, require practical tools to understand and address emotional states. This study explores the application of machine learning models for emotion detection in mental health counseling conversations, focusing on four algorithms: Bernoulli Naive Bayes, Decision Tree, Logistic Regression, and Random Forest. The dataset, derived from transcribed counseling sessions, underwent preprocessing, including stemming, stopword removal, and TF-IDF vectorization to create structured inputs for classification. Emotional categories such as "Depresi" (Depression), "Kecewa" (Dissapointed), "Senang" (Happy), "Bingung" (Confused) and "Stres" (Stress) were analyzed to evaluate model performance. Results indicated that Logistic Regression achieved the highest accuracy at 82%, showcasing its reliability and scalability, followed closely by Random Forest with 81%, demonstrating robustness in handling complex data structures. Bernoulli Naive Bayes performed competitively at 80%, excelling in computational efficiency, while Decision Tree recorded the lowest accuracy at 70%, reflecting its limitations in managing overlapping features and high-dimensional data. These findings highlight the potential of machine learning in addressing the increasing demand for scalable mental health support tools. The study underscores the importance of model selection, balanced datasets, and feature engineering to improve classification accuracy. Future work includes developing AI-driven chatbots for real-time emotion detection and integrating multimodal data to enhance interpretability. This research contributes to advancing automated solutions for mental health care, offering new pathways for timely and personalized interventions.
Integrating Convolutional Neural Networks into Mobile Health: A Study on Lung Disease Detection
This study presents the development and evaluation of a Convolutional Neural Network (CNN) model for lung disease detection from chest X-ray images, complemented by a mobile application for real-time diagnosis. The CNN model was trained on a diverse dataset comprising images labeled as "NORMAL" and "PNEUMONIA," achieving an overall accuracy of 96%. Compared to traditional machine learning methods such as Support Vector Machine (SVM) and Random Forest, which typically achieve accuracies ranging from 85% to 92%, the proposed CNN model demonstrates superior performance in classifying lung conditions. The model achieved high precision (0.98) and recall (0.96) for pneumonia detection, as well as precision (0.89) and recall (0.95) for normal cases, ensuring both sensitivity and specificity in diagnostic performance. These results indicate that the model minimizes false positives and false negatives, which is crucial for reducing misdiagnoses and improving patient outcomes in clinical settings. To enhance accessibility, an Android-based application was developed, allowing users to upload chest X-ray images and receive instant diagnostic results. The application successfully integrated the trained CNN model, offering a user-friendly interface suitable for healthcare professionals and patients alike. User testing demonstrated reliable performance, facilitating timely and accurate lung disease detection, particularly in areas with limited access to radiologists. These findings highlight the potential of CNNs in medical imaging and the critical role of mobile technology in expanding healthcare accessibility. This innovative approach not only improves diagnostic accuracy but also enables real-time disease detection, ultimately supporting clinical decision-making. Future research will focus on expanding the dataset, incorporating additional lung conditions, and optimizing the model for enhanced robustness in diverse clinical scenarios.
Exploring Machine Learning to Support Software Managers' Insights on Software Developer Productivity
Software developer productivity is a complex issue with no single, universally accepted definition or measurement. Emerging technologies like machine learning offer a promising opportunity for more accurate productivity measurement. Semi-structured interviews were conducted to gain qualitative insights into software managers’ perception of developer productivity to identify issues and inform the development of applied machine learning solutions. It was discovered that digital distractions significantly hinder developer productivity and conventional methods to monitor developer activity were often inefficient. Therefore, machine learning models were developed to monitor developer activity by classifying screenshots captured during activity, along with the URL and text content scraped from accessed URLs. Train and test data were obtained from a cooperating software house, supplemented with online sources. For screenshot classification, transfer learning using EfficientNetV2B0 outperformed InceptionV3, Resnet50V2, and VGG16, reaching 99.6% accuracy. This was achieved without fine-tuning, which resulted in the fastest training and lowest resource consumption. For content classification, SVC hyperparameter-tuned using grid search outperformed six other classifiers, reaching 88.5% accuracy. The design concept for a web application that utilizes the developed models to help managers measure developer productivity was well-received by the managers interviewed.
The Influence of Logistics Technology Innovation on the Efficiency of Operations in Small and Medium-Sized Businesses in Thailand
Logistics technology innovation include technology for moving materials and products, such as robotics and automated logistics systems; technology used to transmit information that enables real-time data exchange to optimize material movement; and technology to assist in decision-making as artificial intelligence enhances decision-making. These technologies include the use of digital transformation, automation, and enhanced decision-making tools to increase the efficiency of supply chain operations. This study aimed to examine how environmental factors (legal regulations, market competition, and stakeholder involvement) influence the operational efficiency of small and medium-sized enterprises in Thailand, with logistics technology innovation serving as a mediating factor, and to propose strategic guidelines for improving business performance through innovation. Data were collected from 400 small and medium-sized businesses in the Eastern Special Development Zone which are Chachoengsao, Chonburi, and Rayong provinces. A purposive sampling method was used to select enterprises in logistics-related industries, followed by convenience sampling for survey distribution. The investigation was carried out utilizing structural equation modeling. The findings revealed that environmental variables have a considerable impact on operational efficiency, with logistics technology innovation serving as a mediating variable. The direct effect of environmental factors on innovation technology was strong (β = 0.73), while innovation technology had a significant positive effect on operational efficiency (β = 0.37). Product movement technologies, including robots and automated vehicles, had the greatest influence (β = 0.62), followed by digital data transmission technologies (β = 0.34) and decision support systems (β = 0.06). These results imply that small and medium-sized businesses should emphasize logistics automation, artificial intelligence-driven decision-making, and digital data sharing platforms to increase efficiency. This study offers important insights for corporate executives and politicians in creating a favorable climate.
Dispute on Security Framework Model of MFCC Mixed Methods in Speech Recognition System
An audio recording system device has unprecedented activities of its authorized users which in a particular way cause vulnerability to the system. It starts to get into a fuzzy condition and deteriorate the system sensitivity in detecting unauthorized access to pass through, then the system inclination may occur. One case is when separate users picked speech voices with similar keywords to set their usernames or password. Moreover, when users are siblings or twins that could have merely similar voices. Troublesome of this situation leads to a less sensitive manner of a security system, and in some situations, the system could operate blocking authorized users themselves to get access. This paper defines a proposed method to resolve the situation by combining Mel Frequency Cepstral Coefficient with other methodologies, which have been implemented for many other research’ specific objectives as well. This paper displays to prove its combination with an interval scoring in Fuzzy Relation complements a resolution to tackle the security of fuzzy issues mentioned. The Mel Scale has its capacity of delivering extractions output from audio input data, it is called as spectral centroids which refer to humans’ voices or an individual's voice features. Some spectral centroids get merely similar results due to those inclinations mentioned. This paper exposes Fuzzy Relation method to fit the need of verification procedures thorough its interval scale on any fuzzy features. The objective of verification procedure is to gain consistency measured scales, and security warrant remains valid. The inhouse experiments served to give user A of [0.49, 1.18] interval, user B of [0.76,1.07] interval, and user C of [0.44,0.95] interval, and those interval numbers are proposed to cap other login users accounts unto theirs.
Implementation of Naïve Bayes Gaussian Algorithm for Real-Time Classification of Broiler Cage Conditions
Monitoring large-scale broiler farms poses considerable challenges due to the variable nature of environmental conditions, which have a direct impact on poultry health and productivity. This study proposes a real-time classification system for broiler house conditions, utilizing the Naïve Bayes Gaussian algorithm in conjunction with the Internet of Things (IoT) technology. The system has been developed to address the limitations of manual monitoring by automating the collection of temperature, humidity, and ammonia data through BME-680 and MICS-5524 sensors, which are strategically positioned 30 cm from the floor to optimize ammonia detection. Utilizing a dataset comprising 865 records, meticulously divided into 75% for training (648 records) and 25% for testing (217 records), the model attained an accuracy of 82.03%, a precision of 75.67%, a recall of 82.67%, and an F1-score of 77.67%. A comparative analysis was conducted, which demonstrated significant advantages over alternative classification methods, with Decision Trees achieving 79.5% accuracy and Support Vector Machines reaching 80.8%. The innovation lies in the integration of automated condition classification into an IoT system, enabling rapid responses to environmental changes with processing times of approximately 500 milliseconds from sensing to classification. The system demonstrated an accuracy of 178 data points, with a misclassification rate of 39 out of 217 test samples. The strategic placement of sensors at a height of 30 cm optimizes ammonia detection while ensuring accurate temperature and humidity readings. The system provides historical data, enabling farms to analyze long-term environmental trends, and thereby support data-driven decision-making strategies to enhance broiler welfare and operational efficiency. Usability testing with five poultry farm operators confirmed the dashboard's intuitive design, though recommendations for visual alerts for critical ammonia levels were suggested for future iterations.
Robust Digital Image Watermarking Scheme in the DCT Domain Employing Möbius Transformation
This study introduces a novel digital image watermarking method that integrates Möbius transformations with the Discrete Cosine Transform (DCT) to enhance both resilience and imperceptibility. The primary objective is to address the challenges of watermark embedding in digital images, ensuring robustness against geometric distortions, noise, and compression while maintaining high visual quality. The method employs a genetic algorithm to optimize the Möbius transformation parameters for effective watermark embedding in the DCT domain. Experimental results demonstrate the robustness of the proposed technique, with peak signal-to-noise ratio (PSNR) values consistently above 40 dB, ensuring minimal perceptual distortion. The bit error rate (BER) is significantly lower than that of traditional methods, demonstrating the technique's resilience against a wide range of attacks, including rotation, scaling, Gaussian noise, JPEG compression, and cropping. Compared to existing watermarking schemes, this approach consistently outperforms them in visual quality and resistance to tampering, with the PSNR reaching 60.94 dB for Lena images and achieving an SSIM value close to 1, indicating superior imperceptibility. The novelty of this approach lies in its combination of Möbius transformations with the DCT domain, offering a robust, efficient, and scalable solution for digital rights management and secure media transmission. This technique’s efficiency in terms of computational complexity and potential scalability for broader applications like video and audio watermarking highlights its practical advantages.
Modelling and Investigation of Solar Photovoltaic-Based Converter Configurations with Data Science Approach
Renewable energy sources, such as solar photovoltaic (PV) systems, typically produce low-voltage outputs, necessitating the use of high-gain direct current (DC) converters for efficient energy conversion. This study proposes a high-gain DC-DC converter for PV applications, designed with two MOSFET switches, two inductors, and two capacitors, offering a compact and efficient configuration. The converter achieves a high voltage gain of 6.8 and maintains a conversion efficiency of 97.7%, making it suitable for high-power applications. A data science-driven approach was employed to analyze the converter’s performance, integrating conventional simulation with machine learning techniques. Simulation results, conducted using MATLAB, confirmed the converter's superior performance, achieving an input ripple of 0.05% and an output ripple of 0.01%. Machine learning models, including Linear Regression, Decision Tree, Ridge Regression, and Support Vector Machine (SVM), provided deeper insights into the converter's behavior. Linear Regression accurately predicted output voltage, Ridge Regression minimized overfitting, and the Decision Tree model identified Duty Ratio and Input Voltage as the most critical factors affecting efficiency. SVM effectively classified operating conditions into high, moderate, and low efficiency. The Zero-Voltage Switching (ZVS) technique minimized switching losses, enhancing overall efficiency. This study demonstrates that integrating data science techniques with conventional analysis enhances the understanding and optimization of high-gain converters. The proposed converter provides a scalable and efficient solution for PV applications, offering insights for further optimization as part of process innovation.
Intelligent Solar Panel Monitoring Using Machine Learning and Cloud-Based Predictive Analytics
The increasing global energy demand necessitates reliable and sustainable solutions, with solar photovoltaic (PV) technology emerging as a key carbon-neutral option. However, optimizing solar energy systems requires advanced monitoring and predictive analytics to enhance efficiency and ensure long-term performance. This study introduces an Internet of Things (IoT)-based solar energy monitoring system, integrating machine learning algorithms and cloud computing to enhance real-time performance assessment. The proposed system employs K-Means Clustering for condition classification, Support Vector Machine (SVM) for fault detection, Long Short-Term Memory (LSTM) for energy forecasting, Prophet for time-series predictions, and Isolation Forest for anomaly detection. The system was validated using a 125-watt photovoltaic module, monitoring temperature, solar radiation, voltage, and current. A Wi-Fi-enabled microcontroller collects data, which is processed through a cloud-based platform and visualized via the Blynk application. Experimental results demonstrate 94.2% energy prediction accuracy using LSTM, 89.7% fault classification accuracy with SVM, and 88.5% anomaly detection accuracy with Isolation Forest, confirming high reliability. The system's wireless tracking mechanism minimizes resource consumption, ensuring scalability and adaptability for commercial and industrial applications. The integration of IoT, machine learning, and cloud analytics provides a cost-effective and scalable approach for solar PV optimization. Future enhancements include deep learning models and reinforcement learning algorithms to improve energy forecasting, fault detection, and adaptive optimization, ensuring greater efficiency, resilience, and sustainability in solar energy management.
HOG feature extraction in optimizing FK-NN and CNN for image identification of rice plant diseases
This study compares the performance of FK-NN and CNN models in identifying rice diseases from digital images, focusing on both effectiveness and efficiency. Additionally, this research utilizes HOG for feature extraction from the digital images. The stages include data collection, preprocessing, transformation, classification, and model evaluation. The results show that the FK-NN model achieves a higher accuracy of 86.26%, compared to the CNN model's accuracy of 71.37%. Furthermore, the precision value of the FK-NN model is also higher at 86.88%, compared to the CNN model’s precision of 72.74%. Similarly, the recall value for the FK-NN model is higher at 86.88%, compared to the CNN model’s 71.37%. The F1-score of the FK-NN model is likewise superior, with a value of 86.88%, compared to the CNN model’s F1-score of 71.37%. These findings suggest that the FK-NN model with HOG feature extraction is more effective. However, in terms of inference time, the CNN model is faster, taking 0.000282 seconds compared to FK-NN’s 0.002331 seconds. In conclusion, the FK-NN model with HOG feature extraction excels in identifying rice diseases, while the CNN model offers faster inference time in this study.
Development of a Self-Identity Construction Model for Private Vocational College Students Using Data Science Techniques
This study aimed to synthesize theories of self-identity learning to develop a self-identity development model for private vocational college students in Yunnan Province, China, identify key influencing factors, and evaluate the model's effectiveness. Using purposive sampling, the study involved 17 experts and 1,004 first-year students. Data were collected through a semi-structured questionnaire via Delphi Technique, supported by consultations via email, WeChat video, and in-person interviews. The model’s validity was assessed based on satisfaction levels from students, teachers, and stakeholders. Statistical analyses included weight calculations, means, standard deviations, coefficients of variation, and path analysis. The results showed strong expert consensus, with an average score of M = 4.5008 and CV = 0.1181, forming a model of 27 first-level and 21 second-level indicators. The "career development expectation evaluation" held the highest weight at 26.86% in the initial assessment, while "dynamic feedback loop development" recorded the highest importance at 0.442 in the practical development phase. Practical testing demonstrated significant effectiveness, with satisfaction means ranging from M = 4.059 to 4.341. Regression analysis confirmed significant mutual influences among the model's five modules. Overall, the model effectively addresses the urgent need for personalized development strategies for private vocational college students in Yunnan Province.
Understanding Culinary Tourism Preferences: A Study of Local Food Preferences in Indonesia
To encourage the contribution of tourism to grow from time to time, it is necessary to have a driving factor for the success of tourism. The government also relies on the number of visits from domestic tourists in increasing tourism power so it is very important for the government to pay more attention to domestic tourists. This study explores the factors influencing tourist satisfaction in local food culinary tourism sector, identifying key drivers and challenging existing assumptions. There were 242 tourists participated freely to fill online questionnaire in the study. The research highlights the importance of Perceived Quality (PQ), Costs and Risks (CR), and Gastronomy Tourist Market Assessment (GTME) as primary determinants of satisfaction. PQ emphasizes the significance of service quality and authenticity, while GTME underscores the role of market positioning in enhancing the tourist experience. Unexpectedly, CR was found to positively influence satisfaction, suggesting that moderate costs and perceived risks may enhance the appeal of culinary tourism. In contrast, Local Food Satisfaction (LFD), Destination Image (DI), Perceived Value (PV), and Tourist Expectations (TE) exhibited statistical significance but had a less pronounced practical impact on overall satisfaction. These findings contribute to the tourism satisfaction theory by emphasizing the need for context-specific models and offering actionable insights for tourism stakeholders. The study suggests that improving service quality, strategic market positioning, and finding a balance between affordability and perceived adventure can enhance the culinary tourism experience. This study introduces a novel application of tourism satisfaction theory by demonstrating the importance of contextualizing satisfaction models specifically for culinary tourism—an area often overlooked in broader tourism satisfaction research.
Applying Structural Equation Modelling to Examine the Impact of Environmental Management Accounting on Financial
Environmental management accounting has garnered significant attention from various stakeholders. Enterprises that effectively implement environmental management accounting not only contribute to sustainable development but also enhance organizational performance. This study aims to examine the relationship between environmental management accounting, green innovation, and the financial performance of small and medium-sized enterprises in the context of a developing country like Vietnam. A quantitative research approach was employed to analyze data collected through a structured survey. The dataset comprises responses from 151 small and medium-sized enterprises, with financial managers and management accountants serving as key informants. Data analysis was conducted using the Smart Partial Least Squares software. The findings reveal that environmental management accounting has a direct positive impact on financial performance and an indirect impact through the mediation of green process innovation. While green product innovation exerts a direct impact on financial performance, environmental management accounting appears to have no significant influence on green product innovation. Consequently, green product innovation does not function as a mediating variable in the relationship between environmental management accounting and financial performance. The results underscore the greater significance of green process innovation over green product innovation in driving improvements in the financial performance of small and medium-sized enterprises. These results significantly contribute to the relatively unexplored theoretical relationship between environmental management accounting, green innovation, and the financial performance of small and medium-sized enterprises. Furthermore, the study provides a practical foundation for managers to boost their organizations’ financial performance by practicing environmental accounting and integrating green innovation into business operations.
Cross-Biome Biodiversity Assessment and Anomaly Detection Using AI-Enhanced Acoustic Monitoring
This study proposes a novel AI-powered eco-monitoring framework that integrates acoustic ecology, deep learning, and low-cost IoT devices to enable scalable, real-time biodiversity assessment and ecological anomaly detection across diverse environments. The primary objective is to automate species classification and environmental monitoring using passive audio data captured by solar-powered IoT sensors, thereby reducing reliance on manual ecological surveys. The framework comprises four modules: acoustic data acquisition, dual-representation preprocessing Short-Time Fourier Transform (STFT) and Mel-Frequency Cepstral Coefficients (MFCCs), species classification using CNN and CNN-LSTM models, and anomaly detection via autoencoders and one-class SVM. Field validation and multi-dataset testing were conducted across 250+ species from temperate forests, wetlands, and urban areas. The CNN-LSTM model achieved the highest performance with 93.7% accuracy, 93.0% precision, and a 92.5% F1-score, while anomaly detection reached 89.7% precision with an AUC of 0.94, effectively identifying irregularities such as invasive calls, mechanical noise, and species absence. A forest case study demonstrated the system’s ability to detect circadian acoustic patterns (e.g., dawn chorus of sparrows, nocturnal owl calls), and real-world disturbances with 91% expert validation agreement. The novelty of this work lies in its hybrid AI architecture with real-time unsupervised anomaly detection, cross-biome generalization capability, and deployment readiness on low-power edge devices like Raspberry Pi and Jetson Nano. Inference times as low as 18 ms per sample and bandwidth usage under 3 MB/hour make it feasible for continuous, remote deployment. The framework offers a robust and adaptable solution for conservation efforts, environmental policy, and climate resilience initiatives. Future directions include integrating multimodal data sources and transformer-based continual learning for broader ecological impact. These findings position the system as a scalable and intelligent tool for next-generation, AI-driven environmental monitoring.
A Study to Detect Multi-word Expression from Text Using Deep Learning Models
Detecting Multi-word Expressions (MWEs) is a crucial task in Natural Language Processing (NLP) for applications in machine translation, sentiment analysis, and information retrieval. This study evaluates the performance of several deep learning models on MWE detection using two samples of varying sizes from the major consumer electronic product retailer corpus. The sample is limited to 10,000 and 15,000 rows, with each row contains 15-20 English words. Preprocessing steps include removing special symbols and emojis, converting text to lowercase, and applying the spaCy NLP library for tokenization and part-of-speech (POS) tagging. Syntactic rules are then used to identify MWEs such as verb-noun combinations and phrasal verbs, with BIO tags (B-MWE, I-MWE, O) to mark MWE boundaries. We investigated transformer-based models such as BERT, BERT-CRF, LSTM-CRF and RoBERTa-CRF using a sample of 10,000 rows; BERT, BERT-BiLSTM, BiLSTM-GloVe, and BiLSTM-GloVe-BiGRU uses a sample of 15,000. Results demonstrated that the transformer-based model, RoBERTa-CRF, excels on the smaller sample which achieves the best performance by leveraging the contextual embeddings and sequential dependency modeling. On a larger sample, the BERT-BiLSTM model emerged as the most effective model, showcasing the advantage of combining dynamic embeddings with sequential learning. In contrast, models utilizing static embeddings, such as GloVe, displayed moderate performance, highlighting their limitations in capturing contextual nuances. Comparative analysis across both samples reveals that transformer-based models like RoBERTa-CRF performed optimally on the smaller dataset, whereas hybrid models integrating with sequential architectures like BERT-BiLSTM demonstrated superior performance as dataset size increased. These findings highlight the importance of model selection based on dataset scale to optimize MWE detection. This study underscores the importance of integrating contextual and sequential deep learning techniques to improve MWE detection and provides a basis for developing more robust and scalable systems for diverse linguistic tasks.
Evaluating the Effectiveness of Augmented Reality in Mixed Reality Exhibition Spaces: A Multidisciplinary Perspective
In MR exhibition environments, the integration of AR provides a dynamic approach to increasing visitor engagement, encouraging interactive learning, and enhancing knowledge retention. However, the impact of AR on user experience and educational outcomes in these settings remains underexplored. This study investigates the role of AR in MR exhibitions, aiming to identify both its benefits and challenges. Using a mixed-methods research framework, this study combines qualitative and quantitative approaches to assess AR’s influence on visitor interaction and cognitive processing. Qualitative data is collected through ethnographic observations and structured user interviews, utilizing thematic analysis to identify engagement patterns and usability concerns. Quantitative data is gathered through eye-tracking metrics, user interaction logs, and pre- and post-exhibition knowledge assessments to measure cognitive load, retention rates, and engagement levels. Findings show that AR increases engagement by seamlessly integrating physical and digital elements to create immersive and intuitive experiences. Interactive storytelling and spatial learning mechanisms enhance knowledge retention. However, challenges such as technical limitations, accessibility barriers, and content development complexities restrict widespread adoption. This research contributes to the growing discourse on AR in cultural and educational contexts by offering evidence-based recommendations for optimizing its implementation. By focusing on user-centered design and interdisciplinary collaboration, the study identifies strategies for addressing challenges and maximizing AR’s potential in MR exhibitions. The findings are valuable for curators, designers, and educators seeking to create more effective and engaging exhibition experiences through AR.
Analyzing the Impact of Social Media Advertising on Consumer Interactions, Trust, and Purchase Intentions in the Cosmetics and Personal Care Sector
This study analyses the influence of social media advertisements on consumer purchasing intentions within Malaysia's cosmetics and personal care sector, highlighting the significance of interactivity, informativeness, and trust. Considering the growing prevalence of social media in marketing, comprehending how these elements affect consumer choices is crucial for enhancing digital advertising strategies. This research utilises a quantitative approach to evaluate consumer behaviour. Data were gathered from 384 Malaysian consumers aged 18 and older who actively interact with cosmetic and personal care brands on social media using online survey. The research employed descriptive analysis, reliability and validity assessments, and hypothesis testing to investigate the relationships among principal variables. Research demonstrates that informativeness and trust in social media advertisements substantially increase purchase intentions, with trust acting as a vital mediator. Although interactivity enhances trust, it does not directly affect purchase intention, indicating that its influence may be contingent upon contextual variables. These findings underscore the imperative for brands to deliver transparent, reliable, and informative content to enhance consumer trust and stimulate purchasing behaviour. The study's originality is rooted in its examination of the Malaysian cosmetics and personal care market, providing practical insights for marketers to enhance social media advertising strategies. The research underscores trust as a pivotal factor, offering essential insights for brands aiming to improve their digital engagement. This study enhances the existing literature on digital marketing in emerging markets, providing practical insights for businesses seeking to improve their social media efficacy in Malaysia.
A New Data Preprocessing Framework to Enhance the Accuracy of Herbal Plants Classification Using Deep Learning
This research proposes to solve the problem of herbal plant classification, which plays a key role in Thai pharmacy and traditional medicine. Moreover, there are limitations due to similar physical characteristics of plants and the reliance on specialists to classify herbal plants, which hinder the utilization of herbal plants by the general public at the local level. To solve this problem, this research presents a new preprocessing framework called P4, which integrates 7 techniques as follow: Image Cropping, Resizing, Normalization (0–1), Data Augmentation, Label Noise, Label Cleaning, and Dataset Quality Score (DQS). The prominent point of P4 technique is the combination of intentional mislabeling and label cleaning process, as well as, quantitative data quality assessment and additional expert review in order to filter out potentially inaccurate data before inputting to Deep Learning model. In the experiment, a dataset of 4,211 herbal images covering 30 herbal plant species is used and compared with 3 proposed techniques in previous research (P1–P3) with 5 deep learning architectures, namely DenseNet201, EfficientNetB7, ViT, Swin Transformer, and ConvNeXt. The experimental results showed that the P4 technique combined with DenseNet201 model provided the highest performance in herbal plant classification, with an Accuracy of 92%, Precision of 92%, Recall of 91%, and a training time of merely 22.92 minutes. This was a result of combining the good data quality from the P4 technique, which enhanced to increase efficiency in producing higher quality and more balanced data. When combined with the structural capability of DenseNet201 that supported feature reuse from previous layers, it increased the robustness to mislabeled data and was able to accurately distinguish plants with similar characteristics. The results of this experiment are able be applied as a guideline for future application in Thai traditional medicine support system and herbal plant learning system.
Modeling Female Contraceptive Recommendation Using Hybrid Analytical Hierarchy Process and Profile Matching
Multi-criteria decision-making (MCDM) methodologies have been extensively employed across various domains within healthcare. They can be utilized for disease prediagnosis, aiding clinical decision-making (e.g., surgery), conducting health technology assessments, as well as establishing healthcare priorities. This research presents the outcomes of a hybrid MCDM strategy, integrating the AHP and Profile Matching to facilitate clinical recommendations related to female contraception. Many cases in Indonesia show acceptors' inappropriateness in using available contraceptives, causing side effects resulting in negative effects. The challenges in the Keluarga Berencana or Planned Parenthood program in Indonesia are increasing, based on the decline in the number of new acceptors and the high unmet needs for contraception. Failure to meet the need for contraception has the potential to increase birth rates and maternal mortality rates, which requires serious attention and the development of appropriate strategies. Based on the problem, this study aims to create a decision support model in selecting suitable contraceptives for acceptors. The criteria used in this study consisted of age, medical history, weight (BMI), breastfeeding or not, history of childbirth, period of use, and income. The seven criteria are implemented in AHP with a consistency test result value of 2.2%. Based on the target value of contraceptives obtained from the results of Profile Matching, compatibility was determined with a sample of three acceptor profiles. The results that have been achieved indicate a sample recommendation model for acceptors of IUD-type contraception that can assist midwives or medical personnel in providing recommendations for selecting appropriate contraceptive methods. Future studies can integrate the results of recommendations for health service providers (e.g., hospitals, Public Health Center or Puskesmas) in procuring contraceptives.
Detecting Gender-Based Violence Discourse Using Deep Learning: A CNN-LSTM Hybrid Model Approach
Gender-Based Violence (GBV) is a critical social issue impacting millions worldwide. Social media discussions offer valuable insights into public awareness, sentiment, and advocacy, yet manually analyzing such vast textual data is highly challenging. Traditional text classification methods often struggle with contextual understanding and multi-class categorization, making it difficult to accurately identify discussions on Sexual Violence, Physical Violence, and other topics. To address this, the present study proposes a hybrid deep learning approach combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. CNN is utilized for extracting key linguistic features, while LSTM enhances the classification process by maintaining sequential dependencies. This hybrid CNN+LSTM model is evaluated against standalone CNN and LSTM models to assess its performance in classifying GBV-related tweets. The dataset was sourced from Kaggle, containing real-world Twitter discussions on GBV. Experimental results demonstrate that the hybrid model surpasses both CNN and LSTM models, achieving an accuracy of 89.6%, precision of 88.4%, recall of 89.1%, and F1-score of 88.7%. Confusion matrix and ROC curve analyses further confirm the hybrid model’s superior performance, correctly identifying Sexual Violence (82%), Physical Violence (15%), and Other (3%) cases with reduced misclassification rates. These results suggest that combining CNN’s feature extraction with LSTM’s contextual learning provides a more balanced and effective classification model for GBV-related text. This work supports the development of AI-based tools for social media monitoring, policy-making, and advocacy, helping stakeholders better understand and respond to GBV discussions. Future research could explore transformer-based models like BERT and real-time classification applications to further improve performance.
An Approach for Emotion Detection in Natural Arabic Audio Files Based on Acoustic and Lexical Features
Emotion Detection is a crucial for enhancing human-machine interactions. This paper addresses the challenge of accurately recognizing emotional states from speech, particularly in distinguishing between emotions with similar acoustic characteristics, such as anger, happiness and surprise, which have high pitch and energy. While acoustic features convey significant information about emotional states, they are often inadequate for distinguishing between these emotions. This limitation highlights the need for improved performance in emotion detection systems. The main contribution of this work is the introduction of a multimodal approach that combines both acoustic and lexical features for emotion detection in natural Arabic audio files, focusing on four emotions anger, happiness, sadness and neutral. To the best of our knowledge, this is the first study that employ such a combination in this context, building on our previous work that utilized only acoustic features. Several Machine Learning (ML) classifiers were applied including Sequential Minimal Optimization (SMO), Random Forest (RF), K-Nearest Neighbors (KNN), and Simple Logistic (SL). Two types of experiments were executed: one using only lexical features and another combining various acoustic features sets with lexical features. This approach enhances our previous experiments that used only acoustic features. The experimental results show that SMO classifier achieved the highest performance, with an accuracy 96.11% when using all acoustic features combined with a unigram model, outperforming the other classifiers. These results suggest that combining acoustic and lexical features enhances the performance of emotion detection models, particularly for complex emotions in natural Arabic audio datasets.
A Study of Unified Framework for Extremism Classification, Ideology Detection, Propaganda Analysis, and Flagged Data Detection Using Transformers
The rise of extremism and its rapid dissemination through propaganda channels have become pressing global challenges, threatening peace, security, and social cohesion. This study aligns with the United Nations Sustainable Development Goal 16 by proposing a unified framework leveraging advanced machine learning and large language models to combat extremism through extremism classification, ideology detection, propaganda analysis, and flagged word recognition. This framework introduces process innovation by integrating state-of-the-art transformer models such as BERT, RoBERTa, DistilBERT and XLNet to streamline the analysis process and overcome traditional limitations in extremism detection with exceptional performance: 90.00% accuracy for extremism classification, 98.82% accuracy for ideology detection, and 99.71% accuracy for flagged word recognition. While the proposed approach demonstrates high precision and recall, it faces challenges such as potential data bias, ethical concerns in dataset usage and the risk of false positives, which could lead to misclassification of benign content. The inclusion of multilingual capabilities broadens the applicability of the framework but variations in linguistic structures and cultural contexts introduce complexities in model generalization. Additionally, ethical considerations in handling extremist content, especially in social media data collection, necessitate stringent privacy safeguards to prevent unintended harm. By providing actionable insights, this research contributes to counter-extremism efforts in areas such as online content moderation, law enforcement and intelligence analysis, laying a foundation for future advancements in safeguarding global security which enhance the process innovation.
Exploring the Determinants of User Acceptance for the Digital Diary Application in Type 1 Diabetes Management: A Structural Equation Modeling Approach
Effective management of Type 1 Diabetes (T1D), especially in children, requires continuous monitoring and care. Digital health applications have become vital in supporting routine T1D management, including insulin delivery, glucose monitoring, nutrition, and physical activity tracking. This study investigates factors influencing user acceptance of a digital diary app designed for children with T1D and their families. Using an extended Technology Acceptance Model incorporating Trust, Perceived Risk, Perceived Enjoyment, and Social Influence, a survey was conducted with 114 participants, including parents, physicians, and dietitians. Data were analyzed using Partial Least Squares Structural Equation Modeling. Findings indicate that perceived usefulness, trust, and social influence significantly affect users' attitudes and intentions to use the app, through the accepted hypothesis that considered path coefficients and p-values. Conversely, hypothesis that shows relation between perceived ease of use, enjoyment, and risk toward intention were rejected, showing unsignificant relations toward user intention to use. Furthermore, this study recommends prioritizing robust security features, fostering user trust, and engaging social networks to enhance digital health adoption in pediatric care. Future research should further explore the roles of perceived risk and enjoyment in sustaining long-term engagement
Impact of Sample Size on the Robustness of Machine Learning Algorithms for Detecting Loan Defaults Using Imbalanced Data
This study aimed to assess the impact of sample size on the robustness of five machine learning classifiers: Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), Decision Trees (DT), and K-Nearest Neighbour (K-NN). Although there are data-balancing techniques that aid in addressing data imbalance, they have some limitations which are discussed in this paper. The current study continues the trend in the application of these five ML classifiers for credit default detection, but it makes a contribution by examining whether sample size increment can better their performance when they are trained using a different imbalanced loan default dataset which has not been the focus of previous studies, although most ML algorithms are known to perform well when trained with large datasets. The study used a secondary loan default imbalanced dataset from Kaggle.com, where 85% of participants made loan payments and 15% defaulted. Stratified random sampling was used to select different sample sizes starting with 2% of the total observations, followed by 5%, then 10% up to 90% of the dataset, with the dependent variable being the stratum. The study found no consistent change in the classification metrics with the change in sample size, but RF and DT achieved 100% performance regardless of sample size and are therefore recommended as the most robust to data imbalance in loan default detection. The average classification metrics for NB and K-NN ranged from 72% to 92%, and SVM produced the lowest averages which were between 69% and 75%. NB, K-NN and SVM yielded poor sensitivity rates of 0% to 53%, indicating poor loan payments prediction but they had sensitivity scores in range of 84% to 86%, indicating good loan default classification. Future studies should consider other sampling methods, deep and hybrid learning methods with comparison to RF and DT.
Integrating Moving Average Indicators with Long Short-Term Memory Model in Bitcoin Price Forecasting
Bitcoin price forecasting remains a challenging task due to the market's high volatility and complex nonlinear dynamics. This study proposes a novel forecasting framework by integrating Long Short-Term Memory (LSTM) networks with Moving Average (MA) indicators—specifically Simple Moving Average (SMA), Exponential Moving Average (EMA), and Weighted Moving Average (WMA)—as auxiliary input features to enhance model accuracy. The objective is to examine the frequency-specific effectiveness of these hybrid models across daily and high-frequency datasets. Using historical Bitcoin data from Bitstamp between January 2021 and December 2024, we conducted experiments at four epoch levels (50, 100, 150, 200) to determine optimal model configurations. Empirical results reveal that, on daily data, LSTM combined with a 10-period WMA achieves the lowest Mean Absolute Percentage Error (MAPE) of 2.1661% at 150 epochs, while for high-frequency data, the combination with a 10-period SMA yields superior performance with a MAPE of 0.4895%. Furthermore, increasing epochs beyond the optimal point led to performance degradation, indicating overfitting. Compared to the standalone LSTM model, our integrated approach demonstrates significantly improved adaptability to short-term fluctuations and heightened forecasting precision. This research contributes a comprehensive comparative analysis of MA-enhanced deep learning models for cryptocurrency price prediction, and offers practical insights for algorithmic traders, financial analysts, and decision-support systems in volatile digital asset markets.
The Use of Artificial Intelligence in Accounting Classes: Behavioral Insights from Students
Accounting learning has entered a new transformation with the intensive use of artificial intelligence. This study analyzes student behavior in using artificial intelligence in accounting lectures. The Unified Theory of Acceptance and Use of Technology (UTAUT) framework is used to understand student behavior by adding the construct of experience and competence of information and communication technology. This study contributes to the extended application of UTAUT. This survey took a sample of accounting and accounting education students at the Faculty of Economics and Business, Universitas Negeri Semarang, totaling 124 students. The questionnaire distributed via Google Forms was used as a data collection technique. The data analysis technique used was SEM-PLS. The results of the analysis show moderate student behavior in using artificial intelligence in accounting lectures, there is 2.48 of an average score. ChatGPT and Canva are the types of AI most frequently used by students in accounting courses. SEM-PLS analysis indicates that students' intentions to use artificial intelligence in their accounting lectures are more determined by performance and effort expectancy. The coefficients are 0.519 and 0.382 at a P-value of 0.000. Social influence does not have a significant effect on student intentions, with a P-value of 0.104. Student intentions, ICT experience, and ICT competence significantly influence student behavior to use AI. The coefficients are 0.382, 0.241, and 0.214 at a P-value less than 0.05. Facilitating conditions do not have a substantial effect on actual behavior, with a P-value of 0.210. The practical implication of this study is the importance of highlighting students' ICT experiences and competencies that determine the use of AI for lecture purposes.
Improving MCDM University Rankings through Statistical Validation Using Spearman’s Correlation and THE Benchmark
The evaluation of higher education institutions is a critical field for informing data-driven policy and institutional benchmarking. A key problem in this area is the lack of transparency and consistency in university rankings, particularly when using Multi-Criteria Decision-Making (MCDM) methods such as MABAC and MAIRCA, with limited research on how weighting techniques affect the reliability and alignment of these rankings with international standards like the Times Higher Education (THE) Rankings. This study proposes the use of MABAC and MAIRCA methods combined with two weighting techniques—Rank Order Centroid (ROC) and Rank Sum (RS)—to assess 20 top Indonesian universities based on five performance indicators: research quality, research environment, teaching, industry, and international outlook. Spearman’s rank correlation is used to compare the MCDM-generated rankings with THE Rankings 2025. The study contributes empirical evidence on the impact of weighting schemes on the consistency and reliability of university rankings and demonstrates that the MAIRCA-ROC method achieves the highest agreement with THE Rankings, with a correlation coefficient of 0.8135 and a p-value of 0.00001. These results validate the use of MCDM methods in higher education evaluation and emphasize the importance of selecting appropriate weighting techniques to develop transparent and robust ranking frameworks that support evidence-based policy decisions.
Applied Data Science for Exploring Critical Factors Affecting Systemic Risk of Commercial Banks in Vietnam
The banking system is essential in developing the Vietnamese economy, serving as a capital supply channel for the economy's production, business, and investment activities. In 2024, the banking industry faces many challenges, including global macroeconomic fluctuations, the Russia-Ukraina war, and policy changes. Therefore, the study aims to quantify the impact of these factors on systemic risk using a Structural Equation Modeling (SEM) approach. Furthermore, it seeks to provide empirical evidence and actionable policy recommendations to help mitigate systemic risks, enhance financial stability, and support socio-economic recovery and development. The methodology of this study applied a structural equation model consisting of five factors: (1) Macroeconomic environment, (2) internal factors of commercial banks, (3) legal framework and supervisory authorities, (4) globalization and financial integration, and (5) technology and financial innovation. Data were collected from 450 managers working in the banking sector and processed using Amos software. The study's novelty showed that five critical factors positively impact the systemic risk of commercial banks in Vietnam. In addition, the originality of this research includes introducing technology and financial innovation into the model, a new factor of the banking industry in the digital transformation period of banking. Moreover, the results highlight that robust and timely policy interventions are essential for mitigating systemic vulnerabilities and promoting financial stability. Finally, the practical implications of the article proposed policy recommendations to help managers and policymakers minimize systemic risks due to influences from external-internal factors contributing to socio-economic recovery and development. Finally, managers and policymakers should strengthen regulatory oversight, promote digital risk management, enhance governance practices, and ensure macroeconomic stability to mitigate systemic banking risk.
Designing a Culturally Adaptive Information Framework for Anxiety Disorders: A Mixed-Methods Thematic Analysis in Malaysia
This study addresses critical gaps in Malaysia's mental health landscape by developing a culturally adaptive framework for anxiety disorder resources, where only 28% of adults recognize symptoms due to cultural stigma and poor resource design. Our key contribution is a user-centered framework integrating visual-interactive tools with cultural adaptation strategies to improve accessibility and literacy. The objective was to investigate how information design can overcome barriers, using a mixed-methods approach with 12 anxiety disorder patients (screened via DASS-21). Findings revealed: (1) format preferences (infographics: 40%, videos: 35%, simulations: 25%), (2) accessibility barriers (technical language: 45%, lack of credible sources: 65%, insufficient examples: 30%), and (3) demand for demographic personalization (age-targeted content: 78%, mood-tracking tools: 62%). Quantitative results showed strong alignment between preferred formats and comprehension gains (infographics improved understanding by 40% vs. text). The novelty lies in merging cognitive load theory with Malay cultural values (familial collectivism, Islamic coping mechanisms) into actionable design principles. Our framework demonstrates that culturally tailored visual-interactive content increases engagement by 35-40% compared to generic materials, while simplified Malay Language reduces stigma-related avoidance by 28%. These ideas translate into three evidence-based strategies: (a) minimalist visual formats to reduce cognitive load, (b) family-involved examples to respect collectivism, and (c) hybrid delivery (online/offline) for rural accessibility. The study provides policymakers with metrics-backed guidance, showing SMS-based hybrid tools achieve 58% adherence in low-bandwidth areas versus 22% for chatbots. Future work should validate scalability in larger cohorts and test AR/VR adaptations (requested by 70% of youth participants). This research advances both mental health communication theory and practical interventions for Southeast Asia's multicultural contexts.
The Impact of Supplier-Customer Collaboration on Sustainability-Oriented Capability: The Mediating Role of Communication Effectiveness and Technology Adoption in Micro-Enterprises
With many businesses transforming digitally to lead future trends driven by sustainability, supplier-customer collaboration is key in empowering MSMEs. Nevertheless, effective collaborative practices remain challenging for MSMEs due to resource constraints and operational limitations. This study examines the mediating role of communication effectiveness and technology adoption in the relationship between supplier-customer collaboration and sustainability capabilities. Data was collected from 400 MSMEs in Bangkok and metropolitan area, Thailand, and analyzed using partial least squares-structural equation modeling (PLS-SEM). Results indicate that supplier-customer collaboration positively influences sustainability capability directly (β=0.345, p < 0.001) and indirectly through communication effectiveness (β=0.370, p < 0.001) and technology adoption (β=0.043, p < 0.001). Effectiveness of communication appeared to be the superior mediator, providing evidence that the impact of technology adoption is less influential in resource-constrained environments. The study’s novelty lies in its focus on micro-enterprises’ unique constraints and the creation of a contextualized framework that prioritizes effective communication over technological investments, challenging conventional sustainability models derived from large organizations and offering context-specific policy recommendations for enhancing micro-enterprise sustainability in developing economies.
A Hybrid CNN-Transformer Model with Quantum-Inspired Fourier Transform for Accurate Skin Disease Classification
Skin disease classification is a complex task that requires robust feature extraction, efficient classification, and interpretability. Artificial intelligence-based technologies offer effective solutions for developing a framework for skin disease classification while ensuring explainability for healthcare professionals. This study proposes a novel Hybrid Transformer model comprising of Convolutional Neural Network (CNN) architecture infused with a Quantum-Inspired Fourier Transform (QIFT) to enhance classification accuracy. QIFT is incorporated to emphasize frequency-domain information alongside the spatial features captured by CNNs, potentially improving feature representation and model generalization. For demonstration, a dataset containing four different classes of dermatological images is used. Data augmentation techniques and adaptive learning rate scheduling are employed to optimize the dataset. A weighted cross-entropy loss function is used to address class imbalances in the dataset. In this research, explainability is implemented using a standard attribution technique like Integrated Gradients providing insights into model decision-making, and enhancing trust in medical applications. Performance evaluation involves validating the proposed framework using metrics such as confusion matrix analysis, classification reports, and training-validation curves. Experimental results demonstrate a high classification accuracy of 92.5% across skin disease categories. The findings indicate that integrating QIFT and CNN-based feature extraction with transformer-driven attention mechanisms enhances skin disease classification performance while ensuring interpretability as process innovation.
Modeling Neuroelectrical-Microbiome Crosstalk: AI-Driven Insights into Gut-Brain Bioelectrical Signaling
The gut-brain axis, traditionally understood as a chemical communication network, is reconceptualized in this study as a bidirectional bioelectrical system. This paper introduces a novel framework for exploring host–microbiome interactions through neuroelectrical signaling, integrating Artificial Intelligence (AI)-based modeling with experimental insights. The objective is to assess how microbial metabolites, especially Short-Chain Fatty Acids (SCFAs) such as butyrate (1.5–3.5 mM), modulate host membrane potentials, and how these bioelectrical changes influence microbial behavior. Using a hybrid simulation platform combining Recurrent Neural Networks (RNNs) and Graph Neural Networks (GNNs), we modeled dynamic interactions within a low-inflammation gut environment. Results demonstrated that increasing butyrate concentration from 1.5 to 3.5 mM led to a depolarization of enteric neurons from –70.0 mV to –63.1 mV over 24 hours. This shift was associated with a 2.5-fold increase in microbial diversity index and a suppression of pathogenic Enterobacteriaceae. SHAP (SHapley Additive exPlanations) analysis identified butyrate concentration (+0.43) and potassium channel expression (+0.27) as top contributors to excitability enhancement. Additionally, the simulation predicted improved gut motility and increased abundance of beneficial taxa such as Bifidobacterium. These findings suggest a previously underappreciated electrical layer of gut-brain communication that complements chemical pathways. The novelty of this work lies in its systems-level approach that quantifies and predicts the reciprocal influence between microbial activity and host electrophysiology. By combining bioelectrical principles with AI-driven simulation, the study contributes a mechanistic understanding and virtual testing environment for neuroelectrical-microbiome dynamics. This research opens new avenues for non-invasive interventions—such as dietary modulation or vagus nerve stimulation—to treat microbiome-related neurological and gastrointestinal disorders.
Development of A Deep Learning Model for Mental Health Classification and Early Screening through Draw a Person (DAP) Test Images
Mental health, as defined by the World Health Organization (WHO), is a fundamental aspect of overall well-being. The increasing complexity of modern society, coupled with rising levels of competition and stress, significantly impacts individuals’ mental health. The DAP test is a psychological assessment tool that uses human figure drawings to gain insights into an individual’s personality and mental condition. YOLO (You Only Look Once) is a deep learning algorithm based on Convolutional Neural Networks (CNNs) designed for real-time object detection. This study utilizes a DAP image dataset contributed by adolescents aged 12 to 16 years to develop a model for detecting and classifying objects in DAP images using the YOLOv8 algorithm. Optimal training results were achieved after 150 epochs, yielding a Precision of 0.821, Recall of 0.799, and mAP50 of 0.88. The model evaluation demonstrated an F1-Score of 0.78, indicating a balanced performance between Precision and Recall. Psychological analysis was conducted based on symptoms extracted from the characteristics of DAP images. Mental health conditions were classified according to severity levels consisting of minor, medium, and serious, based on weighted symptomatology derived from DAP image characteristics. The successful development of this model highlights its capability to classify various mental health conditions based on psychological analysis of DAP images. The findings suggest that mental health classification using DAP test images has the potential to support early screening and psychological assessment by providing an innovative and objective approach to identifying psychological indicators.
A Dual-Fusion Hybrid Model with Attention for Stunting Prediction among Children under Five Years
Malnutrition remains a persistent global health challenge, especially among children under five. Traditional assessment methods often rely on static anthropometric measures, which are limited in capturing complex growth patterns. This study aims to develop a robust classification model for predicting the nutritional status of children under five years old, addressing the critical public health challenge of stunting. The model contributes to the growing need for accurate, data-driven early detection systems in child health monitoring by introducing a hybrid framework that combines deep learning and classical machine learning techniques. The proposed approach integrates automatically extracted features from a One-Dimensional Convolutional Neural Network (1D-CNN) with classical anthropometric indicators. These combined features are processed through an additive attention mechanism, highlighting the most informative attributes. The attention-weighted representation is then classified using an ensemble stacking method that aggregates predictions from multiple base classifiers, including decision trees, nearest neighbor algorithms, support vector machines, etc. Synthetic Minority Over-sampling Technique (SMOTE) is applied to the training dataset to mitigate data imbalance, particularly the underrepresentation of severe and moderate malnutrition cases. The research utilizes a dataset comprising 2,789 records of children under five years old collected from community health posts in Indonesia. Data preprocessing included cleaning, normalization, and gender encoding. The model’s performance was evaluated using 5-fold cross-validation and measured by accuracy, precision, recall, and area under the curve metrics. The results show that the proposed model achieved an average accuracy of 99.70% and an area under the curve of 99.99%. An ablation study further demonstrated the significant contribution of each component, feature extraction, fusion mechanism, and ensemble classifier to the final performance. This approach reveals a robust and scalable solution for early nutritional status prediction in healthcare settings.
Formalization of Morphological Rules for Kazakh Nouns in the New Latin Alphabet
This study presents a hybrid computational model for formalizing and predicting morphological inflections of Kazakh nouns written in the new Latin alphabet. The motivation stems from limitations in previous systems based on Cyrillic orthography, which often misrepresented key phonological features such as vowel harmony and consonant assimilation. The main objective is to develop a linguistically informed and computationally efficient system to support Natural Language Processing (NLP) for Kazakh during its transition to Latin script. The methodology combines rule-based grammar formalization with a machine learning approach, specifically a Bayesian Regulation Backpropagation Neural Network (BR-BPNN). A manually curated dataset of 1,000 Latin-script Kazakh nouns was annotated for various morphological forms. Each word was encoded at the character level using a custom dictionary (kazlat_dict), capturing the final four letters as feature vectors. Formal logic and regular expressions were used to model morphological rules such as pluralization and case endings, incorporating vowel harmony, consonant softness, and sonority. These rules provided the training labels for the BR-BPNN model. The trained model achieved 91.5% accuracy, 89.4% precision, and a correlation coefficient (R) above 0.98, confirming the effectiveness of the hybrid system. A user interface prototype was developed to demonstrate practical utility, enabling users to input root nouns and receive suffix predictions with confidence scores and linguistic explanations. The novelty of this work lies in integrating linguistic theory with machine learning for a low-resource Turkic language. It offers a foundation for intelligent Kazakh language tools including spell checkers, grammar correctors, and educational platforms. Future work will extend the system to other parts of speech and explore contextual modeling to improve handling of ambiguous or irregular forms.
Factor Analysis on Teaching Quality Management for Art Design Students Using Data Driven Approach
This study aimed to improve teaching quality management for Art Design students using a data-driven approach through three objectives: (1) synthesizing key factors influencing instructional quality, (2) analyzing those factors using expert consensus, and (3) evaluating student satisfaction after applying the data-driven methodology. The Delphi Method was used to gather insights from 17 education experts, while 30 purposively selected Art Design students participated in satisfaction assessments. Data collection involved questionnaires and interviews, with analysis techniques including mean, standard deviation, Coefficient of Variation (CV), and t-tests. Cronbach’s α was 0.98, indicating high internal reliability. Results showed expert consensus on relevant teaching quality factors (M = 3.92, SD = 0.33, CV = 19.96, p = .002). Key aspects identified included instructional design, digital integration, feedback mechanisms, and curriculum alignment. Post-intervention analysis revealed significant student improvement, with average skill levels increasing from 16.12 (SD = 0.89) to 20.34 (SD = 0.566, p = .002). Student satisfaction reached 78.59%, with a mean of 3.90 (SD = 0.72, CV = 18.78). All statistical terms were properly defined and contextualized. The findings underscore the role of structured data analysis and expert-informed models in enhancing instructional strategies, aligning teaching with professional expectations, and promoting continuous improvement in Art and Design education.
Applied Data Science for Analyzing the Mediating Role of Digital Transformation Influencing Banking Business Efficiency in Vietnam
In the context of accelerated digitalization and growing competitive pressures, commercial banks in emerging economies urgently need to enhance internal capabilities to drive innovation and performance. This study investigates how key organizational factors, including employee engagement, leadership style, corporate culture, financial capacity, and management capacity, influence creative innovation and digital transformation and how these, in turn, impact business efficiency in Vietnamese commercial banks. Grounded in the Resource-Based View and Dynamic Capabilities Theory, the research develops and empirically tests a comprehensive structural model using data collected from 942 banking professionals across 30 commercial banks in Vietnam. A mixed-methods approach was employed, combining expert interviews with a large-scale survey. Structural Equation Modeling (SEM) and bootstrapped mediation analysis were used to test 17 hypotheses. The results reveal that all five internal factors positively influence Creative Innovation (CI) and Digital Transformation (DT). Notably, employee engagement and management capacity emerged as the strongest drivers. Creative innovation exerts the most significant direct effect on business efficiency, while digital transformation plays a complementary but weaker mediating role. The findings validate a multi-layered framework linking organizational dynamics to performance, offering novel insights into how banks can align internal resources with strategic goals. This study contributes to the literature by positioning creative innovation and digital transformation as mediators between organizational capabilities and business outcomes. It also provides actionable recommendations for bank executives seeking to enhance operational efficiency through people-centered, innovation-led strategies tailored to the context of emerging markets. Finally, policymakers and bank leaders should implement digital Key Performance Indicators (KPIs), foster employee-led innovation, invest in managerial training, and align human resource incentives with transformation goals to enhance efficiency and resilience in Vietnam's banking sector.
Incorporate Transformer-Based Models for Anomaly Detection
This paper explores the effectiveness of Transformer-based models, specifically the Time-Series Transformer (TST) and Temporal Fusion Transformer (TFT), for anomaly detection in streaming data. We review related work on anomaly detection models, highlighting traditional methods' limitations in speed, accuracy, and scalability. While LSTM Autoencoders are known for their ability to capture temporal patterns, they suffer from high memory consumption and slower inference times. Though efficient in terms of memory usage, the Matrix Profile provides lower performance in detecting anomalies. To address these challenges, we propose using Transformer-based models, which leverage the self-attention mechanism to capture long-range dependencies in data, process sequences in parallel, and achieve superior performance in both accuracy and efficiency. Our experiments show that TFT outperforms the other models with an F1-score of 0.92 and a Precision-Recall AUC of 0.71, demonstrating significant improvements in anomaly detection. The TST model also shows competitive performance with an F1-score of 0.88 and Precision-Recall AUC of 0.68, offering a more efficient alternative to LSTMs. The results underscore that Transformer models, particularly TST and TFT, provide a robust solution for anomaly detection in real-time applications, offering improved performance, faster inference times, and lower memory usage than traditional models. In conclusion, Transformer-based models stand out as the most effective and scalable solution for large-scale, real-time anomaly detection in streaming time-series data, paving the way for their broader application across various industries. Future work will further focus on optimizing these models and exploring hybrid approaches to enhance detection capabilities and real-time performance.
An IoT-Enabled Smart System Utilizing Linear Regression for Sheep Growth and Health Monitoring
The global livestock industry faces significant pressures from climate change, land constraints, and rising consumer demand, necessitating greater efficiency and sustainability in production. To address these challenges, there is a critical need for accessible, data-driven tools; however, accessible and individualized tools for monitoring the growth and health of livestock like sheep remain underdeveloped, limiting farmers' ability to transition from reactive to proactive management. This study developed and validated an Internet of Things (IoT) smart system for monitoring sheep using an Arduino and ESP32 platform equipped with a DHT22 sensor for temperature and humidity and a load cell for weight. Weekly weight data from 15 sheep were collected over a six-month period. Simple linear regression was then applied to model the individual growth trajectory of each animal. The IoT system was successfully implemented and deployed in a farm setting. The primary finding was that individualized linear regression models provided a highly accurate method for tracking sheep growth, with R² values consistently exceeding 99% for most animals. The system effectively delivered real-time reports on growth trajectories and health-relevant environmental conditions (e.g., temperature and humidity) to a smartphone interface, confirming its practical utility. The primary implication of this research is a validated framework for practical and interpretable precision livestock farming. The system empowers farmers to shift from reactive to proactive management by using individualized growth curves as baselines for early problem detection. This dual-function system enhances productivity through precise growth tracking while supporting animal welfare via environmental monitoring, offering a valuable tool for modern, sustainable sheep farming.
Data-Driven Optimization of UPQC Performance for Solar PV Systems in Weak Grids Using Simulation and Predictive Modeling
The integration of solar photovoltaic (PV) systems into weak power grids presents significant challenges due to low short circuit ratios (SCR), resulting in voltage instability, high harmonic distortion, and diminished fault tolerance. This study proposes a data-driven framework to enhance grid stability and power quality by employing a Unified Power Quality Conditioner (UPQC) integrated with Proportional-Integral (PI) controllers. A comprehensive simulation model was developed using MATLAB/Simulink and validated through hardware-in-the-loop (HIL) experiments. Key electrical performance metrics—such as voltage profiles, total harmonic distortion (THD), and reactive power—were collected and analyzed. To enhance system insight, the dataset was further processed using statistical analysis and predictive modeling techniques to evaluate control response under varying solar irradiance and load conditions. The results demonstrate that the UPQC system maintains stable voltage, reduces THD to within IEEE-519 standards, and improves power factor to 0.98. This research highlights the potential of combining power electronics control with data-centric evaluation to ensure reliable renewable energy integration in weak grid environments. The proposed system contributes toward developing intelligent grid-support solutions for sustainable energy transitions and process innovation.
An Effective Hybrid Approach for Predicting and Optimizing Business Complexity Metrics and Data Insights
This study proposes a hybrid approach for optimizing complexity prediction in the domain of business intelligence by integrating three powerful techniques: the Multi-Objective Complexity Prediction Model (MPK), Principal Component Analysis (PCA), and the XGBoost regression algorithm. The MPK model serves as a state-based simulator to capture system complexity dynamics, while PCA is employed to reduce data dimensionality and eliminate redundancy among features. Subsequently, XGBoost is used as a non-linear predictive model to estimate complexity values based on the refined input features. The results show that this hybrid approach significantly improves prediction accuracy, reduces data noise, and streamlines the modelling process. Quantitative evaluation using Mean Squared Error (MSE), Mean Absolute Error (MAE), and the R-squared (R²) metric demonstrates exceptional performance, with an MAE of 0.000035, an MSE of 6.7 × 10⁻⁹, and an R² of 0.9999999. These results confirm that the integration of MPK, PCA, and XGBoost is highly effective for complexity prediction tasks and can provide accurate and insightful outcomes in business intelligence analytics.
Ball and Plate System Controller Using State Observer and Geometric Approach
The primary objective of this paper is to present a feedback regulator using a Luenberger observer for state estimation of the ball-plate system, which is characterized by high instability and non-linearity. The novelty of this work lies in the design of an innovative control approach that explicitly considers time delay in the feedback loop—an aspect often neglected in prior studies. The adopted methodology involves modeling the system in state space while accounting for delay, and then constructing a state-feedback observer using a geometric approach. Numerical simulations were conducted to validate the proposed design. For instance, with an observer gain of L₂ = [1.58, 1.35], the controller minimizes response time along the x-axis and remains stable for delays up to 0.6364 seconds. Similarly, along the y-axis, a gain of L₅ = [0.58, 0.27] ensures robustness even with delays up to 1.4084 seconds, while effectively reducing initial overshoot. In all tested scenarios, estimation errors converged to zero, confirming the effectiveness of the observer-based controller. These findings support future work on automatic gain tuning based on performance specifications.
Navigating Heart Stroke Terrain: A Cutting-Edge Feed-Forward Neural Network Expedition
Heart stroke remains one of the leading causes of death worldwide, necessitating early and accurate prediction systems to enable timely medical intervention. While a variety of machine learning approaches have been employed to address this issue, including Logistic Regression, Decision Trees, Random Forests, Support Vector Machines, and K-Nearest Neighbors, these models often suffer from limitations such as overfitting, insufficient generalization, poor performance on imbalanced datasets, and inability to capture complex nonlinear patterns in clinical data. Additionally, many existing works do not comprehensively integrate both clinical and demographic features or lack rigorous evaluation metrics beyond accuracy alone. This study proposes a novel Feed-Forward Neural Network (FFNN) model for heart stroke prediction, designed to overcome the shortcomings of conventional models. Unlike shallow classifiers, the FFNN architecture employed here leverages multiple hidden layers and nonlinear activation functions to learn intricate relationships within the dataset. The dataset used comprises various attributes such as age, hypertension, heart disease, BMI, and smoking status, which were preprocessed through normalization, one-hot encoding, and imputation techniques to ensure data quality and model performance. Experiments were conducted using a stratified train-test split, and the model was trained using the Adam optimizer with carefully tuned hyperparameters. Comparative evaluations against baseline models (Logistic Regression, Random Forest, and SVM) were carried out using precision, recall, F1-score, and ROC-AUC as performance metrics. The proposed FFNN achieved the highest accuracy of 96.47%, along with substantial improvements in recall and F1-score, highlighting its superior capability in identifying potential stroke cases even in imbalanced datasets. This work bridges a significant gap in heart stroke prediction by demonstrating the effectiveness of deep learning models—specifically FFNNs—in extracting complex patterns from diverse patient data. It also sets the stage for further exploration of deep learning-based clinical decision support systems.
Firefly Algorithm-Optimized Deep Learning Model for Cyber Intrusion Detection in Wireless Sensor Networks Using SMOTE-Tomek
Wireless Sensor Networks are increasingly vulnerable to sophisticated cyber threats, necessitating effective and intelligent intrusion detection strategies. This paper presents a deep learning-based intrusion detection model that enhances cybersecurity performance through intelligent hyperparameter optimization and advanced data balancing. The main objective of the study is to improve classification accuracy and generalization in intrusion detection systems by employing a dynamic and adaptive optimization framework. A Firefly Algorithm that mimics nature is part of the suggested model. It changes the number of neurons, learning rate, and dropout rate while evaluating the performance of every arrangement with just a little training. It uses the strategy of swarms to find solutions effectively and in an adaptable way. A hybrid method called SMOTE-Tomek is employed to deal with the issues caused by an unequal number of classes in the dataset. The network is built with different dense layers that are enhanced with dropout and batch normalization, and adaptive learning rate adjustment. In preprocessing the data, we encoded categorical variables, made the values consistent with normalization, and balanced the classes by producing artificial data when needed. The model was trained using GPU software for ten epochs and checked for performance using accuracy measurements, confusion matrices, and classification reports. The optimized model obtained an accuracy of 97.82% in classifications, higher than what baseline models and previous machine learning methods could do. It is able to spot and classify various kinds of attacks, completely handles Flooding cases and greatly lowers the chances of mistakes when identifying Blackhole and Grayhole. The study underlines the fact that using swarm intelligence with hybrid resampling enhances the real-time protection of networks against cyber attacks. A deep learning framework is developed at the end of the study that can work well and effectively in cybersecurity tasks.
Human Shoulder Posture Anthropometry System for Detecting Scoliosis Using Mediapipe Library
The system proposed in this research is a posture detection system using real-time computer vision technology with system limitations aimed at detecting shoulder posture as part of anthropometric measurements, because if the shoulder posture is unbalanced and has a very significant height difference, it is called an indication of scoliosis. This research aims to facilitate the detection of scoliosis, especially in one of its symptoms, namely shoulder asymmetry with anthropometric measurements of the ‘Elbow-to-Elbow breadth’ position using the scoliomter method. In addition, common screening methods that can be used for scoliosis, especially in adolescents, include the Adams forward bend test, Cobb angle measurement, and Moire measurement. The anthropometric shoulder posture detection system includes the stages of preparation for detection using a webcam with T-position calibration, then MediaPipe Library processes 33 keypoints, OpenCV and Python to analyze body movements in real time, then this asymmetry is calculated using standard algorithms for pose prediction, vector projection and atan2 to obtain asymmetry angle information. The results of testing the shoulder detection system in the form of shoulder posture according to landmarks on one test subject and keypoint extraction on the user interface display in real time and provide information on the angle of asymmetry of the shoulder and hip in the front and rear facing positions. From testing 16 respondents, the shoulder tilt angle is obtained in the range of 7.42-19.84 degrees which will have a TRUE value if the angle is greater than 15 degrees. Information on the angle of more than 15 degrees can be used as a reference for scoliosis symptoms and further diagnosis by medical practitioners and through this detection system it will be easy to get information related to the results of shoulder posture detection accurately and in real time compared to using only a scoliometer.
Impact of FACTS Devices on Reactive Power Optimization in Hybrid Renewable-Grid Networks
Renewable energy integration with conventional electric power networks creates power-quality and stability difficulties because of their inherent volatility. The reliability improvement of hybrid renewable-grid systems depends heavily on reactive power optimization for achieving voltage control as well as loss reduction. The research explores the application of Flexible AC Transmission System (FACTS) devices with special emphasis on Distribution Static Compensator (DSTATCOM) devices for distributing reactive power compensation at the distribution level. The optimization process utilizes Particle Swarm Optimization (PSO) because it demonstrates both quick convergence and strong abilities for global search within nonlinear systems. The PSO algorithm functions to determine the perfect settings of the DSTATCOM device that enables voltage regulation within safety bounds and improves power factor performance. The hybrid system connects PV array components with wind turbines for power management together with the main grid while dealing with fluctuating load requirements. Under optimized conditions simulation output shows that DSTATCOM reduces reactive power requirements in substantial amounts. DSTATCOM's implementation enables the system to achieve better voltage security together with diminished power losses and superior load power factor levels. Detailed research shows that DSTATCOM proves efficient while being attached to the main grid for real-time compensation operations. The PSO system enables it to function efficiently throughout changing conditions of power generation and load requirements. Smart grid efficiency along with resilience advances because of the combined operation of FACTS devices and swarm intelligence methods. Through its proposed method the system ensures lasting grid sustainability and manages renewable resources intermittency effectively for process innovation.
Multi-Label Classification of Indonesian Voice Phishing Conversations: A Comparative Study of XLM-RoBERTa and ELECTRA
Mobile phones have become a primary means of communication, yet their advancement has also been exploited by cybercriminals, particularly through voice phishing schemes. Voice phishing is a form of social engineering fraud carried out via telephone conversations to illegally obtain personal or financial information. The complexity of voice phishing continues to increase, as a single conversation may involve multiple fraudulent schemes simultaneously, necessitating the application of multi-label classification to comprehensively identify all motives of fraud. Previous studies have predominantly utilized single-label approaches and foreign-language data, making them less relevant to the Indonesian language context and unable to produce speaker segmentation outputs for conversational analysis. This study contributes by developing a multi-label voice phishing classification system specifically for Indonesian telephone conversations to address this gap. Audio data were collected from open sources and simulated recordings, resulting in a total of 300 samples labeled into six categories: five phishing modes and one non-phishing category. The proposed system consists of a preprocessing pipeline that includes noise reduction, speaker segmentation, automatic transcription, and text cleaning to preserve the context of two-way conversations. Two machine learning models based on transformer architectures, XLM-RoBERTa and ELECTRA, are employed to identify various fraud schemes that may occur simultaneously within a single conversation. The dataset was split into training, validation, and testing sets with two division ratios for performance evaluation. Several combinations of hyperparameters were tested to obtain the most optimal model configuration. Evaluation was conducted using a supervised learning approach and various performance metrics. The experimental results show that XLM-RoBERTa achieved the highest average accuracy of 97.04 ± 1.15% and the highest average F1-score of 92.66 ± 2.59%. These results highlight the novelty of applying multi-label classification in the Indonesian language context for voice phishing detection, contributing to more effective fraud identification in real-world telephony systems.
Enhancing Aspect-Based Sentiment Analysis in Tourism Reviews Through Hybrid Data Augmentation
The increasing reliance on online reviews in tourism has made User-Generated Content (UGC) an invaluable resource for understanding visitor perceptions. However, extracting meaningful insights from these reviews remains challenging due to their unstructured nature, aspect imbalance, and the prevalence of code-mixing between languages such as Indonesian and English—particularly in multicultural destinations like Bali. Aspect-Based Sentiment Analysis (ABSA) offers a promising solution by associating sentiment polarity with specific aspects of tourist experiences. Yet, its performance is often constrained by limited and imbalanced datasets, especially for underrepresented aspects such as sanitation and amenities. This study proposes a hybrid data augmentation framework that integrates three complementary strategies: generative augmentation using ChatGPT, semantic filtering via Sentence-BERT (SBERT), and domain refinement through Masked Language Modeling (MLM). The framework is designed to improve ABSA performance on multilingual tourism reviews by generating synthetic aspect-relevant data while preserving semantic integrity and contextual nuance. Using 398 reviews of Kuta Beach in Bali, we evaluate the effectiveness of the proposed approach across five tourism aspects: scenery, dusk, surf, amenities, and sanitation. Results show that the hybrid strategy reduces hallucination rates from 12% (using ChatGPT alone) to 3.8%, increases F1-scores for underrepresented aspects by up to 5.1%, and improves cross-lingual alignment (Cohen’s κ = 0.78). These improvements demonstrate the synergy between generative and semantic augmentation in addressing real-world ABSA challenges. The proposed method not only advances the state of multilingual ABSA but also offers practical implications for tourism analytics, allowing destination managers to better understand and respond to aspect-specific visitor feedback. The framework is extensible to other low-resource domains, were linguistic diversity and data scarcity present similar limitations.
Enhancing SMOTE Using Euclidean Weighting for Imbalanced Classification Dataset
Class imbalance is a significant challenge in machine learning classification tasks because it often causes models to be biased toward the majority class, resulting in poor detection of minority classes. This study proposes a novel enhancement to the Synthetic Minority Over-sampling Technique (SMOTE) by incorporating Euclidean distance-based feature weighting, called Weighted SMOTE. The key idea is to improve the quality of synthetic minority samples by calculating feature importance using a Random Forest model and assigning higher weights to the most relevant features. The objective of this research is to generate more representative synthetic data, reduce model bias, and increase predictive accuracy on highly imbalanced datasets. Experiments were conducted on four benchmark datasets from the KEEL Repository with imbalance ratios ranging from 0.013 to 0.081. The proposed Weighted SMOTE combined with an ensemble voting classifier (Random Forest, AdaBoost, and XGBoost) demonstrated significant improvements compared to standard SMOTE and models without resampling. For example, on the Zoo-3 dataset, the Balanced Accuracy Score (BAS) increased from 75% to 90%, while the F1-score improved from 48% to 94%. On the Cleveland-0_vs_4 dataset, precision improved from 83% to 91% and recall remained high at 99%. Statistical testing using the Wilcoxon signed-rank test confirmed these improvements with p-values < 0.05 for key metrics. The findings show that the proposed method effectively balances sensitivity and precision, generates more meaningful synthetic samples, and reduces the risk of overfitting compared to conventional oversampling. The novelty of this work lies in integrating Euclidean-based feature weighting into the SMOTE process and validating its performance on multiple domains with varying feature types and imbalance ratios. These results indicate that the proposed Weighted SMOTE approach contributes a practical solution for improving classification performance and model stability on severely imbalanced data.
Mathematical Modeling of Water Quality Dynamics in Aquaculture: A Foundation for IoT Integration and Machine Learning-Driven Predictive Analytics
Effective water quality management is paramount for sustainable aquaculture, yet conventional methods often fall short in providing timely and predictive insights. This paper details the development and analysis of a comprehensive suite of mathematical models designed to simulate key water quality dynamics in aquaculture systems. These models encompass critical biogeochemical processes, including the nitrogen cycle (ammonia, nitrite, nitrate, organic nitrogen), phosphorus cycle, Dissolved Oxygen (DO) balance, and Biochemical Oxygen Demand (BOD). Simulation results derived from these models illustrate the temporal evolution of these critical parameters, demonstrating their capability to capture complex interactions and provide a mechanistic understanding of the aquatic environment. This foundational modeling approach offers a robust tool for quantitative analysis and prediction of system responses under various conditions. The core contribution of this work is the articulation of these mathematical models, which serve as a crucial foundation for advanced, data-driven aquaculture management. To enhance their practical utility, we propose a conceptual framework for integrating these models with Internet of Things (IoT) sensor networks. Real-time data acquisition via IoT can be essential for model parameterization, continuous calibration, and validation against operational conditions. Furthermore, this paper discusses how outputs from these validated mechanistic models can serve as robust inputs for Machine Learning (ML) algorithms. This synergy enables the development of sophisticated predictive analytics for critical events, such as forecasting water quality deterioration, and supports optimized, proactive management strategies. This research lays the theoretical and methodological groundwork for developing more precise and resilient decision support systems in aquaculture. By emphasizing the synergistic potential of combining foundational mathematical modeling with data science techniques like IoT and ML, this work aims to contribute to transforming aquaculture into a more productive, sustainable, and environmentally responsible industry. Future efforts should focus on empirical validation and the practical implementation of the proposed integrated framework.
Fine-Grained Sentiment Analysis Approach on Customer Reviews Based on Aspect-Level Emotion Detection
In the era of digital platforms, customer reviews constitute a vital resource for understanding user sentiment and perception toward products and services. Traditional sentiment analysis methods predominantly operate at the document or sentence level, often missing fine-grained emotional cues tied to specific product or service aspects. To address this limitation, this study proposes a novel Fine-Grained Sentiment Analysis (FGSA) framework that performs aspect-level sentiment classification using a joint learning approach. The proposed model employs a hybrid deep learning architecture that integrates transformer-based contextual encoders with Bidirectional Long Short-Term Memory (Bi-LSTM) layers. This design allows the model to capture both rich contextual semantics and sequential dependencies a combination that has not been widely adopted in existing FGSA research. Additionally, we introduce a new annotated dataset of 5,000 customer reviews spanning multiple domains (electronics, food and beverages, and general services), enabling robust training and evaluation. Experimental results show that the model outperforms standard baselines, achieving an F1-score of 82.0% for aspect extraction and an accuracy of 79.8% for sentiment classification. Further analysis reveals consistent patterns, such as positive sentiments linked to design and quality, and negative sentiments associated with customer service and delivery. These insights highlight the practical value of aspect-level sentiment modelling. The key contribution of this work is the integration of a transformer-Bi-LSTM joint architecture for aspect-based sentiment analysis, supported by a domain-diverse benchmark dataset. This framework enhances the interpretability and granularity of sentiment insights and sets a foundation for future research in multilingual and multimodal contexts.
Lora Communication System for Early Detection and Monitoring of Water Toxicity in Floating Net Cages
Floating Net Cages/ Keramba Jaring Apung (KJA) are at risk of polluting the air, which can affect fish farming. Therefore, an early monitoring system is needed that can measure air quality such as temperature, pH, and dissolved oxygen (DO) in real-time. This system utilizes the LoRa RFM95W module to wirelessly transmit environmental data from sensors installed on the cages, which continuously monitor water quality parameters such as temperature, pH, and DO in real-time. The data obtained is then processed to monitor changes in water toxicity in real-time, allowing early detection of potential threats to the ecosystem. Tests were conducted at distances of 50m, 180m, 300m, 340m, and 440m. The results showed that the system worked well up to a distance of 300m with RSSI values between -85 dBm to -120 dBm and SNR more than 2 dB. However, at distances of 340m and 440m, the signal decreased and the delay increased. At a depth of 340m, only one experiment was successful with RSSI -134 dBm and SNR -6 dB, while at a depth of 440m, only a few experiments were successful with RSSI between -122 dBm to -132 dBm and SNR between 1 dB to -6 dB. The prototype system successfully transmitted real-time air quality data to a web-based monitoring center. Data from the sensors were sent via the LoRa network to a central server for further monitoring.
Optimizing Function-Level Source Code Classification Using Meta-Trained CodeBERT in Low-Resource Settings
This study investigates the effectiveness of a meta-trained transformer-based model, CodeBERT, for classifying source code functions in environments with limited labeled data. The primary objective is to improve the accuracy and generalizability of function-level code classification using few-shot learning, a strategy where the model learns from only a few labeled examples per category. We introduce a meta-learning framework designed to enable CodeBERT to adapt to new function types with minimal supervision, addressing a common limitation in traditional code classification methods that require extensive labeled datasets and manual feature engineering. The methodology involves episodic few-shot classification, where each episode simulates a low-resource task using five labeled and five unlabeled samples per function class. A balanced subset of Python functions was sampled from the CodeXGLUE benchmark, consisting of ten function categories with equal representation. The source code was preprocessed by removing comments and docstrings, then tokenized into a fixed length of 128 tokens to fit the model input format. The meta-trained CodeBERT was evaluated across 10 episodes, each representing a different task composition. Results show that the model achieves an average classification accuracy of 73.0%, with high accuracy on function categories characterized by unique syntax patterns, and lower performance on categories with overlapping logic or naming structures. Despite this variability, the model-maintained accuracy above 60% in all episodes. These findings suggest that meta-learning significantly enhances the adaptability of CodeBERT to unseen tasks under data-constrained conditions. This research demonstrates that meta-trained transformer models can serve as practical tools for real-time code analysis, particularly in integrated development environments and continuous integration pipelines. Future work may include extending the framework to other programming languages and incorporating semantic code representations to further reduce classification ambiguity.
Designing a Data-Driven, Innovative Practical Model for Minority Dance Courses in Higher Education Institutions
This study aimed to design and evaluate a data-driven, innovative practical teaching model for minority dance courses in higher education by integrating constructivist learning theory, multicultural education, and experiential learning. The objectives were threefold: (1) to develop a systematic instructional design framework, (2) to measure students' knowledge improvement before and after applying the model, and (3) to assess student satisfaction with the model, particularly regarding cultural identity, learning experience, and engagement. A total of 17 expert instructors from Chinese universities and Kunming University were selected through purposive sampling to contribute to the design process using the Delphi Method. Additionally, 402 first-year dance students participated in evaluating the model’s effectiveness. Quantitative analysis was conducted using means, standard deviations, coefficients of variation, and t-tests. The experts' evaluation of the teaching model yielded a mean of 4.63 (SD = 0.31, CV = 17.84, p = .002), indicating moderate agreement. Student performance significantly improved after intervention, with average skill scores rising from 16.11 (SD = 0.884) to 20.33 (SD = 0.564), p = .002. Student satisfaction reached 78.58% (mean = 3.90, SD = 0.72, CV = 18.78). The hybrid teaching model—blending traditional methods with interactive digital tools and interdisciplinary content (effectively enhanced students' dance proficiency, cultural awareness, and engagement). These findings support the use of blended learning and data-informed instructional strategies to drive innovation and improve outcomes in minority dance education.