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Synthesis of batik motifs using a diffusion - generative adversarial network

Multimedia Tools and Applications 2025年第7期84卷 3407-3438页

作者： Octadion, One Yudistira, Novanto Kurnianingtyas, Diva Informatics Engineering Faculty of Computer Science Universitas Brawijaya Malang Indonesia

This paper presents a novel approach for generating intricate Batik motifs using a modified Diffusion-Generative Adversarial Network (Diffusion-GAN) augmented with StyleGAN2-Ada. Motivated by the rich cultural heritage of Indonesian Batik, our research addresses the challenge of synthesizing high-quality, diverse patterns that capture the artistry and complexity of traditional designs. Traditional generative models often struggle with stability and fidelity in artistic synthesis. We integrate StyleGAN2-Ada and Diffusion techniques to overcome these limitations, optimizing model architecture and employing a curated Batik dataset. Evaluation metrics including Frechet Inception Distance (FID), Kernel Inception Distance (KID), precision, recall, and non-redundancy assess the quality and diversity of generated motifs. Our results demonstrate significant advancements in the realism and authenticity of synthesized Batik patterns, leveraging Diffusion-GAN to enhance detail and artistic fidelity. By exploring the intersection of deep learning and cultural art, this research contributes to the preservation and evolution of Batik as a cultural artifact. References to prior works underscore the evolution of Artificial Intelligence (AI) in cultural heritage preservation, highlighting the potential of modern AI techniques to innovate within traditional art forms like Batik. In summary, this study showcases the efficacy of the proposed Diffusion-GAN methodology in synthesizing original and convincing Batik motifs, while identifying opportunities for further refinement and complexity in motif synthesis. The dataset and code can be accessed here: https://***/octadion/diffusion-stylegan2-ada-pytorch © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Generative adversarial networks

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Enhancing demand forecasting through combination of anomaly detection and continuous improvement

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Soft Computing 2025年第2期29卷 1243-1258页

作者： Jahani, Meysam Zojaji, Zahra Raji, Fatemeh Faculty of Computer Engineering University of Isfahan Isfahan Iran School of Computer Science and Informatics De Montfort University Leicester United Kingdom

Demand forecasting has emerged as a crucial element in supply chain management. It is essential to identify anomalous data and continuously improve the forecasting model with new data. However, existing literature fails to comprehensively cover both aspects of anomaly detection and continuous improvement in demand forecasting. This study proposes an enhanced model to improve accuracy in the demand forecasting. The proposed model introduces a novel data handling method that incorporates an anomaly detection autoencoder, improved with anomaly correction mechanisms. The data handling approach simultaneously detects data anomalies, distinguishes between expected and unexpected anomalies, and corrects anomalous data, ensuring cleaner input for demand forecasting. Then, the proposed model employs a long short-term memory architecture for demand forecasting, enhanced with a continuous improvement method. Thus, the model not only forecasts demand but also retrains the model when the anomaly data surpasses the predetermined threshold, thereby improving the accuracy of forecasting. The results show that the proposed model outperforms other models in detecting data anomalies, achieving an average precision-recall of 0.922, a receiver operating characteristic value of 0.739, and a significance level of less than 0.05. Finally, the model exhibits superior performance in demand forecasting, with average mean squared error, root mean squared error, and mean absolute error values of 33.167, 4.347, and 1.509, respectively, all with a significance level of less than 0.05. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Demand response

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Advanced Design Pattern Selection Methodology through Weighted Semantic Ontologies for Microservices

Advanced Design Pattern Selection Methodology through Weight...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Attia, Rabab Atef Ghoniemy, Samy Hamdy, Abeer The British University in Egypt Faculty of Informatics and Computer Science Cairo Egypt Galala University Computer Science and Engineering Program Cairo Egypt

ISBN: (纸本)9798350367560

Seamless communication between authorities, people, and smart devices is crucial in today's globally interconnected world. Unprecedented demands on software design result from the advent of ubiquitous connectivity, which allows connections at any time and from any location. By structuring applications as separate services that can communicate both internally inside a platform and externally across platforms, microservices (MS) architecture satisfies these requirements. One of the most difficult parts, though, is still controlling communication between MS services, which calls for knowledge of the best design patterns. In order to solve this, we suggest a context-aware recommender system that is adapted to MS design patterns. This system not only makes recommendations for appropriate patterns, but it also gives statistical scores that show how relevant each solution is to the particular issue at hand. Other patterns with ratings indicating their possible feasibility are also displayed by our algorithm. On the other hand, sentence transformers handle intricate, human-like problem descriptions, while our method uses MS Domain Ontologies to distinguish pattern applications, constraints, and best practices. Our methodology provides a refined examination of solution suitability and recommendations through the use of weighing and correlation algorithms. Extensively tested with more than 237 developer-style scenarios, our framework offers qualitative and quantitative insights into its performance and efficacy, showing notable gains over current solutions in terms of offering pattern-specific, contextually accurate guidance for MS communication challenges. © 2024 IEEE.

关键词： Semantics

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Photovoltaic Fault Detection in Remote Areas Using Fuzzy-Based Multiple Linear Regression (FMLR)

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International Journal of Advanced computer science and Applications 2025年第4期16卷 243-249页

作者： Ardianto, Feby Ermatita, Ermatita Sofijan, Armin Doctoral Program in Engineering Science Universitas Sriwijaya Palembang Indonesia Faculty of Computer Science Universitas Sriwijaya Palembang Indonesia Faculty of Engineering Universitas Sriwijaya Palembang Indonesia

This research focused on developing and implementing a fault detection model for photovoltaic (PV) systems in remote areas, utilizing a Fuzzy-Based Multiple Linear Regression (FMLR) approach. The study aimed to address the challenges of monitoring PV systems in locations with limited access to conventional power grids and technical resources. The fault detection system integrated environmental parameters such as solar radiation, temperature, wind speed, and rainfall, alongside PV system parameters like panel voltage, current, battery voltage, and inverter performance. Data collection and preprocessing were conducted over a specified period to identify operational patterns under both normal and faulty conditions, ensuring data accuracy through cleaning, normalization, and categorization. The research was conducted in Pandan Arang Village, Kandis District, Ogan Ilir Regency, South Sumatera, Indonesia, contributing to the improvement of reliability and sustainability of renewable energy sources in isolated communities. The total number of data points for 276 rows with 6 attributes each was 1656 records. The MLR model was developed to predict the output power of the PV system, while fuzzy logic was employed to handle uncertainties in the data, offering a more flexible and adaptive decision-making process. The system applied fuzzy rules to determine the charging status (P3), categorizing it into Optimal Charging, Adjusted Charging, Charging Delay, or Fault Alert. The model was tested with real-time data, and its performance was validated through comparison with manual inspections. The results showed that the FMLR-based fault detection system effectively identified faults and optimized the performance of the PV system, making it suitable for remote areas in South Sumatera. © (2025), (science and Information Organization). All Rights Reserved.

关键词： Multiple linear regression

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User Experience Design of Mobile Application for Tuberculosis Care and Prevention in Indonesia 2

User Experience Design of Mobile Application for Tuberculosi...

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2nd International Conference on Technology Innovation and Its Applications, ICTIIA 2024

作者： Felix Universitas Mikroskil Computer Science Program Informatics Faculty Medan Indonesia

ISBN: (数字)9798350351613

ISBN: (纸本)9798350351613

User experience (UX) is a major concern in m-health (mobile health) application design. In the global attempt to eliminate Covid-19, m-health apps play a remarkable role. With the same spirit, tuberculosis (TB) as the world's top infectious killer after Covid-19 can be suppressed. Unlike Covid-19 where the patient can be either cured or killed in less than a month, TB need at least 6 months to be cured for the first stage of treatment. TB are highly contagious and easily spread in the poor hygiene condition. The purpose of this research is to design a user-oriented UX design. The design then can ease programmers working with Health Ministry or healthcare units to develop a fully functioning m-health app. In order to produce a user-oriented UX design, the research uses design thinking methodology. © 2024 IEEE.

关键词： COVID-19

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Fine-Tuned Attribute Weighted Naïve Bayes with Modified Partial Instances Reduction for Gaming Disorder Classification

HighTech and Innovation Journal

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HighTech and Innovation Journal 2025年第1期6卷 67-80页

作者： Latubessy, Anastasya Wardoyo, Retantyo Musdholifah, Aina Kusrohmaniah, Sri Doctoral Program Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Informatics Engineering Faculty of Engineering Universitas Muria Kudus Indonesia Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Psychology Faculty of Psychology Universitas Gadjah Mada Yogyakarta55281 Indonesia

Fine Tuning Attribute Weighted Naïve Bayes (FTAWNB) is a reliable modified Naïve Bayes model. Even though it is able to provide high accuracy on ordinal data, this model is sensitive to outliers. To improve the performance of FTAWNB, this research modified the Partial Instances Reduction (PIR) technique to make the FTAWNB more adaptive to outliers. Nevertheless, in contrast to the original PIR technique, which substitutes missing values for data values deemed outliers, the PIR technique suggested in this study replaces data values deemed outliers using a Naïve Bayes weighting approach. The attribute values from the outlier data are replaced with the highest probability values for the attributes in the actual class. This PIR technique is referred to as modified PIR. The FTAWNB model with modified PIR has been evaluated using the Gaming Disorder dataset. Replacing the four attributes with the least amount of information resulted in accuracy gains of 99.74%, an increase of 1.53% over the FTAWNB model. The experimental result shows that adding the modified PIR technique to the FTAWNB model can handle the outlier in the data, proving it by increasing the performance in terms of accuracy, precision, and recall without pruning the dataset used. © 2025, Ital Publication. All rights reserved.

关键词： Attribute Weighted Classification Fine-Tune Gaming Disorder Instances Reduction Naïve Bayes

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UNRAVELING THE EFFECTIVENESS OF THE K-NN ALGORITHM FOR IMBALANCED DATA IN RESEARCH TOPIC RECOMMENDATIONS

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Xinan Jiaotong Daxue Xuebao/Journal of Southwest Jiaotong University 2024年第1期59卷 271-282页

作者： Muludi, Kurnia Aisyah, Nonik Nama, Gigih Forda Lumbanraja, Favorisen R. Informatics & Business Institute Darmajaya Bandar Lampung35142 Indonesia Computer Science Department Faculty of Science The University of Lampung Bandar Lampung Indonesia Informatics Engineering Department Faculty of Science The University of Lampung Bandar Lampung Indonesia

This study addresses the challenge of selecting research topics for undergraduate students, focusing on computer science, by evaluating a recommendation model based on the k-Nearest Neighbor algorithm (kNN). The objective is to enhance the accuracy of research topic recommendations in the presence of imbalanced data. The methodology involves data cleaning, transformation, and correlation-based feature selection, with a particular focus on addressing missing values and optimizing the feature set. The Synthetic Minority Over-Sampling Technique (SMOTE) is employed to balance the dataset. The model development includes a comprehensive analysis of various k values, leading to the identification of k=3 with Manhattan distance as the optimal configuration, achieving an accuracy of 82%. The experiment explores different training data proportions, revealing that a 90:10 ratio yields the highest accuracy. This study incorporates a Grid Search technique for hyperparameter tuning, highlighting the importance of selecting appropriate distance metrics and nearest neighbor values. The system implementation is presented with user and administrator interfaces. Findings indicate varying model performance across research categories, emphasizing the need for category-specific evaluation metrics. The study concludes with a discussion on the significance of the results, which contribute to the improvement of research topic recommendation systems for students through the integration of advanced machine learning techniques. © 2024 by the authors.

关键词： Recommender systems

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Machine Learning-Based Prediction of Corrosion Inhibition Efficiency of Expired Pharmaceuticals: Model Development and Application

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Journal of Bio- and Tribo-Corrosion 2025年第1期11卷 1-12页

作者： Putra, Dzaki Asari Surya Ardyansyah, Nibras Bahy Putranto, Nicholaus Verdhy Trisnapradika, Gustina Alfa Akrom, Muhamad Rustad, Supriadi Dipojono, Hermawan Kresno Maezono, Ryo Faculty of Computer Science Study Program in Informatics Engineering Universitas Dian Nuswantoro Semarang50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa923-1292 Japan

Corrosion poses a significant challenge in industries due to material degradation and high maintenance costs, making effective inhibitors essential. Recent studies suggest expired pharmaceuticals as alternative corrosion inhibitors, but identifying the most efficient compounds can be time-consuming. This study applies machine learning models, particularly Gradient Boosting Regressor (GBR), to predict corrosion inhibition efficiency (CIE). The developed model is integrated into a web-based application, enabling real-time, accurate CIE predictions. Results show GBR as the more precise model for predicting CIE, with low error rates. The streamlit-based web application offers a user-friendly platform, facilitating quick identification of suitable inhibitors. It is flexible, supporting analysis beyond corrosion inhibitors, and significantly enhances the efficiency of the evaluation process. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Development of a machine learning model for predicting abnormalities of commercial airplanes

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Data science and Management 2024年第3期7卷 256-265页

作者： Rossi Passarella Siti Nurmaini Muhammad Naufal Rachmatullah Harumi Veny Fara Nissya Nur Hafidzoh Department of Computer Engineering Faculty of Computer ScienceUniversitas Sriwijaya30662Indonesia Intelligent System Research Group(ISysRG) Faculty of Computer ScienceUniversitas Sriwijaya30662Indonesia Department of Informatics Faculty of Computer ScienceUniversitas Sriwijaya30662Indonesia College of Engineering Studies Universiti Teknologi MARA(UiTM)Shah Alam40450Malaysia

Airplanes are a social necessity for movement of humans,goods,and *** are generally safe modes of transportation;however,incidents and accidents occasionally *** prevent aviation accidents,it is necessary to develop a machine-learning model to detect and predict commercial flights using automatic dependent surveillance–broadcast *** study combined data-quality detection,anomaly detection,and abnormality-classification-model *** research methodology involved the following stages:problem statement,data selection and labeling,prediction-model development,deployment,and *** data labeling process was based on the rules framed by the international civil aviation organization for commercial,jet-engine flights and validated by expert commercial *** results showed that the best prediction model,the quadratic-discriminant-analysis,was 93%accurate,indicating a“good fit”.Moreover,the model’s area-under-the-curve results for abnormal and normal detection were 0.97 and 0.96,respectively,thus confirming its“good fit”.

关键词： Automatic dependent surveillance-broadcast data Commercial airplanes accident Data-labeling Machine learning Prediction model

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Stress Detection Using EEG Signals: A SVM-Based Classification and Feature Selection Approach 12

Stress Detection Using EEG Signals: A SVM-Based Classificati...

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12th Electrical Power, Electronics, Communications, Controls and informatics Seminar, EECCIS 2024

作者： Sifaunnufus, Fi Imanur Bachtiar, Fitra Abdurrachman Prasetio, Barlian Henryranu Brawijaya University Faculty of Computer Science Department of Computer Engineering Malang Indonesia Brawijaya University Faculty of Computer Science Department of Informatics Engineering Malang Indonesia

ISBN: (纸本)9798350379914

Stress is a problem with various adverse effects, such as mental disorders and suicide. With these problems arising, a stress detection system is needed as an initial examination of the level of stress experienced. Generally, stress detection is done with a stress test at a hospital. However, medical examinations done in the hospital require high costs. Another alternative to examine stress levels is by using a biosignal electroencephalogram (EEG), which provides more objective and accurate results. In addition, there is a need to develop a portable device that can be used to detect stress. This study tries to detect stress levels using EEG Signals. The data used in this study is primary data that simulates several stress-level activities. In total, 55 subjects participated in this stress detection research. The received signal goes through preprocessing to clean it from noise and artifacts. The data will be extracted using Power Spectral Density (PSD) into power value for each signal frequency. The preprocessed input signals are then classified into indications of stress levels. This study uses Random Forest, SVM, and Decision Tree. The model is stored on a portable system with Raspberry Pi as the main component. EEG signals are taken using electrodes on Muse 2 and sent to the system. The classification model generates predicted stress levels and is displayed on a 16×2 LCD. SVM as the best model test result achieved a testing accuracy of 1, and an average computation time of 0.92. © 2024 IEEE.

关键词： Electroencephalography

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