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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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Techno-economic modeling and analysis of a PV EV charged with battery energy storage system (BESS) on Kalimantan Island

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Global Energy Interconnection 2025年第2期8卷 225-239页

作者： Aripriharta Adiguna, Satria Afandi, Arif N. Bagaskoro, Muhammad Cahyo Department of Electrical Engineering and Informatics Faculty of Engineering State University of Malang Malang65145 Indonesia Indonesia

This research examines the optimal combination of solar panel and battery capacity in hybrid systems in 11 cities on the island of Borneo, utilizing the region's significant solar energy potential and high irradiation levels. This research analyses the optimal combination of solar panels and battery capacity in 11 cities in Kalimantan using particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms to maximize energy output, reduce levelised energy costs, and maximally reduce carbon emissions. Results show Tarakan as the most optimal location, generating 215,804.88 kWh for IDR 916.9/kWh and lowering emissions by 435,884.29 kgCO2e, while Samarinda is the least optimal location. Economically, electricity tariffs of IDR 2,466.78/kWh and IDR 2,000/kWh generate a positive Net Present Value (NPV) with a payback period (PP) of 9–12 years, while a tariff of IDR 1,500/kWh is considered unfavorable. The findings demonstrate the effectiveness of PSO and GWO in optimizing the renewable energy system and confirm the project's financial viability, with a positive NPV and reasonable PP. Implementing renewable energy systems in Kalimantan Island can improve energy efficiency and significantly reduce carbon emissions, supporting environmental sustainability goals. © 2025 Global Energy Interconnection Group Co. Ltd.

关键词： Zero-carbon

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Self-Learning Model for Pattern Recognition in Vision System Based on Adaptive Kernel

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第14期49卷 157-170页

作者： Aradea Rianto Herlina, Nina Hoeronis, Irani Department of Informatics Faculty of Engineering Siliwangi University Kode Pos Tasikmalaya46115 Indonesia

Recently, the solution for recognizing and understanding an object based on visuals is to integrate the adaptation function (continuous machine-driven process) into the system update function involving humans (continuous human-driven process). However, this has created a gap between the adaptation function and the system. This situation requires understanding the system viewed as a dynamic composition of the learning process. This research introduced a self-learning model in the form of an adaptive kernel equipped with the SpinalNet architecture, and the goal of this study is to increase the Convolutional Neural Network (CNN) accuracy. The model consisted of a domain model, contextual knowledge, and adaptive learner developed based on the CNN with SpinalNet. The combination of Adaptive Kernel and SpiralNet in this CNN has a significant impact, allowing the model to adjust the selection of subsequent kernels based on the optimal input from the previous kernel. Moreover, this combination results in lower memory usage during training. The evaluation results show that our proposed model provides better classification accuracy than the SpiralNet model without the Adaptive Kernel. Furthermore, in terms of inference speed, our model outperforms SpiralNet, as evidenced by the use of fewer parameters. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Self-supervised learning

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Thermal Analysis of a Simplified Railway Brake Model with Numerical Simulation

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Periodica Polytechnica Transportation engineering 2025年第1期53卷 1-6页

作者： Bolló, Betti Sarka, Ferenc Voith, Katalin Felhő, Csaba Institute of Energy Engineering and Chemical Machinery Faculty of Mechanical Engineering and Informatics University of Miskolc Egyetemváros MiskolcH-3515 Hungary Institute of Machine and Product Design Faculty of Mechanical Engineering and Informatics University of Miskolc Egyetemváros MiskolcH-3515 Hungary Institute of Manufacturing Science Faculty of Mechanical Engineering and Informatics University of Miskolc Egyetemváros MiskolcH-3515 Hungary

The braking system is the most important safety device in all vehicles. When braking, the kinetic energy of the vehicle is converted into thermal energy. The use of friction brakes can cause problems in the long term, as the brake can heat up, reducing its coefficient of friction. Heating and cooling have a harmful effect on all frictional elements, causing material fatigue. As a result of the negative effects, the hot brake cannot reduce the velocity of the vehicle at the correct rate. In this paper, the propagation of frictional heat is analysed in a test specimen made of cast iron material using different numerical simulations. The computational results are validated with the results obtained during measurement. With proper validation, the brake discs are able to be modelled at different designs and materials without having to manufacture a test specimen for each case. In the study, it is important to point physical quantities to carry out the simulation. © 2025 Budapest University of Technology and Economics. All rights reserved.

关键词： Brakes

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Enhancing Decision-making in Uncertain Domains through Optimized Fuzzy Logic Systems

Periodica Polytechnica Electrical Engineering and Computer S...

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Periodica Polytechnica Electrical engineering and Computer Science 2025年第1期69卷 63-78页

作者： Sekhi, Ihab Kovács, Szilveszter Nehéz, Károly Institute of Information Science Faculty of Mechanical Engineering and Informatics University of Miskolc MiskolcH-3515 Hungary

Fuzzy logic helps manage human-like reasoning in system control, mainly when traditional analysis does not work due to complex control processes. Despite its usefulness, fuzzy logic faces challenges in decision-making, especially in complex business situations and when combined with expert systems. It struggles with uncertainty and relies on various beliefs and assumptions, which is limiting compared to other methods for handling uncertainty. However, fuzzy logic can improve traditional control systems by adding a layer of intelligence. This study adapts mathematical functions like the straight-line point-slope equation, the absolute value function, and the Gaussian equation to develop accurate and flexible membership functions for fuzzy logic systems. By analyzing 10,000 tasks of different sizes, we found our methods significantly more precise than traditional approaches, especially in determining degrees of membership for uncertain and complex environments. Our MATLAB research shows the potential of using varied membership functions to enhance fuzzy logic systems' accuracy and flexibility. © 2025 Budapest University of Technology and Economics. All rights reserved.

关键词： Membership functions

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Comparative Analysis of SVM, Naïve Bayes, and Logistic Regression in Detecting IoT Botnet Attacks

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 338-343页

作者： Siswanto, Apri Aji, Luhur Bayu Efendi, Akmar Alfaruqi, Dhafin Azriansyah, M. Rafli Raihan, Yefrianda Informatics Department Faculty of Engineering Universitas Islam Riau Pekanbaru Indonesia Faculty Data Science and Information Technology INTI International University Malaysia

The rapid proliferation of Internet of Things (IoT) devices has significantly increased the risk of cyberattacks, particularly botnet intrusions, which pose serious security threats to IoT networks. Machine learning-based Intrusion Detection Systems (IDS) have emerged as effective solutions for detecting such attacks. This study presents a comparative analysis of three widely used machine learning classifiers—Support Vector Machine (SVM), Naïve Bayes (NB), and Logistic Regression (LR)—to assess their performance in detecting IoT botnet attacks. The experiment uses the BoTNeTIoT-L01 dataset, applying preprocessing techniques such as data cleaning, normalization, and feature selection to enhance model accuracy. The models are trained and evaluated based on standard performance metrics, including accuracy, precision, recall, F1-score, and AUC-ROC. The results indicate that SVM outperforms the other classifiers in terms of detection accuracy and robustness, particularly in detecting malware based on PE files. These findings offer valuable insights into selecting suitable machine learning models for securing IoT environments. Future work will further explore integrating advanced feature selection techniques and deep learning models to improve detection performance. © (2025), (Science and Information Organization). All rights reserved.

关键词： Logistic regression

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Vision Transformer for Audio-Based Depression Detection on Multi-Lingual Audio Data 24

Vision Transformer for Audio-Based Depression Detection on M...

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7th International Conference on Digital Medicine and Image Processing, DMIP 2024

作者： Pratiwi, Monica Sanjaya, Samuel Ady Department of Computer Engineering Faculty of Engineering and Informatics Universitas Multimedia Nusantara Tangerang Indonesia Department of Information System Faculty of Engineering and Informatics Universitas Multimedia Nusantara Tangerang Indonesia

ISBN: (纸本)9798400709586

Depression has the potential to impact death rates, particularly when it comes to death by suicide. Inadequate diagnosis may result in a delay or unsuitable therapy, which can worsen symptoms of depression. Unaddressed or insufficiently addressed depression can result in deteriorating mental well-being, which includes a higher risk of experiencing suicidal ideation and engaging in self-destructive actions. Voice analysis can be employed to distinguish between individuals with depression and those without depression. However, the research that has been conducted on voice recognition to distinguish between depressed and non-depressed individuals keeps focusing on the use of a single dataset source as input for the classification model that is being developed. This work utilizes the Vision Transformer model and various pre-trained transformers, such as DeiT (Data-Efficient Image Transformer) and Swin Transformer, to detect depression based on voice. The objective was to create a more generic model not restricted to a specific language. Integrating Mel-spectrogram characteristics with a vision transformer-based model can enhance the efficacy of voice recognition models when dealing with multi-language data. The result shows 21% higher accuracy than the previous study that also implemented a cross-dataset test. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Spectrographs

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A Self-adapting Pixel Antenna - Substrate Lens System for Infrared Frequencies

Periodica Polytechnica Electrical Engineering and Computer S...

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Periodica Polytechnica Electrical engineering and Computer Science 2025年第1期69卷 14-25页

作者： Shubbar, Mustafa Rakos, Balázs Department of Automation and Applied Informatics Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3. BudapestH-1111 Hungary

In this work, we propose a concentrator lens - antenna arrangement, potentially suitable for infrared sensing and energy harvesting rectenna systems. The structure consists of a pixel antenna, self-adapting for the direction of the incident infrared radiation, and a concentrator lens, both optimized for the mid-infrared spectrum. The silicon substrate lens is situated above the pixel antenna, enabling the concentration of the incident light on the antenna;silicon was chosen due to its transparency in the IR spectrum. We examine how various parameters of the lens, in conjunction with the different states of the pixel antenna, affects the performance of the system. The simulations show that the gain of the arrangement increases considerably in correlation with the radius of the lens. The results suggest that the energy conversion efficiency of infrared rectenna systems can be enhanced by several orders of magnitude with the utilization of the proposed arrangement. © 2025 Budapest University of Technology and Economics. All rights reserved.

关键词： Rectennas

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Click Count Forecasting with Bi-Directional LSTM and Temporal Feature Fusion

Click Count Forecasting with Bi-Directional LSTM and Tempora...

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2025 International Conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Abdillah, Rifqi Mar'l, Farhanna Deny Pratama, Moch Sonhaji Akbar, Muhammad Cinthya, Monica Widya Purwita, Anggraeni Universitas Negeri Surabaya Faculty of Engineering Department of Informatics Engineering Surabaya Indonesia Universitas Negeri Surabaya Faculty of Vocational Department of Informatics Management Surabaya Indonesia

ISBN: (纸本)9798331513320

In recent years, online advertising has become a fundamental element of the digital economy, with businesses increasingly relying on digital ads to reach potential customers and boost sales, where key metrics like impressions, clicks, click-through rates (CTR), and conversions are used to measure effectiveness. Among these, click counts-representing the frequency of user interactions with an ad-are crucial for evaluating campaign success, and accurately predicting these counts is essential for optimizing advertising expenditures and enhancing user experiences by delivering relevant content at the right time. This research investigates the application of Bi-Directional Long Short-Term Memory (Bi-LSTM) networks with temporal feature fusion to predict click counts in online advertising, presenting a notable advantage over conventional models by capturing non-linear relationships in the data and detecting complex patterns through the integration of past and future temporal features. The proposed model improves prediction accuracy by better understanding the context surrounding ad views, with the results demonstrating that incorporating interaction terms between features leads to superior performance, achieving a Mean Squared Error (MSE) of 2.73, a Root Mean Squared Error (RMSE) of 1.65, a Mean Absolute Error (MAE) of 1.37, and the highest R2 score of 0.88, which highlights the model's ability to account for complex relationships within temporal data. © 2025 IEEE.

关键词： Mean square error

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The double integrals of 2-refined neutrosophic functions and its applications

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Neutrosophic Sets and Systems 2025年 81卷 799-811页

作者： Alhasan, Yaser Ahmad Alshallali, Asma Mohammed Alhassan, Qusay Prince Sattam bin Abdulaziz University Alkharj Saudi Arabia Faculty of Informatics Engineering Cordoba Private University Syria

The importance of this paper comes from the fact that it presented a study of the double integrals of 2-refined neutrosophic functions and its applications, where we presented several theories for the concept of the double integrals of 2-refined neutrosophic functions over a general region and over a rectangle, including the 2-refined neutrosophic Fubini’s theorem. In addition to discussing applications of double integrals of 2-refined neutrosophic functions to calculate the area of region. Also, We took into account the definition of the positive 2-refined neutrosophic number, and the n-refined AH-Isometry. © 2025, University of New Mexico. All rights reserved.

关键词： Choquet integral

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