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3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Ammasai, Viswanathan Katbar, Nek Muhammad Kothalanka, Amarendra Vandadi, Venkata Ramana Udhaya Shankar Latha, Sindhu Sharma, Dilip Kumar Sengan, Sudhakar School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632 014 India School of Mathematics and Statistics Central South University Hunan Changsha410083 China Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Computer Science and Engineering KSRM College of Engineering Andhra Pradesh Kadapa516003 India Department of Information Technology V.S.B. College of Engineering Technical Campus Tamil Nadu Coimbatore642109 India Department of Mathematics Jaypee University of Engineering and Technology Madhya Pradesh Guna473226 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli62745 India

ISBN: (纸本)9789819783540

The realm of Smart Grid Systems (SGS) has witnessed unprecedented progress, driven by increasing energy demands and the integration of Renewable Energy Sources (RES). While these grids promise enhanced efficiency and sustainability, their involved and dynamic nature poses significant optimization challenges. Existing solutions, although innovative, often overlook the intricate interplay between diverse factors, leaving room for potential improvements in optimization approaches. Recognizing this gap, the current study delves into developing a novel method that integrates Ant Colony Optimization (ACO) with domain-specific heuristics. By focusing on analytical sections like Load Forecasting, Renewable Energy Integration, Peak Shaving, and Grid Resilience, this method attempts to bridge the divide that existing approaches miss. The proposed method not only ensures optimal energy distribution but also tailors the solution to address the unique features and requirements of modern SGS. A more reliable, effective, and Renewable Energy (RE) future is within reach according to the outcomes of this investigation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Clean energy

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Generative Adversarial Networks for Semi-Supervised Learning in Clinical Decision Support Systems 3rd

Generative Adversarial Networks for Semi-Supervised Learning...

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3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Sengan, Sudhakar Katbar, Nek Muhammad Kothalanka, Amarendra Shelke, Mayura Chinnathambi, Kalimuthan Sharma, Dilip Kumar Ammasai, Viswanathan Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627451 India School of Mathematics and Statistics Central South University Hunan Changsha410083 China Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of AI & DS AISSMS IOIT Pune411001 India Deprtment of Information Technology V.S.B. College of Engineering Technical Campus Tamil Nadu Coimbatore642109 India Department of Mathematics Jaypee University of Engineering and Technology Madhya Pradesh Guna473226 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (纸本)9789819783540

Clinical Decision Support Systems (CDSS) have emerged as crucial tools in healthcare, leveraging vast amounts of data to aid clinicians in their decision-making processes. However, the challenge of efficiently utilizing limited labelled medical data remains. This paper introduces a novel approach using Semi-supervised DC-GAN to address this challenge. Building upon the foundational principles of Generative Adversarial Networks (GANs), this proposed model augments the capabilities of traditional semi-supervised methods, optimizing their performance in the CDSS landscape. Through extensive evaluations, they compared this approach with three widely accepted semi-supervised algorithms: MixMatch, Mean Teacher, and Π-Model. The results indicate that Semi-supervised DC-GAN enhances the data utilization process and significantly improves model training, especially when working with limited labelled datasets. This research underscores the potential of GANs in revolutionizing semi-supervised learning within CDSS, paving the way for more efficient and informed clinical decisions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Synergetic effects of multiple junction and surface hydroxyl in Cu/CuO/Cu2O/TiO2 heterostructures towards highly efficient photocatalysts for hydrogen generation

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Materials Science for Energy Technologies 2025年 8卷 131-142页

作者： Subagyo, Riki Anindika, Garcelina Rizky Aufkani, Afif Akmal Zhang, Lei Ardhyananta, Hosta Burhan, R.Y. Perry Nugraheni, Zjahra Vianita Akhlus, Syafsir Bahruji, Hasliza Prasetyoko, Didik Wellia, Diana Vanda Ivansyah, Atthar Luqman Arramel Kusumawati, Yuly Department of Chemistry Faculty of Science and Analytic Data Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Center of Excellence Applied Physics and Chemistry Nano Center Indonesia Jl PUSPIPTEK South Tangerang Banten15314 Indonesia Department of Physics National University of Singapore Singapore117551 Singapore Department of Materials and Metallurgical Engineering Faculty of Industrial Technology and Systems Engineering Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Centre of Advanced Material and Energy Science Universiti Brunei Darussalam Jalan Tungku Link BE 1410 Brunei Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Andalas Padang25163 Indonesia Master Program in Computational Science Faculty of Mathematics and Natural Science Institut Teknologi Bandung Jalan Ganesha No. 10 Bandung40132 Indonesia Instrumentation and Computational Physics Research Group Department of Physics Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jalan Ganesha No.10 West Java Bandung40132 Indonesia

The implementation of titanium dioxide (TiO2) as a photocatalyst material in hydrogen (H2) evolution reaction (HER) has embarked renewed interest in the past decade. Rapid electron-hole pairs recombination and wide band gap of a photo-sensitive material of TiO2 are detrimental toward the targeted catalytical reaction. In this study, we present the rational design, fabrication, photocatalytic performance of TiO2-Cu/CuO/Cu2O heterostructures (CuTi) using viable chemical reduction method. The Z-scheme and S-scheme are succesfully generated across the TiO2/CuO/Cu2O interfaces, while the Schottky junction arises on the Cu perimeters. This is evidenced from the blue shifted about 0.3 eV of Cu 2p core level determined by using X-ray photoemission spectroscopy (XPS), in combination with the formation of inverse V-shape of the Mott-Schottky plots. In addition, we find that Cu/CuO/Cu2O facilitates photon absorption range up to the visible region. The multiple heterojunction and the large number of OHsurface enhanced charge carrier transfer are associated to the suppression of photoluminescence (PL) intensity, high surface hydroxyl (OHsurface) density in CuTi probed by XPS, and fast electron transfer based on the electrochemical measurements. The presence of OHsurface inhibits the recombination of electron. A significant H2 photogeneration rate enhancement is achieved when an optimized 5 wt% Cu/CuO/Cu2O concentration is used on TiO2 to achieve 7,157.19 μmol·g−1 (1,789.30 μmol·g−1·h−1). Based on this finding, zero emission energy innitiative could be materialized under multiple heterojunctions in photocatalytic process is beneficial for enhancing the H2 production. © 2025 The Authors

关键词： Titanium dioxide

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Microfluidic droplet detection using synthetic data generated by a Generative Adversarial Network

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engineering Applications of Artificial Intelligence 2025年 156卷

作者： Karimzadeh, Marziyeh Meybodi, Mohsen Alambardar Shams, Mohammadreza Goli-Malekabadi, Zahra Department of Applied Mathematics and Computer Science Faculty of Mathematics and Statistics University of Isfahan Isfahan81746-73441 Iran Department of Computer Engineering Shahreza Campus University of Isfahan Isfahan Iran Department of Medical Technology and Tissue Engineering School of Life science Engineering College of Interdisciplinary Science and Technology University of Tehran Tehran Iran

Cellular heterogeneity, even among genetically identical cells, results in variations in their properties and behaviors, making single-cell analysis crucial for obtaining detailed insights. However, isolating single cells from a cell population poses major challenges, as conventional laboratory techniques often risk cell damage and involve complex procedures. Droplet microfluidics has emerged as a promising approach for encapsulating cells, particularly single cells, into individual droplets without causing harm. Despite this, factors like cell sedimentation and aggregation can reduce encapsulation efficiency and lead to deviations from the expected Poisson distribution. To address these challenges, leveraging artificial intelligence and deep learning to monitor, detect, and regulate encapsulation conditions in real-time is critical for enhancing system performance. However, deep learning models require substantial training data, and issues like microfluidic channel clogging and the scarcity of certain cell types often limit data availability. To overcome this limitation, researchers are turning to synthetic data generation to supplement training datasets and address data scarcity challenges effectively. This study emphasizes the potential of integrating synthetic data with cutting-edge deep learning techniques to enhance the accuracy and efficiency of single-cell analysis within droplet microfluidic systems. A diverse dataset integrating synthetic and real images was used to train the YOLOv8s model for automated detection and classification of microfluidic droplets, enhancing accuracy and system performance. The model trained on a combination of real and synthetic data outperformed the one trained using conventional data augmentation methods, achieving an mAP0.5 of 98% due to the increased diversity of training images. It also demonstrated faster and more stable training. Additionally, the YOLOv8 network, with a detection rate of approximately 2338 droplets per sec

关键词： Deep learning

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Comments on "relaxed Conditions for the Input-to-State Stability of Switched Nonlinear Time-Varying Systems"

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IEEE Transactions on Automatic Control 2025年第6期70卷 4233-4236页

作者： Şahan, Gökhan Trenn, Stephan Izmir Institute of Technology Department of Mathematics and Computational Science and Engineering Program Urla Izmir Turkey University of Groningen Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence Groningen9722BA Netherlands

This study addresses the deficiencies in the assumptions of the results in Chen and Yang, 2017 [1] due to the lack of uniformity. We first show the missing hypothesis by presenting a counterexample. Then we prove why they are wrong in that form and show the errors in the proof of the main result of [1]. Next, we compare the assumptions and related results of [1] with similar works in the literature. Lastly, we give suggestions to complement the shortcomings of the hypotheses and thus correct them. © 1963-2012 IEEE.

关键词： Nonlinear simulations

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Privacy Vulnerabilities in Marginals-based Synthetic Data

Privacy Vulnerabilities in Marginals-based Synthetic Data

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Secure and Trustworthy Machine Learning (SaTML), IEEE Conference on

作者： Steven Golob Sikha Pentyala Anuar Maratkhan Martine De Cock School of Engineering and Technology University of Washington Tacoma USA Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium

ISBN: (数字)9798331517113

ISBN: (纸本)9798331517120

When acting as a privacy-enhancing technology, synthetic data generation (SDG) aims to maintain a resemblance to the real data while excluding personally-identifiable information. Many SDG algorithms provide robust differential privacy (DP) guarantees to this end. However, we show that the strongest class of SDG algorithms-those that preserve marginal probabilities, or similar statistics, from the underlying data-leak information about individuals that can be recovered more efficiently than previously understood. We demonstrate this by presenting a novel membership inference attack, MAMA-MIA, and evaluate it against three seminal DP SDG algorithms: MST, PrivBayes, and Private-GSD. MAMA-MIA leverages knowledge of which SDG algorithm was used, allowing it to learn information about the hidden data more accurately, and orders-of-magnitude faster, than other leading attacks. We use MAMA-MIA to lend insight into existing SDG vulnerabilities. Our approach went on to win the first SNAKE (SaNitization Algorithm under attacK … ε) competition.

关键词： Differential privacy Machine learning algorithms Technology Machine learning Probability Inference algorithms Synthetic data Identification of persons

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Learning with Noisy Labels: the Exploration of Error Bounds in Classification

arXiv

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arXiv 2025年

作者： Liu, Haixia Li, Boxiao Yang, Can Wang, Yang School of Mathematics and Statistics Institute of Interdisciplinary Research for Mathematics and Applied Science Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Hubei Wuhan China School of Mathematics and Statistics Huazhong University of Science and Technology Hubei Wuhan China Department of Mathematics The Hong Kong University of Science and Technology Clear Water Bay China

Numerous studies have shown that label noise can lead to poor generalization performance, negatively affecting classification accuracy. Therefore, understanding the effectiveness of classifiers trained using deep neural networks in the presence of noisy labels is of considerable practical significance. In this paper, we focus on the error bounds of excess risks for classification problems with noisy labels within deep learning frameworks. We begin by exploring loss functions with noise-tolerant properties, ensuring that the empirical minimizer on noisy data aligns with that on the true data. Next, we estimate the error bounds of the excess risks, expressed as a sum of statistical error and approximation error. We estimate the statistical error on a dependent (mixing) sequence, bounding it with the help of the associated independent block sequence. For the approximation error, we first express the classifiers as the composition of the softmax function and a continuous function from [0, 1]d to RK. The main task is then to estimate the approximation error for the continuous function from [0, 1]d to RK. Finally, we focus on the curse of dimensionality based on the low-dimensional manifold assumption. Copyright © 2025, The Authors. All rights reserved.

关键词： Risk assessment

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Cultivating Cluster Enactment in Wireless Sensor Networks Through Hybrid Metaheuristics Intended for Energy Optimization

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SN Computer Science 2025年第3期6卷 1-12页

作者： Kushwaha, Ajay Goswami, Anjali Sharada, K. Sharma, Manmohan Berar, Gagandeep Edara, Deepak Chowdary Department of Computer Science and Engineering Rungta College of Engineering and Technology R1 CG Bhilai India Department of Mathematics and Statistics College of Science and Theoretical Studies Saudi Electronic University Riyadh 13323 Saudi Arabia Department of Computer Science and Engineering GITAM (Deemed to be University) AP Visakhapatnam India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of CSE Vignan’s Foundation for Science Technology and Research Vadlamudi AP Guntur India

Though traditional search methods shine when faced with local goals and a plethora of decision factors, they could struggle when faced with engineering design limitations. On the other hand, metaheuristic approaches promise to find global optima in real-world optimization problems, even in nonlinear and multimodal settings. In order to improve cluster head selection in Wireless Sensor Networks (WSNs), this research presents a new metaheuristic method that combines the principles of Cuckoo Search method (CSA) and Selection by Optimization (SBO). When choosing a Chief Cluster Head (CCH), the CSA algorithm takes hop count into account, whereas SBO finds the best cluster heads by considering communication range. By comparing the suggested approach to both stand-alone SBO and CSA methods, we can see how well it performs in terms of throughput, latency, energy usage, delivery ratio, and packet overhead. The combined SBO-CSA method improves cluster performance, simplifies cluster head selection in WSNs, and increases network efficiency overall, according to the results. To prove its efficacy in enhancing network performance, the suggested solution outperforms standalone SBO and CSA approaches across a range of performance parameters, such as throughput, latency, energy consumption, and packet overhead. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： CCH CH CSA Metaheuristic methods SBO

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Identifiability and Observability of Nonsmooth Systems via Taylor-Like Approximations

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IEEE Transactions on Automatic Control 2025年第6期70卷 4178-4185页

作者： Stechlinski, Peter Eisa, Sameh A. Abdelfattah, Hesham University of Maine Department of Mathematics and Statistics Maine United States University of Cincinnati Department of Aerospace Engineering and Engineering Mechanics OH United States University of Cincinnati Department of Mathematical Sciences OH United States

New sensitivity-based methods are developed for determining identifiability and observability of nonsmooth input-output systems. More specifically, lexicographic derivatives are used to construct nonsmooth sensitivity rank condition (SERC) tests, which we call lexicographic SERC (L-SERC) tests. The introduced L-SERC tests are practically implementable, accurate, and analogous to (and indeed recover) their smooth counterparts. To accomplish this, a novel first-order Taylor-like approximation theory is developed to directly treat nonsmooth (i.e., continuous but nondifferentiable) functions. An L-SERC algorithm is proposed that determines partial structural identifiability or observability, which are useful characterizations in the nonsmooth setting. Lastly, the theory is illustrated through an application in climate modeling. © 1963-2012 IEEE.

关键词： Climate models

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Viscosity Solutions of Fully second-order HJB Equations in the Wasserstein Space

arXiv

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arXiv 2025年

作者： Bayraktar, Erhan Cheung, Hang Ekren, Ibrahim Qiu, Jinniao Tai, Ho Man Zhang, Xin Department of Mathematics University of Michigan United States Department of Mathematics and Statistics University of Calgary Canada School of Mathematical Sciences Dublin City University Ireland Department of Finance and Risk Engineering New York University United States

In this paper, we show that the value functions of mean field control problems with common noise are the unique viscosity solutions to fully second-order Hamilton-Jacobi-Bellman equations, in a Crandall-Lions-like framework. We allow the second-order derivative in measure to be state-dependent and thus infinite-dimensional, rather than derived from a finite-dimensional operator, hence the term "fully". Our argument leverages the construction of smooth approximations from particle systems developed by Cosso, Gozzi, Kharroubi, Pham, and Rosestolato [Trans. Amer. Math. Soc., 2023], and the compactness argument via penalization of measure moments in Soner and Yan [Appl. Math. Optim., 2024]. Our work addresses unbounded dynamics and state-dependent common noise volatility, and to our knowledge, this is the first result of its kind in the *** Codes 49L25, 35Q93, 35B51, 58E30 © 2025, CC BY.

关键词： Equations of state

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