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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Ahluwalia, Gunveen Suresh, Vaibhav Goutham Praveena, M. D. Anto Acharjya, Kalyan Paridaname, Trushna Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798331542375

Cloud automation enables the dynamic management and allocation of resources in a computing environment and is one of the most common uses available. Yet despite their power when cloud systems were simple and small, traditional approaches to allocating resources have proven far less feasible lately. This scenario has also created the need for new evolution methods that will adapt to dynamic cloud environments and improve the system's overall performance. A solution to this challenge comes from machine learning, which has been increasingly used recently. ML algorithms can process vast amounts of data and identify patterns and trends to predict resource use and user needs. Deep learning helps cloud-enabled automation systems adapt in real-time to changing conditions, distribute resources efficiently and improve user experience. For example, reinforcement learning and deep learning are ML techniques that can be applied to resource allocation algorithms or demand prediction models to increase accuracy. Integrating cloud automation and machine learning brings effectiveness in dynamic resource allocation, boosting the overall performance of a system, which leads to savings, better experience, and greater flexibility in using cloud resources. Reinforcement and deep learning are machine learning techniques to optimize order allocation algorithms and enhance demand estimation models. © 2025 IEEE.

关键词： Learning algorithms

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Anomaly-Based Intrusion Detection systems by using Machine Learning based Stacking Ensemble Model 3

Anomaly-Based Intrusion Detection Systems by using Machine L...

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3rd International Conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Buvaneswari, P.R. Madhura, G.K. Alasady, Heba Alagarraja, K. Soni, Mukesh School of Electrical and Electronics Engineering Vit Bhopal University Bhopal India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India University Centre for Research & Development Chandigarh University Department of Computer Science and Engineering Mohali India

ISBN: (纸本)9798331508456

In recent years, network intrusion refers to unauthorized access to systems or networks by external attackers or insiders aimed at stealing information or deploy malicious software. Traditional approaches for anomaly-based intrusion detection systems have faced several challenges which include high computational overhead and limited capability in detecting zero-day attacks due to reliance on predefined attack signatures or handcrafted features. To address these limitations, this research proposes a Machine Learning-based Stacking Ensemble Model (ML-SEM) for anomaly-based intrusion detection, effectively reducing computational overhead and enhancing detection of previously unseen attacks. Initially, data is collected from University of Nevada, Reno Intrusion Detection Dataset (UNR-IDD) which consists of network traffic data and various types of attacks. Then, the collected data is preprocessed by using standard scaler normalization which ensures that all features have a mean of zero and a standard deviation of one. After that, the features are selected using Principal Component Analysis (PCA) which reduces dimensionality and enhances model efficiency by eliminating redundant and less significant features. Finally, anomaly-based intrusion detection is done by using proposed ML-SEM. The proposed ML-SEM achieved better results in terms of accuracy (98.6%), precision (95.5%) and recall (98.3%) when compared with existing Random Forest (RF). © 2025 IEEE.

关键词： Dimensionality reduction

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A distributed algorithm with network-independent step-size and event-triggered mechanism for economic dispatch problem

A distributed algorithm with network-independent step-size a...

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作者： Chen, Baitong Yang, Jianhua Lu, Wei Pedrycz, Witold Sun, Changhai School of Control Science and Engineering Dalian University of Technology Dalian China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Institute of Systems Engineering Macau University of Science and Technology SARTaipa 999078 China Research Center of Performance and Productivity Analysis Istinye University Istanbul Turkey School of Electrical Engineering Dalian University of Technology Dalian China

The economic dispatch problem (EDP) poses a significant challenge in energy management for modern power systems, particularly as these systems undergo expansion. This growth escalates the demand for communication resources and increases the risk of communication failures. To address this challenge, we propose a distributed algorithm with network-independent step sizes and an event-triggered mechanism, which reduces communication requirements and enhances adaptability. Unlike traditional methods, our algorithm uses network-independent step sizes derived from each agent's local cost functions, thus eliminating the need for detailed network topology knowledge. The theoretical derivation identifies a range of step size values that depend solely on the objective function's strong convexity and the gradient's Lipschitz continuity. Furthermore, the proposed algorithm is shown to achieve a linear convergence rate, assuming the event triggering threshold criteria are met for linear convergence. Numerical experiments further validate the effectiveness and advantages of our proposed distributed algorithm by demonstrating its ability to maintain good convergence characteristics while reducing communication frequency. © 2024 John Wiley & Sons Ltd.

关键词： Cost functions

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A Blockchain-Based Distributed Collaborative Sensing and Spectrum Access Approach for Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Peng, Yuhuai Li, Yuan Guo, Yu Zhang, Dawei Khan, Fazlullah Alturki, Ryan Alshawi, Bandar Northeastern University College of Computer Science and Engineering Shenyang110819 China Zhiyuan Research Institute Xihu District Hangzhou China Zhengzhou University School of Computer and Artificial Intelligence Henan Zhengzhou450001 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China KKR and KSR Institute of Technology and Sciences Department of Computer Science and Engineering Andhra Pradesh Guntur India Division of Research and Development Lovely Professional University Phagwara India University Centre of Research and Development Chandigarh University Mohali India Umm Al-Qura University Department of Software Engineering College of Computing Makkah Saudi Arabia Umm Al-Qura University College of Computing Department of Computer and Network Engineering Makkah Saudi Arabia

Cognitive radio (CR) provides key technical support for the seamless communication of consumer electronic (CE) devices through distributed cooperative sensing and spectrum access. However, its openness and dynamic nature introduce potential security risks and challenges. Due to the openness of wireless communication, spectrum sensing and access are vulnerable to malicious attacks, and resource-constrained devices cannot effectively implement artificial intelligence (AI)-driven encryption and access control strategies. To address these limitations, this study proposes a blockchain-based distributed collaborative sensing method for CE devices. In the proposed method, after sensing, the smart contracts verify node legitimacy using the ring signature method. The malicious users are rejected by the outlier detection method, which improves the collaborative anti-interference ability. In addition, by using a centralized training and distributed execution architecture, a secure spectrum sensing strategy based on multi-agent reinforcement learning is designed. The proposed strategy ensures that each node makes independent decisions, which enhances the adaptability of the CE ecosystem. The proposed method is verified by extensive simulations, and the results show that it can maintain spectrum awareness of more than 95.72% and an access success rate of 91.19% under malicious attacks. Moreover, communication overhead is significantly reduced by approximately 93.53%. © 1975-2011 IEEE.

关键词： Blockchain

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Design and demonstration of dual-core spiral package-embedded inductors for Integrated Voltage Regulators

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IEEE Transactions on Components, Packaging and Manufacturing Technology 2025年

作者： Avula, Venkatesh Murali, Prahalad Swaminathan, Madhavan Georgia Institute of Technology School of Electrical and Computer Engineering the3D Systems Packaging Research Center AtlantaGA30332 United States Apple Inc CupertinoCA95014 United States Georgia Institute of Technology School of Materials Science and Engineering 3D Systems Packaging Research Center AtlantaGA30332 United States Penn State University School of Electrical Engineering and Computer Science University ParkPA16802 United States

Novel package-embedded inductors utilizing a dual-core spiral topology, designed for improved performance in integrated power delivery systems, are presented in this paper. The proposed inductor features a simple fabrication process, gapped magnetic cores for stable performance across varying operating conditions, and overlapping spiral windings in adjacent layers, achieving high inductance density. The proposed topology consists of a spiral-shaped conductor winding layer sandwiched between two magnetic core layers. The conductor and insulating dielectric layers separate the cores and act as an air gap for the magnetic flux of the spiral inductor. The air gap causes an increase in the saturation current performance of the inductor. In addition to the single spiral winding, two advanced spiral inductor configurations, namely two spiral windings in series and parallel, are explored. To enable design, a physical model and analytical monomial expressions are provided for inductance calculation. To evaluate the performance of these designs, three inductor samples are fabricated with two different magnetic core materials and air gaps. Overall, package-embedded dual-core spiral inductors with a performance of 115nH inductance with 5 A saturation current and 330nH inductance with 3.5 A saturation current, occupying a 9 mm2 area and having efficiencies ranging from 75% to 80%, are demonstrated. © 2011-2012 IEEE.

关键词： Magnetic cores

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Integrating AI-based adaptive learning into the flipped classroom model to enhance engagement and learning outcomes

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computers and Education: Artificial Intelligence 2025年 8卷

作者： Katona, Jozsef Gyonyoru, Klara Ida Katonane Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest University of Economics and Business Budapest Hungary Department of Organizational Development and Communication Science Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary

This study explores the integration of the AI-powered adaptive learning system within the flipped classroom model and discusses programming education. The increasing demand for the acquisition of programming skills in the digital era generates the development of innovative pedagogical approaches to help students overcome the difficulties in mastering programming concepts. The study investigates how AI-based adaptive feedback influences student engagement, motivation, and learning outcomes. This study was conducted for 13 weeks, with two groups of undergraduate students. In one of the groups, tshe traditional flipped classroom model was applied, while in the other group, AI-driven adaptive learning tools were introduced. In this research methodology, a mixed-methods approach was adopted, integrating quantitative analyses with qualitative data. These showed that the experimental group demonstrated significant improvements in learning results and motivation, thereby realiszing the value of real-time, personalized feedback provided through the AI system. The qualitative findings revealed that with the AI system, there was an increase in student autonomy;thus, there is increased responsibility among students to collaborate in in-class participation, which helps enhance a more dynamic and cooperative learning environment. On the whole, these findings reveal important implications of the integration of AI into flipped classrooms, while at the same time, they reveal how to make the modernization of programming education achieve a better overall experience in student learning. © 2025 The Authors

关键词： Contrastive Learning

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Double Oracle Neural Architecture Search for Game Theoretic Deep Learning Models

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IEEE Transactions on Image Processing 2025年 34卷 2463-2472页

作者： Aung, Aye Phyu Phyu Wang, Xinrun Wang, Ruiyu Chan, Hau An, Bo Li, Xiaoli Senthilnath, J. Singapore138632 Singapore Singapore Management University School of Computing and Information Systems Bras Basah 188065 Singapore KTH Royal Institute of Technology Division of Robotics Perception and Learning Stockholm100 44 Sweden University of Nebraska-Lincoln School of Computing LincolnNE68588 United States Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore A STAR Centre for Frontier AI Research Singapore138632 Singapore

In this paper, we propose a new approach to train deep learning models using game theory concepts including Generative Adversarial Networks (GANs) and Adversarial Training (AT) where we deploy a double-oracle framework using best response oracles. GAN is essentially a two-player zero-sum game between the generator and the discriminator. The same concept can be applied to AT with attacker and classifier as players. Training these models is challenging as a pure Nash equilibrium may not exist and even finding the mixed Nash equilibrium is difficult as training algorithms for both GAN and AT have a large-scale strategy space. Extending our preliminary model DO-GAN, we propose the methods to apply the double oracle framework concept to Adversarial Neural Architecture Search (NAS for GAN) and Adversarial Training (NAS for AT) algorithms. We first generalize the players' strategies as the trained models of generator and discriminator from the best response oracles. We then compute the meta-strategies using a linear program. For scalability of the framework where multiple network models of best responses are stored in the memory, we prune the weakly-dominated players' strategies to keep the oracles from becoming intractable. Finally, we conduct experiments on MNIST, CIFAR-10 and TinyImageNet for DONAS-GAN. We also evaluate the robustness under FGSM and PGD attacks on CIFAR-10, SVHN and TinyImageNet for DONAS-AT. We show that all our variants have significant improvements in both subjective qualitative evaluation and quantitative metrics, compared with their respective base architectures. © 1992-2012 IEEE.

关键词： Generative adversarial networks

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Sustainable Crop Yield Forecasting with the Dual-Branch Geometric Progressive Feedback Cosine Convolutional Network Model 4

Sustainable Crop Yield Forecasting with the Dual-Branch Geom...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Ramkumar, M. Siva Nivetha, R. Gopan, Gokul Kanan, Mohammad Baig, Rahmath Ulla Giri, Jayant Sns College of Technology Dept of Ece India Kumaraguru College of Technology Computer Science and Engineering India Saveetha University Department of Mechanical Engineering Chennai602105 India College of Engineering University of Business and Technology Department of Industrial Engineering Jeddah21448 Saudi Arabia College of Engineering Zarqa University Department of Mechanical Engineering Zarqa Jordan Cambridge Institute of Technology Department of Artificial Intelligence and Machine Learning Bangalore560086 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India

ISBN: (纸本)9798331523923

Using deep learning algorithms to extract important agricultural traits has made crop production prediction based on environmental, soil, water, and crop parameters an important area of research. But conventional approaches have trouble creating a straight linear or non-linear mapping between yield values and raw data, and the quality of the characteristics that are extracted has a significant impact on how well they work. Deep reinforcement learning builds a strong prediction framework by combining the intelligence of deep learning with reinforcement learning to get beyond these drawbacks. In order to anticipate agricultural yields accurately and sustainably, this work aims to create a Dual-Branch Geometric Progressive Feedback Cosine Convolutional Network with Skill Optimization Algorithm (DB-GPFCNet-SOA). Robust Double-Weighted Guided Filtering (RDWGF) is used for data preprocessing in order to minimize noise and improve important components. To ensure a complete prediction model, the N-Branch retrieves local neighbor-based information while the C-Branch records global geometric properties. Moreover, the accuracy is enhanced by the SOA, which enhances loss parameters. The suggested model has RMSE of 0.25 and MAE of 0.15 with exceptional accuracy of 99.2%, precision of 99.1%, recall of 99%, and F1-score of 99%. These results validate the performance of the model, which renders it a suitable approach for real-time agricultural applications. © 2025 IEEE.

关键词： Deep reinforcement learning

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Scalable Real-Time Anomaly Detection in Financial Markets Using a Novel Machine Learning Model 3rd

Scalable Real-Time Anomaly Detection in Financial Markets Us...

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

作者： Andi, Hari Krishnan Bommisetti, Ravi Kumar Subbarao, Buggavarapu V. S. S. Sonthi, Vijaya Krishna Ray, Samrat Ammasai, Viswanathan Sengan, Sudhakar Centre for Postgraduate Studies Asia Metropolitan University Johor Bahru81750 Malaysia Department of Business Administration and Dean-Research and Development Maris Stella College Vijayawada520008 India Department of MBA Kumaraguru College of Technology Tamil Nadu Coimbatore641049 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Dean Head of International Relations IIMS Pune411033 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627451 India

ISBN: (纸本)9789819783540

Stakeholder objectives can be put at risk if anomalies are not detected sufficiently promptly in the constantly evolving financial systems. This paper recommends an innovative Machine Learning (ML) method, the Hybrid Temporal-Feature Embedding Model (HTEM), dedicated to real-time Anomaly Detection (AD). Fundamental to this framework is the use of a distributed system to store and process real-time data that has been collected through a strong Data Ingestion Layer. The HTEM framework is a vital component of this layout. The temporal flow of data collected from LSTM networks is precisely combined with the significance of individual features that are analyzed by autoencoders in this technique. By applying this dual embedding, a complete model of financial data sequences is accomplished. By applying a fully connected layer to provide this combined representation, the research affects an accurate anomaly value for each data point. If the overall rating is higher than a particular threshold, the data point is classified as an anomaly. Training this model frequently on known anomalies enables it to be fine-tuned to a higher level. The timely, accurate, and efficient finding of anomalies can be ensured through the incorporation of the HTEM within the model being proposed, protecting the interests of stakeholders in the financial market environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Speech Separation for Low-Resource Languages

Speech Separation for Low-Resource Languages

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Borsdorf, Marvin Pan, Zexu Himmelmann, Pascal Li, Haizhou Schultz, Tanja Machine Listening Lab University of Bremen Bremen Germany Alibaba Group Singapore Department of Mathematics and Computer Science FernUniversität in Hagen Hagen Germany School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore Cognitive Systems Lab University of Bremen Bremen Germany

ISBN: (纸本)9798350368741

Speech separation aims to equip machines with the human ability of selective listening, i.e. to focus attention on specific information in spoken communication. Studies have shown that the language spoken in a cocktail party scenario matters. While the development of speech separation models can leverage extensive databases, for the majority of languages only very limited data is available. This work presents the very first study on speech separation for low-resource languages. We choose blind source separation as the task to be studied and analyze three strategies to overcome the data scarcity of two low-resource languages from the GlobalPhoneMS2 database. We show that data from other languages can be used to develop models that work for low-resource languages. Finetuning additionally boosts the performance, and training on multiple languages increases both performance and robustness. We show that dynamic mixing in the development helps to find a trade-off between performance and development time. © 2025 IEEE.

关键词： cocktail party problem GlobalPhone low-resource language multilingual speech separation

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