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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Kumar, Avneesh Awasthy, Neeta Jose, A. Sujin Ramesh, Gajula Gupta, Manish Srinija, K. Najm, Rusul Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India Gl Bajaj Group of Institutions Department of Computer Science and Engineering U.P Mathura India New Horizon College of Engineering Bangalore India Griet Department of Cse Bachupally Telangana Hyderabad India Lovely Professional University Phagwara India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq

ISBN: (纸本)9798331527495

Initially, the FLAGS methodology and a concise study schedule are presented. Combining automata and formal grammars, FLAGS has revolutionized computational theory. SAA, GSTA, and GAGD comprise the last three numerals of FLAGS. They exhibit greater adaptability, productivity, and the generation of novel ideas more frequently when they collaborate. GSTA guides the development of the automaton's real-time state through the use of formal language grammars. This results in a comprehensive and precise computational model. Official language signals are appended to the robot's orthography by the SAA. Text can be represented by flags in an extensive variety of arrangements. In order for FLAGS to function in computer and innovative language environments, AGDE modifies formal language standards to accommodate computer requirements. FLAGS is more innovative, adaptable, and practicable than six well-known approaches, according to the study's findings. Values of the Language Harmony Index (LHI) may be utilized to assess the productivity of FLAGs. It evaluates the compatibility and performance of various linguistic forms when combined. The abstract emphasizes the influence of FLAGS on computational theory. Through the integration of structured languages, computer science, and automata theory, this one-of-a-kind method produces results. This approach revolutionizes linked learning due to its computer-friendly nature and utilization of the target language. © 2025 IEEE.

关键词： Turing machines

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Neural secret key enabled secure cloud storage with efficient packet checker algorithm

Cyber Security and Applications

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Cyber Security and Applications 2025年 3卷

作者： Maurya, Satya Prakash Mishra, Rahul Kumari, Upma Department of Computer Science and Engineering Graphic Era (Deemed to be) University Uttarakhand Dehradun 248002 India National Institute of Advanced Manufacturing Technology Jharkhand Ranchi 834003 India Poornima College of Engineering Rajasthan Jaipur 302022 India

Cloud computing technology is utilized for the commercial purpose of implementing virtualization for the storage of data, which is then controlled and made accessible to users via remote servers. With the increased use of various communication devices and direct access to web servers, there is a favorable opportunity to orchestrate attacks and exploit vulnerabilities in a system. Distributed Denial of Service (DDoS) is a commonly used technique employed by attackers in cloud environments. This study introduces a security layer incorporating a Packet Checker Algorithm (PCA) to detect and eliminate counterfeit packets. The algorithm takes into account transmission delay time as well as minimum and maximum thresholds, thereby enhancing the response time of the Intrusion Detection Process (IDP) in a cloud environment that utilizes neural encryption. Neural encryption is used to synchronize neural networks and create new public channel protocols for a secure cloud storage paradigm. This study greatly expands upon the hop count inspection and filtering method. It incorporates the time slot filtering function and implements a unique key set to differentiate between genuine packets and falsified packets. This novel methodology has the capability to detect Distributed Denial of Service (DDoS) attacks and related anomalies during the initial phases of data transmission. The technique considers Time-to-Live (TTL), Hope count, and Transmission-Delay-Time as crucial components. © 2024

关键词： Cloud storage DDoS Hope count Neural encryption Packet checker algorithm

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Physical Layer Security for Pinching-Antenna Systems (PASS)

arXiv

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arXiv 2025年

作者： Sun, Mingjun Ouyang, Chongjun Wu, Shaochuan Liu, Yuanwei School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

The pinching-antenna system (PASS) introduces new degrees of freedom (DoFs) for physical layer security (PLS) through pinching beamforming. In this paper, a couple of scenarios for secure beamforming for PASS are studied. 1) For the case with a single legitimate user (Bob) and a single eavesdropper (Eve), a closed-form expression for the optimal baseband beamformer is derived. On this basis, a gradient-based method is proposed to optimize the activated positions of pinching antennas (PAs). 2) For the case with multiple Bobs and multiple Eves, a fractional programming (FP)-based block coordinate descent (BCD) algorithm, termed FP-BCD, is proposed for optimizing the weighted secrecy sum-rate (WSSR). Specifically, a closed-form baseband beamformer is obtained via Lagrange multiplier method. Furthermore, owing to the non-convex objective function exhibiting numerous stationary points, a low-complexity one-dimensional search is used to find a high-quality solution of the PAs’ activated locations. Numerical results are provided to demonstrate that: i) All proposed algorithms achieve stable convergence within a few iterations, ii) across all considered power ranges, the FP-BCD algorithm outperforms baseline methods using zero-forcing (ZF) and maximal-ratio transmission (MRT) beamforming in terms of the WSSR, and iii) PASS achieves a significantly higher secrecy rate than traditional fixed-antenna systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Lagrange multipliers

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Intelligent Control of Air Combat Maneuvers Using Fuzzy Logic

Intelligent Control of Air Combat Maneuvers Using Fuzzy Logi...

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Mechatronics and Robotics engineering (ICMRE), International Conference on

作者： Mohanad A. Deif Mohamed A. Hafez Hani Attar S.F. Rezeka T. Awad E.M. Attia M.B. Badawi Research Institute of Sciences and Engineering University of Sharjah United Arab Emirates Faculty of Engineering FEQS INTI-IU Universi Nilai Malaysia Department of Energy Engineering Zarqua University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Mechanical Engineering Department Faculty of Engineering Alexandria University Alexandria Egypt Department of Mechanical Engineering Faculty of Engineering Alexandria University Alexandria Egypt Mechanical Engineering Department Faculty of Engineering Alexandria University El-Chatby Alexandria Egypt Department of Computer Science Faculty of Computer Science and Engineering King Salman International University (KSIU) Egypt

ISBN: (数字)9798331509293

ISBN: (纸本)9798331509309

With the expanding capabilities of fighter aircraft in gauging their targets with great accuracy, comes the necessity that fighter pilots possess higher talents in making the correct decision. Because human error is frequently a barrier to making precise and timely decisions, a control mechanism is required to regulate the trajectory of aircraft during combat maneuvers. In this paper, a combat-maneuver fuzzy program is developed and applied to the inverse kinematics of the F-16 aircraft. The program produces a new path for the plane to achieve the maneuver such that the minimum distance between the resulting path and the object aircraft path is larger than a specified distance (critical distance). Also, spatial defensive maneuvers against spatial offensive maneuvers of bandits were realized, accordingly, this research emphasizes the importance of process innovation in the design of adaptive air combat control systems, enabling more effective response strategies.

关键词： Fuzzy logic Technological innovation Process control Turning Reliability engineering Prediction algorithms Trajectory Aircraft Robots Aerospace control

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Machine Learning-Driven Hybrid Adaptive Beamforming for Efficient Interference Mitigation in Dense Wireless Sensor Networks

Machine Learning-Driven Hybrid Adaptive Beamforming for Effi...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Loganathan, P. Priyeshkumar, A.T. Kodeeswari, K. Reena, K Jose Poonguzhali, I. Balaji, M. Department of Electronics and Communication Engineering Namakkal India Mahendra College of Engineering Department of Electronics and Communication Engineering Salem India Department of Electronics and Communication Engineering Erode India B. S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Applications Vandalur India Panimalar Engineering College Department of Electronics and Communication Engineering Chennai India Vivekananda College of Technology for Women Department of Electronics and Communication Engineering Tiruchengode India

ISBN: (纸本)9798331521394

The Adaptive Beamforming Algorithm (ABA) represents a promising approach to suppress interference in dense wireless sensor networks (WSNs), enhancing signal fidelity and network throughput. In dense WSNs, the proximity of sensor nodes leads to high interference levels, which degrade energy efficiency and communication reliability. This is exacerbated by static beamforming methods that oversimplify time and frequency utilization. This work proposes a set of adaptive beamforming solutions, including digital, machine learning (ML)-empowered, and hybrid beamforming approaches. The Adaptive Array Signal Processing (AASP) method introduces real-time interference analysis with null steering to suppress unwanted signals and maximize the signal-to-interference-plus-noise ratio (SINR). Hybrid beamforming, combining analog and digital domains, balances computational cost with performance. Additionally, a deep learning-based beam steering module dynamically adjusts beam patterns based on interference forecasts. Extensive simulations demonstrate the proposed methods' superiority over conventional beamforming, achieving significant SINR improvement, enhanced network throughput, and improved energy efficiency. In ultra-dense environments, the results indicate a 10-fold reduction in interference power alongside better scalability. Proactive interference mitigation using ML models further ensures adaptability to varying network conditions. These techniques establish a baseline for deploying adaptive beamformers in dense WSNs, addressing scalability and real-time computational challenges. They also pave the way for implementing advanced 5G/6G-capable sensor systems, enabling resilient communication in high-interference environments. Key-words: signal to interference plus noise ratio(SINR), adaptive beamforming, interference mitigation, hybrid beamforming, null steering, beam steering, computational efficiency. © 2025 IEEE.

关键词： Beamforming

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Efficient Resource Management for Real-Time AI Systems in the Cloud using Reinforcement Learning

Efficient Resource Management for Real-Time AI Systems in th...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Jagadeesh Vedula Saravanakumar Baskaran Sathyanarayanan Sayiram Rajkumar Muthusamy Ketan Gupta N. Yuvaraj Digital Transformation Houston Texas USA Microsoft Corporation Seattle Washington USA IBM San Jose CA USA NTT Data Irving Texas USA Department of Information Technology University of The Cumberlands Williamsburg KY USA Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and Technology Coimbatore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

The advent of artificial intelligence (AI) has driven an emergence in applications demanding online responses to immense amounts of data. Typically, the cloud-based deployment of these applications requires resource management to be optimized for high performance while meeting cost efficiency. However, conventional static resource allocation methods may only partially apply to the context of real-time AI applications as they are dynamic, unpredictable, and require more efficient & adaptive ways of allocating resources. To address this challenge, and as a solution in this research, we introduce an RL-based method for online resource management of cloud-based AI systems. Reinforcement learning (RL) is a machine-learning algorithm that allows systems to learn how best to perform tasks in changing environments based on feedback from those interactions with the environment. The research utilizes reinforcement learning to enable dynamic resource allocation, ensuring that resources are provisioned on demand rather than relying on static allocation methods defined at design time.

关键词： Cloud computing Costs System performance Reinforcement learning Dynamic scheduling Load management Real-time systems Planning Resource management Artificial intelligence

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Nonequilibrium transport and the fluctuation theorem in the thermodynamic behaviors of nonlinear photonic systems

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Physical Review Research 2025年第1期7卷 013084-013084页

作者： Yang Liu Jincheng Lu Zhongfei Xiong Fan O. Wu Demetrios Christodoulides Yuntian Chen Jian-Hua Jiang School of Physical Science and Technology & Collaborative Innovation Center of Suzhou Nano Science and Technology Soochow University 1 Shizi Street Suzhou 215006 China Key Laboratory of Efficient Low-carbon Energy Conversion and Utilization of Jiangsu Provincial Higher Education Institutions School of Physical Science and Technology Suzhou University of Science and Technology Suzhou 215009 China School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan 430074 China CREOL College of Optics and Photonics University of Central Florida Orlando Florida 32816-2700 USA Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles California 90089 USA School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Hefei 230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou 215123 China School of Physical Sciences University of Science and Technology of China Hefei 230026 China

Nonlinear multimode optical systems have attracted substantial attention due to their rich physical properties. Complex interplay between the nonlinear effects and mode couplings makes it difficult to understand the collective dynamics of photons. Authors of recent studies have shown that such collective phenomena can be effectively described by Rayleigh-Jeans thermodynamics theory, which is a powerful tool for the study of nonlinear multimode photonic systems. These systems, in turn, offer a compelling platform for investigating fundamental issues in statistical physics, attributed to their tunability and the ability to access negative temperature regimes. However, to date, a theory for nonequilibrium transport and fluctuations is yet to be established. Here, we employ full counting statistics theory to study the nonequilibrium transport of particles and energy in nonlinear multimode photonic systems in both positive and negative temperature regimes. Furthermore, we discover that, in situations involving two reservoirs of opposite temperatures and chemical potentials, an intriguing phenomenon known as the loop current effect can arise, wherein the current in the positive energy sector runs counter to that in the negative energy sector. In addition, we numerically confirm that the fluctuation theorem remains applicable in optical thermodynamics systems across all regimes, from positive temperatures to negative ones. Our findings closely align with numerical simulations based on first-principles nonlinear wave equations. In this paper, we seek to deepen the understanding of irreversible nonequilibrium processes and statistical fluctuations in nonlinear many-body photonic systems which will enhance our grasp of collective phenomena of photons and foster a fruitful intersection between optics and statistical physics.

关键词： Chemical potential

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Biomedical data and AI

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science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Recommender Systems in E-Commerce: A Deep Dive into AI-Driven Optimization 1

Recommender Systems in E-Commerce: A Deep Dive into AI-Drive...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Mishra, Manas Kumar Ramavath, Shiva Kumar Gupta, Gopal Kumar Desai, Piyush Bipinkumar Somavarapu, Sushira Katyayan, Shashank Shekhar G.L. Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida201306 India University of North Texas DentonTX United States Nagpur Campus Pune440008 India Tamil Nadu Vellore632014 India Louisiana State University Baton RougeLA70802 United States Eli Broad College of Business Michigan State University East LansingMI United States

ISBN: (纸本)9798331518578

In e-commerce, recommender systems are the most widely used systems that satisfy the user with personalized recommendations, and they have a significant influence on e-business success. In the new era of artificial intelligence (AI) and deep learning techniques, modern recommender systems have evolved from the classical models of content-based filtration systems to highly sophisticated and accurate models. In this post, we take a closer look at specific optimization techniques that are applied in the context of AI-based recommender systems in the e-commerce industry. We start with the data needed to train these systems and the different ways to collect and clean the data. Next, we delve into getting recommendations and the methods behind them - collaborative filtering (CF), content-based filtering (CB), etc. Next, we explain how deep learning approaches such as natural language processing and neural networks improve the accuracy of these systems. We look at what sort of problems you face while implementing these systems (cold-start issue, data sparsity issues et al) and how are you working to address them. We also consider ethical concerns regarding recommender systems: bias and privacy. Finally, we summarize the various use cases of AI-based recommender systems in e-commerce, including product recommendations, personalized promotions, and user profiling. This paper intends to present a general survey of the techniques which are being state-of-the-art in e-commerce AI recommender systems and a brief discussion of how they can massively boost user satisfaction in unison with business goals. © 2025 IEEE.

关键词： Marketplaces

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Data Exchange for the Metaverse with Accountable Decentralized TTPs and Incentive Mechanisms

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IEEE Transactions on Big Data 2025年

作者： Zhang, Liang Wu, Xingyu Ma, Yuhang Kan, Haibin Hainan University School of Cyberspace Security School of Cryptology Haikou570228 China Hong Kong University of Science and Technology Department of Industrial Engineering and Decision Analytics China Fudan University School of Computer Science Shanghai200433 China Shanghai Engineering Research Center of Blockchain Shanghai200433 China Yiwu Research Institute of Fudan University Yiwu City322000 China

As a global virtual environment, the metaverse poses various challenges regarding data storage, sharing, interoperability, and privacy preservation. Typically, a trusted third party (TTP) is considered necessary in these scenarios. However, relying on a single TTP may introduce biases, compromise privacy, or lead to single-point-of-failure problem. To address these challenges and enable secure data exchange in the metaverse, we propose a system based on decentralized TTPs and the Ethereum blockchain. Firstly, we use the threshold ElGamal cryptosystem to create the decentralized TTPs, employing verifiable secret sharing (VSS) to force owners to share data honestly. Secondly, we leverage the Ethereum blockchain to serve as the public communication channel, automatic verification machine, and smart contract engine. Thirdly, we apply discrete logarithm equality (DLEQ) algorithms to generate non-interactive zero knowledge (NIZK) proofs when encrypted data is uploaded to the blockchain. Fourthly, we present an incentive mechanism to benefit data owners and TTPs from data-sharing activities, as well as a penalty policy if malicious behavior is detected. Consequently, we construct a data exchange framework for the metaverse, in which all involved entities are accountable. Finally, we perform comprehensive experiments to demonstrate the feasibility and analyze the properties of the proposed system. © 2015 IEEE.

关键词： Decentralized finance

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