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1st IEEE International Conference on Innovations in High Speed Communication and Signal Processing, IHCSP 2023

作者： Arulkumaran, G. Jayagopalan, Santhosh Thanga Mariappan, L. Balamurugan, P. School of Computing and Information Technology REVA University Bengaluru India British Applied College Department of Computing Umm Al Quwain United Arab Emirates School of Information Technology and Engineering Vellore Institute of Technology Vellore Department of Smart Computing Vellore Dist Tamil Nadu India School of Computing SRM Institute of Science and Technology Department of Networking and Communications Chengalpattu Dist Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350345957

One of the cornerstones of the future generation of computing is cloud computing, a form of internet-based computing. It allows for the instantaneous allocation of Internet-accessible resources whenever they are needed. Services such as software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS) are made available to people and businesses through cloud computing. By giving customers a low-cost, scalable, and financially-independent place to store their data, cloud storage services have seen rapid revenue growth in recent years. An interoperable cloud computing system is built on open architectures and interfaces that support both private and public cloud services. Many businesses today are making the switch to the cloud because of the numerous advantages it offers in terms of data storage, retrieval, management, and availability. While cloud computing is receiving a lot of attention, it is imperative that security problems be addressed first. This study proposes a crypto-stegno mechanism in the cloud as a solution to cloud security problems. This study proposes a practical method for ensuring data privacy and security in cloud storage. In order to obtain their own public and private key, users must first register their information in the cloud and come up with a unique username and password. Cryptography, a means of concealing information and sending information in an unreadable format, is employed in the next level of security. Secure data transmission and storage on the web, electronic commerce, digital media privacy, and ATM transfer are all dependent on cryptography to some degree. Repudiation, integrity, authenticity, and confidentiality are all possible using today's cryptographic methods. Signcryption is a lightweight cryptographic system that combines encryption and signatures in a logical way to achieve higher security with less computing time. © 2023 IEEE.

关键词： Cryptography

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An Overview of Security Threats, Attack Detection and Defense for Large-Scale Multi-Agent Systems (LSMAS) in Internet of Things (IoT)

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 70-81页

作者： Cai, Jijing Wen, Long Feng, Hailin Fang, Kai Chen, Junxin Wei, Wei Wang, Wei Zhejiang A and F University College of Mathematics and Computer Science Hangzhou311300 China Macau University of Science and Technology School of Innovation Engineering Macau999078 China Dalian University of Technology School of Software Dalian116621 China Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China Beijing Institute of Technology School of Medical Technology Beijing100081 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Shenzhen518172 China

With the global advancement of the Internet of Things (IoT), large-scale multi-agent systems (LSMAS) technology has been increasingly adopted across various industries. Despite the widespread use of IoT, vulnerabilities in its software and hardware components pose significant challenges to ensuring security. To address the security concerns faced by LSMAS, this article explores its application in IoT domains such as smart grids, smart manufacturing, and smart healthcare. By examining the structural layers of IoT-the perception layer, network layer, and application layer-this paper analyzes the specific security threats encountered by each layer, along with the defense strategies designed to counter these attacks. Furthermore, we categorize and compare both traditional and AI-based attack detection methods, dividing the latter into machine learning-based, deep learning-based, and transfer learning-based approaches. This analysis highlights the strengths and weaknesses of the various attack detection methods currently deployed in LSMAS, identifying the existing challenges they face. Finally, we outline potential trends for the future development of LSMAS in IoT, offering research directions for experts in related fields. © 2023 IEEE.

关键词： Multi agent systems

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Forecasting of Groundwater Quality Utilizing an Efficient K-Means with Principal Component Analysis Approach 2

Forecasting of Groundwater Quality Utilizing an Efficient K-...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Adnan, Myasar Mundher Manjula, B.M. Mishra, Antim Dev Ganti, Swathi Bala Dhandayuthapani, V. The Islamic University of Babylon Babylon Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering School of Engineering and Technology Sushant College Gurgaon India Department of Nano Technology Andhra University Visakhapatnam India Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Oman

ISBN: (纸本)9798331504960

Groundwater stands as a pivotal freshwater resource in numerous regions worldwide, but the burgeoning population and diverse economic activities have adversely impacted its quality. However, the K-means algorithm is sensitive to the initial placement of centroids, where outliers can significantly influence the position of centroids resulting in suboptimal clusters. To address this, it is recommended to remove or clip outliers before applying K-means scales well to large datasets making it suitable for handling substantial amounts of data. In this proposed method, attributes are first calculated using Principal Component Analysis (PCA) determines the weight of each attribute based on eigenvalues. The PCA, by determining attribute weights and Rapid Centroid Estimation (RCE), by establishing the initial cluster centre, contribute to the overall effectiveness of the method. The classification step utilizes a Linear Support Vector Machine (SVM), suitable for linear hyper line data when the dataset can be classified into two classes using a single straight line. The results obtained showcase the superiority of the proposed method, achieving a Root Mean Squared Error (RMSE) of 1.23, R-squared (R2) of 0.90, Mean Absolute Deviation (MAD) of 0.28 and E of 0.97 on the Indian groundwater dataset. These outcomes affirm better detection and classification performance compared to other existing methods, such as Random Forest (RF), Decision Tree (DT) and K-Nearest Neighbour (KNN). © 2024 IEEE.

关键词： Mean square error

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Artificial Magnetic Conductor as Planar Antenna for 5G Evolution

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Computers, Materials & Continua 2023年第1期74卷 503-522页

作者： Komsan Kanjanasit Pracha Osklang Terapass Jariyanorawiss Akkarat Boonpoonga Chuwong Phongcharoenpanich College of Computing Prince of Songkla UniversityPhuket Campus83120Thailand Faculty of Technical Education Rajamangala University of Technology IsanKhonkaen Campus40000Thailand Department of Electrical Engineering Faculty of EngineeringKasetsart University10900Thailand Research Center of Innovation Digital and Electromagnetic Technology Department of Electrical and Computer EngineeringFaculty of EngineeringKing Mongkut’s University of Technology North BangkokBangkok10800Thailand School of Engineering King Mongkut’s Institute of Technology LadkrabangBangkok10520Thailand

A 5G wireless system requests a high-performance compact antenna *** research work aims to report the characterization and verification of the artificial magnetic conductor(AMC)metamaterial for a high-gain planar *** configuration is formed by a double-side structure on an intrinsic dielectric *** 2-D periodic pattern as an impedance surface is mounted on the top surface,whereas at the bottom surface the ground plane with an inductive narrow aperture source is *** characteristic of the resonant transmission is illustrated based on the electromagnetic virtual object of the AMC resonant structure to reveal the unique property of a magnetic material *** characteristics of the AMC metamaterial and the planar antenna synthesis are investigated and verified by experiment using a low-cost FR4 dielectric *** directional antenna gain is obviously enhanced by guiding a primary field *** loss effect in a dielectric slab is essentially studied having an influence on antenna *** verification shows a peak of the antenna gain around 9.7 dB at broadside which is improved by 6.2 dB in comparison with the primary aperture antenna without the AMC *** thin antenna profile ofλ/37.5 is achieved at 10GHz for *** emission property in an AMCstructure herein contributes to the development of a lowprofile and high-gain planar antenna for a compact wireless component.

关键词： AMC metamaterial planar antenna high-gain antenna

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An Optimized YOLO-based License Plate Recognition System with Integrated Privacy Safeguards 2

An Optimized YOLO-based License Plate Recognition System wit...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Putluru, Aravinda Reddy Pannuru, Vivekananda Reddy Ashokkumar, C. Kumar, C. Jothi School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Department of Computing Technologies Chennai India

ISBN: (纸本)9798331505745

Efficient and accurate license plate recognition (LPR) is crucial for intelligent transportation systems, surveillance, and security applications. This work proposes a Powered Smart License Plate Recognition System utilizing YOLOv8 for real-time detection and Optical Character Recognition (OCR) for character extraction. The system integrates publicly available datasets, including OpenALPR, CCPD, and UFPR-ALPR, along with custom-annotated data to enhance robustness. Data preprocessing involves normalization, augmentation, and privacy protection measures such as Gaussian blurring and encryption to ensure compliance with data protection regulations. The architecture is optimized using convolutional layers for feature extraction and attention mechanisms for improved character recognition. A privacy protection module ensures sensitive information masking, while Edge AI deployment on Jetson Nano or Raspberry Pi enables real-time inference with minimal latency. Model training employs transfer learning with pre-trained YOLOv8 weights, fine-tuned on license plate datasets. Optimizers such as Adam and SGD, along with IoU loss, enhance bounding box accuracy, ensuring precise detection. Performance evaluation demonstrates high mean Average Precision (mAP) exceeding 95% and OCR accuracy surpassing 90%, validating the system's efficiency across diverse environmental conditions. Applications include automated toll collection, traffic monitoring, vehicle tracking, and secure entry management in smart cities. Law enforcement agencies can utilize the system for stolen vehicle identification and traffic rule enforcement. The proposed approach offers a scalable, privacy-aware, and high-accuracy solution for modern LPR applications, ensuring compliance with security and regulatory standards. © 2025 IEEE.

关键词： Optical character recognition

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Smart Environmental Monitoring System for Air and Water Quality Index

Smart Environmental Monitoring System for Air and Water Qual...

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IEEE Students' Conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Ansh Tripathi Saransh Dubry P. Rama V. Angayarkanni J. Kalaivani Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur TN India Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur TN India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai Department of Computing Technologies Faculty of Engineering and Technology SRMIST Kattankulathur

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

The quality of air and water is essential for human health, environmental sustainability, and ecosystem balance. However, pollution from industrial activities, urbanization, and agricultural practices has made effective environmental monitoring a global priority. Traditional monitoring methods, which rely on manual sampling and laboratory analysis, are often time-consuming and lack real-time capabilities. This paper presents a Smart Environmental Monitoring (SEM) system leveraging IoT and sensor technology to monitor air and water quality in real time using two key sensors: the MQ135 for Air Quality Index (AQI) and the pH sensor for Water Quality Index (WQI). By focusing on these two critical parameters, the SEM system offers a streamlined yet robust solution for assessing environmental quality. The system collects data continuously, transmits it to a cloud platform for processing, and employs machine learning algorithms to forecast future pollution trends. A user-friendly dashboard displays real-time AQI and WQI levels, historical trends, and predictive insights, empowering users to respond proactively to environmental risks. Deployment results demonstrate the SEM system’s capability in detecting pollution patterns, identifying high-risk areas, and predicting pollution spikes during peak traffic and in industrial zones. Comparative analysis with traditional monitoring highlights the SEM system’s superior accuracy, scalability, and predictive abilities. Although the SEM system has higher initial setup costs, its operational efficiency and long-term benefits in mitigating environmental risks make it a sustainable choice for public health and environmental management.

关键词： Environmentally friendly manufacturing techniques Scalability Water quality Machine learning Market research Real-time systems Water pollution Indexes Environmental monitoring Public healthcare

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Unbridled Icarus: A Survey of the Potential Perils of Image Inputs in Multimodal Large Language Model Security

Unbridled Icarus: A Survey of the Potential Perils of Image ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Fan, Yihe Cao, Yuxin Zhao, Ziyu Liu, Ziyao Li, Shaofeng School of Electronics and Information Engineering TongJi University China School of Computing National University of Singapore Singapore Fan Gongxiu Honors College Beijing University of Technology China Nanyang Technological University Singapore School of Computer Science and Engineering Southeast University China

ISBN: (纸本)9781665410205

Multimodal Large Language Models (MLLMs) demonstrate remarkable capabilities that increasingly influence various aspects of our daily lives, constantly defining the new boundary of Artificial General Intelligence (AGI). Image modalities, enriched with profound semantic information and a more continuous mathematical nature compared to other modalities, greatly enhance the functionalities of MLLMs when integrated. However, this integration serves as a double-edged sword, providing attackers with expansive vulnerabilities to exploit for highly covert and harmful attacks. The pursuit of reliable AI systems like powerful MLLMs has emerged as a pivotal area of contemporary research. In this paper, we endeavor to demostrate the multifaceted risks associated with the incorporation of image modalities into MLLMs. Initially, we delineate the foundational components and training processes of MLLMs. Subsequently, we construct a threat model, outlining the security vulnerabilities intrinsic to MLLMs. Moreover, we analyze and summarize existing scholarly discourses on MLLMs' attack and defense mechanisms, culminating in suggestions for the future research on MLLM security. Through this comprehensive analysis, we aim to deepen the academic understanding of MLLM security challenges and propel forward the development of trustworthy MLLM systems. © 2024 IEEE.

关键词： Semantics

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A Novel Enhanced Krill Herd Optimization Based Quality Prediction for Health Care Services 1

A Novel Enhanced Krill Herd Optimization Based Quality Predi...

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1st International Conference on Computer Science and Emerging Technologies, CSET 2023

作者： Rajeyyagari, Sivaram Gopal, R. Saravanan, P. Basha, M John Kempanna, M. Sekaran, Ramesh College of Computing and Information Technology Shaqra University Department of Computer Science Shaqra Saudi Arabia College of Engineering University of Buraimi Information and Communication Engineering Al Buraimi Oman School of Computing Science and Engineering Galgotias University Delhi NCR India School of Computer Science and Engineering Jain Deemed to Be University Bengaluru India Bangalore Institute of Technology Bangalore560004 India

ISBN: (纸本)9798350341737

Some patients with a recent diagnosis of coronary heart disease report drastic drops in their health-related quality of life (HRQoL). Factors predicting HRQoL in patients with an acute coronary syndrome diagnosis were the subject of scant prior research. Quality of life is not conceived of as a risk factor for cardiovascular disease (CVD), despite the fact that there are robust associations between self-reported health and mortality. Our goal was to examine whether or not HRQoL was a separate predictor of incident CVD. It further aims at integrating the Data Analytics to enhance the quality of healthcare services such as heart disease examination. In this paper, we develop an improved quality healthcare services using enhanced krill herd optimisation. To increase the KH speed and safeguard against a local optimum, we add the counter-populations during population initialization. We also introduce the impulse operator during the search for the optimal solution to give it the push it needs to quickly escape a local optimum if it ever enters one. The simulation is conducted to test the efficacy of the krill herd optimisation in improving the healthcare quality services in predicting the heart disease problem in humans. The results show improved classification accuracy than the other methods. © 2023 IEEE.

关键词： Data Analytics

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REQUIRED NUMBER OF ITERATIONS FOR SPARSE SIGNAL RECOVERY VIA ORTHOGONAL LEAST SQUARES

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Journal of Computational Mathematics 2023年第1期41卷 1-17页

作者： Haifeng Li Jing Zhang Jinming Wen Dongfang Li Henan Engineering Laboratory for Big Data Statistical Analysis and Optimal Control College of Mathematics and Information ScienceHenan Normal UniversityXinxiang 453007China College of Information Science and Technology Jinan UniversityGuangzhou 510632China School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan 430074China

In countless applications,we need to reconstruct a K-sparse signal x∈R n from noisy measurements y=Φx+v,whereΦ∈R^(m×n)is a sensing matrix and v∈R m is a noise *** least squares(OLS),which selects at each step the column that results in the most significant decrease in the residual power,is one of the most popular sparse recovery *** this paper,we investigate the number of iterations required for recovering x with the OLS *** show that OLS provides a stable reconstruction of all K-sparse signals x in[2.8K]iterations provided thatΦsatisfies the restricted isometry property(RIP).Our result provides a better recovery bound and fewer number of required iterations than those proposed by Foucart in 2013.

关键词： Sparse signal recovery Orthogonal least squares(OLS) Restricted isometry property(RIP)

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Resource Allocation in Edge computing Environment Using Deterministic Policy Gradient Algorithm 10

Resource Allocation in Edge Computing Environment Using Dete...

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10th International Conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Shabariram, C.P. Shanthi, N. Ponnuswamy, P. Priya Subramaniyaswamy, V. Sathana, V. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Erode India School of Computing Sastra University Department of Computer Science and Engineering Thanjavur India

ISBN: (纸本)9798350384369

In recent years, the edge computing paradigm enables the movement of processing units and storage nearer to the data available locations. The mechanism completes the computation in a short span of time in minimum bandwidth. Edge ecosystem is a type of distributed computing that is sensitive to topology and geography;the Internet of Things is a prime instance of this. Rather than referring to a single technology, Edge computing refers to an architecture. This paper proposes a resource allocation methodology that will enliven the situation between users and edge servers. By creating continuous control at the edge servers to determine resource allocation, edge computing improves reaction time, provides high security with decreased risk, scalability, lowers transmission costs, and versatility (offload targets, migration bandwidth and computing resources). The Deterministic Policy Gradient, Deep learning and Quality Network concepts are combined in the proposed system. The continuous action space is achieved by a deterministic policy gradient. The experience relay includes a quality network. In the proposed system, the actor-critic network produces a single continuous action instead of resulting probability based actions. The critic-part uses Q-value from a quality network based on current status and activity. The goal of the proposed system is to develop a Deep Deterministic Policy Gradient methodology to allocate servers for mobile users with the help of the Edge computing while taking computation resources, offloading goals and migration bandwidth into consideration. The simulation result indicates that deterministic policy gradients integrated deep learning models improve the system performance compared with Game theory. © 2024 IEEE.

关键词： Mobile edge computing

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