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2025 International Conference on multi-Agent Systems for Collaborative Intelligence, ICmSCI 2025

作者： Vasudevan, I. malathi, P. Siddik, S. Therasa, P.R. Nasurudeen, m.mohamed Charaan, R.m.Dilip Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Dept.of Cse Chennai India Saveetha College of Liberal Arts and Sciences Simats Dept of Computer Science India Pavaai Engineering College Dept.of Eee Namakkal India R.M.K Engineering College Dept.of Cse Kavaraipettai India V.S.B Engineering College Dept.of Cse Karur India

ISBN: (纸本)9798331509828

Cyber security is one the most challenging task in today's world. most of the existing Li-Fi technology in wireless sensor network is to detect the data within the short range of distance less accuracy. In this research, we proposed a Smart based Secure morse Code (SSmC) technique. Here there are two part, first is encoding and next is decoding. This proposed encoding technique takes user input in text based form and convert these text to standard morse code pattern and decoding does in the opposite direction. The transmitter parts encode the user input and send the pattern through laser module. The receiver part receives the pattern sends to microcontroller and then decodes and displays the data. In our experiment results shows that the proposed technique can transmit data over long distance with help of morse code encoding decoding algorithm. This technique takes 5ms for the whole transmission and process the transmission accurately. © 2025 IEEE.

关键词： Encoding (symbols)

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A Novel and Robust Sensing technique under Cooperative Schemes of IOT Based Industrial WSN in Real Time

A Novel and Robust Sensing Technique under Cooperative Schem...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Reddy, S.P. Vijaya Vardan Jaison, B. Resmi, N.C. Ashok, S. Sahana, S. Prabu, R. Thandaiah Department of Electronics and Communication Engineering R.M.K. Engineering College Chennai India Department of Computer Science and Engineering R.M.K. Engineering College Chennai India Department of Electronics and Communication Engineering Mar Baselios Christian College of Engineering and Technology Kerala India Department of Electronics and Communication Engineering Vel Tech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Simats Chennai India

ISBN: (纸本)9798350334357

The fast growth of IoT-based wireless technologies across industries in recent years is no secret. As the Internet of Things (IoT) has spread into many real-time applications, the WSN (Wireless Sensor Network) has become an integral part of it. Different critical and non-essential uses of the IoT and WSNs now affect almost every facet of our daily lives. This article is to prove that, while the cooperative scheme is less effective under abnormal conditions, it provides better shielding against the severity of jamming, making the WSN more secure by cutting down on upkeep costs, and placing the groundwork for addressing the issue of jamming. This article also includes an algorithm for real-time jamming detection. In the first place, it looks at the aggressor's specific traits and the harm they inflict. And secondly, it demonstrates how the WSN's inherent resistance to attack. Finally, it takes into account performance metrics, particularly those that have an effect on transmission energy consumption. © 2023 IEEE.

关键词： Cooperative communication

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Image Scaling Attack Simulation: A measure of Stealth and Detectability

arXiv

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arXiv 2024年

作者： Kelly, Devon A. Flanery, Sarah A. Chamon, Christiana Department of Computer Science and Engineering Texas A&M University College StationTX United States Department of Electrical and Computer Engineering Texas A&M University College StationTX United States Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA United States

Cybersecurity practices require effort to be maintained, and one weakness is a lack of awareness regarding potential attacks not only in the usage of machine learning models, but also in their development process. Previous studies have determined that preprocessing attacks, such as image scaling attacks, have been difficult to detect by humans (through visual response) and computers (through entropic algorithms). However, these studies fail to address the real-world performance and detectability of these attacks. The purpose of this work is to analyze the relationship between awareness of image scaling attacks with respect to demographic background and experience. We conduct a survey where we gather the subjects’ demographics, analyze the subjects’ experience in cybersecurity, record their responses to a poorly-performing convolutional neural network model that has been unknowingly hindered by an image scaling attack of a used dataset, and document their reactions after it is revealed that the images used within the broken models have been attacked. We find in this study that the overall detection rate of the attack is low enough to be viable in a workplace or academic setting, and even after discovery, subjects cannot conclusively determine benign images from attacked images. © 2024, CC0.

关键词： Cyber attacks

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Automated Object Detection and Recognition in Satellite Imagery Using CNNs

Automated Object Detection and Recognition in Satellite Imag...

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Innovations in Communications, Electrical and computer engineering (ICICEC), International Conference on

作者： Shanthi H J G. Shobana E. Kamalanaban J. Senthil murugan S. Nandhinieswari R. Stalinbabu Department of Computer Applications Hindustan Institute of Technology and Science Deemed to be University Padur School of Computer Science Takshashila University Department of Computer Science and Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Vel Tech High Tech Dr. Rangarajan Dr.Sakunthala Engineering College Kongunadu Arts and Science College Coimbatore Gounder Mills Coimbatore Artificial Intelligence and Machine Learning M. Kumarasamy College of Engineering Karur

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

As increasingly high-resolution satellite photography becomes available, it has led to a growing need for efficient and accurate methods to analyse large databases and extract valuable information. This research presents a comprehensive approach for automated item identification and detection in satellite data using Convolutional Neural Networks (CNNs). This research abstract outlines the critical steps involved in a suggested technique. These steps encompass data cleaning and organisation, model architecture development, training and evaluation, post- processing, deployment, and subsequent management. This study investigates the ability of CNNs to learn intricate spatial characteristics. The research focuses on carefully selecting and annotating training and validation datasets incorporating items of interest. This study uses data augmentation methods to enhance the generalisation capabilities of the model. This research abstract discusses the importance of the preprocessing stage in ensuring the consistent performance of a model by normalising the input data. This research abstract discusses the components of the CNN architecture, namely convolutional, pooling, and fully connected layers. The CNN architecture is specifically designed to capture hierarchical characteristics. This study investigates the utilisation of a cutting-edge optimiser in the training process to optimise a meticulously constructed loss function. This study examines the integration of the model into real-world applications, considering the trade-off between processing speed and resource constraints as crucial considerations for successful deployment. The findings suggest that this methodology holds promise as a valuable tool in various fields.

关键词： Training technological innovation Accuracy Satellites Image recognition Transfer learning Data augmentation Data models Satellite images Convolutional neural networks

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Design and Development of an AI based Intelligent Door for Home Security System 2

Design and Development of an AI based Intelligent Door for H...

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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Sreelakshmy, R. Kumar, Ata. Kishore Titus, Anita Hemajothi, S. Venkatesh, J. Lavanya, A. Veltech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of ECE Chennai India School of Engineering Department of ECE Tirupati India Jeppiaar University Department of ECE Chennai India Vel Tech High Tech Dr.RangarajanDr.Sakunthala Engineering College Chennai India Chennai Institute of Technology Department Computer Science and Engineering Chennai India R.M.K. Engineering College Department of ECE Chennai India

ISBN: (纸本)9798350315905

These days, safety concerns are universal. A house, a factory, a bank, and so on are all things that virtually everyone desires. In today's world, when more and more people have easier access to various resources, it is increasingly vital to ensure that one has a safe place to live. There must be a way to keep us secure since the daily rise in crime rates demands it. We're all aware that there are sophisticated security systems out there, but unfortunately, they're out of reach for a lot of people. Trespassing, intrusion, and other forms of illegal entry are commonplace, with many common targets including banks, business offices, financial organizations, jewellery stores, and government organizations. Our goal is to alleviate this problem by creating a low-priced electronic system that can detect intruders' movements and trigger an alert. To this end, we offer a new Android app called Door Security System that makes use of IoT technology to keep tabs on the door's state, manage the door, and boost home safety. The smart lock system in this instance is an android-based one, and it can function in both single-mode and multi-mode settings. Financial and commercial institutions have an urgent need for this kind of technology. The system also provides features for the average user, who does not need to be the only one with access to the lock's controls. The system's high efficiency, low price, and cutting-edge features and user-friendly design all contribute to its value. In the event that the door is opened without permission, the alarm will sound and a message will be sent to the homeowners. © 2023 IEEE.

关键词： Internet of things

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Pulse Non-Orthogonal multiple Access (PNOmA) technique for Future Generation Wireless Networks

Pulse Non-Orthogonal Multiple Access (PNOMA) Technique for F...

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2023 IEEE International Conference on Research methodologies in Knowledge management, Artificial Intelligence and Telecommunication engineering, RmKmATE 2023

作者： Radhika, S. Hemalatha, S. Suresh, Helina Rajini makanyadevi, K. Andrews, William J. Kamatchi, S. Department of Information Technology R.M.K. Engineering College Tamil Nadu Chennai India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Tamil Nadu Chennai600127 India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering M.Kumarasamy College of Engineering Tamil Nadu Karur India Department of Computer Science and Engineering Panimalar Engineering College Tamil Nadu Chennai India Saveetha School of Engineering SIMATS Department of Electronics and Communication Engineering Saveetha Nagar Thandalam Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350305708

The foundation of Non-orthogonal multiple access (NOmA) is providing services to several consumers at once while using the same resource in terms of frequency, time, and area. In this research, we provide a novel approach to reducing complexity and power consumption by using ideas from pulsed NOmA systems. The strategy is quite versatile and may be used with numerous other systems. The improvement that we suggest differs from the OFDm system in that the amplitude shaping pulse of the OFDm modulation uses pulses with duty cycles. The time domain spectrum of the modulated signals is spread out by pulsing NOmA symbols, resulting in a spreading gain that is inversely proportional to the duty cycle of the pulsed subcarriers. We show that the additional diversity may be completely utilized by a pulsed-NOmA receiver without the need for OFDm receivers. It is demonstrated that while pulsed NOmA receivers outperforms with OFDm receivers, it also offers advantages over NOmA receivers in terms of intrinsic low complexity and low power consumption. © 2023 IEEE.

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Predicting Crops Based on Soil and Environmental Characteristics Using ARImA and LSTm model 4

Predicting Crops Based on Soil and Environmental Characteris...

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4th IEEE International Conference on mobile Networks and Wireless Communications, ICmNWC 2024

作者： Naresh Kumar, U. madhana mohan, K. Pragadeeswaran, S. Scaria, Jobin m. Baseera, A. Seenivasan, D. Annamacharya University Department of Artificial intelligence & Data science Rajampet India Sri Sairam Engineering College Department of Electronics and Instrumentation Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Chennai India School of Information Technology Ibs University Mt. Eriama Campus Port Moresby Papua New Guinea School of Computing Science and Engineering Vit Bhopal University Sehore India M. Kumarasamy College of Engineering Department of Computer Science and Business Systems Karur India

ISBN: (纸本)9798350352931

Food producers are under pressure to meet rising demand as the world's population and natural resources diminish. Overuse of pesticides and fertilizers has caused soil erosion and land degradation. Future food needs will require environmentally sustainable farming strategies. The suggested strategy includes model training, feature extraction, sensor integration, and model preparation. Data preparation involves processing data before storing it machine-readable. SFFS and RFE select features. The model was trained using a hybrid ARImA-LSTm.technique, which blends deep learning and time-series forecasting. The ARImA-LSTm model outperforms solo ARImA and LSTm models with an average accuracy of 93.54%. Increased accuracy shows the model's capacity to anticipate sustainable agriculture approaches. Data-driven and environmentally sustainable ways are needed to overcome global food production concerns, according to this study. When paired with good preprocessing and feature selection, ARImA-LSTm can boost agricultural productivity while minimizing environmental impact. © 2024 IEEE.

关键词： crop productivity long short-term memory (lstm) soil management

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Advancements in Cybersecurity Using Deep Learning techniques Attack Detection for Trojan Horses

Advancements in Cybersecurity Using Deep Learning Techniques...

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2024 International Conference on Electrical, Electronics and Computing technologies, ICEECT 2024

作者： Hosanna Princye, P. Ahila, A. Lakshmi, A. Anna Ragavendran, N. Purushothaman, K.E. Uma maheswari, G. Saravanakumar, R. Sri Sairam College of Engineering Department of Electronics and Communication Engineering Anekal Bangalore India R.M.K. Engineering College Department of Information Technology Tamil Nadu Chennai India RMK College of Engineering and Technology Department of Artificial Intelligence and Data Science Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India RMK College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Software Analyst Iconix Software Solution Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350378092

Today's computer networks are threatened by Trojan horse assaults and other cybersecurity dangers. We propose and evaluate deep learning methods using the Kaggle-hosted Trojan Detection dataset to detect Trojan horse assaults. This project uses these approaches. Our malware detection technology uses CNN classification for accuracy. We clean and enhance the dataset to make the model more versatile. Due to extensive testing and optimization, Trojan horse detection rates reach 98.47%. We found that the deep learning architecture accurately identifies malware. We also discuss CNN model features that illuminate Trojan horse attacks. The technique offers complete cyber security and expands our knowledge of harmful software. Deep learning may boost cybersecurity defenses against emerging attacks like Trojan horses, according to our study. © 2024 IEEE.

关键词： Cyber attacks

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Innovative techniques in the Construction of Very Large Scale Integration Circuits for Low-Power and High-Performance Circuits 2

Innovative Techniques in the Construction of Very Large Scal...

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2nd IEEE Global Conference on Communications and Information technologies, GCCIT 2024

作者： Saravanan, V. Sasikala, G. Gomathi, P. Kirubakaran, D. Rajalingam, A. Pagunuran, Jubert R. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Nano Electronics Materials and Sensors Tamilnadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Avadi Chennai India V.S.B. Engineering College Department of Ece India R.M.K Engineering College Department of Computer Science and Engineering India University of Technology and Applied Sciences College of Engineering and Technology Engineering Department Shinas Oman

ISBN: (纸本)9798350388916

Portable electronic gadgets and high-performance computing systems have increased demand for low-power, high-performance integrated circuits (ICs). VLSI technology, which creates integrated circuits with dozens to millions of transistors on a chip, is leading these efforts. This research investigates low-power, high-performance VLSI circuit construction methods. We start with VLSI design fundamentals and power management and performance optimization concerns. The constraints of traditional VLSI design methods in satisfying current electronics' strict requirements are examined. VLSI design methodology development and evaluation are the focus of this research. Advanced power optimization methods include mTCmOS, adaptive body biasing, and power gating. We also study clock tree improvement, signal integrity enhancement, and new materials and device topologies for high performance. In addition, we study how machine learning techniques automate and optimize circuit placement, routing, and timing in VLSI design. AI handles the complexity of current VLSI designs and finds optimal design parameters for minimal power and high performance. Scaling VLSI circuits to newer technology nodes raises process variance, leakage current, and thermal management difficulties, which the research tackles. These issues can be overcome with new ways to keep VLSI circuits scaling while preserving performance and power efficiency. Finally, case studies and experimental findings show that the offered methods work in real-world applications. These findings demonstrate significant power usage and performance benefits over typical VLSI design. In conclusion, our research advances VLSI technology by developing low-power, high-performance integrated circuits. The proposed VLSI design methods and solutions could lead to more efficient and powerful electronic systems. © 2024 IEEE.

关键词： VLSI circuits

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A Novel Architecture for Efficient mobile Communication based on Scattered and Diverse Combination using Prioritize and Postoperative Soft modulation 7

A Novel Architecture for Efficient Mobile Communication base...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Sumithra, J. mercy Theresa, m. Geetha Priya, S. Jackson, Beulah Samuthira Pandi, V. Balamurugan, A. R. M. D Engineering College Department of Electronics and Communication Engineering Tamil Nadu India SRM Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu India R. M. D Engineering College Department of Computer Science and Engineering Tamil Nadu India Vel Tech Rangarajam Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu India SRM Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu India Chennai Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu India

ISBN: (纸本)9798350397253

The proposed work suggests a novel approach for the cooperation of geographically scattered individuals and the blending of different points of view. This work ensures that the center on heterogeneous networks in which nodes support both long-range low-rate Radio Frequency (RF) interfaces (like cellular) and short-range high-rate RF interfaces (such as IEEE802.11) that communicate over urban Rayleigh fading channels. These interfaces are used to connect the devices. The interfaces are what are used to link the various gadgets. This research work proposes and evaluates a wide variety of hierarchically distributed cooperation strategies that can function despite resource restrictions such as limited short-range RF bandwidth. These strategies are contained within the confines of this paradigm, which serves as the framework for proposed work. Post Soft-Demodulation Combining, often referred to as PSDC, and Priority maximum Ratio Combining are the two approaches that come highly recommended by our team (PmRC). This work provides evidence that using the offered methods increases throughput, reliability, and signal-to-noise ratio. moreover, this study found that PmRC and PSDC have the ability to improve throughput performance by more than an order of magnitude across a wide range of communication circumstances. © 2023 IEEE.

关键词： Signal to noise ratio

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