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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Balasubramani, M. Gohin, B. Jayashankari, J. Samuthira Pandi, V. Ravi, Kiran Chand Arumugam, Deepak V.S.B Engineering College Department of Computer Science and Engineering Tamil Nadu Karur India Swarnandhra College of Engineering and Technolnogy Department of Computer Applications Andhra Pradesh Seetharamapuram India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Tamil Nadu Chennai India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Tamil Nadu Chennai India Mlr Institute of Technology Department of Ece Hyderabad India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electrical and Electronics Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

With an eye towards improving the efficacy of energy monitoring and management, this research offers a thorough approach for the creation of an Intelligent Energy Metre that is based on the Internet of Things. A key component of the system is the use of specialized sensors to continuously measure electrical characteristics like voltage and current. These sensors generate analog signals, which are conditioned, digitized, and processed by a microcontroller. Algorithms within the firmware/software calculate energy consumption metrics, which are transmitted wirelessly to a central server or cloud platform using secure communication protocols. The collected data is then stored and analyzed to provide insights into energy usage patterns, facilitating informed decision-making for optimizing energy efficiency and reducing costs. The methodology encompasses various stages, including market research, hardware and software development, integration, testing, deployment, and maintenance. Through the implementation of this methodology, a robust and scalable IoT-based energy monitoring system can be developed, empowering users to make informed decisions for sustainable energy management. © 2024 IEEE.

关键词： Energy management

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An Improved Hybrid Deep Learning Strategy to Predict Digital Image Forgery using Artificial Intelligence Principle

An Improved Hybrid Deep Learning Strategy to Predict Digital...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： L Hubert Mary Anita Titus M. Priscilla K. Sailaja A. B. Hajira Be Department of ECE Jeppiaar Institute of Technology Chennai Department of Electronics and Communication Engineering Jeppiaar University Chennai Department of ECE S.A. Engineering College Chennai Department of Computer Applications School of Computing Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati Department of Computer Applications Karpaga Vinayaga College of Engineering and Technology Chennai

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Several modern-day application domains rely heavily on digital images. The practice of manipulating a digital image in such a way as to conceal important or useful details is known as image faking. The new researchers will benefit from this study’s information on picture fraud and how to identify it. An image or picture that is digitally represented is called a digital image. A two-dimensional image is represented numerically, typically in binary form. One of the most important ways to disseminate information on the Internet, digital images are employed in practically every industry. National security and commercial secrets make up the vast majority of digital photographs. Scientists and technologists now face the pressing problem of how to ensure the integrity of images due to the proliferation of the Internet and other forms of simple multimedia sharing. Image processing may be defined as a method for improving or extracting useful information from digital images by converting them to a digital format and then doing various operations on them. Many different types of researchers are interested in image processing. In order to create a featured picture that can be used to train a pre-trained model, the difference between the original and compressed versions of the image must be calculated. Training the pre-trained model involves removing the classifier and replacing it with a newly-tuned one. Eight distinct pre-trained models optimized for binary classification are compared. Applying the suggested strategy with the modified pre-trained models outperforms current state-of-the-art approaches, according to the experimental results. For the purpose of easily identifying forgery images, this paper presented a new AI-assisted approach called AIIFI. To assess the efficacy of the proposed scheme, it was cross-validated with an existing learning model called Convolution Neural Network (CNN).

关键词： Deep learning Training Digital images Neural networks Pressing Predictive models Forgery

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A Structured Architecture Development for 6G Technology for the Accurate Communication 4

A Structured Architecture Development for 6G Technology for ...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Verma, Ankit Bagyalakshmi, P. Pavaiyarkarasi, R. Al-Fatlawy, Ramy Riad Rajalakshmi, G. Arunkumar, B. Ies College of Technology Department of Electronics & Communication Engineering M. P Bhopal India Nehru Institute of Engineering and Technology Department of Mechanical Engineering Tamil Nadu T.M.Palayam India R.M.K. Engineering College Department of Ece Tamilnadu RSM Nagar India The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology TN India Karpagam Academy of Higher Education Department of Computer Science Engineering India

ISBN: (纸本)9798350360165

Following the worldwide pandemic, the next age will bring with it never-before-seen possibilities as well as problems due to the revolutionary merging of the biological, digital, and physical domains of human experience. In order to seamlessly link these realms, a whole new infrastructure is needed to envision 6 G, a dramatic paradigm change. Breaking down architecture into its component parts of platform, works, orchestration, and specialization yields new ideas. Within a heterogeneous cloud architecture, the 6 G landscape that is being envisioned embraces an attitude of openness, scalability, flexibility, and platform agnosticism. Extremely specific architecture supports the complex decomposition of converged applications as well as services into micro-services with serverless operations. This innovative framework also has an open resource orchestration architecture. Important characteristics of these architectural possibilities are explained, with a focus on how they might be adjusted to the constantly changing technology landscapes of the future. At the airinterface leveled 6 G plans to use sub-Terahertz spectrum, use state-of-the-art technologies for sharing spectrum, optimize air-interface design using AI/ML methods, combine radio sensing and communication, and meet strict latency, reliability, while synchronization requirements. As this thorough review explains, to fully use these radio technology breakthroughs, 6 G network design advances must occur concurrently. © 2024 IEEE.

关键词： Terahertz spectroscopy

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Design and Evaluation of Connectivity Based Home Automation System Based on Arduino

Design and Evaluation of Connectivity Based Home Automation ...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Kalyan Kumar, G. Mubarak, Suhail Gopila, M. Nalini, N. Abid, Y. Liakath, Javid Ali Rajalakshmi Institute of Technology Department of Computer and Communication Engineering Chennai India Research Institute of Humanities and Social Sciences University of Sharjah Sharjah27272 United Arab Emirates Sona college of Technology Department of Electrical and Electronics Engineering Salem636005 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Department of Vlsi Microelectronics Chennai India St. Joseph's Institute of Technology Department of Information Technology OMR Chennai600119 India

ISBN: (纸本)9798331543617

IoT encompasses interconnected devices that exchange data autonomously, enabling automation. Raspberry Pi, a compact computer, serves as an affordable platform for DIY projects, education, and IoT uses. The CHASA model integrates Arduino Nano and Arduino Mega modules to optimize energy efficiency in home automation networks. The model employs unidirectional communication and AES (Advanced Encryption Standard) encryption to minimize power consumption and enhance security. Experimentation evaluates the model's performance across key metrics, showcasing its superiority over existing protocols. Results reveal CHASA's higher packet delivery ratio, energy efficiency, lower data loss, routing overhead, and average delay. Comparative analysis demonstrates CHASA's effectiveness in enhancing IoT network performance and energy conservation. © 2024 IEEE.

关键词： Advanced Encryption Standard (AES) IoT (Internet of Things)

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Fashion Clothing Design Style Recommendation using Extra Tree Classifier Combined with LSTM

Fashion Clothing Design Style Recommendation using Extra Tre...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Prem Kumar, M. Aluvala, Srinivas Parthipan, V. Buvaneswari, B. Raghunathareddy, M.V. Global Technical Marketing Element 14 Bangalore India Sr University School of Computer Science & Artificial Intelligence Warangal India Ece Sri Eshwar College of Engineering Coimbatore India Panimalar Engineering College Department of Information Technology Chennai India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

Recommendation System plays an vital part in e-commerce requisition as they help the consumer or user to choose the requires items within limited time. Presently, several e-commerce use a text-based artifact search which has problem to fetch the most alike products of the system. However, most cloth recommendation have several limitations such as;high source consumption, inconsistence subjectively cloth labeled. To overcome above limitation this paper is mainly focused to Extra Tree Classifier (ETC) based feature sub categorization method to overcome the high time consuming and provide intelligent recommendation of clothing style. Convolution Neural Network (CNN) used to recognize effective features for future recommendation. To train the identified aspects with to make efficient rating prediction the numerical function of LSTM based feature extraction are used more effective enables user to select the most rated item (clothing) of the function. The proposed ETC-LSTM method increases exploration and exploitation which helps to improve the user recommendation based rating. The production of the ETC-LSTM method in analyzed in the terms of Area Under Cover (AUC), Precision and Recall. The ETC-LSTM method has 95.347% than the existing MAlexNet-JSEPCC, DVESM-CAE and SLT-DT. © 2023 IEEE.

关键词： Amazon e-commerce ETC categorization fashion clothing style ranking recommendation rating prediction user recommendation system

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Optimizing Multiple Sclerosis Detection through ResNet Feature Fusion and SVM Classifier

Optimizing Multiple Sclerosis Detection through ResNet Featu...

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

作者： Jyoti Kumari Santi Kumari Behera Ashoka Kumar Ratha Prabira Kumar Sethy Department of Computer Science and Engineering VSSUT Burla India Department of Electronics Sambalpur University Odisha India Department of ECE Guru Ghasidas Vishwavidyalaya Bilaspur India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

Multiple sclerosis (MS) is a chronic inflammatory disease that affects the central nervous system (CNS) and is characterised by inflammation, demyelination, and persistent nerve damage known as sclerosis. Accurate diagnosis and monitoring are challenging due to the ambiguity of MS symptoms, despite their importance. MRI is essential for the diagnosis of MS by identifying and evaluating lesions that demonstrate disease activity. This study introduces hybrid framework that uses high level features of pre-trained CNN models (ResNet18, ResNet50, and ResNet101) to distinguish between MS and control classes, utilising axial and sagittal MRI scans. The F1 score was 90.53%, and the classification accuracy was 88.10%. This was achieved by combining features from ResNet18 and ResNet50 with a SVM classifier. This paradigm represents a significant advancement in the diagnosis and classification of multiple sclerosis, and it has the potential to have a broad impact on neuroinflammatory disorders.

关键词： Support vector machines Neurological diseases Technological innovation Multiple sclerosis Accuracy Magnetic resonance imaging Feature extraction Lesions Monitoring Residual neural networks

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IoT Applications in Blockchain Technology 1

IoT Applications in Blockchain Technology

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

作者： Bale, Ajay Sudhir Biswas, Ankur Malik, Suraj Uchoi, Enjula Soni, Sabhyata Uppal Soni, Arun New Horizon College of Engineering Dept. of Ece Bengaluru India Adamas University Dept. of Comp. Sc. & Engg. Kolkata India Meerut India Lovely Professional University Computer Science and Engineering Department India Panjab University Uiet Department of Electrical & Electronics Engineering India Mohali India

ISBN: (纸本)9798350341737

Securing the data through blockchain might be crucial to enhancing the IoT network, since data is an especially important part of the system. Secure storage of the information generated by the IoT network's intelligent gadgets is made possible via the integration of blockchain technology. For blockchain to be widely used in the IoTs, it must first conquer technological and financial barriers. To make the most of the IoT, blockchain technology may require some tweaking. The upcoming Internet of Things (IoT) technical breakthrough could involve this. This work emphasizes on bringing on board the recent trends of IoT applications utilizing Blockchain technology. The challenges faced in doing so are also outlined. We're hoping this work might help elucidate the state of blockchain studies and provide a road map for what comes next. © 2023 IEEE.

关键词： Supply chains

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Implementation of A Deep Learning Framework for Intelligent Intrusion Detection in Internet of Things Networks 5

Implementation of A Deep Learning Framework for Intelligent ...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Jeyapriyanga, S. Ravi, C.N. Rathiya, R. Kalaivani, K. Rama Devi, C. Kumar, Kallakunta Ravi St.Joseph's Institute of Technology Department of Information Technology Chennai India Cmr Engineering College Computer Science and Engineering Department Telangana Hyderabad India Information Technology Dr. Ngp Institute of Technology Coimbatore India School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India St.Joseph's College of Engineering Electrical and Electronics Engineering India Koneru Lakshmaiah Education Foundation Department of Ece AP Vaddeswaram India

ISBN: (纸本)9798350321425

Internet of Things (IoT) has ushered in a new age that benefits humanity greatly. From automated healthcare to energy and transportation, the Internet of Things covers many subject topics. IoT devices are vulnerable to several cyberattacks due to their limited resources. As data output has expanded exponentially from a zettabyte to a trillion per year, computer and IoT network expansion has surged. Network growth has created new security issues. In such large data sets, intrusions are hard to spot. Today's networks are used for 'smart' dwellings and cities, grids, devices, objects, online commerce, banking, government, etc. Due to internet data privacy and security concerns, Intrusion Detection Systems (IDS) have proliferated. Privacy, security, and resilience would suffer if IDS protection fails. Traditional defences can't handle modern attacks. Cutting-edge deep learning methods may automatically detect intrusions and network anomalies. This study's main goal is to evaluate intrusion detection using deep learning algorithms and compare the outcomes to other systems. This research work has proposed a novel deep-learning-based IoT invasion detection method. The cutting-edge Internet of Things dataset contains IoT traces and real-world attack traffic including DoS, DDoS, data harvesting, and theft attempts. Finally, all publicly available network-based IDS datasets are analyzed in this research study. The accuracy, false alarm rate, recall, precision, f-1 score, and detection rate of many deep learning IDS algorithms have been examined. Furthermore, this research study has investigated the network security and privacy issues and solutions. © 2023 IEEE.

关键词： Internet of things

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A Highly Energy-Efficient Body-Coupled Transceiver Employing a Power-on-Demand Amplifier

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Cyborg and Bionic Systems 2023年第1期4卷 149-155页

作者： Tao He Yabin Zheng Xu Liang Jiamin Li Longyang Lin Wenfeng Zhao Yongfu Li Jian Zhao Department of Micro/Nano Electronics Shanghai Jiao Tong UniversityShanghaiChina School of Microelectronics Southern University of Science and TechnologyShenzhenChina Department of Electrical and Computer Engineering Binghamton UniversityVestalNYUSA

Wearable body sensor nodes require massive data transmission under limited ***,it suffers from drastically varying channel loss,which limits its energy efficiency in practical *** paper presents a power-driven body-channel transceiver(TRX),whose power consumption can be adaptively tuned against varying channel *** out-band programmable gain amplifier(PGA)is proposed to save power and generate a quasi-linear correlation between PGA gain and *** using the quasi-linear gain-power relationship,we propose an auto gain/power control technique to realize on-demand power *** addition,a differential balanced transmitter is designed to eliminate base-band harmonics in on-off keying modulation and increase the power delivered by the transmitter(TX).The TX and receiver(RX)of the prototype were integrated into 1 chip and fabricated in a 55-nm complementary metal oxide semiconductor *** the measurement,2 chips were configured as TX and RX,*** the TX and the RX were wearable,powered by lithium batteries,and attached to the subject’s *** prototype achieved a 5.25-Mbps data rate with 16-pJ/bit energy efficiency at a 1.5-m straight-line ground path ***,the proposed TRX maintained stable communication within a 1.5-m distance,while dynamically reducing power consumption.

关键词： Highly maintained straight

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Classifying Atrial Fibrillation Using Deep Residual Networks (ResNet) on ECG Signals 2

Classifying Atrial Fibrillation Using Deep Residual Networks...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Shukla, Aasheesh Ankam, Sreejyothsna Hussein, Layth Durgadevi, G. Ashwini, S. Arunachalam, P. GLA University Department of Electronics & Communication Engineering Mathura India GRIET Bachupally Department of CSBS Telangana Hyderabad India College of Technical Engineering Department of Computers Techniques Engineering The Islamic University Najaf Iraq New Prince Shri Bhavani College of Engineering and Technology Department of ECE Tamil Nadu Chennai600073 India CMR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Department of Biomedical Engineering TamilNadu Chennai600062 India

ISBN: (纸本)9798350368109

Atrial Fibrillation (AF) is a complex most prevalent arrhythmia with many complications that affect the affected individual's overall health as his/her propensity to stroke and heart failure. This study seeks to investigate the effectiveness of Deep Residual Networks (ResNet) in the categorisation of AF from ECG signals given that this will help improve the AF diagnostic accuracy and speed. The ResNet model is based on deep architecture and uses residual connections to learn various patterns from ECG data. The process includes data preprocessing: filtering, normalization and seg-mentation;model training and testing. The results show a high accuracy of the method, the sensitivity, and specificity, which is higher than in many traditional and some modern techniques. The use of ResNet-based approach suggested for automated AF detections seems to be promising with its clinical application and the consequent effects on patients technology. Improvements for future work may be made to fine-tune the model and the integration of the proposed solution with other diagnostic aids for easier diagnosis of arrhythmias. © 2024 IEEE.

关键词： Electrocardiography

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