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5th International Conference on electronics and Sustainable Communication Systems, ICESC 2024

作者： Siva Priya, M.S. Manivannan, KiranKumar Vivekanandan, M. Saranya, G. School of Computer Science and Engineering Vellore Institute of Technology Chennai campus India School of Electronics Engineering Vellore Institute of Technology Chennai campus India

ISBN: (纸本)9798350379945

Early and accurate lung cancer detection is crucial for improved patient outcomes. This research proposes a hybrid framework combining traditional image processing techniques and machine learning for enhanced lung cancer diagnosis. By leveraging CT images, the system employs DTCWT and GLCM for feature extraction, followed by ANN-BPN for classification. Fuzzy clustering is utilized for malignant tissue localization. The proposed approach demonstrates superior performance in terms of accuracy, precision, and F1-score compared to conventional methods. This hybrid model offers a promising tool for assisting radiologists in lung cancer diagnosis. © 2024 IEEE.

关键词： Lung cancer

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Review of the latest techniques that address network fraud detection and proposed architectures 1st

Review of the latest techniques that address network fraud d...

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1st International Conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Indarjit, Elisha Balyan, Vipin Adonis, Marco Department of Electrical Electronics and Computer Engineering Cape Peninsula University of Technology Cape Town Bellville South Africa

ISBN: (纸本)9781032874036

The increase of fraudsters and fraud attacks on the communication network plays a major role in the loss of revenue, network abuse, and degradation of services. Communication and cloud networks belong to separate companies and consist of handover points from one network to another. The scope is to bring communication and cloud networks closer and understand network traffic profiles using the Software-Defined Networking (SDN) concept. The SDN controller will serve to route the extracted tapped data to a central server instead of making use of the live network to route traffic. To apply policies to the traffic and identify which user traffic is a fraud case, and further to send a block signal to the network charging element on the communication network. The flow process will be automated, and to protect the network from fraud attacks, using the SDN controller in a seamless manner and maintaining the performance of the network, The journal article explores traditional fraud detection techniques and will define a new framework for communication and cloud providers to enable the detection and blocking of fraud. The research solution will propose three network architectures for the identification of fraud and integration within communication and cloud networks. The aim of the study is to perform real-time data classification, reporting of fraudulent subscribers, applications, and protocols;real-time analytics, and real-time blocking of data that is classified as fraudulent activity within the mobile data network. The main purpose is to achieve a higher energy/service fraud detection rate than similar work in the field, to provide a solution that integrates within the traditional network, and to further block fraud within the network. © 2025 the Author(s).

关键词： cloud network communication network detection fraud software-defined network

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Wireless Gesture-Driven Navigation for Autonomous Robots: An Innovative Human-Machine Interface Approach

Wireless Gesture-Driven Navigation for Autonomous Robots: An...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Zarina Begum, M. Gowthami, V. Ramya, M. Vijaya Shanthi, V. Manakula Vinayagar Institute of Technology Department of Electronics and Communication Engineering Puducherry India Christ Institute of Technology Department of Computer Science Engineering Puducherry India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510022

As more and more features are added to the car's Human- Machine Interface (HMI), device management becomes increasingly difficult. Making the most of the many human senses is consequently a tactic to facilitate using in-car technologies. With the use of an Arduino Uno and this concept, a car robot with wireless navigation can be built. The robot chassis's direction is altered in accordance with the statement specified in the Arduino code, which is run when the position of the hand is monitored and the designed coordinates are taken into consideration as the parameter. in order for it to carry out the necessary actions, such as going ahead, backward, turning left, turning right, and stopping. © 2024 IEEE.

关键词： DC motors

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Audio Deepfake Detection using Deep Scattering Network 9

Audio Deepfake Detection using Deep Scattering Network

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9th International Conference on Communication and electronics Systems, ICCES 2024

作者： Avaiya, Utsav Badlani, Naman Baadkar, Rajas Talele, Kiran Sardar Patel Institute of Technology Department of Computer Engineering Mumbai India Sardar Patel Institute of Technology Department of Electronics and Telecommunication Engineering Mumbai India

ISBN: (纸本)9798350377972

The rise of audio Deepfakes has raised serious concerns about the authenticity of audio content, affecting media, politics, and cybersecurity by enabling misinformation, fraud, and identity theft. To address these challenges, we propose a novel approach to Deepfake detection using a Deep Scattering Network (DSN). Unlike traditional methods that rely on Convolutional Neural Networks (CNNs), which require massive amounts of training data, our approach leverages the Scattering Transform to capture multi-scale time-frequency features, enhancing the detection of subtle anomalies in manipulated audio while requiring significantly less data for training. Additionally, DSNs employ pre-defined multi-scale wavelet filters rather than data-driven linear filters, offering a more interpretable model while retaining a hierarchical structure. Evaluation of a publicly available dataset shows that the DSN outperforms the standard CNN-based models, particularly in scenarios with low-quality or heavily distorted audio. This research underscores the potential of scattering networks in bolstering the security and reliability of audio authentication systems. © 2024 IEEE.

关键词： Authentication

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A Review of Enhancement in Security of an IoT System Using BlockChain 3

A Review of Enhancement in Security of an IoT System Using B...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Karankar, Nilima Seth, Anita Institute of Engineering and Technology DAVV Department of Computer Engg. Indore India Institute of Engineering and Technology DAVV Department of Electronics and Telecommunication Indore India

ISBN: (纸本)9798350396157

The Internet of Things is thriving rapidly in terms of quality and quantity, helping us to provide better services every day. With the wide-spread growth of the Internet of Things, it introduces new technical challenges, such as managing a vast number of devices. In almost every field, like smart homes, smart transactions, automobiles, and healthcare, IoT is being used very effectively. Even though so many architectures and frameworks are proposed to overcome these challenges, security and scalability are major challenges for an IoT system. Additionally, IoT technology has been merged with other advanced technologies, such as cloud computing, big data, and blockchain, to form a strong IoT system. Blockchain is an emerging technology that is based on distributed protocols. It has the potential to ensure the security, scalability, and reliability of an IoT system and can provide secure digital transactions. This paper attempts to identify the current contributions to IoT security using blockchain and highlights various research gaps in this area. The paper concludes with a discussion on open challenges and research issues. © 2024 IEEE.

关键词： Scalability

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Secure and Efficient Data Synchronization Techniques for Digital Twins in Fog-Edge Cloud Environments 7

Secure and Efficient Data Synchronization Techniques for Dig...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Jegadeesan, S. Hayvita, R.K. Nishath, A.G. Ramalakshmi, S. Krishnan, R. Santhana Essaki Muthu, A. Velammal College of Engineering And Technology Department of Information Technology Madurai India Scad College of Engineering And Technology Department of Computer Science And Engineering Cheranmahadevi India Scad College of Engineering And Technology Department of Electronics And Communication Engineering Cheranmahadevi India

ISBN: (纸本)9798350359299

Data synchronization is a critical aspect in the deployment of Digital Twins within Fog-Edge Cloud environments, ensuring consistency and reliability across distributed systems. However, the security and efficiency of synchronization techniques remain significant challenges. The paper presents a novel algorithm named TwinCrypt, which is designed based on the combination of appropriate encryption algorithm called as Homomorphic Encryption and suitable computing mechanism scheme called as Secure Multi-Party Computation. The algorithm is aimed to achieve secure and efficient data synchronization for Digital Twins in Fog-Edge Cloud environments. The proposed algorithm aims to address vulnerabilities associated with data transmission and storage, offering robust protection against unauthorized access and data breaches. The performance effectiveness of TwinCrypt is analysed by conducting suitable simulations and the simulation results are compared with are conducted comparing it with the existing algorithms used for data synchronization viz. RSA Encryption, AES Encryption, and Zero-Knowledge Proofs in similar environments. Simulation metrics such as latency, throughput, and security overhead are employed to assess the performance of TwinCrypt against its counterparts. The results establish that TwinCrypt outperforms existing algorithms in both security and efficiency, showcasing its suitability for real- world deployment scenarios. © 2024 IEEE.

关键词： Fog computing

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Advanced Smart Metering for Electricity Theft Detection and Power Monitoring 1

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Control Instrumentation System Conference, CISCON 2023

作者： Gaikwad, Vijay Thopte, Kaushalya Dave, Tilak Rajendra Deshmukh, Akash Abhay Afasarpasha, Patil Jiyan Tathe, Ayush Ramchandra Department of Electronics and Telecommunication Engineering Vishwakarma Institute of Technology Pune Pune India Department of Computer Engineering Vishwakarma Institute of Technology Pune Pune India

ISBN: (数字)9789819758661

ISBN: (纸本)9789819758654

Electricity theft is a major problem that causes financial losses for utility companies and household users. To address this issue, we propose an IoT-based solution that detects electricity theft by bypassing or hooking methods. The proposed system utilizes a current measuring and comparing approach to monitor energy consumption indirectly from the electric pole to an intermediate distributor box and then to individual houses. The system periodically measures the current in the distributor box and electric meter and posts the data to the server database. During installation, the user's details are stored in the server database through a user-friendly mobile application, including the address, latitude, longitude using mobile GPS. The system uses statistical algorithms to compare the current values from the distributor box and electric meter. If a marginal difference between the currents is detected, the system identifies electricity theft and alerts the authorized mobile application with the user's details and shutdowns the electricity flow for particular that area. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electric power measurement

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Design and Development of an Intelligent Energy Management System for a Smart Grid to Enhance the Power Quality

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Energy engineering 2023年第8期120卷 1747-1761页

作者： Nisha Vasudevan Vasudevan Venkatraman A.Ramkumar T.Muthukumar A.Sheela M.Vetrivel R.J.Vijaya Saraswathi F.T.Josh Department of Electrical and Electronics Engineering Kalasalingam Academy of Research and EducationTamil NaduIndia Department of Computer Science Engineering Kalasalingam Academy of Research and EducationTamil NaduIndia Department of Electrical and Electronics Engineering Kongunadu College of Engineering and TechnologyTamil NaduIndia Department of Electrical and Electronics Engineering Kongu Engineering CollegeTamil NaduIndia Department of Electrical and Electronics Engineering Narasu’s Sarathy Institute of TechnologyTamil NaduIndia Department of Electrical and Electronics Engineering Panimalar Engineering CollegeTamil NaduIndia Department of Electrical and Electronics Engineering Karunya Institute of Technology&SciencesTamil NaduIndia

MigroGrid(MG)has emerged to resolve the growing demand for *** because of its inconsistent output,it can result in various power quality(PQ)*** is a problem that is becoming more and more important for the reliability of power systems that use renewable energy ***,the employment of nonlinear loads will introduce harmonics into the system and,as a result,cause distortions in the current and voltage waveforms as well as low power quality issues in the supply ***,this research focuses on power quality enhancement in the MG using hybrid shunt ***,the performance of the filter mainly depends upon the design,and stability of the *** efficiency of the proposed filter is enhanced by incorporating an enhanced adaptive fuzzy neural network(AFNN)*** performance of the proposed topology is examined in a MATLAB/Simulink environment,and experimental findings are provided to validate the effectiveness of this ***,the results of the proposed controller are compared with Adaptive Fuzzy Back-Stepping(AFBS)and Adaptive Fuzzy Sliding(AFS)to prove its superiority over power quality improvement in *** the analysis,it can be observed that the proposed system reduces the total harmonic distortion by about 1.8%,which is less than the acceptable limit standard.

关键词： Artificial intelligence resistive inductive load shunt hybrid filter smart grid adaptive fuzzy back-stepping power factor

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Artificial Intelligence-Based Secured Smart Mirror Control Systems with Enhanced Vehicle and Signboard Detection 11th

Artificial Intelligence-Based Secured Smart Mirror Control S...

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11th EAI International Conference on Advancement of Science and technology, ICAST 2023

作者： Sanjay, S. Nithish Soorya, S.S. Vengatesh, R. Prakasam, P. Annamalai, Pushparaghavan School of Electronics Engineering Vellore Institute of Technology Vellore India Faculty of Electrical and Computer Engineering Bahirdar University Bahir Dar Ethiopia

ISBN: (纸本)9783031641503

Vehicle accidents have a significant impact on society, and they have a number of detrimental repercussions on individuals, families, and communities. Vehicles are an essential part of everyone’s daily lives in the modern world;therefore, it is very important to drive them in a cautious manner to prevent accidents. A side mirror is very important equipment in a vehicle as it helps drivers with a view of the area behind and to the sides of the vehicle. The proposed work focuses on the enhancement of artificial intelligence (AI)-based automatic side mirror folding systems in addition with vehicle and signboard detection as driver alert measures. The system proposed inspects authorized persons with the help of face detection and fingerprint verification applications provided via individual phones. The signboard detection helps the driver to notice signboards to make necessary adjustments in very crowded traffic areas, and vehicle detection helps the driver to drive in a cautious manner, specifically when taking turns in highly congested areas with the help of Internet of Things (IOT) applications. The system checks for the presence of signboards, monitors the surroundings, and supervises the security of the vehicle using face detection and android app-based fingerprint verification. The proposed AI-based method achieved the 95.5% testing accuracy for unregistered persons with the help of face detection and fingerprint verification. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mirrors

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IoT-Assisted AI Framework for Preserving Biodiversity: Monitoring Rare Trees in Dynamic Forest Ecosystems

IoT-Assisted AI Framework for Preserving Biodiversity: Monit...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Rajan, K. Palanivel Raj, J. Relin Francis Krishnan, R. Santhana Balamuralikrishnan, G. Vidyasree, G. Jothi, J. Nirmal Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Sri Sai Ranganathan Engineering College Department of Computer Science and Engineering Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi India

ISBN: (纸本)9798331509828

The preservation of biodiversity, particularly the monitoring of rare tree species in dynamic forest ecosystems, presents significant challenges due to the complexities in detecting and analyzing forest changes. This research introduces a novel IoT -assisted AI framework, termed FoLiA-Net (Forest Lightweight Adaptive Network), designed to improve the efficiency and accuracy of monitoring rare trees. The proposed algorithm combines IoT sensor data with machine learning techniques to adaptively detect, classify, and track rare tree species across large and dynamically changing forest areas. The framework utilizes a lightweight neural network structure to ensure real-time processing while minimizing computational resources, making it suitable for deployment in resource-constrained environments. Simulation experiments were conducted to evaluate the performance of FoLiA-Net in comparison with existing algorithms, including Faster R-CNN (F-R-CNN), Random Forest Classifier (RFC), and Gradient Boosting Machines (GBM). The evaluation was based on several key performance metrics such as accuracy, latency and detection efficiency. The results revealed that FoLiA-Net outperformed the existing algorithms in terms of accuracy and computational efficiency, demonstrating a notable improvement in the monitoring of rare tree species in diverse forest ecosystems. The proposed framework is particularly effective in large-scale forest monitoring, providing a valuable tool for biodiversity conservation efforts © 2025 IEEE.

关键词： Biodiversity Forest Ecosystems Machine Learning Rare Trees Simulation Sustainability

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