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2nd International Conference on Power Electronics and Energy, ICPEE 2023

作者： Pattanaik, Debasish Swain, Sarat Chandra Samanta, Indu Sekhar Rout, Ullash Kumar Dash, Ritesh Swain, Kunjabihari Kiit Deemed to Be University School of Electrical Engineering Bhubaneswar India Iter Soa University Dept.of Computer Science and Engineering Bhubaneswar India Odisha University of Technology and Research Dept. of Electrical Engineering Bhubaneswar India Reva University Dept.of Electrical and Electronics Engineering Bangalore India Nist Institute of Science and Technology Dept. of Electrical and Electronics Engineering Berhampur India

ISBN: (纸本)9781665470582

Microgrid monitoring has gained popularity in recent years, owing to their popularity and the power quality disturbances (PQD) caused by renewable energies. Many artificial intelligence-based PQD monitoring strategies have been proposed by researchers. This paper presents a novel method for detecting, classifying and monitoring the power quality disturbances in solar integrated microgrids. Power quality events are detected by analysing the frequency signal with the wavelet synchro-squeezing transform (WSST). The features of power quality disturbances are used to classify them. For PQD classification, the retrieved features are fed into a Convolutional neural network (CNN) classifier, using GoogLeNet and SqueezeNetfier. Thus the accuracy was found to be 99.90% and 93.76% respectively. © 2023 IEEE.

关键词： Artificial Neural Network Convolutional Neural Network GoogLeNet neural network Innovation Micro Grid Power Quality Disturbances Renewable Energy Sources Wavelet synchro-squeezing transform

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Fuzzy Logic-based Energy Management for WSNs in Smart Grid Applications 4

Fuzzy Logic-based Energy Management for WSNs in Smart Grid A...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Vengatesan, K. Vijayalakshmi, R. Samee, Sayyad Chopade, Neha Mahalle, Shilpa Bashkaran, K. Department of AIDS Sanjivani University Kopargaon India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Dept.of Computer Science Al Sadara School Al Ain Abu Dhabi United Arab Emirates SIES College of Management Studies India Kongunadu College of Engineering and Technology Thottiam India

ISBN: (纸本)9798331515720

Fuzzy Logic-Based Energy Management (FLBEM) is an innovative approach for optimizing energy consumption in Wireless Sensor Networks (WSNs) utilized in Smart Grid applications. This technique employs fuzzy logic principles to dynamically adjust network parameters, such as transmission power and data sampling rates, to enhance energy efficiency while maintaining reliable communication. By intelligently adapting to network conditions and environmental factors, FLBEM extends the operational lifetime of sensor nodes and ensures the stability of the WSN, thus facilitating real-Time monitoring and control of Smart Grid systems. The proposed FLBEM approach encompasses a comprehensive framework that includes the design of fuzzy logic controllers, the development of energy-Aware routing protocols, and the integration of renewable energy sources for powering sensor nodes. Simulation results demonstrate significant improvements in energy efficiency, network lifetime, and overall performance, validating the effectiveness of the FLBEM strategy in the context of Smart Grid applications. As energy management remains a critical concern for WSNs, the implementation of FLBEM offers a promising solution to address the challenges of energy conservation and sustainability in modern power systems. © 2024 IEEE.

关键词： Smart power grids

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Block-Hash Signature (BHS) for Transaction Validation in Smart Contracts for Security and Privacy using Blockchain

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Journal of Mobile Multimedia 2023年第4期19卷 935-962页

作者： Bhatnagar, Sonika Dayal, Mohit Singh, Deepti Upreti, Shitiz Upreti, Kamal Kumar, Jitender Dept. of Computer Science & Engineering Government Polytechnic Baheri Bareilly India Dept. of Applied Science Bharati Vidyapeeth’s College of Engineering New Delhi India Dept. of Information Technology ABES Institute of Technology Ghaziabad India Dept.of Information Technology Asian Education Group (AEG) Noida India Dept. of Computer Science & Engineering Dr. Akhilesh Das Gupta Institute of Technology and Management New Delhi India Dept. of Computer Science & Engineering J.C Bose University of Science & Technology YMCA Haryana Faridabad India

Some of the well-known signature techniques like Winternitz and Lamport are not considered to be very appropriate for the usage of hashing or smart contracts in Blockchains security because of their size O(n²), which is prominently too high. Although in Blockchain, the security concern is on the top priority because of its distributed P2P design still, the security enhancement is required to sign and verify the documents forwarded to the peers, especially in Hyperledger Fabric. Here, this paper presents a new signature technique “Block-Hash” to enhance Blockchain security by using it in smart contracts as well as hashing with size 3Xn bits (n = 256, generally for SHA-256 Hashing) and which can score 112 bits security. The proposed signature can be used appropriately for signing a smart contract by the endorser or committer node. Also, it can be used with a hash algorithm in forming a Merkle tree. Apart from the description and implementation of Block-Hash Signature, this paper has covered the analysis of its security and correctness measures with a table for result comparison. © 2023 River Publishers.

关键词： blockchain security Digital signature hyperledger fabric smart contract

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An Intelligent Security Systems in Cloud Computing using Cryptography Techniques 5

An Intelligent Security Systems in Cloud Computing using Cry...

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

作者： Srilakshmi, V. Chetana, V. Lakshmi Dornala, Raghunadha Reddi Vallabaneni, S. Parvathi Ramanjaiah, Ganji School of Computer Science and Engineering Vit -AP University Andhra Pradesh Amaravati522237 India School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amaravati Campus India Walgreens United States Pvp Siddhartha Institute of Technology Department of It Vijayawada India R v Institute of Technology Dept.of Cse Guntur India

ISBN: (纸本)9798350379945

Delivering computing resources over the internet has seen a paradigm shift towards cloud computing. However, there are serious security risks because cloud environments are distributed and shared. Intelligent Security Systems (ISS) that use cryptographic techniques are now necessary to overcome these issues, such as data confidentiality, integrity, and authenticity;this study developed an ISS for cloud computing, concentrating on cryptographic techniques. The RSA-OAEP (Optimal Asymmetric Encryption Padding) and X.509 algorithms are combined to create the ISS and further cover a variety of cryptographic methods, emphasizing their functions in protecting cloud-based systems, including encryption, digital signatures, and key management. It also looks at improving security systems' threat detection and response capabilities by integrating artificial intelligence (AI) algorithms like Grubbs' Test. In addition, several issues and potential paths for intelligent security systems in cloud computing are examined, highlighting the necessity of ongoing study and development to remain ahead of developing cyber threats. © 2024 IEEE.

关键词： Cloud security

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A System to Track Fabricated Content in Social media using Machine Learning Approach

A System to Track Fabricated Content in Social media using M...

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2024 IEEE International Conference on Recent Advances in science and engineering technology, ICRASET 2024

作者： Hegde, Ramakrishna Soumyasri, S.M. Shivashankara, S. Kavyashree, M.K. Pemula, Rambabu Harshitha, M. Vidyavardhaka College of Engineering Affiliated to Visvesvaraya Technological University Dept. of Computer Science and Engineering Mysuru India Vidya Vikas Institute of Engineering and Technology Affiliated to Visvesvaraya Technological University Dept. of Computer Applications Mysuru India Kr Pete Krishna Govt. Engg. College Dept. of Computer Science and Engineering Krishnarajapet Mandya India Sjce Jssstu Mysore India Vidya Jyothi Institute of Technology Dept. of Artificial Intelligence Hyderabad India Vidya Vikas Institute of Engineering and Technology Dept.of Information Science and Engg. Mysore India

ISBN: (纸本)9798350388602

In today's society, when the internet is pervasive, everyone gets their news from a variety of online sources. Information can swiftly reach millions of users in a short amount of time due to the growing usage of social media sites like Facebook, Twitter, and others. Fake news has far-reaching effects, ranging from swaying election outcomes in favor of particular politicians to fostering prejudiced viewpoints. The primary platforms for disseminating fake news are Instagram, WhatsApp, and numerous other social media platforms. By presenting a machine learning-based fake news detection algorithm, this work provides a remedy. This model needs fictitious data that was gathered from multiple information sources. Data is extracted using web analytics, which is mostly used to build datasets. Real data sets and fake data sets are the two primary categories into which the data is separated. Random forest, logistic regression, decision trees, KNN, and gradient boosting are the classifiers used to classify the data. The data is categorized as true or false data based on the version that was received. The user will be able to determine whether or not the information on the website is fraudulent based on this. © 2024 IEEE.

关键词： Adversarial machine learning

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Genetic Testing of Huntington's Disease by Facial Examination of a Mankind using AI Programs 3

Genetic Testing of Huntington's Disease by Facial Examinatio...

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3rd International Conference on Innovations in computer science and Software engineering, ICONICS 2022

作者： Atruba Feroze, S. Faiza Nasim, S. NED University of Engineering and Technology Dept.of Computer and Information System Engineering Karachi Pakistan NED University of Engineering and Technology Dept. of Computer Science and I.T NEDUET Karachi Pakistan

ISBN: (纸本)9781665461412

This Genetic disorder is considered as one of the major challenges faced by Medical sciences as it is inherited by humans from one generation to another. With an increasing number of natives of the World at a higher rate, it causes a major impact on information society socially and economically during their lifetime. The citizens of Pakistan and Indian sub continents are facing genomic disease in probably high frequency as compared to other continents. Occurrence of congenital diseases in Pakistan results in higher percentages of unusual genetic diseases especially in children. After analyzing all the current scenarios, some goals and objectives were settled in order to facilitate and give general awareness to people. The objectives of systematic review are evaluation of diagnostic accuracy of AI for the identification of genome contributions to the pathogenesis of Huntington's disease through facial probe AI to accurately identify genetic disorders. The narrative review is performed in Ned university. In this proceeding sequence, 30 cases of both Normal and Genetically affected Patients were documented in which some certain sorts of tests were performed by using facial recognition. For this FR test, at first a combo list of information of all six emotions of a human being i.e. (Happy, Sorrow, Amazed, Fear, Anger, Revulsion) is gathered in the form of mutated images, which was then later be arranged into a pseudo-random order. Now with the help of Vision technology we have applied some CV Techniques for the extraction of large pattern sets of features. Lastly the purpose of ML and DL techniques is to receive and compare those morphed image data from the Facial Database in order to identify each of six different emotions of HDNormal Person. This whole procedure is conducted in real time. Hence in this way we may recognize the HD genetic disorder among the people in an efficient way. The detailed procedure is explained in the Methodology section of this narrative revi

关键词： Genome

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Smart Conversations: Enhancing User Engagement through NLP in IoT Environments 11

Smart Conversations: Enhancing User Engagement through NLP i...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Muthugurunathan, G. Padmapriya, S. Leelavathy, L. Talukdar, Veera Gupta, Ankur Mittal, Manisha Madanapalle Institute of Technology & Science Dept. of Cse Andhra Pradesh India Srm Trichy Arts and Science College Dept. of Computer Science TN Irungalur India D y Patil International University Dept.of Cs Akurdi Maharashtra Pune India Vaish College of Engineering Dept.of Cse Haryana Rohtak India Guru Tegh Bahadur Institute of Technology Ggsipu Dept. of Ece New Delhi India

ISBN: (纸本)9798350350357

The integration of NLP into IoT settings has emerged as a revolutionary framework, providing consumers with a more intuitive and participatory experience. This study examines the capacity of NLP to improve user involvement in IoT ecosystems by facilitating intelligent interactions between people and linked objects. The exponential growth of IoT devices has resulted in a surge of data, necessitating the development of efficient and user-friendly interfaces to facilitate smooth interactions. NLP is crucial in connecting people and IoT devices by enabling the comprehension and production of language that resembles human communication. By adding NLP, IoT environments may give users with a conversational interface, allowing them to manage and monitor equipment with natural language instructions. This study focuses on doing a comprehensive examination of current NLP approaches and their suitability for use in IoT environments. This work examines the problems and potential of integrating NLP in various IoT situations. It specifically focuses on tackling concerns related to language variety, contextual comprehension, and the need for real-time processing. Moreover, the research examines the influence of intelligent dialogues on user involvement, highlighting the possibility of heightened user contentment, effectiveness, and availability. Case studies and practical examples demonstrate effective use of NLP in IoT applications. © 2024 IEEE.

关键词： Natural language processing systems

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APOA based Multi-scale Parallel Convolution Blocks with Hybrid Deep Learning for Gastric Cancer Prediction from Endoscopic Images 1

APOA based Multi-scale Parallel Convolution Blocks with Hybr...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Padthe, Adithya Mathapati, Mahantesh Priyanka, M.S. Nandihal, Priya University of the Cumberlands Department of Information Technology United States Dept.of Cse India Nitte Meenakshi Institute of Technology Dept. of Aiml Bengaluru India Dayananda Sagar Academy of Technology and Management Dept of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350316469

In terms of mortality rates, gastric cancer is second only to lung cancer. Manual gastric slice pathology examination is labor-intensive and prone to observer bias. Endoscopy of the upper digestive tract is commonly used for the screening of gastric cancer. An object identification model, a kind of deep learning, was presented as a means of automating the diagnosis of early stomach cancer using endoscopic pictures. However, difficulties were encountered while attempting to reduce the sum of false positives in the identified findings. Tumour segmentation from the preprocessed pictures was carried out in this study, which is often more challenging and crucial. The research suggests a productive approach that makes use of multi-scale parallel convolution blocks (MPCs). Multi-scale parallel convolutions (MPCs) use filters of variable sizes to extract characteristics that are relevant across a range of tumour sizes. To further aid feature extraction with fewer parameters, residual connections and residual blocks can be used. In addition, the suggested study uses an Artificial plant optimisation algorithm (APOA) to fine-tune the segmentation model's parameters without resorting to post-processing approaches. Finally, gastric cancer detection from endoscopic pictures is accomplished using a hybrid classification strategy that incorporates Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Experiments employing 1208 photos from healthy people and 533 photographs from patients with stomach cancer examined detection performance using the 5-fold cross-validation approach. These findings show promise for the suggested method's application in automated early stomach cancer diagnosis using endoscopic images. © 2023 IEEE.

关键词： Artificial plant optimization algorithm Convolutional Neural Network Endoscopic images Gastric cancer Multi-scale parallel convolution blocks Recurrent Neural Network

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Bidirectional Laser Based OCDMA-VLC System for Smart Home Applications 6

Bidirectional Laser Based OCDMA-VLC System for Smart Home Ap...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Arya, Vivek Kumari, Meet Chauhan, Rahul Sharma, Neha Dept. of ECE FET Haridwar India Chandigarh University Dept.of ECE Mohali Gharuan India Graphic Era Hill University Computer Science and Engineering Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9798350304480

A bidirectional symmetric 3 x35Gbps optical code division multiple access (OCDMA) with visible light communication (VLC) system is presented. The performance is measured using multi-diagonal (MD) code for different users with channel impairments. The simulation results indicate that faithful VLC range of 45m is achieved successfully. Also, the system employing MD code can supports 80 numbers of end users in both downlink as well as upstream transmission. Also, the system shows that best performance over other existing ones for smart home applications. © 2023 IEEE.

关键词： multi-diagonal (MD) optical code division multiple access (OCDMA) smart home visible light communication (VLC)

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Bi-Directional Gated Recurrent Unit Approach for Detecting and Classifying High Impedance Fault in Power Distribution 3

Bi-Directional Gated Recurrent Unit Approach for Detecting a...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Irfan, Md. Mujahid Supriya, R. Reddy, P. Malleswara Alabdeli, Haider Gurumoorthy, Sasikumar Sr University Department Of Electrical And Electronics Engineering Warangal India Nitte Meenakshi Institute Of Technology Dept.of Ise Bengaluru India Department Of Eee Ananthapuramu Gooty India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq School Of Engineering And Technology Dhanalakshmi Srinivasan University Computer Science And Engineering Tamil Nadu Tiruchirappalli India

ISBN: (纸本)9798350318609

In the recent days, detecting the High Impedance Faults (HIF) is an extremely difficult task because it is unpredictable, asymmetric, and nonlinear. Due to the size of the fault current is typically much lower than the normal load current, these faults are typically undetectable and impossible to isolate using traditional over-current approaches. Therefore, this research introduced a novel technique name called Bi-directional Gated Recurrent Unit (Bi-GRU) for power distribution-related HIF detection and isolation. The devised technique makes use of voltage as well as data from current sensors. In order to clarify the procedure, many types of algorithms were utilized during the detecting phase. For classification purposes, the algorithm Bi-GRU is applied on IEEE 13- distribution of node networks to test this method's ability to recognize and detect HIF with both damaged and unbroken wires. The suggested Bi-GRU provides fast and flawless output when troubleshooting high impedance issues. Bi-GRU considerably increases input processing and enhances classification accuracy. From the overall analysis, suggested Bi-GRU combines the forward and reverse GRUs in reverse order, which allows it to complete the operation more quickly and accurately than existing Morphological Fault Detector by achieving 95.63 % accuracy rate. © 2024 IEEE.

关键词： Principal component analysis

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