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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Rajawat, Anand Singh Goyal, S.B. Goyal, Aarti Rajawat, Kavita Raboaca, Maria Simona Verma, Chaman Mihaltan, Traian Candin School of Computer Science & Engineering Sandip University Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Mind Intellect Technology Indore India National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea Romania Eötvös Loránd University Faculty of Informatics Department of Media and Educational Informatics Budapest Hungary Technical University of Cluj-Napoca Faculty of Building Services Cluj-Napoca Romania

ISBN: (纸本)9798350321388

This study examines how blockchain technology and consensus mechanisms can safeguard and grow the metaverse ecosystem. Blockchain is transparent and decentralized. It is unchangeable, cryptographically verified, and runs without a central authority. Blockchain technology can secure the metaverse and users' data. This study compares blockchain-based consensus algorithms such as POW(PoW), PoS, and PBFT to ensure metaverse safety. These consensus mechanisms provide safe, decentralized transaction verification, waste reduction, and network consensus. Studying blockchain technology's metaverse scaling restrictions and scaling solutions, including sharding, sidechains, and off-chain protocols. These methods increase throughput, parallel computing, and transaction speed. They improve the metaverse's eco-system. Quantitative analysis, simulations, and experiments determine the security and scalability of blockchain-based metaverse consensus processes. Changes will be quantified by measuring transaction speed, network throughput, and resource consumption. This study enables safe, scalable metaverse environments. Blockchain-based consensus mechanisms allow the metaverse to retain user data safely, halt undesirable conduct, and allow users to join without trusting each other. Scalability solutions declutter networks, enabling the metaverse to handle more individuals and transactions. Blockchain-based consensus mechanisms could make the metaverse more secure, private, and scalable. Users will trust each other, create new apps, and be more open to this groundbreaking digital world. © 2023 IEEE.

关键词： Scalability

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Energy Consumption in Wireless Sensor Networks using Genetic Algorithm and Ant Colony Optimization

Energy Consumption in Wireless Sensor Networks using Genetic...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Naziya Hussain Anoop Singh Raed Hamyd Divyaraj G N R Maranan Department of Computer Science and Engineering Mukesh Patel School of Technology Management & Engineering SVKM's NMIMS Deemed to be University Shirpur India Group of IPS Academy Indore India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of AI & ML Nitte Meenakshi Institute of Technology Bengaluru India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

Now-a-days adaptive scheduling plays a crucial role to reduce energy consumption in Wireless Sensor Networks (WSNs) by adjusting network operations on real-time conditions that increasing the network lifespan. Even though, the existing approaches faced challenges limited battery life and during data transforming and while balancing the energy consumption. To overcome these challenges, this research proposed a robust model using with Genetic Algorithm and Ant Colony Optimization (GA-ACO) model. Initially, Soft Actor Critic (SAC) model is utilized for adjusting transmission power and sensing duration. Further, a hybrid approach using GA- ACO is incorporated to optimize data routing and energy efficient paths. Then, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is employed for handling noise, outlier especially anomalous energy consumption patterns caused by sensor failures. Finally, Bayesian Optimization (BO) is imposed to learns the best energy configurations dynamically. the proposed SAC-GA-ACO attained improved results in terms of Prevalence Threshold ( Pth ) (91.52%), Critical Success Index (CSI) (92.18%), Computation of Delta-P ΔP (95.21%), MCC (91.44%) and Fowlkes-Mallows Index (FMI) (93.82%) when compared to existing Enhanced Energy Optimization Model (EEOM).

关键词： Energy consumption Wireless sensor networks Ant colony optimization Computational modeling Optimization models Noise Sensors Optimization Genetic algorithms Load modeling

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DETR-DC5 Approaches for Improved Spatial Object Detection in Satellite Imagery

DETR-DC5 Approaches for Improved Spatial Object Detection in...

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Intelligent Computing and Control for engineering and Business Systems (ICCEBS)

作者： Radhika Baskar Rajat Kumar Dwibedi Makhan Kumbhkar Swati Sah Harshal Patil Firos A Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Aarupadai Veedu Institute of Technology VMRF Chennai India Department of Computer Application Indian Institute of Soybean Research Indore India School of SET Sharda university Greater Noida India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Department of computer Science and Engineering Rajiv Gandhi University (A Central University) Rono-Hills Doimukh Arunachal Pradesh India

Satellite spatial object detection is crucial for urban planning, environmental monitoring, and catastrophe management. The DETR-DC5 (Data Efficient Transformer with DC5 Backbone) is a cutting-edge object detection model. This study introduces DETR-DC5 framework-based satellite spatial object detection improvements. DETR-DC5's strengths are used to improve feature extraction and post-processing. First, a specific feature augmentation technique extracts richer and more discriminative spatial features to let the model capture subtle object attributes. Second, a semi-supervised learning training technique is used to address the lack of labeled satellite imagery data and improve the model's generalization. A new satellite imagery post-processing refining method uses non-maximum suppression methods. This refinement stage reduces false positives and improves localization, especially in densely populated metropolitan regions and complicated natural surroundings. On benchmark satellite imagery datasets, the suggested methods outperform baseline DETR-DC5 models in spatial object detection. Experimental findings indicate significant improvements in precision, recall, and F1-score, proving the upgrades work. This study uses the DETR-DC5 architecture, targeted feature augmentation, semi-supervised training, and refined post-processing algorithms to recognize spatial objects in satellite data. These techniques have the potential to dramatically improve satellite-based object detection, which has implications for urban planning, environmental monitoring, and disaster response.

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IoT enabled Wireless Sensor Networks Novel Tree-based Efficient Data Aggregation Dissemination: EDOT

IoT enabled Wireless Sensor Networks Novel Tree-based Effici...

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2021 International Conference on Technological Advancements and Innovations, ICTAI 2021

作者： Dhotre, Virendrakumar Anna Borole, Yogini Dilip Verma, Avnish Raj Shrivastava, Amit Tikore, Swapna Vijay Hemavathi, S. Maharishi University of Information Technology Department of Computer Science and Engineering Lucknow India Gh Raisoni Institute of Engineering and Technology Department of Electronics and Telecommunications Engineering Pune India Svits University Department of Cse Indore India Oriflame Private Limited Pandharpur India Central Electrochemical Research Institute Csir Chennai India

ISBN: (纸本)9781665420877

Randomly remote sensors are conveyed to shape a specially appointed organization in a non-deterministic environment. Different hierarchical clustering supporting algorithms are available for efficient coverage, information processing and connectivity in the literature. For increasing efficiency of these highly supporting tiny notes very few researchers have considered employing mobile agents. Affirmation of detected information transmission to the sink hub is one of the key problems for WSNs in the presence of failed sensor nodes. For the data aggregation and dispersion styles this arises as a severe problem. The strategy of information collection, becomes the key necessities, for example, consider less number of messages for assembling steering tree, huge number of covering courses, guaranteed data transmission, and increased data complete rate. Work on the course of information conglomeration and transmission is the aim of EDOT as its name indicates. EDOT protocol has a good property is to fabricate directing tree with a modest number of messages, higher covering ways in number, expanded collection rate, information, accumulation, dependability and assured information spread by most brief way tree (SPT) and information combination arrangement. EDOT actual purpose is built with the shortest paths energy efficient and reliable tree connecting all the source sensor hubs to the planned sink increasing performance of information accumulation and dispersal. When contrasted with condition of-workmanship conventions the recreation results requested that EDOT works on the exhibition. © 2021 IEEE.

关键词： Mobile agents

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Improving IoT Security Using Elliptic Curve Integrated Encryption Scheme with Primary Structure-Based Block Chain technology 4th

Improving IoT Security Using Elliptic Curve Integrated Encry...

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4th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2022

作者： Chauhan, Chetan Ramaiya, Manoj Kumar Rajawat, Anand Singh Goyal, S.B. Verma, Chaman Raboaca, Maria Simona Faculty of Engineering and Technology SAGE University Indore India School of Computer Science and Engineering Sandip University Maharshtra Nashik India Faculty of Information Technology City University Malaysia Department of Media and Educational Informatics Faculty of Informatics Eötvös Loránd University Budapest1053 Hungary ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technolog Ramnicu Valcea240050 Romania

Security and privacy remain as a significant challenge in the distributed Internet of Things (IoT) framework. IoT has already demonstrated that it can significantly impact our daily lives. The internet sends and receives a large volume of data from different devices present worldwide. IoT comprises of heterogeneous units present across a wide variety of networks to communicate sensitive and non-critical data, resulting in significant challenges in terms of protecting and ownership of the data collected from users. The Internet security architecture developed in a constrained location, such as healthcare, is presented in this research work. Across a range of networks and PCs, the system offers safe data sharing and authentication mechanism. The proposed design is based on the blockchain technology's underlying structure and the usage of secure hazarding. This research study has attempted to propose a technique for improving IoT security using elliptic curve integrated encryption scheme with primary structure based blockchain technology. © 2023 The Authors. Published by Elsevier B.V.

关键词： Blockchain

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Design, Modelling, and Analysis of Novel Solar PV System using MATLAB

Design, Modelling, and Analysis of Novel Solar PV System usi...

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International Conference on Computations in Materials and Applied engineering, CMAE 2021

作者： Banik, Ayan Shrivastava, Anurag Manohar Potdar, Ravindra Kumar Jain, Sanjiv Gopal Nagpure, Santosh Soni, Mukesh National Institute of Technical Teachers' Training and Research Kolkata India Principal and Professor ECE Lakshmi Narain College of Technology and Science Indore India Department of EandTC Engineering Bhilai Institute of Technology Chhattisgarh India Department of Electrical Engineering Medi-Caps University Indore India Department of EandTC Engineering D. Y. Patil School of Engineering Academy Pune India Department of Computer Science and Engineering Jagran Lakecity University Bhopal India

In order to meet the world's future energy demand, alternative renewable green energy supplies must be widely promoted. Among all DERs, the most prevalent is Solar PV for a number of important reasons. In this study, the authors analyze several elements of the Single-Phase Solar Photovoltaic Rooftop System with MATLAB. In order to analyze the characteristics of the output current, a new grid synchronization methodology has been devised. For maximum power tracking, the INC control technique has been adopted. The output of the solar DC power system has been synchronized with the grid through hysteresis current loop control. Extensive testing verifies the practicality of the proposed power grid model and promising outcomes and results. © 2021 Elsevier Ltd. All rights reserved.

关键词： Electric inverters

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Geospatial Object Detection in Hyperspectral Imagery Using Spectral-Spatial Networks

Geospatial Object Detection in Hyperspectral Imagery Using S...

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Intelligent Computing and Control for engineering and Business Systems (ICCEBS)

作者： Prabakar D S. Pranavan Makhan Kumbhkar Swati Sah Harshal Patil Firos A Professor/Computer Science and Engineering Karpagam College of Engineering Coimbatore India Department of Civil Engineering Dhanalakshmi Srinivasan College of Engineering Navakkarai Coimbatore India Young Professional I Department of Computer Application Indian Institute of Soybean Research Indore India School of SET Sharda university Greater Noida India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Department of computer Science and Engineering Rajiv Gandhi University (A Central University) Rono-Hills Doimukh Arunachal Pradesh India

By recording a wide range of spectral bands across the electromagnetic spectrum, hyperspectral imagery (HSI) delivers extensive information. Due to the complicated interaction between spectral and spatial properties, extracting meaningful geographical information from HSI is difficult. This article uses Spectral-Spatial Networks to detect geographical objects in hyperspectral data. The proposed deep learning system blends spectral and spatial information to improve object discrimination in complicated scenarios. SSN uses convolutional neural networks (CNNs) and spectral attention mechanisms to adaptively weigh spectral bands and capture spatial correlations. A large dataset of annotated hyperspectral photography with ground truth object identification trains the SSN. The collection includes different lighting, topography, and item scales. The object detection task is fine-tuned after pre-training on a large HSI dataset. Experimental results on benchmark hyperspectral datasets show that the SSN outperforms state-of-the-art approaches. Precision, recall, and F1-score for numerous object classes reveal that the suggested technique can distinguish objects in complicated and congested situations. The suggested SSN framework is also resilient to illumination, climatic conditions, and sensor properties, making it suitable for real-world applications. The SSN architecture's scalability is further examined, showing its suitability for heterogeneous HSI datasets with different spectral resolutions.

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Deep Learning and IoT Based Robotics to Monitor the Traffic 2nd

Deep Learning and IoT Based Robotics to Monitor the Traffic

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2nd International Conference on Cyber Warfare, Security and Space Computing, SpacSec 2024

作者： Priya, V. Vishwa Pande, Soumitra S. Ilyas, Md Jayasudha, R. Ramesh, Janjhyam Venkata Naga Suganthi, D. Department of Computer Science Vels Institute of Science Technology and Advanced Studies Chennai India Ericsson Global India Private Limited Bangalore India Prestige Institute of Engineering Management and Research Indore India Dr. N. G. P. Institute of Technology Coimbatore India Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur Vaddeswaram India Saveetha College of Liberal Arts and Sciences SIMATS Thandalam Chennai India

ISBN: (纸本)9783031734939

In densely populated areas, managing traffic laws has become more difficult due to the rising number of cars on the road. One of the most critical components of effective traffic management and improved mobility is real-time traffic monitoring systems. Consequently, precise and dependable real-time traffic information has always been essential for cars and drivers. Many other approaches have been suggested as potential answers to the issues plaguing traffic conditions recently. A different approach is VCC or vehicle cloud computing. In addition, an advanced model called an Iot-aided robotic (IoRT), which uses cameras and Internet of Things (IoT) detector nodes to gather actual traffic information, has been created. Two deep learning methods, one based on improved LeNet-5 for real-time signs for traffic identification and the other on the Inception-V3 model for detecting and identifying traffic lights, are the critical contributions provided by this research effort. Additionally, to decrease road accidents, the ideal distance between barriers and ultrasonic sensors was determined by analyzing the timing and speed of ultrasonic waves. Data acquired by cameras and sensors is analyzed using a variety of image processing algorithms before being uploaded to the cloud and made accessible to commuters and drivers via a mobile app. According to the test findings, the suggested models are far more accurate. The expanded GTSRB (EGTSRB) dataset achieved 98.89% accuracy while the German Traffic Sign Recognition Benchmark (GTSRB) dataset achieved 98.23% accuracy using the modified LeNet-5. The second model achieved a precision of 99.7 percent using data from the Laboratory for the Intelligent and Safe Automobiles (LISA). The results of this study beat previous research on comparable traffic monitoring systems by 2.13% for traffic light recognition and detection and by 4.89% for traffic sign identification. © The Author(s), under exclusive license to Springer Nature Switzerland AG

关键词： Traffic signs

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A deep Bi-CapsNet for analysing ECG signals to classify cardiac arrhythmia

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computers in Biology and Medicine 2025年 189卷 109924-109924页

作者： Anitha, T. Aanjankumar, S. Dhanaraj, Rajesh Kumar Pamucar, Dragan Simic, Vladimir Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India VIT Bhopal University Bhopal- Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India Pune India Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Department of Applied Mathematical Science College of Science and Technology Korea University Sejong30019 Korea Republic of Department of Mechanics and Mathematics Western Caspian University Baku Azerbaijan University of Belgrade Faculty of Transport and Traffic Engineering Vojvode Stepe 305 Belgrade11010 Serbia Yuan Ze University College of Engineering Department of Industrial Engineering and Management Taoyuan City320315 Taiwan

— In recent times, the electrocardiogram (ECG) has been considered as a significant and effective screening mode in clinical practice to assess cardiac arrhythmias. Precise feature extraction and classification are considered as essential concerns in the automated prediction of heart disease. A deep bi-directional capsule network (Bi-CapsNet) uses a new method based on an intelligent deep learning (DL) classifier model to make the classification process very accurate. Initially, the input ECG signal data are acquired and the preprocessing steps such as DC drift, normalization, LPF filtering, spectrogram analysis, and artifact removal are applied. After preprocessing the data, the Deep Ensemble CNN-RNN approach is employed for feature extraction. Finally, the Deep Bi-CapsNet model is used to predict and classify the cardiac arrhythmia. For performance validation, the dataset is referred to the MIT-BIH arrhythmia database, which selects five different types of arrhythmias from the ECG waveform to estimate the proposed model. Various ECG arrhythmia categories, including Normal (NOR), Right Bundle Branch Block (RBBB), Premature Ventricular Contraction (PVC), Atrial Premature Beat (APB), and Left Bundle Branch Block (LBBB) have been identified. For performance analysis, the metrics such as precision, accuracy, F1-score, error rate, sensitivity, false positive rate, specificity, Mathew coefficient, Kappa coefficient, and outcomes are included and compared with the traditional methods to validate the effectiveness of the implemented scheme. The proposed scheme has achieved an overall accuracy rate of approximately 97.19 % compared to the traditional deep learning models like CNN (89.87 %), FTBO (85 %), and Capsule Network (97.0 %). The comparison results indicate that the proposed hybrid model outperforms these traditional models. © 2025 Elsevier Ltd

关键词： Electrocardiograms

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Design, Modelling, and Analysis of Novel Solar PV System using MATLAB

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Materials Today: Proceedings 2022年 51卷 756-763页

作者： Ayan Banik Anurag Shrivastava Ravindra Manohar Potdar Sanjiv Kumar Jain Santosh Gopal Nagpure Mukesh Soni National Institute of Technical Teachers' Training & Research Kolkata India Principal and Professor ECE Lakshmi Narain College of Technology and Science Indore India Associate Professor Department of E&TC Engineering Bhilai Institute of Technology Chhattisgarh India Assistant Professor Department of Electrical Engineering Medi-Caps University Indore India Assistant Professor Department of E&TC Engineering D. Y. Patil School of Engineering Academy Pune India Research Scholar Department of Computer Science and Engineering Jagran Lakecity University Bhopal India

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