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International Conference on Power engineering and Intelligent Systems, PEIS 2023

作者： Maurya, Vijendra Kumar Kothari, Rakshit Sachdev, Payal Rathore, Narendra Singh Associate Professor Department of Computer Science and Engineering Geetanjali Institute of Technical Studies Dabok Udaipur India Assistant Professor Department of Computer Science and Engineering Geetanjali Institute of Technical Studies Dabok Udaipur India Campus Director Geetanjali Institute of Technical Studies Dabok Udaipur India

ISBN: (纸本)9789819972159

The use of smart grid technologies, in the energy sector has brought about upgrade, such as high efficiency, lower operating costs and better energy management. However, it has also established security aspects. Safeguarding the smart grid is of importance due to its role as essential infrastructure. The innovative potential of blockchain holds promise in enhancing the security of the grid. This paper aims to explore how blockchain technology can be utilized for protecting the grid. It delves into the security issues associated with grids and investigates how blockchain can address them. Additionally, it provides an analysis of studies on blockchain and its application, in grid protection while considering the pros and cons of utilizing blockchain based solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Smart power grids

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A Novel Adaptive Fault Tolerance Algorithm Towards Robust and Reliable Distributed Applications to Reuse System Components 10th

A Novel Adaptive Fault Tolerance Algorithm Towards Robust an...

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10th International Conference on Innovations in computer science and engineering, ICICSE 2022

作者： Banothu, Lalu Chandra Mohan, M. Sunil Kumar, Charupalli Professor Department of Computer Science and Engineering JNTUH College of Engineering Hyderabad India Professor & Dean in CSE Apollo University Andhra Pradesh Chittoor India

ISBN: (纸本)9789811974540

Distributed component-based systems relay on components that run in different servers located geographically. In large distributed applications experiencing faults is common due to complexity, heterogeneity and a host of other reasons. Therefore, realizing a reliable and fault-tolerant distributed system is challenging. Existing techniques such as N-version programming and modular redundancy have certain drawbacks such as increased complexity and error prone. In this paper, we proposed an algorithm known as Adaptive Fault Tolerance (AFT) algorithm which evaluates different functions or components in the system and assigns weights to them besides following a novel redundancy strategy. The algorithm exploits function calls and frequency of calls to arrive at finding importance of functions. This know-how helps in identifying functions that are vulnerable and the functions that are reliable. The AFT helps in achieving fault-tolerant distributed component-based applications comprising of untrusted components. Experimental results showed that AFT outperforms many existing fault-tolerant approaches. Thus, the proposed algorithm helps in realizing robust and reliable distributed applications that reuse components and drive home businesses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fault tolerance

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A novel enhanced lemurs optimization-based hybrid attention network for classifying arrthymia from ECG signal using optimal weighted feature

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Australian Journal of Electrical and Electronics engineering 2024年第3期21卷 311-335页

作者： Wajgi, Rakhi Brammya, G. Ramesh, Gopisetty Deotare, Vilas Vasudeo Govindasamy, Chinnaraj Preetha, N.S. Ninu Department of Computer Technology Yeshwantrao Chavan College of Engineering Nagpur India Department of Electronics and Communication Engineering Resbee Info Technologies Private Limited Chennai India Department of EIE CVR College of Engineering Mangalpalli India Professor in E&TC Savitribai Phule Pune University Pune India Department of Computer Science and Engineering Saveetha School of Engineering Kuthambakkam India

An arrhythmia is just a disease associated with irregular heart rhythm. Any kind of arrhythmia can be determined with an ECG test. However, this requires long and excellent-quality of ECG samples. This makes an affordable, reliable, and automated system for recognizing and diagnosing arrhythmias. To meet the aforementioned criteria, a new technique with the aid of an attention-aided deep learning approach is proposed to classify arrhythmia in this paper. "Higher-order statistical (HOS)" features are extracted from the original EEG signal and the deep features are obtained from the analyzed QRS complex signals and RR interval by means of a "One-Dimensional Convolution Neural Network (1DCNN)". The Risk Rate Enhanced Lemurs Optimization Algorithm (RRE-LOA) is utilized to modify the weight, and the extracted features are selected optimally before carrying out the feature selection followed by feature concatenation with weight fusion in order to generate the Weighted Fused Features (WFF). A range of indicators is used to compare the performance of the executed classification model with the traditional models. The results demonstrate that the proposed methodology for classifying arrhythmias offers an enhanced performance while classifying the arrhythmia thus aiding in faster treatment of the arrhythmia disease. ©, Engineers Australia.

关键词： Electrocardiograms

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Real-time Face mask Detection Using Deep Learning and MobileNet V2

Real-time Face mask Detection Using Deep Learning and Mobile...

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International Conference on VLSI and Microwave and Wireless Technologies, ICVMWT 2021

作者： Verma, Akshay Srivastava, Manish Kumar Master of Technology Department of Computer Science and Engineering MMM University of Technology Gorakhpur India Assistant Professor Department of Computer Science and Engineering MMM University of Technology Gorakhpur India

ISBN: (纸本)9789811903113

COVID-19 has affected the whole world severely. Lockdowns and quarantines are imposed all over the world to prevent its spread. Hand sanitizers and face masks were made compulsory for individuals to apply for safety of their own and their society. This project will check the presence or the absence of masks on the face of a person. There could be more than a single person in the input provided, and the input could vary from images to GIFs to Livestreams. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer vision

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Real-Time Sign Language Recognition System for Physically Challenged Community Using Deep Learning Technique 8th

Real-Time Sign Language Recognition System for Physically Ch...

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8th International Conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Devi, V. Anjana Bhuvaneswari, E. Tummala, Rama Krishna Professor Department of Computer Science and Engineering Rajalakshmi Institute of Technology Tamil Nadu Chennai India Associate Professor Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamil Nadu Chennai India Accenture Solutions Pvt. Ltd Chennai India

ISBN: (纸本)9789819784219

Language disparities have always existed in human civilization. In their community, deaf individuals use sign languages to communicate with one another, but it can be difficult to do so when interacting with the wider population. The project comprises a real-time sign language recognition system that enables hearing-impaired individuals and persons who do not know sign language to effortlessly communicate. The most used sign language in South Asian nations is Indian sign language. Convolution neural networks, specifically Tensor Flow object detection, are utilized to assess and train the model on the images that are used. The transfer training approach is used to train the deep learning model with various hand movements. Loss is anticipated to be decreased. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Selective Text Encryption Using RSA for E-governance Applications for Pdf Document

Selective Text Encryption Using RSA for E-governance Applic...

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International Conference on Network Security and Blockchain Technology, ICNSBT 2023

作者： Adhikari, Subhajit Karforma, Sunil Assistant Professor BSH Department Institute of Engineering and Management University of Engineering and Management Kolkata India Research Scholar Department of Computer Science University of Burdwan Burdwan India Faculty Department of Computer Science The University of Burdwan Burdwan India

ISBN: (纸本)9789819944323

When data travels through an insecure medium, security must be enforced. The confidentiality of the exchanged data must be guaranteed with the help of encryption techniques. Selective encryption is a very powerful tool for encrypting textual data in a resource constrained environment. In the context of e-governance, textual data is very important and must be protected from any kind of security threat using selective text encryption. In this paper, a fast and efficient selective encryption technique based on an RSA asymmetric key encoding approach is presented. After fetching the whole textual information in the encryption phase, the user will search for a particular word or phrase using regular expression. After that, the selected data will be encrypted using the RSA-1024-bit algorithm and written to a new encoded document with the remaining text data. In the phase of decoding the data, only the encoded text of the document is considered. The experimental result confirms that the encrypting method is secure in terms of statistical security tests and fast in terms of computation time. Our encoding method can be implemented to encrypt any multimedia data like images, audio, and videos. This proposed technique can be used in IOT devices where resources are limited. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cryptography

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Plant Disease Detection Using Convolution Neural Network 2

Plant Disease Detection Using Convolution Neural Network

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Rani, K. Pushpa Lavanya, G. Madhuri, K. Sai Vamsinath, J. Sabitha, Ch. Rao, K. Srinivas Department of Computer Science and Engineering MLR Institute of Technology Dundigal Hyderabad 500043 Assistant Professor Department of CSE VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur 522302

ISBN: (纸本)9798350370188

Today the persistence of plant and plant diseases is a substantial problem for cultivators, disease diagnosis is important in the farm. To preserve the production of crops, adequate consideration is a must. To isolate and detect infections in conventional situation, a lot of workforce, time, and frequent surveillance of the farm is involved. If no proper safety measures are not taken in protecting the crops, then it may damage the production often diseases make the plants weak by interrupting the photosynthesis. This paper shows in-depth study methods used in disease diagnosing. © 2024 IEEE.

关键词： Convolutional neural networks

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Performance analysis of machine learning algorithms on automated sleep staging feature sets

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CAAI Transactions on Intelligence Technology 2021年第2期6卷 155-174页

作者： Santosh Satapathy D Loganathan Hari Kishan Kondaveeti RamaKrushna Rath Puducherry Research Scholar of Computer Science and EngineeringPondicherry Engineering CollegePuducherryIndia Professor of Computer Science and Engineering Pondicherry Engineering CollegePuducherryPuducherryIndia Assistant Professor of Computer Science and Engineering VIT UniversityAmaravatiAndhra PradeshIndia Research Scholar of Computer Science and Engineering Anna UniversityChennaiIndia

With the speeding up of social activities,rapid changes in lifestyles,and an increase in the pressure in professional fields,people are suffering from several types of sleep-related *** is a very tedious task for clinicians to monitor the entire sleep durations of the subjects and analyse the sleep staging in traditional and manual laboratory environmental *** the purpose of accurate diagnosis of different sleep disorders,we have considered the automated analysis of sleep epochs,which were collected from the subjects during sleep *** complete process of an automated approach of sleep stages5 classification is majorly executed through four steps:pre-processing the raw signals,feature extraction,feature selection,and *** this study,we have extracted 12 statistical properties from input *** proposed models are tested in three different combinations of features *** the first experiment,the feature set contained all the 12 *** second and third experiments were conducted with the nine and five best *** patient records come from the ISRUC-Sleep *** highest classification accuracy was achieved for sleep staging through combinations with the five feature *** the categories of the subjects,the reported accuracy results were found to exceed above 90%.As per the outcome from the proposed system the random forest classification techniques achieved best accuracy incomparable to that of the other two classifiers.

关键词： sleep staging diagnosis

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An optimal group-wise non-rigid image registration technique based clinical decision support for dementia prediction

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Multimedia Tools and Applications 2025年 1-26页

作者： Mary, F. Sheeja Asanambigai, V. Fred, A. Lenin Assistant Professor Senior Grade School of Computing Dept. of CSE Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India Computer Science and Engineering Annamalai University Chidambaram India Dept. of CSE Mar Ephraem College of Engineering and Technology Marthandam India

The fundamental challenge in clinical practice is predicting whether patients with cognitive symptoms or impairment will improve or stay stable. Population ageing is a worldwide phenomena with various impacts. Among dementia, Alzheimer disease (AD) is common in the elderly which is the most common form of dementia and may contribute to 60–70% of cases. An early and precise AD diagnosis enhances patient quality of life. In this study, we propose an optimal group-wise non-rigid image registration technique for dementia (AD) prediction. We introduce an improved cuckoo search optimization (ICSO) algorithm to search optimal group-wise dense correspondence in large image sets in non-rigid image registration which reduce dimensionality of the search space. Then, we illustrate a teacher-student learning based optimization algorithm which cooperates with PLS regression to extract deformation which separates the relationship information suitable for each other from the raw relationship information. After that, we compute the distribution of morphologies of age by fitting smooth percentile curves to these scores using hybrid capsule neural network with least Median Square (hybrid CNN-LMS) curve fitting model. Finally, the simulation results of proposed hybrid CNN-LMS technique are compared to the existing methods to analyze the measures. We observed that the accuracy of our proposed hybrid CNN-LMS technique is 96.23% and 95.82% for RSS and ANDI datasets respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Neurodegenerative diseases

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Semantic Segmentation of Areal Images using Pixel Wise Segmentation

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Procedia computer science 2025年 259卷 463-472页

作者： Swathi Gowroju V. Swathi Narsimhulu K. Shilpa Choudhary Associate Professor Department of Artificial Intelligence & Machine Learning Sreyas Institute of Engineering and Technology Hyderabad Telangana Assistant Professor Department of Computer Science Sreyas Institute of Engineering and Technology Hyderabad Telangana Assistant Professor Department of Computer Science Matrusri Engineering College Hyderabad Telangana Associate Professor Department of Computer Science NGIT Hyderabad Telangana

Particularly in regions hit by flooding, precise semantic segmentation of aerial photographs is essential for post-disaster evaluation and urban planning. The high-resolution aerial photography collection FloodNet, created for pixel-by-pixel segmentation of flooded and non-flooded landscapes, is presented in this work. Aerial photos taken from various geographic regions make up the collection, and segmentation masks are used to detect flooded roads, houses, cars, plants, and other vital infrastructure. In order to conduct semantic segmentation on aerial photos and capture the fine-grained information necessary for flood damage assessments, the suggested model makes use of a U-Net architecture. Morphological post-processing is also used to improve object delineation and smooth segmentation borders. The model showed consistent performance with little overfitting, achieving training accuracy of 84% and validation accuracy of 81% over 200 epochs. Effective generalization to unknown data was demonstrated by the validation loss stabilizing at 0.55 and the training loss declining from 1.1 to 0.45. The findings show that the suggested method can reliably identify and categorize areas damaged by flooding from aerial photos, enabling automated flood assessment and speeding up disaster relief activities. There is a lot of promise for integrating this model and dataset combination into real-time catastrophe monitoring, urban planning, and geospatial analytic pipelines.

关键词： Satellite images areal images semantic segmentation UNet using morphology Pixel wise segmentation

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