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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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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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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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Blockchain Technology for Secure Smart Grid Access Control

Blockchain Technology for Secure Smart Grid Access Control

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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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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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Intelligent Irrigation Mechanism to Enhance The Sensor Network Topology by Using Machine Learning And Artificial Intelligence 2

Intelligent Irrigation Mechanism to Enhance The Sensor Netwo...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Sriram, K.P. Anbalagan, E. Sasikumar, S. Kumar, M. Guru Vimal Paramesh, J. Sujatha, P. Kola Assistant Professor Information technology St.Joseph's Institute of Technology OMR Chennai – 600119 Professor Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Professor Department of Computer Science and Engineering Saveetha Engineering College Chennai Associate Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Professor Department of Computer Science and Engineering Mohamed Sathak A.J. College of Engineering Ekattur OMR Siruseri Chennai Associate Professor Department of Information Technology MIT Campus Anna University Chennai Tamil Nadu India – 600044

ISBN: (纸本)9798350383867

This research paper is about the effectiveness of a fusion of machine learning and artificial intelligence into smart irrigation systems in terms of both safety and benefit of the environment and the economy in farming. We've experienced a intensive process of experiment and application of this succeeded method in a real world scenario and we've also detailed the performance of the dynamic irrigation scheduling and the adapter sensor network optimization. Results indicate a significant improvement in key metrics: scheduling was done dynamically which on average was a 10% rise in crop yield and 15% water saving, on the other hand, sensor network optimization with adaptive technology resulted into a 15% increase in cover area and 20% improvement achieved in data transmission reliability. Furthermore, the introduction of the smart irrigation system got impressive results signified by a 10 kg more production per hectare, irrigation efficiency that rose from 70% to 85%, and saving 20% of the water consumed. This calls to mind the capability of cutting-edge technologies to contribute to innovative water management paradigms, improved resource use, and security in food production in face of unpredictable climatic variability. ©2024 IEEE.

关键词： Adversarial machine learning

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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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Identification of Autism Spectrum Disorder from Functional MRI Using Deep Learning 4th

Identification of Autism Spectrum Disorder from Functional M...

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4th International Conference on Advances in Information Communication Technology and Computing, AICTC 2022

作者： Pavithra, R. Abirami, S.P. Krithika, S. Sabitha, S. Tharanidharan, P. Assistant Professor Department of Computer Science and Engineering Coimbatore Institute of Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering Coimbatore Institute of Technology Tamil Nadu Coimbatore India

ISBN: (纸本)9789811998874

Autism spectrum disorder (ASD) is a collection of a way of continuing neurodevelopment illnesses identified by limited and repetitive behavioral patterns, as well as social and communication impairments. Despite the fact that symptoms are most common in childhood, diagnosis is sometimes delayed. Because the current ASD diagnostic technique is solely uncertain and questionnaire, requiring the physician to review the behavior and developmental history of a child. It's been suggested that behavioral symptoms in ASD are linked to brain findings of increased short-distance and diminished long-distance connections. The suggested approach makes use of brain imaging data from Autism Brain Imaging Data Exchange (ABIDE), a global multi-site database. For ASD identification, the proposed approach uses functional connectivity characteristics extracted from resting state functional MRI data. ASD patients’ brain connections could be disturbed. The presented method extracts time series from 122 regions of interest defined by the Bootstrap Analysis of Stable Clusters (BASC) and also extracts time series from 48 regions of interest defined by the Harvard Oxford (HO) brain atlas to create an efficient functional connectivity matrix for all individuals. Synthetic Minority Over-sampling Technique (SMOTE) an oversampling procedure employed for artificial data generation. The classification challenge is carried out using a Recurrent Convolutional Neural Network (RCNN) and Convolutional Neural Network (CNN) and results were compared. In both models, fivefold Cross-Validation is applied. The model performed better in the modified experiment, with RCNN achieving an accuracy of nearly 85%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electronic data interchange

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A novel random finite element model for holistically modeling of the frost effects on soils and cold region pavements

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Journal of Infrastructure Preservation and Resilience 2022年第1期3卷 1-14页

作者： Dong, Shaoyang Jiang, Yusheng Yu, Xiong Graduate Research Assistant Department of Civil and Environmental Engineering Case Western Reserve University ClevelandOH United States Opal J. and Richard A. Vanderhoof Professor and Chair Department of Civil and Environmental Engineering Professor of Electrical Engineering and Computer Science Case Western Reserve University ClevelandOH United States

This paper describes the development of a random finite element model (RFEM) that allows holistic simulation of frozen soil behaviors, including the effects of phase transition and the consequent internal stress and volume changes. The performance of the model is firstly validated with laboratory experiments. The model is implemented to simulate the effects of frost action on pavement. The coupled thermal-mechanical actions including the mechanical responses of subgrade soils subjected to freezing temperature and its effects on the pavement structure are analyzed. The results show that the frost action and expansion of ice lenses change the interaction modes between pavement layers. This leads to increase of stress and deformation in the pavement layer. Methods to mitigate the effects of frost heave are analyzed with this model. The simulation results indicate that the detrimental effects of frost heave on the pavement structure can be mitigated by increasing the thickness of base layer, use of thermal insulation layer or improve drainage in the subgrade layer. The RFEM combines the advantages of discrete element model (DEM) in holistically describing the microstructure effects and in the finite element model (FEM) in terms of computational efficiency. This allows to focus research on understanding the behaviors of individual soils phase and their interfacial interactions. © The Author(s) 2022.

关键词： Pavements

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Effect of addition of glass fibre on sugar cane bagasse ash under compressive loading

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Materials Today: Proceedings 2022年 61卷 1109-1114页

作者： Nikhade, Harshal R. Ram Rathan Lal, B. Research Scholar Kavikulguru Institute of Technology and Science Assistant Professor Dept. of Civil Engineering Yashwantrao Chavan College of Engineering RTMNU Maharashtra India Dept. of Civil Engineering Kavikulguru Institute of Technology and Science Ramtek RTMNU India

This research defines the result of studies on a Sugar cane bagasse ash (SCBA) material through laboratory experiment. The material prepared with mix ratio (0.2 % to 1.0 %), Blast furnace slag as mix ratio proportion (10%), glass fibre as reinforcing materials, cement (10%,15% and 20%) and water (50%,60%) for mix in to homogeneous slurry were added to material. The consequence of mix ratio percentages on density, compressive strength are studied. It was observed that the density of materials with inclusion of glass fibre ranging between 1101 kg/m3 to 1326 kg/m3. The compressive strength of material increases as percentages of cement increase from 10 to 20%. The compressive strength of materials influenced considerably by mix ratio 0.2 % to 1.0 %. The stress–strain outline was observed to be nonlinear for every mix ratio percentage and curing duration. © 2021

关键词： Slags

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