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

作者： Jagannathan, J. Ponmalar, A. Pandiarajan, Rajkumar Sudha, I. Ramesh, P.S. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India R.M.K. Engineering College Tamilnadu Chennai India School of Computing Kalasalingam Academy Of Research and Education Department of Information and Technology Krishnankoil Virudhunagar India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350388602

The research is aimed at improvement of multi-spectral image analysis for remote sensing and monitoring the environment at various ecosystems. A combined spectral imagery encompasses several spectrums at a time compared to single spectral imagery that takes only one spectrum at an instance or at a given area to cover all these spectrums. The study employs digital high resolution satellite images and ground based data to quantify the vegetation vigour, water quality, soil characteristics and changes in land cover. Features, indexes, and classification routines are employed to process the data that is collected with the aid of avionics and other peripherals. This provincial level modelling highlights important gains in environmental status, which includes the general improvement in vegetation condition, water quality as well as physical properties of the soil in the province. This study focuses on the complementary possibilities of integration of multi-spectral imaging with other remote sensing technologies and the high performance of data processing. These techniques play significant roles if used to give current and comprehensive information in the management and conservation of the environment as highlighted in the study. © 2024 IEEE.

关键词： data analysis environmental monitoring image processing techniques machine learning Multi-spectral imaging remote sensing soil properties vegetation health water quality

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Detection of Parkinson disease for handwriting dataset using deep learning algorithms 3

Detection of Parkinson disease for handwriting dataset using...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Malathi, A. Ramalakshmi, R. Gandhi, Vaibhav Anand Institute of Higher Technology Department of Computer Science and Engineering Chennai India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Srivilliputhur India Middlesex University Department of Design Engineering London United Kingdom

ISBN: (纸本)9798350399264

The most common neurodegenerative diseases are Parkinson's disease. People who have reached the age of 70 or older are affected. Parkinson's disease is the second incurable illness after Almeria's disease. The numerous characteristics that distinguish between people with Parkinson's disease and those who are healthy, and earlier diagnosis of the disease results in reduced disease severity. These are the typical signs of Parkinson's disease, which include both motor and non-motor deficiency in patients. The handwritten data set, which includes spiral and wave drawings, was retrieved from UCI equipment. EfficientnetB3 provides high accuracy in comparison to the current CNN model. To increase efficiency, EfflcientnetB3 shrinks the parameter size and equally scales each dimension using a predetermined set of scaling dimensions. The proposed model Adam -EfficientNet achieves 98.3 % which is the best accuracy compared to all other existing CNN models. © 2023 IEEE.

关键词： Neurodegenerative diseases

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GS-FFO Based Meta-Heuristic Clustering for Discovering Colossal Patterns in High-Dimensional Data 2nd

GS-FFO Based Meta-Heuristic Clustering for Discovering Colos...

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2nd Congress on Control, Robotics, and Mechatronics, CRM 2024

作者： Reddy, T. Sreenivasula Sathya, R. Nuka, Mallikharjuna Rao Research Scholar Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Annamalai Nagar Tamil Nadu Chidambaram608002 India Assistant Professor Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Annamalai Nagar Tamil Nadu Chidambaram608002 India Professor Department of Computer Applications Annamacharya Institute of Technology & Sciences YSR Kadapa Andhra Pradesh Rajampet516115 India

ISBN: (纸本)9789819768097

Colossal pattern mining, which involves deriving important relationships and insights from large and complex datasets, is an essential part of data analysis. Extracting association rules that satisfy preset minimum support and confidence criteria from a given database is the aim of association rule mining (ARM). Nevertheless, ARM often generates an unusually large number of association rules that are hard for end users to comprehend or validate, which lessens the significance of data mining discoveries. Based on the clustering methodology, this research study presents a new mining strategy. The process begins with a method called the Expressed Data Matrix (EDM), which transforms unstructured data into a structured format and enhances the quality and organisation of the information. Primary data cleaning processes are used to eliminate noise and inconsistencies from the data in order to further improve it. To address high-dimensionality issues and boost mining productivity, dimensionality reduction—which lowers the dataset to its most valuable features—is accomplished using Eigen-Principal Component Analysis (PCA). The core of this study is the hybridised clustering approach, or GS-FFO, which combines the Fruit Fly Optimisation technique (FOA) with Glow Swarm Optimisation (GSO). This technique improves the quality of clustering and reduces the probability of local optima convergence by integrating the benefits of both approaches. The suggested GS-FFO model outperforms with four datasets, including accident, pumbs*, retail, and yeast, with an accuracy of up to 98.5%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Dimensionality reduction

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Advanced Machine Learning Algorithms for 1Predictive Analytics in Healthcare to Enhance Patient Outcomes with Data-Driven Insights

Advanced Machine Learning Algorithms for 1Predictive Analyti...

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

作者： Pandiarajan, Rajkumar Jagannathan, J. Ramesh, P.S. Ponmalar, A. Sudha School of Computing Kalasalingam Academy Of Research and Education Department of Information and Technology Krishnankoil Virudhunagar India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India R.M.K. Engineering College Tamilnadu Chennai India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350388602

This research aims to explore the use of modern complex defensive machine learning algorithms in the provision of predictive analytics for health improvement. Incorporating electronic health records, medical image information, and genomic data the work proposes and assesses multiple machine learning models including deep learning, ensemble learning, and reinforcement learning. The main findings shown very high performance in disease prediction, in chronic illness and in the application of efficiency improvements in hospitals as a whole. Overall, such threats as data quality, data privacy, and model interpretability have been discussed in the research and emphasize the importance of the machine learning approach in healthcare. It is highlighted that further development and integration of the interdisciplinary approaches as well as the use of the transparent and interpretable artificial intelligence and machine learning models can be urgent to increase the acceptance among the healthcare staff. This paper enhances the literature on applying machine learning in healthcare systems adding to the repertoire of knowledge for improved patient care. © 2024 IEEE.

关键词： Electronic health record

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Role of Blockchain in Higher Education Industries: A Survey 3

Role of Blockchain in Higher Education Industries: A Survey

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Verma, Ravi Nigam, Shekhar Jain, Ruchi Jain, Nelesh School of Computing Science Engineering Vit Bhopal University Bhopal India School of Computer Science and Engineering Sam Global University Bhopal India Lnct & S Dept. of Computer Science and Engineering Bhopal India Trinity Institute of Technology and Research Dept. of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350343274

The Blockchain technology are recommended to add an extra security layer with banking, financial and education industries. The basic structure of Blockchain technology serve the security and privacy features using a concept of Block, Blocks has been mined in a way that distribute the transaction to other blocks of the Blockchain network these blocks are managing the data through transaction process in immutable and temper proof mode that makes Blockchain technology very secure throughout the mode of data transmission. In this study, we wish to examine how blockchain technology has impacted colleges in terms of information and technology in order to raise the standard of universities. Reviewing earlier research publications that are relevant to the study is the process utilized. As a result, it is anticipated that this research will make a positive impact by helping to solve issues with blockchain implementation in higher education, in the mean of how Blockchain transforms the education system and its role in near future with various growing industries as banking system, medical and education industries. © 2023 IEEE.

关键词： Smart contract

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Image Emotion Classification Network Based on Multilayer Attentional Interaction,Adaptive Feature Aggregation

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computers, Materials & Continua 2023年第5期75卷 4273-4291页

作者： Xiaorui Zhang Chunlin Yuan Wei Sun Sunil Kumar Jha Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China Adani University AhmedabadGujaratIndia

The image emotion classification task aims to use the model to automatically predict the emotional response of people when they see the *** have shown that certain local regions are more likely to inspire an emotional response than the whole ***,existing methods perform poorly in predicting the details of emotional regions and are prone to overfitting during training due to the small size of the ***,this study proposes an image emotion classification network based on multilayer attentional interaction and adaptive feature *** perform more accurate emotional region prediction,this study designs a multilayer attentional interaction *** module calculates spatial attention maps for higher-layer semantic features and fusion features through amultilayer shuffle attention *** layer-by-layer up-sampling and gating operations,the higher-layer features guide the lower-layer features to learn,eventually achieving sentiment region prediction at the optimal *** complement the important information lost by layer-by-layer fusion,this study not only adds an intra-layer fusion to the multilayer attention interaction module but also designs an adaptive feature aggregation *** module uses global average pooling to compress spatial information and connect channel information from all ***,the module adaptively generates a set of aggregated weights through two fully connected layers to augment the original features of each ***,the semantics and details of the different layers are aggregated through gating operations and residual connectivity to complement the lost *** reduce overfitting on small datasets,the network is pre-trained on the FI dataset,and further weight fine-tuning is performed on the small *** experimental results on the FI,Twitter I and Emotion ROI(Region of Interest)datasets show that the proposed network exceeds existing image emotion classification methods

关键词： Attentionmechanism emotional region prediction image emotion classification transfer learning

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Scavenger: Better Space-Time Trade-Offs for Key-Value Separated LSM-trees 40

Scavenger: Better Space-Time Trade-Offs for Key-Value Separa...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Zhang, Jianshun Wang, Fang Qiu, Sheng Wang, Yi Ou, Jiaxin Huang, Junxun Li, Baoquan Fang, Peng Feng, Dan School of Computer Science and Technology Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China China Shenzhen Huazhong University of Science and Technology Research Institute China Bytefrance Inc.

ISBN: (纸本)9798350317152

Key-Value Stores (KVS) implemented with log-structured merge-tree (LSM-tree) have gained widespread ac-ceptance in storage systems. Nonetheless, a significant challenge arises in the form of high write amplification due to the compaction process. While KV-separated LSM-trees successfully tackle this issue, they also bring about substantial space am-plification problems, a concern that cannot be overlooked in cost-sensitive scenarios. Garbage collection (GC) holds significant promise for space amplification reduction, yet existing GC strategies often fall short in optimization performance, lacking thorough consideration of workload characteristics. Additionally, current KV-separated LSM-trees also ignore the adverse effect of the space amplification in the index LSM-tree. In this paper, we systematically analyze the sources of space amplification of KV-separated LSM-trees and introduce Scavenger, which achieves a better trade-off between performance and space amplification. Scavenger initially proposes an I/O-efficient garbage collection scheme to reduce I/O overhead and incorporates a space-aware compaction strategy based on compensated size to minimize the space amplification of index LSM-trees. Extensive experiments show that Scavenger significantly improves write performance and achieves lower space amplification than other KV-separated LSM-trees (including BlobDB, Titan, and TerarkDB). © 2024 IEEE.

关键词： Economic and social effects

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Federated Learning on Internet of Things:Extensive and Systematic Review

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computers, Materials & Continua 2024年第5期79卷 1795-1834页

作者： Meenakshi Aggarwal Vikas Khullar Sunita Rani Thomas AndréProla Shyama Barna Bhattacharjee Sarowar Morshed Shawon Nitin Goyal Chitkara University Institute of Engineering and Technology Chitkara UniversityRajpuraPunjab140401India Department of CSE&IT BPS Mahila VishwavidyalayaKhanpur KalanSonipatHaryana131305India Engineering Research&Innovation Group Universidad Europea del AtlánticoC/Isabel Torres 21Santander39011Spain Department of Project Management Universidad Internacional IberoamericanaAreciboPR00613USA Department of Project Management Universidade Internacional do CuanzaEstrada Nacional 250Bairro KaluapandaCuito-BiéAngola Department of Computer Science and Engineering University of Science and Technology Chittagong(USTC)Chattogram4202Bangladesh Deptartment of EEE University of Science and Technology Chittagong(USTC)Chattogram4202Bangladesh Department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendragarhHaryana123031India

The proliferation of IoT devices requires innovative approaches to gaining insights while preserving privacy and resources amid unprecedented data ***,FL development for IoT is still in its infancy and needs to be explored in various areas to understand the key challenges for deployment in real-world *** paper systematically reviewed the available literature using the PRISMA guiding *** study aims to provide a detailed overview of the increasing use of FL in IoT networks,including the architecture and challenges.A systematic review approach is used to collect,categorize and analyze FL-IoT-based *** was performed in the IEEE,Elsevier,Arxiv,ACM,and WOS databases and 92 articles were finally *** measures were published in English and with the keywords“FL”and“IoT”.The methodology begins with an overview of recent advances in FL and the IoT,followed by a discussion of how these two technologies can be *** be more specific,we examine and evaluate the capabilities of FL by talking about communication protocols,frameworks and *** then present a comprehensive analysis of the use of FL in a number of key IoT applications,including smart healthcare,smart transportation,smart cities,smart industry,smart finance,and smart *** key findings from this analysis of FL IoT services and applications are also ***,we performed a comparative analysis with FL IID(independent and identical data)and non-ID,traditional centralized deep learning(DL)*** concluded that FL has better performance,especially in terms of privacy protection and resource *** is excellent for preserving privacy becausemodel training takes place on individual devices or edge nodes,eliminating the need for centralized data aggregation,which poses significant privacy *** facilitate development in this rapidly evolving field,the insights presented are intended to help practitioners and re

关键词： Internet of Things federated learning PRISMA framework of FL applications of FL data privacy communication

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Automatic Speed Measurement and License Plate Reader for Motor Vehicles using CNN 7

Automatic Speed Measurement and License Plate Reader for Mot...

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7th International Conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Poornima, D. Gopinath, N. Harish, L. Manju Kumar, Santosh Tamilselvi, C. Sathyabama Institute of Science and Technology Department of Computer and Science Engineering Tamil Nadu India B. S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science Engineering Tamil Nadu India CMR Institute of Technology Department of Information Science and Engineering Karnataka India Government Engineering College Department of Electrical Engineering Bihar India Gaya Colleg of Engineering Department of Electrical and Electronics Engineering Bihar India Dr. MGR Educational and Research Institute Department of Information Technology Tamil Nadu India

ISBN: (纸本)9798350340600

Assessing traffic surveillance data is a prevalent utilization of deep learning methodologies. Video processing techniques were employed to develop the system aimed at determining the velocity of vehicles. The analysis of video speed is conducted without the requirement of camera calibration. Frame subtraction and masking techniques are employed to effectively segregate moving vehicles. The suggested system comprises four primary components. Object detection techniques are employed in the initial phase to identify and locate moving vehicles within a given footage. Real-time vehicle recognition leverages the utilization of R-CNN and deep learning techniques. In the subsequent section, algorithms are employed to observe the movement of objects within individual video frames. Optical flow methods are employed in the third segment to ascertain the velocities of the respective vehicles. In the preceding part, the proposed work have utilized image processing techniques in conjunction with deep learning methodologies to accurately decipher the license plates of the automobiles under our surveillance. The experimental evaluation involving the utilization of traffic film demonstrated a system performance of 97% in precisely quantifying speed while achieving a license plate identification accuracy of 95%. The incorporation of state-of-the-art traffic management systems has facilitated the implementation of real-time speed monitoring and license plate reading capabilities. This facilitates the deterrence of automobile theft and the implementation of traffic regulations. Additionally, the methodology has the capability to identify traffic patterns and pinpoint regions of congestion, thus assisting in the effective management of traffic. © 2023 IEEE.

关键词： Object detection

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Classification of Quantum Graph State Codes using Decision Trees

Classification of Quantum Graph State Codes using Decision T...

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2022 International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2022

作者： Belanje, Vishnu Shetty Raina, Ankur Vellore Institute of Technology School of Computer Science and Engineering Vellore India Indian Institute of Science Education Research Department of Electrical Engineering and Computer Science Bhopal India

ISBN: (纸本)9781665454995

Graph states are multipartite quantum states that exhibit rich entanglement because of their topology. We consider graph states for studying a family of quantum error correcting codes such as [[n,\ 1,\ d]] encoding one logical qubit into n physical qubits of the graph state. We investigate the impact of edge connectivity on the minimum d of the resulting graph state codes (GSCs). © 2022 IEEE.

关键词： Decision trees

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