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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Palanimani, P.G. Jenifer Jothi Mary, A. Kumar, V. Suresh Kasturiwala, Sanket B. Ahmareen, Shafaque Bohra, Sneha Erode Sengunthar Engineering College Department of Mathematics Anna University Tamil Nadu India Department of Computer Science Bharathidasan University Tamil Nadu Tiruchirappalli India SRM Valliammai Engineering College Department of ECE Tamil Nadu Kanchipuram India G.H.Raisoni Institute of Engineering and Technology Department of Electronics & Telecommunication Engg. Nagpur India Ministry of Education Department of Computer Science United Arab Emirates G H Raisoni University Department of Computer Science and Engineering Maharashtra Amravati India

ISBN: (纸本)9781665456876

Supporting medical decisions with data mining techniques allows for more efficient detection and treatment of disease while reducing the burden on doctors. Using data mining methods to diagnose diabetes in advance. Diseases of the kidney, eye, and heart are all linked to diabetes mellitus, which has the fourth highest fatality rate of any disease in the world. As a result, many people today suffer from the effects of this illness. This is where the bulk of current study focuses. Using data mining techniques aids in making accurate medical diagnoses. Different parts of the body are impacted by the chronic disease diabetes mellitus. The ability to accurately predict the spread of illnesses early on has the potential to save lives and give us command of them. Historically, diabetes has been diagnosed using a battery of physical exams;however, these procedures have been shown to be inaccurate. This study predicts the condition by utilising a variety of Data Mining techniques for the purpose of both predicting and diagnosing diabetes mellitus, therefore overcoming this constraint. Naive Bayes, K-Nearest Neighbor, Support Vector Machine, and Decision Tree are the most popular data mining methods. Seven hundred and sixty-eight occurrences from the Pima Indian Diabetes Dataset were used to create this dataset. Diabetic risk is scored and assigned to one of three categories: mild, moderate, and severe. In addition, the effectiveness of various algorithms for diabetes diagnosis has been analysed using this data. The outcomes obtained demonstrate how effective our classification method. © 2022 IEEE.

关键词： Diseases

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Retraction Note: Sub-network modeling and integration for low-light enhancement of aerial images

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Optical and Quantum electronics 2024年第10期56卷 1-2页

作者： Uganya, G. Devi, C. H. Sarada Chaturvedi, Abhay Shankar, B. B. Ramesh, Janjhyam Venkata Naga Kiran, Ajmeera Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of CSE Meenakshi College of Engineering Chennai India Department of Electronics and Communication Engineering GLA University Mathura India Department of Electronics and Communication Engineering NITTE NITTE (Deemed to Be University) NMAM Institute of Technology (NMAMIT) Karkala India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur India Department of Computer Science and Engineering MLR Institute of Technology Dundigal Hyderabad India

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A New Method for Scene Classification from the Remote Sensing Images

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computers, Materials & Continua 2022年第7期72卷 1339-1355页

作者： Purnachand Kollapudi Saleh Alghamdi Neenavath Veeraiah Youseef Alotaibi Sushma Thotakura Abdulmajeed Alsufyani Department of CSE B V Raju Institute of TechnologyNarsapurMedakTelanganaIndia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Electronics and Communications DVR&DHS MIC Engineering CollegeKanchikacharlaVijayawadaA.P.India Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of ECE P.V.P Siddhartha Institute of TechnologyVijayawadaIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

The mission of classifying remote sensing pictures based on their contents has a range of applications in a variety of *** recent years,a lot of interest has been generated in researching remote sensing image scene *** sensing image scene retrieval,and scene-driven remote sensing image object identification are included in the Remote sensing image scene understanding(RSISU)*** the last several years,the number of deep learning(DL)methods that have emerged has caused the creation of new approaches to remote sensing image classification to gain major breakthroughs,providing new research and development possibilities for RS image classification.A new network called Pass Over(POEP)is proposed that utilizes both feature learning and end-to-end learning to solve the problem of picture scene comprehension using remote sensing imagery(RSISU).This article presents a method that combines feature fusion and extraction methods with classification algorithms for remote sensing for scene *** benefits(POEP)include two *** multi-resolution feature mapping is done first,using the POEP connections,and combines the several resolution-specific feature maps generated by the CNN,resulting in critical advantages for addressing the variation in RSISU data ***,we are able to use Enhanced pooling tomake the most use of themulti-resolution feature maps that include second-order *** enablesCNNs to better cope with(RSISU)issues by providing more representative feature *** data for this paper is stored in a UCI dataset with 21 types of *** the beginning,the picture was pre-processed,then the features were retrieved using RESNET-50,Alexnet,and VGG-16 integration of *** characteristics have been amalgamated and sent to the attention layer,after this characteristic has been fused,the process of classifying the data will take *** utilize an ensemble classifier in our classification a

关键词： Remote sensing RSISU DL RESNET-50 VGG-16

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Eog Based Virtual Keypad Design for Human computer Interface

Eog Based Virtual Keypad Design for Human Computer Interface

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Dhanalakshmi Subramanian Poongothai Marimuthu Monisha L Sathvika I Shri Harinii K Ayodeji Olalekan Salau Sofiya Parveen A Department of ECE Coimbatore Institute of Technology Coimbatore India Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado Ekiti Nigeria Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Communication plays an important role nowadays,hence the people with Amyotrophic lateral sclerosis(ALS) have difficulty to communicate with ***,HCI acts as a tool between the people with disabilities. Hence the biomedical signals like EEG and EOG used for implementing the communication systems. The EOG based virtual keypad mainly used for the people with ALS to communicate with their personal care partner for sending the urgent messages. This project aims at use of the latest sensor like Bio-amp EXG pill for sensing the EOG signal from eye which replaces the conventional systems and provides the accuracy of the data collection.A self-designed keypad of ten keys consists of urgent messages was *** system provides an overall accuracy of 75% than the conventional systems.

关键词： Electrooculography Humanities Accuracy Noise User interfaces People with disabilities Software Hardware Electroencephalography Sensors

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Prostate Cancer Classification using Cat Swarm Optimization with Deep Learning on MRI Images 7

Prostate Cancer Classification using Cat Swarm Optimization ...

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7th International Conference on electronics, Communication and Aerospace Technology, ICECA 2023

作者： Haritha, V. Archana, K.V. Balasubramaniam, D. Uma Maheswari, S. Sasireka, P. Suja, G.P. RMK Engineering College Department of CSE Chennai Kavarapettai India School of Engineering Department of Electronics and Communication Engineering Avinashilingam Institute for Home Science and Higher Education for Women Coimbatore India VelTech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Department of Biomedical Engineering Tamilnadu Chennai India Saveetha Institute of Medical and Technical Sciences Department of Biosciences Saveetha School of Engineering Chennai India S. A. Engineering College ECE Department Chennai India Research Center of Computer Science Muslim Arts College Kanyakumari Thiruvithancode629174 India

ISBN: (纸本)9798350340600

Prostate Cancer (PC) is a common malignancy amongst men globally, and accurate classification plays a significant part in its diagnoses and treatment planning. Due to its superior soft tissue contrast, Magnetic Resonance Imaging (MRI) has developed as a valuable imaging modality for the assessment of PC. PC classification on MRI is a major task in medical imaging analysis, and deep learning (DL) approaches are employed to accomplish automated and accurate classification. In recent times, Convolutional Neural Network (CNN) structure was selected for the classification of PC. This manuscript introduces an automated Prostate Cancer Classification using Cat Swarm Optimization with Deep Learning (PCC-CSODL) technique on MRI Images. The main purpose of the PCC-CSODL algorithm lies in the effectual and accurate identification of PC. For achieving this, the PCC-CSODL technique involves NASNet feature extractor at the initial stages. In addition, the CSO algorithm is used for the optimum hyperparameter selection of the NASNet model. For classification purposes, attention based long short term memory (ALSTM) model can be used. The results demonstrated the effectiveness of the PCC-CSODL technique in accurately classifying PC on MRI. It has the potential to assist clinicians in making informed decisions and enhancing patient outcomes in PC management. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Autism Spectrum Disorder Detection using Attention-Based CNN and ML Classifiers

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Procedia computer Science 2025年 258卷 4216-4227页

作者： Abhinav Chaitanya R Dharun Muthaiah N Sanjai S H Arjun M Harshavardhan S Yugander P Jagannath M School of Electronics Engineering Vellore Institute of Technology (VIT) Chennai Tamil Nadu 600127 India School of Computer Science and Engineering Vellore Institute of Technology (VIT) Chennai Tamil Nadu 600127 India Department of ECE Kakatiya Institute of Technology and Science Warangal Telangana 506015 India

Autism spectrum disorder (ASD) is a common condition affecting how children interact and communicate. ASD can manifest in various ways, such as difficulty in social interactions, repetitive behaviours, and verbal and non-verbal communication challenges. These difficulties can significantly impact a child’s ability to form relationships, participate in everyday activities, and navigate social situations. Currently, diagnosing ASD relies a lot on understanding behaviours and developmental disorders, as there are no clear physical markers for diagnosis. This study explores how advanced computer techniques, like deep learning and machine learning, can help detect ASD. Utilizing functional magnetic resonance imaging (fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE) dataset, we have developed a technique to distinguish between ASD accurately and typically developed (TD) individuals. We focus on building a program that can understand the patterns in brain scans using convolutional neural networks (CNNs). Additionally, we used methods like multihead attention and squeeze and excitation to extract more features in the data. We then used machine learning (ML) algorithms to classify the extracted features. The outcomes indicate that our proposed model can detect ASD with an accuracy of 98.59%.

关键词： Autism Spectrum Disorder Deep Learning Convolutional Neural Networks Feature Extraction Squeeze Excitation

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Deep Learning-Based EEG Signal Classification of Epileptic Patients 4

Deep Learning-Based EEG Signal Classification of Epileptic P...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Kumar, V. Senthil Karpagavalli, C. Pradeep, S. Divya, S. Taqui, Syed Noeman Vinayagam, Nadanakumar SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Tiruchirapalli621105 India Ramco Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Rajapalayam626117 India S.A. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai600077 India Dr. N. G.P. Institute of Technology Department of Biomedical Engineering Tamil Nadu Coimbatore641048 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of VLSI Microelectronics Tamil Nadu Chennai602105 India Hindustan Institute of Technology and Science Department of Automobile Engineering Padur Tamil Nadu Chennai603103 India

ISBN: (纸本)9798350385793

Epilepsy is a neurological disorder characterized by repeated seizures throughout the day. Epilepsy affects around 65 million people worldwide. For people with focal epilepsy, surgery is a possibility but medicine is the standard treatment for generalized epilepsy. Unfortunately, these medications fail to control epileptic seizures in more than 30% of patients. Yet, this ends up in accidents and significantly shortens the patient's life expectancy. An electroencephalogram (EEG) is a critical diagnostic tool for epilepsy. The conventional method for detecting epileptic activity is expert human interpretation of multi-channel EEG readings. This is a complex and time-consuming procedure, thus many researchers have tried to automate it using Machine Learning (ML) and Deep Learning (DL) methods. Unfortunately, the investigation did not produce satisfactory results. This study aims to fill this research gap by proposing a new CNN-BLSTM (Convolutional Neural Network-Bidirectional Long Short-Term Memory) based hybrid DL model for epileptic detection. The EEG data from Kaggle was collected and processed. This study uses the same amount of data and epochs for training, validation, and testing all four DL models such as AlexNet, VGG-16, InceptionV3, and the proposed model. According to the experiment findings, the proposed model has a better level of accuracy (98.37%) than the other three models. © 2024 IEEE.

关键词： Electroencephalography

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A Novel Deep Learning based IoT Enabled computerized Vehicle Number Plate Recognition System using OCR Principles

A Novel Deep Learning based IoT Enabled Computerized Vehicle...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： J. Bhuvaneswari N. Gireesh G. Srimathy Subrahmanyam Nandigam V. Nanammal Department of AI & DS R.M.K. College of Engineering and Technology Puduvoyal Chennai Department of ECE Mohan Babu University (Erstwhile Sree Vidya Nikethan Engineering College Tirupati Electrical and Electronics Engineering S.A Engineering College Chennai Department of Computer Science and Engineering Dr.M.G.R Educational and Research Institute Chennai Department of ECE Jeppiaar Engineering College Chennai

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

A kind of intelligent transportation and detecting method is the license plate recognition system. The system can automatically recognize and read the license id of a vehicle’s number plate from digitally taken photographs thanks to this mix of technologies. The term “automatically capturing the license plate” refers to a technique that detects license plates and converts their pixel data into plain text or ASCII text. Modified Learning based Character Recognition (MLCR) is an analytical operation-based approach to vehicle number plate recognition that is part of this system. For the purpose of evaluating the suggested scheme’s efficiency, it is cross-validated with the traditional learning model known as Convolution Neural Network (CNN). The primary goal is to properly recognize and translate a specific vehicle’s license plate by using and combining several morphological techniques. Image enhancement, grayscale transformation, filtering, edge detection, and number plate extraction from vehicle images are some of the operations upon which this is built. Following the completion of the aforementioned processes, the segmentation process is being utilized to detect the text on the number plate. This is achieved by utilizing template matching and optical character recognition. From a photograph of the car, our method can swiftly and reliably deduce the license plate.

关键词： Vibrations Statistical analysis Neural networks Transportation Cameras Character recognition Reliability

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Impact of Robotic Process Automation on Enterprise Resource Planning Systems

Impact of Robotic Process Automation on Enterprise Resource ...

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2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management, IC-RVITM 2023

作者： Yendluri, Dileep Kumar Ponnala, Jainath Thatikonda, Ramya Kempanna, M. Tatikonda, Reshmi Bhuvanesh, A. University of the Cumberlands Masters in Computer Science United States Jawaharlal Technological University Bachelors in Ece Hyderbad India University of the Cumberlands PhD in Information Technology Williamsburg United States Bangalore Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Bengaluru India University of the Central Missouri Masters in Computer Information Systems Warrensburg United States Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350306088

ERP systems are merging their powers to generate a dynamic collaboration. By examining the integration between RPA (Robotic Process Automation) and ERP (Enterprise Resource Planning) systems, we can identify their shared potential for enhancing process flow. Combining automation and ERP integration makes RPA a powerful tool for process management across multiple industries. The paper examines the basics of RPA and its ERP integration, highlighting the advantages of enhanced productivity and fewer errors. Addressing security, compliance, and change management issues simultaneously. In addition to top-down and bottom-up methods, process analysis and selecting suitable processes are covered. Practical examples offer insightful observations on this integration's success. The union of RPA and ERP creates a powerful combination that can elevate operational effectiveness, facilitate strategic insights, and reshape the workplace. © 2023 IEEE.

关键词： Integration

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Chaotic Particle Swarm Optimization Based Shortest Path Routing in VANET

Chaotic Particle Swarm Optimization Based Shortest Path Rout...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： R. Ramya A. Yazhini T. Geetha R. Sahila Devi Kavin K. S. S. Dhanalakshmi Department of Nano Electronics Materials and Sensors ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai India Department of Computer Science & Engineering College of Engineering Anna University Chennai India Department of Artificial Intelligence and Data Science Agni College of Engineering and Technology Chennai India Department of Computer Science and Engineering Rohini College of Engineering and Technology Palkulam India Department of Electrical and Electronics Engineering Government College of Engineering Tirunelveli Tamilnadu India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam Erode India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

The present study examines the Vehicle Ad hoc Networks (VANETs) exhibit unexpected behavior due to their high mobility and random network structure. Including ad hoc networks, a variety of problems, including repeated connected breakdowns, flexibility, multi-hop transmission of information, and information loss, impact Transmission Control Protocols (TCP). To overcome this problem, this paper suggests employing zone-based routing in VANETs while taking mobility into account. The routing technique is implemented with a hybrid optimization strategy. The most significant swarm intelligence (SI) algorithm for solving optimization issues is the chaotic particle swarm optimization (CPSO) algorithm. Due to its simplicity, which involves just a few parameters, CPSO has grown to be considered one of the most frequently used optimization techniques. When resolving challenging optimization problems, the routing algorithms experience high overhead or incompatibilities. A CPSO based shortest path routing method is presented for VANET that offers a high-quality path for connection among nodes, improving the distribution of packets percentage and preventing end-to-end delay, in order to enhanced routing effectiveness and throughput in prevailing approaches. These achievements show that CPSO performs adequately and is experiencing significant progress.

关键词： TCP Protocols Vehicular ad hoc networks Spread spectrum communication Routing Throughput Search problems

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