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Automated Process for Retinal Image Segmentation and Classification Via Deep Learning based CNN Model

Automated Process for Retinal Image Segmentation and Classif...

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International Conference on Inventive Computation Technologies (ICICT)

作者： D. Abitha Kumari S. Rama Lakshmi R. Revathy S. Sundararajan R. Santhana Krishnan K. Lakshmi Narayanan Department of Computer Science and Engineering RMK Engineering College Thiruvallur India Department of Computer Science and Engineering SCAD College of Engineering and Technology Cheranmahadevi Tirunelveli Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Mechanical Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department Electronics and Communication Engineering Francis Xavier Engineering College Tirunelveli India

Retinal vessel segmentation is a critical problem for the identification of a variety of retinal diseases, and manual segmentation is a time-consuming process. Automatic segmentation of such vessels might help in eye disease diagnosis. Because various studies on the automated mechanism for retinal illness were conducted, there were certain drawbacks such as lesser accuracy, a lower prediction rate, and so on. For the classification and diagnosis of diabetes disease in retinal images, an efficient convolutional neural network (CNN) based classification algorithm is used. The input dataset is preprocessed using the extraction of the green channel, picture enhancement using a histogram-based technique, and noise reduction. The vessel is extracted using a thresholding method. The watershed algorithm is then used to segment the preprocessed picture. The features are retrieved from the segmented picture by the Grey level co-occurrence matrix (GLCM) approach, then selected using the principal component analysis (PCA) algorithm. The anomaly is found when the specified characteristics are categorized. Finally, a performance analysis is done and related to current methodologies in order to demonstrate the effectiveness of the suggested mechanism. For performance assessment, the three publicly accessible databases DRIVE, STARE, and CHASE DB1 are employed. As a consequence of the outcomes, it was learned that the proposed approach outperforms other current processes.

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LoRaWAN Mesh Networks: An efficient mesh formation of LoRa devices in rural energy system

LoRaWAN Mesh Networks: An efficient mesh formation of LoRa d...

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2022 International Conference on Innovative Computing, Intelligent communication and Smart Electrical Systems, ICSES 2022

作者： Farzhana, I. Chandrasekar, A. Diwakaran, S. Hemavathi, S. Ramkumar, G. Department of Computer Science and Engineering VelTechRangarajanDr.Sagunthala R and D Institute of Science and Technology Avadi Chennai India St. Joseph's College of Engineering Department of Computer Science and Engineering Chennai602117 India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamil Nadu Srivilliputhur626128 India Tamil Nadu Chennai600 113 India Academy of Scientific & Innovative Research Uttar Pradesh Ghaziabad201 002 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai602105 India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

For Iot systems, LoRa (Long Range) offers the lowest wide-area network solution. The use of Iot systems for remote monitoring and control is growing in popularity with the growth of the Internet of Things. Compared to conventional metres, digital metres have several benefits, and smart metering is one of the applications of the Internet of Things (IoT). Because of its distant load measurement and management abilities, it aids in the integration of new concepts into the old power system, such as smart grids, blockchain technologies, and automated systems. However, because of the high installation and operational costs and the unpredictability of smart metres, classic analogue metres are still favored in many applications. Because of the absence of dependable and economical communication systems, these difficulties may be solved by deploying a LoRa-based network. The rural energy system is discussed in this article in relation to LoRa technology and how it might be used to address smart metering issues. As part of a simulation-based research, a rural microgrid has been examined to see whether the LoRa technology can be used in various architectures, as well as to discover the most cost-effective and reliable approach to integrate smart metres. © 2022 IEEE.

关键词： Internet of things

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Small Object Detection for UAVs Using Deep Learning Models on Edge Computing: A Comparative Analysis

Small Object Detection for UAVs Using Deep Learning Models o...

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International Conference on Circuits, communication, Control and Computing

作者： M S Saathvik Vasishta Abhinava Teja Reddy Amireddy Pranjal Shrivastava Raghuram S Viswanath Talasila Prasad N Shastry Dept. of Electronics and Telecommunication Engineering Ramaiah Institute of Technology Bangalore India Dept. of Electronics and Communication Engineering Ramaiah Institue of Technology Bangalore India Dept. of Computer Science Engineering Ramaiah Institute of Technology Bangalore India Dept. of Electronics and Communication Engineering Ramaiah Institute of Technology Bangalore India Dept. of Electrical and Computer Engineering Bradley University Peoria Illinois U.S.A

ISBN: (数字)9798331528539

ISBN: (纸本)9798331528546

This paper presents a comparative analysis of DNN-models, including TensorFlow Lite, SSD-MobileNet, Mask R-CNN and YOLOv8, for the high-precision detection and classification of small objects in aerial search and rescue scenarios. The study features a proprietary dataset collected through drone flights over an extended period. The model performance was evaluated on compact computing platforms like Jetson Nano and Raspberry Pi, emphasizing detection accuracy and computational efficiency. Results reveal trade-offs between model complexity and real-time processing capabilities. The findings guide the selection of on-board computer models, thereby enhancing drone capabilities in critical operations

关键词： Deep learning Analytical models Accuracy Computational modeling Object detection Search problems Real-time systems Integrated circuit modeling Drones Testing

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Optimizing Parkinson’s Diseases Detection from Vowel Phonation: A Bayesian Approach with K-Nearest Neighbours

Optimizing Parkinson’s Diseases Detection from Vowel Phonat...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Jyoti Kumari Prabira Kumar Sethy Santi Kumari Behera Ashoka Kumar Ratha Department of Computer Science and Engineering VSSUT Burla Odisha India Department of Electronics and Communication Engineering Guru Ghasidas Vishwavidyalaya Bilaspur C.G. India Department of Electronics Sambalpur University Odisha India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

This study explores the optimization of Parkinson’s Diseases (PD) detection utilizing vowel phonation data through a Bayesian approach coupled with the K-nearest neighbours (KNN) algorithm. Using a variety of speech signal processing techniques, 754 characteristics were retrieved from sustained 'a' vowel phonation recording from 252 participants (188 PD patients and 64 healthy controls) using an open-source dataset from the University of California Irvine. Each subject contributed three recordings, yielding 756 instances in the dataset. Employing Bayesian optimization, we fine-tuned the parameters of the KNN model and achieved promising validation and test accuracies of 92.6% and 93.3%, respectively. The model's effectiveness was also confirmed by the area under the receiver operating characteristic curve (AUC), which had values of 0.9543 and 0.9685 for the test and validation sets, respectively. Notably, the minimum classification error obtained was 0.073727, demonstrating the robustness of the optimized model. The optimal configuration of the KNN model involves setting the number of neighbours to one and employing the Spearman distance metric with inverse distance weighting. These results highlight how Bayesian optimization might improve the ability of machine learning models to diagnose Parkinson's disease, especially when it comes to vowel phonation analysis.

关键词： Analytical models Accuracy Signal processing algorithms Receivers Nearest neighbor methods Robustness Bayes methods Recording Speech processing Optimization

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Cloud-Based Smart Sensing System for Real-Time Health Management of Dementia Patients 2

Cloud-Based Smart Sensing System for Real-Time Health Manage...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Latha, R. Arul, U. Dhanalakshmi, S. Ramya, R. Mishra, Nilamadhab Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Tamil Nadu Trichy India Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Saveetha Nagar Thandalam Tamil Nadu Chennai India Department of Electronics and Communication Engineering Sri Muthukumaran Institute of Technology Tamil Nadu Chennai India Department of Mathematics S.A Engineering College Tamil Nadu Chennai India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India

ISBN: (纸本)9798350305418

The paper describes an innovative technique of monitoring updates on the health of people with dementia by integrating smart sensing technologies with a cloud-based platform. Because dementia is a degenerative neuropsychiatric disease that develops with time, constant patient monitoring is essential for ensuring the health and safety of those living with the condition. The proposed system uses smart sensing devices, including wearable and ambient sensors, to monitor dementia patients' health in real-time. Data like heart rates, movements used, and ambient conditions are collected and sent to a server in the cloud for analysis. The camera can monitor and analyze visual data, capture crucial information, and help evaluate the patient's well-being. Multiple patients can be monitored effectively in real time because of the cloud-based system's scalability, accessibility, and storage capacities. For healthcare providers to recognize irregular movements, identify possible risks to health, and implement preventative measures, doctors utilize the moment data analytics devices to gather valuable insights from the acquired data. In addition, the cloud-based solution improves collaborative care and enhances patient support by allowing for continuous communication and information sent among caregivers, doctors, nurses, and family members. Predefined thresholds or anomalous occurrences may trigger alerts and messages, allowing for quick action in reaction to emergencies. Modern technology and data analytics increase the health and happiness of people with dementia. © 2023 IEEE.

关键词： Cloud platform Dementia patients Healthcare Real-time monitoring Sensors

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Semantic Nexus: Knowledge Graph Creation from CrossNER Data using NLP

Semantic Nexus: Knowledge Graph Creation from CrossNER Data ...

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Advanced Computing & communication Technologies (ICACCTech), International Conference on

作者： Arun Kumar M Harsha Prada S Caroline Dorathy Esther J Sabareshwaran M T Kavitha Devi M K Computer science and Engineering Thiagarajar College of Engineering Madurai Tamil Nadu Electronics and communication engineering Thiagarajar College of Vellore Institute of Technology Vellore Tamil Nadu

There is an abundance of raw text which contains plentiful information which could be classified into entities,relations and knowledge graphs. We could obtain this by using NLP *** comprehensive CrossNER dataset,which contains Named Entity Recognition (NER) data from multiple areas, is used in this research study to offer a method for creating Knowledge Graphs(KGs). We especially employ NER, Relation Extraction (RE),and Knowledge Graph creation within the context of Artificial Intelligence from the CrossNER *** method uses this dataset to generate Knowledge Graphs,which provide an organized representation of the data. Crucial steps in the procedure include phrase segmentation, entity extraction, and relationship identification. This technique helps organize and extract knowledge for a variety of uses. The resulting Knowledge Graphs are priceless tools for knowledge management, improving information retrieval, and getting insights into certain domains.

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Advancements in Fake News Detection: Integrating NLP and Multi-Modal Approaches

Advancements in Fake News Detection: Integrating NLP and Mul...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Baskar, Radhika Sah, Swati Shyam, R. Kumar, K. Sambath Patil, Harshal Reddy, Gosu Nageswara Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India School of Set Sharda University Greater Noida India University Department of Computer Applications Banglore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Computer Science and Engineering Pune India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Chemistry Chennai India

ISBN: (纸本)9798350394580

This comprehensive investigation uses powerful NLP and multi-modal integration to detect fake information on social media. Our technique was tested using 10,000 news pieces from various social media networks. Advanced NLP models are crucial, with the state-of-the-art transformer model, BERT, collecting subtle contextual information with 94.2% accuracy. SVM and BERT had the greatest classification accuracy at 95.6% when combined with ensemble approaches. Multi-modal information sources including picture captions and user metadata improved classification performance, with the textual-image model achieving the greatest accuracy at 96.8%. The research was ethical, incorporating fairness analysis and transparency measures, to ensure detection trustworthiness and fairness. These findings show that sophisticated NLP models and multi-modal integration can improve fake news detection, enabling the creation of strong, real-world apps to combat disinformation and protect digital discourse. © 2023 IEEE.

关键词： Ethical considerations Fake news Machine learning models NLP-based approaches Social media

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Deep Facial Emotion Recognition System Under Facial Mask Occlusion 5th

Deep Facial Emotion Recognition System Under Facial Mask Occ...

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5th International Conference on computer Vision and Image Processing, CVIP 2020

作者： Saxena, Suchitra Tripathi, Shikha Sudarshan, T.S.B. Department of Electronics and Communication Engineering PES University Bangalore India Department of Computer Science and Engineering PES University Bangalore India

ISBN: (纸本)9789811610912

Over the past few decades, automated machine based Facial Emotion Recognition (FER) has made a significant progress, guided by its relevance for applications in various areas of neuroscience, health, defense, entertainment and Human Robot Interaction (HRI). Most of the work on FER is focused on controlled conditions for non-occluded faces. In the present Covid-19 scenario, when more individuals cover their face partially to prevent spread of corona virus, developing a system/technique that can recognize facial emotion with facial mask constraint is desirable. In this research work, a FER system built using Convolution Neural Networks (CNN) technique is proposed. In the proposed work, eyes and forehead segment is used to train model. This system is implemented using Raspberry Pi 3 B + and Robotic Process automation (RPA) for HRI. The proposed system achieves 79.87% average accuracy with 5-fold cross validation and also tested in real time scenario. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Emotion Recognition

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LSTM Powered Identification of Clickbait Content on Entertainment and News Websites 13

LSTM Powered Identification of Clickbait Content on Entertai...

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13th IEEE International Conference on Computational Intelligence and communication Networks, CICN 2021

作者： Bhoj, Naman Dwivedi, Adarsh Raj Tripathi, Ashutosh Pandey, Bishwajeet Department of Computer Science and Engineering Uttarakhand Bhimtal263136 India Department of Electronics and Communication Engineering Uttarakhand Bhimtal263136 India

ISBN: (纸本)9781728176956

Clickbait content on online platforms, is exaggerating content that doesn't deliver what it promises. The main motive of such content is to mislead the reader to 'click' on them. These are widely responsible for delivering false information to the user and damaging their online experience. Many online creators deliberately use them to get more views and generate more revenue. In light of potential difficulties created by clickbait content, this paper aims to create a clickbait detection model for entertainment and news websites utilizing the power of the machine and deep learning models. Empirical results of our experiments indicate that LSTM models are best suited for identifying clickbait content containing text by achieving an accuracy of 95.031 % which is 1.138 times greater than the Random Forest and 1.709 times greater than the Naive Bayes model. © 2021 IEEE.

关键词： Decision trees

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Deep Learning Cerebral Splitting: Adaptation for Spine Elimination

Deep Learning Cerebral Splitting: Adaptation for Spine Elimi...

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communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： M. Balasubramani B. J. Job Karuna Sagar K. C. Rajheshwari E. Saranya B. Aishwarya P. Murugeswari Assistant Professor Department of Computer Science and Engineering V.S.B. Engineering College Professor Department of Computer Science and Engineering Joginpally B.R Engineering College Hyderabad India Department of Computer Science and Engineering Sona College of Technology Assistant Professor Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Assistant Professor Department of Computer Science and Engineering(Cyber Security) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Department of Electronics and Communication Engineering Theni Kammavar Sangam College of Technology

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The posterior fossa volume and the total intracranial volume are important factors to consider when doing a brain volumetric analysis using structural electromagnetic resonance imaging. The thorough segmentation of the whole cortex offers a method for non-invasively evaluating different brain regions. In addition, the extraction of skulls in a way that maintains anonymity is increasingly being used as a means of disseminating large amounts of data obtained from brain images. Therefore, generalizing machine learning brain regions for intracranial measurements after skull removal is an interesting task. However, data collecting is difficult due to the small number of handwritten and whole-brain atlases marked with his TICV/PFV. In this study, we use U-Net tiles to estimate intracranial volume and perform whole-brain segmentation with and without cranial characteristics simultaneously. In the absence of individually recorded total cortical volume, a transfer learning approach has been shown to estimate extra her TICV and PFV labeling during whole-brain delineation on T1-weighted MRI. More specifically, U-Net tiles are pre-trained using a big Brain COLOR atlas generated using multi-atlas segmentation, and do not include his PFV labels. The pre-trained model is improved to learn more His TICV and PFV labels using the limited Brain COLOR atlas. We also elaborate on his method for processing MR images of the brain in the absence of a skull. The results show that our method produces reasonable segmentation and size estimation results in terms of average contact distance, cubic similarity index, and total volumetric similarity for both brains within and outside the skull. is showing off. This technology is currently available as open source to the general public.

关键词： Deep learning Image segmentation Image color analysis Magnetic resonance imaging Transfer learning Pipelines Estimation Skull Resonance Telecommunication computing

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