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A Design of Disease Diagnosis based smart Healthcare Model using Deep Learning Technique

A Design of Disease Diagnosis based Smart Healthcare Model u...

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2022 International Conference on Electronics and Renewable systems, ICEARs 2022

作者： Pavan Kumar, Illa Mahaveerakannan, R Praveen Kumar, K Basu, Indranil Anil Kumar, T. Ch. Choche, Manasi Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology Department of Information Technology Hyderabad India Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Kakatiya Institute of Technology and Science Department of Information Technology Warangal India Institute of Engineering and Management Dept of Electronics and Communication Engineering Kolkata India Vignan's Foundation for Science Technology and Research Vadlamudi Department of Mechanical Engineering Guntur Dt. Andhra Pradesh India Kc College of Engineering and Management Studies and Research Thane India

ISBN: (纸本)9781665484251

A smart Healthcare system (sHs) is developed from traditional healthcare by integrating the Internet of Things (IoT) with Artificial Intelligence (AI). The data are captured by millions of devices and sensors, where it is exchanged continuously with medical staff to monitor the health of patients. An important message can then be analyzed using various machine learning (ML) / deep learning (DL) algorithms to predict the severity of diseases and then shared through wireless connectivity with medical professionals who can make appropriate recommendations. The main aim of the research work is to develop a disease detection model based on sHs for diabetic disease using DL classifiers. The method considered both collected and public datasets stored in the cloud for building sHs to allow accurate time monitoring of patient health conditions. IoT devices as sensors enable smooth data gathering, while AI algorithms use the data to diagnose diseases. For disease diagnosis, Restricted Boltzmann Machine based generative adversarial network (RBM -GAN) model has only three stages for the full prediction process. The experiments are carried out in two datasets, where the performance of the proposed RBM-GAN model is compared with existing DL classifiers. The simulation results show that the proposed model increased the accuracy by 5% on both two datasets than current DL classifiers. The proposed RBM-GAN model is used as a suitable illness analysis tool for sHs from these results. © 2022 IEEE.

关键词： Internet of things

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Multiauthority Cloud storage with secure and Efficient Attribute-Based Access Control

Multiauthority Cloud Storage with Secure and Efficient Attri...

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Information Technologies and Communications (GCITC), Global Conference on

作者： Upadhyay Darshan P Faizah Mahendi Nawaz Kureshi shravan Khunti T Vijay Muni Ravi Kumar Tata s. Kalarani Dept. of Civil Engineering S. V. National Institute Of Technology Surat Gujarat India Dept. of Computer Science and Engineering Vellore Institute of Technology Chennai India Dept. of Aerospace Engineering Chandigarh University Ludhiana Chandigarh State Hwy Punjab India Dept. of Electrical and Electronics Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Dept. of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Information Technology St Joseph’s Institute of Technology Chennai India

Cloud storage benefits both individuals and organisations by making it simpler to properly share their data over the internet. This simplifies the process of transferring data. As a consequence of this, there is a significant margin for error associated with the management of the entry of shared information. In this section, we provide insights based on finances and ownership that may be used to govern distributed storage systems. There is no centralised authority, and all real estate agents and brokers independently distribute secret keys to customers; 2) each characteristic authority has the ability to gradually evict any customer from its space, with the end goal of such waived customers no longer being able to obtain information that has been redistributed in this manner; 3) cloud servers have the ability to update encoded information; and 3) we construct an inter-authority system. CPABE conspiracy

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Control of LED using BOLT Wi-Fi Module through Bubble web app over Internet

Control of LED using BOLT Wi-Fi Module through Bubble web ap...

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Computational Intelligence for Green and sustainable Technologies (ICCIGsT), International Conference on

作者： Abhishek Raj Arnab De Bappadittya Roy Ankan Bhattacharya Koyndrik Bhattacharjee Anup Kumar Bhattacharjee Dept. of ECE National Institute of Technology Durgapur India Dept. of ACSE School of Computing and Informatics Vignan’s Foundation for Science Technology & Research Guntur AP School of Electronics Engg VIT-AP University Amaravati AP India Dept. of ECE Hooghly Engineering & Technology College Hooghly India Dept. of Civil Engg Dr. B.C. Roy Engineering College Durgapur India

ISBN: (数字)9798350381887

ISBN: (纸本)9798350381894

This paper presents an economical and reliable solution for LED control by utilizing the Bolt Wi-Fi module through a Bubble web app integrated with the Internet of Things (IoT), eliminating the need for programming skills. Leveraging a User Interface (UI) builder, Application Programming Interface (API), and the Bolt Wi-Fi module EsP 8266MOD (IsM-2.4GHz, PA +25dBm, 802.11b/g/n) operating at 2.4 GHz frequency, LED control is achieved. The paper introduces a block control system for LED ON and LED OFF, allowing users to select appearance styles - a success button for LED ON and a danger button for LED OFF. Digital wire plugins are employed for both LED states, utilizing pin zero with a high status for LED ON and low for LED OFF.

关键词： Automation Fasteners Light emitting diodes safety security Internet of Things Wire Wearable devices Wireless fidelity Application programming interfaces

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Enhancing Data Transmission Reliability in Cluttered Environments using Adaptive Wireless sensors

Enhancing Data Transmission Reliability in Cluttered Environ...

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Communication, security and Artificial Intelligence (ICCsAI), International Conference on

作者： R Dinesh P. Vanathi N. susitha P. Priya Ambikavathi C syed sumera Ali Department of ECE Excel Engineering College Nammakkal India Department of Information Technology Adhiyamaan College of Engineering Hosur India Dept. of Computer Science Vinayaka Mission's Chennai Campus Vinayaka Mission's Resarch Foundation DU School of Arts and Science Department of Computer Science and Engineering M. Kumarasamy College of Engineering Karur Tamilnadu India Department of Computer Science Vinayaka Mission Research Foundation School of Arts and Sciences Paiyanoor Tamilnadu India Electronics and Computer Engineering Department CSMSS Chh Shahu College of Engineering Aurangabad Maharashtra India

Wireless data transmission in complicated terrains like cities or deep woodlands sometimes raises dependability concerns. These issues are caused by barriers, multipath fading, and signal interference. Our study uses adaptive wireless sensors to improve data transmission reliability to solve this problem. These sensors are meant to dynamically modify gearbox parameters in real time, unlike standard sensors. This distinguishes them. The study uses key research methods. Dynamic Path selection chooses the best transmission paths for data packets. Adaptive Modulation and Coding (AMC) alters modulation and coding rates based on real-time environmental information. The study also uses spatial Diversity using Beamforming. This method concentrates energy using multi-antenna systems, increasing signal intensity and decreasing interference. Practical experiments in clutter-filled regions showed improved gearbox reliability. Compared to standard systems, adaptive sensors reduced packet loss by 45% and increased throughput by 30%. Adaptive wireless sensors increase connection reliability in difficult conditions. These sensors may be utilised for many applications, including urban IoT frameworks and autonomous navigation.

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Telephony speech Enhancement for Elderly People 4

Telephony Speech Enhancement for Elderly People

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4th International Conference on computer, Communication and signal Processing, ICCCsP 2020

作者： Praveen Kumar Emani, R. Telagathoti, Pitchaiah Prasad, N. Vignan's Foundation for Science Technology and Research Dept. of Electronics and Communication Engineering Guntur India Dept. of Ece Bhimavaram India

ISBN: (纸本)9781728165097

One of the limiting factors of performance of any communication system is bandwidth. In telephone systems, the operational voice frequency ranges from 300 to 3400 Hz and is called Narrow Band (NB). This NB causes remarkable depreciation of speech in terms of quality and intelligibility, specifically for elderly persons. speech Bandwidth Extension (sBE) technique is used to increase the quality and intelligibility of NB signal. In sBE method, additional data about the lost speech frequency components is transmitted along with NB signal. A better-quality Wide Band (WB) signal is reconstructed at the receiver end using the transmitted missing speech frequency components. This paper explores the ability of sBE to enhance the quality and intelligibility of NB signal for elderly persons. The enhancement in the quality and intelligibility of NB speech for elderly persons is reasserted by subjective listening and objective tests. © 2020 IEEE.

关键词： speech enhancement

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Exploring The Impact Of Artificial Intelligence On Visual Communication In Unmasking Deep Fake And Ensuring Public Trust

Exploring The Impact Of Artificial Intelligence On Visual Co...

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Intelligent systems and Machine Learning Conference (IsML)

作者： Jagendra singh Neeraj Abbas Thajeel Rhaif Alsahlanee Abha Kiran Rajpoot Gaurav Agrawal Mohit Tiwari Computer Science Engineering Alliance School of Advanced Computing Alliance University Banglore India School of Computer Science Engineering and Technology Bennett University Greater Noida India Development and Continuous Education Center University of Thi-Qar Thi-Qar Iraq Department of CSE(AIML) KIET Group of Institution Ghaziabad India Department of CSE(AI) KIET Group of Institution Ghaziabad India Dept. of Computer Science & Engineering Bharati Vidyapeeth’s College of Engineering Delhi India

ISBN: (数字)9798350343878

ISBN: (纸本)9798350343885

In this groundbreaking study, we delve into the pivotal role of the Vision Transformer (V-iT) in the realm of deepfake detection, seeking to unravel its effectiveness in navigating the intricate landscape of manipulated visual content. As image-generating technologies advance, the proliferation of Dfakes poses profound challenges to the integrity of digital media, sparking concerns about misinformation and eroding trust in visual communication. Against this backdrop, V-iT emerges as a cutting-edge solution, leveraging its unique ability to comprehend intricate patterns in visual data. Our research is anchored in a meticulous methodology, involving comprehensive experiments designed to assess V-iT's performance in deepfake detection. The results, spanning 10 diverse samples, reveal consistently high accuracy, precision, recall, and F1 rate values. V-iT's capacity to achieve accurate classifications, minimize false positives, and sensitively detect manipulated content underscores its robustness in addressing the complexities of deceptive media practices. This paper goes beyond a mere validation of V-iT's capabilities, offering a nuanced understanding of its performance metrics. The high precision values emphasize V-iT's accuracy in identifying manipulated content, while exceptional recall values highlight its sensitivity in capturing true positive instances. The balanced F1 rate provides a harmonious measure of V-iT's overall effectiveness, demonstrating its potential as a dependable tool in upholding the authenticity of visual communication.

关键词： Deepfakes Visualization computer vision Accuracy sensitivity Visual communication Weapons Resists Transformers Robustness

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Moving Vehicle Registration Plate Detection Using Machine Learning

Moving Vehicle Registration Plate Detection Using Machine Le...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： B sreedhar Gowtham Prasad T V s sivudu Macherla K. s. Raghuram M. sunil Kumar Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram A.P India Dept of Electronics and Mohan Babu University (erstwhile Sree Vidyanikethan Engineering College) Tirupathi AP India Department of IT PACE Institute of Technology and Sciences Ongole AP India Department of Mechanical Engineering Vignan's Institute of Information Technology (A) Visakhapatnam A. P. India Department of Computer Science and Engineering School of Computing Mohan Babu University (erstwhile Sree Vidyanikethan Engineering College) Tirupathi AP India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

This research investigates the application of machine learning techniques in the detection of moving vehicle registration plates. The study explores the effectiveness of various algorithms in accurately identifying and localizing license plates in dynamic and real-time scenarios. By utilizing image processing and pattern recognition methods, the research aims to develop a robust and efficient system capable of detecting license plates under challenging conditions, such as varying lighting conditions and different vehicle orientations. The proposed approach offers potential benefits for law enforcement, traffic monitoring, and parking management systems. Through empirical evaluations, the study aims to validate the effectiveness and reliability of the machine learning-based approach for moving vehicle registration plate detection, providing insights for future research and development in this field.

关键词： Quantum computing Heuristic algorithms Machine learning Real-time systems Pattern recognition Reliability Vehicle dynamics

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Evaluation of Deep Training and Adapting Algorithms in Medical with De-Noising EEG 3

Evaluation of Deep Training and Adapting Algorithms in Medic...

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

作者： Abdulwahid, Ali Hadi Karetla, Girija Rani Ganesh, K.V.B. Kumar, A suresh Radha, K. shivakumar, K. Southern Technical University Engineering Technical College Control and Automation Engineering Department Basra Iraq Western Sydney University School of Computer Data and Mathematical Sciences Australia Koneru Lakshmaiah Education Foundation KL Business School Guntur Andhra Pradesh India Graphic Era Deemed to Be University Department of CSE India St. Martin's Engineering College Dept. of IT Secunderabad Telangana India SR University School of Computer Science and Artificial Intelligence Warangal Telangana India

ISBN: (纸本)9798350399264

An electroencephalogram that was captured with electrodes placed can easily get contaminated with a variety of artifacts. Here is a comparative of various electroencephalogram (EEG) de-noising techniques. Three alternative techniques are used to eliminate five different kinds of noise, and also the outcomes are evaluated. These three methods are just the Least Mean square (LMs) technique, the Connected Neural Network, and also the Recursive Least-square (RLs) adaptable algorithm (FCNN). Period charts of the actual EEG signal, the noise EEG signal, and thus the predicted EEG signal are used to display the results. RRMsE and signal-to-noise ratio were utilized to compare the effectiveness of the 3 de-noising strategies in this study. Investigating the model parameters in this case, we discover the FCNN forecasts a better outcome compared to the other two methods. © 2023 IEEE.

关键词： Electroencephalography

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Experimental Analysis of Radioscopy Images based Pneumonia Disease Detection using Improved Neural Network Methodology

Experimental Analysis of Radioscopy Images based Pneumonia D...

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Image Processing and Capsule Networks (ICIPCN), International Conference on

作者： sajja suneel B.M.s. Rani M. Amina Begum Jayendra Gopal Thatipudi Ajay singh Yadav D. Muthukumaran Department of CSE(Data Science) Institute of Aeronautical Engineering Hyderabad Telangana India Department of ECE Vignan’s Nirula Institute of technology and science for women Department of ECE Oxford College of Engineering Venmani Tamilnadu India Dept. of Computer Science University of North Texas Department of Mathematics SRM Institute of Science and Technology Delhi-NCR Campus Ghaziabad U.P India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

The bacterium streptococcus pneumonia is the most prevalent causative agent of pneumonia, a severe and potentially fatal lung infection in humans. According to the World Health Organization, pneumonia accounts for one in three deaths in India. Assessing chest radiographs to detect pneumonia typically requires skilled radiologists, making an automatic detection method crucial for rapid treatment, especially in rural areas. This study introduces an Improved Neural Network (INN) learning method, cross-validated against existing Convolutional Neural Networks (CNNs), reflecting the success of deep-learning algorithms in medical image analysis. The research highlights the utility of these methods for disease classification. The features learned by INN and CNN models on large datasets are particularly valuable for image classification tasks. By using feature extractors programmed on trained INN and CNN frameworks, this study compares the performance of various classifiers in identifying normal and abnormal chest radiographs. statistical results indicate that pre-trained INN models combined with supervised classifier approaches significantly enhance the evaluation of chest radiographs, particularly in detecting pneumonia. This advancement is pivotal for improving diagnosis and treatment outcomes in resource-limited environment.

关键词： Learning systems Pneumonia Neural networks Lung Organizations Feature extraction Convolutional neural networks

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Experimental Evaluation of Deep Learning Assisted Autism spectrum Disorder Prediction scheme using Convolutional Classification Principle

Experimental Evaluation of Deep Learning Assisted Autism Spe...

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Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： Abhinav Kumar sonika Thapak Jayendra Gopal Thatipudi C. Rameshkumar R. Krishnamoorthy Divya Muralitharan Department of Computer Science and Engineering IES College of Technology Bhopal Dept. of Computer Science University of North Texas Department Physics Sathyabama Institute of Science and Technology Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai India Department of C.S.E Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Autism spectrum Disorder (AsD) is characterized by challenges in social communication and restricted or repetitive behaviors. With rising AsD incidence, early diagnosis and intervention become increasingly crucial for improving developmental trajectories and quality of life. Traditional diagnostic methods are often subjective and limited. This study evaluates a deep learning-based approach for AsD prediction using Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Leveraging the Autism Brain Imaging Data Exchange (ABIDE) dataset, which includes neuroimaging, genetic, and behavioral data, the model integrates spatial and temporal features to enhance prediction accuracy. The combined feature set yielded the highest accuracy of 93.7%, demonstrating the efficacy of integrating multiple data types. The proposed Convolutional Recurrent Hybrid (CRH) model achieved a superior accuracy of 98%, outperforming traditional models. This research highlights the potential of deep learning techniques in improving AsD diagnostics.

关键词： Neuroimaging Deep learning Autism Accuracy Recurrent neural networks Predictive models Brain modeling Genetics Trajectory Biological neural networks

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