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检索条件"机构=Departments of Computer Science and Engineering and Electronics and Communication Engineering"

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IoT-Enabled Alzheimer Disease Detection: A Convolutional Encoder-Decoder Approach Enhanced by Alpine Skiing Optimization

IoT-Enabled Alzheimer Disease Detection: A Convolutional Enc...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： S.V. Evangelin Sonia Narendra B Mustare V. Sailaja Pamarthi Sunitha Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Department of EIE CVR College of Engineering Hyderabad India Department of Electronics and Communication Engineering Pragati Engineering College Surampalem Andhrapradesh India Department of Electronics and Communication Engineering Aditya University Surampalem Andhrapradesh India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Alzheimer's disease (AD) represents a significant challenge in the treatment of dementia among older adults, with early diagnosis being crucial for improving patient outcomes and extending life expectancy. This study introduces a novel Alzheimer's prediction model that leverages Internet of Things (IoT) technology and a hybrid Convolutional Encoder-Decoder architecture optimized with Alpine Skiing Optimization. Utilizing the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the proposed approach begins with Adaptive Self-Guided Filter (ASGF)-based pre-processing to refine raw brain images by removing irrelevant areas. A Convolutional Encoder-Decoder framework, combining U-Net and V-Net architectures with a Point Transformer Network (Con-EDUV-PTransNet), is employed for robust classification of AD. This hybrid method integrates region-of-interest (ROI) segmentation, feature extraction, and classification, and is optimized using Alpine Skiing Optimization to minimize the loss function. The results demonstrate superior performance with an F-Score of 99.18%, accuracy of 99.4%, precision of 99.26%, and recall of 99.87%. These metrics underscore the effectiveness of the Con-EDUV-PTransNet model in distinguishing between healthy individuals and those with AD. The experimental results affirm the model's effectiveness, offering a highly accurate and reliable method for AD diagnosis that could significantly enhance patient outcomes through improved brain imaging analysis.

关键词： Neuroimaging Accuracy Predictive models Brain modeling Transformers Information filters Feature extraction Internet of Things Alzheimer's disease Optimization

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Correlation and Causation in the Perspective of COVID-19: An Empirical Study on Canonical Correlation Analysis

Correlation and Causation in the Perspective of COVID-19: An...

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2022 International Conference on Smart Applications, communications and Networking, SmartNets 2022

作者： Vinod, Dasari Naga Lebekwe, Casper K. Zungeru, Adamu Murtala Menaka, D. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai600062 India Botswana International University of Science and Technology Department of Electrical Computer and Telecommunications Engineering Palapye Botswana Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai600119 India

ISBN: (纸本)9781665487580

COVID-19 has an immense effect on the Globe, crossing 53,86,95,729 affected in more than 220 nations, with 63,18,093 individuals deceased. Various countries released COVID-19 protocols to enclose its spread to control the pandemic. This research article illustrates the Effect of COVID-19 on aged people (age>50), diabetes individuals, and individuals with smoking habits concerning the cause of death. An attempt has been made to identify the predominant variables for the cause of death due to COVID-19. IBM SPSS statistical tool enabled by Canonical Correlation Analysis (CCA) is used for simulation. Data were gathered from the Kaggle, an open repository for 2020. Based on the results obtained, predictions regarding the Cause and Effect of COVID-19 are discussed. © 2022 IEEE.

关键词： COVID-19

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Deep Learning Network for Object Detection Under the Poor Lighting Condition 1

Deep Learning Network for Object Detection Under the Poor Li...

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1st IEEE International Conference on Automation, Computing and Renewable Systems, ICACRS 2022

作者： Chethan, L.S. Maheswari, S. Uma Rao, Seeram Srinivasa Mukkapati, Naveen Ganesh, C.S. Sundar Kumar, Ashok Computer Science and Engineering PESITM SHIMOGA Karnataka Shivamogga577204 India Department of Electronics and Communication Engineering Easwari Engineering College Tamil Nadu Chennai600089 India Department of Mechanical Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Department of Computer Science and Engineering RVR & JC College of Engineering Andhra Pradesh Guntur522019 India Department of Electrical and Electronics Engineering Karpagam College of Engineering Myleripalayam Tamil Nadu Coimbatore641032 India Department of Computer Science Banasthali Vidyapith Rajasthan Banasthali304022 India

ISBN: (纸本)9781665460842

Object detection is a part of image processing that holds great importance in this modern world. To locate things in images or videos, a computer vision approach called object detection is utilized. To identify items in both still photos and moving films, numerous algorithms and models have been created. One of the main issues with such object detection algorithms is that they struggle to accurately identify things in images with poor lighting. This study tries to develop the best deep learning algorithm that can be used to predict items from photos with very little or no lighting to address this issue. For this, from GitHub, a dataset of images of various objects, such as tables, cats, and dogs, has been compiled. After categorization, the collected dataset is examined using two factors. The images are then preprocessed utilizing picture format conversion and histogram equalization. Two different deep learning models were produced by two different algorithms. The YOLO algorithm and the speedier RCNN algorithm are the two algorithms. The models are then trained and evaluated using the preprocessed dataset. The performance of the models during training and validation is assessed using a metric called the AP score. The YOLO algorithm has the better AP score overall, according to the analysis of the AP score. The YOLO is once again determined to be superior when the findings are displayed in a bar graph for easier understanding. The YOLO algorithm's prediction results are also examined, and it is discovered that even under conditions of extremely poor lighting, the algorithm can correctly predict the presence of one or more objects. © 2022 IEEE

关键词： Object detection

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Performance Analysis of Differential Amplifiers Using Cadence with GPDK45, GPDK90, and GPDK180 6

Performance Analysis of Differential Amplifiers Using Cadenc...

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6th International Conference on Devices for Integrated Circuit, DevIC 2025

作者： Sannyashi, Tomal Suvro Akhtar, Sk Babul Roy, Tanmay Sinha Jana, Biswajit Ghosh, Soumen Swami Vivekananda University Electronics and Communication Engineering Kolkata700121 India Institute of Engineering and Management Electrical and Electronics Engineering Kolkata700091 India Bennett University School of Computer Science Engineering and Technology UP 201310 India West Bengal 721657 India

ISBN: (纸本)9798350391107

This paper presents a comprehensive analysis of differential amplifier circuits designed using three process technologies: GPDK45, GPDK90, and GPDK180. Utilizing Cadence design and simulation tools, we perform detailed simulations to evaluate and compare key performance parameters of these architectures. The analysis focuses on critical metrics such as propagation delay, gain, power consumption, and bandwidth, which play a pivotal role in determining the overall efficiency and suitability of differential amplifiers for various applications. The simulation results highlight the impact of technology scaling on the performance characteristics, offering insights into trade-offs and optimization strategies. By systematically comparing the outputs across these process nodes, the study provides valuable guidelines for the selection of process technology based on specific design requirements and application domains. © 2025 IEEE.

关键词： Differential amplifiers

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DWT Algorithm for Macro & Micro Block based Multiple Histogram Shifting for Video Data Hiding 1

DWT Algorithm for Macro & Micro Block based Multiple Histogr...

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1st IEEE International Conference on Automation, Computing and Renewable Systems, ICACRS 2022

作者： Venkata Ramana, K. Sowntharya, C. Jithesh, K. Reddy, Poli Lokeshwara Apoorva, M.C. Kumar, Ashok Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad500090 India Department of Computer Science and Engineering Sri Ramakrishna Engineering College Tamil Nadu Coimbatore641022 India Department of Computer Science Mahatma Gandhi College Kerala Kannur670703 India Department of Electronics and Communication Engineering Anurag University Telangana Hyderabad500088 India School of Computer Science and Applications REVA University Karnataka Bengaluru560064 India Department of Computer Science Banasthali Vidyapith Rajasthan 304022 India

ISBN: (纸本)9781665460842

With the help of video compression and compressive sensing technology, this study demonstrates how to conceal data. Video data was concealed using a linear sensing technique for greater quality. By reducing changes across pixels to obtain discrete micro-block features while keeping their statistical properties, micro-block residuals can be assessed using CS. In order to create many histograms, micro blocks with related features are grouped together using CS. The seamless integration of data into video is the project's main objective. The picture quality ratio and PSNR rate of the image are improved using this method. This method uses a micro-block-based approach to increase PSNR and improve security. In order to eliminate shifting distortions, data embedding is used on each of these histograms to extend the outermost bins while maintaining the size of the inner bins. Numerous RDH strategies based on multiple HS (MHS) based on spatial correlations of multimedia are offered to protect copyright, in which several histograms are constructed using some strict criteria. Compared to photographs, video has more three-dimensional and temporal linkages, and it's simple to obtain histograms with greater clarity. Reduced embedding distortions enable lossless restoration of the cover thanks to the reversible data hiding (RDH) technology. Histogram shifting is one RDH strategy that is widely employed (HS). © 2022 IEEE

关键词： Image compression

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An Approach to Analyze the Permanent Magnet Motor Torque Ripple for Improving Torque Power Curves used in Electric Vehicles

An Approach to Analyze the Permanent Magnet Motor Torque Rip...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Varatharaj Neelakandan B Prabakaran D Balaji P Suresh S Maheswaran N Indhumathi Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore TamilNadu India Department of Electronics and Communication Engineering Kongu Engineering College Erode TamilNadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

An electric car’s permanent magnet synchronous motor (PMSM) will be designed and evaluated for performance in this research article. In order to satisfy the unique needs of the electric car and improve its overall performance, the goal is to achieve the highest torque density and the lowest torque ripple. The study’s goal is to increase the electric vehicle’s power density to do this, which calls for the use of a high-power-density motor. This is addressed by conducting an optimization procedure to increase the generated torque while making sure that the chosen motor stays within the defined size and volume restrictions. The efficient use of permanent magnet (PM) materials with strong thermal limiting capacity, which can enhance the power, is a critical component of this improvement. The efficient use of permanent magnet (PM) materials with high thermal limiting capacity, which may boost the power output under both peak and continuous loads, is a vital component of this optimization. The most important motor design parameters are carefully chosen throughout the optimization process to have the greatest influence on motor performance. Using the finite element software program “CAE Software,” the research team tests and executes the suggested motor design. The overall goal of this project is to build a PMSM that is highly effective and adapted to the needs of electric vehicles. The goal of the study is to optimize critical motor parameters and use modern PM materials with higher thermal properties in order to increase torque density and reduce torque ripple of the motor.

关键词： Torque Limiting Motors Electric vehicles Permanent magnet motors Software Permanent magnets Torque measurement Thermal loading Optimization

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Design of deep learning models for the identifications of harmful attack activities in IIOT

Design of deep learning models for the identifications of ha...

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2023 International Conference on Artificial Intelligence and Smart communication, AISC 2023

作者： Rajbalaji, S. Raman, Ramakrishnan Pant, Bhasker Rathour, Navjot Rajagopa, Balaji Ramkumar Prasad, Ch. Raghava Rathinam College of Arts and Science Department of Computer Science Tamil Nadu Coimbatore641021 India Symbiosis Institute of Business Management Maharashtra Pune India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Graphic Era Hill University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Uttaranchal University Uttaranchal Institute of Technology India Cognizant Technology Solutions Tamilnadu Chennai India Koneru Lakshmaiah Education Foundation Department of Electronics and Communication Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350322309

IICSs, which connect technical systems with physical systems to deliver services to businesses, have become a new field of study since they are susceptible to various cyber attacks that could compromise their ability to continue supply services to businesses. Potential risks result in lost revenue, and reputational risk for businesses. It is challenging to collect information for use in constructing a smart NIDS that can successfully recognize both current and novel assaults, despite the fact that Network Intrusion Detection Systems (NIDSs) have been recommended as a way of protection from them. This paper presents an anomalies recognition system for IICSs using DL algorithms that may train and evaluate data obtained from TCP/IP package in order to address this problem. A well network datasets NSL-KDD & UNSW-NB15 are used to assess the training courses for deep auto-encoder and deep feed-forward (FF) NN designs. The research findings result that this methodology could be used in actual IICS situations because it has a lower false positive rate & a greater detection accuracy compared to previous methods. © 2023 IEEE.

关键词： Network security

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Utilizing Extreme Gradient Boosting (XGBoost) for Apple Scab Disease Detection

Utilizing Extreme Gradient Boosting (XGBoost) for Apple Scab...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Sowjanya Kumari Y Jayakrishna K Dhanasekar J Miniappan PK Balasundaram N Vijayakumari G Department of Computer Science Engineering QIS College of Engineering and Technology Ongole India Department of Master of Computer Application QIS College of Engineering and Technology Ongole India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore India Department of Mechanical Engineering Karpagam Academy of Higher Education Coimbatore India Department of Civil Engineering Karpagam Academy of Higher Education Coimbatore India Department of Electronics and Communication Engineering Builders Engineering College Kangeyam India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

The research in this paper aims at evaluating a technique known as Apple Scab Disease detection using XGBoost (ASD-XG) Extreme Gradient Boosting (XGBoost) in the identification of apple scab on the apples crop which is a detrimental disease globally. The work feeds on a collection of 100 high resolution images of apple leaves on which image processing methodology is used to derive features pertinent to scab condition including color, texture and shape. The above-mentioned features of the dataset have been used to train the XGBoost model on the dataset, which showed a test accuracy of 95% as against the models like SVM, RF, CNN etc. Consequently, the experimental evaluations prove XGBoost’s higher accuracy and readability for early discovery of diseases in agriculturally important crops. The work done in this study indicates that XGBoost could underline the improvement of sustainable farming.

关键词： Support vector machines Accuracy Shape Crops Feature extraction Boosting Agriculture Real-time systems Convolutional neural networks Diseases

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Covid Precuationary Alerting Robot 12

Covid Precuationary Alerting Robot

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12th International Conference on computer communication and Informatics, ICCCI 2022

作者： Rooban, S. Manimegalai, R. Eshwar, Idupulapati Venkata Sai Vasista, Ranga P.L.N. Gupta, Puvvada Venkata Madhava Electronics and Communication Engineering Koneru LakshmaiahEducation Foundation Andhra Pradesh Guntur522502 India Psg Institute of Technology and Applied Research Department of Computer Science and Engineering TN Coimbatore District 641062 India

ISBN: (纸本)9781665480352

This paper presents the precautionary robot which can warn and also help the people from affecting with deadly corona virus. We know that the Novel Corona virus had shown a great impact on the world population, many of the people are dying due to this deadly virus because of not following the safety measures and not taking any precautions to overcome the ***-care workersare playing a major role in this pandemic by helping the affected patients to recover from the virus infections. Not only health-care workers but also robots are playing a major part in this pandemic. Robots are being used in laboratories and hospitals for various purposes like disinfection, spraying, manufacturing of medicines/vaccines, surveillance and many more applications. Robots are also equally contributed as healthcare workers around the world to adapt the present needs in order to combat with COVID-19. Mainly in this pandemic robot act as a tool, used in applications which are focused primarily in the area of health and services. The proposed work is to design and implement a robot for the covid applications. This robot is proposed by analyzing the present day's scenario on this pandemic. The proposed robot is user friendly and works particularly for 4 functions like mask detection on people, sanitizer spraying/disinfecting, temperature detection, and finally moving from one place to another place in the hospital or wherever it is required. © 2022 IEEE.

关键词： Robots

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Energy Efficiency in 5G Networks Using Gaussian Mixture Models

Energy Efficiency in 5G Networks Using Gaussian Mixture Mode...

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2024 IEEE International Conference on communication, Computing and Signal Processing, IICCCS 2024

作者： Alsalami, Zaid Kasthuri, R. Sowmiya, S. Rani, Pooja Gupta, Deepak Kumar Gowtham Kumar Reddy, I.V. The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Sri Ramakrishna Engineering College Department of Mathematics Tamilnadu Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Science Engineering Punjab Mohali140307 India Ies College of Technology Department of Electronics & Communication Engineering M.P. Bhopal India Saveetha Institute of Medical and Technical Sciences Department of CSE-AI Tamilnadu Chennai India

ISBN: (纸本)9798350390759

In this research, a new approach for improving energy efficiency of the networks in the 5G context is presented: utilizing the Gaussian Mixture Models. The process includes acquiring data in the first step, training the model in the second step, and using the model to make real-time decisions to minimize resources and inefficient energy consumption in the last step. This includes the collection of data on context and complete flow details of the course of traffic passage as well as actions committed by users. The GMM is then trained using the Expectation-Maximization (EM) algorithm for developing the model and reinforcement learning for real-time decision-making on the developed model. In case studies analyzed, there is evidence of the enhancement of energy consumption by 14% on average absolute reduction in energy per traffic unit, by 11% of the average absolute increase in network throughput and by 49% of the mean absolute reduction of latency in comparison with baseline conditions. © 2024 IEEE.

关键词： Expectation maximization algorithm

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