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Performance Comparison of Deep learning Models for Ultrasound-Based Kidney Disease Diagnosis 2

Performance Comparison of Deep Learning Models for Ultrasoun...

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2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Mehta, Shiva Bhalla, Anubhav Centre for Research Impact & Outcome Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh 174103 India

ISBN: (纸本)9798350366570

This paper presents a comparative analysis of three advanced deep learning models, U-Net, SegNet, and DeepLabv3+, to segment anatomical structures of kidneys in ultrasound images to promote the diagnosis of kidney disorders. The models were assessed using essential performance metrics: Sensitivity, Dice Similarity Coefficient (DSC), and Intersection over Union (IoU). In all the parameters, including Dice score, IoU, as well as the Sensitivity, U-Net had higher scores with 0.91, 0.88 and 0.93 respectively. Encoder-decoder configuration connected skip connection means spatial information retention and fine features recovery while making it perfect for accurate kidney segmentation. The whole architecture was tested on the bone class, gaining a Dice score of 0.89, an IoU of 0.85, a Sensitivity of 0.91. The model introduced atrous convolution and atrous spatial pyramid pooling for a better way of handling multiscalar information;however, the model painted out worse performance in perceiving minute structures in comparison to U-net. Nevertheless, SegNet which was the most computationally efficient had the lowest score of a Dice score of 0.85, IoU of 0.81, and sensitivity of 0.87. The reduced performance was caused by a reduction of spatial information during level max-pooling and up-sampling to extract borders of the kidneys. In this paper, it is established that the U-Net architecture is optimal in the context of kidney ultrasound segmentation, demonstrating state-of-the-art performance for clinical applications in kidney disease diagnosis. Future research could focus on other models, different from the pure ones, in order to improve the effectiveness of segmentation. © 2024 IEEE.

关键词： Clinical research

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Research on Optimization System of machine learning Algorithm to Improve Wireless Power Transmission 2

Research on Optimization System of Machine Learning Algorith...

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2nd IEEE international conference on Control, Electronics and Computer Technology, ICCECT 2024

作者： Tang, Taiqing Wei, Xiwen Pang, Peng Tian, Cong Heilongjiang University of Technology Heilongjiang Jixi China

ISBN: (纸本)9798350380958

As wireless power transmission systems are widely used in modern communications, improving their performance and reliability has become an urgent task. However, frequent failures in the system pose challenges to communication quality and reliability. Traditional fault detection methods often rely on rules or manually defined features, which are difficult to cope with complex and changeable actual environments. To address this challenge, this study proposes a novel machine learning model named C-BiLSTM to improve the optimization of wireless power transmission systems. The C-BiLSTM model synergizes features from a convolutional neural network (CNN) and a bidirectional long short-term memory network (BiLSTM). This sophisticated neural network effectively leverages the CNN's adept feature extraction and the BiLSTM's temporal modeling prowess for enhancing wireless power transmission. The transmission signals of the system are used for fault analysis and prediction. Specifically, the model accepts a one-dimensional transmission signal sequence as input, extracts local features through CNN, captures long-term dependencies through BiLSTM, and finally outputs fault classification or prediction results through a fully connected layer to provide a basis for subsequent fault processing. The C-BiLSTM model combines CNN and BiLSTM to accurately predict and analyze faults in wireless power transmission systems. The method of this study not only improves the accuracy of fault prediction, but also reduces the dependence on experts in related fields, which has higher practical significance. © 2024 IEEE.

关键词： Convolutional neural networks

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2nd international conference on signal processing and Communication, ICSPC 2019 - proceedings

2nd International Conference on Signal Processing and Commun...

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2nd international conference on signal processing and Communication, ICSPC 2019

ISBN: (纸本)9781728118499

The proceedings contain 82 papers. The topics discussed include: comparative analysis of various machine learning techniques for intrusion detection system;influence of assorted back barriers on AlGaN/GaN HEMT for 5G K-band applications;a low-cost Arduino based automatic irrigation system using soil moisture sensor: design and analysis;sequential segmentation of EEG signals for epileptic seizure detection using machine learning;classification of lung images using deep convolutional neural network;a four grade brain tumor classification system using deep neural network;feature selection and classification for analysis of breast thermograms;multi-frame image super-resolution by interpolation and iterative backward projection;state of art of network on chip;and a hybrid secure and energy efficient cluster based intrusion detection system for wireless sensing environment.

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2nd international conference on Sustainable Technology and Advanced Computing in Electrical Engineering, ICSTACE 2023

2nd International Conference on Sustainable Technology and A...

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2nd international conference on Sustainable Technology and Advanced Computing in Electrical Engineering, ICSTACE 2023

ISBN: (纸本)9789819992348

The proceedings contain 40 papers. The special focus in this conference is on Sustainable Technology and Advanced Computing in Electrical Engineering. The topics include: Performance Analysis of Various Converters in PV Systems Using PSO-Based MPPT;Ultra-Gain Two-Input Two-Output DC–DC Converter for Electric Vehicle Application;Modified Switched Capacitor-Based Non-isolated Bidirectional DC–DC Converter for Obtaining High VTR;wavelet-Based Quadrature Rule to Evaluating Quasi-Type Singular Integral Equations;Spherical Induction Motor—Speed Control Using VFD;a machine learning and Fuzzy-Based Reliable Data Collection and Communication in AioT—Fog Computing Environment;smart Home Energy Management with a Coordinated Neighborhood Energy Sharing;Dynamic Operation of Islanded DC Microgrid with Fuel Cell Using Hybrid Energy Storage Systems;fault Detection, Classification, and Identification of Shunt Compensated Distribution System Through Wavelet Transforms;Optimal Sizing of PV/Wind/Battery Grid-Connected Hybrid Renewable Energy Systems Using TLBO Algorithm;diagnosis of Clustered Microcalcifications in Breast Cancer Using Mammograms;Stochastic Optimal Allocation of Photovoltaic-Based DG and DSTATCOM in Distribution System Considering Uncertainties of Loads and Solar Irradiance;breast Cancer: Automatic Detection from Mammogram Images by Utilizing Deep learning Methods;Standalone Photovoltaic Fed Water Pumping System with Better Performance Using P&O MPPT Techniques;Accurate Estimation of Amplitude, Phase, and Frequency of a Sinusoidal signal Contaminated with Harmonics and DC Decaying Components;experimental Investigation and Pollution Contaminant Severity Analysis of Glass and Porcelain Insulators Under Controlled Conditions;Classification of Dry and Wet Snow of Glaciers in Western Himalayas Using SAR Polarimetric Decomposition—An Application of Satellite Image processing;optimum Overcurrent Relay Coordination for Radial Distribution Networks Using Improved Mathematical Fo

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Design of MIMO CSI Feedback with Deep learning 2

Design of MIMO CSI Feedback with Deep Learning

引用

2nd international conference on Artificial Intelligence and Intelligent Information processing, AIIIP 2023

作者： Xie, Bin Dai, Bangqiang College of Information Engineering Jiangxi University of Science and Technology Ganzhou China

ISBN: (纸本)9798350371451

In MIMO systems, effective channel state information (CSI) feedback is crucial for optimizing signal transmission and improving system performance. However, with the increasing complexity of CSI, traditional CSI feedback methods are limited in terms of computational error and complexity, making the feedback performance has become a bottleneck. In this paper, we propose a new CSI feedback network based on deep learning approach, which aims to utilize the powerful properties of neural networks to achieve better feedback performance in a more accurate and efficient manner. The network is divided into two parts: encoder and decoder. In the encoding part, it utilizes the coordinated attention mechanism to extract richer CSI features;in the decoding part, it achieves better CSI recovery through different numbers of convolutional channels with multiple receptive fields. Experimental results show that the feedback method proposed in this paper achieves better CSI feedback performance in different scenarios and at different compression rates. © 2023 IEEE.

关键词： MIMO systems

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Land Use Classification of Kathgodam Region using Transfer learning-based approach 2

Land Use Classification of Kathgodam Region using Transfer l...

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2nd international conference on Artificial Intelligence and signal processing, AISP 2022

作者： Pargaien, Saurabh Singh, Devendra Prakash, Rishi Dubey, Ved Prakash Pant, Himanshu Pargaien, Amrita Verma Department of Electronics and Communication Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering Graphic Era Hill University Bhimtal Campus India College of Pharmacy Graphic Era Hill University Bhimtal India

ISBN: (纸本)9781665442909

Transfer learning is a new concept that has the potential to propel machine learning further in research and industry. One of the key reasons to use it is the absence of data on specific jobs, as gathering and classifying data can be costly and time-consuming. In machine learning, transfer learning is used to reprocess a formerly trained model on an original problem. In transfer learning, a machine uses knowledge from a previous assignment to improve the prediction about a new task. Transfer learning allows CNNs to learn with small amounts of data by transferring knowledge from models that have been pre-trained on big datasets. In this paper we have used various pre-trained models like Inception v3, SqueezeNet, VGG-19 and VGG-16 to study the performance of pre-trained models to classify Agriculture land and non-Agriculture land in Kathgodam Region. The capabilities of the four pre-trained CNN models SqueezeNet, Inception V3, VGG16, and VGG19 for extracting features from images were explored by the authors. The accuracy of 98.5% is obtained using Inception V3. © 2022 IEEE.

关键词： Agriculture

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Hybrid machine learning Approach: SVM with ANN Kernel for HSI Medical Image Enhancement 2

Hybrid Machine Learning Approach: SVM with ANN Kernel for HS...

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2nd IEEE international conference on Emerging Research in Computational Science, ICERCS 2024

作者： Aravindhan, K. Revathi, K. Priyanka, S. Priyadharshini, S. Keerthana, P.K. Muthulakshmi, K. REVA University School of Computing and Information Technology Karnataka Bengaluru India SNS College of Engineering Department of Information Technology Tamil Nadu Coimbatore India SNS College of Engineering Department of Artificial Intelligence and Data Science Tamilnadu Coimbatore India KGiSL Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Manipal Institute of Technology Department of Information Technology Karnataka Bengaluru India

ISBN: (纸本)9798331534950

In medicine image processing provides a perceptive study on disease diagnosis and identification as well as treatment preparation. Since it can acquire complete spectral information over a wide spectrum of wavelengths, especially hyperspectral imaging (HSI) has shown great potential for the non-invasive characterization of tissue. On the other hand, due to their high-dimensional character as well as the presence of noise HSI medical images present a great challenge for processing and enhancement. Even if traditional methods are effective since they sometimes struggle to manage the complexity of these datasets, more sophisticated approaches are needed and demanded. The proposed a hybrid machine learning model including a support vector machine (SVM) classifier and an artificial neural network (ANN) kernel inside the scope of this work to improve and classify HSI images. The ANN kernel is designed to control the inherently complex and non-linear elements in HSI data. Acting as a mapping tool, the artificial neural network kernel transforms the input space into a feature space easily managed in which the SVM can operate with higher accuracy. Main goals are to lower image noise and increase contrast to improve diagnosis correctness. The method was evaluated with a readily available general public HSI medical image collection. In the experimental results, image quality and classification accuracy show appreciable rise. Comparatively to the traditional SVM methods, the signal-to-noise ratio (SNR) increased by 15%;hence, the general classification accuracy was found to be 94.8%. Moreover, the better images show more clear tissue limits and spectral features, which helps clinical interpretation of the results. The results imply that the SVM with ANN kernel model provides a strong solution for the enhancement and classification of HSI medical images, so opening a new path for the improvement of medical diagnostics. © 2024 IEEE.

关键词： Support vector machines

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machine learning Aided Resource Allocation in a Downlink Multicarrier NOMA network with Coordinated Direct and Relay Transmission 2

Machine Learning Aided Resource Allocation in a Downlink Mul...

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2nd international conference on Artificial Intelligence and signal processing, AISP 2022

作者： Joan, Romera S. Manimekalai, T. Laxmikandan, T. Dept. of ECE College of Engineering Anna University Guindy Chennai India

ISBN: (纸本)9781665442909

In this paper we propose an Artificial Neural Network (ANN) based approach to reduce the computational complexity on solving the combinatorial optimization problem of resource allocation in a downlink multicarrier non-orthogonal multiple access (MC-NOMA) network aided by coordinated direct and relay transmission (CDRT) in the presence of underlay cognitive radio (CR) users. The combinatorial optimization involves optimal user pairing, relay selection, subcarrier pairing and assignment which, when solved by exhaustive search, incurs a high computational complexity and processing delay. We show that an ANN trained by stochastic gradient descent (SGD) based supervised learning algorithm can do the same with low complexity and can provide more than 50% reduction in processing delay. © 2022 IEEE.

关键词： Combinatorial optimization

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Honey Adulteration Detection using Hyperspectral Imaging and machine learning 2

Honey Adulteration Detection using Hyperspectral Imaging and...

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2nd international conference on Artificial Intelligence and signal processing, AISP 2022

作者： Al-Awadhi, Mokhtar A. Deshmukh, Ratnadeep R. Department of Computer Science and IT Dr. Babasaheb Ambedkar Marathwada University Aurangabad India

ISBN: (纸本)9781665442909

This paper aims to develop a machine learning-based system for automatically detecting honey adulteration with sugar syrup, based on honey hyperspectral imaging data. First, the floral source of a honey sample is classified by a botanical origin identification subsystem. Then, the sugar syrup adulteration is identified, and its concentration is quantified by an adulteration detection subsystem. Both subsystems consist of two steps. The first step involves extracting relevant features from the honey sample using Linear Discriminant Analysis (LDA). In the second step, we utilize the K-Nearest Neighbors (KNN) model to classify the honey botanical origin in the first subsystem and identify the adulteration level in the second subsystem. We assess the proposed system performance on a public honey hyperspectral image dataset. The result indicates that the proposed system can detect adulteration in honey with an overall cross-validation accuracy of 96.39%, making it an appropriate alternative to the current chemical-based detection methods. © 2022 IEEE.

关键词： Hyperspectral imaging

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Comprehensive Analysis on the Performance of Antenna Design Using machine learning Techniques 1

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2nd international conference on signal and Data processing, ICSDP 2022

作者： Muthumeenakshi, K. Radha, S. Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu Chennai India

ISBN: (数字)9789819914104

ISBN: (纸本)9789819914098

Optimization of antenna design using machine learning techniques is investigated in this paper. Popular machine learning algorithms such as linear regression, support vector machines, k Nearest Neighbor, Gaussian Process Regression and artificial neural networks are employed for a reference microstrip patch antenna designed initially for 2.45 GHz. Optimal design parameters are obtained using these techniques. A thorough analysis using this machine learning technique shows exceptional performance for the frequency band from 1 to 3 GHz. The accuracy obtained using these techniques is validated by performing CST simulations as well. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural networks

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