Synthetic Aperture Radar (SAR) has the advantages of high penetration, high resolution, all-weather, and all-time, but due to the special imaging mechanism of SAR images, there are a large number of coherent speckle n...
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Predicting liver disease usually entails estimating a person's chance of contracting ailments related to the liver using a variety of techniques. The prediction is made using the Novel Decision Tree algorithm and ...
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This paper proposes a novel TCN-LSSVM model for precise trajectory prediction. An approach that integrates the image recognition and the trajectory prediction is designed, which improves the prediction process. By com...
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The proceedings contain 31 papers. The special focus in this conference is on Internet of Everything and Quantum Information processing. The topics include: Revolutionizing Agriculture: A Mobile App for Rapid Plant Di...
ISBN:
(纸本)9783031619281
The proceedings contain 31 papers. The special focus in this conference is on Internet of Everything and Quantum Information processing. The topics include: Revolutionizing Agriculture: A Mobile App for Rapid Plant Disease Prediction and Sustainable Food Security;EMG Based Human Machine Integration for IoT Based Instruments;medrack: Bridging Trust and Technology for Safer Drug Supply Chain Using Ethereum and IoT;a Review on Tuberculosis Pattern Detection Based on Various Machine Learning Techniques;sensor Based Hand Gesture Identification for Human Machine Interface;an Improved Detection System Using Genetic Algorithm and Decision Tree;a Detailed Analysis of Colorectal Polyp Segmentation with U-Network;a Review on Internet of Things (IoT): Parkinson’s Disease Monitoring Device;Machine Learning-Based Prediction of Temperature Rise in Squirrel Cage Induction Motor (SCIM);quantum Many-Body Problems: Quantum Machine Learning Applications;Experimental Study on the Impact of Airborne Dust Deposition on PV Modules Using Internet of Things;bidirectional Converter with Time Utilization-Based Tariff Investigation and IoT Monitoring of Charging Parameters Based on G2V and V2G Operations;predictive Analysis of Telecom Customer Churn Using Machine Learning Techniques;baker’s Map Based Chaotic image Encryption in Military Surveillance systems;Cyber Security Investigation of GPS-Spoofing Attack in Military UAV Networks;ioT Based Enhanced Safety Monitoring System for Underground Coal Mines Using LoRa Technology;ioT Based Hydroponic System for Sustainable Organic Farming;predicting Stride Length from Acceleration Signals Using Lightweight Machine Learning algorithms;unveiling Hate: Multimodal Perspectives and Knowledge Graphs;vision-Based Toddler Activity Recognition: Challenges and Applications;automated W-Sitting Posture Detection in Toddlers.
Recent pre-exascale and exascale supercomputers have driven the development of increasingly sophisticated AI models for diverse applications, including image recognition and classification, natural language processing...
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Atmospheric turbulence deteriorates the quality of images captured by long-range imaging systems by introducing blur and geometric distortions to the captured scene. This leads to a drastic drop in performance when co...
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ISBN:
(数字)9781665496209
ISBN:
(纸本)9781665496209
Atmospheric turbulence deteriorates the quality of images captured by long-range imaging systems by introducing blur and geometric distortions to the captured scene. This leads to a drastic drop in performance when computer vision algorithms like object/face recognition and detection are performed on these images. In recent years, various deep learning-based atmospheric turbulence mitigation methods have been proposed in the literature. These methods are often trained using synthetically generated images and tested on real-world images. Hence, the performance of these restoration methods depends on the type of simulation used for training the network. In this paper, we systematically evaluate the effectiveness of various turbulence simulation methods on image restoration. In particular, we evaluate the performance of two state-or-the-art restoration networks using six simulations method on a real-world LRFID dataset consisting of face images degraded by turbulence. This paper will provide guidance to the researchers and practitioners working in this field to choose the suitable data generation models for training deep models for turbulence mitigation. The implementation codes for the simulation methods, source codes for the networks and the pre-trained models are available at https://***/Nithin-GK/Turbulence-Simulations
This paper presents a unique solution to challenges in medical imageprocessing by incorporating an adaptive curve grey wolf optimization (ACGWO) algorithm into neural network backpropagation. Neural networks show pot...
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This work presents a modified hyperchaotic system based on two memristors with unique Lyapunov exponents, offering a unique method for image encryption. The main goals are to use dynamic analysis to evaluate the compl...
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This paper presents a novel approach to detecting equipment failures in modern power systems by leveraging machine learning techniques applied to thermography inspection data. Particularly segmentation and pixel proce...
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ISBN:
(纸本)9798350310665
This paper presents a novel approach to detecting equipment failures in modern power systems by leveraging machine learning techniques applied to thermography inspection data. Particularly segmentation and pixel processing to improve accurateness is highlighted in the methodology. The proposed method is capable of identifying early warning signs of equipment failure and predicting when the failure is likely to occur. The proposed approach demonstrates the potential for early detection of equipment failure in modern power systems with accurate clustering. The use of machine learning algorithms applied to thermography inspection data provides a reliable and effective way to identify and predict equipment failures, ultimately leading to improved system reliability and reduced maintenance costs.
It provides a classic method for blood image based system for blood group classification techniques. The suggested approach reduces reliance on traditional processes by automating the analysis of blood sample images t...
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ISBN:
(数字)9798331512088
ISBN:
(纸本)9798331512095
It provides a classic method for blood image based system for blood group classification techniques. The suggested approach reduces reliance on traditional processes by automating the analysis of blood sample images through the use of sophisticated imageprocessingalgorithms. This strategy seeks to offer a dependable and expandable solution that is especially useful for application in distant or resource-constrained environments.
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