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An Effectual Methodology to Objective Quality Assessment for Noisy and Compressed Images Using Full and Blind Reference Quality Metrics 3

An Effectual Methodology to Objective Quality Assessment for...

引用

3rd IEEE International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2023

作者： Ravichandran, M. Alexander, T. Jerry Sathish, B.S. Joseph, L.M.I. Leo Ganesan, P. Sajiv, G. Vel Tech Rangarajan Dr. Sagunthala R &d Institute of Science and Technology Dept. of Computer Science Engineering Chennai India Swarnandhra College of Engineering and Technology Dept. of Ece AP Narsapur India Sri Venkateshwara College of Engineering Dept. of Computer Science Engineering Ai Bengaluru India Sr University Dept. of Electronics and Communication Engineering Telangana Warangal India Vidya Jyothi Institute of Technology Dept. of Electronics and Communication Engineering Hyderabad India Saveetha School of Engineering Simats Chennai India

ISBN: (纸本)9798350358742

The proposed work elucidates the role of metrics to decide the superiority of noisy and compressed images. The metrics approximate the superiority of degraded image (noisy or compressed) with or without the reference images. The computed numerical value based on this comparison finalizes the excellence of the image under test. The degradation in any stage of image processing (acquisition, transmission and reception) deteriorates the eminence of an image. It is challenging to understand the source and reason for low quality of the image. The suggested work analyzes the quality of salt and pepper noisy image and compressed image using blind and complete reference metrics. The analysis using full reference metrics is an unpretentious process. However, they do not related to human perception. In contrast, blind quality metrics demonstrated its hegemony in this facet. © 2023 IEEE.

关键词： Salt and pepper noise

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Predicting Liver Disease with Machine Learning Methods 2

Predicting Liver Disease with Machine Learning Methods

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Dhondiyal, Shiv Ashish Luthra, Pooja Aparna Taluja, Tannu Uniyal, Ishita Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science & Engineering Chandigarh University Gharuan Punjab Mohali India Graphic Era Deemed to be University India Department of Electrical Engineering Graphic Era Hill University India

ISBN: (纸本)9798350383867

With time, illnesses pertaining to the heart and liver are getting more and more common. These will only rise in the future due to ongoing technological improvements. The issue will persist despite the fact that individuals are taking yoga and dancing classes and are becoming more health conscious, as well as continuing to introduce and improve their sedentary lifestyles and indulgences. The aging population combined with an increase in the frequency and prevalence of chronic illnesses raises the risk of liver disease-related hospitalization or mortality. Three algorithms are chosen: Random Forest, K-Nearest Neighbours (K-NN), and Support Vector Machine (SVM). Python is used to compare the accuracy and determine which categorization technique best predicts liver illness. Using these methods we find the KNN method gives maximum good result than SVM and Random Forest *** gives 0.993 Accurate value. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Wrist Fracture Detection and Classification through Deep Learning and XAI

Enhancing Wrist Fracture Detection and Classification throug...

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European Workshop on Visual Information Processing (EUVIP)

作者： Subhan Ali Ali Shariq Imran Zenun Kastrati Sher Muhammad Daudpota Faouzi Alaya Cheikh Mohib Ullah Dept. of Computer Science Norwegian University of Science & Technology (NTNU) Gjovik Norway Dept. of Informatics Linnaeus University Vaxjo Sweden Dept. of Computer Science Sukkur IBA University Sukkur Pakistan

ISBN: (数字)9798350391145

ISBN: (纸本)9798350391152

According to WHO, approximately 1.71 billion people worldwide have musculoskeletal conditions, which in- clude various issues such as intact muscles, bones, joints, and fractures. Among those, fractures are the most common. Due to the emergency nature of diagnosing fractures, there is a high chance of misdiagnosis for several reasons, such as the unavailability of radiologists, physicians' lack of experience, and other factors. Fracture diagnostic or X-ray interpretation errors can be reduced if radiographs are always read instantly by radiologists or automatically. In our study, we are focusing on automating wrist fracture diagnosis, where we are utilizing the publicly available GRAZPEDWRI-DX dataset, which consists of 20,327 wrist radiographs. We employed the YOLOv9 model for fracture detection, achieving an mAP@50 of 0.677, which surpasses previous benchmarks. For fracture classification, we trained several state-of-the-art deep learning models, including VGG16, VGG19, ResNet50, EfficientNetB7, DenseNet121, MobileNet, and ConvNeXtXLarge. In particular, the YOLOv8-cls model surpassed all others in accuracy (0.93), precision (0.9352), and recall (0.8855), reaching its peak performance at epoch 70. To elucidate the decision-making process of both the detection and classification models, we generated explainable saliency maps through EigenCAM, making the models more explainable and interpretable.

关键词： Wrist Deep learning Visualization Accuracy Explainable AI Computational modeling Focusing Medical diagnosis Diagnostic radiography Medical diagnostic imaging

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Fringing Effects in a Capacitive Micromachined Ultrasonic Transducer with a SiC Actuation Layer Insulated by High Dielectric Material 3

Fringing Effects in a Capacitive Micromachined Ultrasonic Tr...

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3rd IEEE International Conference of Electron Devices Society Kolkata Chapter, EDKCON 2024

作者： Ahamed, Sk. Sohel Pal, Moumita Chakraborty, Pritam Neogi, Biswarup Maity, Niladri Das, Reshmi Future Institute of Engineering and Management Dept. of electronics and communication kolkata India JIS College of Engineering Dept. of electronics and communication Kalyani India Institute of Engineering and Management Dept. of computer science and application Kolkata India NITTTR Dept. of technical education Kolkata India Mizoram University Dept. of electronics and communication Mizoram India

ISBN: (纸本)9798350374643

Sealed capacitive micromachined ultrasonic transducer (CMUT) mostly consists of two parallel electrodes separated by a structural layer of silicon nitride (Si3N4) and a vacuum cavity. Biasing the transducer element generates electrostatic force of attraction where the lines in the field bulge at the boundaries of the layers contributing to fringing effects. These fringing capacitances result in overall increment of the device capacitance. To capture this effect a very simple model of Landau and Lifschitz is incorporated, where the analytical model outputs are well matched with commercially available PZFlex FEM simulated results. Because silicon carbide (SiC) has a higher Young's modulus and lower residual stress than Si3N4, it is used as a structural material in the CMUT. The improvement in device capacitance is approximately 13.1% through adjustments in the thickness of gaps and insulation. However, factoring in the relative dielectric constant and the electrode radius, this improvement reduces to around 7.9%. Additionally, introducing a high-k insulating layer enables the upper electrode to be structured on the lower face of the SiC layer. This modification leads to an augmentation in the device's total capacitance. This article also examines HfO2 materials of varied thicknesses. © 2024 IEEE.

关键词： Silicon nitride

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Optimization of YOLOv8 for Defect Detection and Inspection in Aircraft Surface Maintenance using Enhanced Hyper Parameter Tuning

Optimization of YOLOv8 for Defect Detection and Inspection i...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Pasupuleti, Shailaja Ramalakshmi, K. Pattisapu, Varaha Madhavi Alliance University AU-Centre of Excellence Computer Vision Alliance College of Engineering and Design Bangalore India Bharath Institute of Higher Education and Research Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350378092

Aviation repair is governed by strict standards, because maintaining the integrity of aircraft surfaces is essential. Conventional inspection techniques, which frequently rely on manual examinations, are time-consuming and prone to human mistake. Through a SEM model picture collection of aircraft surface flaws, this study investigates the improved use of YOLOv8 (You Only Look Once version 8) for diagnosing and identifying surface concerns in aviation maintenance. In order to precisely identify, locate, and classify different defect kinds, such as aggressive pitches and thin resists, it presents a revolutionary ensemble deep learning approach. In order to particularly assess SEM pictures with defects including gaps, potential gaps, line collapses, bridges, and micro bridges, the research entails training MobileNet backbone models. To further enhance detection and classification performance, the suggested approach combines predictions from several models using a Weighted Box Fusion (WBF) ensembling strategy. According to experimental findings, the optimized YOLOv8 model outperforms conventional YOLOv8 models in terms of accuracy and speed thanks to its WBF ensembling and MobileNet backbone. This sophisticated model is excellent at spotting a variety of surface flaws, such as corrosion, fractures, and other irregularities. Aircraft surface restoration has advanced significantly with the integration of MobileNet and WBF into the improved YOLOv8 model, improving Mean Average Precision (mAP) for difficult defect categories. Metrics like the F1-score, accuracy, precision, recall, specificity, and confidence score are used to assess how well this method is at reliably detecting and localizing different types of defects in SEM pictures by utilizing deep learning. © 2024 IEEE.

关键词： Deep learning

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Enhancing Device Authentication in IoT Security Application using Artificial Intelligence: A Survey 3

Enhancing Device Authentication in IoT Security Application ...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Brogya, Maame Serwah Dumfeh Ayuso, Enrique Shukla, Aasheesh Goyal, Amit Kumar Sain, Mangal Dongseo University Dept. of Computer Engineering Busan Korea Republic of Gla University Department of Electronics and Communication Mathura India Chandigarh University Department of Computer Science and Engineering Mohali140413 India

ISBN: (纸本)9798331528201

Technological advancement over the last decade has seen significant technological development, particularly in IoT, which is the most used and commercialized. This is due to its convenience, as it gives users remote control of their appliances via portable devices like smartphones and tablets, without needing to be physically present or close to those devices. As IoT technologies continue to grow and new innovative solutions for everyday applications are being developed there are more interconnected devices and large volumes of data being generated every second. However, the rapid growth of IoT has heightened security concerns, especially around device authentication. This paper reviews IoT authentication complexities and explores Machine Learning solutions under AI, comparing various algorithms and, providing a foundation for researchers and developers in the field. © 2024 IEEE.

关键词： Adversarial machine learning

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Transfer Learning Approaches for Channel State Prediction in Heterogeneous Fading Networks

Transfer Learning Approaches for Channel State Prediction in...

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Intelligent Signal Processing and Effective Communication Technologies (INSPECT), IEEE International Conference on

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Abhay Upadhyay Nookala Venu Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Data Science and Quantum Computing ABV Indian Institute of Information Technology and Management Gwalior India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

In this work, we leverage state-of-the-art machine learning algorithms to predict Channel State Information (CSI), for enhancing performance of wireless communication systems. The simulation and analysis stop with transfer learning to finetune, adaptive domain adaptations, multitasking (MTL) as well a few shot meta-learning models framework under different network packet loss rate scenarios. By generalizing better from one domain to another, transfer learning pushes the performance beyond what traditional methods can obtain. Further, fine-tuning also helps to align the source and target domain distributions in feature space by a partial (or full) reduction of re-stylization error. This work proposes multi-Task learning as a tool to mitigate loses in light of predicting across fading scenarios. Considering multi-task learning also provides benefits from shared learnings, so experience is pooled between the tasks which indirectly improve prediction as well and it helps in training both of these algorithms together. Moreover, the few-shot learning models prove to be highly desirable as they deliver state-of-the-art performance with minimal training data for difficult problems. These results show the potential of AI-based methods to improve CSI prediction, and have implications for improving reliability and throughput when deploying real wireless communication systems.

关键词： Wireless communication Fading channels Machine learning algorithms Transfer learning Training data Predictive models Multitasking data models Few shot learning Channel state information

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Indian Food Segmentation and Calorie Estimation Using CatBoost and Masked Convolutional Neural Networks

Indian Food Segmentation and Calorie Estimation Using CatBoo...

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Acharya, Nabin Singh, Ayush Kumar Dwivedi, Anshuman Kumar Jayanthi, M.G. Kannadaguli, Prashant Cambridge Institute of Technology Dept. of Information Science and Engineering Bengaluru India Cambridge Institute of Technology Dept. of Artificial Intelligence and Machine Learning Bengaluru India Cambridge Institute of Technology Dept. of Computer Science and Engineering Bengaluru India Dhaarini Academy of Technical Education Bengaluru India

ISBN: (纸本)9798350300826

Accurate food segmentation and calorie estimation play a pivotal role in effective dietary monitoring and health management. In this paper, after extensive experimentation on a diverse Indian food dataset of 20 classes collected by us, we present two separate innovative models designed to address these tasks with exceptional precision. The first model employs a machine learning approach by incorporating the CatBoost algorithm, while the second model leverages a deep learning technique utilizing the U-Net architecture for high-quality image segmentation. Additionally, we merge the U-Net outcomes with a convolutional neural network (CNN) to enhance the deep learning-based classification. Our models effectively handle categorical features and address imbalanced data, resulting in significantly improved accuracy in food item delineation within images. This advancement enables a more reliable and comprehensive analysis of dietary patterns. We demonstrate the effectiveness and robustness of our proposed models. Comparative evaluations against state-of-the-art methods verify exceptional performance in terms of segmentation accuracy, precision in food classification, and estimation of calorie intake. Notably, our models are specifically tailored for food images and do not take ingredients or any other information into consideration. In comparison CNN model is performs better than CatBoost, achieving an accuracy of 78%. © 2023 IEEE.

关键词： Convolutional neural networks

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IIoT-based Realtime Environmental Monitoring System for an Underground Coal Mines

IIoT-based Realtime Environmental Monitoring System for an U...

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International Conference on Computing and Networking Technology (ICCNT)

作者： G. Thirumal Vivek Kumar Gautam Chiranjeev Kumar Himanshu Ranjan Seth Shaily Priyanka Bodra Dept. Computer Science and Engg. Indian Institute of Technology (ISM) Dhanbad Jharkhand India Data Science Oyo Vacation Homes Lucknow India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Underground coal mines are harsh in the working environment, which leads to more causality. Therefore, modern mines are using sensor networks to monitor the mines regularly to minimize mine accidents. Hence, very limited real-time monitoring systems are developed using a Real-time operating system (RTOS). Contiki-NG is a popular embedded application development framework for Wireless sensor networks and the internet of things (IoT). This paper provides a detailed roadmap to design a real-time monitoring system for underground coal using Contiki-NG. The System design comprises designing sensor node with modified RPL protocol and border router using TI CC2650 launchpad and raspberry pi 3. The Node-RED middleware collects data from the application environment using a sensor node. Store the data into the MongoDB database and visualizes sensed data in a web browser in the monitoring center.

关键词： Wireless communication Wireless sensor networks data visualization Coal Real-time systems Coal mining Visual databases Internet of Things Environmental monitoring Middleware

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Stochastic Large-scale Machine Learning Algorithms with Distributed Features and Observations

Stochastic Large-scale Machine Learning Algorithms with Dist...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Fang, Biyi Klabjan, Diego Vo, Truong Northwestern University Dept. of Engineering and Applied Mathematics EvanstonIL60208 United States Northwestern University Dept. of Industrial Engineering and Management Sciences EvanstonIL60208 United States Northwestern University Dept. of Machine Learning and Data Science EvanstonIL60208 United States

ISBN: (纸本)9798350362480

As the size of modern datasets exceeds the disk and memory capacities of a single computer, machine learning practitioners have resorted to parallel and distributed computing. Given that optimization is one of the pillars of machine learning and predictive modeling, distributed optimization methods have recently garnered ample attention, in particular when either observations or features are distributed, but not both. We propose a general stochastic algorithm where observations, features, and gradient components can be sampled in a double distributed setting, i.e., with both features and observations distributed. Very technical analyses establish convergence properties of the algorithm under different conditions on the learning rate (diminishing to zero or constant). Computational experiments in Spark demonstrate a superior performance of our algorithm versus a benchmark in early iterations of the algorithm, which is due to the stochastic components of the algorithm. © 2024 IEEE.

关键词： Contrastive Learning

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