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检索条件"任意字段=Conference on Applications of Artificial Neural Networks in Image Processing"

共 21063 条记录，以下是4791-4800 订阅

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Detailed review on Integrated Healthcare Prediction System Using artificial Intelligence and Machine Learning 3

Detailed review on Integrated Healthcare Prediction System U...

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3rd International conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Parasar, Deepa Ali, Arif Pillai, N. Muthuvairavan Shahi, Archana Alfurhood, Badria Sulaiman Pant, Kumud Amity University Maharashtra Cse India Dev Bhoomi Uttarakhand University Department of Cse Uttarakhand Dehradun India R.M.D Engineering College Computer Science and Business Systems Kavarapettai India Chitkara University Chitkara Business School India Princess Nourahbint Abdulrahman University College of Computer and Information Sciences Department of Computer Sciences Saudi Arabia Graphic Era Deemed to Be University Department of Biotechnology Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

The intricacy and expansion of statistics in increased care sector will lead to an increase in the application of artificial intelligence (AI). There are several different AI techniques in use today by consumers, health professionals, and sciences companies. Guidelines for care and prognosis, patient and commitment, and organizational chores are the key application areas. Challenges will prevent the work of health professionals from being substantially computerized for a considerable length of time, despite the fact that AI can do healthcare tasks in many instances equally as well or better than people. Ethics issues and the implementation of artificial intelligence in healthcare are also discussed. AI technology is essential for producing effective healthcare applications. The definition of several effective software programmers may help in the growth of the health predictive model. Computer vision and cloud services are critical elements for building smart healthcare *** collection and analysis of a wide range of health care data may provide useful insights quickly. artificial intelligence is also crucial in the process of illness detection and prognosis. This smart and efficient approach may help with early illness diagnosis. Sensors connected through wireless networks make it possible to track the actions of a whole population. Sensors embedded in the human body may aid in the recognition of emotions and facial expressions by using neural networks. There is a plethora of technological implementations that contribute to a more advanced data processing *** primary focus of this research is to determine how effectively various efficient technology's function when used in the development of healthcare prediction models. AI uses a wide variety of sensor-based methods to boost healthcare's efficacy. Another area of study has substantial implications for the healthcare system. To help identify problems in biomedical goods, modelling tools may be pro

关键词： Forecasting

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EGIS: Entropy Guided image Synthesis for Dataset-Agnostic Testing of RRAM-Based DNNs

EGIS: Entropy Guided Image Synthesis for Dataset-Agnostic Te...

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Design, Automation and Test in Europe conference and Exhibition

作者： Anurup Saha Chandramouli Amarnath Kwondo Ma Abhijit Chatterjee School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

While resistive random access memory (RRAM) based deep neural networks (DNN) are important for low-power inference in IoT and edge applications, they are vulnerable to the effects of manufacturing process variations that degrade their performance (classification accuracy). However, to test the same post-manufacture, the (image) dataset used to train the associated machine learning applications may not be available to the RRAM crossbar manufacturer for privacy reasons. As such, the performance of DNNs needs to be assessed with carefully crafted dataset-agnostic synthetic test images that expose anomalies in the crossbar manufacturing process to the maximum extent possible. In this work, we propose a dataset-agnostic post-manufacture testing framework for RRAM-based DNNs using Entropy Guided image Synthesis (EGIS). We first create a synthetic image dataset such that the DNN outputs corresponding to the synthetic images minimize an entropy-based loss metric. Next, a small subset (consisting of 10–20 images) of the synthetic image dataset, called the compact image dataset, is created to expedite testing. The response of the device under test (DUT) to the compact image dataset is passed to a machine learning based outlier detector for pass/fail labeling of the DUT. It is seen that the test accuracy using such synthetic test images is very close to that of contemporary test methods.

关键词： Data privacy Accuracy Manufacturing processes image synthesis Resistive RAM artificial neural networks Machine learning Production Entropy Testing

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CAD Model for Biomedical image processing for Digital Assistance 1

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2nd International conference on Emerging Trends in Expert applications and Security, ICE-TEAS 2023

作者： Sharma, Hitesh Kumar Choudhury, Tanupriya Choudhary, Richa Um, Jung Sup Sharma, Aarav School of Computer Science University of Petroleum & Energy Studies Bidholi Energy Acres Uttarakhand Dehradun248007 India Kyungpook National University Daegu Korea Republic of Uttarakhand Dehradun248007 India Adjunct Professor CSE Dept. Graphic Era Hill University Uttarakhand Dehradun248002 India The AI University CutbankMT59427 United States

ISBN: (数字)9789819919468

ISBN: (纸本)9789819919451

Insufficiency of doctors produces a high challenge for Biomedical Engineering to develop efficient assistance tools or applications which can be used as a supporting system (e.g., CAD) for doctors to diagnose diseases using biomedical images like CT-Scan, X-ray, MRI etc. COVID-19 is a highly communicable and extremely dangerous disease. Almost 1.36 billion people in the word are affected by the coronavirus till date. Two million people have died. This deadly disease has affected more than 150 countries in the world and still thousands of people are getting exposed to this disease daily. So early detection of the disease is really important and critical to save a person’s life. Coronavirus can be significantly detected through chest X-rays. With the help of Deep Learning and neural Network, detection of COVID can be done quickly and cheaply. So, we are making use of a CNN Model to quickly detect whether a person has COVID-19 or not by inputting the X-ray images. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Tympanic Membrane Generation with Generative Adversarial networks 29

Tympanic Membrane Generation with Generative Adversarial Net...

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29th IEEE conference on Signal processing and Communications applications (SIU)

作者： Eseoglu, Mustafa Furkan Karsligil, M. Elif Kocak, Ismail Yildiz Tekn Univ Bilgisayar Muhendisligi Bolumu Akilli Sistemler Lab Istanbul Turkey DrVoice Clin Istanbul Turkey

ISBN: (纸本)9781665436496

Obtaining sufficient original data in most studies in the field of medical pattern recognition is a difficult and time consuming process. Different data augmentation methods are used to increase the amount of data to be used to train these systems. In this study, a generative adversarial networks based system that produces artificial images by using the tympanic membrane images taken from otoscope devices for data augmentation has been designed and trained. artificial images that has been generated by different generative adversarial networks have been evaluated by specialist physicians with a Visual Turing test. Preliminary results show that artificial medical images can be perceived as real with high confidence scores.

关键词： generative adversarial networks medical image processing data augmentation Visual Turing test

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27th International conference series on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2024

27th International Conference series on Climbing and Walking...

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27th International conference series on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2024

ISBN: (纸本)9783031713002

The proceedings contain 28 papers. The special focus in this conference is on Climbing and Walking Robots and the Support Technologies for Mobile Machines. The topics include: 27 Years of Climbing and Walking Robots – Are We There?;Torque Controlled or Intrinsically Compliant? DLR’s Perspective on Robust and Efficient Biped and Quadruped Locomotion;Efficient Stream-Based Active Learning Initialization for Legged Robots Based on a PCA/K-Means image Selection Approach;precision Vehicle Pose Estimation with Uncertainty-aware neural Network;HAPmamba: Linear-Time Sequence Modeling for Terrain Classification by Legged Robots;neural-Based Self-collision Checking for a Quadruped Robot;omnidirectional Climbing Robot for Maintenance Services on Hard to Reach Places of Ship Hulls;demonstration of a Micro Wall-Climbing Robot Moving on Metal Surfaces;linkage Length Optimization of a Climbing Inspection Robot Using an Area Overlap Method;Rotary Push-in Mechanism for Variable Outer Diameter PIGs with Multiple-Connected Using Pneumatic artificial Muscles;basic Study on a Peristaltic Motion-Type In-Pipe Inspection Robot Using a Hyper-Extension Unit for Improving Locomotion Speed;proposal of Operation Methods of the Square-Duct Cleaning Machine with Multistage Planetary Gear Mechanism;earth-Shaping with Heterogeneous Robotic Teams: From Sim to Real;mobile Victim Signs Monitoring Through Non-invasive Robotic System;Multi-UAV Coverage Path Planning for Agricultural applications;autonomous Landing Pad with a Closed Cover for a Medium-Sized Drone to Support Typical Research and Reconaissance Tasks in the Local Environment;concept of Pneumatic Soft Robot: Suction-Driven Locomotion;climbing Robot Inspired by Inchworms: Adaptable for Tubular and Flat Surfaces with Multi-plane Work Capability;high-Propulsive Trunk Flexion–Extension Mechanism Using Cheetah-Inspired S-Shaped Spine;self-organized Locomotion with Multiple Stepping Frequencies in an Insect-Like Robot Under Decentralized Adaptive

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The Role of AI and Machine Learning in the Evolution of 5G and Beyond networks

The Role of AI and Machine Learning in the Evolution of 5G a...

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Intelligent Signal processing and Effective Communication Technologies (INSPECT), IEEE International conference on

作者： Y.L. Malathi Latha Sk. Rabbani Chiranjeevi Manike Sandip J. Gami M Kishore Kumar Siva Koteswara Rao Katta Department of Information Technology Stanely College of Engineering and Technology for Women (A) Hyderabad India Department of Computer Science and Engineering MallaReddy Engineering College(A) Maisammaguda India Data Science MLR Institute of Technology Hyderabad Independent Researcher Brambleton VA Department of ECE Aditya College of Engineering & Technology Surampalem AP Department of Mechanical KITEW Kakinada

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

This article will examine the current capabilities of 5G and B5G networks in terms of artificial intelligence and machine learning. The research delves into several applications of AI and ML within the telecom industry. neural networks are introduced as a class of tools for decision-making and statistical data modelling that are often nonlinear. Data pattern discovery and the representation of complex connections between input and output parameters in systems are common uses for these. This class of neural networks includes recurrent, convolutional, feed-forward, and deep neural networks. When improving a system property or maximising a collective reward, reinforcement learning focuses on the behaviours that intelligent agents must do. One advantage of deep reinforcement learning is its ability to handle unstructured input. This is achieved by combining deep neural networks. We offer hybrid solutions, like expert knowledge aided machine learning and mixed analytical and machine learning models. Lastly, further targeted approaches are showcased, including GANs, unsupervised learning, and clustering. CF M-MIMO technology, which stands for cell-free massive MIMO, is the most promising idea in B5G. The appealing aspects of CF M-MIMO systems, such as power distribution and channel estimation, are with their downsides. There have been numerous successful applications of deep learning (DL) across many scientific domains, including wireless communications. According to the research by Lazaros Alexios Iliadis et al., CF M-MIMO networks use cutting-edge DL techniques. We provide the most popular DL models and review cell-free network fundamentals here. The endnotes emphasise the potential avenues for further study.

关键词： Wireless communication Cloud computing 5G mobile communication Biological system modeling Tourism industry artificial neural networks Signal processing Telecommunications Trajectory Unsupervised learning

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A Study of Dynamic Convolutional neural Network Technique for SCOTUS Legal Opinions Data Classification 8th

A Study of Dynamic Convolutional Neural Network Technique f...

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8th Annual International conference on Information Management and Big Data, SIMBig 2021

作者： Hammami, Eya Faiz, Rim Slama, Sami Ben LARODEC Laboratory ISG University of Tunis Tunis Tunisia LARODEC Laboratory IHEC University of Carthage Tunis Tunisia King Abdul Aziz University Jeddah Saudi Arabia

ISBN: (纸本)9783031044465

The quantity of legal information that is being produced on a daily basis in courts is growing enormously. The processing of such data has been gaining considerable attention thanks to their availability in an electronic form and the advancement made in artificial Intelligence applications. Indeed, deep learning has offered promising results when used in the field of natural language processing (NLP). neural networks such as recurrent neural network and convolutional neural networks have been used for different NLP tasks like information retrieval, document classification and sentiment analysis. In this paper, we present a neural Network based model with a dynamic input length for classifying legal opinions from cases seen by the Supreme Court of the United States (SCOTUS) (https://***/gqfiddler/scotus-opinions ). The proposed model, tested over a real-world legal opinions dataset, by the way, proved better performance than the other baseline methods. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sentiment analysis

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SeqEnhance: A Lightweight image processing PipeLine for Low-Light image Enhancement

SeqEnhance: A Lightweight Image Processing PipeLine for Low-...

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artificial Intelligence Technology (ACAIT), Asian conference on

作者： Runlin Zhou Peiliang Huang Dingwen Zhang Jun Ren AHU-IAI AI Joint Laboratory Anhui University Hefei China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei China School of Automation Northwestern Polytechnical University Xi’an China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9798350359145

ISBN: (纸本)9798350359152

Low-light image enhancement (LLIE) is an important task in computer vision, aiming to improve the visual perception or interpretability of images captured in poorly illuminated environments. Recently, deep learning based methods have been extensively explored to address this issue. While many of these methods have achieved significant advancements in various evaluation metrics for LLIE, only a few have made progress in improving inference speed for the resulting images. As a result, achieving both high-quality enhancements and efficient inference speed remains a challenge. To tackle this challenge, we propose SeqEnhance, a fast LLIE method based on a predefined parameterized image processing pipeline. Our approach combines the inference capabilities of deep neural networks for parameter estimation and the efficient processing capabilities of image processing pipelines to generate enhanced images in an end-to-end manner. The experimental results demonstrate that the proposed method achieves competitive performance on image quality evaluation metrics such as PSNR and SSIM with a fast inference speed.

关键词： Measurement image quality Deep learning Computer vision Parameter estimation Pipelines artificial neural networks

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FM-P2L: An Algorithm Hardware Co-design of Fixed-Point MSBs with Power-of-2 LSBs in CNN Accelerators 41

FM-P2L: An Algorithm Hardware Co-design of Fixed-Point MSBs ...

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41st IEEE International conference on Computer Design (ICCD)

作者： Wu, Jun-Shen Liu, Ren-Shuo Natl Tsing Hua Univ Hsinchu Taiwan

ISBN: (纸本)9798350342918

Convolutional neural networks (CNNs) are a focal point for advancing the field of artificial intelligence, enabling significant advances in image and speech recognition, natural language processing, and other complex tasks. However, due to the high requirements of memory storage and computational resources, implementing CNNs can be challenging. To alleviate these deficiencies, this work presents a novel algorithm hardware co-design centered on a new number format, fixed-point MSBs with power-of-2 LSBs (FM-P2L), which can significantly reduce the requirement of computational resources of the CNN accelerators by trading negligible accuracy loss. First, we propose the novel FM-P2L number format that uses fixed-point to represent MSBs and power-of-2 to represent LSBs, which can reduce the computation complexity of multiplications. The optimal bitwidths of MSBs and LSBs are determined from our proposed algorithm with the capability to preserve accuracy. Second, we propose a novel multiplier that best matches FM-P2L with CNN accelerators, which can significantly reduce the area and power of the computational units. Finally, to evaluate the benefits of FM-P2L, we compared FM-P2L with fixed-point and low-bitwidith floating-point on a weight-stationary based systolic array accelerator with vector-vector multiplication PEs, which is adopted by Google TPU and NVDLA, in TSMC 40nm technology. Our evaluation results demonstrate that FM-P2L can achieve up to 44% computing power and 50% area reduction compared with fixed-point and up to 55% computing power and 65% area reduction compared with floating-point, while maintaining negligible inference accuracy loss on the state-of-the-art CNN models.

关键词： neural network accelerator approximate computing

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AI for Space Operations: the next generation of Mission Operations for Earth Observation constellations 74

AI for Space Operations: the next generation of Mission Oper...

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74th International Astronautical Congress, IAC 2023

作者： Schandeler, Baptiste Lamothe, Matthieu Chouteau, Florient Future Programmes Department Airbus Defence and Space 31 rue des cosmonautes Toulouse31400 France Image and Mission Engineering Department Airbus Defence and Space 31 rue des cosmonautes Toulouse31400 France Computer Vision & Advanced Data Processing Department Airbus Defence and Space 31 rue des cosmonautes Toulouse31400 France

Earth Observation Satellite constellations are more and more requested by customers to fit their strategic needs. They offer the ability to collect large amounts of data from various places, from various sensors. They offer versatility, robustness and an undeniable strategic advantage of revisit. Classical way of operations, where every satellite, subsystems and the ground segment were operated by a team, does fit neither technically nor commercially for constellations. artificial intelligence (AI) has proven its value in multiple applications. Automated mission operations, especially in multi-missions context, have a high impact in terms of efficiency where reduction of human interactions is foreseen: routine operations, monitoring, data processing and ground segment health. Airbus has already developed automated collision avoidance maneuver (CAM), automated mission plan uplink and advanced monitoring that already allow us to operate with a limited number of operators. AI-based image production and analysis is one of the latest developments Airbus has performed: change detection and Deepzoom are now available on the market. AI is set to play a major role in the automation of future EO systems, enabled by the significant advances in Machine Learning techniques of recent years. Data-driven AI-based solutions can be developed to improve operations effectiveness. Multi-variable multi-subsystems analysis (based on neural networks) for time series can be used to predict future behaviors. Identifying a potential upcoming failure before it occurs and proposing preventive procedures to reduce the downtime is one of the leaps where the classical way of operations is outdated. The spacecraft can no longer be controlled individually by operators: the impact of on-board AI will drastically shake current CONOPS. Today, Airbus is working on artificial intelligence concepts for the next-gen EO constellations: AI on board and on ground to reach the lights-out center, where AI and a

关键词： artificial Intelligence Constellations Earth Observation Machine Learning Satellite operations

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