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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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A Recurrent Self-learning Labeler for Building Network Traffic Ground Truth 26

A Recurrent Self-learning Labeler for Building Network Traff...

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26th international conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Yang, Qingya Liu, Chang Fu, Peipei Wang, Bingxu Gou, Gaopeng Xiong, Gang University of Chinese Academy of Sciences Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security Beijing China

ISBN: (纸本)9798350331684

With the increasing number of traffic category, machine learning-based methods have gradually become the mainstream way of traffic classification to support network security and management. machine learning-based methods require a large amount of high quality labelled data to learn network behavior patterns to achieve better recognition results. In the field of network traffic labeling, manual labeling can achieve more accurate labeling results, but the labeling efficiency is low and the labor cost is high. Deep packet inspection (DPI) technology can greatly improve labeling efficiency and reduce labor costs, but DPI labeling suffers from the problem of inaccurate and incomplete labeling. In this paper, we propose a recurrent self-learning framework (RSL-Labeler) for traffic labeling, which can solve the problem of inaccurate and incomplete DPI labeling. this framework consists of three components: high-quality data generation, class behavior pattern learning, and confidence filtering. Based on high-quality data labeled by multiple DPIs, we build three classifiers to learn the behavior patterns of DPI labeling intelligently from three perspectives. then, we propose the idea of confidence filtering, which combines the pseudo-labeled data and the confidence values of three learning models to filter the credible samples by combined voting. these samples are added to the self-learning model for recurrent training. Experiments show that our method is able to label application traffic with accuracy of 99%, which is at least 8% better than single DPI. © 2023 IEEE.

关键词： Wages

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A review of computing offloading and caching in edge computing based on reinforcement learning 23

A review of computing offloading and caching in edge computi...

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4th international conference on Computing, Networks and Internet of things, CNIOT 2023

作者： Guo, Mengke Wang, Wushuang Graduate School of Comprehensive Human Sciences University of Tsukuba Ibaraki Japan Graduate School of Science and Technology University of Tsukuba Ibaraki Japan

ISBN: (纸本)9798400700705

Edge Computing (EC) leverages edge devices to provide computational and analytical capabilities, reducing the load and response time of cloud servers and ensuring the real-time processing of tasks. However, the capacity of edge devices is limited and requires rational allocation of computational and storage resources to reduce delay, energy consumption, and cost. AS one of the research hotspots in machine learning, Reinforcement learning (RL) can optimize resource allocation strategies for EC to maximize task rewards and make edge devices more intelligent. this study comprehensively reviews the literature on offloading, caching, joint offloading and caching in EC by RL, as well as classifies and compares them. the works of the computational offloading problem are further divided into three parts according to optimization objectives: reducing delay, reducing energy consumption, and the trade-off between energy consumption and delay. EC for different application scenarios is also summarized. Lastly, this paper outlines the main directions for future research and the challenges faced in this field. CCS CONCEPTS •Computing Methodologies © 2023 ACM.

关键词： Reinforcement learning

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Unveiling the Power of Collective Intelligence: A Voting-based Approach for Dementia Classification 7

Unveiling the Power of Collective Intelligence: A Voting-bas...

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7th international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Pandya, Darshanaben D. Patel, Pranav Arvindkumar Patel, Hansaben Haribhai Goswami, Akashgiri Jashvantgiri Degadwala, Sheshang Vyas, Dhairya Gujarat Visnagar India Insb Bca & Pgdca Collge Idar Gujarat India Sspc Spu Electrical Department Gujarat Visnagar India Silver Oak Univercity Silver Oak College of Computer Application Gujarat Ahmedabad India Sigma University Department of Computer Engineering Gujarat India The Maharaja Sayajirao University of Baroda Gujarat Vadodara India

ISBN: (纸本)9798350341485

Dementia, a debilitating neurodegenerative condition affecting millions worldwide, demands early and accurate diagnosis to facilitate timely intervention and improved patient outcomes. this study proposes an innovative approach for dementia classification that harnesses the power of collective intelligence through a voting-based methodology. Proposed method combines machine learning algorithms with the wisdom of the crowd by aggregating predictions from both experts and non-experts. By leveraging the diverse perspectives and expertise of the collective, the system achieves heightened accuracy and robustness in dementia classification. this study presents extensive experimental results demonstrating the effectiveness of this approach and showcase its potential to revolutionize medical decision-making, offering a promising avenue for advancing dementia diagnosis and personalized patient care. the findings unveil the untapped potential of collective intelligence in the healthcare domain and encourage further exploration of collaborative models in medical research and diagnostics. © 2023 IEEE.

关键词： machine learning

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proceedings - 2022 4th international conference on computer Science and Technologies in Education, CSTE 2022

Proceedings - 2022 4th International Conference on Computer ...

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4th international conference on computer Science and Technologies in Education, CSTE 2022

ISBN: (纸本)9781665481885

the proceedings contain 59 papers. the topics discussed include: application of Apriori algorithm in professional elective course data mining for university;programming education in Japanese elementary schools integrated with multiple subjects: origins, practical paths and implications;the application of AES in international Chinese language smart education;COVID-19, from crisis to opportunity: assessing teaching programming languages online using statistical methods;an empirical study on the factors influencing primary school teachers' acceptance towards stem teaching;the employment of testing dojo as a collaborative learning methodology for teaching failure analysis: an experience report;impact of instructors’ content knowledge on their online intercultural English teaching instructional beliefs;and construction and application of scratch gamification instruction design framework based on growing problem-based learning.

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Role of Big Data, AI and Deep learning in Medical Image Training Models and Decision Support System Using I4.0 Technologies 10th

Role of Big Data, AI and Deep Learning in Medical Image Trai...

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10th EAI international conference on IoT Technologies for Health-Care, HealthyIoT 2023, and 4th EAI international conference on Wearables in Healthcare, HealthWear 2023

作者： Bhatt, Kavita Kumar, S. Mohan School of Engineering and Technology CMR University Bangalore560037 India

ISBN: (纸本)9783031719103

the area of diagnostic decision assistance in radiology is going through a rapid shift with the availability of a lot of patient information and the advancement of new AI (Artificial Intelligence) techniques of ML (machine learning), like DL (Deep learning). they have the potential to offer imaging professionals tools that will increase the precision and effectiveness of diagnosis and therapy. this paper will discuss the development of the area of radiology and general trends emphasizing advancements in diagnostic decision assistance from the earliest rule-based expert systems to contemporary cognitive assistants utilizing I4.0 technology. the accuracy, dependability, and productivity of electronic equipment in the healthcare industry must be improved with the use of the IoMT (Internet of Medical things). Researchers are building a digital healthcare system by connecting the already available medical resources and healthcare services. Although IoT is converging across many disciplines, our attention is on the scientific contributions of IoT in the healthcare domain. In terms of medical services in healthcare, this article discusses IoT applications, user contributions, and upcoming problems specifically in medical imaging hence the name Internet of Medical Imaging things. It also gives a complete overview of the latest developments of ML approaches broadly utilized for Medical Imaging Diagnostic using I4.0 by classifying the study as per the ML methods, equipment, and machinery used and a foundation for future research. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Radiology

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application and Practice of Intelligent Algorithms in computer Software Design and Testing 4

Application and Practice of Intelligent Algorithms in Comput...

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4th international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Zhang, Yun Wuhan Polytechnic Hubei Wuhan430074 China

With the rapid development of information technology, software has become a part of social life. However, for complex software systems, traditional design and testing methods face a series of problems such as low testing coverage and low efficiency. the main purpose of this article is to study the optimization of software design and testing processes using intelligent algorithms, in order to improve the quality and efficiency of software. this article established a real-time optimization and feedback adjustment mechanism during the software design and testing phase, enabling intelligent algorithms to respond promptly to the environment and requirements. Monitoring and control strategies were designed to ensure the algorithm runs stably for a long time. Test method 1 (traditional method) had 100 test cases, a total execution time of 1200 seconds, and an average execution time of 12 seconds per test case;test method 2 (Intelligent Algorithm Optimization) had 100 test cases, a total execution time of 800 seconds, and an average execution time of 8 seconds per test case. the research results of this article play a good promoting role in improving software testing coverage and software structure optimization, and also provide new ideas and methods for the promotion of intelligent algorithms in software engineering. © 2024 the Authors.

关键词： Software testing

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Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and Data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on machine learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and Workshop on Multimodal learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

Workshop on Longitudinal Disease Tracking and Modeling with ...

引用

Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and Data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on machine learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and Workshop on Multimodal learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031845246

the proceedings contain 21 papers. the special focus in this conference is on Longitudinal Disease Tracking and Modeling with Medical Images and Data. the topics include: Disease Progression Modelling and Stratification for Detecting Sub-trajectories in the Natural History of Pathologies: application to Parkinson’s Disease Trajectory Modelling;back to the Future: Challenges of Sparse and Irregular Medical Image Time Series;Individualized Multi-horizon MRI Trajectory Prediction for Alzheimer’s Disease;towards Longitudinal Characterization of Multiple Sclerosis Atrophy Employing SynthSeg Framework and Normative Modeling;segHeD: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints;Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting;registration of Longitudinal Liver Examinations for Tumor Progress Assessment;Tracking Lesion Evolution Using a Boundary Enhanced Approach for MS Change Segmentation (BEAMS);a Radiological-Based Coordinate System for the Human Body: A Proof-of-Concept;language Models Meet Anomaly Detection for Better Interpretability and Generalizability;A Diffusion Model Embedded WCSAU-Net for 3D MRI Brain Tumor Segmentation;predicting Human Brain States with Transformer;Cross-Modality Image Quality Prediction for Time-Resolved CT from Breathing Signals;RATNUS: Rapid, Automatic thalamic Nuclei Segmentation Using Multimodal MRI Inputs;HyperMM: Robust Multimodal learning with Varying-Sized Inputs;HEMIT: H&E to Multiplex-Immunohistochemistry Image Translation with Dual-Branch Pix2pix Generator;Physics-Informed Latent Diffusion for Multimodal Brain MRI Synthesis;medPromptX: Grounded Multimodal Prompting for Chest X-Ray Diagnosis;predicting Stroke through Retinal Graphs and Multimodal Self-supervised learning;multimodality for Diagnosis of Asian Choroidal Vasculopathy: Results from a Novel Dataset and Deep-learning Experiments;multimodality Frequency Feature Customized learning for Pediatric Ventricu

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4th international conference on Advances in Emerging Trends and Technologies, ICAETT 2022

4th International Conference on Advances in Emerging Trends ...

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4th international conference on Advances in Emerging Trends and Technologies, ICAETT 2022

ISBN: (纸本)9783031259418

the proceedings contain 54 papers. the special focus in this conference is on Advances in Emerging Trends and Technologies. the topics include: Micro-enterprise Management Towards Scenario Building for Decision Making;analysis of Business Efficiency Considering the Influence of the Particular Events on Sales Increase Period 2016–2020;income from Ordinary Activities and Its Tax Impact on Companies in the Automotive Sector in Ecuador;proof of Concepts of Corda Blockchain Technology Applied on the Supply Chain Area;spread Virus: Usability Evaluation on a Mobile Augmented Reality Videogame;neural Networks on Noninvasive Electrocardiographic Imaging Reconstructions: Preliminary Results;security Mechanisms and Log Correlation Systems;identification of Corn Leaves Diseases Images Using MobileNet Architecture in SmartPhones;experimental Assessment of Photovoltaic Systems Using One-Axis Tracking and Positioning Strategies in Equatorial Regions;Evaluation of the Reliability of a LiDAR Sensor through a Geometric Model in applications to Autonomous Driving;Design of Optimal Controllers Applying Reinforcement learning on an Inverted Pendulum Using Co-simulation NX/Simulink;socio-spatial Segregation Using Computational Algorithms: Case Study in Ambato, Ecuador;expert System with Facial Recognition Implemented in Human-machine Conversation Services for the Automation of Multi-platform Remote Processes in the Identification of People Reported Missing;radar Probability of Detection in Multipath Environments;Low Detection Symbol Algorithm for MIMO Systems with Big Number of Antennas;preface;Recognition and Classification of Cardiac Arrhythmias Using Discrete Wavelet Transform (DWT) and machine learning Techniques;deployable Networks. An Alternative for Communications in Critical Environments;application for the Study of Underwater Wireless Sensor Networks: Case Study;learning Performance Indicators a Statistical Analysis on the Subject of Natural Sciences During the COVID-19 Pandemic

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Enhancing Transistor Sizing in Analog IC Design using a Circuit-Focused Semi-Supervised learning 4

Enhancing Transistor Sizing in Analog IC Design using a Circ...

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4th IEEE international Multidisciplinary conference on Engineering Technology, IMCET 2023

作者： Mina, Rayan Sakr, George E. Nassif, Houssam Saint-Joseph University of Beirut Department of Electrical and Mechanical Engineering Mar Roukoz Lebanon Virgil Systems TorontoON Canada Meta SeattleWA United States

ISBN: (纸本)9798350313826

this work investigates the application of artificial neural networks to predict transistor dimensions in analog integrated circuits using semi-supervised learning. Traditionally, circuit designers apply a time-consuming iterative approach to find the optimal dimensions of transistors that satisfy a set of performance metrics. To address this problem, we propose to use artificial neural networks combined with an innovative approach wherein each transistor's dimensions were first predicted using its own network to identify potential learning behavior differences. Some transistors exhibited favorable validation loss levels, while others displayed up to 3 times higher loss values. Building upon this observation, a focused approach was developed, involving the splitting of prediction tasks into two individual networks. the first targeted transistors with low validation losses and relied solely on circuit performance metrics as inputs. the second, designed for challenging transistors, introduced a novel input structure encompassing not only the performance metrics but also the dimensions of other well-trained transistors. this adjustment led to a notable reduction in both training and validation losses by 3.5 times, thus enhancing prediction accuracy for the challenging transistors. these findings underscore the importance of tailored artificial neural networks in enabling more efficient transistor sizing and present a promising approach for advancing analog integrated circuit design automation. Furthermore, this study contributes to the understanding of machine learning efficiency in the context of analog design. © 2023 IEEE.

关键词： Neural networks

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Developing an Effective Model for Skin Disease Detection: Techniques and Approaches 4

Developing an Effective Model for Skin Disease Detection: Te...

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4th international conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Yadav, Nikhil Kumar Tiwari, Amit Gaurav Verma, Harsh Kumar Saxena, Arpna Ajay Kumar Garg Engineering College Varanasi India Ajay Kumar Garg Engineering College Muzaffarpur India Ajay Kumar Garg Engineering College Meerut India Ajay Kumar Garg Engineering College Ghaziabad India Ajay Kumar Garg Engineering College Master of Computer Application Ghaziabad India

ISBN: (纸本)9798350364729

this research highlights the critical role of the skin as the body's primary defense against damage and infection. Despite its importance, the skin can be vulnerable to serious infections caused by fungi, viruses, or even dust, leading to conditions ranging from acne to eczema. Some skin diseases are highly contagious and can be spread by simple contact, underscoring the need for proper diagnosis and treatment to alleviate suffering and prevent transmission. the study focuses on developing a prototype for identifying skin diseases using convolutional neural networks (CNNs), a choice based on their effectiveness in image processing tasks. this research uses CNNs to classify specific skin conditions, including hand dermatitis, hand eczema, subacute eczema, lichen simplex, stasis dermatitis, and ulcers are all different skin conditions.. the methodology integrates image processing techniques with machine learning and uses the processed images to train a CNN model. the training data consists of five classes of skin conditions from the Dermnet dataset, which includes 19600 images. the prototype achieved an accuracy of 92%, highlighting the system's potential. Research suggests that with further improvements and a larger data set, the system could significantly improve early detection and treatment of skin ailments representing a substantial advance in dermatology care. © 2024 IEEE.

关键词： Dermatitis

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