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检索条件"任意字段=7th International Conference on Machine Learning and Soft Computing, ICMLSC 2023"

共 787 条记录，以下是131-140 订阅

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Omni Channeling Healthcare: Chaining Details, Information and Predictions for Extemporize Service Delivery 7

Omni Channeling Healthcare: Chaining Details, Information an...

引用

2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Manohar, Sridhar Nag, Isha Mittal, Amit Nair, Arjun J Chitkara Business School Chitkara University Punjab India

ISBN: (纸本)9798350304268

the major purpose of this study is to incorporate the latest technologies such as Blockchain and AI with the concept of omni channeling for better and improved healthcare services. the study also highlights how the transformation could benefit the service providers on data management and the receivers by addressing communication and delivery gaps. However, the technologies needed to be merged and handled to an extent that it does not hamper the safety, security and trust between the partners. Additionally, explores the potential for frugal innovation and the role of developed nations in supporting healthcare systems in emerging economies. Overall, provides insights into the opportunities and challenges presented by emerging technologies in healthcare and their potential to transform the field. © 2023 IEEE.

关键词： AI Blockchain Health Care machine learning Omni Channeling

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Test the Capability of Arduino TinyML for machine learning 7th

Test the Capability of Arduino TinyML for Machine Learning

引用

7th EAI international conference on Robotics and Networks, ROSENET 2023

作者： Caravella, Valentin Yassine, Sahar Kadry, Seifedine Nimer Polytech Marseille Marseille France Noroff University College Kristiansand Norway Department of Applied Data Science Noroff University Kristiansand Norway Ajman University Ajman United Arab Emirates

ISBN: (纸本)9783031644948

machine learning can be used for anything in our daily lives using TinyML, with low power and memory constraints. this chapter shows a test of the capability of Arduino Nano 33 BLE Sense board to perform machine learning. For this, TinyML board with its sensors will be compared with equivalent machine learning using a computer. the results of these two approaches will be compared to know whether the board shows satisfying results. this board is still not widely used, and acknowledging what it can do will allow us to use it more effectively. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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A Deep learning-based System for Classification and Quality Evaluation of Seeds 7

A Deep Learning-based System for Classification and Quality ...

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Tedla, Balaji Simhadri, Madhuri Yarra, Venkata Jagadeesh Nukala, Geethika Natta, Vamsi Seshadri Rao Gudlavalleru Engineering College Department of Information Technology Andhra Pradesh India

ISBN: (纸本)9798350397253

Farmers and agro-industries must automate seed segregation because it is a time-consuming and labor-intensive task when performed manually. Deep learning (DL) and machine learning (ML) based algorithms have exhibited promising results in object recognition, classification, and pattern recognition. Despite this, no research has been conducted on the classification of seed types for crops grown with several crop kinds and varying quality criteria. the objective of this research is to develop a system based on deep learning dubbed the Mixed Cropping Seed Classifier and Quality Tester (McscQT). this system will be capable of analyzing the quality of seeds based on their shape, color, and texture. the system is trained with labelled images of healthy and damaged pearl millet and maize seeds, and it has a recall and accuracy of 98.9%. the MCSCQT is an essential piece of equipment in the food sector since it can distinguish between healthy and damaged maize seeds. © 2023 IEEE.

关键词： Object detection

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ISMSI 2023 - 2023 7th international conference on Intelligent Systems, Metaheuristics and Swarm Intelligence

ISMSI 2023 - 2023 7th International Conference on Intelligen...

引用

7th international conference on Intelligent Systems, Metaheuristics and Swarm Intelligence, ISMSI 2023

ISBN: (纸本)9781450399920

the proceedings contain 22 papers. the topics discussed include: machine learning for socially responsible portfolio optimization;leveraging deep learning approaches for deepfake detection: a review;predicting open parking space using deep learning and support vector regression;habitat prediction and knowledge extraction for marine bivalves using machine learning techniques;optimized computational diabetes prediction with feature selection algorithms;chaos gray wolf global optimization algorithm based on opposition-based learning;a learnheuristic approach to a constrained multi-objective portfolio optimization problem;analyzing the computing time to solve single row facility layout problems by simulated annealing in a Python framework;feature selection using gravitational search algorithm in customer churn prediction;and set-based particle swarm optimization for data clustering: comparison and analysis of control parameters.

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MECBench: A Framework for Benchmarking Multi-Access Edge computing Platforms 7

MECBench: A Framework for Benchmarking Multi-Access Edge Com...

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7th IEEE international conference on Edge computing and Communications (IEEE EDGE) / IEEE World Congress on Services (SERVICES)

作者： Naman, Omar Qadi, Hala Karsten, Martin Al-Kiswany, Samer Univ Waterloo Waterloo ON Canada Acronis Res Toronto ON Canada

ISBN: (纸本)9798350304831

We present MECBench, an extensible benchmarking framework for multi-access edge computing. MECBench is configurable, and can emulate networks with different capabilities and conditions, can scale the generated workloads to mimic a large number of clients, and can generate a range of workload patterns. MECBench is extensible;it can be extended to change the generated workload, use new datasets, and integrate new applications. MECBench's implementation includes machine learning and synthetic edge applications. We demonstrate MECBench's capabilities through two scenarios: an object detection scheme for drone navigation and a natural language processing application. Our evaluation shows that MECBench can be used to answer complex what-if questions pertaining to design and deployment decisions of MEC platforms and applications. Our evaluation explores the impact of different combinations of applications, hardware, and network conditions, as well as the cost-benefit tradeoff of different designs and configurations.

关键词： Benchmarking Containers Edge computing machine learning performance Multi Access Edge computing Network Emulation

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Statistical Analysis of Design Aspects on Various Graph Embedding learning Classifiers 7

Statistical Analysis of Design Aspects on Various Graph Embe...

引用

7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Madhuri, A. Devi, T. Uma Visakhapatnam530045 India Department of Computer Science and Engineering Prasad V. Potluri Siddhartha Institute of Technology Andhra Pradesh Vijayawada India

ISBN: (纸本)9781665464086

Graphs representation learning is an emerging research topic. Graph representation learning generates vectors that capture immense graphs' structure and properties. the quality of graph representation vectors affects machine learning downward tasks like node categorization, edge estimation and anomaly detection. For efficient graph representation vectorization, both traditional graph embedding techniques and methods based on graph neural networks (GNN) have been described. these approaches work on "static & dynamic graphs". In contrast to dynamic graphs which constantly adds new nodes and edges, static graphs consist of a single unchanging structure. this paper reviews traditional and GNN-based embedding approaches for both static and dynamic graphs. this paper summarizes GNN challenges and proposes a framework. Finally, real world applications of these techniques are explored. © 2023 IEEE.

关键词： Graph neural networks

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Explainable Artificial Intelligence Techniques for Image Classification Models in Diverse Domains 7

Explainable Artificial Intelligence Techniques for Image Cla...

引用

2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Joshi, Pranjali P. Bagade, Anant M. SCTR's Pune Institute of Computer Technology Department of Computer Engineering Pune India SCTR's Pune Institute of Computer Technology Department of Information Technology Pune India

ISBN: (纸本)9798350304268

Over the past few decades, Deep learning (DL), which is a subpart of machine learning (ML), has gained recognition as the dominant technique in diverse domains such as Computer Vision, Natural Language Processing, and Image Recognition. However, despite the exceptional performance of DL models, they are often referred to as of 'Black Box' nature, due to their limited ability to explain the decisions. this has led to a need for explainability of DL models. therefore to address this issue of lack of explainability in DL models, Explainable Artificial Intelligence (XAI) techniques have been improving in recent era. Unlike DL, XAI has the ability to provide both decision-making and model explanations for data. this research review comprises articles that utilize XAI techniques for image classification tasks. the purpose of this paper is to showcase the utility of the most recent XAI techniques employed in Image Classification Models (ICM). this paper will assist researchers to develop a better intepretation of XAI techniques for image classification as well as related challenges and future research directions. © 2023 IEEE.

关键词： Deep learning (DL) Explainable Artificial Intelligence (XAI) Image Classification Model (ICM) machine learning (ML)

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Using machine learning for Detection and Classification of Cyber Attacks in Edge IoT 7

Using Machine Learning for Detection and Classification of C...

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7th IEEE international conference on Edge computing and Communications (IEEE EDGE) / IEEE World Congress on Services (SERVICES)

作者： Becker, Elena Gupta, Maanak Aryal, Kshitiz Tennessee Technol Univ Dept Comp Sci Cookeville TN 38505 USA

ISBN: (纸本)9798350304831

Internet of things (IoT) devices are omnipresent due to their ease of use and level of connectivity. Because of wide deployment, IoT network traffic security is a large issue, especially as the devices become more common at the edge of the connected ecosystem. In general, low-powered IoT devices themselves are not inherently secure, so tailored security mechanisms are needed to make the ecosystem secure. the incorporation of the cloud also adds new security issues with the cloud service provider (CSP). In addition, several smart applications necessitate deploying edge-based infrastructure due to their real-time computation and communication requirements, while also having the ability to detect and mitigate different cyber attacks and remain light-weight. In this paper, we propose a machine learning-based approach to detect and classify different edge IoT network traffic driven cyber attacks, and evaluate their strengths and weaknesses. Particularly, we will compare eleven machine learning models to determine the best security agent trained for attack detection and classification on an edge IoT cyber security dataset with fourteen different attacks. We also provide experimental evaluation and analysis of our work, followed by our conclusion.

关键词： Internet of things Edge IoT machine learning Attack Detection and Classification

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Classification and Prediction of Alzheimer's Disease using Deep learning 7

Classification and Prediction of Alzheimer's Disease using D...

引用

7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Brindha, D. Ebenezer, V. Selvaraj, E. Jackson Bevin Geoff, D.M. Dileep, B. Bethina Nambi, M. thirumalai Dept. of Computer Science and Engineering Coimbatore India Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397253

this study has proposed a novel technique for performing object detection and image classification with a Convolutional Neural Network (CNN) architecture. Nevertheless, employing a 2D CNN architecture to identify a lot of patients as AD or MCI based on 3D MR Images becomes challenging when dealing with hundreds of MRI images that are highly similar in nature. It provides a solution to this problem that streamlines the concept of classification of patients based on 3D MRI by exploiting 2D data produced by the CNN structure. In this method, 2D characteristics from MRI are extracted and then converted by using an appropriate machine learning (ML) algorithm for categorization. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Development of machine learning Framework for the Protection of IoT Devices 7

Development of Machine Learning Framework for the Protection...

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7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Mohandas, R. Sivapriya, N. Sanyasi Rao, A. Radhakrishna, K. Sahaai, Madona B. Department of Electronics and Communication Engineering Balaji Institute of Technology & Science Telangana Warangal India Department of Computer Applications Cauvery College of Women Tamil Nadu Trichy India Tamil Nadu Chennai India

ISBN: (纸本)9781665464086

Internet of things (IoT) has a wide range of threats to businesses, according to security experts. Organizations need an intelligent system that can automatically detect suspicious IoT devices linked to their networks. this study introduces a unique security framework powered by machine learning (ML) that automatically adapts to the growing security needs of the IoT sector. there should be a way to identify IoT devices that aren't on a trusted white list. In this article, a machine learning method has been used to recognize IoT device types from a white list by using network traffic data. Seventeen separate IoT devices, each representing one of nine different categories of IoT devices, were manually tagged to train and assess multi-class classifiers. the majority rule was used to classify block listed devices accurately using unidentified in 86% of trial forms, while authorized expedient categories stayed appropriately identified through the real kinds with 88% of forms. the detection times varied for different types of IoT devices. In addition, it shows how the machine learning-based IoT white-listing system can defend itself against hostile attacks. © 2023 IEEE.

关键词： Internet of things

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