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检索条件"任意字段=International Conference on Machine Learning and Computing"

共 65467 条记录，以下是4701-4710 订阅

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Classification and Featurization of Motherboard Components Using YOLO Through machine learning

Classification and Featurization of Motherboard Components U...

引用

2024 IEEE international conference on computing, Applications and Systems, COMPAS 2024

作者： Rahman Fahim, Md. Imadur Mahazabin Senthia, Sabrina Ferdows, Rubayea Ahmed, Fuad IUBAT-International University of Business Agriculture and Technology Dept of Computer Science and Engineering Bangladesh Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798331529765

The role of technology is vital and can be observed through the 5th industrial revolution. As a matter of fact, the impact is so severe, it can be felt almost everywhere. As technology advances, one of the most promising frontiers lies in the realm of Artificial Intelligence (AI), with machine learning at its forefront. Object detection is a crucial such part with applications in robotics and image analysis. This paper presents a comprehensive study on vision AI, focusing on the detection of motherboard components utilizing the YOLOv8 object detection framework. The results demonstrate the effectiveness and real time effectiveness of YOLOv8 in terms of motherboard object detection. Simultaneously, this work validates the feasibility of employing YOLOv8 for in-depth analysis and object detection on various circuit boards. This will allow many automated devices to repair circuit boards with the help of artificial intelligence. Consequently, allowing robots to detect flaws and repair integrated hardware components in situations considered difficult for humans. © 2024 IEEE.

关键词： Robot vision

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Examine the Incorporation of Integration of Imaging Data for Orthodontic Prediction 6

Examine the Incorporation of Integration of Imaging Data for...

引用

6th IEEE international conference on Emerging Smart computing and Informatics, ESCI 2024

作者： Ramyaalakshmi, A. Poonguzhali, S. Vels Institute of Science Technology and Advanced Studies India

ISBN: (纸本)9798350306613

This study examines the crucial but final decision about tooth extraction during orthodontic therapy. The oral health and general wellbeing of individuals are significantly impacted by this choice. In the past, orthodontists' knowledge and clinical assessments were used to make these decisions. However, the development of machine learning (ML) and artificial intelligence (AI) offers a chance to improve the accuracy and predictability of these choices. With the goal of revolutionising orthodontic practise, this study will create and evaluate an AI-based orthodontist prediction model. An informed and accurate approach is required due to the significant effects that tooth extraction decisions have on orthodontic treatment. By developing an AI-based orthodontist prediction model, our research initiative aims to close this gap. This model can be used as a guide for people interested in pursuing careers in orthodontics, in addition to helping the current orthodontic practise by offering insightful information on tooth extraction choices. There usher in a new era of oral healthcare by embracing technology breakthroughs in orthodontics, which will result in better patient results and well-informed decision-making. © 2024 IEEE.

关键词： Artificial Neural Networks Decision Tree K-Nearest Neighbour machine learning and Orthodontic

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Enhancing Organ Donation: Web-based System for Promoting Awareness with machine learning for Optimized Matching 3

Enhancing Organ Donation: Web-based System for Promoting Awa...

引用

3rd international conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Patil, Aditi Mahamuni, Chaitanya V Patil, Kaustubh Kunjumon, Roy Nadar, Kingston Dept. of Computer Engineering Fcrit Vashi Navi Mumbai India

ISBN: (纸本)9798350375190

Thisresearch work presents a web-based system designed to enhance organ donation by optimizing donor-recipient matching and raising public awareness. Motivated by the urgent need to improve transplant success rates amidst organ shortages, our work integrates machine learning to optimize the matching process. Multiple linear regression is utilized for pairing donors and recipients due to its capability to model the complex relationship between multiple attributes, such as blood type compatibility and age, thereby optimizing the matching process for organ transplantation. The system uses HTML, CSS, JavaScript, PHP, and SQL technologies to facilitate donor registration, matching algorithms, and hospital workflows. Results demonstrate improved efficiency in donor-recipient pairing, showcasing potential impact on transplant outcomes. Future work includes algorithm refinement, expanded outreach efforts, and implementation of robust privacy measures. This research contributes to the organ donation field by offering technological solutions to enhance transplant success rates and address organ shortage challenges. © 2024 IEEE.

关键词： Geographic information systems

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Detecting DDoS attacks in Software Define Networking: A machine learning Based Approach

Detecting DDoS attacks in Software Define Networking: A Mach...

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2023 international conference on Next-Generation computing, IoT and machine learning, NCIM 2023

作者： Bala, Shimul Ahsan, Sk. Md. Masudul Khulna University of Engineering & Technology Department of CSE Khulna Bangladesh

ISBN: (纸本)9798350316001

Software-Defined Networking (SDN) is a type of network strategy that enables the separation of a network's control plane and data plane, thereby allowing centralized management and control of network traffic. Although SDN has many remarkable advancements, it encounters several security concerns like DDoS attack. The Distributed Denial-of-Service (DDoS) attack is a fast-growing threat that endangers the future of internet technology. In this paper, some machine learning classifiers namely support vector machine (SVM) using linear based kernel and radial basis function (RBF) based kernel, random forest (RF), decision tree (DT), logistic regression (LR), and k-nearest neighbors (KNN) are evaluated to identify and analyze DDoS assaults within an SDN environment. A comparative performance study according to the results from the experiments is described to determine the best suitable techniques for DDoS detection. The results show that some classifiers reached up to 100% accuracy and the performance is validated using ROC-AUC curve. By utilizing proper classifiers derived from machine learning and taking the benefits of SDN, the controller can be safeguarded against DDoS attacks. © 2023 IEEE.

关键词： Denial-of-service attack

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Early Detection of Type-2 Diabetes Mellitus using machine learning based Prediction Models 18

Early Detection of Type-2 Diabetes Mellitus using Machine Le...

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18th INDIAcom;11th international conference on computing for Sustainable Global Development, INDIACom 2024

作者： Khanam, Asra Masoodi, Faheem Syeed University of Kashmir Department of Computer Science Kashmir India

ISBN: (纸本)9789380544519

Diabetes, a condition that lasts for a long time, or we say, is a lifetime problem where the body cannot produce enough or no insulin, does not use the body's insulin effectively, or can exhibit both. As a result of which body cells cannot take the blood sugar, eventually, the body's sugar level is elevated. Diabetes today is an almost door-to-door problem, causes slow but significant changes in the body. So, we must address it more vigorously. As we know it, ML, a part of AI, is widely being used in predicting many diseases like cancers, tumors, etc. Here we are involved in detecting the chances of type-2 Diabetes in people using ML at an early stage and comparative analysis of the model's results in detecting type-2 Diabetes. The models used are KNN, LR, SVM, RF, and DT. The dataset used is the local dataset from SKIMS Soura, Kashmir. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Decision Tree Diabetes KNN Logistic Regression machine learning Random Forest SVM

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machine learning-Driven Distributed Intrusion Detection System for the Internet of Vehicles 13

Machine Learning-Driven Distributed Intrusion Detection Syst...

引用

13th international conference on Research in computing at Feminine, RIF 2024

作者： AlFarra, Joud Alam, Leena Marir, Naila Sarirete, Akila Computer Science Department Effat College of Engineering Effat University Jeddah Saudi Arabia LIRE Laboratory Constantine 2-Abdelhamid Mehri University Constantine Algeria

The Internet of Vehicles (IoV), integrating vehicles with EV charging stations, faces significant vulnerabilities to cyber threats. Designing an efficient Intrusion Detection System (IDS) to detect sophisticated attacks within EV charging stations is crucial for safeguarding this interconnected network. However, creating IDS models using centralized machine learning approaches requires overcoming challenges such as scalability, time-consuming analysis, and real-time processing requirements. In this context, we propose a novel distributed system to IDS design for the IoV, leveraging distributed learning techniques to address these challenges and enhance EV charging station security. This study utilizes different machine learning models to improve the effectiveness of abnormal behavior detection. Based on the robustness and adaptability of machine learning models, the detection and classification of intrusion data are significantly enhanced. Our presented system is validated using the CICEV2023 dataset. Simulation results demonstrate that our algorithm achieves higher efficiency and accuracy compared to existing centralized schemes. © 2024 Copyright for this paper by its authors.

关键词： Network intrusion

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machine learning-Optimized Blockchain Protocols for Enhanced Data Integrity in Clinical Trials

Machine Learning-Optimized Blockchain Protocols for Enhanced...

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2024 OPJU international Technology conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Thethi, H. Pal Rajitha, Akula Revathi, V. Prithviraj, Jitendra Yadav, Dinesh Kumar Saud, Haider Radhi Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Applied Sciences Bangalore India GyanGanga Institute of Technology and Sciences DoCSE M.P Jabalpur India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology College of Health and Medical Technology Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

This article reports the findings of a comprehensive investigation that found blockchain technology developed for machine learning can improve clinical trial data security. This research was first submitted to Transactions on Blockchain Technology. Secure Decentralized Data Management System (SDDMS) that successfully addresses data stability, security, and privacy is possible. This is feasible. This makes switching to safer, more transparent medical data processing easy. Blockchain encryption, consensus-driven validation, and federated learning provide the secure and efficient management of patient personal information by the Secure Distributed Database Administration System (SDDMS). As a result, the clinical trial operating environment becomes more trustworthy and transparent. The suggested system has been successfully implemented, suggesting that it has the potential to revolutionize data management, foster collaborative research, and empower individuals to make better and more informed healthcare decisions. Blockchain and machine intelligence are likely to change trustworthy data management in clinical trials and other healthcare research. This is likely. Because this combination is likely to combine the two technologies. © 2024 IEEE.

关键词： Federated learning

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HOTS: A Containers Resource Allocation Hybrid Method Using machine learning and Optimization 7th

HOTS: A Containers Resource Allocation Hybrid Method Using M...

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7th international conference on Optimization and learning

作者： Leclercq, E. Rivalan, Jonathan Smile Ile De France France

ISBN: (纸本)9783031779404;9783031779411

One of the main challenges in cloud computing is resource management, the ability to schedule workloads and services over the infrastructure in the most automated way. By optimizing cloud assignment and resource usage, energy can be saved, production incident can be anticipated and services QoS improved. With the recent years emergence of light virtualisation, known as containerization, the resource allocation problem was brought back, notably to support containers elasticity, hence the dynamic allocation of ressource at runtime at a single service scale. In this paper we show that using an hybrid loop system, which combines unsupervised learning and optimization techniques, our algorithm provides and iteratively improves scheduling solutions to containers resource assignment, enabling capacity planning over dynamic resource loads. Within our benchmarks, these solutions outperform state of the art algorithms, by an average of 6.3%, while providing more expressivity and control over input parameters. We describe also the implementation of this method, through an open source Python library called HOTS, which allows hybrid optimization for time series based use cases.

关键词： Clustering Continuous Optimization Containerized Applications Resource Allocation

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Analysis and Implementation of Microservices Using Docker 2nd

Analysis and Implementation of Microservices Using Docker

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2nd international conference on Advanced Network Technologies and Intelligent computing (ANTIC)

作者： Sharma, Keshav Verma, Anshul Verma, Pradeepika Banaras Hindu Univ Dept Comp Sci Varanasi Uttar Pradesh India Indian Inst Technol Technol Innovat Hub Patna Bihar India

ISBN: (纸本)9783031281792;9783031281808

Implementation of micro-services is among the most challenging and important tasks in the field of Computer Science. For implementing the microservice architecture, Docker has been used in the current scenario. Creating a machine learning model that works on one computer is not really difficult. But creating a model that can scale and run on all types of servers around the world, it's more challenging. This work proposed a way to deploy a certain type of trained model in microservice architecture using Docker to use anywhere. For practical implementation, two models for machine learning has been used. One that addresses the handwritten digit recognition problem which has been an open problem in pattern classification since a long time, and another model resolves the issue of house pricing based on several attributes. The purpose of implementation of machine learning model in microservice architecture using Docker is to enable a method from which anyone can use a machine learning model without worrying for their machine configuration and dependencies of the machine learning model.

关键词： Container Docker Cloud Microservices machine learning model Dependencies Server

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7th international conference on Mobile Internet Security, MobiSec 2023

7th International Conference on Mobile Internet Security, Mo...

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7th international conference on Mobile Internet Security, MobiSec 2023

ISBN: (纸本)9789819744640

The proceedings contain 21 papers. The special focus in this conference is on Mobile Internet Security. The topics include: computing Resource Allocation Based on Multi-base Station and Multi-user Scenario in Mobile Edge computing;lattice-Based Multi-entity Identification Protocols;Hardware Implementation of SM4 Encryption Algorithm with Hybrid Stochastic computing;Privacy-Preserving Fair Outsourcing Polynomial Computation Without FHE and FPR;SFPDML: Securer and Faster Privacy-Preserving Distributed machine learning Based on MKTFHE;existence of Equivalent Keys in a Random Number Generator with Field Trace and a Dynamic Mapping;a Heterogeneous computing Framework for Accelerating Fully Homomorphic Encryption;generic Construction of Forward-Secure Chameleon Hash Function;exploring Leakage Characteristics and Attacks Through Profiles of Screaming Channels;Revisiting an Extension of Kannan’s Embedding for Ring-LWE;revisiting the Algorithms for the Quaternion -Isogeny Path Problems;the Impact of Data Scaling Approaches on Deep learning, Random Forest and Nearest Neighbour-Based Network Intrusion Detection Systems for DoS Detection in IoT Networks;integrating machine learning Models into the Linux Kernel: Opportunities and Challenges;early Weather Warning System with Real-Time Monitoring by IoT Sensors Considering Scalability and Reliability;privacy Preserving Multi Factor Authentication Protocol for Next Generation Grids Deployed in Smart Cities;research Directions in Formal Verification of Network Configurations Toward Verification of Mobile Networks;Destructive Malwares on MITRE ATT&CK Tactics for Cyber Warfare: A Brief Survey and Analysis;intelligent Cooperative Routing for Services in computing Integration Networking;A Biometric-Based Data Protection Scheme for RSDs.

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