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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

共 1863 条记录，以下是231-240 订阅

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application of A Question Recognition Strategy for Small Data Sets in Medical Insurance QA Systems 24

Application of A Question Recognition Strategy for Small Dat...

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3rd international conference on Frontiers of Artificial Intelligence and machine learning (FAIML)

作者： Zhang, Shun Fu, Weiwei Hu, Yurong Chen, Huafeng Jingchu Univ Technol Internet Intelligences Applicat Innovat Res Ctr Jingmen 448000 Peoples R China Jingmen Duodao Dist Med Secur Bur Jingmen Peoples R China Jingmen Cryptometry Applicat Technol Res Ctr Jingmen 448000 Peoples R China Jingchu Univ Technol Sch Comp Engn Jingmen 448000 Peoples R China

ISBN: (纸本)9798400709777

Question recognition is crucial in Q&A systems. An effective recognition method significantly enhances the accuracy and efficiency of information retrieval. Advances in natural language processing (NLP) technology have spurred the development of statement recognition techniques. However, many methods rely on extensive datasets, and numerous sectors struggle to keep pace with intelligent advancements due to data scarcity. Policy-related questions, which often involve a plethora of technical terms, exemplify this challenge. For colloquial questions with intricate structures, models require enhanced natural language understanding capabilities. These models must prioritize key information and filter out irrelevant data, particularly when data availability is limited. To address these issues, this study utilized medical insurance approval items and related questions as data sources. It employed sentence vectors and question structures as analytical frameworks and explored innovative solutions. Specifically, the twin network fine-tuning pre-training model RoBERTa was applied to classify medical insurance items, integrating syntactic structure and question word features to pinpoint question targets. This method outperformed standard text classification models in the medical insurance domain and has potential applicability in other policy-oriented Q&A systems, thereby offering precise services to the public.

关键词： Pre-trained model Multiple text classification Sentence structure Question recognition

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Human profiling based on computer vision : A survey with application 3

Human profiling based on computer vision : A survey with app...

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3rd IEEE international conference on Electronics, Control, Optimization and computer Science, ICECOCS 2022

作者： Elbaghazaoui, Bahaa Eddine Amnai, Mohamed Fakhri, Youssef Choukri, Ali Gherabi, Noreddine IbnTofail University Laboratory of Computer Sciences Faculty of Sciences Kenitra Kenitra Morocco Sultan Moulay Slimane University National School of Applied Sciences Khouribga Morocco

ISBN: (数字)9781665457231

ISBN: (纸本)9781665457231

machine learning is currently a prominent issue due to the direct training of machines with minimal human input. In the present period, the scenario of manual machine feeding has changed, it now learns automatically. machine learning approaches are utilized for a variety of tasks, such as feature extraction, pattern recognition, object recognition, and classification. In computer vision, machine learning is used to extract important information from visual data. computer vision contributes to a variety of disciplines, including surveillance, robotics, suspect detection, optical character recognition, and numerous other technologies. computer vision helps in extracting metadata from humans, such as emotions, sentiments, personality, and so on. This paper presents a thorough yet current survey of human profiling based on computer vision. However, we present a taxonomy of data profiling and computer vision as well as how to profile humans from image data. As an experience, we will discuss our program about human faces recognising emotions from a person's image using deep learning. In any event, the outcomes we have achieved are critical and important. © 2022 IEEE.

关键词： computer vision

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Capturing Temporal Information with LSTM to Stabilize A Rotating machine 3

Capturing Temporal Information with LSTM to Stabilize A Rota...

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3rd international conference on computer Vision, Image and Deep learning and international conference on computer Engineering and applications, CVIDL and ICCEA 2022

作者： Wei, Yaoyao Qu, Weiming Zhang, Tao Deng, Yian Hu, Fan Zhang, Xiangqi Liu, Tianlin Bolchover, Paul Yu, Tao Zheng, Guishui Zhang, Ping Liu, Qing Luo, Dingsheng Peking University School of Artificial Intelligence Beijing China Schlumberger

ISBN: (纸本)9781665459112

Rotor stability, which indicates the performance of a rotor keeping the system in good working order without transverse vibration, is one of the important research contents in rotor dynamics area. Actually, in a real working environment, it is difficult to stabilize a rotating machine because violent transverse vibrations often happen due to the influence of air resistance, ground friction, errors in manufacture of the rotor, and other factors. The vibrations not only cause instability of the system, but also greatly limit the increase of the rotational speed. We think that the temporal information of the rotating machine, which records the history of its motion, is useful to help reduce the vibrations and stabilize the rotating machine. A recurrent neural network architecture, Long Short-Term Memory (LSTM) is employed to capture the complex and changeable temporal information. So, in this research, an intelligent control approach, that is deep reinforcement learning coupled with LSTM capturing temporal information, is proposed to stabilize a rotating machine. Experimental results in simulation platform demonstrate the effectiveness and robustness of the approach. © 2022 IEEE.

关键词： Reinforcement learning

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Migration Techniques for Virtual machine Distribution Using machine learning Heuristics application in Cloud Computing 3

Migration Techniques for Virtual Machine Distribution Using ...

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3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Panesar, Gurpreet Singh Chadha, Raman Chandigarh University Computer Science and Engineering Chandigarh India

ISBN: (纸本)9798350342338

Cloud computing has revolutionized the way IT resources are provisioned, deployed, and managed. Virtual machine (VM) migration is a crucial aspect of cloud computing that facilitates resource optimization, load balancing, and fault tolerance. In recent years, machine learning (ML) heuristics have emerged as effective tools for VM distribution, allowing dynamic and intelligent allocation of resources. This research review paper presents a comprehensive analysis of various migration techniques that leverage ML heuristics for VM distribution in cloud computing environments. The paper reviews relevant literature, compares different methodologies, and discusses their advantages, limitations, and potential applications. © 2023 IEEE.

关键词： Cloud computing Fault tolerance Load balancing machine learning heuristics Resource allocation Virtual machine (VM) migration

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machine learning Approach for Cloud Computing Security 3

Machine Learning Approach for Cloud Computing Security

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3rd international conference on Intelligent Engineering and Management, ICIEM 2022

作者： Garima Quraishi, Suhail Javed Chandigarh University Department of Computer Science and Engineering Punjab Mohali India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

The fast proliferation of Cloud Computing affects the way an industrial application is designed, delivered, and deployed. This acceptance of Cloud Computing has massive repercussions for the IT sector and associated industries since it does not only alter the customers' business models but also the other midfielders in the enterprise *** the constant enhancement of the domain comes the responsibility to maintain the security of the cloud data and retreat ways to threats that may be encountered *** paper intends on discovering the potential security issues, threats, and attacks that individuals or an organization may experience while working on cloud or other distributed computing architecture and how it can be dealt with by employing technology like machine *** have explored numerous machine learning approaches that are utilized to tackle cloud security challenges covering supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Whereupon, we evaluated the efficiency and draw a comparison for every approach based on its characteristics, benefits, and shortcomings. Furthermore, we highlight potential research paths to safeguard cloud frameworks. © 2022 IEEE.

关键词： computer architecture

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machine learning Model-Based Financial Market Sentiment Prediction and application 3

Machine Learning Model-Based Financial Market Sentiment Pred...

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

作者： Kumar Chaudhary, Jitendra Tyagi, Swati Prapan Sharma, Hari Vaseem Akram, Shaik Sisodia, Dharini Raje Kapila, Dhiraj School of Computing Graphic Era Hill University Bhimtal Campus Uttrakhand India University of Delaware Newark DE United States Institute of Business Management Gla University Uttar Pradesh Mathura India Uttaranchal Institute of Technology Uttaranchal University India Army Institute of Management & Technology Department of Management Uttar Pradesh Greater Noida India Lovely Professional University Department of Computer Science & Engineering Punjab Phagwara India

ISBN: (纸本)9798350399264

Academic interest in stock market forecasting has risen dramatically in recent years. An in-depth understanding of stock movement data and strong analytical abilities are prerequisites for making reliable stock market forecasts. This responsibility calls for innovative methods of execution to provide the highest level of accuracy in the prediction and, by extension, the best potential profits for investors. The major emphasis of this article is on developing a robust stock market forecasting system by using effective machine learning techniques. The research has three parts: (1) stock market data set preprocessing;(2) application of two supervised machine learning algorithms, K-Nearest Neighbor (KNN) and Random Forest (RF);and (3) evaluation of the accuracy and efficacy of predicting for the two recommended models. The experimental findings showed, while both recommended models performed well in terms of accuracy ratio (with values of 90.23 percent, 91.12 percent, and 90.17 percent for precision, recall, and F-measure, respectively). © 2023 IEEE.

关键词： Forecasting

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3rd international conference on computer and Communication Engineering, CCCE 2023

3rd International Conference on Computer and Communication E...

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3rd international conference on computer and Communication Engineering, CCCE 2023

ISBN: (纸本)9783031352980

The proceedings contain 18 papers. The special focus in this conference is on computer and Communication Engineering. The topics include: Development of Home-Bot with Accident-Sensory System Using IOT and Android application for Control;application of machine learning in Predicting Crime Links on Specialized Features;an Empirical Analysis on Lossless Compression Techniques;an Analysis of the Performance Changes of the Model by Reducing the Input Feature Dimension in Stock Price Forecasting;a Hybrid Algorithm by Incorporating Neural Network and Metaheuristic Algorithms for Function Approximation and Demand Prediction Estimation;Deep QA: An Open-Domain Dataset of Deep Questions and Comprehensive Answers;tropical Cyclone Analysis and Accumulated Precipitation Predictive Model Using Regression machine learning Algorithm;mapping learning Algorithms on Data, a Promising Novel Methodology to Compare learning Algorithms;rough Rice Grading in the Philippines Using Infrared Thermography;an Interpretable Hybrid Recommender Based on Graph Convolution to Address Serendipity;A Geometry-Based Strategic Placement of RISs in Millimeter Wave Device to Device Communication;obstacle Aware Link Selection for Stable Multicast D2D Communications;mobility Aware Path Selection for Millimeterwave 5G Networks in the Presence of Obstacles;A Probabilistic Analysis of the Delay in RIS Assisted SISO D2D Communication Using Chernoff’s Bounds;enhancing IoT Security Through Deep learning-Based Intrusion Detection;a Security and Vulnerability Assessment on Android Gambling applications.

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Prediction of Distributed Denial of Service Attacks in SDN using machine learning Techniques 3

Prediction of Distributed Denial of Service Attacks in SDN u...

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3rd IEEE international conference on Intelligent Technologies, CONIT 2023

作者： Sekar, R. Raja Jenny, Ardhala Mounika Sreshta, Dubba Vikas, M. Ajay, Dasari Badri Nageshwar Ganesh, Mankena Kalasalingam Academy of Research and Education Department of Computer Science Amd Engineering Virudhnagar Krishnankovil India

ISBN: (纸本)9798350338607

A network architecture known as "software-defined networking"(SDN) enables the design and creation of hardware elements remotely. Due to their permanent connections, you can modify on the go. Even if SDN remains fantastic approach, Internet-threatening DDoS occurrences may exploit it. DDoS occurrences may be stopped with the use of machine learning techniques. When numerous systems work together to assault a server, it is called a DDoS attack. A control layer software manages the devices of the infrastructure layer and enables SDN by linking the application layer to the infrastructure layer. [1] The methods for identifying malicious traffic that we discuss in this work include decision trees, Cat Boost, and Extra trees. The outcomes of our tests demonstrate that Decision Tree, Cat Boost, and Extra Tree are capable of determining if the attack is safe or not. © 2023 IEEE.

关键词： Decision trees

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An Approach to Loan Approval prediction Using Boosting Ensemble learning 3

An Approach to Loan Approval prediction Using Boosting Ensem...

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3rd international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Anusha, Chintam Rajendra Kumar, G. Praveen, M.A. Vignan's Institute of Information Technology Computer Science and Engineering Andhra Pradesh Vishakapatanam India

ISBN: (纸本)9781665494007

Now a days banks are receiving lakhs of loan applications daily. This is delay in approval of loan to the customer as humans need to verify each of them manually. So even after having enough resources to approve loans it is big problem for banks to approve applications in time. In order to resolve this problem, we are planning to build an ML application which can reduce the time required to approve a loan using ML based prediction model to approve the loan with minimal human intervention by filtering huge number of applications and forward very few applications for human verification. In this work used several popular machine learning models are Random Forest tree, Logistic Regression, Decision tree, Support Vector machine and XGB. The results demonstrated that XGB has provided higher accuracy in the presenting assessment of the models than all other models combined. © 2023 IEEE.

关键词： Forecasting

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A Novel Eye-tracking and Audio Hybrid System for Autism Spectrum Disorder Early Detection 3

A Novel Eye-tracking and Audio Hybrid System for Autism Spec...

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2023 IEEE 3rd international conference on Data Science and computer application, ICDSCA 2023

作者： Zhang, Alec The Harker School San JoseCA United States

ISBN: (纸本)9798350341546

Autism spectrum disorder (ASD) is a lifelong neurodevelopmental condition marked by challenges in social communication and the presence of repetitive behaviors or limited interests. The early diagnosis of ASD is crucial for enhancing the quality of life for those affected and maximizing the effectiveness of early intervention strategies. This paper presents a novel approach that utilizes hybrid eye tracking and speech data within a supervised learning framework, aiming to enhance accuracy and accessibility for the early detection of ASD in children. A dataset containing both of these data modalities was collected from 30 ASD and 78 non-ASD children. Data processing methods, such as scanpath visualization for eye tracking data and Mel-frequency cepstral coefficients (MFCCs) extraction for speech data, were employed on our collected dataset. Classifiers such as XGBoost and neural networks were then applied to this altered dataset to develop predictive models for ASD classification, with a classification accuracy of up to 82% obtained. The results demonstrate that our multimodal approach significantly outperforms the models trained on individual data types alone, showcasing the potential of this approach for early, cost-effective, and scalable ASD screening. © 2023 IEEE.

关键词： ASD Audio Eye-tracking machine learning

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