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检索条件"任意字段=2023 International Conference on Computer Engineering and Distance Learning, CEDL 2023"

共 40714 条记录，以下是4751-4760 订阅

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Decoding and Analysing Consumer Feedback for Companies and Goods using Machine learning 3

Decoding and Analysing Consumer Feedback for Companies and G...

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3rd international conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Joel, M. Robinson Ebenezer, V. Jeyaraj, K. Arul Navaneethakrishnan, M. Arunadevi, R. Jenifa, D. Rejees Kings Engineering College Department of Information Technology Chennai India Department of Computer Science and Engineering Coimbatore India St. Joseph College of Engineering Sriperumbudur Department of Computer Science and Engineering Chennai India Technology Prince Shri Venkateshwara Padmavati Engineering College Department of Information Chennai India Psna College of Engineering and Technology Department of Ece Dindigul India

ISBN: (纸本)9781665462167

Over the past ten years, the number of internet users has increased tremendously. Due to the e- commerce industry's fast growth, practically all goods are now sold online. Before making a purchasing choice, the customer gets comprehensive information on the items they are interested in. Customer opinions and reviews assist businesses and other consumers in making wise decisions and enhancing the purchasing experience. It is getting harder to comprehend and keep track of every customer's wants since the number of individuals leaving evaluations is increasing quickly. Some evaluations, which make it more time-consuming and challenging to analyse for business, are purposefully overly lengthy, unclear, incomplete, and short. A customer Feedback Evaluation System is needed to address this issue since it will track every one of those evaluations and offer statistical data for improving the manufacturer's quality and functionality. The suggested approach uses machine learning to give each word a certain amount of weight. © 2023 IEEE.

关键词： Support vector machines

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Role of Ontology-Informed Machine learning in computer Vision 6

Role of Ontology-Informed Machine Learning in Computer Visio...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Kumar, Vipin Goswami, Rakesh Giri Pandya, Diptanshu Prasad, M.S.R. Kumar, Sachin Jain, Abhishek College of Smart Computing Department of Cyber Security COER University U.K. Roorkee India Medi-Cap University Department of CSE Madhya Pradesh Indore India Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering Andhra Pradesh Vaddeswaram India Uttranchal University Department of CSE U.K. Dehradun India

ISBN: (纸本)9798350304480

Ontologies are used in practically every major biological database to formalise and reason about domain information in the life sciences. Ontologies are increasingly employed to offer background information in similarity-based analysis and machine learning models. Combining ontologies with machine learning is still experimental. We discuss how semantic similarity measures and ontology embeddings can use ontologies'background knowledge and provide constraints to improve machine learning models. Ontological research can help in machine learning by introducing the broad aspects of the future scope. The ontology supports structured, semi-structured, and unstructured data types for building a piece of valuable information. © 2023 IEEE.

关键词： Artificial intelligence Data-science Machine learning Ontology Philosophy Regression tree model

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AI-ML-Enabled Clinical Decisions for Pregnant Women and their Doctors with Special Attention to international Legal Aspects 10

AI-ML-Enabled Clinical Decisions for Pregnant Women and thei...

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10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Patil, Priya Bahuguna, Rajesh Memoria, Minakshi Law College Dehradun Uttaranchal University Uttarakhand Dehradun India Uttaranchal Institute of Technology Faculty of Uttaranchal University Uttarakhand Dehradun India

ISBN: (纸本)9798350382471

The utilization of AI and machine learning (ML) algorithms in the field of maternity care has proven to be highly effective. This study assesses the application of AI in the field of women's health, specifically examining its integration into clinical decision-making processes. Additionally, it explores the present state of maternal health and the well-being of unborn children. The writers admit that the issues of female feticide, illegal abortion, and hazardous delivery are of utmost importance and have been the focus of attention by numerous states and the global community. The aforementioned publications encompassed a variety of topics pertaining to pregnancy and the transdisciplinary uses of artificial intelligence. There is a limited body of research pertaining to the intersection of pregnancy, artificial intelligence (AI), and pharmacology. As a result, we undertake a meticulous examination of these works. The research investigations have limitations in terms of the approaches and frameworks presented, which restricts their generalizability. Our analysis of the use of artificial intelligence (AI) in maternal health includes the examination of preconception, prenatal, perinatal, and postnatal medical difficulties. Furthermore, we examined the legal dimensions that were addressed on both an international and national level by several countries, such as India. © 2023 IEEE.

关键词： ANN Artificial Intelligence Machine learning Maternity Maternity Protection Convention PNDT Preeclampsia RBF

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Comparative Analysis of Covid Detection using Chest X-rays by SVM-PCA and Deep learning Techniques 1

Comparative Analysis of Covid Detection using Chest X-rays b...

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1st IEEE international conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Jaswanth, Kunisetty Nikhil, Masabattula Teja Siddhartha, Mutyala Sai Sri Jayan, Sarada Amrita Vishwa Vidyapeetham Amrita School of Engineering Dept. of Computer Science Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Dept. of Mathematics Bengaluru India

ISBN: (纸本)9798350348057

In the medical field, it is very crucial to obtain the results accurately in less amount of time. nowadays the covid cases have been increasing gradually and as a reason, the time of computations has to be decreased. In this paper, an SVM-based covid detection model on chest x-rays has been proposed with a relatively small dataset by preprocessing the images with a pooling operation and performing principal component analysis. © 2023 IEEE.

关键词： Principal component analysis

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Rain Predictive Model using Machine learning Techniques

Rain Predictive Model using Machine learning Techniques

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2023 international Multi-Disciplinary conference in Emerging Research Trends, IMCERT 2023

作者： Muneer, Muhammad Shahbaz Nabeel Mustafa, Syed Muhammad Zehra, Syeda Sundus Maqsood, Haniya NED University of Engineering and Technology Dpertment of Computational Finance Karachi Pakistan Sir Syed Uuniversity of Engineering and Technology Dept. of Software Engineering Karachi Pakistan NED University of Engineering and Technology Department of Computer and Information Systems Engineering Karachi Pakistan

ISBN: (纸本)9798350347708

Climate is rapidly changing around the world. Over time, there have been significant changes in the weather. Rainfall is now erratic due to climate change. The frequency of extreme weather events like droughts and floods has increased due to climate change, necessitating the need for more precise and timely rainfall forecasts. For strategic reasons including agriculture, water resource management, and architectural design, rain forecasting is crucial. The naturally occurring non-stationary component in the rainfall time series impairs model performance for practical hydrologists and drought risk assessors. We present a rain predicting model based on machine learning to address the forecasting issue. In our work, we predict the possibility of rain the next day on the basis of last 10 years' data. The variables that were calculated during the experiments were humidity, pressure, evaporation, sunshine, rainfall, and so on. Random Forest gave the 90% accuracy with 0.904 Area under Curve, highest out of all the algorithms. The model's performance will significantly aid in the rain forecast. © 2023 IEEE.

关键词： Rain

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Creating An Artificial Intelligence-Powered Image Classification Model For Specially Abled Persons 9

Creating An Artificial Intelligence-Powered Image Classifica...

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9th international conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Srikanth, Kandula Venuthurumilli, Munni Manaswini, Nune Vl Somayajulu, M.V.N.N.S.S.R.K.S. Seshadri Rao Gudlavalleru Engineering College Department of Information Technology Andhra Pradesh India PB Siddhartha College of Arts and Science Department of Computer Science Andhra Pradesh Vijayawada India Nalanda Degree College Department of Computer Science Andhra Pradesh Vijayawada India Krishna University Machilipatnam Department of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798350397376

This article describes how to use Python's Keras deep learning package to build an Artificial Intelligence-powered image classification model. Speech, text, and other types of input are transformed into machine-readable form by the system using natural language processing (NLP) and speech recognition technology. Other assistive technologies can be adapted to and used with the system. The purpose of this study is to show how straightforward and simple it is to use Keras to create an accurate picture categorization model. Convolutional neural network (CNN) architecture, a kind of deep learning model best suited for image classification tasks, is built first in the procedure. Following that, the model is assembled and trained on a sizable dataset, where it discovers patterns and features in the photos. The model is tested and evaluated on fresh data after training to ensure accuracy. The outcomes demonstrate that Keras can efficiently and accurately construct an AI-powered picture classification model, making it a useful tool for resolving practical computer vision issues. This demonstrates the capabilities of AI in this area and offers a thorough manual for people wishing to create their own picture classification models using Keras. © 2023 IEEE.

关键词： computer vision

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Prediction of Traffic Accidents Using Deep learning Ensemble Model

Prediction of Traffic Accidents Using Deep Learning Ensemble...

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2023 international conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Sekhar, G Soma Mahadik, Shubhangi Ramana, Attili Venkata Chandra Shaker Reddy, Pundru Pughazendi, N. Asif, S. Computer Science and Engineering Vardhaman College of Engineering Telangana Hyderabad India Bharati Vidyapeeth's Institute of Management & Information Technology Master of Computer Applications Navi Mumbai India Hyderabad India SR University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India Computer Science and Engineering Panimalar Engineering College Tamil Nadu Chennai India Marri Laxman Reddy Institute of Technology and Management CSE-Data Science Telangana Hyderabad India

ISBN: (纸本)9798350319200

Because of the widespread negative effects that traffic accidents can have on people's lives, the study of how to assess and anticipate them using cutting-edge technology is an important area of study. The research of traffic accidents has traditionally relied on data made public by traffic-entities and road-police forces. However, with the advent of social-media, researchers now have access to detailed, real-time data on road accidents occurring across a region, allowing for more comprehensive analyses that take into account previously unreported incidents. This paper uses an ensemble-Deep-learning(EDL) strategy made up of Gated-Recurrent Units(GRUs) and Convolutional-Neural-Networks(CNNs) to offer a model to forecast traffic accidents utilizing data mined from social-media and the open web. The acquired results are compared to those of published baseline methods and other researchers. The outcomes are encouraging, showing that the proposed EDL approach outperforms the conventional models and other DL approaches published in the literature when applied to the problem setting. Authorities in charge of preventing accidents on the roads can use the data generated by the program. © 2023 IEEE.

关键词： DL ML risk prediction traffic accident traffic accidents

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Deep learning Convolutional Neural Networks Classification Based on Brain Cancer MRI

Deep Learning Convolutional Neural Networks Classification B...

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2023 international conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Zeebaree, Diyar Qader Asaad Zebari, Dilovan Zeebaree, Imad Majed Abduljabbar Mohammed, Mohammed Mohammed, Bafreen Abduljabbar Asaad Zebari, Nechirvan Duhok Polytechnic University Technical College of Informatic Department of Computer Network and Information Security Kurdistan Region Duhok42001 Iraq Nawroz University College of Science Department of Computer Science Kurdistan Region Duhok42001 Iraq Duhok Private Technical Institute Department of Health Management Information Systems Kurdistan Region Duhok42001 Iraq University of Duhok College of Education Department of Mathematic Kurdistan Region Duhok42001 Iraq

ISBN: (纸本)9798350322972

Brain tumor diagnosis and classification is a major challenge for medical field. The majority of the time, computer-Aided Diagnostic Systems (CADS) and methods involving Deep learning (DL) are utilized to find brain tumors quickly and precisely. Artificial diagnostic systems, on the other hand, are not very accurate, the physician is not using them very well when diagnosing Magnetic Resonance Images (MRI). By applying image processing principles, it is possible to conceptualize the different human tissue structures. It is difficult to detect diseased structures in the human brain using only the most fundamental imaging methods. In this research, we present a model-based Convolutional Neural Network (CNN) structure for identifying and classifying brain tumors. The proposed method consists of two parts: pre-processing and classification. In this study, CNN architecture outperformed in state-of-the-art methods, with 99.87% accuracy. F1-score 99.88%, 99.76% precision. © 2023 IEEE.

关键词： Magnetic resonance

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Pyramid Input-Based Network for Single Image Dehazing 3

Pyramid Input-Based Network for Single Image Dehazing

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3rd international conference on Machine learning and computer Application, ICMLCA 2022

作者： Wei, Yinxue Li, Jinjiang Hua, Zhen School of Information and Electronic Engineering Shandong Technology and Business University Yantai China Co-innovation Center of Shandong Colleges Universities: Future Intelligent Computing Yantai China Yantai China

ISBN: (纸本)9781510664814

Images taken in hazy weather are susceptible to the effects of haze, resulting in blurred images and low contrast to the extent that important information is lost in the image. Therefore, this is necessary to dehaze haze images, process the image information and ensure the normal operation of other computer vision tasks. Traditional deep learning-based image dehazing methods often suffer from uneven haze removal, colour bias and loss of detail. To solve this problem, this paper proposes a single image dehazing method (IMNet) based on pyramidal input for image dehazing. The network is divided into three modules: an intensive feature extraction module, a pyramid input branch and a detail deepening module. This paper uses two loss functions in combination, which can help preserve texture details more effectively. Experimental results have shown that IMNet outperforms other dehazing algorithms in terms of metrics and visual effects. © 2023 SPIE.

关键词： Feature extraction

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AI-Enable Generating Human Faces using Deep learning 12

AI-Enable Generating Human Faces using Deep Learning

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12th IEEE international conference on Communication Systems and Network Technologies, CSNT 2023

作者： Varshney, Neeraj Kumar, Gaurav Kumar, Ankit Pandey, Saroj Kumar Singh, Teekam Singh, Kamred Udham Gla University Department of Computer Engineering & Appications UP Mathura India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Graphic Era Hill University School of Computing Dehradun India

ISBN: (纸本)9781665462617

Lately, sensible Image processing using deep neural networks has become a fervently discussed issue in machine learning and computer vision. Image can be made at the pixel level by learning from a gigantic variety of pictures. learning to make splendid movement pictures from highdifference draws is not only a captivating investigation issue yet also a reasonable application in innovative delight. In this research, we research the sketch-to-picture mix issue by using prohibitive generative poorly arranged networks. The model can normally deliver reasonable shadings for a sketch. The new model is not only prepared for painting hand-drawn sketches with real tones, yet also allows customers to exhibit supported tones. Test results on two sketch datasets show that the autopainter performs better contrasted with existing picture-topicture methodologies. With creating interest in the development of film, the interest in building a computerized structure to change over the authentic video into action is higher than at some other time. The edge-by-diagram modification of the action age measure is costly and dreary. To help with moving quickly and with no issue in a robotized collaboration we proposed a generative model that changes over genuine pictures into contrasting energy pictures without losing critical nuances of the source picture. We used an assortment of the generative hostile association as a fundamental plan with the custom incident ability to ensure the substance of the source picture, which changed over to an exuberance image. © 2023 IEEE.

关键词： Deep neural networks

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