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

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A Research On The New Generation Artificial Intelligence Technology Generative Pretraining Transformer 3 3

A Research On The New Generation Artificial Intelligence Tec...

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3rd international Informatics and Software Engineering conference, IISEC 2022

作者： Aydin, Nazif Ayhan Erdem, O. Gazi University Graduate School of Natural and Applied Sciences Department of Computer Engineering Ankara Turkey Gazi University Faculty of Technology Department of Computer Engineering Ankara Turkey

ISBN: (数字)9781665459952

ISBN: (纸本)9781665459952

In the digitalizing world, Artificial Intelligence (AI) paves the way for the automation of routine work done by humans and makes life easier. Recently, there is no area where AI and its applications are not used in daily life, from health to education, from transportation to energy, and from agriculture to tourism. AI applications are making rapid progress in the direction of important and current developments, especially in Natural Language Processing (NLP) and Deep learning (DL). A concrete example of progress in these areas is the GPT-3 (Generative Pre-trained Transformer 3) language model. AI-assisted GPT-3 technology is the DL model that is effectively used in many NLP fields, which can produce long and consistent content similar to the texts written by people using pre-trained algorithms. The GPT-3 architecture has reached a level that can compete with people in many areas by producing optimum solutions for all kinds of inputs by using the Transformer-based language model, which is an attention-based deep learning technique. This article aims to convey the efficiency, structure and potential of the Transformer-assisted GPT-3 model, which is one of the most up-to-date NLP technologies, to the reader. Since the number of Turkish studies in the field of GPT-3 AI is quite limited, it is considered that this study will contribute to the literature in terms of both quantity and quality. In addition, the performance parameters of the model were examined by making a customized fine-tuned sample application in the beta version of the GPT-3 model. © 2022 IEEE.

关键词： Deep learning

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Leveraging Support Vector machine for Accurate Sentiment Analysis in Healthcare

Leveraging Support Vector Machine for Accurate Sentiment Ana...

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Disruptive Technologies (ICDT), international conference on

作者： Palvi Gupta Himani Tyagi Nidhi Gupta Aditya Dayal Tyagi Computer Science & Application Sharda University Greater Noida India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Sentiment AI leverages artificial intelligence to analyze human emotions across various fields. This research focuses on understanding patient experiences with healthcare facilities by using a Support Vector machine (SVM) algorithm in machine learning. The primary goal is to train a model to analyze patient reviews, identifying key features of each facility, such as cleanliness, doctor accessibility, and patient treatment. Implemented in Python with necessary machine learning libraries, the model can extract insights from patient responses and assess their sentiments. This study aims to assist patients in selecting suitable healthcare facilities by calculating a “goodness score” for each one, representing advancements in AI and machine learning in healthcare

关键词： Support vector machines Sentiment analysis Reviews Stars Medical treatment Medical services machine learning Transforms Feature extraction Numerical models

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The Whole Process Education Management System Based on machine learning Algorithm 1

引用

3rd EAI international conference on application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Ma, Jun Nanning University Guangxi Nanning530200 China

ISBN: (数字)9783031631399

ISBN: (纸本)9783031631382

In recent years, with the development of education and teaching in our country and the continuous deepening of new curriculum reform, the subject of ideology and politics occupies an increasingly important teaching position in the education and teaching of university in our country. How to improve the quality of education and teaching of political thought theory courses in university is very important. Therefore, in the education and teaching process of political thought theory courses in university, it is necessary to adhere to the integrity and continuity of education guidance, so as to provide high-quality political thought theory teaching for college students, and guide students to use political thought theory in real life. Only by solving problems with theoretical knowledge content can we improve the teaching quality of political thought theory courses in university. This article will focus on the establishment of an political thought theory course education system that keeps pace with the times, the establishment of a "monitoring mechanism" for moral education behaviors to do a good job in post-grant service, and the introduction of topic-based teaching into the political thought theory course education in university. From these three aspects This paper analyzes the strategies for improving the education quality of political thought theory courses in university under the whole-process education model, in order to provide constructive suggestions for the improvement of the teaching quality of political thought education in university. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Teaching

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Supervised machine learning Approach for Crack Detection in Digital Images 3rd

Supervised Machine Learning Approach for Crack Detection in ...

引用

3rd international conference on machine learning, Advances in Computing, Renewable Energy and Communication, MARC 2021

作者： Vadicherla, Deepti Gadicha, Vijay Department of Computer Science and Engineering GHRU Maharashtra Amravati India

ISBN: (纸本)9789811928277

Cracks are very common in concrete wall, railway sleeper, pavement construction, old paintings and underwater walls of dam. Crack detection is a challenging task in terms of accuracy. Manual inspection of cracks has many qualitative drawbacks. Therefore, automation in the process of crack detection is essential. machine learning has become fast growing area of research that covers many fields including image classification. This paper reviews few supervised machine learning-based crack detection methods, namely random forest, support vector machine and Naive Bayesian. These methods have been investigated for solving many challenging problems in classification. Literature in the paper presents machine learning techniques to detect cracks on the surface of images automatically. In this paper, some of the recent research articles on crack detection have been reviewed and also checked their feasibility toward the crack detection task. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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Social Media Sentiment Analysis Using machine learning 3

Social Media Sentiment Analysis Using Machine Learning

引用

3rd international conference on Disruptive Technologies, ICDT 2025

作者： Deokar, Om Rajendra Gaikwad, Tushar Raju Gawali, Kaushal Ramesh Ghanghav, Piyush Dyaneshwar Shivarkar, Sandip Ashok Muzumdar, Ajit Ashok Sanjivani College of Engineering Department of Computer Engineering Kopargaon India

ISBN: (纸本)9798331519582

In recent years, social media has emerged as a crucial platform for public expression, enabling individuals to share opinions and sentiments on a wide range of topics. This study explores the application of machine learning techniques-Support Vector machines (SVM), Decision Trees, Naive Bayes, and Random Forest for sentiment analysis of social media content. By leveraging a diverse dataset of user-generated posts, we aim to classify sentiments into positive, negative, and neutral categories. We evaluate the performance of each algorithm based on accuracy, providing a comparative analysis of their effectiveness. Our findings reveal that SVM significantly outperforms the other methods in terms of accuracy establishing it as the most effective model for sentiment classification in this domain. The superior performance of SVM is attributed to its capability to manage high-dimensional data and create optimal hyperplanes for classification. These results emphasize the critical importance of selecting appropriate machine learning models for sentiment analysis, especially in the dynamic landscape of social media. © 2025 IEEE.

关键词： Support vector machines

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Speech Disorders Classification by CNN in Phonetic E-learning System 3rd

Speech Disorders Classification by CNN in Phonetic E-Learnin...

引用

3rd international conference on Artificial Intelligence in HCI (AI-HCI) Held as Part of the 24th international conference on Human-computer Interaction (HCII)

作者： Liu, Jueting Ren, Chang Luan, Yaoxuan Li, Sicheng Xie, Tianshi Seals, Cheryl Atkins, Marisha Speights Auburn Univ Auburn AL 36849 USA Northwestern Univ Evanston IL 60208 USA

ISBN: (纸本)9783031056437;9783031056420

Speech disorders may affect the process of phonetic transcriptions. In the Automated Phonetic Transcription-the grading tool (APTgt), a linguistic E-learning system, to reduce the influence of disordered speech in the phonetic exams, we proposed a speech disorders classification-module that aims to classify disordered speech and non-disordered speech. The Mel-frequency cepstral coefficients (MFCCs) are utilized to represent the features of the speech sound files. With the two different formats of MFCCs, we adopted two approaches to classifying the MFCCs: calculating the similarity between MFCC values by dynamic time warping (DTW) algorithm and classifying the distances by support vector machine (SVM);directly image classification by the convolutional neural network (CNN). We will focus on the second approach in this paper.

关键词： E-learning Phonetic transcription Speech disorders Mel-frequency cepstral coefficients Dynamic time warping Convolutional neural network

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The application of Building Information Model in the Structural Design of Intelligent Buildings 3

The Application of Building Information Model in the Structu...

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3rd international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT2023

作者： Zhao, Huizhi Architecture and Environmental Engineering Dongying Vocational Institute Shandong Dongying257091 China

Due to the irreversibility of building structures in the construction process, unreasonable construction methods are likely to cause economic losses and great negative social impacts. Therefore, the study of scientific and efficient intelligent construction methods for building structures has become a research hotspot in recent years, mainly including intelligent building design, construction control and safety analysis methods. Therefore, it is very meaningful and necessary to carry out research on intelligent construction technology based on BIM technology. By applying BIM technology to the process of intelligent construction of structures, this paper intends to achieve six major objectives such as building performance analysis and parametric design of structures, and independently develop a BIM construction process management platform, which combines construction progress, quality, safety, cost and green construction through BIM technology for information management of projects, making it more convenient for owners and construction unit staff to software application, thus greatly improving the communication and use efficiency, and providing important guidance for the construction process control and progress control of the project. © 2023 Elsevier B.V.. All rights reserved.

关键词： Information management

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application of Intelligent Optimization Algorithms in the Distribution Network Planning and Evaluation Models 3

Application of Intelligent Optimization Algorithms in the Di...

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3rd international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT2023

作者： Du, Xin Huo, Jiali Chen, Lin Wang, Mingchang Wu, Zhaoshun Zhang, Chengliang Hangzhou Power Supply Company of State Grid Zhejiang Electric Power Company Zhejiang Hangzhou310000 China

In order to reasonably plan distribution network evaluation strategies and improve the efficiency and accuracy of power system planning, this study aims to explore the application of intelligent optimization algorithms in distribution network planning and evaluation. It includes the principle, method and model construction of intelligent optimization algorithm, and designs a distribution network planning evaluation model using Ant colony optimization algorithms combined with GIS (Geographic Information System). This article applies its example to the distribution network planning of a certain area, and the results show that compared with traditional algorithms, the intelligent optimization algorithm has a voltage value of 230.2V at the highest and 220.2V at the lowest;The highest load value is 2.75 Megawatt, and the lowest is 1.19 Megawatt;The longest running time is 8913ms, surpassing traditional algorithms in all aspects. This algorithm improves the voltage stability and load balance of the distribution network, and reduces operating time. This study has certain promotion and application value for the research and practical application of power system planning, which can improve the efficiency and accuracy of power system planning and provide scientific basis for the reliability and effectiveness of power supply. © 2023 Elsevier B.V.. All rights reserved.

关键词： Efficiency

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Fair and Private CT Contrast Agent Detection 2nd

Fair and Private CT Contrast Agent Detection

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2nd international Workshop on Fairness of AI in Medical Imaging, FAIMI 2024, and 3rd international Workshop on Ethical and Philosophical Issues in Medical Imaging, EPIMI 2024, Held in Conjunction with the international conference on Medical Image Computing and computer Assisted Interventions, MICCAI 2024

作者： Kaess, Philipp Ziller, Alexander Mantz, Lea Rueckert, Daniel Fintelmann, Florian J. Kaissis, Georgios AI in Healthcare and Medicine Technical University of Munich Munich Germany Department of Radiology Massachusetts General Hospital Boston United States Department of Diagnostic and Interventional Radiology University Medical Center of the Johannes Gutenberg University Mainz Mainz Germany Department of Computing Imperial College London London United Kingdom Institute of Machine Learning in Biomedical Imaging Helmholtz Munich Germany

ISBN: (纸本)9783031727863

Intravenous (IV) contrast agents are an established medical tool to enhance the visibility of certain structures. However, their application substantially changes the appearance of Computed Tomography (CT) images, which - if unknown - can significantly deteriorate the diagnostic performance of neural networks. Artificial Intelligence (AI) can help to detect IV contrast, reducing the need for labour-intensive and error-prone manual labeling. However, we demonstrate that automated contrast detection can lead to discrimination against demographic subgroups. Moreover, it has been shown repeatedly that AI models can leak private training data. In this work, we analyse the fairness of conventional and privacy-preserving AI models during the detection of IV contrast on CT images. Specifically, we present models which are substantially fairer compared to a previously published baseline. For better comparability, we extend existing metrics to quantify the fairness of a model on a protected attribute in a single value. We provide a model, fulfilling a strict Differential Privacy protection of (Ε,δ)=(8,2.8·10-3), which with an accuracy of 97.42% performs 5%-points better than the baseline. Additionally, while confirming prior works, that strict privacy preservation increases the discrimination against underrepresented subgroups, the proposed model is fairer than the baseline over all metrics considering race and sex as protected attributes, which extends to age for a more relaxed privacy guarantee. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Differential privacy

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Hippocampus MRI diagnosis based on deep learning in application of preliminary screening of Alzheimer's disease 3

Hippocampus MRI diagnosis based on deep learning in applicat...

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3rd international conference on Artificial Intelligence and computer Engineering, ICAICE 2022

作者： Zhang, Bingchen Yu, Wuhan Lü, Yang Yang, Zhifang Yu, Juan Fang, Xinxin Chen, Lihua State Key Laboratory of Power Transmission Equipment and System Security and New Technology College of Electrical Engineering Chongqing University Chongqing China Department of Geriatrics The First Affiliated Hospital of Chongqing Medical University Chongqing China

ISBN: (纸本)9781510663473

The morphological changes of the hippocampus in brain magnetic resonance imaging (MRI) images are of great significance for the early screening of Alzheimer's disease. Currently, in clinical practice, the diagnosis of the hippocampus is achieved manually by doctors with experience. Because the hippocampus has the characteristics of small size, complex shape, and indistinct boundary with surrounding structures, manual segmentation, and grading of the hippocampus in brain MRI is time-consuming and labor-intensive, which is susceptible to errors because of human subjective judgment. To address that, this paper proposes a hippocampal MRI diagnosis algorithm based on Faster R-CNN and Mask R-CNN. The main contributions are 1) automatic identification of hippocampus in brain MRI by Faster R-CNN neural network, 2) precisely segmenting the hippocampus and judging the atrophy level through Mask R-CNN. Case studies are performed on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database and the medical records of the First Affiliated Hospital of Chongqing Medical University. Results indicate that the proposed method achieves a good segmentation effect on the hippocampus in the coronal MRI image of the brain and accurately grades the level of hippocampal atrophy, which can better assist doctors in diagnosing Alzheimer's disease. © 2023 SPIE.

关键词： Magnetic resonance imaging

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