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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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Public Perception and Sentiment of ChatGPT: machine learning Analysis on Weibo Posts 3

Public Perception and Sentiment of ChatGPT: Machine Learning...

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3rd international conference on Electronic Information Engineering and computer, EIECT 2023

作者： Liao, Fei Wuhan Business University School of Foreign Languages Wuhan China

ISBN: (纸本)9798350357707

ChatGPT, a large, pre-trained language model, which is developed by OpenAI, has exerted a great influence on society in all aspects since its launch. Knowing the adopters' perspectives, preferences and sentiment is of critical significance for a technological company to promote and improve its products. Such social media as YouTube and Weibo, offer a convenient and time-saving channel for opinion mining. In this paper, 67,985 Weibo posts ranging from December 1, 2022 to September 30, 2023 were analyzed to explore the main topics and sentiment of users by employing machine learning techniques, including latent Dirichlet allocation (LDA) topic modeling algorithm and sentiment analysis. The results suggest that more than half of the adopters expressed a positive attitude towards all topics. However, a small portion of users have expressed worry and concern about the impact of ChatGPT on education and career compared to the other two topics, the development of the AI industry and the introduction of ChatGPT. This paper, while further proving the applicability of social media to explore the public's opinion toward a new technology, offers insights into and contributes to the development and popularity of ChatGPT by figuring out Chinese users' perspectives and sentiment. © 2023 IEEE.

关键词： Sentiment analysis

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Deep learning-Based Automatic Speaker Recognition Using Self-Organized Feature Mapping 3rd

Deep Learning-Based Automatic Speaker Recognition Using Self...

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3rd international conference on computer Vision, High Performance Computing, Smart Devices, and Networks, CHSN 2022

作者： Preethi, K. Prasad, C.V.P.R. Telangana State Hyderabad India

ISBN: (纸本)9789819966899

Automatic speaker recognition (ASR) plays the major role in many applications including forensics, dictionary learning, voice verification, biometric systems, and so on. The performance of these application depends on efficiency of ASR system. However, the conventional ASR systems were developed using standard machine learning algorithms, which resulted in low recognition performance. Therefore, this work is focused on development of deep learning-based ASR system. Initially, voice features are extracted using Mel-frequency cepstral coefficients (MFCC), which analyzed the spectral properties of various voice samples. Then, self-organized feature map (SOFM) is applied to reduce the number of available features, which selects the best features using Euclidian similarity between features. Further, deep learning convolutional neural network (DLCNN) model is used to train the features and forms the feature database. Finally, a test voice sample is applied to the trained DLCNN model, which recognizes the speaker detail. The simulations carried out on Anaconda (TensorFlow) showed that the proposed ASRNet system resulted in superior recognition performance as compared to conventional systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolution

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3rd international conference on Innovations in Computational Intelligence and computer Vision, ICICV 2022

3rd International Conference on Innovations in Computational...

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3rd international conference on Innovations in Computational Intelligence and computer Vision, ICICV 2022

ISBN: (纸本)9789819926015

The proceedings contain 57 papers. The special focus in this conference is on Innovations in Computational Intelligence and computer Vision. The topics include: Attention Deficit Hyperactivity Disorder Prediction Using Resting-State Networks;Neuroinformatics Deep learning Synthesizer Based on Impulse Control Disorder Using LSTM Cells;Wasserstein GANs-Enabled Spectral Normalization on Credit Card Fraud Detection;Classification of Bipolar Disorder Using Deep learning Models on fMRI Data;Predicting Schizophrenia from fMRI Using Deep learning;patrolling Robot with Facial Detection;a Graph-Based Relook Beyond Metadata for Music Recommendation;an Interpretability Assisted Empirical Study of Affective Traits in Visual Content of Disinformation;multi-scale Fusion-Based Object Detection Network for Advance Driver Assistance Systems;secured Face Recognition System Based on Blockchain with machine learning;Vectorization of Python Programs Using Recursive LSTM Autoencoders;phenology Detection for Croplands Using Sentinel-2 and computer Vision Techniques;deep learning-Based Safety Assurance of Construction Workers: Real-Time Safety Kit Detection;Automatic Detection and Classification of Melanoma Using the Combination of CNN and SVM;CyINSAT: Cyclone Dataset from Indian National Satellite for Forecasting;classification of Ocular Diseases: A Vision Transformer-Based Approach;Analyzing Performance of Masked R-CNN Under the Influence of Distortions;COVID-19 Detection in Chest X-Ray Images Using Non-iterative Deterministic learning Classifier;multimodal Classification via Visual and Lingual Feature Layer Fusion;a Review on Rural Women’s Entrepreneurship Using machine learning Models;classifying Paintings/Artworks Using Deep learning Techniques;handling Class Imbalance Problem Using Feature Selection Techniques: A Review;an Intelligent Human Pose Recommendation System Using Feature Fusion Technique;ensemble machine learning Algorithms for Predicting Cardiovascular Disease;AI-Based Open

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Determining the Course Interests of Gifted Students using machine learning 3

Determining the Course Interests of Gifted Students using Ma...

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3rd international Informatics and Software Engineering conference, IISEC 2022

作者： Arapoglu, Ozkan Ciftci, Rohat Tuna International Computer Institute Ege University Izmir Turkey Konak Sehit Omer Halisdemir Science and Art Center Izmir Turkey

ISBN: (数字)9781665459952

ISBN: (纸本)9781665459952

The Science and Art Centers in Turkiye provides special education to the gifted students according to their interests. In this paper, we developed a machine learning model to determine the course interest of the gifted students. We worked with 100 gifted students based on 10 different courses. The results obtained by using machine learning algorithms are compared with each other for each course as well as the classical test theory. The accuracy of our model is 1.74 times better than the classical test theory © 2022 IEEE.

关键词： Students

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Analysis of Crop Yield Prediction Using machine learning Algorithm 3rd

Analysis of Crop Yield Prediction Using Machine Learning Alg...

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3rd international conference on Communication, Networks and Computing, CNC 2022

作者： Shrivastava, Chanchal Garg, Neha Garg, Sonali Khan, Asif Kumar, Akash India

ISBN: (纸本)9783031431449

We are all aware of how significant Indian agriculture is. In this study, yield estimates are provided for nearly all of the Indian crops. By using straightforward factors like temperature, humidity, season, weather, and location to produce a book, the user may predict the agricultural output in any given year. The yield is predicted by the article using sophisticated regression machine learning methods. A crucial role is played by machine learning (ML), which offers a tool for predicting crop yields. This study examines various machine learning (ML) algorithms used to estimate agricultural yields and offers a complete analysis of the approaches’ accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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A Statistical Review on machine learning Based Medical Diagnostic Systems for Chronic Kidney Disease 3

A Statistical Review on Machine Learning Based Medical Diagn...

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3rd international conference on Computation, Automation and Knowledge Management, ICCAKM 2022

作者： Sharma, Arvind Singh, Dalwinder Lovely Professional University Phagwara Department of Computer Application Punjab India Lovely Professional University Phagwara Department of Computer Science and Engineering Punjab India

ISBN: (数字)9781665453196

ISBN: (纸本)9781665453196

The decision making is a very challenging and complex process while doing the diagnosis of any specific illness. Huge ambiguities and problems are faced in the process of diagnosing by using the human visual system, which leads to conclusions that are inappropriate and unsuitable for a chronic kidney disease diagnosis. The employment of artificial intelligence approaches can overcome these limitations as these techniques help an expert doctor to offer precise and high-quality care to the patent suffering from CKD. Numerous publications enlighten the usage of machine learning methodologies along with its benefits, and the number of publications is elevated day by day. The primary focus of this study is to statistically review the various proposed and developed medical diagnostic systems for the diagnosis of chronic kidney disease using machine learning algorithms. The conducted review is categorized based on some criteria such as the objective of research work, its publication year, research gap and results obtained by researchers. © 2022 IEEE.

关键词： Expert systems

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3rd international conference on computer, Big Data and Artificial Intelligence, ICCBDAI 2022

3rd International conference on Computer, Big Data and Artif...

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3rd international conference on computer, Big Data and Artificial Intelligence, ICCBDAI 2022

The proceedings contain 68 papers. The topics discussed include: multiclass support vector data description in extreme learning machine feature space;wheeled robot visual odometer based on two-dimensional iterative closest point algorithm;document-level relation extraction based on heterogeneous graph convolutional network and local semantic fusion;salient object detection from unlabeled images;nested deep feature attention module for underwater image enhancement;document-level event extraction - a survey of methods and applications;novel robust span-based model for event detection;densely stacked rebar counting based on image semantic segmentation in wide scene;UAV swarm cooperative situation awareness theory model construction and scenario analysis;object detection from point clouds for driverless vehicles;simple and effective complementary label learning based on mean square error loss;and optical image change detection method based on depth convolution difference map fusion.

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Research Trends in Perceived Behavioral Control from a Bibliometric Perspective 3

Research Trends in Perceived Behavioral Control from a Bibli...

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3rd international conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Purnama, Suryari Sunarjo, Richard Andre Rakhmansyah, Mohamad Rizky, Agung Esa Unggul University Faculty of Economies and Business Tangerang Indonesia Universitas of Raharja Faculty of Economies and Business Tangerang Indonesia University of Raharja Faculty Sains of Technology Tangerang Indonesia Pandawan Sejahtera Indonesia Faculty of Digital Business Tangerang Indonesia

ISBN: (纸本)9798350367492

Perceived Behavioral Control (PBC), a central element of the Theory of Planned Behavior, is essential for understanding and predicting human behavior across diverse fields such as psychology, business, and education. PBC refers to an individual's perceived ease or difficulty in executing a particular behavior. This study employs the Dimensions database and advanced bibliometric tools, leveraging artificial intelligence and machine learning algorithms to investigate the global influence and utilization of PBC. The analysis identifies key trends in PBC research, leading contributors, prominent institutions, and its interdisciplinary scope, particularly focusing on the intersection of PBC with technology adoption and human-computer interaction. Data from 68 countries highlight major contributions from China and Indonesia. Noteworthy institutions include Universiti Malaysia Kelantan, University of Malaya, and Binus University, with influential researchers such as Meng Bo and Arkorful. Leading journals such as Sustainability and the international Journal of Environmental Research and Public Health reflect PBC's interdisciplinary significance, particularly in the context of technological advancements and digital behavior analysis. Key-word co-occurrence analysis reveals PBC's frequent associations with attitudes, intentions, and subjective norms, which are crucial to the Theory of Planned Behavior and its application in technology acceptance models. This study underscores PBC's global significance in behavior prediction, showcasing its widespread application and the international collaboration fueling current research. By incorporating advanced data analytics and machine learning techniques, this research lays the groundwork for future studies to delve deeper into PBC's implications and applications in the realm of technology and digital interactions, making significant contributions to the IEEE community. © 2024 IEEE.

关键词： international cooperation

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A preprocessing study of serum mass spectrometry data using machine learning as a base 3

A preprocessing study of serum mass spectrometry data using ...

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3rd international conference on Electronic Information Engineering, Big Data, and computer Technology, EIBDCT 2024

作者： Han, Jiaxin Dong, Ruimin Liang, Jinjin School of Computer Science Xi'an Shiyou University China Xi'an710065 China School of Science Xi'an Shiyou University China Xi'an710065 China

ISBN: (纸本)9781510680449

Mass spectrometry data preprocessing is the most basic and important part of mass spectrometry data application. Aiming at the problems of high dimension of serum mass spectrometry data, an efficient and usable preprocessing method is proposed which deals with the inconsistency of dimensions in each group, non-normalization, and non-unique attribute columns. Firstly, the method of rounding down the Mass value is used to determine the attribute column name;Secondly, four ways are used to determine the unique value for the problem that the existence of the same Mass term after rounding down the Mass value;then four ways of filling the missing values are done, and then the transposition of the overall data is carried out. Since four treatments are done for both the same Mass term and missing values, then 16 permutations are available, with a total of 16 new datasets;Finally, three machine learning methods are used to demonstrate the performances of different data preprocessing approaches on the datasets, which are SVM(linear), Random Forest and Logistic Regression. The numerical results show that the accuracies of classification of the three classifiers respectively reach 0.8296, 0.8906, and 0.8088, which validate that the preprocessing algorithms proposed in this paper can efficiently process the raw mass spectrometry data into valid and normalized data © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Mass spectrometry

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Architecture Design of Intelligent Education and Teaching System Based on machine learning Algorithm 3rd

Architecture Design of Intelligent Education and Teaching Sy...

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

作者： Zhu, Libo Ma, Xin Liang, Xinlong Zhang, Jun Zhou, Yufei Zibo Normal College Shandong Zibo255100 China Yunnan University of Business Management Yunnan Anning650300 China

ISBN: (纸本)9783031631382

With the great success of machine learning (ML) algorithms represented by deep learning in vision, speech recognition and other fields, while looking forward to and promoting the large-scale educational application of artificial intelligence (AI), data center network is an important infrastructure supporting big data and cloud computing platforms, which is widely used in data-intensive and large-scale modular parallel computing tasks. AI has entered an unprecedented period of rapid adoption and stability. At the beginning of architecture design, the traditional distributed big data processing system didn't optimize the ML task, but ensured the training convergence efficiency, improved the iterative computing speed and improved the model quality. The application of AI education is limited by the common sense and symbolic grounding and transfer of AI technology. Based on the above analysis, this paper aims to solve the efficiency bottleneck of distributed system in dealing with ML tasks, and design and implement a high-performance distributed ML system for heterogeneous environment in data centers, so as to promote the intelligent development of education and teaching by applying heuristic AI teaching ideas. Overall, the degree of automation that AI technology can achieve in education is still very limited. It is more realistic to strengthen teacher design than to replace it. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Efficiency

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