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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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Enhancing Online Teaching Effectiveness through Hand-Raising Detection 4

Enhancing Online Teaching Effectiveness Through Hand-Raising...

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4th international conference on Information Science and Education, ICISE-IE 2023

作者： Zhu, Xiaoke Dong, Zhiwei Chen, Xiaopan Shi, Yufeng Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China

ISBN: (纸本)9798350394610

After the outbreak of the COVID-19 pandemic, online teaching has gradually emerged as an indispensable component of education. Despite its convenience, online education lacks the immediate interactive experience inherent in traditional classrooms. this paper presents a meticulously designed DS-YOLOv8 network model, specifically tailored for real-time recognition of students raising their hands under cameras. On our proprietary dataset, this network model exhibits exceptional accuracy in hand-raising detection, showcasing a 1.3% improvement in the AP50 index compared to the baseline YOLOv8 model. Our focus lies on the application of hand-raising detection within online classrooms with an objective to provide enhanced support for teachers by promptly identifying students raising their hands and thereby improving teaching efficiency while enriching the interactive experience of students in virtual learning environments. © 2023 IEEE.

关键词： Students

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machine Vision-Based Structural Diagnosis application 4th

Machine Vision-Based Structural Diagnosis Application

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4th international conference on Advances in Civil and Ecological Engineering Research, ACEER 2022

作者： Hong, Weixing Noori, Mohammad Jiang, Hanqing Liu, Yuhan Altabey, Wael A. Nanjing Zhixing Information Technology Co. Ltd. Jiangsu Nanjing China Jiangsu Advance Transportation Institute Jiangsu Nanjing China Department of Mechanical Engineering California Polytechnic State University San Luis ObispoCA93405 United States Department of Mechanical Engineering Faculty of Engineering Alexandria University Alexandria21544 Egypt

ISBN: (纸本)9789811957826

the wide development in deep learning (DL) application in different fields such as speech recognition, computer vision, and natural language processing has been done by different architectures of networks, such as the most applied network named convolutional neural networks (CNN). this work discusses the framework for monitoring the structural surface cracks based on DL. the proposed model is combined with unmanned aerial vehicles (UAVs) to collect the surface cracks images automatically to detect the cracks in real-time. Equipped with a comparatively heterogeneous dataset, the use of DL allows the crack detection development system. In this proposed work, the results dataset has a high accuracy rate (P%), regression rate (R%), and F-score (F%), and the overall performance is $$95.5{\text{\% }},{ }93.78{\text{\% }}$$, and $$91.55{\text{\% }}$$, respectively. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer vision

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Predicting High-Risk Areas for Crime Hotspot Using Hybrid KNN machine learning Framework 5

Predicting High-Risk Areas for Crime Hotspot Using Hybrid KN...

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5th international conference on Inventive Research in Computing applications, ICIRCA 2023

作者： Vinothkumar, K. Ranjith Kumar, S. Vikram Raj, R. Mekala, N. Sasirekha, S.P. Reshma, R. Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350321425

this research presents the application of the K-Nearest Neighbour (KNN) machine learning algorithm for predicting crime hotspots. the KNN algorithm is a supervised learning method that uses distance between data points to forecast outcomes. the system was used to find trends and forecast potential hotspots after being trained on previous crime data. the study also takes into account social and environmental elements that influence the frequency of crimes in hotspots. the study's findings give insight into the fundamental causes of criminal activity and show that the KNN algorithm may be successfully used to identify crime hotspots. In order to lower crime rates in high-risk locations, law enforcement agencies and urban planners can benefit from this research in establishing focused crime prevention efforts. However, the quality of the data and the algorithm's capacity to recognize pertinent patterns both affect how accurate the forecasts turn out to be. Exploring the potential and restrictions of this method to crime prediction will require more research. In general, using the KNN algorithm to anticipate crime hotspots has shown promise and has the potential to help create efficient crime prevention tactics. © 2023 IEEE.

关键词： Forecasting

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Crop Yield Prediction Using machine learning Algorithms 4th

Crop Yield Prediction Using Machine Learning Algorithms

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4th international conference on computer and Communication Technologies, IC3T 2022

作者： Rama Devi, Boddu Ragam, Prashanth Godishala, Sruthi Priya Gandham, Venkat Sai Kedari Nath Panuganti, Ganesh Annavajjula, Sharvani Sharma Department of ECE Kakatiya Instistute of Technology and Science Telangana Warangal506015 India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Vijayawada522237 India

ISBN: (纸本)9789811985621

Agriculture is the most crucial aspect in ensuring survival. Climate and other environmental changes have become a significant threat to agriculture. Estimating the crop yield before the harvest would assist farmers in choosing marketing and storage strategies. machine learning algorithms are used for developing practical and efficient solutions to predict the yield. Historical data, such as rainfall, temperature, fertilizer, and past crop yield data, are used to predict crop yield. this paper focuses mostly on estimating yield by utilizing a variety of machine learning methods. the models utilized here are ensemble XGBoost-RF, gradient boosting, random forest, and XGBoost out of which ensemble XGBoost-RF showed maximum accuracy with the R2 of 0.976111 and MSE of 0.002163. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Digital storage

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SBSI 2023 - proceedings of the 19th Brazilian Symposium on Information Systems: "Green Information Systems for Environmental Sustainability"

SBSI 2023 - Proceedings of the 19th Brazilian Symposium on I...

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19th Brazilian Symposium on Information Systems: Green Information Systems for Environmental Sustainability, SBSI 2023

ISBN: (纸本)9798400707599

the proceedings contain 59 papers. the topics discussed include: 4Experts: a task recommendation approach using machine learning and biometric data;machine learning applied to the classification of technical inspection recommendations regarding the trend to increase criticality;characterizing toolkits for platform independent chatbot development;Assis: online semi-automatic dialog annotation tool;using machine learning to classify process model elements for process infrastructure analysis;an approach based on machine learning for predicting software design problems;the use of users’ personal data to improve online retail services: an analysis from a systematic literature review;towards a blockchain-based API to ensure data interoperability and transparency in the registration and inspection processes of Brazilian water dams;inference of properties of a natural gas processing plant through the application of machine learning to time series;and a score approach to identify the risk of students dropout: an experiment with information systems course.

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Almond Leaf Diagnostics: Federated learning and CNN for Accurate Disease Prediction 4

Almond Leaf Diagnostics: Federated Learning and CNN for Accu...

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4th IEEE international conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

作者： Sharma, Vikrant Kukreja, Vinay Mehta, Shiva Joshi, Kireet Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun India Chitkara University Institute Of Engineering And Technology Chitkara University Punjab India Graphic Era Deemed To Be University Computer Science And Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350384628

this study dives into the novel use of federated learning with convolutional neural networks (CNN) for the diagnosis of illnesses in almond leaves. this paper examines five types of diseases using data from five different customers. In the investigation, initially trained separate models using the local data of each client. Performance metrics, including accuracy, recall, and F1-scores, varied widely amongst illness classes in these locally modelled findings. Client op-1, for example, had an impressively high average accuracy of 97%, with precision ranging from 91.81% to 95.33.3% and recall from 91.52% to 94.05.0% across all classes. Client op-2 performed between moderately well (71.13% and 83.03%) and poorly (84.50% and 90.0%) on accuracy and recall, respectively. Clients op-3, op-4, and op-5 also underwent similar studies but with variable degrees of model success. Using federated averaging, to were able to aggregate the knowledge from all of the clients' local models into one complete global one. this method united the disparate bodies of information at the regional level. the estimated averages after the federation demonstrated the efficacy of this strategy. For instance, the macro average scores after federation ranged from 79.55% (op-2) to 96.33% (op-4), the weighted averages from 79.70% (op-2) to 96.33% (op-4), and the micro averages from 79.62% (op-2) to 96.33% (op-4). this shows that model performance got a lot better, especially for clients with lower metrics to begin with. In conclusion, it shows that federated learning mixed with CNN has promise for detecting agricultural illnesses, in the case of those affecting almond leaves. © 2023 IEEE.

关键词： Data privacy

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application of Novel machine learning Methods in the Analysis of Safety and Efficiency of Stem Cell Treatment 4

Application of Novel Machine Learning Methods in the Analysi...

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4th international conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Vaithiyanathan, M. Bala, K. Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Tamil Nadu Chennai600073 India School of Computing Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Tamil Nadu Chennai600073 India

ISBN: (纸本)9798331508845

the implementation of ML in basic biomedical research has been instrumental for yielding novel insights into biological processes and confirming multiple biological processes. this trend is gradually being experienced in stem cell research especially in the characterization of stem cells and differentiation processes. As we proceed further, it becomes necessary to use computational approaches to assess the biosafety and bio efficacy of stem cells for clinical applications, mainly with respect to the highly important issue of tumorigenicity in stem cell therapy. Stem cells are very sensitive to the conditions used to initiate tumorigenicity as this is impacted on by many factors not reflected well in conventional laboratories. Furthermore, since tumorigenicity is shaped by numerous factors it is unlikely that there is a single method suitable for evaluation of these risks;such an approach might compromise safety and efficacy of stem cell therapies. As the availability and scopes of biological datasets have grown beyond genomics and expanded to include phenotype and function, together with the recent developments in informatics and artificial intelligence, there is potential for identifying safety and efficacy of stem cell therapies. In this regard, several ML methodologies, and we put forward standardized processes to enable stem cell studies. With regards to area 3, the following is the focus: Our overall principle is to balance the opportunities of these methods for assessing biosafety and bio efficacy risks. the purpose is to build accurate decision support systems to assist clinicians in deriving informed decisions about stem cell treatments that ultimately improve patient care and safety. Using ML, we propose an approach to stem cell therapy to handle enhanced difficulties in risk assessment in clinical contexts. © 2024 IEEE.

关键词： Tumors

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A Comparative Review of GPT-4's applications in Medicine and High Decision Making 4

A Comparative Review of GPT-4's Applications in Medicine and...

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4th international conference on Computing, Networking, Telecommunications and Engineering Sciences applications, CoNTESA 2023

作者： Bitri, Rea Ali, Maaruf Jena Germany Universiteti Metropolitan Tirana Department of Computer Science Tirana Albania

ISBN: (纸本)9798350382754

this paper provides a comprehensive assessment of GPT-4's (Generative Pre-trained Transformer - 4) application in the domain of medicine, highlighting its advancements compared to previous models. It delves into the model's potential, limitations and significant experimental findings - offering a thorough perspective on its rôle in transforming the medical field. Emphasizing the implications of GPT-4's integration in critical decision-making contexts, the study explores the pivotal rôle of prompt engineering in extracting precise and valuable outputs from extensive language models like GPT-4. By examining prompt design intricacies, the research sheds light on its far-reaching consequences and its direct influence on enhancing GPT-4's effectiveness, especially its rôle as a medical assistant. © 2023 IEEE.

关键词： Decision making

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Finding Optimal Monte-Carlo Run Numbers for Stochastic Data under Gaussian Distribution 4

Finding Optimal Monte-Carlo Run Numbers for Stochastic Data ...

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4th international Informatics and Software Engineering conference, IISEC 2023

作者： Erkus, Ekin Can Yildiz, Alper Aciksoz, Sevim Huawei Turkey RandD Center Intelligent Application DC Dept. Istanbul Turkey

ISBN: (纸本)9798350318036

this study investigates the optimal number of Monte Carlo runs under Gaussian conditions to enhance the efficiency of stochastic model assessments. the research involves synthetic data generation with varying lengths and error rates. the mean optimal run numbers are computed and are found to decrease as the data length increases, indicating larger datasets require fewer runs for convergence. Conversely, as the error rate decreases, more runs are needed for reliable convergence. To understand this relationship, exponential regression models are fitted for each error rate, providing a predictive tool for estimating optimal run numbers based on specific error rates and data lengths. the models exhibit an exponential decay, emphasizing the diminishing returns of additional Monte Carlo runs. Findings offer insights into stochastic model dynamics, particularly in recommender systems, machine learning, and AI. Regression models that have been fitted enable us to easily find the ideal number of runs before starting the Monte Carlo simulations and hence reduce the computational times to reach convergence. the utility of stochastic models across dynamic domains is shaped by this study's advancement of our understanding and mapping of future research directions in the context of evolving computational techniques and AI applications. © 2023 IEEE.

关键词： Stochastic models

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Corrosion Prediction of Iron-based Metals by the Prophet Model 4

Corrosion Prediction of Iron-based Metals by the Prophet Mod...

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4th international conference on Artificial Intelligence and Electromechanical Automation, AIEA 2023

作者： Shi, Quan Wu, Xuan Shen, Lin Li, Xueqin Research Laboratory of Waste Utilization and Environmental Equipment Development Guangxi University of Science and Technology Liuzhou545006 China School of Mechanical and Automotive Engineering Guangxi University of Science and Technology Liuzhou545006 China

ISBN: (纸本)9781510666412

this paper proposes the Prophet model for corrosion prediction of iron-based materials. the corrosive degree of iron-based materials under different conditions is assessed though the weight loss method and the experimental results are fed to the proposed model as the training data. It indicated that HT200, Q345B, and 45 steel are easily and rapidly corroded in the periodic immersion condition. the predicted results from the Prophet model are compared with the experimental results to evaluate the performance of the prediction model. the highest RMSE is 2.35×10-4 and the largest MAPE is 2.90×10-4 %. that means the proposed model is a reliable machine learning model for corrosion prediction of iron-based materials. © 2023 SPIE.

关键词： machine learning

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