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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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Cooperative Blended learning Model Applied to the Next Generation Digital learning Environment 23

Cooperative Blended Learning Model Applied to the Next Gener...

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proceedings of the 2023 4th international conference on computer Science and Management Technology

作者： Xu Cui Shandong Urban Construction Vocational College China

ISBN: (纸本)9798400709517

the paper designs a cooperative blended learning model applied to the next generation digital learning environment which called Online learning Platform (OLP 2.0). In OLP 2.0, teachers cooperate with each other to decompose relevant knowledge information, produce meta-data information, collect teaching cases and form game maps with the support of three courses which are Data structure, Algorithm design, and Program design course group; learners are guided into the learning situation in which the knowledge sequences are set up referring to the implementation process of teaching cases; learners can learn knowledge and gain abilities with the help of game maps. three types of representative game maps which are linear maps, tree maps, and graph maps are designed to meet the demands of learning knowledge and cultivating cooperation abilities. Each map needs to be completed by at least two learners. And after several rounds of cooperation, each learner would complete all the tasks in the map and get the corresponding scores. the design and implementation of OLP 2.0 have been highly evaluated by experts and technical staff. Teachers and learners who have adopting the platform also believe that the platform is not only convenient for teaching and learning, but also plays an important role in improving learning efficiency and creative abilities of learners.

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A Chinese Patent Classification Method Based on Prompt learning 23

A Chinese Patent Classification Method Based on Prompt Learn...

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proceedings of the 4th international conference on Artificial Intelligence and computer Engineering

作者： Xu Yang Yu Bai Xinyu Zhao Xiaole Wu Yu Han HCI Research Center Shenyang Aerospace University China Global Envoy Software Company Ltd China

ISBN: (纸本)9798400708831

Patent literature is the world's largest source of information technology, and patent classification is very important for patent literature management, retrieval and protection of intellectual property rights. the automatic classification of patent literature can assist patent inventors and patent examiners to better conduct patent application and patent examination. Taking the patent literature classification as the research task, the data set of this experiment is constructed in the database of tens of millions of patents collected by a company, and a Chinese patent classification method based on prompt learning is proposed. this method uses the prompt learning strategy of BERT pre-training model to obtain the category distribution probability vector, integrates the information of BERT model to the text coding, and the use of summary information to enhance the original data. On the self-built patent text data set, the experimental results of this model reached 75.19% precision rate, 75.06% recall rate and 74.69% F1 value, which significantly improved the experimental results of several other baseline methods.

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Comparative Study of different machine learning Algorithms to Analyze Sentiments with a Case Study of Two Person's Microblogs on Twitter 22

Comparative Study of different Machine Learning Algorithms t...

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proceedings of the 4th international conference on Information Management & machine Intelligence

作者： Anil Kumar Purushottam Lal Bhari Uday Pratap Singh Vivek Saxena CS PIET Jaipur India CSE PIET India

ISBN: (纸本)9781450399937

In recent years, social media has become a powerful tool for individuals to express their opinions and attitudes. Twitter, a microblogging platform, has emerged as a prominent source of such information. Sentiment analysis, the process of extracting attitudes and opinions from text data, has become increasingly popular in the field of machine learning. this study aims to perform a comparative analysis of different machine learning algorithms for sentiment analysis, with a case study of two individuals' microblogs on Twitter. A publicly available labeled dataset from Kaggle was used for the study. the pre-processing of the tweets was performed to make them manageable for common language management strategies. the study compared the performance of Naive Bayes, logistic regression, and support vector machine algorithms for sentiment analysis on the tweets. the results showed that all three algorithms performed well in classifying the sentiments, with support vector machines providing the highest accuracy. this study highlights the potential of machine learning algorithms in analyzing sentiments on Twitter and can serve as a reference for future research in this field.

关键词： Data mining

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Exploring the Experimental Possibilities of an Intelligent Blood Pressure Prediction Scheme using IoT enabled machine learning Principles

Exploring the Experimental Possibilities of an Intelligent B...

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Intelligent Technologies (CONIT), international conference on

作者： T. thilagam S. Rajarajeswari D Shobana S. Ramkumar M. Bharathi Balamurugan Annamalai Department of Computer Science & Engineering S.A Engineering college Chennai Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Mechatronics Engineering Rajalakshmi Engineering College Chennai Department of Electronics and communication engineering Sri Eshwar College of Engineering Coimbatore Department of Electronics and Communication Engineering Jeppiaar Institute of Technology Kunnam Tamil Nadu India Department of Electrical and Electronics Engineering Chennai Institute of Technology Chennai

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Blood pressure is a severe worldwide public health issue that has an impact on every person. High blood pressure is a dangerous medical disease that can occasionally result in death and damage vital organs including the heart, brain, and kidneys. It is one of the unsolved issues in the medical field. One major risk factor for cardiovascular disease (CVD) is hypertension. It is imperative to identify these high-risk individuals in order to avoid wasting time or money on more complex, invasive, or expensive diagnostic procedures. Recent advances in machine learning may be used to partially achieve this goal. More specifically, a number of affordable, non-invasive, and readily-obtained indicator traits of high-risk individuals may serve as the foundation for the creation of a prediction model. this research presents a new machine learning model, learning based Intelligent Blood Pressure Prediction (LIBPP), and assesses its efficacy by cross-validating it against the traditional learning model, Naive Bayes Classifier (NBC). First and foremost, this research aims to improve our understanding of the importance of variables in hypertension modelling. Second, the study aims to assess the suggested approach’s predictive performance. Young and middle-aged persons who might not take regular blood pressure readings can benefit from early warnings provided by this innovative approach of forecasting systolic blood pressure. Additionally, because isolated blood pressure readings are known to fluctuate frequently, our prediction can give medical professionals an additional point of reference. Our experimental findings demonstrate the suitability of LIBPP for systolic blood pressure modelling and prediction.

关键词： Hypertension Accuracy Sociology machine learning Predictive models Naive Bayes methods Forecasting

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Blockchain-Enhanced Federated learning: A New Paradigm for Secure Distributed machine learning

Blockchain-Enhanced Federated Learning: A New Paradigm for S...

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Intelligent Technologies (CONIT), international conference on

作者： Shiva Mehta Amanveer Singh Centre for Research Impact & Outcome Chitkara University Rajpura Punjab India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Merging blockchain technology into Federated learning makes a novel approach to boost security, trust, and scalability, which are critical questions about the formation of distributed machine learning. this given industry study examines which obstacles are and possible ways of mitigating them to implement blockchain technology in such settings and attempts to solve the primary weakness of distributed learning exhibited. this is supported by our study concerning the experimental approach with an emphasis on security policy changes indicated by our results. We were able to find the risk sources $25 \%$ up, and we were able to prevent the everyday common data leaks and model poisoning risks. Also, the jump from 70 to 95 security scores shows that providers have significantly improved their cybersecurity. the integration process, however, brings along a computational overhead, which can be estimated from the $\mathbf{9 0 \%}$ performance reduction from which the system efficiency score alters from 90 to 75. the designed framework increases scaling-level issues and trust among people groups. Scalability ratings have been improved from 80 to 85. Trust metrics, however, are now $30 \%$ higher, which took the metrics from 60 to 90. these statistical data show clearly that in the pursuit of security and trust, tradeoffs may arise in the form of increased computational resources. the tut gives a good reason for using blockchain technology in the FL model. It provides a comprehensive description of both pros and cons. It proposes principles that can be followed in subsequent research to advance and strengthen the system’s resilience in conjunction with its operation. the research that is covered in this paper contributes a new framework to the volatile subject of safe and scalable machine learning. this framework is designed to elevate the trustworthiness and reliability of distributed learning systems.

关键词： Measurement computer aided instruction Federated learning Distance learning Scalability Computational modeling Data models

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A Multi-Objective Evolutionary Algorithm Based on Indicator and Performing Local Searches

A Multi-Objective Evolutionary Algorithm Based on Indicator ...

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Information Science and Education (ICISE-IE), international conference on

作者： Hanning Chen Xiangrui Zeng Kun Jia Yabao Hu Guopeng Wang School of Computer Science and technology Tiangong University Tianjin China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education Beijing China School of Artificial Intelligence Tiangong University Tianjin China Jingwei Textile Machinery Co. Ltd Beijing China College of Artificial Intelligence University of Science and Technology Tianjin China The Open University of China

the indicator-based multi-objective optimization evolutionary algorithm has gained significant attention for its strong performance across various optimization problems. We draw on the advantages and try to make improvements in the environmental selection process based on different selection criteria. From this, we developed a distinctive multi-objective evolutionary algorithm that based on indicator and performing local searches, namely INLS. the INLS algorithm includes a novel selection mechanism that does not rely on weight vectors, which employs a strategy choosing solutions with relatively poor convergence but good diversity to perturb the decision variables of the selected solutions, aiming to explore and discover solutions that demonstrate enhanced convergence and diversity characteristics. the experimental results obtained from conducting tests on the ZDT and DTLZ test suites provide clear evidence of the remarkable performance exhibited by the proposed INLS in comparison to various other well-known MOEAs. these results serve to underscore the competitiveness of the proposed method.

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Coffee Bean High Accuracy Classification with eXplainable Artificial Intelligence 23

Coffee Bean High Accuracy Classification with eXplainable Ar...

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proceedings of the 2023 4th international conference on Big Data Economy and Information Management

作者： Jiaqi Liu Dept. of Computer Science Hong Kong Baptist University China

ISBN: (纸本)9798400716669

With the development of exploration and trade, the coffee industry blossomed, and coffee gradually became an essential social drink for its unique taste and captivating aroma. Among various industrial coffee-producing processes, coffee bean classification is of great importance, which involves classifying beans into different qualities as multiple kinds of beans contain distinct characteristics, such as fermentation, sucrose, and sour degrees, influencing coffee beverage quality control, price setting, and even consumers` health. To the best of our knowledge, a prevailing approach for classification is machine learning based on neural networks. Unfortunately, even though high accuracy could be accomplished, it is hard to convey convincing results to researchers or industry personnel due to the ‘black-box’ property of the neural network, which indicates that we have little information about the inner mechanism in the neural network except for its inputs and outputs, jeopardizing the transparency and reliability of the neural network. Excellent interpretability is meaningful for retailers and consumers to know why the outputs are reliable. To begin with, in this paper, we will stress the importance of the high-accuracy classified coffee bean. Secondly, we will introduce the deep neural network technique to improve its high-accuracy classification. Last, we will demonstrate the XAI technique (GradCAM, etc.) to enhance explainability and interpretability. We improved the interpretability of the heatmap results produced by GradCAM and GradCAM++, and we also found out that they are presented differently. As XAI approaches, the versatile GradCAM provides high-resolution heatmaps to assist in understanding the regions of pictures that contribute much to the classification results, and the GradCAM++ produces much more precise results on accurately locating the contributing regions, including a better presentation of the unique distinct features of each class.

关键词： Coffee Bean GradCAM GradCAM++ XAI classification deep learning heatmap machine learning

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Stony Coral Species Recognition System Using Deep learning

Stony Coral Species Recognition System Using Deep Learning

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international conference on Artificial Intelligence and Data Sciences (AiDAS)

作者： Nur Nadhirah Rusli Itaza Afiani Mohtar Computer Science Studies College of Computing Informatics and Mathematics Universiti Teknologi MARA Perak Malaysia

Existing applications for identifying coral species are difficult to find as compared to other artificial intelligence applications, for example, plant recognition applications. People usually recognize coral species by rough observation and identify the information by themselves. It is difficult to recognize the coral species because each species has a wide range of colors, textures, shapes and are very similar to each other, making it rather hard to differentiate unless by trained eyesight. therefore, a web application system utilizing Convolutional Neural Network (CNN), to identify the name, family name and other related information of a stony coral's species is proposed. 10 Stony coral species were selected for this project. A total of 3831 images were split to train (80%), test (10%) and validate (10%) the model. the model was able to achieve training accuracy of 99%, validation accuracy of 97% and testing accuracy of 91.9% after 50 epochs. the precision, recall and F1 score achieved were 0.88, 0.96, and 0.92 respectively. As a conclusion, the model can classify the stony coral species. this project can be integrated with other platforms in the future. such as mobile applications.

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Personalized Book Recommendations for Adults Using Deep learning and Sophisticated Filtering Approaches 15th

Personalized Book Recommendations for Adults Using Deep Lear...

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15th international Joint conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2023

作者： Ng, Yiu-Kai Computer Science Department Brigham Young University ProvoUT3361 TMCB United States

ISBN: (纸本)9783031875687

Reading improves the reader’s vocabulary and knowledge of the world. It can open minds to different ideas which may challenge the reader to view things in a different light. Reading books benefits both physical and mental health of the reader, and those benefits can last a lifetime. It begins in early childhood and continue through the senior years. A good book should make the reader curious to learn more, and excited to share with others. For some readers, their reluctance to read is due to competing interests such as sports. For others, it is because reading is difficult and they associate it with frustration and strain. A lack of imagination can turn reading into a rather boring activity. In order to encourage adults to read, we propose an elegant book recommender for adults based on either the Vector Space model or a machine-learning model, both are content-based approaches without utilizing the actual content of a book, which is often unavailable due to the copyright constraint. the Vector Space model is simply an algebraic model for representing text documents as vectors of identifiers, whereas the machine-learning model solely relies on a trained model using a pre-defined labeled item set. With that, we offer the user the choice between an algebraic model or a machine learning model to be considered. In order to analyze the performance of the proposed book recommender, BkRec, we have included in the empirical study an online evaluation to verify the novelty of BkRec in recommending books to adults, besides conducting an offline evaluation. the online evaluation determines whether recommended book are perceived as preferable by ordinary users, i.e., adults, which offers another perspective on the performance of the recommender. this evaluation is based on real users’ assessments of BkRec which goes beyond the offline performance analysis conducted. In the online performance evaluation, we determine the ideal number of appraisers and test cases extracted from t

关键词： Algebra

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Interpretable Deep learning for Diagnosis of Maize Streak Disease

Interpretable Deep Learning for Diagnosis of Maize Streak Di...

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Advancements of Artificial Intelligence in African Context (AAIAC), international conference on the

作者： Micheal Francis Kalyango Kyebambe Moses Ntanda Department Of Computer Science Makerere University Kampala Uganda Department Of Networks Makerere University Kampala Uganda

Maize Streak Disease (MSD) caused by the maize streak virus remains a significant agricultural challenge during the 4th industrial revolutions, as it poses a substantial threat to global maize crops, resulting in severe yield reductions. Effective and timely identification of the disease is pivotal in curbing its propagation and enacting appropriate control strategies. the emergence of the 5th industrial revolutions has witnessed the integration of advanced technologies into various sectors, with deep learning methodologies standing out for their prowess in diverse domains, including disease detection. Nonetheless, the inherent complexity of deep learning models hinders visibility into their transparency, preventing users from comprehending the underlying features and decision-making processes. this opacity often leads to reduced user trust, even when the models demonstrate high accuracy *** this research, we introduce an interpretable machine learning framework through explainable AI (XAI) that harnesses the capabilities of deep learning, specifically Convolutional Neural Networks (CNNs), for the identification of the Maize Streak Disease. this framework not only exploits the potential of deep learning for precise disease classification but also integrates techniques for model interpretability. the proposed methodology capitalizes on CNN architectures to extract discerning features from an extensive dataset of MSD-affected leaf images. the trained CNN model is then coupled with interpretable techniques such as SHAP and *** findings highlight that this easy-to-understand deep learning approach is very effective in correctly diagnosing the Maize Streak Disease. the TensorFlow-designed model achieves a commendable accuracy of 96%.In essence, this study presents a novel interpretable machine learning framework that seamlessly integrates deep learning with interpretability methods to tackle the challenge of Maize Streak Disease. Beyond achieving exceptio

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