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检索条件"任意字段=5th International Conference on Machine Learning and Computer Application, ICMLCA 2024"

共 2979 条记录，以下是111-120 订阅

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Integrating AI into Biology Education: Classifying Edible and Poisonous Mushrooms Using machine learning 5

Integrating AI into Biology Education: Classifying Edible an...

引用

5th international conference on computer Engineering and Intelligent Control, ICCEIC 2024

作者： Huang, Yangfan Ling, Long Liu, Yaodong Nanning Cuizhu Experimental School Guangxi China Nanning No.14 Middle School Guangxi China Nanning Wuxiang NO.1 Experimental Primary Schooll Guangxi China

ISBN: (纸本)9798331507992

As artificial intelligence (AI) becomes increasingly prevalent in various domains, its integration into education offers unique opportunities to enhance learning experiences. this paper explores the application of AI, specifically machine learning, in biology education through the classification of edible and poisonous mushrooms. Leveraging a dataset of gilled mushrooms from the Agaricus and Lepiota families, this study demonstrates how AI techniques can be used to classify species based on morphological features such as cap shape, gill attachment, and color. the use of AI not only improves students' understanding of species identification and biological classification but also introduces them to key concepts in data science, such as feature selection, model training, and validation. By implementing machine learning algorithms like decision trees and support vector machines, students can engage in interactive learning experiences that merge biology with modern technological tools. the results show that integrating AI into biology curricula can foster both scientific literacy and computational thinking, offering a more comprehensive and engaging approach to science education. this paper outlines a practical framework for educators to incorporate AI in biology lessons, preparing students for future academic and career challenges in an increasingly digital world. © 2024 IEEE.

关键词： Adversarial machine learning

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X-Module: An Plug-and-Play Edge Enhancement Module Based on Connectivity Data 5

X-Module: An Plug-and-Play Edge Enhancement Module Based on ...

引用

5th international conference on computer Engineering and application (ICCEA)

作者： Cao, Hao Lin, Yizheng Wang, Jiayi Guilin Univ Elect Technol Sch Comp & Informat Secur Guilin Peoples R China

ISBN: (纸本)9798350386783;9798350386776

In the field of image segmentation, addressing the limitations of existing techniques in capturing both edge details and global information in images, this paper innovatively develops a novel image segmentation algorithm module named X-Module. this technology integrates finely tailored and reassembled feature maps, deep analysis of edge connectivity, and efficient hierarchical convolutional structures, achieving significant enhancement and refinement of edge information in images. through testing on a series of publicly available datasets using the integrated segmentation network with X-Module, the results demonstrate that this technology exhibits significant advantages over existing techniques, particularly in improving the accuracy of image segmentation and performance in edge processing, showcasing better detail recovery and object recognition capabilities.

关键词： Image Segmentation Deep learning Image Edges X-Module Plug-and-Play

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A Comparison of Stock Price Prediction Using machine learning Techniques 5

A Comparison of Stock Price Prediction Using Machine Learnin...

引用

5th international conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Shaik, Mohammed Ali Sri, Nallani Lakshmi School of Computer Science & Artificial Intelligence Sr University Telangana State Warangal India

ISBN: (纸本)9798350379945

Technological advancements have revolutionized stock market forecasting, with machine learning methods proving more accurate than traditional statistical approaches. Comparing various models, this study found that algorithms like Random Forest results in high performance in predicting Tesla's stock closing prices. these methods mitigate risks associated with investing while maximizing dividends, which are crucial for effective resource allocation and macroeconomic expansion in a country's financial market. © 2024 IEEE.

关键词： Contrastive learning

来源：评论

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Data Analysis and Visualization for Electrical Safety Tools and Equipment 5

Data Analysis and Visualization for Electrical Safety Tools ...

引用

5th international conference on computer Engineering and application (ICCEA)

作者： Xue, Liang Yang, Lei Suzhou City Univ Comp Sci & Artificial Intelligence Coll Suzhou Peoples R China

ISBN: (纸本)9798350386783;9798350386776

Electric safety tool data has the characteristics of large data volume, high dimensionality, and strong effectiveness. Traditional data analysis still has problems such as insufficient accuracy, slow processing speed, and imprecise feature selection when processing this type of data. this paper proposes a method for predicting the loss of safety tools based on classification algorithms, which provides data support for procurement by predicting the loss. In addition, a supplier scoring model based on scoring indicators was constructed to explore the potential value of the data. First, the data is characterized by construction, elimination, fusion, and reconstruction, and correlation coefficients are used for feature selection and tested on different prediction models;Secondly, a data classification model based on scoring indicators is established to form a new data dimension, including after-sales service, product quality, scale indicators, and customer feedback;Finally, visualize the data set from the perspective of the overall table and sub-tables, and the changing trends of key evaluation indicators were analyzed. this method can not only provide data for predicting the loss of electric power tools but also solve the problem of supplier evaluation and realize multi-dimensional in-depth mining and comprehensive visualization of data.

关键词： component machine learning Feature engineering Classification algorithm Data analysis Data visualization

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Analysing Depression in Social Media Data Using machine learning Techniques 5

Analysing Depression in Social Media Data Using Machine Lear...

引用

5th international conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Jayapradha, B. Velmurugan, T. Dr. Ambedkar Government Arts and Science College Pg and Research Department of Computer Science Chennai India Dwaraka Doss Govardhan Doss Vaishnav College Pg and Research Department of Computer Science Chennai India

ISBN: (纸本)9798350379945

In the present digital era, a significant volume of text data has started to emerge post-COVID with the increase in the number of people who have started using social media. the text thus generated encapsules a hidden layer of emotion with in it. this emotion hidden in the textual data conveys a lot of understanding of the people who are responsible for the creation of the text and helps to know the person better. However, extracting the emotions from the text data is quiet a challenging task as it involves a series of pre-processing pipelines and also the application of machine learning techniques to the text involved in the research for the purpose of extracting the emotion from it. the primary objective of this research work focusses on extracting depression based emotional text using some of the machine learning techniques. It also gives a comparative analysis between the machine learning techniques in terms of accuracy. this work has utilized machine learning techniques such as Naïve Bayes and Random Forest to find the emotion particularly depressions in the Kaggle data set, particularly, text-based information taken into account for the analysis. Finally, this work concludes that the performance of machine learning algorithms based on its efficiency. this information can then enable appropriate counselling interventions, preventing the person under depression from escalating into suicidal thoughts or to other mental health issues. © 2024 IEEE.

关键词： Adversarial machine learning

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application of machine learning and Data Mining Techniques for Accurate Cloud Burst Prediction 5

Application of Machine Learning and Data Mining Techniques f...

引用

5th international conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Siddique Ibrahim, S.P. Venkata Harika, N. Mohitha Reddy, N. Srija, K. Sahithi, P.Shiva Kohima, A. School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India

ISBN: (纸本)9798331518097

Cloudbursts are often characterized by intense rainfall over smaller areas, during catastrophic floods and landslides in vulnerable areas. Traditional models and meteorological departments struggle to predict the occurrences due to unstable weather variations. Approximate forecasting plays an important role in meteorology;it will influence the environment and people as it decreases the impact of cloudbursts. this study leverages the power of data mining and machine learning techniques to present the cloudburst prediction system, improving accuracy and providing early warnings. All the data contained is real time and factual. machine learning techniques like neural networks, decision trees, along with the old meteorological data and satellite imagery are used to find the events and patterns of cloudbursts in the data. Data mining is used to preprocess the data, remove inconsistencies, smooth the data to find the associative rules, which tells us which combination of data items lead to cloudbursts. © 2024 IEEE.

关键词： Adversarial machine learning

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Multi-Task Reinforcement learning with Cost-based HTN Planning 5

Multi-Task Reinforcement Learning with Cost-based HTN Planni...

引用

5th international conference on computer Engineering and application (ICCEA)

作者： Hu, Yuyong Zhuo, Hankz Hankui Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China

ISBN: (纸本)9798350386783;9798350386776

Multi-task Reinforcement learning (MT-RL) faces key challenges in accomplishing complex long-horizon tasks, particularly related to scarce rewards, inefficient sample usage, and low transferability. these challenges are exacerbated in real world scenarios where tasks can often be done by completing different intermediate subtasks, complicating intermediate reward allocation. To address those issues, we introduce a novel framework integrating a strategic planner, a pre-trained language module, and a reinforcement learning policy. this framework strategically decomposes complex tasks into observable sub-task lists using the planner, adapting the plan based on sub-task completion, while the incorporation of the pre-trained language module aids in the task list understanding. We evaluated our framework in a single-agent overcooked environment, chosen for its relevance in the long-horizon tasks. Our results demonstrate notable improvements in time efficiency and adaptability, showcasing the framework's potential to enhance MT-RL applications.

关键词： Multi-Task Reinforcement learning Planning Hierarchical Task Network Pre-trained Language Models

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Automated machine learning in Bankruptcy Prediction of Manufacturing Companies 5

Automated Machine Learning in Bankruptcy Prediction of Manuf...

引用

5th international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Papik, Mario Papikova, Lenka Comenius Univ Odbojarov 10POB 95 Bratislava 82005 Slovakia

Industry 4.0 uses artificial intelligence and machine learning algorithms to optimize processes. the main goal of the manuscript is to analyze the possibilities of applying automated machine learning to predict company bankruptcy. the data sample consists of financial data of 9,771 manufacturing companies for the years 2020 and 2021 collected from the Finstat database. Two methods of automated machine learning, AutoML and H2O, were tested. the results were compared with five other methods - linear discriminant analysis, logistic regression, naive Bayes classifier, CatBoost and XGBoost. the resulting model was cross-validated through the 10-fold approach. the best results were achieved by H2O automated machine learning algorithm with an AUC of 90.13%, followed by the gradient boosting methods CatBoost with AUC of 90.05% and XGBoost (AUC of 88.61%) and another automated learning algorithm AutoML with AUC of 81.17%. the findings of this paper indicate possibilities to apply automated machine learning methods in predicting bankruptcy. However, it is necessary to distinguish between individual automated machine learning algorithms since they provide a different range of results. (C) 2024 the Authors. Published by Elsevier B.V. this is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Industry 4.0 manufacturing companies bankruptcy models automated machine learning predictions

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TLDALRLNMT: Low-Resource Language Neural machine Translation Based on Transfer learning and Dynamic Attention Enhancement 24

TLDALRLNMT: Low-Resource Language Neural Machine Translation...

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5th international conference on computer Science and Management Technology, ICCSMT 2024

作者： Liu, Zhenqing Zhu, Rong Dai, Lingyun School of Computer Science Qufu Normal University Shandong Rizhao China

ISBN: (纸本)9798400709999

With the increasing pace of globalization, machine translation has become crucial for facilitating cross-cultural ***, translating low-resource languages remains challenging due to the scarcity of large parallel *** address this issue, an innovative approach was introduced, combining transfer learning and a dynamic attention *** method enhances translation quality by utilizing high-resource language models to transfer knowledge and by dynamically concentrating on important elements of the *** experiments with various low-resource language pairs, the proposed model demonstrated notable improvements, with BLEU score gains ranging from 1.30 to 4.44, validating the approach's effectiveness. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Dynamic attention

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Research on Network Information Security applications Based on Deep learning Algorithms 5

Research on Network Information Security Applications Based ...

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5th international conference on computer Vision, Image and Deep learning, CVIDL 2024

作者： Chen, Dan Jinan University Guangzhou Guangdong China

ISBN: (纸本)9798350373820

According to the principles of security management, intuitive scientificity, and scalability, an information security system architecture based on representation and metric deep learning algorithms was designed. Two key algorithm technologies, interactive interfaces, neural network algorithms, and convolutional operation algorithms, were used for information security prediction analysis and information security level interpretation. By optimizing the explanation mechanism, deep learning algorithm inference, and core knowledge acquisition, the security normalization management of application data was achieved. © 2024 IEEE.

关键词： learning algorithms

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