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Research on Hemorrhagic Stroke Based on K-medoids Cluster Analysis and data mining 4

Research on Hemorrhagic Stroke Based on K-medoids Cluster An...

引用

4th international Signal Processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Sun, Chongjing Yang, Zhongji Gao, Mengqi Ma, Shenao China University of Geosciences Beijing Beijing China

ISBN: (纸本)9798331528676

data mining is an effective computer-aided analysis technology, which can uncover hidden information from large data sets and provide basis for decision-making. At the same time, clustering analysis, as an important branch of data mining, is also widely used in various fields, among which the k-medoids clustering algorithm has better robustness and anti-noise ability than the traditional k-means algorithm. In order to expand the value of these two computer analysis techniques in medical applications, this paper attempts to combine k-medoids cluster analysis and data mining methods to apply them to the analysis and research of clinical hematoma. Specifically, this research first used feedforward neural network to construct a simulation of edema volume changes, which laid a foundation for subsequent cluster analysis. then, the k-medoids algorithm was used to divide the different edema patients into three clusters. On this basis, the therapeutic effect of multiple treatment modalities on link edema was analyzed by controlling for a single variable. Finally, Pearson correlation analysis was used to explore the treatment options most related to the volume of hematoma. In general, this research successfully applied the data mining technique and k-medoids clustering algorithm to the field of clinical medicine, which provides a useful reference for the application of more computer analysis methods in the field of medical and health care. In the future, we can further explore how to integrate more machine learning algorithms into medical data analysis, to provide more intelligent support for clinical diagnosis and treatment. ©2024 IEEE.

关键词： Cluster analysis

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Application of Virtualization Digital Technology in Intelligent Platform Construction pattern Analysis System 4

Application of Virtualization Digital Technology in Intellig...

引用

4th international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Yuan, Hongyan Shandong Institute of Commerce and Technology Shandong Jinan250103 China

Intelligent platforms are mainly service platforms composed of machine learning integrations that rely on continuous reasoning and learning. this technology has been widely used in today's the Internet Age, and its analysis system, as an important customer oriented feedback link, is also very important. therefore, this article would optimize the intelligent platform analysis system by applying virtual digital technology, and use intelligent platforms for ship shipping and smart home platforms as examples. © 2024 the Authors.

关键词： Smart homes

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Examining Policy Entropy of Reinforcement learning Agents for Personalization Tasks 4th

Examining Policy Entropy of Reinforcement Learning Agents f...

引用

4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

作者： Dereventsov, Anton Starnes, Andrew Webster, Clayton Lirio AI Research Lirio LLC KnoxvilleTN37830 United States

ISBN: (纸本)9789819787012

this paper examines the behavior of reinforcement learning systems in personalization environments and details the differences in policy entropy associated with the type of learning algorithm utilized. We observe that as agents evolve towards the optimal policy, the trajectory of the learned policy is intricately linked to the chosen learning paradigm. through a series of numerical experiments, we consistently observe differences in policy entropy values between Policy Optimization and Q-learning agents during the training process. Our empirical findings are complimented by a theoretical analysis that sheds light on this phenomenon, which has not yet been explored in existing literature. © the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Diffusion-Augmented learning for Long-tail recognition 4

Diffusion-Augmented learning for Long-tail recognition

引用

4th international conference on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Wang, Xiaochun Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

In general, natural data are long-tail distributed over semantic classes. Existing recognition methods tackle the imbalanced classification problem by designing architectures that focus more on the tail classes without altering the imbalance in the original dataset. In this paper, we propose an augmentation learning strategy, which not only alleviates the bias of the classifier towards head classes but also generates a balanced augmented dataset. Our method is called Diffusion-Augmented learning (DA), which can be easily applied to any long-tail recognition model. Its core idea is to leverage the generative capabilities of diffusion models to generate a balanced augmented dataset with high confidence and rich feature information. Subsequently, through a specific sampling strategy, we mix the original dataset with the augmented dataset to tackle long-tail recognition. the purpose of our method is twofold: 1) to utilize the generative capability of diffusion models to add sufficient and reliable augmented data for training, which improves the robustness of the model, and 2). to introduce more augmented data for tail classes through both probabilistically replacing strategy and an improved diffusion model, which alleviates the model's bias towards head classes. We carried out a series of contrast experiments on the CIFAR10-LT, CIFAR100-LT, and ImageNet-LT datasets, and the experimental results demonstrate the superiority of our proposed method and that the augmented dataset is versatile. © 2025 SPIE.

关键词： Adversarial machine learning

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Mushroom Classification Based on Deep Residual Network 4

Mushroom Classification Based on Deep Residual Network

引用

4th IEEE international conference on pattern recognition and machine learning, PRML 2023

作者： Feng, Ju Ling, Xufeng Wang, Yubo Yang, Jie Shanghai Normal University Tianhua College School of Artificial Intelligence Shanghai China Shanghai Acoustics Laboratory Chinese Academy of Sciences Shanghai China Inst. of Image Proc. and Pattern Recog. and Inst. of Medical Robotics Shanghai Jiaotong University Shanghai China

ISBN: (纸本)9798350324303

Due to the similarity in mushroom features and the difficulty in distinguishing between poisonous and nonpoisonous varieties, mushrooms pose a threat to human health. To address the challenge of mushroom classification and identification, this paper proposes a mushroom classification method based on residual networks. Firstly, a network architecture with multiple residual blocks is designed, and it is trained using an image dataset. then, a transfer learning strategy is employed to initialize the network parameters from a pre-Trained model, followed by fine-Tuning to adapt to the mushroom classification task. Finally, multiple testing experiments are conducted to evaluate the effectiveness of the proposed method. the experimental results demonstrate excellent performance of the proposed method in mushroom classification tasks. Compared to traditional feature extraction methods, it can better capture the details and texture features of mushrooms, thereby improving classification accuracy. In conclusion, the mushroom classification method based on residual networks exhibits high accuracy and generalization capability. this method has potential applications in the field of mushroom classification, aiding in the better identification and differentiation of poisonous mushrooms, thereby protecting human health. © 2023 IEEE.

关键词： Convolutional neural networks

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Stress Detection During Social Interactions with Natural Language Processing and machine learning 4

Stress Detection During Social Interactions with Natural Lan...

引用

4th international conference on Expert Clouds and Applications, ICOECA 2024

作者： Singh, Manish Kumar Kumar, Sunil University Institute of Sciences Chandigarh University Department of Mathematics Gharuan Punjab Mohali India

ISBN: (纸本)9798350385793

the field of Natural Language Processing (NLP) is witnessing a growing recognition of the significance of investigating stress-related phenomena. this research study proposes a new framework to address the diagnostic challenges associated with mental health conditions such as anxiety, depression, and stress, which can worsen if left unaddressed. Within this research study, social media postings and comments are used to enhance sentiment and emotion analysis for assessing individuals' stress levels. Specifically, text analysis of social media data, including Twitter tweets, serves as a pivotal application. the proposed system is implemented by employing a combination of machine learning (ML), Natural Language Processing (NLP), and Deep learning (DL) methodologies. the proposed system utilizes Convolutional Neural Network (CNN), BERT transfer learning techniques, as well as machine learning (ML) algorithms such as Naive Bayes, Support Vector machine (SVM), and K-Nearest Neighbors (KNN). this study aims to predict five distinct emotional categories: 'Happy, ' 'Angry, ' 'Depression, ' 'Sad, ' and 'Fear.' Remarkably, the proposed system has achieved an accuracy and F1 score exceeding 93%, outperforming the performance of other existing human emotion recognition systems. © 2024 IEEE.

关键词： Convolutional neural networks

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Prediction of Chronic Diseases Using Parallel 1D-CNN Feature Extraction and SVM Classification Based on SMOTE 4th

Prediction of Chronic Diseases Using Parallel 1D-CNN Feature...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Mesbah, Fatma Zohra Tassadit Ait Abidine, M'hamed Bilal Fergani, Belkacem Univ Sci & Technol Houari Boumediene USTHB Lab Ingn Syst Intelligents & Commun LISIC Fac Elect Engn BP 32 El Alia Algiers 16111 Algeria

ISBN: (纸本)9783031821523;9783031821530

Due to their severity and prevalence, the prediction of chronic diseases (CDs) has become an important area of research, particularly with advances in deep learning. In this paper, we propose a new automated technique for detecting these diseases. A deep network architecture using a parallel unidimensional convolutional neural network (1D-PCNN) is employed to extract deep features. Subsequently, the Support Vector machine (SVM) technique is applied for CD classification. the uniqueness of our framework lies in the design of the 1D-PCNN, which can learn the deep features of the input layer through parallel convolutional layers. As a result, the deep features of each parallel branch are simultaneously extracted before being combined in the fusion layer. Furthermore, in order to improve the efficiency of the proposed model, the Synthetic Minority Oversampling Technique (SMOTE) is used. this strategy manages class imbalance in CD databases. the suggested model is analysed against standard 1D-CNN, 1D-CNN model combined with conventional machine learning methods and other existing state-of-the-art models. the effectiveness of the suggested method was tested using two known databases the Pima Indian Diabetes database (PIDD) and the Cleveland Heart Disease database (CHDD). Results, from these databases indicate that the proposed approach yielded an accuracy rate of 83% and 88% an F score of 73% and 90% and an AUC of 80% and 87% correspondingly. Finally, after applying the SMOTE method, accuracy was improved to 86% and 92%, Fscore to 86% and 93%, and AUC to 86% and 92%, respectively, and outperforms other methods.

关键词： Chronic diseases data classification SMOTE Parallel unidimensional convolutional neural network (1D-PCNN) SVM

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Real Time Flower Identification by Artificial Intelligence 4

Real Time Flower Identification by Artificial Intelligence

引用

4th international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Rahman, Moshiur Joy, Taushik Ahmed Akter, Samia Sattar, Abdus Daffodil International University Department of Management Information System Daffodil Smart City Dhaka Bangladesh North South University Department of Electrical and Computer Engineering Bashundhara Dhaka1229 Bangladesh Daffodil International University Department of Computer Science and Engineering Daffodil Smart City Dhaka Bangladesh

ISBN: (纸本)9798350343670

In the realm of flower-rich Bangladesh, the presence of these blossoms enriches our everyday experiences, whether encountered during leisurely strolls, along railway tracks, or within our gardens. However, the beauty of these flowers often remains unexplored due to our limited knowledge about their names and attributes. To address this, a project was initiated to close this gap and acquaint people with these unfamiliar yet frequently encountered blooms. Our endeavor involves an innovative mobile application that employs real-time camera recognition to identify flowers, powered by neural networks, particularly the Tensorflow-based image classifier on the Android platform. machine learning's expansive applications in computer science have propelled our interest in this arena, specifically focusing on Convolutional Neural Networks (CNN) and Tensorflow for image classification. While our current application marks the inception, aspiration to further enrich and refine our system for the future. Our ultimate aim is to share the benefits of our work, enabling individuals to gain profound insights into the enchanting floral world that envelops them daily. © 2024 IEEE.

关键词： Artificial Intelligence data Science Deep learning machine learning Monitoring Smartphone Sensors

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AI and machine learning Techniques for Managing Complexity, Changes and Uncertainties in Manufacturing 4

AI and Machine Learning Techniques for Managing Complexity, ...

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4th international conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： William, P. Nirsanametla, Yadaiah Al-Samalek, Ahmed Hussain, Ahmed Varshney, Neeraj Rao, A.L.N. Kopargaon Sppu Sanjivani College of Engineering Department of Information Technology MH Pune India Chitkara University Chitkara Center for Research and Development Himachal Pradesh India Al-Zahrawi University College Department of Medical Laboratory Technics Karbala Iraq Al-Farahidi University Medical Technical College Iraq GLA University Department of Computer Engineering and Applications Mathura India Lloyd Institute of Engineering& Technology Greater Noida India

ISBN: (纸本)9798350307757

Artificial neural networks (ANNs), fuzzy systems, expert systems, pattern recognition methods, and modern hybrid approaches to artificial intelligence (AI) may all be considered as phases of a progression that began more than 20 years ago. this trend began with the creation of artificial neural networks (ANNs). this research article includes a number of original discoveries and focuses on hybrid "artificial intelligence' (AI) and multi-strategy machine learning approaches. this new knowledge is presented as well as a discussion of the essential stages that comprise this process. One of the possible uses for agent-based holonic systems has been identified as being the management of complexity, changes, and interruptions in production systems. It is envisaged that more approaches would be incorporated together. In the event that one so chooses, the subject of defect detection might be rethought as one of binary categorization. Both the classification task machine learning technique and the choice of the features that make up the data and are most significant to the process's quality were decided on the basis of the l1-regularized logistic regression. the establishment of a brand-new manufacturing industry that is being referred to as Smart Manufacturing. this was done in order to guarantee the highest possible level of quality throughout the whole of the process. this allowed for optimal efficiency in both areas. Because of this, it is feasible to combine the most relevant facts about the procedure. the suggested strategy is supported by a cutting-edge hybrid feature removal technique and the best classification threshold search algorithm currently available. the outcomes of the tests reveal that flaws can always be precisely detected without fail. © 2024 IEEE.

关键词： Expert systems

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Research on mental health status evaluation based on data mining model 4

Research on mental health status evaluation based on data mi...

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4th international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Liu, Haiqi Wang, Kaizheng Zhao, Feilong College of Education Linyi University Linyi276000 China

this paper puts forward a new mental health evaluation model based on data mining technology, and analyzes the factors that affect mental health. the model understands the influencing factors according to the mental health status of different student groups, then reduces the dimensionality of the original data through principal component analysis and discriminant analysis, and finally establishes a dependent variable prediction model through regression analysis. the experimental results show that the method proposed in this paper can effectively predict students' mental health status. © 2024 the Authors.

关键词： Academic performance data mining Mental health

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