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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3282 条记录，以下是591-600 订阅

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A Novel Multi-view Fuzzy Clustering Algorithm Based on Fuzzy C-Means 1

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3rd international conference on Artificial Intelligence Logic and Applications, AILA 2023

作者： Lin, Ze Chen, Yixiang National Engineering Research Center of Trustworthy Embedded Software East China Normal University Shanghai200062 China

ISBN: (数字)9789819978694

ISBN: (纸本)9789819978687

Fuzzy c-means (i.e., FCM) is a representative clustering method that is widely used in machine learning and pattern recognition. It can describe the degree of fuzziness of objects to clusters using memberships, but it cannot assess the degree of fuzziness of the clustering results. Motivated by such issues, in this paper, we propose a multi-view fuzzy clustering method based on FCM and a fuzzy assessment is introduced to assess the fuzziness of clustering results. Specifically, we constructed a multi-view FCM to obtain a reasonable membership matrix by fully integrating multi-view information. Based on this, we propose a fuzzy assessment method to evaluate the fuzziness of the clustering results and mark the objects between the different clusters (i.e., the object has the feature of multiple clusters). The experimental result shows that the proposed multi-view fuzzy clustering method achieves better performance than several related clustering methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： pattern recognition

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Research on clustering algorithms in data mining 3

Research on clustering algorithms in data mining

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3rd international conference on Big data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2022

作者： Xu, Haoxiang Shanxi Agricultural University School of Software Engineering Shanxi China

ISBN: (数字)9781665451604

ISBN: (纸本)9781665451604

Clustering is a necessary data pre-processing method in data mining research, which aims to obtain the intrinsic distribution structure of valuable datasets from unlabeled datasets and thus simplify the description of the datasets. data mining technology can discover potential and valuable knowledge from a large amount of data, giving a new meaning to the massive amount of data accumulated by people in the information age-new meaning. With the rapid development of data mining technology, as an essential part of it, grid clustering technology has been widely used in data analysis images. As a mainstream data mining method, cluster analysis facilitates data processing in machine learning by implementing clustering algorithms to analyze data and the proper operation of machines. Through the continuous experiments and proofs of previous generations, the algorithms on cluster analysis are becoming more and more mature. The text lists an overview of the evolution of high-quality clustering algorithms to understand the applications better and explore multiple clustering approaches. This study reviews papers that refer to 16 more well-known and research-discussed developmental academic articles to understand their applications on clustering and explore the development of clustering in various fields. This systematic review aims to summarize the cluster analysis and grouping techniques used to date and make recommendations for future developments. © 2022 IEEE.

关键词： data mining

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Reliable Navigation learning in Stochastic Transportation Networks with Uncertain Topology 3

Reliable Navigation Learning in Stochastic Transportation Ne...

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3rd international conference on pattern recognition and machine learning, PRML 2022

作者： Guo, Hongliang Shi, Rui Deng, Yiran Chen, Qiming Zhou, Yingjie Institute for Infocomm Research Agency for Science Technology and Research Singapore Singapore School of Automation Engineering Chengdu China College of Computer Science Chengdu China College of Communication Engineering Chengdu China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

This paper studies the reliable navigation problem in stochastic transportation networks with uncertain topology. The objective is to find a dynamic routing policy, which navigates the ego vehicle to the destination with the minimal linear combination of mean and standard deviation (mean-std) of travel time. Different from almost all other stochastic routing problems, which focus on the stochasticity of travel time and assume a fixed topology of the underlying transportation network, the reliable navigation problem considered in this paper involves an additional layer of stochasticity, namely the uncertain network topology. To reach reliable navigation, we advance the canonical reinforcement learning (RL) method, which could originally find a routing policy with the minimal mean travel time, to simultaneously estimate and minimize the linear combination of the mean and standard deviation of a routing policy's travel time. Additionally, to cope with the uncertain network topology problem, we enhance RL's underlying Markov decision process (MDP) with a variational action set as well as a novel decision-list policy that outputs a list of ranked actions instead of a single one. We evaluate and compare the proposed method with state-of-the-art mean-std shortest path solutions in a range of transportation networks, and demonstrate that the proposed method achieves superior performance with the lowest response time. © 2022 IEEE.

关键词： Travel time

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A recognition technology for the output pattern of likely metamorphic relation of neutron diffusion program 3

A recognition technology for the output pattern of likely me...

引用

3rd international conference on Electronics and Communication;Network and Computer Technology (ECNCT)

作者： Wen, Zhaoyu Yang, Xiaohua Yan, Shiyu Li, Meng Univ South China Sch Comp Sci Hengyang 421001 Peoples R China

ISBN: (数字)9781510652118

ISBN: (纸本)9781510652118;9781510652101

The metamorphic test is a method to alleviate the unexpected value of the Oracle problem. The key point is the identification of the metamorphic relations. The identification of the metamorphic relations of scientific calculation programs is also a complex problem, and the likely of the metamorphic relation of the program can provide enlightening information for the identification of the metamorphic relations. The likely metamorphic relation can be regarded as the implicit expression of the input pattern and output pattern. This paper proposes an output pattern recognition technology based on the likely metamorphic relations of GEP. The technology is mainly aimed at the core neutron diffusion calculation program. The input pattern of the program, and then generate input data and run the program. Finally, in the corresponding output data results, through GEP data mining technology, the output pattern expressed in a variety of functional forms is obtained, which is further compared with analytical solutions and verified to be reliable likely metamorphic relations.

关键词： metamorphic testing metamorphic relation likely metamorphic relation GEP neutron diffusion program

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A Speech recognition Method Based on Transfer learning for PSC Topic Speaking Section 3

A Speech Recognition Method Based on Transfer Learning for P...

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3rd international conference on pattern recognition and machine learning, PRML 2022

作者： Meng, Huazhen Liu, Jiajun Shen, Yunfei Fan, Zhixing Wumaier, Aishan Zheng, Linna Yang, Ruotong Xinjiang University School of Information Science and Engineering Urumqi China Xinjiang University School of Software Urumqi China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

Many automatic speech scoring systems employ an Automatic Speech recognition (ASR) system to get the speech's transcription, then carry out the other scoring process. The accuracy of the ASR system output, which will be used to calculate the scoring model's features, significantly impacts achieving high scoring accuracy. The National Putonghua Proficiency Test (NPPT) is an official proficiency test in China. The NPPT has four sections: monosyllabic-word reading, multisyllabic-word reading, short text reading, and topic speaking. At present, the topic speaking part still needs to be scored by two certificated human testers. In order to develop an automatic speech scoring system for the NPPT, a robust ASR system is needed. Although the general mandarin ASR systems could also get higher accuracy, most popular general ASR systems have significantly lower performance on the topic of speaking speeches, which contains more mistakes than the general level. This paper builds a speech recognition corpus to develop a specialized ASR for the NPPT topic speaking speech scoring task and adopts the transformer and conformer model to implement the ASR. We also implemented a transfer-learning-based ASR model based on the popular pre-trained Mandarin ASR framework WeNet. The experimental results show that our model could significantly reduce the error rate of lower proficiency speech while achieving higher performance than popular Mandarin ASR systems for general-purpose speech recognition. © 2022 IEEE.

关键词： Speech recognition

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Plant Disease Identification Tracking and Forecasting Using machine learning 3

Plant Disease Identification Tracking and Forecasting Using ...

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3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Vani, M. Sowmya Girinath, S. Hemasree, V. Havardhan, Lomada Hars Sandhya, Poli Department of Masters of Computer Applications AP Tirupati India

ISBN: (纸本)9798350342338

How safe our food supply is and how productive our agriculture is are both significantly impacted by plant diseases. For prompt cures and efficient management of plant diseases, accurate diagnosis, close observation, and foresight are necessary. Methods like deep learning, feature extraction, and picture recognition are frequently used in disease detection. For farmers, agronomists, and decision-makers, machine learning can be used to distinguish, monitor, and forecast illness affecting plants. The Plant Village data set is meticulously segmented for disease prediction training and testing;as a result, several plant species are acknowledged and given new names to make an accurate database. The classifier is then trained using training data, and the output will then be detected with the highest accuracy possible. Therefore, this study presented a CNN-based system for disease detection in plants and also evaluated the overall performance of various classifiers at the study's records set to decide which had the most accuracy. The performance and usability of machine learning models must be improved through ongoing research and development if we are to eventually see more effective and sustainable farming practices. The created model also maps the soil and crop database and recommends appropriate crops depending on the number of nutrients available in the soil, enabling farmers to choose the right crops to be sown in their fields. Therefore, it is important to identify crop diseases as soon as possible. Farmers will profit from using a quick, creative approach and a crop recommendation system. © 2023 IEEE.

关键词： A profound understanding Disease detection diseased plants Neural networks (NN) Neural networks with convolutions Plant Village Predictive Analytics Training a machine

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Classification of Currencies as Authentic or Counterfeit: A data-Driven Approach 3rd

Classification of Currencies as Authentic or Counterfeit: A ...

引用

3rd international conference on Signal and data Processing, ICSDP 2023

作者： Koushik, K. Venkat Sai, G. Chaurasiya, Rahul K. Department of ECE Maulana Azad National Institute of Technology Bhopal India

ISBN: (纸本)9789819795772

The pervasive issue of counterfeit currency poses a significant threat to global economies, leading to substantial financial losses and eroding trust in financial institutions. While traditional methods of counterfeit detection rely on visual inspections and security feature verification, the advent of sophisticated counterfeiting techniques necessitates more advanced solutions. This study explores the application of machine learning (ML) algorithms to the problem of currency authentication, utilizing a dataset comprising images of banknotes. We employed a convolutional neural network (CNN) architecture, tailored for feature extraction from image data, which has demonstrated exceptional performance in pattern recognition tasks. Our proposed model introduces a novel preprocessing algorithm that enhances the distinctive characteristics of genuine and counterfeit notes, improving CNN's classification efficacy. We benchmarked the performance of our model against traditional ML classifiers, such as Support Vector machines (SVM) and Random Forests. The analysis reveals the strengths of our model, including its robustness to variations in image quality and its ability to generalize from limited training data. However, limitations were observed in terms of computational intensity and the need for a substantial dataset to optimize model parameters. The findings underscore the potential of automated, ML-driven techniques in revolutionizing the detection of counterfeit currencies, presenting a scalable and effective solution to a complex challenge. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Feature Selection

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From data Pre-processing to Hate Speech Detection: An Interdisciplinary Study on Women-targeted Online Abuse 3

From Data Pre-processing to Hate Speech Detection: An Interd...

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3rd IEEE international conference on Intelligent Technologies, CONIT 2023

作者： Rathod, Rutuja G. Barve, Yashoda Saini, Jatinderkumar R. Rathod, Sourav Surayadatta College of Management Information & Research Technology Pune India Pune India AI&ML Deparment Pune India

ISBN: (纸本)9798350338607

The term 'misogyny' conveys hatred and disrespect for women. It is a type of sexism that stems from the idea that women are less valuable than men and can take many forms, such as verbal abuse, sexual harassment, and physical violence. One area where misogyny is particularly prevalent is in online spaces. Women who speak out and share their experiences online often face vicious attacks, including violence, rape, and death threats. This phenomenon, often referred to as 'online harassment,' can have devastating effects on women's mental health and well-being. Despite the growing recognition of this issue, there is still a lack of understanding about the nature and extent of hate speech toward women on social media. In this paper, the authors aim to address this gap by analyzing a dataset of tweets on Twitter containing hate speech toward women. The authors introduced the Measuring Hate Speech corpus, a dataset used while studying hate speech towards women. From this dataset, the authors extract some of them to build the model. And authors proposed machine learning algorithms like Logistic Regression with 0.92% accuracy. And to look further to fill the gap, the authors developed Support Vector machine (SVM) algorithm, Topic Modeling technique to extract the topics from the corpus. This study contributes to a better understanding of hate speech on social media platforms as Twitter informs strategies for resisting this pervasive problem. © 2023 IEEE.

关键词： Support vector machines

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Comparative Analysis of machine learning Techniques for Heart Disease Prediction 3

Comparative Analysis of Machine Learning Techniques for Hear...

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3rd international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2023

作者： Susitha, N. Selvi, H. Mahesh Kumar, N.B. Nagarajan, V.R. Ali, Syed Sumera Kumar, L. Sathish TamilNadu Chennai India Saveetha College of Liberal Arts and Sciences TamilNadu Chennai India School of Computing Science and Hindusthan Institute of Technology Tamil Nadu Coimbatore India Karpagam Academy of Higher Education Faculty of Arts Science Commerce and Management Tamil Nadu Coimbatore India Csmss Chh Shahu College of Engineering Maharashtra Aurangabad India Vit Bhopal University Scse Madhya Pradesh Kothrikalan India

ISBN: (纸本)9798350306989

Due to its substantial effects on society, efforts to enhance heart disease diagnosis and treatment have increased. Through data mining and the archiving of medical records, improved patient management is now possible thanks to the fusion of technology and medical diagnoses. Understanding the interaction between risk factors in the histories of patients and their impact on their prospects for heart disease is of utmost importance. This study aims to accurately predict cardiac disease by analyzing various data elements from patients. The selection and feature extraction methods are the most efficient choices for prediction system for heart disease. The following variables, such as age, sex, occupation, smoking, obesity, diet, exercise, mental stress levels, type of chest pain, history of chest pain, pressure, ECG, and results, have all been identified as significant factors in diagnosing heart disease. Various machine learning methods, such as Multilayer Perceptron (MLP), Naive Bayes (NB), K-nearest Neighbor (K-NN) and Support Vector machine (SVM) were utilized to analyze the heart disease dataset. These datasets consisted of one with all features and another with only selected features. The objective was to compare the performance of these methods and determine which ones yielded accurate predictions. The results revealed that random forest, using the chosen features, outperformed other artificial intelligence algorithms, including those using all input features, achieving a maximum accuracy rate of 90%. As a support framework for predicting cardiac disease in its early stages, the suggested approach shows promise. This study advances the prognosis of cardiac disease, enables prompt therapies, and enhances patient outcomes by fusing the strength of data-driven AI algorithms with thorough feature selection. © 2023 IEEE.

关键词： Forecasting

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A Systematic Review: How Computer Vision is Transforming Agriculture in Economic Growth 3rd

A Systematic Review: How Computer Vision is Transforming Agr...

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3rd international conference on Artificial Intelligence and Knowledge Processing (AIKP)

作者： Karanam, Santoshachandra Rao Kumar, A. B. Pradeep Yandrapati, Prakash Babu Tangudu, Naresh Peddada, Nagamani Bollipelly, PruthviRaj Goud GITAM Deemed Univ CSE Dept Hyderabad India Aditya Inst Technol & Management CSD & MCA Dept Tekkali Srikakulam India Anurag Univ IT Dept Hyderabad India

ISBN: (纸本)9783031686160;9783031686177

Recent years have seen the emergence of computer vision, a subfield of artificial intelligence (AI), as a technology that has the potential to revolutionize agricultural practices. This might have an impact on many agricultural practices and crop management techniques. This is due to the fact that computer vision can examine pictures and identify patterns of data. This article provides a summary of the uses of computer vision in agriculture as well as the consequences such applications have had. The issues of precision agriculture, disease diagnosis, crop monitoring, and yield computation may be overcome with the use of computer vision technologies such as image recognition, object detection, and pattern analysis. Specifically, it investigates the ways in which these strategies are useful. In addition to this, it investigates the benefits, drawbacks, and possible future applications of computer vision in agriculture, with a particular emphasis on the potential for the sector to improve its levels of productivity, sustainability, and profitability. According to this in-depth analysis, computer vision has revolutionized the agricultural industry and contributed significantly to economic growth. In order to evaluate the financial impacts that computer vision has had on the agricultural industry, this study looks at a wide range of academic papers, publications, and reports. The findings highlight the advancements, benefits, challenges, and future opportunities presented by computer vision technology in the areas of crop monitoring, precision farming, animal management, and harvesting. According to the evaluation, computer vision has the potential to improve farming in terms of productivity, resource allocation, cost reduction, and sustainable practices.

关键词： Computer Vision Artificial Intelligence Economic Growth Agriculture Management Agricultural Automation machine learning

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