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

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Study on the Type of Campus One Card Student Consumption Based on Clustering Algorithm

Study on the Type of Campus One Card Student Consumption Bas...

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2017 3rd international conference on Future Mechatronics and Automation (ICMA 2017)

作者： Yantao Lan Jinshuai Qu Meina Zhao University Key Laboratory of Wireless Sensor Networks in Yunnan Province Yunnan University of Nationalities

With the rapid development of information technology, people are facing more and more data problems. The world of big data has quietly come to the world. data analysis and processing has become one of the most important research topics at present. In the process of data research, the processing method of large data is particularly important, especially the data mining technology has become the most important. In the processing of large data, clustering analysis is an indispensable means. It is widely used in many fields, such as medical diagnosis, image processing, pattern recognition, data analysis, data mining, decision support and marketing. Because the clustering algorithm is relatively simple, easy to realize and has good application effect in practical application, it has also attracted extensive attention of many experts and scholars. In this paper, based on the basic principle and application of clustering algorithm under the background of big data on the previous experience, the students of Yunnan Minzu University consumption expenditure data, and cluster analysis was performed on the data using Matlab software to analyze the consumption level of different types of students and what the students the consumption level.

关键词： Large data data mining Clustering algorithm Matlab Cluster analysis

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Intervention of Artificial Intelligence in Education to Reduce Illiteracy Rate

Intervention of Artificial Intelligence in Education to Redu...

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Sentiment Analysis and Deep learning (ICSADL), international conference on

作者： Prerna Negi V.K. Shrivastava Shweta Pandey Kanegonda Ravi Chythanya Prafful Negi Manish Gupta School of Applied and Life Sciences Uttaranchal University Dehradun India LCD Division of Research & Innovation Uttaranchal University Dehradun India School of Computer Science and Artificial Intelligence SR University Warangal India Uttaranchal Institute of Technology Uttaranchal University Dehradun India Lovely Professional University Punjab India

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

Artificial Intelligence (AI) can speed up the progress, provide novel teaching and learning techniques, and address some of the largest issues in education. It combines cognition, machine learning, interaction of human and computer, storage of data, recognition of emotion, and decision making to make machines think and act like humans. Digitalization is a process that involves orientation, digital preparation, interactive presence, teamwork, follow-up and performance, reflection and motivation, and examination evaluation. Technology Enhanced learning (TEL) is being developed to help students in their academic pursuits with AI-supported time and task planning (AI4TTP). AI can be used to influence people in a variety of ways, such as through chatbots, e-assessment, and anti-cheating systems. learning analytics (LA) is a new area that aims to improve education and instruction by assessing raw information analytically and identifying trends to recognize behaviour of students, anticipate learner actions, and present quick feedback. Individualized instruction is tailored to each learner's individualized learning inclinations. There has been discussion of suggestions based on the analysis.

关键词： Sentiment analysis Education learning (artificial intelligence) Transforms Market research Reflection Teamwork

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Graph-Theoretic Measures for Supporting Diagnosis and Identification of Biomarkers for Borderline Personality Disorder

Graph-Theoretic Measures for Supporting Diagnosis and Identi...

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data, Decision and Systems (ICDDS), IEEE international conference on

ISBN: (数字)9798350363890

ISBN: (纸本)9798350363906

Borderline Personality Disorder (BPD) is a mental health disorder which renders an individual a victim of severe mood swings, impulsive behaviour, self-destructive behaviour and a distorted self-image. The diagnosis of BPD is usually conducted based on subjective assessments. However with the growing developments in neuroimaging, computational techniques can be developed leading to faster and more accurate diagnosis. In our study, we utilize neuroimaging techniques, specifically functional Magnetic Resonance Imaging (fMRI), to explore how different regions of the brain communicate. We employ graph theoretical methods to model this intricate neural data with the aim of objective diagnosis of the disorder and identifying neurological markers that could improve the diagnostic process for BPD. To achieve this, we make use of publicly available, annotated task-fMRI datasets designed to differentiate between individuals with BPD and healthy controls (HC). Through rigorous statistical analysis, correlation studies, and the application of graph-based metrics, we aim to not only identify whether an individual is suffering from the disorder or not, but also uncover specific brain regions that exhibit distinct activity patterns in individuals with BPD compared to HC. This research seeks to deepen our comprehension of BPD and potentially enhance diagnostic procedures by acting as an aid to health professionals.

关键词： Neuroimaging Measurement Statistical analysis Mood Mental health machine learning Functional magnetic resonance imaging Graph theory data models Object recognition

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A machine learning Approach for Detection Plant Disease: Taking Orchid as Example 2019

A Machine Learning Approach for Detection Plant Disease: Tak...

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Proceedings of the 3rd international conference on Vision, Image and Signal Processing

作者： Li-Hua Li Yu-Sheng Chu Jui-Yuan Chu Shian-Hau Guo Chaoyang University of Technology Taichung Taiwan

ISBN: (纸本)9781450376259

The export value of flower production is very high in many countries. Among these export flowers, orchids are considered one of the most valuable flowers. In order to increase orchid production, it is important to take closely care of orchid in the cultivation period. Due to the plant diseases may cause the economic losses in agricultural industry, the daily inspection and early recognition of plant diseases are *** and prevention of plant diseases are a worldwide agricultural problem. Many researchers have proposed biocontrol or IT technology to handle plant disease problems. Some researchers applied image recognition to find out leaf problems such as banana leaves, alfalfa leaves, and citrus leaves. However, these researches all focus on trees or fruits in the lab. They did not provide the long-distance leaf identification for orchid flowers. To help flower farmers and to enhance the quality of production, this research has proposed a machine learning method to capture the image of orchid leaf and to identify the leaf *** research analyzes the leaf image with feature space, i.e., HSI, RGB, and grayscale. We use histogram to analysis the leaf color and we analyze the green color threshold so that the image can be classified into various color zones. Based on the generated threshold, this research is able to segment the leaf image into healthy area and unhealthy area. Finally, we use the Artificial Neural Network (ANN) and Deep learning ANN to learn the image patterns of orchid leaf. Our proposed method is then applied to identify the orchid leaves and to determine whether the orchid is healthy or sick. With our proposed model, the accuracy of recognizing the leaf disease can achieve 100% for training data and 90% for testing data. This research enables flower farmers to recognize the orchid disease and can prevent the disease in early stage. As a result, the farmer can take better care to the orchid plants and enhance the cultivation of orc

关键词： machine learning Orchid Image Processing Neural Network Plant Disease Deep learning

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Frequency Band Power-based EEG Channel Selection for Emotion recognition

Frequency Band Power-based EEG Channel Selection for Emotion...

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Advancement in Technology (ICONAT), international conference for

作者： Priyam Kar Jupitara Hazarika Electronics and Instrumentation Engineering National Institute of Technology Silchar Silchar India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Effective management of human emotion is of vital importance in maintaining general health and well-being. Consequently, with the growing frequency of human-machine interaction in everyday life, the presence of a computer system capable of adapting to human emotions could be of immense importance. Furthermore, an emotion-adaptive Brain-Computer Interface (BCI) system has the capacity to not only improve the healthcare system but also boost the overall quality of life for individuals. Currently, most emotion-adaptive Brain-Computer Interface (BCI) systems primarily rely on Electroencephalogram (EEG) components due to their non-invasive nature and greater temporal resolution. However, most of the recent efforts in the area of EEG-based emotion detection are primarily focused on improving the effectiveness of the classifier for data collected from a substantially large number of EEG channels. Nevertheless, choosing a smaller set of EEG channels that are pertinent to different emotional states can result in a more resilient and less computationally demanding emotion-adaptive BCI system. Thus, in this work, we have introduced a channel selection technique based on EEG band power to classify positive and negative emotions. To assess the effectiveness of the proposed technique, we used a publicly available dataset and conducted a comparison with the approach that does not include channel selection and also with state-of-the-art literature. The results showed that the suggested method outperformed the state-of-the-art approaches.

关键词： Deep learning Human computer interaction Emotion recognition Correlation Neural networks Medical services Electroencephalography Brain-computer interfaces Complexity theory Character recognition

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The Fast $k-\text{NN}$ Algorithm Based on a Fixed Grid Method

The Fast $k-\text{NN}$ Algorithm Based on a Fixed Grid Metho...

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Artificial Intelligence, Robotics and Control (AIRC), international conference on

作者： Gene Eu Jan Kuan-Lin Su Hui-Ching Hsieh Chaomin Luo President&#x0027 s Office Tainan National University of the Arts Tainan City Taiwan Department of Electrical Engineering National Taipei University New Taipei City Taiwan Tainan National University of the Arts Tainan City Taiwan Department of Electrical and Computer Engineering Mississippi State University Mississippi State U. S. A.

ISBN: (数字)9781665459471

ISBN: (纸本)9781665459488

$k-\text{Nearest}$ Neighbor $(k-\text{NN})$ is a well-known instance-based learning algorithm; widely used in pattern recognition. A $k-\text{NN}$ classifier can generate highly accurate predictions if provided with sufficient training instances. Thus, it plays a pivotal role in many fields. However, though its accuracy can improve with more data, the need for computational resources increases as well. In this paper, we propose a novel approach which pre-partitions instance space into smaller cells in order to reduce computational cost and greatly reduce the time complexity. Assume every instance is mapped to a point in the $d-\text{dimensional}$ Euclidean space and a training set $D$ contains $n$ training instances. Given a query instance, the brute force $k-\text{NN}$ algorithm has the $O(nd)$ time complexity for predicting the class of a query instance. This algorithm can improve the time complexity.

关键词： Training Force Aerospace electronics Prediction algorithms Extraterrestrial measurements Partitioning algorithms pattern recognition

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Decision Fusion in Cooperative Adaptive Systems

Decision Fusion in Cooperative Adaptive Systems

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The 8th international conference on Computer Supported Cooperative Work in Design(第八届计算机支持的协同工作设计国际会议)(CSCWD2004)

作者： Mohamed Kamel Pattern Analysis and Machine Intelligence Lab.University of Waterloo Waterloo OntarioCanadaN2L 3G1

Research on cooperative, adaptive intelligentsystems, involves studying, developing and evaluatingarchitectures and methods to solve complex problemsusing adaptive and cooperative systems. These systemsmay range from simple software modules (such as aclustering or a classification algorithm) to physicalsystems (such as autonomous robots, machines orsensors).The main characteristic of these systems is that theyare adaptive and cooperative. By adaptive, it is meantthat the systems have a learning ability that makes themadjust their behaviour or performance to cope withchanging situations. The systems are willing tocooperate together to solve complex problems or toachieve common *** pattern recognition, there are notable contributionson the use of multiple classifiers. The most dominantdecomposition model used is an ensemble of classifiers(identical structures) that are trained differently. Mostof the innovations are in the combining methods. Thereare weighting and voting approaches, probabilisticapproaches and approximate and fuzzy logicapproaches. In the area of sensor fusion, there havebeen some interesting ideas for fusing the data anddecisions of the sensors. However, most of thesecombining schemes are usually applied as a postprocessing *** this work are concerned with investigatingarchitectures and methods of aggregating decisions in amulti-classifier or multi-agent environment. Newarchitectures that allow active cooperation will bedeveloped. The classifiers (or agents) have to knowsome knowledge about others in the system. Differentforms of cooperation will be reported. In order for thesearchitectures to allow for dynamic decision fusion, theaggregation procedures have to have the flexibility toadapt to changes in the input and output and adjust toimprove on the final output. Changes will be learned bymeans of extracting features using feature *** of these architectures to problems inclassification of data, distributed data mining a

关键词： Adaptive systems Intelligent sensors Sensor fusion data mining Feature extraction Computer vision Intelligent systems Intelligent robots machine intelligence Computer architecture

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Smart Crop Cultivation: Harnessing machine and Deep learning for Informed Agricultural Practices

Smart Crop Cultivation: Harnessing Machine and Deep Learning...

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Applied Artificial Intelligence and Computing (ICAAIC), international conference on

作者： Smit Meghani Jay Vele Kunal Madrecha Abhijeet Salunke Department of Computer Engineering Sardar Patel Institute of Technology Mumbai India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

Amongst various occupations in India, farming is the most common. It boosts the Indian economy significantly and provides Indians with a wide range of job opportunities. The ideal crop for each type of soil is no longer chosen by farmers. This has an immediate effect on agricultural yields. It is causing serious financial losses for the farmers in the area. Cultivating a certain crop on a particular type of soil now involves several factors to take into account. The optimal crop to plant is determined by taking into account a number of soil specifications. This novel opinion mining method will consider the following parameters: temperature, air humidity, soil pH, nitrogen, potassium, phosphorus, and annual rainfall. The model aids in determining the kind of crop that has to be planted by taking into account the data collected from prior years. In order to support the mission, various algorithms are analyzed and evaluated on multiple fronts, including comparative research and analysis. In addition, the ideal fertilizer will be recommended depending on crop-related environmental factors, temperature, pH level, and soil moisture content.

关键词： Temperature dependence Sentiment analysis Temperature Rain Soil moisture Crops Phosphorus

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Drug-protein Interaction Prediction based on Improved AdaBoost Algorithm

Drug-protein Interaction Prediction based on Improved AdaBoo...

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IEEE international conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Wanrong Gu Xianfen Xie Ziye Zhang Yijun Mao Zaoqing Liang Yichen He School of mathematics and information South China Agricultural University Guangzhou China School of Economy Jinan University Guangzhou China School of Mathematical South China University of Technology Guangzhou China

ISBN: (数字)9781728198279

ISBN: (纸本)9781728181585

The drug-protein interaction prediction can be used in the discovery of new drug effects. Recent studies often focus on the prediction with an independent matrix filling algorithm. The single-model matrix-filling algorithm has low accuracy, so it is difficult to obtain satisfactory results in the prediction of drug-protein research. The AdaBoost algorithm is an algorithm framework of a powerful classifier composed of multiple subclassifiers. Its usefulness and effectiveness has been proved in the research field of classification. The prediction of drug-protein is a matrix filling problem, which is a process of scoring prediction. Therefore, we improved the algorithm of AdaBoost. To transform the matrix filling problem into a classification problem with improved AdaBoost algorithm. The AdaBoost algorithm framework could be fully utilized to integrate multiple weak classifiers to improve prediction performance. Then we can make an accurate prediction of the drug-protein interaction. The experimental results based on the public data set show that the proposed algorithm outperforms most classical and recent algorithms in predicting accuracy. The limitation of single algorithm based on machine learning is overcome well. Our method improves the accuracy of prediction by mining the hidden factors better.

关键词： Prediction algorithms Drugs Proteins Computational modeling Matrix decomposition machine learning algorithms Predictive models

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Neural Network Approaches for Magnetization Surface Prediction in Switched Reluctance Motors: Classical, Radial Basis Function, and Physics-Informed Models

Neural Network Approaches for Magnetization Surface Predicti...

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Informatics, Electronics, and Radio Engineering (PIERE), international conference Problems of

作者： Galina Demidova Andrey Bogdanov Andrei Iaremenko Zhu Xirong Hao Chen Alecksey Anuchin Electric Drive Department Moscow Power Engineering Institute Moscow Russia Faculty of Control System and Robotics ITMO University Saint-Petersburg Russia School of Electrical and Power Engineering China University of Mining and Technology Xuzhou China

ISBN: (数字)9798331516321

ISBN: (纸本)9798331516338

This paper investigates the prediction of magnetization surfaces in Switched Reluctance Motors using three distinct methods: classical Neural Networks, Radial Basis Function networks, and Physics-Informed Neural Networks. The study utilizes experimental data from a 7.5 kW SRM to assess the performance of these approaches, with all methods implemented in Python. Among the methods analyzed, PINN demonstrates the highest accuracy in predicting the magnetization surfaces, effectively capturing the underlying physical behavior of the motor. The computational costs of each method are evaluated, providing insights into training times, inference speeds, and floating-point operations per sample. The findings reveal that while NN offers a reasonable balance between performance and efficiency, the RBF method excels in training speed but shows limitations in capturing the physical dynamics of the system. In contrast, the PINN not only delivers superior predictive performance but also emphasizes the significance of incorporating physical principles into machine learning frameworks. The results highlight the applicability of these methods across various types of SRMs, suggesting their potential for enhancing control strategies in electrical and power engineering applications.

关键词： Training Power engineering Electric potential Accuracy Magnetization Radial basis function networks machine learning Artificial neural networks Switched reluctance motors Computational efficiency

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