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Research on K-means Optimized Cluster Analysis Method for Agricultural Housing Construction data

Research on K-means Optimized Cluster Analysis Method for Ag...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Mu Bie Lian Tang Hang Lin School of Information Engineering Chongqing Jianzhu College Chongqing China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Cluster analysis of agricultural housing construction data plays an important role in understanding the characteristics of rural housing distribution, assessing building quality and planning rural infrastructure construction. Although the traditional K-means clustering method is simple and easy to use, it suffers from the problems of local optimization and sensitivity to the initial center of mass when dealing with complex datasets. In order to solve these problems, the study introduced three optimization algorithms: distributed particle swarm optimization (DPSO), artificial fish swarm optimization (AFSA) and sparrow search algorithm (SSA), to improve the K-means clustering method and compare their effects in the analysis of agricultural housing construction data. the experimental results show that all three optimization algorithms can significantly improve the effect of K-means clustering, especially on complex datasets. Among them, SSA algorithm performs the best in clustering effect, with clear cluster boundaries and accurate data point allocation; although DPSO and AFSA perform well in global search ability, their clustering effectiveness in boundary regions is slightly inferior to SSA. therefore, SSA is the preferred method to improve K-means clustering, and it is suitable for clustering analysis of agricultural housing construction data. Meanwhile, future research directions are proposed, including multi-algorithm fusion, parameter optimization, application scenario extension and intelligent decision support, to further improve the effect and application breadth of clustering analysis. the research provides effective data analysis methods and technical support for rural construction and management, helping to improve the science and efficiency of infrastructure construction and management.

关键词： Sensitivity Clustering methods Clustering algorithms machine learning Search problems Fish Planning Resource management Particle swarm optimization Optimization

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Support Vector machine and Boosted Regression Tree as Effective data mining Tools for Predicting Students’ Academic Performance

Support Vector Machine and Boosted Regression Tree as Effect...

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IEEE international conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Nnaemeka M. Oparauwah Donatus O. Njoku Uchenna C. Onyemauche Chidimma L. Okpalla Juliet N. Odii Janefrances E. Jibiri Stanley A. Okolie Department of Computer Science Federal University of Technology Owerri Imo State Nigeria Department of Information Technology Federal University of Technology Owerri Imo State Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

this paper reports the application of data mining techniques; Support Vector Regression (SVR) and Boosted Regression Tree (BRT) in prediction of students’ academic performance in schools. the experimental approach in this study compared the performances of the two data mining techniques to ascertain the best performing model on an academic dataset. Publicly available dataset was used for this study and was retrieved from UCI machine learning Repository, Center for machine learning and Intelligent Systems. the data was collated from school reports and constructed questionnaires from two public secondary schools in Portugal regarding students’ performance in two distinct subjects: Mathematics (mat) and Portuguese language (por). data analysis was conducted in two schemes, first with the complete feature sets and then with the reduced feature set (containing 5 selected best performing features). Evaluation results showed that the BRT model outperformed the SVR model with 0.97% and 0.96% accuracy on the complete and reduced feature sets respectively.

关键词： Support vector machines Humanities data analysis Accuracy machine learning Mathematical models data models data mining Intelligent systems Regression tree analysis

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A Gait Perspective Generation Method Based on the 3D Model

A Gait Perspective Generation Method Based on the 3D Model

引用

pattern recognition and machine learning (PRML), IEEE international conference on

作者： Dongliang Yang Xianglei Xing Changjiang Song Intelligent Manufacturing Research Institute Heilongjiang Academy of Sciences Harbin China College of Intelligent Science and Engineering Harbin Engineering University Harbin China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Aiming at the problem of gait recognition, a multi- view gait recognition method based on the human pose estimation model is proposed. this method can effectively solve the problem of low gait recognition rates caused by limited viewpoints. the VIBE method is used to extract human pose parameters of video frames, but there are errors in human pose parameters estimated by VIBE, and direct rotation with attitude parameters will inevitably cause greater errors. the attitude parameters were corrected in the design experiment. Finally, we used the Rodrigues rotation matrix to generate human pose parameters from other angles for VIBE to complete the conversion of character angles. the calibration network, including the attitude average and angle correction model, is designed. the attitude parameters under are input as the mean value as our final attitude parameters and then sent into the angle correction model to correct the root node. Finally, the corrected gait sequence is obtained. We can intuitively observe that the human model has obvious effects, and the accuracy of gait recognition is verified by Gaitset, which verifies that the model has good qualitative and quantitative effects. After correcting the correction network, we can generate gait sequences from other perspectives through the existing perspective. this method can well expand the perspective of the database and obtain more accurate gait sequence models from other perspectives.

关键词： Solid modeling Accuracy three-dimensional displays databases Pose estimation Clothing machine learning pattern recognition Calibration Gait recognition

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Enhancing Situational Awareness in Industrial Automation and Cybersecurity through an Integrated Framework that Leverages Both machine learning and Deep learning Technologies

Enhancing Situational Awareness in Industrial Automation and...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Xingcheng Lu Kechen Wu Yixuan Chen Nanjing University of Finance and Economics Nanjing China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

With the rapid development of artificial intelligence technology, machine learning and deep learning are widely applied in various fields, showing great potential and value. this article is aimed to explore the integrated application of machine learning with deep learning technologies in industrial automation and cybersecurity situational awareness. Utilizing deep learning technology to facilitate real-time monitoring and early detection of network security threats. this study first reviews the application status of machine learning in industrial safety, including feature selection, model construction, and performance evaluation. then, the key technologies of deep learning in cybersecurity situational awareness, such as anomaly detection and intrusion identification, are discussed in depth. Furthermore, this paper proposes a framework that integrates machine learning and deep learning to improve their liability and network security protection capabilities of industrial control systems. By conducting experiments, the proposed framework demonstrates its ability to accurately forecast potential equipment failures in industrial settings and promptly detect security threats within the network, offering a novel approach to enhancing industrial automation and safeguarding network security. After the experiment, the accuracy of the improved algorithm is more than 85%, the prediction is more than 75, the regression line is more than 55%, and the modified algorithm is better than XGBost and other algorithms. the concentration of the improved CNN in identifying attack information can reach 95%.

关键词： Deep learning Performance evaluation Automation machine learning algorithms Reviews Network security Prediction algorithms Real-time systems Computer security Protection

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Development of an Intent-Based Network Incorporating machine learning for Service Assurance of E-Commerce Online Stores 1

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5th international conference on machine learning for Networking, MLN 2022

作者： Hurtado, Remigio Torres, Mario Pintado, Bryan Muñoz, Arantxa Universidad Politécnica Salesiana Calle Vieja 12-30 y Elia Liut Cuenca Ecuador

ISBN: (数字)9783031361838

ISBN: (纸本)9783031361821

Managing the large number of network devices is a major challenge for organizations. the management of these devices requires meticulous care since manual configurations of traditional networks often lead to errors. Manual configurations result in inefficient troubleshooting. Instead of worrying about the development of new services, the organizations’ staff is exhausted by the arduous task of maintaining network devices. Specifically, one of the organizations’ core services is their e-commerce service available through their website, through which they present and sell products. Companies that have had a great growth require optimizing the use of CPU, memory, bandwidth resources, among others, in order to provide an available and scalable service for the large number of customers. Due to this, this paper proposes: 1) an Intent Based Network (IBN) based on Cisco’s IBN architecture applied to online stores in e-commerce, 2) a scalable and efficient method that uses Neural Networks (NN) for resource prediction in order to adapt to the large number of customers and the growing demand in this era of Big data. the IBN architecture provides automation, flexibility and unlike Software Defined Networks (SDN) includes the integration of artificial intelligence and data mining techniques in order to analyze future trends and problems for network service assurance. the neural network predicts the appropriate bandwidth value based on the trend of the number of users that the online store server will consume and other resources such as CPU, memory and storage usage. this value is automatically configured in the network, so that the service always has a high availability. We have created a dataset by collecting data from the network we have developed. We have defined a base neural network model and subsequently optimized this model by minimizing the Mean Absolute Error (MAE). We have shown that the optimized neural network outperforms other machine learning methods such as Random

关键词： Websites

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Identification Method for Rice Pests with Small Sample Size Problems Combining Deep learning and Metric learning 5th

Identification Method for Rice Pests with Small Sample Size ...

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5th Chinese conference on pattern recognition and Computer Vision (PRCV)

作者： Hu, Gensheng Tang, Xin Zeng, Weihui Liang, Dong Yang, Xiaowei Anhui Univ Natl Engn Res Ctr Agroecol Big Data Anal & Applic Hefei 230601 Peoples R China Univ Sci & Technol China Guo Chuang Software Co Ltd Hefei 230088 Peoples R China

ISBN: (纸本)9783031189159;9783031189166

To achieve accurate identification of rice pests with small sample size problems under complex backgrounds, we proposed a rice pest identification method combining deep learning and metric learning. To overcome the effect of the complex background of the rice pest image, we used the U-Net network which can well retain target information and has a good segmentation effect on rice pests with small sample size problems to remove the background. We also improved the backbone network of VGG16 to extract a more effective feature for identification. Finally, we introduced metric learning to project the feature vector of the pest image to a new feature space for similarity matching and solve the lower accuracy caused by the small sample size. Experimental results showed that the accuracy of the proposed method is better than that of SVM, kNN, AlexNet, VGGNet and Mobilenet. thus, our method could accurately identify rice pests with small sample size problems under complex rice backgrounds.

关键词： Pest identification Small sample size problems Deep learning Metric learning machine learning

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Predicting Water Quality Parameters in Lake Pontchartrain using machine learning 2021

Predicting Water Quality Parameters in Lake Pontchartrain us...

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5th international conference on Information System and data mining (ICISDM)

作者： Daniels, Alexis Koutsougeras, Cris Southeastern Louisiana Univ Hammond LA 70402 USA

ISBN: (纸本)9781450389549

this work is about the use of machine learning methods to improve the monitoring of water quality. the work aims to use machine learning to predict the normal values of a quality indicator (pH, salinity, etc.). Upon significant deviation from actual measurements, monitoring scientists would be alerted to the need to inspect the water way more closely thereby reducing the possibility of missing a problem and speeding up determinations of issues regarding water quality. this study compares methods to predict water quality parameters using water data from Lake Pontchartrain in Southeast Louisiana. K-Nearest neighbors, decision trees, and an artificial neural network have been used to determine which method most accurately predicted water quality parameters such as pH, temperature, salinity, specific conductance, and dissolved oxygen. the decision tree and k-nearest neighbors algorithms produced similar results which were only slightly below the standard deviation of the data. However, a neural network was able to predict the values with a much higher accuracy.

关键词： Environmental Science Water quality machine learning

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A Lightweight Object Detection Method for Bank Operation and Maintenance Scenarios 5

A Lightweight Object Detection Method for Bank Operation and...

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5th international conference on pattern recognition and Artificial Intelligence, PRAI 2022

作者： Xue, Junmin Rao, Yunbo Wu, Jiangang Gou, Haosong Liu, Yong Yang, Yang University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China Data Center of Postal Savings Bank of China Beijing China Chengdu Aerospace Science and Industry Big Data Research Institute Corporation Limited Chengdu China Sichuan Institute of Computer Sciences Chengdu China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

Due to the complex background of bank operation and maintenance scenarios, it is extremely difficult to detect and analyze the object of operation and maintenance scenarios. the traditional target detection algorithms, such as F-RCNN, are not suitable for object detection. We propose a lightweight object detection method for bank operation and maintenance scenarios based on deep learning. the proposed method highlights two improvements based on Yolov5. the first adjustment of it is this method constructs a new convolution method and a new activation function. Also, we use the multi-scale output strategy of Feature Pyramid Networks (FPN) to develop a new lightweight feature extraction algorithm. these changes we made are based on the real-life situation, such as bank operation and maintenance scenarios. Experiments have been carried out on the same data set with multiple object detection algorithms, and the experimental results show that the proposed method reduces the complexity of the activation function. Meanwhile, the module is flexible, and can be easily applied to other similar business scenario detection models. © 2022 IEEE.

关键词： Object detection

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A Novel Predictive Analysis and Classification of Land Subsidence Vulnerability Mapping based on GIS using Hybrid Optimized machine learning Techniques and Computer Vision 5

A Novel Predictive Analysis and Classification of Land Subsi...

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5th international conference on Innovative data Communication Technologies and Application, ICIDCA 2024

作者： Senthil, G.A. Prabha, R. Arun, M. Sahaya Anselin Nisha, A. Prabu, S. Department of Information Technology Agni College of Technology Chennai India Department of Electronics and Communication Engineering Sri Sairam Institute of Technology Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

Land subsidence is a natural disaster caused by the extraction of the earth's material strength on the surface that causes the earth's surface to settle and sink. Land subsidence can also result in significant financial losses since it causes structural damage and expensive maintenance costs for roads, trains, barriers, pipelines, ground water, oil, natural gas, mines, and buildings. the Geographic Information System (GIS) and Google Earth Platform (GEP) can be used to map this land subsidence. this provides a comprehensive view of land pattern subsidence and its interactions with other geographical elements. It applies to both progressive subsidence caused by fluid extraction (groundwater, oil, or natural gas) and rapid surface subsidence caused by the collapse of an underground mine. Risk mitigation is the most effective way to deal with the experimental model. this research study focuses on the use of Predictive data Analysis (PDA), a powerful environment in data analytics that provides a set of tools that make it easier to evaluate optimal models. the proposed model makes use of a dataset that includes specific land subsidence circumstances as input. the prediction result is achieved using a hybrid machine learning technique that combines Stochastic Gradient Descent (SGD) with Long Short-Term Memory (LSTM) and is compared to K-Nearest Neighbor (KNN), Generative Adversarial Network (GAN), and Artificial Neural Network (ANN). the proposed system employs LSTM and SGD as optimizers, resulting in a hybrid model that improves accuracy. Among the four algorithms investigated the LSTM-SGD network has the highest accuracy (97%), followed by KNN (96%), GAN (90%), and ANN (89%). Due to their ability to capture complicated nonlinear correlations in data and manage temporal dependencies, optimized hybrid ML models LSTM-SGD and KNN models appear to be particularly well-suited for land subsidence prediction. © 2024 the Authors. Published by Elsevier B.V.

关键词： Long short-term memory

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learning and Application of Different machine learning Methods (KNN, SVM, Decision Tree) in Different datasets

Learning and Application of Different Machine Learning Metho...

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machine learning and Computer Application (ICMLCA), international conference on

作者： Chaoqun Zhu School of Computer Science and Technology(School of Artificial Intelligence) Zhejiang Normal University Jinhua Zhejiang China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

With the continuous development of artificial intelligence, machine learning has shown great ability in classification and regression problems. For example, logistic regression, SVM methods, and neural networks are widely used in credit evaluation, image recognition, and sales forecasting. In this paper, KNN method, SVM method and decision tree method are used for iris data set, aircraft delay data set and diabetes dataset was tested by linear regression and Lasso regression ridge regression. We can conclude that different machine learning methods have different sensitivities to the partitioning of the dataset (controlled by random_state and test-size). When selecting the model parameters, the influence of these factors on the model performance should be considered.

关键词： Support vector machines Technological innovation Sensitivity Accuracy machine learning Nearest neighbor methods Strategic planning Reliability engineering Reproducibility of results Regression tree analysis

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