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Traffic pattern recognition Method with XGBoost Based on Multi-scale Features

Traffic Pattern Recognition Method with XGBoost Based on Mul...

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Electronic Communication and Artificial Intelligence (IWECAI), international Workshop on

作者： Yunlong Song Hao Wang Institute of Big Data and Artificial Intelligence China Telecom Corporation Limited Beijing Research Institute Beijing China

Traffic pattern recognition belongs to a branch of scene recognition and has become a hot research field. Correctly identifying the transportation mode used by users to travel plays a vital role in promoting the development of situational recognition. In the field of traffic pattern recognition research, many recognition methods use GPS, the combination of GPS and WiFi, and the combination of GSM and WiFi to obtain data, and use LR, SVM and deep learning models to make predictions. However, these methods have some disadvantages, such as poor WiFi signal in some outdoor environments, resulting in failure to obtain user data, GPS being interfered by the external environment, resulting in inaccurate data acquisition and other issues, and the models they use have problems such as low prediction accuracy or prolonged prediction consumption. In response to these problems, this paper uses multi-source sensors in mobile phones to obtain data, and preprocesses sensor data through statistics and signal processing methods to generate features at different scales, and finally uses XGBoost to identify various traffic modes. Extensive experiments are carried out on the method proposed in this paper, and the effectiveness of the proposed method is demonstrated by comparing with two state-of-the-art methods.

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Research on Transferable Patent recognition Based on machine learning 2021

Research on Transferable Patent Recognition Based on Machine...

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4th international conference on data Science and Information Technology, DSIT 2021

作者： Hu, Yifei Yang, Shiwei Shi, Anjie Beijing University of Technology China

ISBN: (纸本)9781450390248

the construction of a transferable patent recognition model is of great significance for increasing patent transformation. To construct the index system affecting patent transformation, this paper adds cross-border dimension index to the existing three-dimensional patent value evaluation index. the results show that the recognition accuracy of Gradient boosting decision tree and Random forest is as high as 85.92%, which indicates that machine learning method can support transferable patent recognition in massive data. © 2021 ACM.

关键词： Decision trees

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Predictive modeling toward identification of sex from lip prints - machine learning in cheiloscopy 4

Predictive modeling toward identification of sex from lip pr...

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Joint of the 4th international conference on Applied Informatics Workshops, ICAIW 2021, 2nd international Workshop on Applied Artificial Intelligence, WAAI 2021, 3rd international Workshop on Applied Informatics for Economy, Society, and Development, AIESD 2021, 4th international Workshop on data Engineering and Analytics, WDEA 2021, 1st international Workshop on Knowledge Management and Information Technologies, WKMIT 2021 and 4th international Workshop on Smart and Sustainable Cities, WSSC 2021

作者： Sabelli, Agustín F. Chatterjee, Parag Pollo-Cattaneo, Ma Florencia Universidad Tecnológica Nacional Facultad Regional Buenos Aires Buenos Aires Argentina

Cheiloscopy is a forensic investigation technique that deals with identification of humans based on lips traces. Lip traces hold multifarious features and could be analyzed in different ways to identify the links with personal identifying features. machine learning holds strong application in this domain, especially for pattern recognition and further interpretation. this work is focused on a brief survey of existing machine learning approaches in Cheiloscopy. Also, a comparative study of predictive models has been presented based on an original dataset of lip prints where supervised models have been used to predict the biological sex of the persons using their lip traces. machine learning on one hand automatizes the identification process and poses a significant potential in analyzing huge number of lip traces with considerable accuracy. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Image processing

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Research on Bearing Fault Diagnosis Base on Deep learning 4

Research on Bearing Fault Diagnosis Base on Deep Learning

引用

4th international conference on Artificial Intelligence and Big data, ICAIBD 2021

作者： Xu, Weipeng Shanghai Co. Ltd. China Coal Technology and Engineering Group Shanghai China

ISBN: (纸本)9780738131702

In order to improve the diagnosis rate of bearing signals, the bearing fault diagnosis model ODCNN based on one-dimensional convolutional neural network is proposed to adapt to one-dimensional time-domain signals on the basis of AlexNet. the model is composed of a multilevel alternating convolutional layer and a pooling layer, which can complete the adaptive extraction of the original input signal features, and combine the fully connected layer to realize fault classification and recognition. Support vector machine, convolutional neural network classic AlexNet architecture, LeNet-5, particle swarm optimization algorithm support vector machine and BP neural network are introduced for comparison and verification by using the Shanghai Tiandi bearing test data set for fault diagnosis test. the results indicate that the algorithm proposed in this paper has high recognition accuracy. Finally, Principal component analysis is used to verify the model's feature learning and classification capabilities of vibration signals. © 2021 IEEE.

关键词： Failure analysis

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patterns recognition of Sentimental patterns from the X-dataset using Deep learning Algorithm

Patterns Recognition of Sentimental Patterns from the X-Data...

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Soft Computing for Security Applications (ICSCSA), international conference on

作者： Praveen S R Konduri Department of Data Science School of Computing Mohan Babu University Tirupati India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

this research investigates the application of deep learning techniques to sentiment analysis, a field focused on mining online platforms for subjective assessments. By combining deep neural networks (DNNs) with particle swarm optimization (PSO), we propose a hybrid method to improve feature selection and enhance the accuracy of sentiment classification. the study employs a dataset collected from Twitter using the Ruby Twitter API and evaluates the performance of the proposed method using k-fold cross-validation. the results are compared with the previously used firefly search (FS) method to demonstrate the effectiveness of the hybrid approach. this research contributes to the advancement of sentiment analysis techniques and their application in various domains, such as social media monitoring and market research.

关键词： Deep learning Sentiment analysis Accuracy Social networking (online) Reviews Blogs Artificial neural networks pattern recognition Security Optimization

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Handwriting recognition using Deep learning with Effective data Augmentation Techniques 4

Handwriting Recognition using Deep Learning with Effective D...

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4th international conference on Artificial Intelligence, Big data, Computing and data Communication Systems, icABCD 2021

作者： Brown, Dane Lidzhade, Ipfi Computer Science Rhodes University Grahamstown South Africa

ISBN: (纸本)9781728185927

machine learning techniques have been successfully used in deciphering handwritten text. Deep learning has made further improvements in this regard. However, they require substantial amounts of training data. this research aims to improve the effectiveness of classification accuracy in the presence of limited training data on handwriting recognition. the main focus thus involves enabling deep models to converge during training on smaller datasets using data augmentation. this will allow for broader use of these systems across more regions, greater accessibility, and future related systems to be less reliant on the amount of data available. therefore, the proposed research includes an image processing and machine learning approach to handwriting recognition while generating more sample data in various ways. Applying random cropping as an augmentation technique resulted in higher accuracy than several other augmentation techniques examined in this paper. Some of these techniques performed worse than on unaugmented data. © 2021 IEEE.

关键词： Image processing

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Comprehensive Study on machine learning Enabled Robot for Plant Disease Detection and recognition

Comprehensive Study on Machine Learning Enabled Robot for Pl...

引用

Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： Bhargavi K Praveena K S Darshan M L Achyuth Shetter Bhoomika M P Geetha C R Department of Electronics and Communication Engineering Vidyavardhaka College of Engineering Mysore India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

Plant diseases are a major problem for global food production because they reduce crop yields and cause big financial losses. Traditional ways of detecting these diseases involve manual checks, which are slow, tiring, and can easily lead to mistakes. this survey looks at how machine learning, especially using Convolutional Neural Networks (CNNs), combined with robots, can make detecting plant diseases faster and more accurate. the review includes various machine learning models and methods used to identify different types of plant infections like bacterial, viral, and fungal diseases. It also discusses how IoT devices and remote sensing can help monitor plants in real time. By automating disease detection, these technologies can give early warnings, reduce the need for chemical treatments, and improve plant health management, leading to more sustainable farming. the paper also talks about the challenges of current models, the importance of having diverse data for training, and how these technologies can be improved and used more widely in the future.

关键词： Training Surveys Plant diseases Computational modeling machine learning Manuals data models Real-time systems Internet of things Convolutional neural networks

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Fake News Detection Using Supervised Binary Classification Techniques

Fake News Detection Using Supervised Binary Classification T...

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Innovative Practices in Technology and Management (ICIPTM), international conference on

作者： Riya Kukreti Girish Singh Bisht Ritu Pal Jagdish Chandra Patni Vaibhavi Painuly Computer Science and Engineering UIT Uttaranchal University Dehradun Computer Science and Engineering Tulas Institute Dehradun Symbiosis Institute of Technology Pune India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

With easy access and aggressive data growth via social media networks, it has become challenging to differentiate between falsified and real information. Due to the ease with which information can be communicated, the amount of information that can be misrepresented has increased exponentially. When fake material is widely disseminated, the credibility of social media networks is jeopardised. Consequently, automatically validating the source, substance, and publisher of information to classify it as false or true has become a research problem. machine learning has been helpful in data classification but has significant limitations. this research offers multiple machine learning (ML) techniques for identifying falsified and constructed information.

关键词： Deep learning Social networking (online) Computational modeling Speech recognition Fake news

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Multidimensional Time Series Analysis for Anomaly pattern Detection and Interpretation

Multidimensional Time Series Analysis for Anomaly Pattern De...

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Ziling Deng Jiahua Kang Xiaoxiao Wang Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

the interpretability of multivariate time series anomaly detection is crucial for understanding the reasons behind anomalies, enhancing the usability and credibility of models, and ensuring successful real-world applications. Although there has been considerable research on the interpretability of time series, most are based on black-box models and have certain limitations in revealing the underlying patterns of anomalies. To address this issue, this paper focuses on identifying anomaly patterns in multivariate time series using a white-box machine learning approach, which overcomes the interpretability limitations of traditional methods in high-dimensional dynamic data processing. this approach innovatively enhances the application of Dynamic Time Warping (DTW) in higher dimensions to effectively capture anomaly patterns of any length, while identifying both short-term and long-term anomalies. Experiments on the Yahoo dataset demonstrate its high accuracy and reliability in anomaly pattern detection, particularly in explaining the reasons and impacts of identified anomaly patterns. this innovation marks a breakthrough in anomaly pattern interpretation, showing significant practicality and interpretability in real-world applications, and provides vital support for decision-making and predictive modeling in time series analysis.

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4th international conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Soumya, T.R. Manohar, S.Solai Ganapathy, N.Bala Sundara Nelson, Leema Mohan, A. Pandian, M.thurai Prathyusha Engineering College Dept. of Cse Tamilnadu Thiruvallur India Cm R Institute of Technology Deptartment of Eee Karnataka Bengaluru India Panimalar Engineering College Department of It Tamilnadu Chennai India Chitkara University Institute of Engineering & Technology Chitkara University Department of Computer Science & Engineering Punjab India Simats Schools of Engineering Saveetha University Department of Information Security Tamilnadu India School of Cse Vellore Institute of Technology Vellore India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

Now-a-days., On-line Social Networks (OSN) plays a major role. It helps to maintain a social bond across world. People can share their information like personal, political and other career related work within minutes. High number of people started indulging in social networks because of its time complexity and easiest way of sharing details. On the other hand, the threats on social networks increased in a rapid way to gain sensitive information of a people sharing important details like passwords, bank details, personal details etc. the online privacy and security threats reports gone to top. It made researchers to involve more on this area of threats in order to protect individual privacy and security regarding social networks. Attackers started to exploit the vulnerabilities of OSNs which highly attracted malicious entities. Each OSNs had ten to hundred million users collectively generate billions of personal sensitive data content. To protect users from exploitation is one of the major challenges. the current research are mainly focused to protect the privacy of an individual's online profile in OSN. the creator constructs a fake profile to pretend himself as a genuine person in these sites. the aim in this paper is to check and detect the trusted and fake profiles created in OSN through machine learning based techniques. By using algorithms like (SVM)Support vector machine, (DNN) Deep neural network, Random Forest to overcome processes such as treating missing value, identification of variable, cleaning and validation of data will be done on the entire dataset. Here, python language is used for improving the efficiency of code. With help of this, millions of fake profiles can be detected automatically. Additionally, various machine learning algorithms are compared and discussed along with the classification report. By identifying the confusion matrix, the results show the effectiveness of the proposed machine learning algorithm, which has best accuracy, precisio

关键词： Social networking (online)

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