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Research on Transferable Patent recognition Based on machine learning 2021

Research on Transferable Patent Recognition Based on Machine...

引用

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...

引用

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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Sentiment Prediction based on Movie Reviews using machine learning Techniques

Sentiment Prediction based on Movie Reviews using Machine Le...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： S Anitha P. Gnanasekaran Department of Computer Applications B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai Department of Information Technology B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai

Internet has become a rich source of user-generated his/her general opinion for products, services, social events, company strategies etc. this has created a need for emotion mining using sentiment analysis. there is a wealth of information and data available today because technology is the foundation of every business. As a result, a recommendation system is useful for managing this massive quantity of data and rapidly and in accordance with the user’ s tastes filtering out the relevant data. this paper describes a system for making movie recommendations. the major goal of this study has been to examine several machine learning techniques to classify a text into positive, neutral and negative emotion polarities. An algorithm used to train a Movie review data by support vector machine (SVM), Logistic regression (LR), XGB classifier models.

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Research on Ornamental Fish recognition Base on Improved Shufflenet V2 Network

Research on Ornamental Fish Recognition Base on Improved Shu...

引用

Big data & Artificial Intelligence & Software Engineering (ICBASE), international conference on

作者： Xingyun Zhang Hongming Chen Longqing Zhang Qian Chen Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhejiang Ocean University Zhoushan China School of Computer Science Guangdong University of Science and Technology Dongguan China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China

With the advancement of computer vision technology, fish recognition plays a crucial role in various fields such as aquaculture processing and disseminating fish knowledge. In this study, we propose an enhanced and lightweight fish recognition algorithm to accomplish fish classification recognition with limited data samples. Based on the ShuffleNet V2 network, we incorporate Transfer learning and the SE attention mechanism. Additionally, we replace the ReLu activation function with the AM-Softmax activation function. Comparative evaluations are conducted against Resnet50, MobileNet V2, MobileNet V3, and the original ShuffleNet V2 network model. Our experimental findings demonstrate that the proposed algorithm outperforms the aforementioned models, resulting in recognition accuracy improvements of 16.01%, 19.05%, 15.04%, and 14.44% respectively. the final recognition accuracy achieved is 97.20%, successfully attaining the desired outcome.

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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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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...

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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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mining of Behavioral Characteristics of Power Users Based on Multidimensional User Profiles and Big data

Mining of Behavioral Characteristics of Power Users Based on...

引用

Computer Science and Blockchain (CCSB), international conference on

作者： Xin Gao Shijie Wang Zhanfeng Wang Ying Liu Shiyu Song State Grid Northwest Electric Power Company Limited Xi'an Shannxi China State Grid Shaanxi Electric Power Co. Ltd. Information and Communication Company Xi'an Shannxi China Shaanxi Daqin Electric Energy Group Co. Ltd. Information and Communication Branch Xi'an Shannxi China

ISBN: (数字)9798350356502

ISBN: (纸本)9798350356519

this paper proposes a method for mining behavioral characteristics of power users based on multidimensional user profiles and big data, aiming to deeply explore the behavioral characteristics of power users through the construction of multidimensional user profiles and big data analysis techniques. Establish a user multi-source feature label system from three dimensions: user behavior, electricity consumption characteristics, and consumption habits, and construct a multi-dimensional user portrait model. By utilizing advanced data mining and machine learning algorithms, key features such as user electricity consumption patterns, demand response potential, and energy-saving behavior preferences are analyzed to provide personalized services, optimize power resource allocation, and promote energy conservation and emission reduction for power companies. the experimental results show that this study can not only accurately and efficiently explore the behavioral characteristics of power users, but also help improve the intelligence level of power services and effectively enhance user satisfaction, promoting the development of the power industry towards a more efficient, green, and sustainable direction.

关键词： Electric potential machine learning algorithms Electricity Energy conservation Big data User experience Power industry Demand response data mining Resource management

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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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Active Prewarning Technology for Smart Grid Based on Unsupervised learning

Active Prewarning Technology for Smart Grid Based on Unsuper...

引用

machine learning and Intelligent Systems Engineering (MLISE), international conference on

作者： Ruiting Qu Xiaolei Tian Shuo Chen State Grid Liaoning Information and Communication Company Shenyang China

ISBN: (数字)9798350375077

ISBN: (纸本)9798350375084

this paper proposes an early warning technology for power grid cloud platform microservice architecture alarm information based on unsupervised learning, discovers potential abnormal patterns and rules, sends out early warning signals in a timely manner, and improves the ability to identify and process power grid cloud platform microservice architecture alarms. According to the above steps, based on the establishment of the power grid business microservice operation alarm log alarm information knowledge base, this article will study the cloud platform operation monitoring data scoring algorithm, classify the cloud platform monitoring information, and then filter out the suspected abnormal data. For different business types of complex scenarios, research on abnormal information classification mining technology based on statistical models, proximity, clustering and classification, apply improved K-means clustering[1] and LOF outlier detection technology[2] to establish a combined detection model, and detect anomalies the data is clustered and outliers detected to achieve active early warning technology of unsupervised learning.

关键词： Knowledge based systems Microservice architectures Clustering algorithms data models Smart grids Classification algorithms Unsupervised learning

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