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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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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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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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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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Research on Power Safety Monitoring Action recognition Algorithm through Neural Network and Deep learning

Research on Power Safety Monitoring Action Recognition Algor...

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

作者： Xingtao Bai Ningguo Wang Yongliang Li Hairong Luo Bo Gao State Grid Ningxia Electric Power Co. LTD. Electric Power Research Institute Yinchuan Ningxia China State Grid Ningxia Power Co. LTD Yinchuan Ningxia China

this paper studied the methods for improving the accuracy and efficiency of the action of power employees by deeply studying the application of neural network algorithm in power safety supervision video. Firstly, this paper summarizes the research status of power safety monitoring system and related action recognition algorithms. For the power industry, timely and accurate identification of workers' movements is essential for accident prevention and management. In this context, various motion recognition algorithms are emerging, among which neural network algorithm has attracted much attention due to its excellent performance in image processing and pattern recognition. through deep learning, neural networks can automatically learn key features from a large number of video data, providing a more reliable means for human action recognition. Secondly, this paper introduces in detail the proposed method of power safety monitoring video personnel action recognition based on neural network algorithm. through the optimization of neural network structure and the careful selection of training data, we construct an efficient and accurate action recognition model. the model can quickly and accurately identify the actions of different personnel at work by monitoring the video of electric power operation, including common operation actions and special actions in emergency situations. Our method can more comprehensively understand the various actions of personnel in the electric power environment. through the analysis of a large number of experimental results, we verify the effectiveness and robustness of the proposed algorithm. Compared with traditional methods, the motion recognition algorithm based on neural network has achieved significant improvement in accuracy and response speed. this proves the practical application prospect of this method in the field of power safety supervision.

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Human or machine? It Is Not What You Write, But How You Write It 25

Human or Machine? It Is Not What You Write, But How You Writ...

引用

25th international conference on pattern recognition (ICPR)

作者： Leiva, Luis A. Diaz, Moises Ferrer, Miguel A. Plamondon, Rejean Aalto Univ Espoo Finland Univ Atlantico Medio Las Palmas Gran Canaria Spain Univ Las Palmas Gran Canaria Las Palmas Gran Canaria Spain Polytech Montreal Montreal PQ Canada

ISBN: (纸本)9781728188089

Online fraud often involves identity theft. Since most security measures are weak or can be spoofed, we investigate a more nuanced and less explored avenue: behavioral biometrics via handwriting movements. this kind of data can be used to verify whether a user is operating a device or a computer application, so it is important to distinguish between human and machine-generated movements reliably. For this purpose, we study handwritten symbols (isolated characters, digits, gestures, and signatures) produced by humans and machines, and compare and contrast several deep learning models. We find that if symbols are presented as static images, they can fool state-of-the-art classifiers (near 75% accuracy in the best case) but can be distinguished with remarkable accuracy if they are presented as temporal sequences (95% accuracy in the average case). We conclude that an accurate detection of fake movements has more to do with how users write, rather than what they write. Our work has implications for computerized systems that need to authenticate or verify legitimate human users, and provides an additional layer of security to keep attackers at bay.

关键词： Handwriting Biometrics Verification Classification Liveness Detection Kinematic Models Deep learning

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Developments in Text Extraction from Images: Comparative Evaluation of Various Techniques

Developments in Text Extraction from Images: Comparative Eva...

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

作者： Akash Burnwal Nishi Choudhary Saurav Kumar Mamata P. Wagh Hrishika Computer Science And Engineering C.V. Raman Global University Bhubaneswar India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

Extracting text from images is essential in image processing and computer vision, with applications in document digitization and automated text recognition. this paper reviews various text extraction techniques, categorized into thresholding, rough set and fuzzy set methods, clustering, edge detection, and machine learning. thresholding techniques such as Gaussian, Otsu, adaptive, and double-edge methods are explored. Rough set and fuzzy set methods, which handle uncertainty in image data and improve text segmentation, are reviewed. Clustering techniques, such as K-means and density-based methods, are studied for their effectiveness in grouping pixels for text isolation. Edge detection techniques, including Sobel, Roberts, Canny, Morphological Component Analysis (MCA), and Laplacian, are examined for their role in enhancing text boundary identification. machine learning approaches, such as Support Vector machines (SVM), Contrastive Language-Image Pre-training (CLIP), Hidden Markov Models (HMM), and hybrid BiLSTM-CNN models, are analyzed for their ability to improve accuracy in noisy environments. this review compares these techniques, highlighting their strengths, challenges, and applications for text extraction.

关键词： Support vector machines Fuzzy sets thresholding (Imaging) Accuracy Text recognition Reviews Image edge detection Hidden Markov models Rough sets machine learning

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Deep learning-based vehicle tracking and traffic event detection

Deep learning-based vehicle tracking and traffic event detec...

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Computer Vision, Image and Deep learning (CVIDL), international conference on

作者： Guohao Sun Kehuan Yan Chengbin Fan School of Mathematics Tianjin University Tianjing China School of Computer and Data Science Fuzhou University Fujian China School of Electrical Engineering and Automation Xiamen University Tan Kah Kee College Fujian China

Time-series data mining plays an important role in big data decision making because it can reveal the development pattern of things. Similar concatenation of temporal data is a fundamental prerequisite for data twinning., whose core objective is to find all similar temporal data pairs according to a given similarity metric. Dynamic temporal regularization (DTW) has been widely used in many fields., such as target detection., trend prediction and fault identification., as the best data alignment method on temporal data. We use deep learning to extract vehicle trajectories and perform behavioral pattern learning., and finally use an improved DTW algorithm to pre-process the trajectory data and solve the distance function to achieve matching between the trajectories of the event sequence to be measured and the typical trajectory data patterns. By comparing the indicators with the unimproved DTW algorithm., the research results show that this traffic condition recognition method is stable and reliable., and can maintain high matching accuracy with significantly reduced computation., high success rate and good real-time performance.

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A combined data-driven method for electricity theft identification 4

A combined data-driven method for electricity theft identifi...

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4th international conference on pattern recognition and Artificial Intelligence, PRAI 2021

作者： Li, Qilin Zhang, Zhemin Li, Yun Jin, Wenxing Metering Center of Sichuan Electric Power Corporation Chengdu610045 China College of Computer and Science and Technology Shanghai University of Electric Power Shanghai200090 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9781665413220

Electricity theft is one of the important sources of non-technical losses in the power grid, which has been an important problem faced by electricity enterprises. the data collected by the advanced metering infrastructure (AMI) makes it possible to identify electricity theft based on a data-driven method. However, among the data-driven methods, the methods based on supervised learning, unsupervised learning and state estimation have their advantages and disadvantages, which can not play a full role. this paper presents a combined data-driven identification framework, which combines the advantages of the supervised method (day-week-month load convolutional neural network (DWML-CNN)), unsupervised method (clustering by fast search and find of density peaks (CFSFDP)) and state estimation method. Firstly, the features are extracted based on the state estimation, then the unsupervised and supervised methods are used to determine the suspicion ranks respectively. Finally, a comprehensive evaluation is made according to the two suspicion ranks. Experimental results show that the framework has good performance on the data set of the State Grid Corporation of China (SGCC). © 2021 IEEE.

关键词： State estimation

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