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Analysis and application of mine water inrush warning and prevention technology based on machine learning 4

Analysis and application of mine water inrush warning and pr...

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4th international conference on Advanced Electronic Materials, Computers and Software Engineering, AEMCSE 2021

作者： Xiao, Yigai Sun, Guoquan Liu, Yunqiu Central South University School of Resources and Safety Engineering Changsha410083 China Sinosteel Maanshan General Institute of Mining Research Co. Ltd. Maanshan Anhui243000 China Sinosteel Nanjing Huaxin Technology Co. Ltd. Nanjing211100 China

ISBN: (纸本)9781665415965

In the process of mine construction and production, mine water hazards often cause heavy casualties and property losses, and even environmental pollution problems that are difficult to recover. To prevent such accidents, various countries and mine enterprises have paid great attention to them and based on conventional prevention and control methods, they have continuously tried to introduce new technologies and new methods such as informatization and intelligence. this makes the hydrological data generated during mine mining explode, but these data have never been fully and effectively used. therefore, how to use mine hydrological data to better serve the safety of mine water inrush has become an important breakthrough in mine water inrush prevention. A mine water inrush safety guarantee system based on machine learning is implemented to realize a mutually connected system closed-loop, timely warn mine water inrush accidents, and recommend targeted solutions. the technical support for multi-angle is comprehensively provided to systematically prevent and control mine water inrush accidents. © 2021 IEEE.

关键词： machine learning

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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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Deep learning based Human Activity recognition in Smart Home

Deep Learning based Human Activity Recognition in Smart Home

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data Engineering and Communication Systems (ICDECS), international conference on

作者： K. Anbazhagan G. Swamy R. Janani Amarsinh Farakte Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Vignana Bharathi Institute of Technology Andhra Pradesh India Department of Computer Science and Business Systems Panimalar Engineering College Chennai India Department of Electronics and Telecommunication Engineering Sant Gajanan Maharaj College of Engineering Maharashtra India

Human activity recognition (HAR) in the context of smart homes has attracted considerable interest because to its potential to increase residents’ quality of life, safety, and energy efficiency. this study dives deeper into the use of deep learning (DL) algorithms for human activity recognition in a smart home scenario. A realistic approach for distinguishing distinct human behaviors like as walking, sitting, cooking, and sleeping is provided by the combination of artificial intelligence and sensor technology. this study offers FCN-LSTM, a revolutionary technique for HAR in smart homes that combines a Fully Convolutional Network (FCN) architecture with a Long Short-Term Memory (LSTM). the proposed FCN-LSTM model capitalises on the strengths of both architectures, combining the FCN’s ability to capture specific information spatially with the LSTM’s experience modelling temporal correlations. this hybrid method efficiently captures both spatial and temporal variability inherent in human activities, overcoming the HAR issues. the FCN-LSTM model is trained on a huge dataset acquired from sensors deployed in a smart home setting, which includes a wide range of actions encompassing ordinary daily routines. this method is used to the Wireless Sensor data mining (WISDM) dataset, which was produced from data collected from a large number of people who engaged in six different activities: walking, sitting, downstairs, running, standing, and upstairs. According on the experimental data, the FCN-LSTM model outperforms established approaches and standalone systems. the results show that the FCN-LSTM algorithm performs well in recognizing human actions. the FCN-LSTM algorithm achieves a testing precision of 95.99%, with recall and f-measure of 97.11% and 99.99%, respectively.

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A Review-and-Reviewer based approach for Fake Review Detection 4

A Review-and-Reviewer based approach for Fake Review Detecti...

引用

4th international conference on Electrical, Computer and Communication Technologies, ICECCT 2021

作者： Bhopale, Janhavi Bhise, Rugved Mane, Arthav Talele, Kiran Sardar Patel Institute of Technology Department of Electronics Engineering Mumbai India

ISBN: (纸本)9781665414807

this paper presents an approach to fake review detection, essentially for online hotel reviews, by combining the review-based approach and reviewer-based approach. Different Natural Language Processing techniques such as tokenization, lemmatization, vectorization, etc. are used to extract insightful features from the review text data. After text mining, the data is used to train different classification models using machine learning algorithms that detect fake reviews. After evaluating the models, a comparison is made based on the performance metrics. Furthermore, a web based user interface is created to provide a platform that combines the knowledge of the input user information with the chosen machine learning model to perform fake review detection on the input data. © 2021 IEEE.

关键词： machine learning

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Jaśkowski–Fitch Natural Deduction Proofs for Metadata Extraction through machine learning Systems

Jaśkowski–Fitch Natural Deduction Proofs for Metadata Ext...

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6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

作者： Cuconato, Simone Department of Humanities University of Calabria CS Rende87036 Italy LUISS University - Rome Rome00198 Italy

ISBN: (纸本)9789819780921

In the present paper, I will focus on propositional knowledge, proof theory, and the deductive transmission of information in data mining. the application of proof theory to machine learning-based metadata extraction systems will allow us to intend metadata extraction as mathematical objects. More in detail, I will consider a recent first-order logic (FOL)-based applied ontology for metadata extraction, by the name MAke Decision for Metadata Extraction (MADME), and I will build a specific logical system for declarative sentences applied to the extraction of metadata through machine learning systems. MADME’s strength is that it bases its decision-making procedure on a rigorous logical structure. the objective of this paper is to demonstrate how the logic in MADME can be optimized using Jaśkowski–Fitch natural deduction proofs. Jaśkowski–Fitch systems will determine new operations among the best machine learning-based metadata extraction systems from heterogeneous documents sources and, consequently, will support the research community in knowledge extraction and management. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decision making

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User Behavior Analysis to Detect Insider threat by Using machine learning Algorithms

User Behavior Analysis to Detect Insider Threat by Using Mac...

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

作者： Nitin Dixit Rishi Gupta Pradeep Yadav Computer Science & Engineering Manipal University Jaipur India Computer Science & Engineering Institute of Technology & Management Gwalior India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

Insider threats, in which someone within an organization poses a risk, are widely regarded as the most dangerous in cybersecurity. Many analysts are working hard to identify and prevent these risks. However, the most difficult aspect of cybersecurity is determining which computers and users are affected. these computers are connected to servers or monitored by them, but there are no clear indications that attackers are targeting specific users. It is critical for the organization to defend against both internal and external cyberattacks. Cyber security measures should be up to date is critical for protecting our organizations. External threats can be identified easily but to identify insider threats is a complex task. Recently, there has been an increased emphasis on computer security, particularly in developing internal risk detection systems. Artificial intelligence, Internet of things, distributed computing, data mining, portable ledgers, and data discovery are some of the advanced methods that have gained popularity for mitigating insider risk. Each of these approaches is unique and takes insider threats into account to a varying degree. In this paper, we use different machine learning models to find out the insiders.

关键词： Computers machine learning algorithms Distributed databases Organizations machine learning Servers Internet of things

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Early Detection of Chronic Diseases Using machine and Deep learning Algorithms

Early Detection of Chronic Diseases Using Machine and Deep L...

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

作者： Bhushan Fulkar Trishul Dhale Utkarsha Pacharaney Shruti Deshmukh Department of Artificial intelligence And Data science Datta Meghe institute of Higher Education and Research Wardha India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Today's persons suffer from a wide variety of diseases due to various influences and choices made at the community level. thus, to prevent the occurrence of such illnesses persistent identification and prediction are paramount. Manually determining the disorders is generally challenging for the doctors to be accurate with the exact numbers. the objective of this study is thus to use these chronic conditions and an accompanying test to select people who will be predominantly affected by these diseases. this might be done by making certain that this categorization correctly singles out people with chronic diseases employing the most advanced data learning technique. Another tough issue is a prediction concerning health conditions. It can therefore be concluded that data mining is an important component of sickness prediction. Employing automatic feature extraction and disease prediction by CNN, distance computation employing KNN for identification of the closest match or data within the data set, and the last disease prognosis of a broad disease, the proposed system enables the determination of patient's symptoms. the patient's life history, medical history of consultations, and illness symptoms were used to construct the data client set. It is on this comprehensive sickness prediction that all of these details were considered. the above said method is compared with logistic regression, Decision tree and Naive Bayes in the conclusion part of the paper.

关键词： Deep learning Accuracy machine learning algorithms Medical services Feature extraction Classification algorithms History Convolutional neural networks Diseases Biomedical imaging

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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...

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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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