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Risk Assessment of Stock Market analysis using Time Series analysis 14

Risk Assessment of Stock Market Analysis using Time Series A...

引用

14th International conference on Advances in Computing, control, and Telecommunication Technologies, ACT 2023

作者： Patni, Sayem Gadekar, Amit R. School of Computer Sciences and Engineering Sandip University Maharashtra India

Stock market forecasting and risk assessment heavily influence investors' financial decisions. Many depend on news announcements to judge the purchase or sale of risky stocks. Due to the complexity and ambiguity of natural languages, however, reliable modeling and risk management of stock market patterns derived from news releases is challenging. In contrast to past work in this field, which generally uses bag-of-words to extract tens of thousands of characteristics to construct a prediction model, this study employs a novel approach that extracts tens of thousands of features directly from the text itself. We present a Time Series Forecasting based method for financial/stock market prediction with risk assessment using a histology dataset. In particular, Time Series Forecasting is performed at the pre-processing stage to extract time period-related characteristics from financial news. Using the collected features, a time series-based metaheuristic CNN (MCNN) model is used to construct a prediction with risk assessment. Using a model based on MCNN, we hope to get better outcomes than previous methods in terms of speed and precision while minimizing risk. The performance is attributable to the time analysis performed during the pre-processing step since it decreases the feature dimensions substantially. Using the suggested method, we aim to enhance the accuracy of stock price forecasts based on a selection of data sets. © Grenze Scientific Society, 2023.

关键词： Risk assessment

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Research on Robust Passenger Flow control Model of Metro Under a Short-Turning Operation Pattern

Research on Robust Passenger Flow Control Model of Metro Und...

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International conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Xue, Hongjiao Jia, Limin Peng, Fei Sun, Xuan Wang, Hongyan Li, Ying School of Automotive Engineering Beijing Polytechnic Beijing100176 China Beijing Automation Engineering School Beijing100101 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9789819639564

Passenger flow control is the most direct and effective way to solve the problem of metro line congestion. To solve the problem that the random arrival characteristics of passenger flow affect the reliability of the passenger flow control strategy, this paper provides a robust passenger flow control strategy for a metro line under a short-turning operation pattern in peak hours according to the dynamic, time-varying and random characteristics of passenger flow. Firstly, a short-turning operation pattern environment is constructed, and the arrival rate of historical inbound passenger flow is obtained by statistical analysis. Poisson process is used as a parameter to generate multi-scene random arrival passenger flow data. Secondly, a robust passenger flow control and ride strategy is proposed considering the passenger flow OD of the stations inside and outside the short-turning sections. Combining with the strategy, a robust passenger flow collaborative control model is constructed with multi-scene random arrival passenger flow data as input. Finally, an empirical study was carried out on the Beijing Fangshan metro line. The results show that the robust passenger flow control strategy is applicable to the three passenger flow scenarios, and can ensure the passengers’ safety in the three passenger flow scenarios. Moreover, compared with the separate passenger flow control in a single passenger flow scenario, the passenger waiting time under the robust passenger flow control strategy only increases by 1.19%, 4.47% and 4.42% respectively, which basically ensures the travel efficiency of passengers. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Urban transportation

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Design and analysis of a Comprehensive database Containing 24,000 Polycrystalline Diamond Compact Cutter Scrape Tests

Design and Analysis of a Comprehensive Database Containing 2...

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2022 IADC/SPE International Drilling conference and Exhibition, DC 2022

作者： Murat Panayirci, H. Skoff, Greg Shen, Yuelin Schlumberger United States

ISBN: (纸本)9781613998427

This paper presents the design and analysis of a database containing more than 24,000 polycrystalline diamond compact (PDC) cutter scrape tests performed using a pressurized experimental laboratory setup. This unique data set contains high-frequency (2 kHz) measurements of the three-axis forces acting on the cutter in various rocks, cutter types and sizes, cutter orientations, depths-of-cut (DOC), and confining pressures. The database can be accessed, visualized, sorted, classified, and analyzed in detail, offering substantial opportunities to increase the knowledge of the rock-cutting process by PDC cutters. Using this database, the authors initially analyzed the rock type, cutter orientation, and DOC effects on the forces acting on the cutter. By using signal processing techniques, algorithms were developed to extract features from the force data. For example, each test was scored based on the rock chipping that occurred during cutting, which manifests itself as sawtooth patterns in the force data. These data allowed for qualitative evaluation of the effects of confining pressure and DOC on the chipping. In addition, the rock types most prone to chipping were identified. This massive dataset allowed for statistical quantification of the forces acting on the cutters, which resulted in new ways for modeling the rock-cutting process, which is inherently stochastic. Furthermore, it is also possible to quantify the efficiency improvement with different cutter shapes when cutting different rocks, which assists in selecting the correct cutter shape for the targeted application to improve drilling performance. In summary, this extensive database provides novel understandings of the rock-cutting process, which are fundamental to the efficiency of the drilling operations, drilling dynamics modeling, and drilling tool development. Copyright 2022, IADC/SPE International Drilling conference and Exhibition.

关键词： Rocks

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Application of Decisions' Roots for data analysis on Example of dataset with Magnetic Susceptibility Values of the Brain Veins and the Alzheimer's Disease 4

Application of Decisions' Roots for Data Analysis on Example...

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4th International conference on control Systems, Mathematical modeling, Automation and Energy Efficiency, SUMMA 2022

作者： Kozhemyakin, Leonid V. Alekseev, Aleksandr O. Nikitin, Vladislav N. Perm National Research Polytechnic University Perm Russia

ISBN: (数字)9781665456593

ISBN: (纸本)9781665456593

In this paper, an attempt was made to approximate medical data and establish the relationship between the magnetic susceptibility values of the cerebral veins and the Alzheimer's disease in the form of integrated rating mechanisms, or as they are also called recently 'decisions' roots'. Quantitative susceptibility mapping indicators are used as medical data. These data allowed us to obtain the value of magnetic susceptibility and provides information on oxygen metabolism. This information is important in assessing neuronal metabolism. Significant factors (the values of the magnetic susceptibility of the cerebral veins) were determined using the methods of system-cognitive analysis. These factors can affect the cognitive state of the patient. Discrete function was constructed using the decision roots identification algorithm. This function described with high reliability the initial set of research data (values of the magnetic susceptibility of the cerebral veins) on the condition of patients for the presence of Alzheimer's disease. © 2022 IEEE.

关键词： Magnetic susceptibility

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Research and Application of Security control Technology Based on AGX ORIN 24

Research and Application of Security Control Technology Base...

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4th International conference on Artificial Intelligence, Big data and Algorithms, CAIBDA 2024

作者： Wen, Qianqian Zhang, Xu Lv, Yongqiang Li, Dulin Gao, Fangyu Huang, Lijun Xuchang XJ Software Technology Co. Ltd. Xuchang Henan China China Electric Equipment Group Co. Ltd. Shanghai China

ISBN: (纸本)9798400710247

The frequent occurrence of accidents in substation operation and maintenance scenarios is mainly caused by personnel's illegal operations. To ensure the safe operation of substation operation and maintenance personnel and improve the performance of the substation safety management and control system, this article designs and implements a real-time safety management and control system for substations based on the edge computing device AGX ORIN. This system captures the operation scenes of operation and maintenance personnel in real-time through cameras, utilizes AGX ORIN to perform parallel processing of video data, and employs the YOLOv5s algorithm to conduct real-time detection and analysis of the operation process. It can quickly and accurately identify illegal behaviors, effectively improving the real-time performance and response speed of safety management and control. At the same time, the system combines multi-channel RTSP video stream real-time transmission technology and MQTT protocol-based abnormal information sending technology, enabling background management personnel to monitor the on-site working conditions in a timely and comprehensive manner, achieving intelligent safety management of the substation. The system can detect 14 types of safety risks, with an average detection rate of 91.8% and a reasoning speed of up to 21FPS. With low cost, strong performance, and high algorithm accuracy, the system has been successfully deployed and operated in a 220kV substation in Henan, greatly ensuring the safety of substation operation and maintenance. © 2024 Copyright held by the owner/author(s).

关键词： Crime

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ML-based failure detection approach for predictive maintenance in an industry 4.0 oriented web manufacturing control application 7

ML-based failure detection approach for predictive maintenan...

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IEEE 7th International conference on Advanced Technologies, Signal and Image processing (ATSIP)

作者： Smaoui, Sirine Baklouti, Mouna Univ Sfax Natl Engn Sch Sfax CES Lab Sfax Tunisia

ISBN: (纸本)9798350351491;9798350351484

With the increasing reach of Industry 4.0, the Internet of Things (IoT) and Artificial Intelligence (AI) and many other technologies, have emerged as pivotal components in the era of information technology. Recent progress in AI, and its sub-field machine learning (ML), have influenced numerous research domains inside the industry, achieving improvements that were not possible with conventional optimization techniques, notably illustrated in the case of predictive maintenance. A significant component of predictive maintenance is fault detection. The goal of predictive maintenance is to avoid future machine failure and consequent downtime by anticipating faults in machinery. As a result, the aim of this research is to employ ML models for fault identification process. A concrete dataset was employed with real sensors' outputs allowing to make analysis of faults in machines mounted with various types of sensors. Four distinct supervised ML algorithms were applied in our work in which their hyper-parameters were tuned with both Grid Search and Random Search methods. A comparative study between the algorithms' performances was made in order to select the best one with highest metrics percentages. A web-based application was also developed, to monitor data and send alerts when detecting anomalies either via SMS, email or inside the provided web interface.

关键词： Industry 4.0 IoT AI ML Predictive Maintenance Fault Detection Grid Search Random Search Web Application

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data Analytics for Volunteer-Based Educational Application 16

Data Analytics for Volunteer-Based Educational Application

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16th IEEE AFRICON, AFRICON 2023

作者： Sherif, Omar Zaghloul, Yasmine Sharaf, Nada Informatics and Computer Science The German International University Cairo Egypt German International University Faculty of Engineering Cairo Egypt

ISBN: (纸本)9798350336214

In the world of technology, data have been available easily and in huge amounts. Because of the large amounts of data, Educational data Mining (EDM) is increasingly gaining more importance. Educational data mining is trending as it is the analysis method for analyzing educational data. It involves checking the relationship between the characteristics of students and which features affect their final grades the most. It can also involve predictive modeling by predicting the final grades of students in the future to help educational institutes rescue failing students before they actually fail. Predictive analysis was the main focus in past years where most researchers targeted predicting grades of students. However, not all educational institutes are able to collect the amount of data suitable for machine learning models to achieve good accuracy. That is why the main target of the work presented in this paper is to develop an interactive interface that gives the ability to educational institutes to check by themselves the relation between different factors using correlation mining. The output of the tool is visual with message boxes to make it understandable by users. That is the best way to discover hidden patterns and trends without the need for a large amount of data in addition to removing the cost of deploying machine learning models. The second goal of the work is to give teachers the ability to check the quality of the content of their slides to help them provide a better learning process. © 2023 IEEE.

关键词： Students

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Investigating Welded Region Stability for Thermal analysis with YOLOV3 15

Investigating Welded Region Stability for Thermal Analysis w...

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15th International conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Veeranan, Arunprasad Amalraj, J.Leo Kandavalli, Sumanth Ratna. Bandili, Sunil Kumar Rajendiran, M. Meenakshi, S. Theni Kammavar Sangam College of Technology Department of Mechanical Engineering Tamilnadu India RMK College of Engineering and Technology Science and Humanities Tamilnadu India New York University Tandon School of Engineering Department of Mechanical Engineering New York United States Saint Louis University School for Professional Studies Department of Computer and Information Sciences St. LouisMO63103 United States Panimalar Engineering College Department Computer Science and Engineering Tamilnadu Chennai India Tamilnadu Kavarapettai India

ISBN: (纸本)9798350370249

This paper presents a novel methodology that integrates YOLOv3, a cutting-edge object identification model, with thermal analysis approaches to examine and evaluate the structural integrity of welded areas. The technology being presented utilizes the sophisticated object detection capabilities of YOLOv3 to reliably find and track specific regions of interest inside a welded structure. By incorporating this object detection model into thermal analysis tools, it becomes possible to see and study the temporal temperature distribution and thermal characteristics of the discovered locations. The study entails the collection of thermal data through the utilization of sophisticated sensors and thermal imaging technologies. This approach aims to acquire a thorough comprehension of the distribution and dissipation of heat inside the welded area under different operational circumstances. This methodology enables the continuous monitoring of operations in real-Time, enabling the identification of possible concerns such as excessive heat, concentrated stress, or material distortions. These factors have the potential to undermine the integrity and security of welded structures. The findings of this study make a valuable contribution towards improving the quality control and maintenance practices of welded components. © 2024 IEEE.

关键词： Thermal modeling

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Convolutional Neural Network Method Accuracy analysis in Detecting Greek Alphabet Letters 3

Convolutional Neural Network Method Accuracy Analysis in Det...

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3rd International conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Muryanto, Muhammad Maulana Aryo Gunawan, Putu Harry Indwiarti Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9798350394559

The Greek alphabet is a typewriting system developed by the Greeks in 1000 BC. The Greek alphabet also belongs to one of the ancestors of the writing system used in the modern era after the Latin alphabets, and to this day, the original Greek Alphabet still exists and uses some of its alphabets in mathematical formula equations such as alpha (α), beta (β), gamma (γ), and many more. Each of these symbols is now more commonly used in the mathematical formula theorems that many civilians recognize from the symbols. In its development, the researchers built a system of deep learning with the image classifier of the symbol using the CNN method. The research aims to learn how to implement CNN algorithms with SoftMax, SGD, and ReLU activation functions for image classification and to analyze the performance of the CNN Algorithm with the activation of softmax and SGD in the Greek alphabet symbol classification. In image classification, CNN was employed due to its proficiency in extracting intricate visual features, comprehending spatial arrangements, and preserving translational invariance. This enables CNN to proficiently capture crucial image-specific attributes, establishing its preeminence in image analysis and classification. The data set used is a two-dimensional image of the Greek alphabet symbol created with writing using the Microsoft Paint application. The collected data amounts to 1000 images, with 100 images per class, and has different resolution and size in each class. The data will then be divided into 70% training data, 20% validation data, and 10% test data. CNN method modeling built as many as nine models with four layers with different activation functions (different and previously defined), with one layer having a filter size of 64 and another layer having a matrix size of 2x2. The training process of the CNN model with the training data that has been equalized will have the optimal resolution. In the performance matrix analysis of the CNN model, it will

关键词： Convolutional neural networks

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Creation of a Knowledge Space by Semantically Linking data Repository and Knowledge Management System - a Use Case from Production Engineering 10th

Creation of a Knowledge Space by Semantically Linking Data R...

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10th IFAC Triennial conference on Manufacturing Modelling, Management and control (MIM)

作者： Sheveleva, Tatyana Wawer, Max Leo Oladazimi, Pooya Koepler, Oliver Nuernberger, Florian Lachmayer, Roland Auer, Soeren Mozgova, Iryna TIB Leibniz Informat Ctr Sci & Technol Welfengarten 1B D-30167 Hannover Germany Leibniz Univ Hannover Inst Prod Dev Univ 1 D-30823 Hannover Germany Leibniz Univ Hannover Inst Werkstoffkunde Mat Sci Univ 2 D-30823 Hannover Germany

The seamless documentation of research data flows from generation, processing, analysis, publication, and reuse is of utmost importance when dealing with large amounts of data. Semantic linking of process documentation and gathered data creates a knowledge space enabling the discovery of relations between steps of process chains. This paper shows the design of two systems for data deposit and for process documentation using semantic annotations and linking on a use case of a process chain step of the Tailored Forming Technology. Copyright (C) 2022 The Authors.

关键词： Knowledge Management in Production Semantic Annotation Research data Management

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