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Application of Wave Cluster Algorithm Based on Kernel Optimization in pattern recognition of High-Frequency Financial data

Application of Wave Cluster Algorithm Based on Kernel Optimi...

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Cloud Computing, Big data Applications and Software Engineering (CBASE), international conference on

作者： Dazhi Yue Business School Xiamen Institute of Technology Xiamen China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

High-frequency financial data has the characteristics of high nonlinearity, non-stationarity and noise, which brings great challenges to traditional data mining algorithms. In this study, we propose a Wave Cluster algorithm based on kernel function optimization, which aims to improve its performance in pattern recognition of high-frequency financial data. The Wave Cluster algorithm has attracted much attention for its robustness to noise and multiresolution analysis capability, but it still has limitations when dealing with complex nonlinear patterns. In this paper, kernel-based functions are introduced to map the original high-dimensional financial data to a higher-dimensional feature space. This nonlinear transformation can effectively capture the potentially complex relationships in the data. Subsequently, the modified Wave Cluster algorithm is applied in the transformed feature space, and high-frequency trading data from the China stock market, including price, trading volume and order book information, are used in the empirical study. Experimental results show that compared with traditional methods, the proposed algorithm achieves significant improvement in the tasks of identifying market microstructure, detecting abnormal trading patterns and predicting short-term price movements. Especially during periods of high volatility, the algorithm shows greater stability and accuracy.

关键词： Accuracy Software algorithms Noise Time series analysis Clustering algorithms Prediction algorithms Stability analysis Kernel Stock markets Optimization

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A New SAFR Tool for Face recognition Using EGV-LBP-CMI-LDA Wrapped with Secured DWT Based Steganography

A New SAFR Tool for Face Recognition Using EGV-LBP-CMI-LDA W...

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IEEE international Advance Computing conference

作者： Shami Jhodge Girija Chiddarwar Gitanjali Shinde Department of MCA Sinhgad College of Management Department of Computer Engineering Sinhgad College of Engineering Department of Computer Engineering SKN College of Engineering

ISBN: (纸本)9781467345279

Face recognition technique nowadays is emerging as the most significant and challenging aspects in terms of security for identification of images in various fields viz. banking, police records, biometric etc. other than an individual's thumb and documented identification proofs. Till date for efficient net banking to be initiated, one has to provide the appropriate user name and password for purpose of authentication. This project introduces a vehicle to take a step forward in easy and more reliable authentication of an individual by providing Face Image along with User Name and Password to the system. In this an individual's face is identified by biometric authentication support with which, only a person whose account is, can access it. However while transferring this sensitive data of user image, from client machine to bank server it has to be protected from hackers and intruders from manhandling it, hence it is transferred using covert communication called Wavelet Decomposition based steganography. As face images are affected by different expressions, poses, occlusions, illuminations and aging over a period of time and it differs from the same person than those from different ones is the main difficult task in face recognition. Whenever image information is jointly co-ordinated in three aspects viz. image space, scale and orientation domains they carry much higher clues than seen in each domain individually. In the proposed method combination of Local Binary pattern (LBP) and Gabor features are used to increase the face recognition performance significantly to compare individual's face presentations. Hence face recognition and representation of Gabor faces are done using E-GV-LBP and CMI-LDA based feature recognition method. Gabor faces uses space, scale and orientation to support accurate face recognition, making net banking easier, authentic, reliable and user friendly.

关键词： DWT (Discrete Wavelet Transformation) E-GV-LBP (Effective Gabor Volume Linear Binary pattern) CMI (Conditional Mutual Information)

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Post-COVID Cardiovascular Risk Assessment Using an Efficient machine learning Model

Post-COVID Cardiovascular Risk Assessment Using an Efficient...

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Advancement in Technology (ICONAT), international conference for

作者： D Srinuvasa Rao Desidi Narsimha Reddy Lova Naga Babu Ramisetti M S Radha Manga Mani Harikrishna Pathipati Siva Kumar Pathuri Mohana Prasad Mendu Dept of CSE GMR Institute of Technology Rajam AP India Soniks Consulting LLC 101 E park blvd Plano TX USA Information Technology 10333 Harwin Dr. #375e Houston TX Department of Information Technology SRKR Engineering College Bhimavaram AP India Information Technology ITG Technologies10998 S Wilcrest Dr Houston TX USA Dept of CSE Koneru Lakshmaih Education Foundation Vaddeswaram AP India Dept of CSE Malineni Suseelamma Womens’s Engineering College PrakasamDst Singarayakonda AP India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Individuals who were exposed to COVID-19 in different parts of their bodies, including heart attacks, faced major consequences. The frequency of cardiovascular diseases is increasing in the healthcare industry. This illness most commonly affects people aged 20 to 48. In supervised learning, data mining mainly depends on categorization algorithms. Using various combinations of calculations and algorithms, the information obtained from hospital data analysis is used for early-stage prediction of post-Covid cardiovascular disease. machine learning is one of the strange inventions that have been widely used in a variety of fields, including the application of medical services to disease prediction. We examined the accuracy of machine learning algorithms that can be used in this study to predict heart disease analyzes and predict overall risk. One of the most current data mining methods is classification. The classification method’s primary goal is to keep the data in the correct class. In this work, classification methods such as Logistic Regression Classifier, Naive-Bayes, and Ensemble Cardiovascular Prediction Classification Algorithm were used (ECVDPCA) and the dataset is taken from Kaggle repository. The goal of this study is to find the most exact classification approach and to improve accuracy in predicting Post-Covid Cardio-Vascular illness. Python was used to get the ideal solution in this case to predict the accuracy of the classifier in which the proposed classifier gave the best accuracy when compared with the other two classifiers i.e., 95

关键词： COVID-19 Technological innovation Accuracy machine learning algorithms Supervised learning Stacking Predictive models Prediction algorithms Classification algorithms data mining

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A Hybrid Approach for Intrusion Detection System using data Minining and Artificial Neural Network

A Hybrid Approach for Intrusion Detection System using Data ...

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： Anjali Pathania Vivek Ghai Chandigarh University Mohali Candigarh India

ISBN: (纸本)9781665438124

This paper is going to present a hybrid model for IDS using ANN and data mining approaches. it is important to deploy a hybrid network using ANN based and data mining based IDS systems that can enable the system in making decisions on intrusion in a mobile environment. MANET is an infrastructure-less ad-hoc network. It is a collection of mobile nodes that are connected in an arbitrary and dynamic manner. ANN (Artificial Neural Network) is a type of machine learning scientific and statistical model that is used by computer systems in order to perform a task without involvement of explicit intrusion, rather than relying on interference and patterns instead. As there is node mobility that can impose different security issues, MANETs are found more susceptible in security provision. In order to resolve this security issue, some authentication and encryption techniques can be proposed for the first-line defensein order to mitigate the security risks. However, complete eradication of these types of risks is next to impossible. For theSecond line defense, the necessity of an Intrusion Detection System (IDS) is essential in this case. IDS can be referred as a method tool or resource that can help to detect access and warn for any activity of unapproved or unauthorized network activity. The current research has been done to prevent the network from the intruders. The number of attackers has been increased in the network because of more usage of technology and wireless network. data is not allowed by the attackers to transmit successfully from source node to destination node. These intruders want to steal data and let to the unsuccessful transmission. In this research we are deploying 48 to 66 nodes in a defined communication area of 1000*1000 height and width. While starting the transmission source node and destination node is defined. After that while using IDS and AODV protocol route is created between destination and source node. If network decreases its performance will

关键词： Intrusion detection Artificial neural networks data models Classification algorithms Encryption Security data mining

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Efficient Classification of Bird Species Using Photographic Images: A MobileViT Based Approach

Efficient Classification of Bird Species Using Photographic ...

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Artificial Intelligence For Internet of Things (AIIoT), international conference on

作者： Venkata Ramana Murthy Polisetty Sp Chokkalingam Department of Computer Science and Engineering (AIE) Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

abstract-In the quest to enhance biodiversity conservation and support ecological monitoring, the identification of bird species through automatic recognition systems has emerged as a vital tool. This study introduces a novel approach employing the Mobile Vision Transformer model, capitalizing on its efficiency in handling complex visual data across a comprehensive dataset encompassing 525 bird species. MobileViT integrates the advantages of Convolutional Neural Networks (CNNs) with Transformer technology, surpassing typical deep learning models in terms of feature extraction and contextual comprehension abilities. Incorporating advanced data augmentation methods has bolstered the dataset’s variability, ensuring the model’s preparedness for a wide spectrum of imaging scenarios. With a direct and impressive outcome, the model achieved a 97.5% accuracy rate in species identification. The adoption of MobileViT promises a scalable and potent solution for bird species recognition within ornithological research, marking a forward leap in employing machine learning for environmental conservation.

关键词： Deep learning Biological system modeling Birds Transformers data augmentation data models Convolutional neural networks

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Research on Evaluation Index and Index System of Athletes' Physical Fitness Training Based on data mining

Research on Evaluation Index and Index System of Athletes' P...

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2019 3rd international conference on Economics,Management Engineering and Education Technology(ICEMEET 2019)

作者： Li Li Hunan Vocational College of Modern

With the development of modern sports, the requirement for the comprehensive quality of athletes is getting higher and higher. According to the characteristics of sports, the level of athletes and the tasks of different training stages, the training proportion of the two is reasonably arranged, and the physical training and technical and tactical training are effectively combined. Therefore, in the process of training and training athletes, countries strive to standardize, scientific and innovative. On the basis of defining the concept of physical fitness training, this paper expounds the main composition of athletes' physical fitness level. data mining is the process of extracting valuable knowledge from a large amount of data by using machine learning method. The athletes' sports training assisted decision support system constructed by data mining technology realizes the organic integration of different aspects of athletes, applies scientific training theory and advanced training methods to sports training management, and studies the athlete's special physical ability from the perspective of evaluation, to conduct a deep theoretical analysis of the athlete's special physical fitness characteristics, and build an athlete's special physical fitness evaluation index system.

关键词： data mining Athletes Physical Training Evaluation Index

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Identify Hidden Phenotypes in Electronic Health Records Using machine learning Technique

Identify Hidden Phenotypes in Electronic Health Records Usin...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Arun Kumar Takuli Prakash Joshi Sony Nayan Nitendra Singh Manish Saraswat Department of Information Technology G.L. Bajaj Institute of Technology and Management (GLBITM) Greater Noida U.P. India Department of Computer Science & Engineering Raj Kumar Goel Institute of Technology Ghaziabad U.P. India CSE-AIML ABES Engineering College Ghaziabad UP India Department of Computer Science & Engineering HMRITM Hamidpur Delhi India Mechanical Engineering Lloyd Institute of Engineering & Technology Affiliated to AKTU Greater Noida UP India

scientific information, such as EHRs, have become increasingly famous because of their comfort and price savings related to their use. On the side of providing an extra efficient manner of storing and reviewing affected person facts, using EHRs allows healthcare companies to study and examine healthcare tendencies and outcomes extra effectively. As the use of EHRs grows, so does the need for brand-new methods to perceive and extract medically helpful facts from the facts. Lately, there has been a growing frame of labor that uses device learning strategies to discover hidden phenotypes in EHRs. These hidden phenotypes are defined as “information or characteristics that are not conveniently obvious from the raw EHRs information however, may be gleaned through automatic data mining and evaluation.” This newsletter focuses on the various tactics used to pick out hidden phenotypes in EHRs through machine studying, natural language processing (NLP), deep gaining knowledge of, and supervised mastering techniques. It additionally gives an outline of the most promising algorithms and discusses the challenges and possibilities associated with the usage of this era.

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Traffic Speed Prediction and Congestion Source Exploration: A Deep learning Method

Traffic Speed Prediction and Congestion Source Exploration: ...

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IEEE international conference on data mining (ICDM)

作者： Jingyuan Wang Qian Gu Junjie Wu Guannan Liu Zhang Xiong School of Computer Science and Engineering Beihang University Beijing China Beihang University Beijing CN School of Economics and Management Beihang University Beijing China Research Institute of Beihang University in Shenzhen Shenzhen China

ISBN: (纸本)9781509054749

Traffic speed prediction is a long-standing and critically important topic in the area of Intelligent Transportation Systems (ITS). Recent years have witnessed the encouraging potentials of deep neural networks for real-life applications of various domains. Traffic speed prediction, however, is still in its initial stage without making full use of spatio-temporal traffic information. In light of this, in this paper, we propose a deep learning method with an Error-feedback Recurrent Convolutional Neural Network structure (eRCNN) for continuous traffic speed prediction. By integrating the spatio-temporal traffic speeds of contiguous road segments as an input matrix, eRCNN explicitly leverages the implicit correlations among nearby segments to improve the predictive accuracy. By further introducing separate error feedback neurons to the recurrent layer, eRCNN learns from prediction errors so as to meet predictive challenges rising from abrupt traffic events such as morning peaks and traffic accidents. Extensive experiments on real-life speed data of taxis running on the 2nd and 3rd ring roads of Beijing city demonstrate the strong predictive power of eRCNN in comparison to some state-of-the-art competitors. The necessity of weight pre-training using a transfer learning notion has also been testified. More interestingly, we design a novel influence function based on the deep learning model, and showcase how to leverage it to recognize the congestion sources of the ring roads in Beijing.

关键词： Neurons Roads Convolution machine learning Biological neural networks Feature extraction Training

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Soft Computing Signal Processing for Health Monitoring of Tie-Bar of Rotor Head Structure

Soft Computing Signal Processing for Health Monitoring of Ti...

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Intelligent Sensors, Sensor Networks and Information Processing conference (ISSNIP)

作者： P. J. Escamilla-Ambrosio N. Lieven Department of AeroSpace Engineering University of Bristol Bristol UK

The need for robust health monitoring and prognostics of structural components in remote or difficult-to-access locations, e.g. helicopter rotor-head structure, is driving the advancement of wireless intelligent sensor devices (WISD). Damage detection techniques, combined with advanced signal processing, are the core components of a structural health monitoring (SHM) system. In this context, feature extraction is an essential component of a SHM system that converts raw sensor data into useful information about the structure health condition. The level of signal processing that can be performed in a WISD depends on the capability of the processing element in terms of speed, memory and energy consumption. But the real bottleneck for energy efficiency is the fact that communications dominate the WISD energy consumption. Therefore, running intelligent local data interrogation algorithms on-board the WISD is a mechanism through which considerable battery power can be preserved. In that sense, in this paper a soft histogram feature extraction algorithm is developed to extract damage-sensitive information from measured response data of tie-bar component of the main rotor hub of a Lynx helicopter. In addition, a method for pattern recognition and critical degradation detection of tie-bar is proposed based on the extracted features and a combination of statistical process control and fuzzy sets theory. Results show the applicability of the proposed approaches.

关键词： Signal processing Remote monitoring Feature extraction Signal processing algorithms Helicopters Intelligent sensors Energy consumption data mining Robustness Wireless sensor networks

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Recent Developments in data Science and Business Analytics 1st ed. 2018

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丛书名： Springer Proceedings in Business and Economics

2018年

作者： Madjid Tavana Srikanta Patnaik

ISBN: (数字)9783319727455

ISBN: (纸本)9783319727448

This edited volume is brought out from the contributions of the research papers presented in the international conference on data Science and Business Analytics (ICDSBA- 2017), which was held during September 23-25 2017 in ChangSha, China. As we all know, the field of data science and business analytics is emerging at the intersection of the fields of mathematics, statistics, operations research, information systems, computer science and engineering. data science and business analytics is an interdisciplinary field about processes and systems to extract knowledge or insights from data. data science and business analytics employ techniques and theories drawn from many fields including signal processing, probability models, machine learning, statistical learning, data mining, database, data engineering, pattern recognition, visualization, descriptive analytics, predictive analytics, prescriptive analytics, uncertainty modeling, big data, data warehousing, data compression, computer programming, business intelligence, computational intelligence, and high performance computing among others. The volume contains 55 contributions from diverse areas of data Science and Business Analytics, which has been categorized into five sections, namely: i) Marketing and Supply Chain Analytics; ii) Logistics and Operations Analytics; iii) Financial Analytics. iv) Predictive Modeling and data Analytics; v) Communications and Information Systems Analytics. The readers shall not only receive the theoretical knowledge about this upcoming area but also cutting edge applications of this domains.

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