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A Strategic Innovations for Stock Market Optimization by Using data Science & Explainable AI in High Frequency Trading 5

A Strategic Innovations for Stock Market Optimization by Usi...

引用

5th international conference on IoT Based Control Networks and intelligent systems, ICICNIS 2024

作者： Devi, Pooja Desai, Swati Subhash Kumar, Rakesh Naruka, Mahaveer Singh Tripathi, Vikas Uttaranchal Institute of Management Uttaranchal University India SJJTU Department of Statistics Rajasthan Jhunjhunu India Graphic Era Deemed to Be University Department of Management Studies Dehradun India G.L. Bajaj Institute of Technology and Management Greater Noida India

ISBN: (纸本)9798331518097

In the era of technology, financial markets especially stock market has been completely transformed through the integration of data science and explainable artificial intelligence. This paper presents a comprehensive analysis of evolution in the high frequency trading with the support of XAI and data science which made it possible to do financial transactions in extremely high speeds, often within milliseconds. But with the execution of these techniques HFT raises few concerns regarding transparency, interpretability and complexity. This study explores how data science shapes the strategies of HFT such as market making, arbitrage strategies: simple arbitrage, triangle arbitrage, statistical arbitrage etc. This study also explores how XAI and data science techniques such as machine learning, big data analytics, momentum trading and quantum computing are being employed in optimizing trading strategies to improve the decision making and liquidity in stock market. It includes regulations of high frequency trading at global level and challenge faced by financial sector in adopting these technologies. This study investigates the qualitative Scopus indexed journal papers and ieee explores paper for the referencing the data to find out the authenticated literature review. This study will help out to market participants such as traders, investors, policymakers who aims to navigate the future of finance in India. © 2024 ieee.

关键词： Marketplaces

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Steering Towards Maritime Safety with True Motion Predictions Ensemble 5

Steering Towards Maritime Safety with True Motion Prediction...

引用

5th ieee international conference on Autonomic computing and Self-Organizing systems Companion, ACSOS-C 2024

作者： Al-Falouji, Ghassan Beyer, Tom Gao, Shang Tomforde, Sven Intelligent Systems Christian-Albrechts-Universität zu Kiel Germany

ISBN: (纸本)9798350389760

Maritime transportation is vital for global trade, yet safety challenges persist, especially with increasing vessel density and narrow waterways. The standard Closest Point of Approach (CPA) method, used to estimate collision risk, has limitations due to its reliance on constant Course Over Ground (COG) and Speed Over Ground (SOG) assumptions, neglecting dynamic vessel behaviours, true ship dimensions, and environmental influences. Addressing these limitations, this paper introduces an enhanced CPA (eCPA) method that counters these deficits by incorporating ship trajectory predictions, probabilistic risk assessment, and real-time data integration. The eCPA method includes user-adjustable tools for trajectory interpolation, prediction, and anomaly elimination, making it adaptable to various maritime scenarios. Extensive testing in encounter scenarios demonstrates the effectiveness of our approach in improving maritime safety through robust and probabilistic estimation of encounter points. This method aligns with the Self-Integration and Self-Organising systems (SISSY) initiative, exhibiting how autonomous maritime operations can benefit from self-improving and self-integrating systems to enhance navigation safety and efficiency. © 2024 ieee.

关键词： Risk assessment

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Empowering Leather Quality Assurance: Leveraging Convolutional Neural Networks for Precise Defect Detection and Classification 3

Empowering Leather Quality Assurance: Leveraging Convolution...

引用

3rd international conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Logeswaran, K. Suresh, P. Savitha, S. Sah, Swati Kr, Prasanna Kumar Rajadevi, R. Vasugi, M. Sujit, B. Akilesh, T. Kongu Engineering College Center of Excellence in Real-Time Analytic Department of Artificial Intelligence Tamilnaud Erode India Vellore Institute of Technology Department of Database Systems Tamil Nadu Vellore India K.S.R College of Engineering Department of Computer Science and Engineering Tamil Nadu Tiruchengode India Sharda University School of Engineering and Technology Uttar Pradesh Greater Noida India Kongu Engineering College Department of Information Technology Tamilnaud Erode India Kongu Engineering College Department of Artificial Intelligence Tamilnaud Erode India

ISBN: (纸本)9798331523893

Ensuring the development of high-quality leather products in the industrial sector requires a strong focus on leather quality assurance. In this study, a unique method for enhancing leather quality assurance using Convolutional Neural Networks (CNNs) is presented. Our suggested solution is able to detect and classify leather defects with amazing accuracy and efficiency by utilizing deep learning. CNN integration for leather fault categorization and detection offers a more reliable and affordable solution, ensuring better product quality and satisfied customers. Existing methods for inspecting leather products often suffer from inconsistent quality control, subjective manual inspection processes, and the complexity of accurately identifying and classifying various types of leather defects. By addressing these challenges, the proposed system aims to empower leather manufacturers with a reliable and efficient solution for ensuring consistent product quality and reducing production costs. Convolutional Neural Networks (CNNs) are integrated to provide a highly automated and effective solution that overcomes these scaling challenges. The proposed CNN-based method uses deep learning algorithms to decode images, which allows the system to swiftly and precisely analyse enormous volumes of visual data. To make sure the CNN can differentiate between various defect classes and keep a low rate of false positives and false negatives, its accuracy, precision, recall, and F1 score should be carefully assessed. © 2025 ieee.

关键词： Fault tolerance

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An Effective Heart Disease Prediction Method using Extreme Gradient Boosting Algorithm Compared with Convolutional Neural Networks 9

An Effective Heart Disease Prediction Method using Extreme G...

引用

9th international conference on advanced computing and Communication systems, ICACCS 2023

作者： Anitha, Cuddapah Rajkumar, S. Dhanalakshmi, R. Mohan Babu University Computer Science and Systems Engineering Sree Vidyaniketan Engineering College Andhra Pradesh Tirupati India Vellore Institute of Technology School of Computer Science and Engineering Chennai India Saveetha School of Engineering SIMATS Department of Computer Science and Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350397376

Heart disease is one of the most serious and common problems to human beings around the face;% to 90% of people are affected by heart problems in which, and Several methods exist to identify heart disease in an earlier stage, but few methods fail to manage the accuracy due to the large volume of data. In addition, the methods require more time to process those features. To overcome these research issues, effective intelligent techniques are proposed to predict heart disease in an earlier stage. Additionally, our model's balanced accuracy (89.5%) outperforms the XGBoost classifiers' accuracy rates. Excellent recall values and area under the curve, together with high specificity and test accuracy, are obtained using the CNN classifier. Designers contrast the XGboost, KNN, SVM, and Naive Bayes Classifiers' classification precision on various healthcare datasets. Finally, this leads to a reduction in the overall error rate and also improves the recognition accuracy by more than 90%. © 2023 ieee.

关键词： Convolutional neural networks

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A Vibration Compensation Method for Pavement Depth data Based on LightGBM with Bayesian Optimization 3

A Vibration Compensation Method for Pavement Depth Data Base...

引用

3rd international conference on Digital Society and intelligent systems, DSInS 2023

作者： Kong, Xianglong Yang, Ming Yuan, Bo Ding, Jangang Li, Wei School of Information Engineering Chang'an University Shanxi Xi'an710018 China

ISBN: (纸本)9798350331387

para>To salve the issue of local distortion and feature distortion caused by vibration during the data acquisition process of 3D pavement detection vehicles, this research proposed a Bayesian optimization-based LightGBM method for compensating for vibration in pavement depth data. Firstly, the least squares method is used to remove trend of the acceleration data and interpolate missing values. Then, the pavement depth data and acceleration signals are matched using timestamps to obtain the dataset. Finally, the vibration compensation for pavement depth data is achieved through Bayesian optimization-based LightGBM. By comparing Bayesian optimization, genetic algorithm, and grid search parameter optimization methods, the results demonstrate the superiority of the Bayesian optimization-based LightGBM, with an R2Score reaching 0.84. Additionally, the proposed method is validated with real- scene pavement depth data, further demonstrating its feasibility, effectiveness, and generalizability. © 2023 ieee.

关键词： Pavements

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Application of Blockchain in Digital Healthcare

Application of Blockchain in Digital Healthcare

引用

2023 ieee international conference on intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2023

作者： Baskar, Sandhiya Gopirajan, P.V. Saveetha Engineering College Department of Computer Science and Engineering Tamilnnadu Chennai India

ISBN: (纸本)9781665462631

To protect personal patient information and comply with regulations, data security is a necessary aspect of the healthcare business. systems in the healthcare business that are based on the block chain are beneficial to both medical professionals and patients. The use of block chain technology in the healthcare industry as a useful tool for decentralizing patient health information, monitoring pharmaceutical goods, and increasing payment options is swiftly gaining support. Because patient data is written on paper and stored in filing cabinets, it is simple to protect and secure it. However, all data is now saved digitally, such as patient records, which are now stored on computers, servers, and storage devices. Poor wireless network security, data breaches, legacy system use, malware, viruses, and other hostile attacks are all risk considerations in health care. When compared to other businesses, one of the key reasons to safeguard health care data is that healthcare companies gather and keep extremely detailed patient records, including patient names, dates of birth, addresses, and social security numbers. This paper aims to discuss the issues and solutions to achieving digital healthcare using blockchain technology. © 2023 ieee.

关键词： Legacy systems

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ieee international conference on Advances in Electronics, Communication, computing and intelligent Information systems, ICAECIS 2023 - Proceedings

IEEE International Conference on Advances in Electronics, Co...

引用

1st ieee international conference on Advances in Electronics, Communication, computing and intelligent Information systems, ICAECIS 2023

ISBN: (纸本)9798350348057

The proceedings contain 127 papers. The topics discussed include: automated Bengali abusive text classification: using deep learning techniques;bio algorithms for resource optimization and analysis of data;prediction of rheumatoid arthritis susceptibility using gene mutation rate;a survey paper on emerging techniques used to translate audio or text to sign language;a novel development of blockchain based messaging application;deep machine learning based usage pattern and application classifier in network traffic for anomaly detection;analyzing paralinguistic information from human speech and its applications in medicine;strategic placement of electric vehicle charging stations using grading algorithm;malware detection in android applications using machine learning;the impact of online reviews on product perception and purchase intention;design and implementation of smart classroom using cisco packet tracer;intrusion detection in networks using gradient boosting;real-time lane detection and departure caution gadget for automobiles based on Raspberry pi;development of a Bengali speech-based emotion analysis system;large vocabulary continuous speech recognition system for Marathi;and low-cost smart glasses for people with visual impairments.

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Fake news detection using social media data for Khasi language

Fake news detection using social media data for Khasi langua...

引用

2023 international conference on intelligent systems, advanced computing and Communication, ISACC 2023

作者： Warjri, Sunita Pakray, Partha Lyngdoh, Saralin A. Maji, Arnab K. Gandhi Institute of Technology and Management Department of Cse Bangalore India National Institute of Technology Department of Cse Silchar India North-Eastern Hill University Department of Linguistics Shillong India North-Eastern Hill University Department of Information Technology Shillong India

ISBN: (纸本)9798350399387

With the advancement in technology, web technol-ogy in the form of social media is one of the main origins of information worldwide. Web technology has helped people to enhance their ability to know, learn, and gain knowledge about things around them. The benefits that technological advancement offers are boundless. However, apart from these, social media also has major issues related to problems and challenges concerning filtering out the right information from the wrong ones. The sources of information become highly unreliable at times, and it is difficult to differentiate and decipher real news or real information from fake ones. Cybercrime, through fraud mechanisms, is a pervasive menace permeating media technology every single day. Hence, this article reports an attempt to fake news detection in Khasi social media data. To execute this work, the data analyzed are extracted from different Internet platforms mainly from social media articles and posts. The dataset consists of fake news and also real news based on COVID-19, and also other forms of wrong information disseminated throughout the pandemic period. We have manually annotated the assembled Khasi news and the data set consists of 116 news data. We have used three machine learning techniques in our experiment, the Decision Tree, the Logistic Regression, and the Random Forest approach. We have observed in the experimental results that the Decision Tree-based approach yielded accurate results with an accuracy of 87%, whereas the Logistic Regression approach yielded an accuracy of 82% and the Random Forest approach yielded an accuracy of 75%. © 2023 ieee.

关键词： Decision trees

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A Domain-Driven Model Generation Framework for Cyber-Physical Production systems

A Domain-Driven Model Generation Framework for Cyber-Physica...

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ACM/ieee international conference on Model Driven Engineering Languages and systems (MODELS)

作者： Majumder, Mainak Johannes Kepler Univ Linz LIT CPS Lab Linz Austria

ISBN: (纸本)9798350324983

The growing influence of Information Technologies in the manufacturing domain has led to the fourth industrial revolution (Industry 4.0). Cyber-Physical Production System (CPPS) is one of the fundamental concepts of Industry 4.0 that aims to develop an intelligent manufacturing environment by leveraging concepts like the Internet of Things (IoT), cloud computing, virtualization, and Artificial Intelligence (AI). However, the challenges originating from the technological heterogeneity in the manufacturing domain remain primary obstacles towards realising a fully automated CPPS. Among them, semantic heterogeneity in manufacturing information is the most crucial which can be attributed to technology and vendor-specific information modelling mechanisms. A CPPS requires seamless machine-to-machine communication which could be hindered due to the non-interoperability among machine data on a semantic level. Therefore, the primary focus of this thesis work is to understand the semantic interoperability challenges of CPPS and propose solutions to address those challenges. The proposed solution revolves around the development of semantic domain models using the modelling philosophies of Domain-Driven Design (DDD).

关键词： CPPS Domain-Driven Design (DDD) Information Model Industry 4.0

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Design of loop shaping filters based on convex optimization guaranteeing robust performance

Design of loop shaping filters based on convex optimization ...

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ieee/ASME international conference on advanced intelligent Mechatronics (AIM)

作者： Wang, Xiaoke Ohnishi, Wataru Atsumi, Takenori Univ Tokyo Dept Elect Engn & Informat Syst Tokyo Japan Chiba Inst Technol Dept Mech Engn Chiba Japan

ISBN: (数字)9781665413084

ISBN: (纸本)9781665413084

For hard disk drive systems (HDD), designing a robust controller that guarantees favorable disturbance rejection performance is crucial in increasing the storage. However, the design process is arduous. To decrease the design effort, employing convex optimization to sequentially design the loop shaping filter is presented in this paper. Optimization constraints are the robust performance and the system's stability. The optimization objective is selected as the 2-norm of the error signal. The satisfying initial condition for optimization is selected based on the RCBode plot. Finally, the effectiveness of the proposed design procedure is validated through a numerical case study.

关键词： Robust performance frequency response data RCBode plot

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