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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...

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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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HyperSurf: Quadruped Robot Leg Capable of Surface Recognition with GRU and Real-to-Sim Transferring

HyperSurf: Quadruped Robot Leg Capable of Surface Recognitio...

引用

2024 ieee international conference on systems, Man, and Cybernetics, SMC 2024

作者： Satsevich, Sergei Savotin, Yaroslav Belov, Danil Pestova, Elizaveta Erhov, Artem Khabibullin, Batyr Bazhenov, Artem Kovalev, Vyacheslav Fedoseev, Aleksey Tsetserukou, Dzmitry Cde Skolkovo Institute of Science and Technology Intelligent Space Robotics Lab-oratory Bolshoy Boulevard 30 bld. 1 Moscow121205 Russia

ISBN: (纸本)9781665410205

This paper introduces a system of data collection acceleration and real-to-sim transferring for surface recognition on a quadruped robot. The system features a mechanical single-leg setup capable of stepping on various easily interchangeable surfaces. Additionally, it incorporates a GRU-based Surface Recognition System, inspired by the system detailed in the Dog-Surf paper [1]. This setup facilitates the expansion of dataset collection for model training, enabling data acquisition from hard-to-reach surfaces in laboratory conditions. Furthermore, it opens avenues for transferring surface properties from reality to simulation, thereby allowing the training of optimal gaits for legged robots in simulation environments using a pre-prepared library of digital twins of surfaces. Moreover, enhancements have been made to the GRU-based Surface Recognition System, allowing for the integration of data from both the quadruped robot and the single-leg setup. The dataset and code have been made publicly available. © 2024 ieee.

关键词： Multipurpose robots

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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...

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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...

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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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A Comprehensive Review of Autonomous Navigation systems for In-Pipe Inspection Robots Using advanced SLAM Techniques 5

A Comprehensive Review of Autonomous Navigation Systems for ...

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5th ieee international conference on Electro-computing Technologies for Humanity, NIGERCON 2024

作者： Emmanuel, Owodolu Opeoluwa Salami, Momoh Jimoh Lawan, Rominiyi Oluwasina Afe Babalola University Dept. Mechanical/ Mechatronics Ekiti Ado-Ekiti Nigeria

ISBN: (纸本)9798331542559

Pipeline system is the most common means of transportation of gases and liquids, including sewage, water, oil, and gasoline. But pipelines are aging and getting damaged all the time, which can lead to serious resource loss and environmental contamination. It is vital to do routine maintenance and inspections for pipelines to operate efficiently and prevent overspending. Inspection, including condition assessment, fluid quality monitoring, leak detection of pipelines, is difficult since pipes are frequently buried and come in a variety of sizes and shapes. In the near future, groups of tiny robots will be used to carry out intelligent autonomous navigating system of underground pipe networks. With all the computational power and diverse sensors (thermal, inertial, optical, and others) these robots will be able to self-localize, navigate, and inspect pipelines in addition to providing real-time condition data about the in-pipes and any issues, such as leaks or blockages, for monitoring and decision support. Teams of autonomous navigating inspection robots, capable of long-term operations over vast spatial scales, will supplant the current practice of primarily manned pipe network inspection. In this paper, first analyze the mechanical systems of the in-pipe robots, and then we look at the four functions these robots perform: inspection, localization, and navigation and mapping (SLAM), this is an approximation of the robot's location and its immediate surroundings with the use of advanced SLAM. The present article provides an overview of the most recent work on water pipe network robot simultaneous localization and mapping (SLAM). It takes into account many facets of the SLAM issue in pipes, including the rationale behind it, the demands of the water business, contemporary SLAM techniques, and sensors that work well with pipes. We additionally offer a list of sensors utilized by in-pipe robots, categorized according to their purpose and mode of operation. © 2024 ieee.

关键词： intelligent robots

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Modeling an intelligent Solar Power Plant Control System Using Colored Petri Nets 11

Modeling an Intelligent Solar Power Plant Control System Usi...

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11th ieee international conference on intelligent data acquisition and advanced computing systems - Technology and Applications (IDAACS)

作者： Gozhyj, Aleksandr Kalinina, Irina Nechakhin, Vladyslav Gozhyj, Victor Vysotska, Victoria Petro Mohila Black Sea Natl Univ 68 Desantnikov Str10 Nikolayev Ukraine Natl Univ Lvivska Politech Bandery Av 12 Lvov Ukraine

ISBN: (纸本)9781665426053

The article describes the modeling of a solar power plant control system based on colored Petri nets. With the help of hierarchical colored Petri nets, a simulation model of a solar power plant was built. The features of constructing models of individual components of a solar power plant are considered. Various modes of their operation have been investigated. To improve the efficiency of system control, an intelligent controller based on a neural network is used. It is proposed to use a neural network to track the maximum power point for more efficient charge control. The neural network drives controllers that control the process and provide an effective current boost only when the solar panel's output voltage exceeds the battery voltage. With the help of the constructed models, various operating modes of a solar power plant are investigated. The results of increasing the system efficiency are presented.

关键词： solar power plant control system color Petri net model intelligent system

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Fake news detection using social media data for Khasi language

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

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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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Hiding Messages in Audio Files Using Direct Spread Spectrum 11

Hiding Messages in Audio Files Using Direct Spread Spectrum

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11th ieee international conference on intelligent data acquisition and advanced computing systems - Technology and Applications (IDAACS)

作者： Kuznetsov, Alexandr Smirnov, Oleksii Zhora, Victor Onikiychuk, Alexander Pieshkova, Olha Kharkov Natl Univ Svobody Sq 4 UA-61022 Kharkiv Ukraine JSC Inst Informat Technol Bakulin St 12 UA-61166 Kharkiv Ukraine Cent Ukrainian Natl Tech Univ Ave Univ 8 UA-25006 Kropivnitskiy Ukraine State Serv Special Commun & Informat Protect Ukra Solomianska 13 Str UA-03110 Kiev Ukraine

ISBN: (纸本)9781665426053

Direct spread spectrum technology is traditionally used in radio communication systems with multiple access. It ensures high security and reliability of information transfer. In addition, direct sequence spreading of spectrum makes the transmitted signals noise alike. We are researching this technology for other uses. The present paper explores the technique of hiding information in multimedia files, in particular, audio containers. The paper investigates various methods of forming wideband signals (chip codes), and studies the possibility of their usage for hiding information by using direct spread spectrum technology. We conduct experimental studies, in particular, we estimate the bit error rate (BER) of information data, as well as the distortion of the audio container by mean squared error (MSE) and peak signal-to-noise ratio (PSNR). We show which method of forming chip codes is preferable for reliable and safe information hiding.

关键词： hiding data audio direct spread spectrum technology steganography

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The Role of Artificial Intelligence in Information acquisition 5

The Role of Artificial Intelligence in Information Acquisiti...

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5th ieee international conference on Advances in computing, Communication Control and Networking, ICAC3N 2023

作者： Singh, J.N. Saxena, Saurabh Saini, Riyanshu Chauan, Manali Saini, Kavita Galgotias University School Of Computing Science And Engineering UP Greater Noida India

ISBN: (纸本)9798350330861

Search engines use artificial intelligence to find potential leads. Early concepts in both fields are introduced in Bush and Turing's publications, which are also utilized to define artificial intelligence and data mining in this article. The foundation for further exploration of intellectual notions of art and their application to information retrieval is a straightforward model of an information retrieval system. Pattern recognition, representation, problem solving and planning, reasoning, and learning are among the concepts covered. This paper finishes by outlining future directions for artificial intelligence research in data mining systems. © 2023 ieee.

关键词： Search engines

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A Smart Agriculture Friendly intelligent Flying Robot Design to Identify Plant Leaf Diseases by using Digital Image Processing with Learning Approach

A Smart Agriculture Friendly Intelligent Flying Robot Design...

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2023 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2023

作者： Kavitha, M.S. Harikrishnan, R. Shankar, S. Vijay Krishnakumari, S. Sherly, S. Irin R.M.K.Engineering College Department ECE Chennai India S.A. Engineering College Department of ECE Chennai India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Loyola ICAM College of Engineering and Technology Department of ECE Chennai India Panimalar Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350319200

Smart agriculture practices have become crucial in meeting the growing global food demand while minimizing resource use. In this context, the development of intelligent systems and automation plays a pivotal role. This paper introduces a novel solution: a smart agriculture-friendly intelligent flying robot equipped with cutting-edge digital image processing techniques and a learning approach to identify plant leaf diseases. The suggested system merges state-of-the-art robotics and aerial technology with advanced machine learning techniques, specifically the VGG-SVM model. The flying robot is equipped with a high-resolution camera to capture images of plant leaves in real-time, and the VGG-SVM model processes these images for disease detection. VGG offers deep feature extraction capabilities, while SVM provides accurate classification, making the model adept at discerning healthy from diseased leaves. Through digital image processing, the robot captures high-resolution images of plant leaves, which are subsequently subjected to advanced machine learning algorithms. These algorithms analyze and classify the images, providing real-time insights into the health of crops. The combination of precision aerial data collection and machine learning ensures early disease detection and targeted intervention, optimizing crop yield, reducing resource consumption, and ultimately contributing to sustainable agriculture. Proposed model excels with the highest accuracy of 0.97, indicating an overall high rate of correct predictions. © 2023 ieee.

关键词： Deep Learning Drones Plant Leaf Diseases SVM VGG

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