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共 1031 条记录，以下是151-160 订阅

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Optimized Fetal ECG Feature Extraction with Genetic Algorithm Based Heart Rate Detection

Optimized Fetal ECG Feature Extraction with Genetic Algorith...

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international conference on ICT in Business Industry & Government (ICTBIG)

作者： Kiran Sree Pokkuluri Nidhi Soni Amit Sahu Tilak Mukherjee Y. Prasanthi Disha Sharma Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Bhimavaram India Department of Computer Science & Engineering IES Institute of Technology and Management IES University Bhopal M.P India Dept of CSE Gyan Ganga Institute of Technology & Sciences Jabalpur Electronics and Communication Engg. Dept Haldia Institute of Technology Haldia West Bengal India Dept of CSE-Data Science KKR & KSR Institute of Technology and Sciences Guntur GGITS Jabalpur MP India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Congenital heart problems are the most common primary cause of death that can be attributed to birth defects. It is possible to identify cardiac defects by analyzing the geomorphology of cardiac electric indications. Additionally, the examination of fetal cardiac signals, which does not involve any intrusive procedures, can be an efficient method for monitoring the health of the fetal heart and identifying cardiac abnormalities at an early stage. One method that can be utilized to evaluate the health and condition of the fetus is the ECG signal, which is a graphical copy of the electric potential that is generated in conjunction with the action of the heart. However, there are several technical issues that arise during the process of non-invasive FECG extraction. these issues include interference from maternal electrocardiograms and maternal electromyograms, interference from power lines operating at 50 Hz, and baseline drift. Extraction and analysis of the fetal electrocardiogram signal are the key goals of electronic fetal monitoring; they are the primary objectives. In the process of extracting features from the fetal ECG indication, which is a nonlinear mix of the gentle ECG, the fetal ECG indication, and other interfering signals, digital signal processing techniques constitute a key part of the process. It is possible to find solutions to problems involving abnormality detection by employing data mining techniques and soft computing approaches for categorization.

关键词： Wavelet transforms Accuracy Fetal heart rate Interference Digital signal processing Electrocardiography Feature extraction Complexity theory Monitoring Genetic algorithms

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An intelligent system for heart disease prediction using adaptive neuro-fuzzy inference systems and genetic algorithm 4

An intelligent system for heart disease prediction using ada...

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2021 4th international conference on Computer Information science and Application Technology, CISAT 2021

作者： Feng, Jindong Wang, Qian Li, Na Northwest Minzu University Lanzhou China Key Laboratory of Streaming Data Computing and Application Northwest Minzu University Lanzhou China

Cardiovascular disease remains the leading cause of death worldwide over the past two decades. Because of a large number of clinical data and the complexity of the disease, it is often challenging to diagnose and make the proper treatment. Over the past decade, as a soft computing method, fuzzy expert systems have been applied in disease diagnosis by many researches because of its superiority in dealing with uncertain and ambiguous problems. this study proposes an Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to diagnose heart disease. Parameters related to membership functions in ANFIS are optimized by applying the genetic algorithm. the experiment was conducted on the public UCI heart disease datasets. the experimental result shows that 91.25% accuracy was obtained on the testing set, which was found to be satisfying based on comparison. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 Licence.

关键词： Genetic algorithms

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Enhancement of Convolutional Neural Networks Classifier Performance in the Classification of IoT Big data 2020

Enhancement of Convolutional Neural Networks Classifier Perf...

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4th international conference on Machine Learning and soft computing (ICMLSC)

作者： Eloanyi Samson Amaechi Hai Van Pham Univ Sci & Technol HUST Hanoi Sch Informat & Telecommun Technol Hanoi Vietnam

ISBN: (纸本)9781450376310

Current developments in technologies occupy a central role in weather forecasting and the Internet-of-things for both organizations and the IT sector. Big-data analytics and the classification of data (derived from many sources including importantly the Internet-of-things) provides significant information on which organizations can optimize their current and future business planning. this paper considers convolutional neural networks and data classification as it relates to big-data and presents a novel approach to weather forecasting. the proposed approach targets the enhancement of convolutional neural networks and data classification to enable improved classification performance for big-data classifiers. Our contribution combines the positive benefits of convolutional neural networks with expert knowledge represented by fuzzy rules for prepared data sets in time series, the aim being to achieve improvements in the predictive quality of weather forecasting. Experimental testing demonstrates that the proposed enhanced convolutional network approach achieves a high level of accuracy in weather forecasting when compared to alternative methods evaluated.

关键词： Internet of things (loT) Big data Neural Network Convolutional Neural Network fuzzy rules

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Deep Convolutional Neural Network for Improving Intrusion Detection. A Spectogram based NIDS Framework

Deep Convolutional Neural Network for Improving Intrusion De...

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Intelligent Technologies (CONIT), international conference on

作者： Sameer Kumar Vepuri Sanbeeth Chakraboorthy S. Prabakeran V. Sujatha Department of Networking and Communication School of Computing College of Engineering and Technology (CET) SRM Institute of Science and Technolog Kattankulathur Tamil Nadu India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

In the past few years, computer networks have changed drastically as the number of connected devices and applications increased significantly. Because of the network’s increased scale and our reliance on it for all parts of our lives, hostile parties have launched a slew of new attacks against the network, as well as mutations of existing attacks. When it comes to safeguarding devices and network nodes, along with the data they contain, against possible breaches, these attacks provide a number of challenges for network security professionals. One of the best security solutions is a system to detect network intrusions which continuously monitors network traffic to safeguard network entry points. When it comes to identifying new assaults, the false alarm rate (FAR) for NIDS remains high even after substantial scientific efforts. We introduce a novel architecture for Network Intrusion Detection Systems (NIDS) employing a deep convolutional neural network alongside spectrogram images generated through the short-time Fourier transform. the effectiveness of our approach was assessed using the CIC-IDS2018 dataset. Our experimental findings revealed a reduction in False Alarm Rate (FAR) by 4.3

关键词： Accuracy Fourier transforms Force Network intrusion detection Telecommunication traffic Predictive models Network security

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Building an international Entrepreneurship Index using the PSR framework

Building an International Entrepreneurship Index using the P...

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

作者： Hitaishi Shivaram Kritika Dutta Shine Raju Kappil Department of Data Science CHRIST (Deemed to be University) Bangalore India

this paper builds an international Index for Entrepreneurship (IIE) for the year 2018, by using a conceptual framework named PSR (Pressures-State-Response) to encapsulate the contextual aspect of entrepreneurship globally. In the past, the indices have used a methodological framework of composite indices. this paper uses the PSR framework to show how these indicators fall into the categories of pressure, state, and response, and concentrates on how these subsystems are interrelated. the study considers 41 countries for the construction of the index. We also check the correlation between the IIE and other growth indicators such as the corruption perception index, the economic freedom summary index, GDP per capita, and trade openness using suitable statistical *** correlation analysis demonstrates that the IIE and the Economic Freedom Summary Index have a positive association.

关键词： Correlation Economic indicators Force Buildings Entrepreneurship Indexes Sustainable development

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Feature-Based Analytical Crop Recommendation System 4

Feature-Based Analytical Crop Recommendation System

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4th IEEE Pune Section international conference, PuneCon 2021

作者： Oswal, Manas Mahajan, Karan Pagare, Shubham Kasa, Vishal Malhotra, Jyoti Sarode, Sambhaji MIT ADT University School of Engineering Dept. of Computer Science Engineering Pune India

ISBN: (纸本)9781665444798

India, the country with the largest occupation in Agriculture, ranks as the second-largest producer for various fruits and vegetables. Farmers have been cultivating crops by following the traditional pattern that they have been using for decades. this worked well, until recent years, where the land is poised by land degradation and the climate has become more unstable. thus, it becomes imperative that we select a crop by taking these parameters into consideration. We aim to solve the problem of crop selection by making a system that takes these parameters into consideration and suggests the optimal crop. Research is being done to create similar systems using Machine Learning or other AI-based algorithms. In this manuscript, we are trying to explore the use of soft computing and Fuzzy logic to create a system to suggest the optimal or 'best-suited' crop. For this purpose, the system only requires the optimal data for each crop and doesn't require huge datasets as required for training a traditional neural network or an ML based model. © 2021 IEEE.

关键词： Crops

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Construction of Educational Information System Model Based on BP Algorithm 4th

Construction of Educational Information System Model Based o...

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4th international conference on Innovative computing, IC 2021

作者： Chen, Haohua City College of Dongguan University of Technology Guangdong Dongguan523000 China

ISBN: (纸本)9789811642579

With the rapid progress of Internet technology, the application scope of science and technology in people's life is becoming more and more extensive, which leads to earth shaking changes in the traditional way of thinking and life, which is also reflected in the education industry. Ideological and political education is one of the necessary courses for students to establish correct values and world outlook. In the era of big data, ideological and political education methods also have new development and innovation. this paper analyzes the characteristics of the era of big data, in view of the opportunities and challenges brought by the era of big data for ideological and political education methods, obtains the development and innovation of Ideological and political education methods in the era of big data, and explores the development direction for the development of Ideological and Political Education methods. In order to improve the accuracy of Ideological and political education informatization, this paper proposes a method of Ideological and political classroom teaching quality evaluation based on da-bp. Firstly, the analytic hierarchy process is used to construct the evaluation index system of Ideological and political classroom teaching quality, including 5 first-class indicators and 25 s-class indicators. Secondly, the scores of 25 evaluation indexes affecting the quality of Ideological and political education are taken as the input of da-bp, and the comprehensive score of Ideological and political teaching quality in Colleges and universities is taken as the output of da-bp. the results show that da-bp can effectively improve the accuracy of Ideological and political education informatization, and provides a new method and way for the evaluation of Ideological and political classroom teaching quality. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Big data

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Efficient Detection of DDoS Attacks in E-Government Clouds Using Sparse Neural Networks

Efficient Detection of DDoS Attacks in E-Government Clouds U...

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international conference on ICT in Business Industry & Government (ICTBIG)

作者： A. Bixapathi H. Mohammed Ali R Maranan Sudhakar AVV Beschi I S M. Ramya Department of Computer Science Engineering -Data Science Geetanjali College of Engineering and Technology Department Mechanical Engineering SRM Institute of Science and Technology-Ramapuram Campus Chennai Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Department of Electrical and Electronics Engineering SR University Warangal Telangana Department of Computer Applications St. Joseph's College (Arts & Science) Chennai Division of Research and Development Lovely Professional University Jalandhar Punjab India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

E-government (Electronic Government) refers to the use of information and communication technology (ICT) by governmental bodies to provide public services, manage government operations, and facilitate communication with citizens, businesses, and other government entities. E-government platforms have expanded significantly in recent years, offering a range of services such as online tax filing, license renewals, healthcare services, and more. these services, often hosted on cloud-based infrastructures, benefit from improved scalability, efficiency, and accessibility through the use of cloud computing technologies. However, the growing volume of legitimate traffic from a diverse range of users on e-government platforms makes distinguishing between normal and malicious traffic, such as DDoS (Distributed Denial of Service) attacks, increasingly difficult. To address this challenge, this manuscript proposes the use of Sparse Neural Networks (SNN) for DDoS attack detection in cloud-based e-government systems. Initially, data from the CSE-CIC-IDS2018 dataset is collected and processed through a feature selection phase. the selected features are selected using the Energy Valley Optimizer. these optimized features are fed into the Sparse Neural Network, which categorizes traffic as benign, DDoS, or DoS. To further enhance the accuracy of DDoS detection, the Ladybug Beetle Optimization (LBOA) is employed to optimize the Sparse Neural Network's parameters, ensuring optimal performance in classifying attacks. this approach aims to safeguard cloud-based e-government systems from cyber threats while maintaining reliable public service delivery. In comparison to current methods such as data clustering for the detection of DDoS attacks in the cloud for e-government (DBSCAN), e-government impact factor analysis cleverly using enhanced machine learning (ML-EGOV), and Problems with Machine Learning in Cloud computing E-Government Models in Developing Nations, the proposed DCNN-QSA-PDO

关键词： Cloud computing Neural networks Machine learning Denial-of-service attack Feature extraction Information and communication technology Reliability Computer crime Optimization Electronic government

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Development of soft Skills by Doctoral Students 2nd

Development of Soft Skills by Doctoral Students

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22nd international conference on Interactive Collaborative Learning (ICL) / 48th IGIP international conference on Engineering Pedagogy - Impact of the 4th Industrial Revolution on Engineering Education

作者： Valeeva, Elvira Ziyatdinova, Julia Galeeva, Farida Kazan Natl Res Technol Univ Kazan Russia

ISBN: (纸本)9783030402716;9783030402709

this paper has been prepared as a part of the capacity building project "Modernization of Doctoral Education in science and Improvement of Teaching Methodologies" made possible by the programs of Erasmus+. the main aim of this project is to improve the quality of doctoral education by implementing new teaching methodologies based on interdisciplinary approaches and international cooperation. the author presents Russian system of Doctoral education, its structure, content and main competences required for training highly qualified researchers. the paper focuses on developing communicative skills by PhD students in the framework of Foreign Language Course which is included Doctoral degree programs.

关键词： Doctoral degree programs soft skills

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A Comprehensive Survey on Parametric and Non-Parametric Machine Learning Approaches for Bus Arrival Time Prediction

A Comprehensive Survey on Parametric and Non-Parametric Mach...

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

作者： Chinmayee P Anusha H Patil Bhavika V K Ashwini B P R Sumathi Department of Computer Science and Engineering Siddaganga Institute of Technology Tumakuru India

Public transportation plays a major role in modern society. Bus transportation is vitally important in public transportation, and it reduces more than 60% of private vehicle usage. It reduces fuel exhaustion and traffic burdens. Hence, promoting the usage of public transit as against private has become very important recently. the prediction of bus travel and arrival time for the passenger and service provider improves the reliability of the service by reducing the waiting time and uncertainty for the passengers. Several research works have focused on predicting the travel and arrival time of public transit buses in the past. A review of the existing works using parametric and non-parametric machine learning models for bus travel arrival time is conducted in this article. Parameters such as data sources, the spatial-temporal scales, and the study location are considered for the study. It is observed from the review that parametric models are widely used; especially network models, most models use data sourced from the GPS, spatially; segmented routes are well studied, and temporally, all-day data are the most studied. the existing works are well spread across the globe irrespective of developed and developing countries. Overall, it is observed from the survey that bus arrival time prediction is a well-studied area, but still, there is scope for innovative solutions in the future.

关键词： Uncertainty soft sensors Machine learning Predictive models data models Spatial databases Parametric statistics

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