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Human Disease Prognosis and Diagnosis Using machine learning 2nd

Human Disease Prognosis and Diagnosis Using Machine Learning

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2nd international conference on Emerging Technologies for Computing, Communications, and Smart Cities, ETCCS 2021

作者： Kumar, Sunil Kumar, Harish Agarwal, Rashi Pathak, V.K. J C Bose University of Science and Technology YMCA Haryana Faridabad India University Institute of Engineering and Technology CSJM University UP Kanpur India CSJM University UP Kanpur India

ISBN: (纸本)9789811902833

The medical or healthcare sector is one of the trending and challenging fields of machine learning (ML). machine learning is a branch of artificial intelligence (AI) that is concerned with machine training and learning. "It is the defining technology of this decade, though its impact on healthcare has been meagre"—stated James Collin of MIT in the USA. Healthcare-related machine learning encompasses a broad range of techniques that can be used to assess efficiency, scope, versatility, and ability in various machine learning applications. Many multinational corporations and research organizations do more research and development work in the medical sector, which can be associated with machine learning to find some fruitful solutions for human beings, as humans face a variety of health-related issues. Disease prognosis and diagnosis are new challenges of the medical or healthcare sector, in which computer technology can utilize. Disease prognosis and diagnosis are critical tasks in the medical or healthcare, in which the prediction and accuracy of the results must be up to the mark. The significance of machine learning in medical or healthcare systems has been discussed in this paper for further exploration. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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A Comprehensive Survey on Identification of Malware Types and Malware Classification Using machine learning Techniques 2

A Comprehensive Survey on Identification of Malware Types an...

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2nd international conference on Smart Electronics and Communication, ICOSEC 2021

作者： Pachhala, Nagababu Jothilakshmi, S. Battula, Bhanu Prakash Annamalai University Department of IT Tamil Nadu Annamalainagar608002 India KKR KSR Institute of Technology Sciences Department of CSE Andhra Pradesh Guntur522017 India

ISBN: (纸本)9781665433686

Malware is malicious code that has an effect on the user or device and allows an attacker to do significant harm to the machine. Malware is a kind of computer virus that increases in number and severity with each passing day, posing a major danger to the security of the Internet. This is a never-ending fight between security experts and malware producers, with the sophistication of malware increasing at the same rate as technological advancement. Current state-of-the-art research focuses on the development and use of machine learning methods for malware detection owing to the capacity of these techniques to stay up with malware evolution and keep up with the speed of technological advancement. The purpose of this study is to provide a systematic and comprehensive review of machine learning methods for malware detection, with a special emphasis on deep learning techniques, in order to aid in the identification of malware. The paper's primary contributions are (i) it provides a comprehensive description of the methods and features used in a traditional machine learning workflow for malware detection and classification;(ii) it examines the challenges and limitations of traditional machine learning;and (iii) it examines recent trends and progress in the field, with a particular emphasis on deep learning approaches. Furthermore, (iv) it addresses the research problems and unresolved obstacles associated with state-of-the-art methods, and (v) it discusses the future directions of study in the field. A better knowledge of malware detection and the new advances and research paths being explored by the scientific community to combat the issue is provided by the survey results, which aid researchers in their research efforts. © 2021 IEEE.

关键词： Static analysis

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Data Analysis Techniques Applied to the MathE Database 2nd

Data Analysis Techniques Applied to the MathE Database

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2nd international conference on Optimization, learning Algorithms and applications (OL2A)

作者： Azevedo, Beatriz Flamia Romanenko, Sofia F. Pacheco, Maria de Fatima Fernandes, Florbela P. Pereira, Ana I. Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI P-5300252 Braganca Portugal Univ Minho Algoritmi Res Ctr P-4800058 Guimaraes Portugal Univ Aveiro Ctr Res & Dev Math & Applicat CIDMA Aveiro Portugal Univ Coimbra Coimbra Portugal Inst Politecn Braganca Lab Sustentabilidade & Tecnol Regioes Montanha Su P-5300253 Braganca Portugal

ISBN: (纸本)9783031232350;9783031232367

MathE is an international online platform that aims to provide a resource for in-class support as well as an alternative instrument to teach and study mathematics. This work focuses on the investigations of the students' behavior when answering the training questions available in the platform. In order to draw conclusions about the value of the platform, the ways in which the students use it and what are the most wanted mathematical topics, thus deepening the knowledge about the difficulties faced by the users and finding how to make the platform more efficient, the data collected since the it was launched (3 years ago) is analyzed through the use of data mining and machine learning techniques. In a first moment, a general analysis was performed in order to identify the students' behavior as well as the topics that require reorganization;it was followed by a second iteration, according to the students' country of origin, in order to identify the existence of differences in the behavior of students from distinct countries. The results point out that the advanced level of the platform's questions is not adequate and that the questions should be reorganized in order to ensure a more consistent support for the students' learning process. Besides, with this analysis it was possible to identify the topics that require more attention through the addition of more questions. Furthermore, it was not possible to identify significant disparities in the students behavior in what concerns the students' country of origin.

关键词： E-learning platforms Mathematics Active learning Education technologies Data analysis

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Exploration of College English Blended learning Model under the Background of Educational Information 2

Exploration of College English Blended Learning Model under ...

引用

2nd international conference on Big Data and Informatization Education, ICBDIE 2021

作者： Wang, Ning School of Foreign Languages Dalian Neusoft University of Information Dalian Liaoning China

ISBN: (纸本)9781665438704

Driven by the rapid development of computer network application technology, various technologies such as cloud computing, big data, and artificial intelligence have appeared in the public's field of vision, and are quickly and widely used in various fields. Among them, the theory of education and teaching integration information technology and technological innovation are among them. The model. The introduction of Blended learning Model realizes the organic integration of information technology theory and ideological and political education and teaching, as well as the scientific planning and full use of learning time and space inside and outside the class, which enriches the method of education and teaching system. The importance of blended learning models under the background of educational informationization is becoming increasingly prominent. This article attempts to explore the blended learning mode of college English from three aspects: online learning, offline learning and extracurricular development. It aims to promote students' initiative, autonomy and cooperation in learning, and achieve effective learning results. © 2021 IEEE.

关键词： learning systems

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Lightweight Convolutional Neural Networks Framework for Really Small TinyML Devices 2nd

Lightweight Convolutional Neural Networks Framework for Real...

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2nd international conference on Smart Technologies, Systems and applications (SmartTech-IC)

作者： Estrebou, Cesar A. Fleming, Martin Saavedra, Marcos D. Adra, Federico De Giusti, Armando E. Natl Univ La Plata Informat Res Inst LIDI CICs Associated Res Ctr RA-1900 La Plata Argentina Natl Univ La Plata Sch Informat RA-1900 La Plata Argentina CONICET Natl Council Sci & Tech Res Buenos Aires DF Argentina

ISBN: (纸本)9783030991708;9783030991692

This paper presents a lightweight and compact framework designed to perform convolutional neural network inference on severely hardware constrained microcontrollers. A review of similar open source libraries is included and experiments are developed to compare their capabilities on several different microcontrollers. The proposed framework implementation shows at least a three-time improvement over the Google Tensorflow Lite Micro implementation with respect to memory usage and inference time.

关键词： machine learning Convolutional neural networks TinyML Microcontrollers Framework

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application of Teaching Innovation Based on robotics engineering 2

Application of Teaching Innovation Based on robotics enginee...

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2nd international conference on Education, Knowledge and Information Management, ICEKIM 2021

作者： Na, Liu Yanling, Cao Huanqing, Han Shaoyong, Cao School of Industrial Automation Beijing Institute of Technology Zhuhai China

ISBN: (纸本)9781728168340

As the core major of 'Internet + Industrial Intelligence', robotics engineering is an upgrade and reconstruction of traditional engineering major. The industrial robot course is the professional core course of the Robotics Engineering. It is also a comprehensive course of multi-discipline integration, which involved mechanical engineering, automatic control, computer, sensor, electronic technology, artificial intelligence and other multi-disciplinary content. Robotics Engineering is characterized by broad foundation, great difficulty, emphasis on practice, rapid development and application of new knowledge. In the process of implementation of the teaching innovation, the new concept of engineering education was applied to propose a new form of curriculum system. Taking the projects of engineering as the study objects, disassemble the knowledge points involved in industrial robots, break the course boundaries, reshape the knowledge system, draw knowledge maps and then design teaching activities. In teaching innovation, teachers extend classroom through formation of subject competition teams, promote teaching and promote learning by competition, realize the integration of 'teaching, class and competition', build a bridge between theory and practice, then complete the transformation from knowledge learning to ability training. Besides, they also keep contact with intelligent manufacturing enterprises in Zhuhai and the Bay Area to obtain real-time new developments in enterprises. Thus, the latest information was introduced into classroom. Therefore, the meaning of 'production, teaching, research and application' has been deepened. According to the characteristics of the knowledge points of the course, experts were invited to make special lectures for students which can bring them with international perspective and frontier knowledge. © 2021 IEEE.

关键词： machine design

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Synchronous Monitoring Method of Multi-manipulator Trajectory Signals Based on machine learning 2nd

Synchronous Monitoring Method of Multi-manipulator Trajector...

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2nd international conference on IoT and Big Data Technologies for HealthCare, IoTCare 2021

作者： Wan, Xiao-zheng Zhang, Song Zhang, Ji-ming Chai, Hui Zhao, Huan-yu Qingdao266061 China College of New Energy China University of Petroleum Qingdao266580 China

ISBN: (纸本)9783030941819

The traditional trajectory signal monitoring method uses a combination of sensors and mathematical models to detect the trajectory signal of the manipulator, which is susceptible to interference from uncertain factors, resulting in low monitoring accuracy and poor real-time performance. In response to the above problems, this research designed a method for synchronous monitoring of multi-manipulator trajectory signals based on machine learning. After the multi-manipulator dynamic model is established, the coordinate system of the trajectory acquisition equipment is calibrated according to the principle of binocular vision. After preprocessing the trajectory image of the manipulator, the CLDNN model is used to realize the synchronous monitoring of the trajectory signal. The simulation experiment results show that the monitoring method shortens the monitoring time by about 62.5%, and has higher monitoring accuracy, which proves that its performance is better. © 2022, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Manipulators

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Tagging Continuous Labels for EEG-based Emotion Classification

Tagging Continuous Labels for EEG-based Emotion Classificati...

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45th Annual international conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Gu, Rong-Fei Zhao, Li-Ming Zheng, Wei-Long Lu, Bao-Liang Shanghai Jiao Tong Univ Ctr Brain Like Comp & Machine Intelligence Dept Comp Sci & Engn Key LabShanghai Educ Commiss Intelligent Interac 800 Dongchuan Rd Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Brain Sci & Technol Res Ctr 800 Dongchuan Rd Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ RuiJin Hosp Clin Neurosci Ctr RuiJin Mihoyo LabSch Med 197 Ruijin 2nd Rd Shanghai 200020 Peoples R China

ISBN: (纸本)9798350324471

EEG-based emotion classification has long been a critical task in the field of affective brain-computer interface (aBCI). The majority of leading researches construct supervised learning models based on labeled datasets. Several datasets have been released, including different kinds of emotions while utilizing various forms of stimulus materials. However, they adopt discrete labeling methods, in which the EEG data collected during the same stimulus material are given a same label. These methods neglect the fact that emotion changes continuously, and mislabeled data possibly exist. The imprecision of discrete labels may hinder the progress of emotion classification in concerned works. Therefore, we develop an efficient system in this paper to support continuous labeling by giving each sample a unique label, and construct a continuously labeled EEG emotion dataset. Using our dataset with continuous labels, we demonstrate the superiority of continuous labeling in emotion classification through experiments on several classification models. We further utilize the continuous labels to identify the EEG features under induced and non-induced emotions in both our dataset and a public dataset. Our experimental results reveal the learnability and generality of the relation between the EEG features and their continuous labels.

关键词： Classification (of information)

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A Review on The application of machine learning To Predict The Battery State That Enables A Smart, Low-Cost, Self-Sufficient Drying And Storage System for Agricultural Purposes 22

A Review on The Application of Machine Learning To Predict T...

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2nd international conference on Engineering and Information Technology for Sustainable Industry, ICONETSI 2022

作者： Redi, Anak Agung Ngurah Perwira Widjaja, Ryo Geoffrey Agustono, Iwan Asrol, Muhammad Budiman, Arief S. Gunawan, Fergyanto E. Industrial Engineering Sampoerna University Indonesia Bina Nusantara University Indonesia

ISBN: (纸本)9781450397186

This study reviews studies on a more viable battery for the energy storage system, the development of battery technology is towards a high capacity, low cost, and long battery lifespan. An accurate prediction of battery state, such as the state of charge, is important to help control the battery charging and discharging and extend the battery lifespan. Several reviews have provided an insightful summary regarding the development of methods to predict battery state for energy storage. This study provides a review that explores the application of machine learning to predict the battery state, including state of charge, state of health, and remaining useful life. Recent studies within this review shown that 64.7% researcher used Neural Network to do prediction with few studies do method combination to further overcome battery dynamic condition in real world application with less computational time and cost to enable integration with IoT technology. Furthermore, the opportunity to implement the energy storage system techniques to enable a smart, low-cost, self-sufficient implementation of the smart solar dryer for agricultural purposes is also elaborated © 2022 ACM.

关键词： machine learning

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A Survey on SARS-COV-2 (COVID-19) using machine learning Techniques 2

A Survey on SARS-COV-2 (COVID-19) using Machine Learning Tec...

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2nd international conference on Smart Electronics and Communication, ICOSEC 2021

作者： Mary, L. William Albert Antony Raj, S. SRM Institute of Science and Technology Department of Computer Applications Chennai Kattankulathur India

ISBN: (纸本)9781665433686

SARS-CoV-2, widely known as (COVID-19), is a deadly contagious disease globally. The new suspicious virus is spreading quickly. Several studies have found that SARS and COVID-19 have comparable patterns of an inflammatory outbreak. The Coronavirus causes a severe respiratory disease that affects lung functions, and the transmission can occur through direct, close, or indirect contact from infected secretions or droplets. The typical symptom includes fever, persistent cough, cold, shortness of breath, pneumonia, decreased sense of smell, insomnia, brain fog, and many organ dysfunctions. Antibody tests and RT-PCR tests are used to diagnose fatal diseases. This literature review examines the occurrence and pathogenicity of COVID-19 infection along with radiological images, machine learning classifiers, feature extraction methods and disease prediction algorithms, and pattern recognition. This overview also summarizes the ML and DL algorithms and tools used for analysis. © 2021 IEEE.

关键词： Coronavirus

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