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检索条件"任意字段=2021 2nd International Conference on Machine Learning and Computer Application, ICMLCA 2021"

共 1034 条记录，以下是151-160 订阅

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application and verification of DSP+FPGA in SINS navigation computer circuit design 2

Application and verification of DSP+FPGA in SINS navigation ...

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2nd international conference on computer Vision, Image, and Deep learning

作者： Yan, Qiang Xi'an Eurasia University Shaanxi Xi'an710061 China

ISBN: (数字)9781510646827

ISBN: (纸本)9781510646810

Navigation computer circuit is the core component of SINS. The design precision of navigation computer directly affects the performance of SINS and even the whole control system. This paper uses DSP+ FPGA to design and complete the hardware platform and interface program of the navigation computer circuit, builds the SINS/GPS vehicle system, and carries out the sports car experiment, which effectively verifies the performance of the SINS navigation computer circuit. © 2021 SPIE.

关键词： Timing circuits

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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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Active learning for Arabic Text Classification 2

Active Learning for Arabic Text Classification

引用

2nd IEEE international conference on Computational Intelligence and Knowledge Economy, ICCIKE 2021

作者： Al-Tamimi, Abdel-Karim Bani-Isaa, Esraa Al-Alami, Ahmed Yarmouk Univeristy Higher Colleges of Technology Cis Department-Computer Eng. Dept Al Ain United Arab Emirates Yarmouk University Computer Engineering Dept Irbid Jordan

ISBN: (纸本)9781665429214

Active learning explores the use of minimal human intervention to improve the efficiency of supervised machine learning algorithms during the learning/training phase. Active learning improves machine learning algorithms performance, especially for ambiguous or unknown cases that are not clearly defined in the classification criteria applied to data. In machine learning, the quality of used data greatly determines the quality of the classification task outcomes. Especially with the current abundance of data resources, the data labeling process represents a major hurdle to data classification. In this paper, we share our results of using active learning approach for Arabic text classification. We demonstrate in this work how active learning approach greatly improves the efficiency of machine learning systems when compared to traditional passive learning approaches. This work introduces our preliminary results of using active learning approach to help annotate the ever-growing Arabic data corpora using state-of-the-art learning techniques. © 2021 IEEE.

关键词： Efficiency

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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 ...

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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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A Review on Malicious Software Detection using machine learning Algorithms 2

A Review on Malicious Software Detection using Machine Learn...

引用

2nd international conference on Electronics and Sustainable Communication Systems, ICESC 2021

作者： Tirandasu, Ravi Kumar Prasanth, Y. Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Vaddeswaram India

ISBN: (纸本)9781665428675

Malware recognition is a significant factor in the security of the PC frameworks. Be that as it may, right now used traditional strategies can't detect exact malicious software detection. That is the reason the requirement for machine learning algorithm identification emerges. The motive for this exertion was to decide the best component extraction, highlight portrayal, furthermore, grouping strategies that outcome in the greatest exactness once utilized on the highest point of Cuckoo Sandbox. In particular, KNN, DT, SVM, Innocent Bayes and RF classifiers were assessed. This work presents suggested review techniques for machine learning constructed malicious [30], [31] characterization and identification, rules for its execution. Moreover, the review made be able to valuable as a base for additional exploration in the arena of malicious software investigation with AI techniques. © 2021 IEEE.

关键词： Malware

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Mask Detection in Crowded Environment using machine learning 2

Mask Detection in Crowded Environment using Machine Learning

引用

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

作者： Balasundaram, A. Naveen Kumar, M. Kumar Sivaraman, Arun Vincent, Rajiv Rajesh, M. Centre For Cyber Physical Systems School of Computer Science and Engineering Chennai India School of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665433686

COVID-19 epidemic has affected many people across the world creating a health crisis around the globe. World Health Organization (WHO) recommends that covering face with mask is one of the best protection to minimize COVID infection. COVID-19 pandemic has forced governments across the planet to impose strict lockdowns to stop the spread of virus. Many health experts says that wearing facemasks while at public places clearly reduces the spread of the virus. A machine learning (ML) based face mask detection with face recognition and alert has been proposed. As part of this work, a face mask detector along with face recognition with CV and ML using Python has been developed. In this system, the input would be a real time input image via laptop webcam. Assuming if anyone is not wearing a mask, an automatic alert is generated and the respective person snapshot is sent to the admin e-mail. © 2021 IEEE.

关键词： COVID-19

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machine learning Based Platform and Recommendation System for Food Ordering Services within Premises 2

Machine Learning Based Platform and Recommendation System fo...

引用

2nd Global conference for Advancement in Technology, GCAT 2021

作者： Aditya, G M Hoode, Aditya Anvesh Rai, K Biradar, Gangadhar Ajay Kumara, M.A Manoj Kumar, M V Prashanth, B S Sneha, H R Shivadarshan, S L Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Bengaluru India Lenior-Rhyne University Dh Schort School of Computer Science and Mathematics HickoryNC United States Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9780738132150

Normally, the long queues and crowd can be seen at the canteens/hotels whenever there is a lunch break in an organization or within a campus. This paper proposes a solution for eliminating the queue system and introduces the facility to remotely place food orders. Further, this paper proposes a real-time food recommendation system to suggest the dishes to users based on their past orders. The solution has been implemented through a mobile application built using Flutter. The mobile application has been empowered with a machine learning model for recommending the items that the user might like. The proposed method has been tested with 2 vendors catering to 60 customers successfully. The accuracy of the recommendation has been satisfactory. © 2021 IEEE.

关键词： Recommender systems

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Multilingual healthcare chatbot using machine learning 2

Multilingual healthcare chatbot using machine learning

引用

2nd international conference for Emerging Technology, INCET 2021

作者： Badlani, Sagar Aditya, Tanvi Dave, Meet Chaudhari, Sheetal Sardar Patel Institute of Technology Department of Information Technology Mumbai India

ISBN: (纸本)9781728170299

The healthcare sector is one of the largest focus areas in the world today. Health problems are becoming increasingly common. India faces a huge challenge in terms of managing rural healthcare. Early diagnosis and treatment of diseases can play an instrumental role. Physical consultation, particularly in the rural areas, is costly and time consuming. The solution is adopting healthcare chatbots. The proposed solution describes a multilingual healthcare chatbot application that can perform disease diagnosis based on user symptoms. It also responds to user queries by calculating sentence similarity by using TF-IDF and Cosine Similarity techniques and choosing the most appropriate response from its knowledge database. The multilingual capabilities of the chatbot system make it highly suitable for use in rural India. The chatbot application currently supports three languages namely English, Hindi and Gujarati. The chatbot application converses with the user using concepts of Natural Language Processing and also supports speech to text and text to speech conversion so that the user can also communicate using voice. Five different machine learning algorithms have been analyzed for disease prediction. The Random Forest Classifier produces the best results and gives an accuracy of 98.43%. Thus, it is used as the system's core classifier. © 2021 IEEE.

关键词： Decision trees

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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...

引用

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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Comparative Analysis of Heterogeneous Ensemble learning using Feature Selection Techniques for Predicting Academic Performance of Students 2

Comparative Analysis of Heterogeneous Ensemble Learning usin...

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2nd international conference on Computational Methods in Science and Technology, ICCMST 2021

作者： Nidhi, Nidhi Kumar, Mukesh Agarwal, Shweta Chandigarh University CSE Punjab India School of Computer Application Lovely Professional University CSE Punjab Phagwara India

ISBN: (纸本)9781665458665

Data stored in digital form is increasing daily, and so its complexity. Processing a massive volume of data needs efficient technology. Data mining and machine learning researchers are focused on finding a suitable algorithm that can find important information after processing that data. In educational data mining, most of the students' records are also stored in digital form. So, the researchers are also trying to find some informative knowledge that can be helpful for the students, teachers, and management to improve their working towards the success of the students and institution also. In predictive modelling, the main challenge is finding the most effective predictive techniques that help achieve an acceptable accuracy level. This article, therefore, proposes a hybrid or heterogeneous approach of Correlation Attribute Evaluation, Ensemble learning like Stacking, Voting and MultiScheme, in conjunction with seven different machine learning algorithms to improve the prediction accuracy up to an acceptable level. Here, k-fold cross-validation was used as a test method to evaluate the predictive performance of the classification algorithms. © 2021 IEEE.

关键词： Students

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