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COVID-19 Detection using Transfer learning with Convolutional Neural Network

COVID-19 Detection using Transfer Learning with Convolutiona...

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international conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Pramit Dutta Tanny Roy Nafisa Anjum Chittagong University of Engineering and Technology Chittagong Bangladesh

ISBN: (数字)9781665415767

ISBN: (纸本)9781665415774

The Novel Coronavirus disease 2019 (COVID-19) is a fatal infectious disease, first recognized in December 2019 in Wuhan, Hubei, China, and has gone on an epidemic situation. Under these circumstances, it became more important to detect COVID-19 in infected people. Nowadays, the testing kits are gradually lessening in number compared to the number of infected population. Under recent prevailing conditions, the diagnosis of lung disease by analyzing chest CT (Computed Tomography) images has become an important tool for both diagnosis and prophecy of COVID-19 patients. In this study, a Transfer learning strategy (CNN) for detecting COVID-19 infection from CT images has been proposed. In the proposed model, a multilayer Convolutional neural network (CNN) with Transfer learning model Inception V3 has been designed. Similar to CNN, it uses convolution and pooling to extract features, but this transfer learning model contains weights of dataset Imagenet. Thus it can detect features very effectively which gives it an upper hand for achieving better accuracy.

关键词： COVID-19 Computed tomography Computational modeling Transfer learning Feature extraction Data models Convolutional neural networks

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Computer-Aided Diagnosis System for Investigation and Detection of Epilepsy Using machine learning Techniques 2nd

Computer-Aided Diagnosis System for Investigation and Detect...

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2nd international conference on Innovative Computing and Communication, ICICC 2019

作者： Naren, J. Sarada Pyngas, A.B. Subhiksha, S. SASTRA Deemed University Tirumalaisamudram Thanjavur613 401 India

ISBN: (纸本)9789811512858

Epilepsy, a neurological disorder seems to be very chronic and affects a large number of people. Epilepsy is the third most common disease among all other mental disorders whose identification and cure is very hard. In the proposed work, a thorough investigation of the various techniques employed in various papers pertaining to Epilepsy detection and cure using machine learning have been taken and analysed. Various EEG signal preprocessing techniques and machine learning classifiers used in many papers were analysed with samples and conclusion on preprocessing techniques combined with classifiers gives the most accurate prediction of the disorder for preliminary investigation. Various classifiers, namely SVM, Random Forest, Neural Network, Logistic Regression were used on the dataset. The Random Forest Classifier gave the most accurate result. The accuracy was 58.9%. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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An Adaptive Graph Cut Algorithm for Spammer Group Detection from Weighted One Mode Projection of Bipartite Graph

An Adaptive Graph Cut Algorithm for Spammer Group Detection ...

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international conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Naznin Sultana Sellappan Palaniappan Pratim Saha School of Science and Engineering Malaysia University of Science & Technology Petaling Jaya Malaysia Daffodil International University Dhaka Bangladesh

ISBN: (数字)9781665415767

ISBN: (纸本)9781665415774

Now-a-days online reviews have become an indispensable resource for customers'decision making before purchasing any product. Product reviews provide valuable information that helps customers, retailers, and product owners in making decision to buy or sell product online. The rise of ecommerce platforms has led to an increase volume of customer review data available in the internet. Though most of the customers write review about the product solidness, quality, usability or else but it has also been also found that some people write intentional collusive review to mislead customer's decision-making process and damage the online business as well. These intentional fake reviews are written in order to rise or down a business and they are called spam reviews. The persons who writes fake review are called spammer. Sometimes review spammer works in group to control the overall sentiment or notion about a product, item or a business. When the review spammer works in collaboration they are called group spammer. Most of the research works found in the literature worked on individual spammer detection using the traditional machine learning or neural network-based algorithms. Very few researchers worked on group spammer detection. This paper focuses on group spammer detection from online reviews and proposed a weighted one mode bipartite projection graph-based algorithm that extract the structural, behavioral and time centric features of reviews to detect group spammer from online marketplace.

关键词： machine learning algorithms signal processing algorithms signal processing Feature extraction Usability Robots Business

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The Role of Big Data Analytics in Smart Grid Management 2nd

The Role of Big Data Analytics in Smart Grid Management

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2nd international conference on Computing, Communications and Data Engineering (CCODE)

作者： Dhupia, Bhawna Rani, M. Usha Alameen, Abdalla SPMVV Dept Comp Sci Tirupati Andhra Pradesh India Prince Sattam Bin Abdulaziz Univ Al Kharj Saudi Arabia

ISBN: (纸本)9789811501357;9789811501340

Data analytics is playing a vital role in the modern industrial era. Electricity is one of the industries that have adapted data analytics techniques to a great extent. The data collected in the smart grid through smart meters and other sensors installed is very huge. The processing of such a huge heterogeneous data is not possible without the use of big data analytics technique. Big data analytics and machine learning algorithms play a vital role in electricity transmission and distribution network for data collection, storage and analysis of the data, prediction for data forecasting, and maintenance of the system. These techniques can help to optimally deliver energy at a lower cost with high quality and can also improve the customer service as well as social welfare. This article will review the use of big data analysis techniques along with the machine learning for various applications that can be mapped for smart grid environment. This article will also discuss the various methods and algorithm to be used for the applications for smart grid. A comparative analysis will also be done to show the best methods and algorithm to be used for a particular application.

关键词： Smart grid machine learning Applications in smart grid Data analytics algorithms and methods

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Traffic Sign and Vehicle Classification based on machine learning 20

Traffic Sign and Vehicle Classification based on Machine Lea...

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2020 2nd international conference on Video, signal and Image processing

作者： Malichenko Viktor Han Honggui Beijing University of Technology China

ISBN: (纸本)9781450388931

In this paper, we offer a machine learning classifier model, later considered as MLCM, for classifying objects such as road signs and vehicles. Showing the influence of vocabulary size on accuracy of SVM using SURF. Based on SURF method used bag-of-words model as feature extractor. Due to its simplifying representation, it accelerates the first stage of our MLCM. We tested and analyzed accuracy of Support Vector machines, including Linear, Quadratic and Medium Gaussian SVM as flowed step model and automatically use best result for further estimation. Furthermore, we provide a brief introduction of applied methods and experimental results analysis. MLCM introduces combination of SURF method and several SVMs as well as optimized SVM. This technique shows good performance with minimum failures. Thereafter, it will be implemented for real-time video sequences. The achieved goal can be implemented in the use of self-driving of industrial machines with a safe speed.

关键词： self-driving vehicle Support Vector machines machine learning SURF method

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Intelligent condition monitoring of a CI engine using machine learning and artificial neural networks 1

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2nd international conference on machine Intelligence and signal processing, MISP 2019

作者： Naveen Kumar, P. Jeeva Karunya, S.R. Sakthivel, G. Jegadeeshwaran, R. School of Mechanical and Building Sciences VIT Chennai India

ISBN: (数字)9789811513664

ISBN: (纸本)9789811513657

The evolving automotive industry with the rapid growth of using fuel resources causes a shortfall of fuel availability. The engine maintenance has great importance and it is essential to develop a fault detection system and condition monitoring is done to reduce the damage-causing circumstances to improve engine safety. The engine tested at the rated speed of 1800 rpm by varying the loads. Captured vibration data is analyzed by using feature extraction and classification algorithms to obtain the best performance and minimum vibrations of blends. The best classification accuracy blend results are selected and used to develop a neural network model and to predict the vibration signatures. The neural network model is developed with a feedforward backpropagation algorithm and using Levenberg–Marquardt as training function. The developed ANN model is trained and vibration signatures are predicted for the selected blend B25 with the classification accuracy of 97%. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Neural network models

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Cellular Level Based Deep learning Framework for Early Detection of Dysplasia in Oral Squamous Epithelium 2nd

Cellular Level Based Deep Learning Framework for Early Detec...

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2nd international conference on Recent Innovations in Computing (ICRIC)

作者： Gupta, Rachit Kumar Kaur, Mandeep Manhas, Jatinder Univ Jammu Dept Comp Sci & IT Bhaderwah Campus Bhadarwah Jammu & Kashmir India Indira Gandhi Govt Dent Coll Dept Oral Pathol Jammu Jammu & Kashmir India

ISBN: (纸本)9783030294076;9783030294069

Over the past few decades, the artificial intelligence is being employed in diverse fields like pattern classification, image processing, object identification, recommender systems, speech recognition, etc. machine learning has made it possible to develop intelligent systems through training that equip machines to handle different tasks, exactly on the analogy similar to humans. In medical field, machine learning algorithms are being used for prediction, early detection and prognosis of various diseases. These algorithms suffer a certain threshold due to their inability to handle large amount of data. Deep learning based techniques are emerging as efficient tools and can easily overcome the above difficulties in processing data related to medical imaging that includes mammographs, CT scans, MRIs and histopathology slide images. Deep learning has already achieved greater accuracy in early detection, diagnosis and prognosis of various diseases especially in cancer. Dysplasia is considered to be a pathway that leads to cancer. So, in order to diagnose oral cancer at its early stage, it is highly recommended to firstly detect dysplastic cells in the oral epithelial squamous layer. In our research work, we have proposed a deep learning based framework (convolutional neural network) to classify images of dysplastic cells from oral squamous epithelium layer. The proposed framework has classified the images of dysplastic cells into four different classes, namely normal cells, mild dysplastic cells, moderate dysplastic cells and severe dysplastic cells. The dataset undertaken for analysis consists of 2557 images of epithelial squamous cells of the oral cavity taken from 52 patients. Results show that on training the proposed framework gave an accuracy of 94.6% whereas, in testing it gave an accuracy of 90.22%. The results produced by our framework has also been tested and validated by comparing the manual results recorded by the medical experts working in this area.

关键词： Deep learning machine learning Dysplasia Oral cancer Oral squamous epithelium Dysplastic cells Convolutional neural network

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A Study on ELM(Election pledge management for Local governors Model) Based on machine learning -Focused on On-Nara Document System- 2019

A Study on ELM(Election pledge management for Local governor...

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2nd international conference on Software Engineering and Information Management (ICSIM) / 2nd international conference on Big Data and Smart Computing (ICBDSC)

作者： Lee, Hong-Jae Han, Kyeong-Seok Kwon, Tae-Hyun Han, Sang-Ung Soongsil Univ Dept IT Policy Management Seoul 06978 South Korea Soongsil Univ Dept Management Seoul 06978 South Korea

ISBN: (纸本)9781450366427

The background of this paper is new social trend of more public's interest in the implementation of the pledge of the local governors who were elected by citizens. In these days the election pledge for enhanced local governmental policies became more important. The objective of this paper is to suggest the model of election pledge management for local government heads based on machine learning focused on On-Nara document system. The system is currently used by Korean governmental organizations for document processes. The methods to prove a comparative advantage of the proposed model are the comparison tests between As-Is system and To-Be system based on a few criteria such as time, efficiency and extraction rate. Through this model, local governors could present systematic goals and road map of pledges in order to get closer to citizens and local residents. In other words, this study proposes a model, so called ELM(Election pledge management for Local governors Model), for efficiently extracting necessary data from planned and implemented details of pledge projects that are prepared in the form of unstructured documents. We carried out research to prove empirically our machine learning-based model is more efficient than current semi-manual system with some automated processes in order to manage efficiently the pledge project implementation of local governors to get the results. In conclusion, this research proved that the proposed model is more competitive than the existing models. In the 4 th industrial revolution era the new approach using machine learning and big data will become more popular.

关键词： machine learning Big Data Natural Language processing Unstructured Database Election pledge management Text mining

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Convolutional neural networks application in cardiovascular decision support systems 2

Convolutional neural networks application in cardiovascular ...

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2nd international conference on Image and Video processing, and Artificial Intelligence (IPVAI)

作者： Natalia, Konnova Mikhail, Basarab Michael, Khachatryan Anna, Domracheva Igor, Ivanov Bauman Moscow State Tech Univ Comp Sci & Control Syst Dept 2 Ya Baumanskaya St5 Moscow 105005 Russia

ISBN: (数字)9781510634107

ISBN: (纸本)9781510634107

The paper considers the possibilities of using neural network methods of machine learning to diagnose the states of the human cardiovascular system and support decision-making in cardiology and cardiac surgery. The issues of processing and preparation of electrocardiography signals, selection of architecture and tuning of neural network parameters for automation of diagnosis are discussed. Here, the results obtained with the help of multilayer perceptrons and convolutional neural networks to assign the submitted input cardiovascular data to one of the classes of states in the selected space are examined. Based on a specialized developed software, the proprietary numerical experiments with real clinical data were carried out. Given the above results, demonstrating the applicability of the used deep learning methods and algorithms to diagnostic automation, a model of a hierarchical decision support system is proposed.

关键词： decision support system deep learning digital signal processing machine learning neural networks

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Predictive Technique Of Task Scheduling For BigData In Cloud 2

Predictive Technique Of Task Scheduling For BigData In Cloud

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2nd IEEE international conference on Emerging Smart Computing and Informatics (ESCI)

作者： Sarraiu, Sridevi Bien, Franklin Ulsan Natl Inst Sci & Technol Sch Elect & Comp Sci Engn Ulsan South Korea

ISBN: (纸本)9781728152639

In the current era, Big data utilizes MapReduce strategies in task scheduling, most notably, Apache Hadoop, a programming library and framework that considers the distributed processing of enormous data across clusters of computers using simple programming models. Still, there persist longer wait times with MapReduce technique. Because, the scheduling in Cloud with huge data causes frequent obstructions to effective computing, prompting prolonged makespan, longer waiting time and expenses acquired by the client and the server end. Task Scheduling with a huge measure of information data can cause obstacles to proficient processing. The motive of this work is to accomplish the desired solution to overcome the issues obstructing the effective CPU/task Scheduling of VMs in the Cloud environment with massive data without the use of any complex algorithms. This paper presents a predictive task scheduling approach and introduces PCA (Principal component analysis) and utilizes nine different machine classifiers and compares the results of the accuracy and time obtained by each ML classifiers with and without the use of PCA. Results are visualized and the percentage variation of comparison is discussed. Experiments are carried out in the Hadoop Environment, using MapReduce the dataset is generated and ML classifiers are executed in Python.

关键词： Hadoop PCA machine learning Task Scheduling

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