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AI and Big Data for the management of COVID-19

AI and Big Data for the management of COVID-19

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Renewable Solutions for Ecosystems: Towards a Sustainable Energy Transition (ICRSEtoSET), international conference on

作者： Dina El Kayaly Sherifa Elhady Nahla Hazem Tawfik Ismail Irene Samy Fahim International Business New Giza University Cairo Egypt Sesc Research Center Nile University Sheikh Zayed Giza Egypt Management of Technology Nile University Sheikh Zayed Giza Egypt Wireless Intelligent Networks Center (WINC) Nile University Giza Egypt

ISBN: (数字)9798350346336

ISBN: (纸本)9798350346343

In March 2020, World Health Organization (WHO) recognized COVID-19 as a pandemic and urged governments to exert maximum efforts to prevent its spreading through political decisions together with public awareness campaigns positively impacting personal behaviors. Moreover, the WHO recommends collecting facts and data from reliable sources to help accurately determine the risks, accordingly governments take reasonable precautions. There are several ways to fight against corona such as accelerating research for the doctors, scientists and organizations working to find a vaccine or a medicine to defeat the COVID-19 virus, cleaning and sterilizing facilities, ensure health and productivity of people while changing their workplace, provide supercomputers to fight the virus. Artificial Intelligence (AI) possesses remarkable potency in its capacity to assist in combating the COVID-19 pandemic. This is achieved through a diverse range of methodologies such as machine learning, Natural Language processing, and Computer Vision applications. By instructing computers to effectively employ models based on extensive data sets, the objective of pattern recognition, explication, and prognostication is pursued [1]. These techniques will generate knowledge that can be useful in diagnosing, predicting, and treating COVID-19. [2]–[3] AI can also help in detecting patterns that help us to manage COVID 19 socio-economic impacts [4]. Since the outbreak of the pandemic, there has been a scramble to use AI. This article aims to overview the possible applications of AI and Big Data in facing COVID 19 pandemic. Four possible applications are identified, namely effective alert helping in monitoring the outbreaks instantaneously; diagnostic cases of COVID 19 and tailor medication; facilitating the implementation of Public Health interventions and resource optimizations; and Building cities with smart healthcare services.

关键词： COVID-19 Pandemics Computer viruses Government Urban areas Big Data Supercomputers

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Silicosis Detection Using Extended Transfer learning Model 6th

Silicosis Detection Using Extended Transfer Learning Model

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6th international conference on Recent Trends in image processing and pattern recognition, RTIP2R 2023

作者： Sharma, Gulshan Kumar Harjule, Priyanka Agarwal, Basant Kumar, Rajesh Malaviya National Institute of Technology Jaipur Jaipur India Central University of Rajasthan Kishangarh Ajmer India

ISBN: (纸本)9783031530845

Silicosis is a prevalent and damaging occupational disease in India. The detection of silicosis on X-ray images heavily relies on the expertise of radiologists, resulting in delayed detection and diagnosis. To overcome this challenge, machine learning-based computer-aided detection methods are being explored. However, due to the lack of publicly available large databases of silicosis images, highly accurate deep-learning models for silicosis detection through X-rays are difficult to obtain. In this study, we propose a method for silicosis detection using transfer learning techniques on X-ray radiographs. We trained and tested well-known deep transfer learning models, including Vgg16, Vgg19, ResNet50, ResNet101, Densenet121, Densenet169, Densenet201, Xception, Mobilnet, InceptionV3, and Efficient B7 models. Our proposed model is based on ResNet50 in which we add additional layers, which achieved an F1 score of 0.8166. Additionally, we applied the Grad-CAM technique to produce heatmap images that highlight the important features extracted from the X-ray images This approach could serve as a feasible alternative in countries with limited access to specialized radiological equipment and trained radiologists for detecting silicosis on X-ray images. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolutional neural networks

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processing of Human Motions using Cost Effective EEG Sensor and machine learning Approach

Processing of Human Motions using Cost Effective EEG Sensor ...

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Artificial Intelligence and Data Analytics (CAIDA), international conference on

作者： Shaikh Abdul Waheed S. Revathi Mohammed Abdul Matheen Amairullah Khan Lodhi Mohammed Ashrafuddin G.S. Maboobatcha B.S. Abdur Rahman Crescent Institute of Science & Technology Chennai India Common First Year King Saud University Riyadh Saudi Arabia Shadan College of Engineering and Technology Hyderabad India

ISBN: (纸本)9781665430418

Emotions are different biological states brought on by neurophysiological changes associated with the nervous system which are affected and results in thoughts, feelings, behavioral responses, and a degree of pleasure or displeasure. These emotions play a vital role in understanding the human response towards any means of action they experience. The system uses an EEG headband device made out of IoT and various machine learning algorithms to understand the human's emotion of any incident they undergo. In this paper, we propose a method to detect and recognize the emotional changes in a human who is exposed to various images.

关键词： Emotion recognition machine learning algorithms image recognition Data analysis machine learning Nervous system Electroencephalography

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Offline Handwritten Signature Verification and recognition Based on Deep Transfer learning 1

Offline Handwritten Signature Verification and Recognition B...

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1st international conference on machine Vision and image processing, MVIP 2020

作者： Foroozandeh, Atefeh Askari Hemmat, Ataollah Rabbani, Hossein Department of Applied Mathematics Sciences and Modern Technology Graduate University of Advanced Technology Kerman Iran Department of Applied Mathematics Mathematics and Computer Shahid Bahonar University of Kerman Kerman Iran Department of Biomedical Engineering School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Isfahan Iran

Recently, deep convolutional neural networks have been successfully applied in different fields of computer vision and pattern recognition. Offline handwritten signature is one of the most important biometrics applied in banking systems, administrative and financial applications, which is a challenging task and still hard. The aim of this study is to review of the presented signature verification/recognition methods based on the convolutional neural networks and also evaluate the performance of some prominent available deep convolutional neural networks in offline handwritten signature verification/recognition as feature extractor using transfer learning. This is done using four pretrained models as the most used general models in computer vision tasks including VGG16, VGG19, ResNet50, and InceptionV3 and also two pre-trained models especially presented for signature processing tasks including SigNet and SigNet-F. Experiments have been conducted using two benchmark signature datasets: GPDS Synthetic signature dataset and MCYT-75 as Latin signature datasets, and two Persian datasets: UTSig and FUM-PHSD. Obtained experimental results, in comparison with literature, verify the effectiveness of the models: VGG16 and SigNet for signature verification and the superiority of VGG16 in signature recognition task. © 2020 IEEE.

关键词： Convolutional neural networks

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Iranian License Plate recognition using Deep learning 1

Iranian License Plate Recognition using Deep Learning

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1st international conference on machine Vision and image processing, MVIP 2020

作者： Rashtehroudi, Atefeh Ranjkesh Shahbahrami, Asadollah Akoushideh, Alireza University of Guilan Department of Computer Engineering Rasht Iran

ISBN: (纸本)9781728168326

Automated License Plate recognition (ALPR) has many applications in intelligent transport system. The ALPR has three main steps, License Plate (LP) localization, segmentation and Optical Character recognition (OCR). Each step needs different techniques in real condition and each technique has its specific characteristics. The LP localization techniques detect the LP after that segmentation algorithms should segment and isolate each character from each other. Finally, the OCR step is applied to recognize the separated characters. The final accuracy depends on the accuracy of each step. To improve the OCR step performance, we combine both segmentation and OCR steps as a single-stage using deep learning techniques such as the You Only Look Once (YOLO) framework. Our experimental results show that this proposed approach recognizes the Iranian LP characters with accuracy 99.2% compared to previous works. © 2020 IEEE.

关键词： Optical character recognition

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Yemeni Paper Currency recognition System Using Deep learning Approach 1st

Yemeni Paper Currency Recognition System Using Deep Learning...

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1st international conference of Advanced Computing and Informatics, ICACIN 2020

作者： Ba Alawi, Abdulfattah E. Saeed, Ahmed Y. A. Al-Zekri, Burhan T. Radman, Borhan M. N. Saif, Alaa A. Alshami, Maged Alshadadi, Ahmed Mohammad, Muneef A. Mohammed, Ala’a M. Saeed, Osama Y. A. Saeed, Adeeb M. Department of Software Engineering Al-Saeed Faculty of Engineering & IT Taiz University Taiz Yemen Department of Communication and Computer Engineering Al-Saeed Faculty of Engineering & IT Taiz University Taiz Yemen

ISBN: (纸本)9789811560477

The recognition of paper currency with various designs is a difficult task for automated banking machines and people with low visions. Due to the wide use of automated system such as ATM, for getting cash, safe and precise methods for recognizing paper currency are highly demanded. Recently, the use of an efficient automatic classifying system becomes one of the most important needs of current banking services. The main aim of this paper is to introduce an intelligent model that can distinguish between different types of Yemeni paper currencies using convolution neural networks (CNN). The system is based on image processing methods to classify different types of currencies efficiently. Moreover, deep learning techniques are used to perform the classification process effectively. The obtained results in the actual implementation of this model are encouraging. The accuracy rate reaches 92.7% in the testing phases. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Development of an Algorithm for Determining the Railway Tracks on Video image 1

引用

1st international Scientific conference on Telecommunications, Computing and Control, TELECCON 2019

作者： Deylid, Ivan Molodyakov, Sergey Tyutin, Boris Peter the Great St. Petersburg Polytechnic University St. Petersburg Russia Opera Software Gothenburg Sweden

ISBN: (数字)9789813366329

ISBN: (纸本)9789813366312

The subject of the work is in the field of creating autopilot railway transport. One of the problems related to railway safety is considered. This paper presents a developed algorithm for determining the railway track from a video image. It is the first step in determining the free path or obstacles in front of the locomotive. The proposed algorithm is a combination of several machine learning algorithms that are applied sequentially (boosting). The first stage of the algorithm is the extraction and classification of features from the image. In this stage, the speeded up robust features or SURF-method is used. At the output of the SURF-stage, we obtain information in the form coordinates of key points and their descriptors. In the second stage, the selected key points are classified. Combinations of two classification methods are used: the K-nearest neighbors or KNN-method and the support vector machines or SVM-method. The final step is the compilation of a railway track mask. For this, the nearest neighbor graph method is used. For practical use of the found mask, the inverse perspective transformation is performed. The efficiency of the developed algorithm is shown experimentally. It can be considered as one of the ways of image segmentation. The main advantages of the algorithm are associated with minimal preparation of the training sample and the ability to analyze its work for further improvement. The results of processing real video images obtained from a video camera mounted on a locomotive are presented. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Nearest neighbor search

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1st international conference on machine learning and Information processing, ICMLIP 2019

1st International Conference on Machine Learning and Informa...

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1st international conference on machine learning and Information processing, ICMLIP 2019

ISBN: (纸本)9789811518836

The proceedings contain 49 papers. The special focus in this conference is on machine learning and Information processing. The topics include: Virtual conversation with real-time prediction of body moments/gestures on video streaming data;automatic diagnosis of attention deficit/hyperactivity disorder;classification of types of automobile fractures using convolutional neural networks;a subjectivity detection-based approach to sentiment analysis;image processing and machine learning techniques to detect and classify paddy leaf diseases: A review;a comparative study of classifiers for extractive text summarization;capabilities of chatbots and its performance enhancements in machine learning;a deep learning-based approach for predicting the outcome of H-1B visa application;data leakage detection and prevention: Review and research directions;stress detection in speech signal using machine learning and AI;a blockchain based model to eliminate drug counterfeiting;a comparative analysis of predictive modeling techniques: A case study of device failure;Python and OpenCV in automation of live surveillance;Privacy preservation in ROI of medical images using LSB manipulation;novel approach for measuring nutrition values using smartphone;detection of hate speech in hinglish language;enhancing multi-level cache performance using dynamic R-F characteristics;denoising documents using image processing for digital restoration;humming-Based song recognition;a new data structure for representation of relational databases for application in the normalization process;e-Classroom Using IOT to revolutionize the classical way of knowledge sharing;real drowsiness detection using viola–jones algorithm in tensorflow;smart sound system controller for restricted zones;predictive analysis of co-seismic rock fall hazard in hualien county Taiwan.

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Challenges and Difficulties of Multi-Spectral MRI Based Brain Tumor Detection and Segmentation

Challenges and Difficulties of Multi-Spectral MRI Based Brai...

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Health Science and Technology (ICHst), international conference on

作者： László Szilágyi Ágnes Györfi Lehel Dénes-Fazakas Szabolcs Csaholczi Ioan-Marius Pisak-Lukáts Levente Kovács Physiological Controls Research Center Óbuda University Budapest Hungary Comput. Intell. Research Group Sapientia Univ. Tg. Muteş Romania BME IIT Budapest Hungary

ISBN: (数字)9798350315714

ISBN: (纸本)9798350315721

Introduction: The automatic detection and segmentation of brain tumors based on multi-spectral MRI data represents a widely investigated topic, which captured special attention with the Brain Tumor Segmentation challenges organized yearly since 2012 jointly with MICCAI conferences. Up to one thousand different solutions may have been presented by the research community, involving the whole arsenal of artificial intelligence starting with classical machine learning methods and developing together with convolutional neural networks and deep learning. Methodology: This paper proposes to discuss the general challenges and difficulties of automatic brain tumor segmentation methods, comparing the needs and capabilities of a classical machine learning based method using ensemble learning, and a U-net solution involving deep learning, both very popular at their respective time. The main difference of the two approaches is that the first relies on handcrafted features extracted for each pixel separately and applies a classification to estimate the tissue to which each pixel belongs, while the other works with whole slices or volumes at once and extracts its own features needed for decision making. Our team investigated the necessary preprocessing steps of MRI data, and their optimization to suit the needs of both approaches, and trialed various post-processing techniques that can improve the final segmentation outcome after the decision made by the classification algorithm. Results and Discussion: The U-net based solution can achieve finer segmentation quality, reaching Dice scores approximately 2 % higher than the ensemble learning, but its stability is weaker meaning that two different training sessions of the same network may lead to significantly different results. Another main difference consists in the most suitable histogram uniformization technique required, as the ensemble learning works better when the method of Nyul et al (IEEE T. Med. Imag. 19:143-150, 2000) is e

关键词： Deep learning Training image segmentation Magnetic resonance imaging Feature extraction Resonance Ensemble learning

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Deployment of Scalable Cloud Based image processing Application Using Fuzzy Coupled learning Technique

Deployment of Scalable Cloud Based Image Processing Applicat...

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international conference on Computational Science and Technology (ICCst)

作者： B. Mathivanan K.R. Nandhashree C. Radhakrishnan S. Ananthi P. Suganthi R. Asokan Department of CSE Sri Ramakrishna Engineering College Coimbatore Department of Cyber Security SRM Valliammai Engineering College Department of CSE Kongunadu College of Engineering and Technology Trichy Department of ECE Kongunadu College of Engineering and Technology Trichy

ISBN: (纸本)9781665476560

Because image processing typically uses a lot of memory and computational time, a sophisticated platform is needed. As a result, it's critical to adopt cost-effective solutions to replace outdated ones. We chose to use cloud computing to fulfill our needs for large-scale data processing as a result of these factors. In the meantime, this strategy offers quick access to on-demand services that are highly available and scalable. By outsourcing computations to a third party and utilizing cloud services rather than internal applications, healthcare organizations can reduce operating costs. However, robust data protection against both unauthorized users and unreliable clouds is required to avoid the disclosure of malicious data. Users can now assemble and execute their data using the cloud thanks to the development of numerous frameworks. Distributed systems, cryptosystems, or a combination of the two are frequently used in their construction. Almost all current implementations use homomorphism cryptosystems, Service Oriented Architecture, secret share schemes, and secure multi-party computation as their primary security mechanisms. The main disadvantage of utilizing these techniques for large-scale data analysis on the cloud is the cost of computing image processing tasks. Keeping medical records & sensitive health-related information secure is a problem. We suggest a fresh method for protecting data processing in cloud environments that is based on machine learning methods. We typically employ Fuzzy C-means Clustering and Multi-Dense Convolution Neural Network to more accurately classify image pixels. To further reduce the danger of a possible leak of medical data, we add a third level, the CloudSaS-TPM module, to the conventional two-layered design. The results show some encouraging discoveries that provide new angles for creating cloud storage for the medical industry.

关键词： Training Cloud computing Costs Sensitivity Scientific computing Neural networks Service-oriented architecture

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