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CoviNet: Role of Convolution Neural Networks (CNN) for an Efficient Diagnosis of COVID-19 1

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9th international conference on Frontiers in Intelligent computing: Theory and Applications, FICTA 2021

作者： Indira, D.N.V.S.L.S. Abinaya, R. Department of Information Technology Gudlavalleru Engineering College AP Gudlavalleru521356 India Department of Computer Science and Engineering Gudlavalleru Engineering College AP Gudlavalleru521356 India

ISBN: (数字)9789811666247

ISBN: (纸本)9789811666230

Coronaviruses are a large family of viruses that can cause a human being to become critically sick. In different forms, COVID-19 affects various people. Since COVID-19 has begun its rampant expansion, isolating COVID-19 infected individuals is the best way to deal with it. This can be accomplished by monitoring individuals by running recurring COVID tests. The use of computed tomography (CT-scan) has demonstrated good results in evaluating patients with possible COVID-19 infection. Patients with COVID will heal with the help of antibiotic therapy from vitamin C supplements. Patients with these symptoms need a faster response using non-clinical methods such as machine learning and deep neural networks in order to manage and address additional COVID-19 spreads worldwide. Here, in this paper we are diagnosis the covid-19 patients with CT-scan images by applying XGBoost classifier. Developed a web application which basically accepts a patient CT-scan to classify COVID positive or negative. After that, the negative class patients with symptoms are suggested with a danger rate with the help of age groups, health-related issues, and the area he/she belongs to. Three machine learning algorithms, the decision tree, random forest, and k-nearest neighbor algorithms, were used for this. The results of the current investigation showed that the model built with the decision tree data mining algorithm is more fruitful in foreseeing the risk rate of 93.31 percent overall accuracy of infected patients. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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TransformerFusionNet: A Real-Time Multimodal Framework for ICU Heart Failure Mortality Prediction Using Big data Streaming

TransformerFusionNet: A Real-Time Multimodal Framework for I...

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international conference on Computer and Applications (ICCA)

作者： Hager Saleh Michael McCann Shaker El-Sappagh John G. Breslin Faculty of Computers and Artificial Intelligence Hurghada University Hurghada Egypt Insight Research Ireland Centre for Data Analytics University of Galway Galway Ireland Atlantic Technological University Letterkenny Ireland Department of Computing Atlantic Technological University Letterkenny Ireland Faculty of Computer Science and Engineering Galala University Suez Egypt Faculty of Computers and Artificial Intelligence Benha University Benha Egypt School of Engineering University of Galway Galway Ireland

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

This paper presents a real-time multimodal frame-work to enhance ICU mortality prediction for heart disease patients by integrating structured data, clinical notes, and big data streaming platforms. The proposed framework comprises two components: an offline multimodal model and a real-time pipeline. In the offline component, we proposed the Transformer-FusionNet model leverages BioBERT-Transformer for clinical notes and a Recurrent Neural Network (RNN) for structured data. The model integrates outputs through a concatenation layer to make predictions and is rigorously evaluated using the MIMIC-III dataset. It demonstrates superior performance across evaluation metrics and achieves an accuracy of 91.720%, precision of 91.85%, recall of 91.720%, and F1-score of 91.693%, outperforming other single and multimodal models. The real-time pipeline component incorporates Apache Spark and Apache Kafka for ingesting, preprocessing, and streaming structured data and clinical notes. This integration enables real-time mor-tality prediction with TransformerFusionNet, addressing critical gaps in the utilization of clinical notes in healthcare analytics. The framework significantly advances multimodal deep learning applications in healthcare by combining state-of-the-art models with scalable streaming technologies, offering a practical solution for improving clinical decision support systems and patient outcomes in real-time, especially in intensive care unit settings.

关键词： Heart Accuracy Recurrent neural networks Biological system modeling Pipelines Mortality Medical services Big data Transformers Real-time systems

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Tobacco Plant Disease dataset 14

Tobacco Plant Disease Dataset

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14th international conference on Digital Image Processing, ICDIP 2022

作者： Lin, Hong Tse, Rita Tang, Su-Kit Qiang, Zhenping Ou, Jinliang Pau, Giovanni Faculty of Applied Sciences Macao Polytechnic University China Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence of Ministry of Education Macao Polytechnic University China College of Big Data and Intelligent Engineering Southwest Forestry University KunMing China Department of Computer Science and Engineering-DISI University of Bologna Italy Samueli Computer Science Department University of California Los Angeles United States

ISBN: (纸本)9781510657564

Tobacco is a valuable plant in agricultural and commercial industry. Any disease infection to the plant may lower the harvest and interfere the operation of supply chain in the market. Image-based deep learning methods are cutting-edge technologies that can facilitate the diagnosis of diseases efficiently and effectively when large-scale dataset is available for training. However, there is not a public dataset about tobacco currently. A comprehensive dataset is appealed to take advantage of deep learning methods in tobacco cultivation urgently. In this paper, we propose to create a specific dataset for tobacco diseases, called Tobacco Plant Disease dataset (TPDD). 2721 tobacco leaf images are taken in field. The dataset serves for two purposes: disease classification and leaf detection. For classification, we identify 12 classes and provide two types of disease annotations: 1) Whole Leaf Section;2) Disease Fragment Section. For leaf detection, we provide two kinds of bounding box: rectangle bounding box and polygon bounding box. In addition, we conduct baseline experiments to illustrate the usefulness of TPDD: 1) using deep learning model to detect single disease and multiple diseases;2) using YOLO-v3 and Mask-RCNN to detect leaves. We hope that the dataset could support the tobacco industry, also be a benchmark in fine-grained vision classification. © 2022 SPIE.

关键词： Computer vision

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Inflated 3D Convolution Network for Detecting Anomalies in Surveillance Videos

Inflated 3D Convolution Network for Detecting Anomalies in S...

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

作者： Rajesh Kumar Yadav Rajiv Kumar Computer Science and Engineering Delhi Technological University Delhi India

All cities are getting smart with the intervention of latest technologies, there infrastructure is getting upgraded with each day. Critical informations is provided to us by these *** the rise in popularity of AI, there is a requirement for a real-time system that can aid in spotting crimes as they *** surveillance platform’s information may include both aberrant and conventional footage. We propose developing an aberrant event identification system based on weakly annotated training videos, and so when such behaviour is discovered, suitable action may be *** extraction of features, we deployed I3D-Resnet-50, a deep deep residual model. The Kinetics video action dataset was used to train this network. There are 13 unique abnormalities in our dataset. Crime, Attack, Firing, Burglaries, Thieving, Prison, Fight, Thefts, Breaking and entering, Bomb, Criminal damage, Torture, and Traffic Accident are all unusual incidents. The proposed approach for visual anomaly detection achieves considerable improvements in terms of correctness and recall.

关键词： Training Visualization Three-dimensional displays Surveillance Weapons Urban areas Feature extraction

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Large-scale Information Management using Hadoop Platform

Large-scale Information Management using Hadoop Platform

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Advanced technologies in Intelligent Control, Environment, computing & Communication Engineering (ICATIECE), international conference on

作者： Vinay Kumar Mishra Avdesh Singh Pundir Department of Computing Science & IT Teerthanker Mahaveer University Moradabad India Department of Engineering & Technology Jaipur National University Jaipur India

ISBN: (纸本)9781665493970

The information produced today is enormous and is increasing more quickly than computer speeds can keep up. As a result, it is no longer advantageous and often takes a long time to maintain or process information using traditional methods, or as we may say, a single machine. Under the terms of the Apache License, In the cloud hardware of the distributed file system, this research also covers optimizing the number of computing nodes and the message passing pathways. According to ***, the Apache HIVE data storage software makes it easier to read, write, and manage huge datasets that are stored in distributed storage using SQL. The installation and launch of Hadoop and HIVE, setting up the rules in a Hadoop cluster, and a few use cases for performance testing are all covered at the end of the chapter.

关键词： Computational modeling Message passing Memory Streaming media Licenses Virtual machining Software

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Learning and Preserving Relationship Privacy in Photo Sharing

Learning and Preserving Relationship Privacy in Photo Sharin...

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IEEE/ACM international Symposium on Big data computing (BDC)

作者： Jialin Liu Lin Li Na Li Department of Computer Science Prairie View A&M University Prairie View USA

In recent years, Online Social Networks (OSN) have become popular content-sharing environments. With the emergence of smartphones with high-quality cameras, people like to share photos of their life moments on OSNs. The photos, however, often contain private information that people do not intend to share with others (e.g., their sensitive relationship). Solely relying on OSN users to manually process photos to protect their relationship can be tedious and error-prone. Therefore, we designed a system to automatically discover sensitive relations in a photo to be shared online and preserve the relations by face blocking techniques. We first used the Decision Tree model to learn sensitive relations from the photos labeled private or public by OSN users. Then we defined a face blocking problem and developed a linear programming model to optimize the tradeoff between preserving relationship privacy and maintaining the photo utility. In this paper, we generated synthetic data and used it to evaluate our system performance in terms of privacy protection and photo utility loss.

关键词： Training Privacy data privacy Social networking (online) Face recognition System performance Linear programming

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Compression scenarios for Federated Learning in Smart Manufacturing 4

Compression scenarios for Federated Learning in Smart Manufa...

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4th international conference on Industry 4.0 and Smart Manufacturing, ISM 2022

作者： Nasri, Seif Allah El Mesloul Ullah, Ihsan Madden, Michael G Departement of Electronic Badji Mokhtar University Annaba23000 Algeria School of Computer Science University of Galway GalwayH91 TK33 Ireland

Recent advances in Industrial Internet of Things (IIoT) and communication technologies have provided new concepts of smart manufacturing and paved the way for the new era of Industry 4.0. The huge amount of generated data from machinery, connected systems, and edge devices can be exploited to extract actionable insights that can enhance the decision-making process in smart manufacturing, targeting to improve the return of investment. Conventionally, smart decisions are obtained from centralised data using predefined machine/deep learning models. To achieve better accuracy, these models require training based on diverse, unbiased, and comprehensive datasets, which may not be feasible in distributed and remote facilities due to the high scalability of smart manufacturing and data privacy concerns. Federated learning stands as a primary solution to these issues by bringing collaborative smart manufacturing without requiring centralised data training rather than model sharing. However, these models with many parameters result in big size, which put pressure on the communication channels due to frequent transfers from server to clients and vice versa and may face communication delays and model corruption. In addition, bigger models create issues for edge devices with limited memory and processing capacity which needs consideration. Therefore, effective compression methods are mandatory to ensure Communication-Efficient Federated Learning. In this paper, first, we discuss some possible application opportunities of FL in smart manufacturing, especially for the automotive industry but that can be extended for others. Secondly, we highlight FL communication challenge in smart manufacturing for which we present the compression concept and objectives in FL. It is followed by discussing the state-of-the-art compression techniques in FL. Thirdly, a special focus is given to NNR and DeepCABAC for their demonstrated high compression gains with a wide variety of deep models. Finall

关键词： Decision making

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Research analysis on the application of big data and artificial intelligence in Chinese medicine diagnosis

Research analysis on the application of big data and artific...

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Cloud computing, Big data and Internet of Things (3CBIT), international conference on

作者： Yanping Zhuang Xuan Zhang Simin Luo Guiling Lin Xiaoyu Tian Yi Chen Mingyin Lai Aimin Gong Department of Traditional Chinese Medicine Hainan Medical University Haikou China

ISBN: (纸本)9781665492263

The application of modern technologies such as big data and artificial intelligence in life and work has inspired scholars to apply them to Chinese medicine diagnosis. And the application of big data is just the right way to digitise and organise and classify millennia of knowledge, experience and literature on Chinese medicine diagnosis. Based on this, the robot will be made intelligent through artificial intelligence-related technology, thus promoting the transmission, development and innovation of Chinese medicine diagnosis. In this paper, the literature is obtained by searching the title "Big data TCM" or "Artificial Intelligence TCM" on CNKI. The obtained literature was read, analyzed and summarized in order to provide theoretical help for further realizing the objectification of TCM diagnosis.

关键词： Technological innovation Cloud computing Big data Internet of Things Artificial intelligence Robots

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Research Landscapes, Hotspots and Emerging Frontiers of Combinatorial Optimization Algorithms on Graphs: A Scientometric Analysis (1987 - 2022)

Research Landscapes, Hotspots and Emerging Frontiers of Comb...

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international conference on Big data and Information Analytics (BigDIA)

作者： Boliang Sun Hai Chen Ziqi You Laboratory for Big Data and Decision National University of Defense Technology ChangSha People’s Republic of China College of System Engineering National University of Defense Technology ChangSha People’s Republic of China

This paper presents a scientometric analysis of the research landscapes, hotspots, and emerging frontiers of combinatorial optimization algorithms on graphs from 1987 to 2022. The study investigates technical maturity, country/region collaboration, institutional cooperation, journal co-citation, dual graph overlay, and hotspot frontier analysis based on co-citation and keyword co-occurrence using tools like CiteSpace, Bibliometrics R, and VOSviewer. The analysis reveals that research in this field has entered a mature stage, and countries such as the United States, China, and Japan have produced rich output in this field. The University of Tokyo, MIT, the Chinese Academy of sciences, Shanghai Jiao Tong University, Stanford University, and the University of California have formed 10 distinct collaborative clusters. The findings also suggest that research in the field of combination optimization mainly comes from two clusters, namely, computer science and mathematics, and that cross-disciplinary knowledge is also involved. In the field of graph-based combination optimization, deep learning, machine learning, graph neural networks, and reinforcement learning are now areas of intense investigation. The frontier direction of research in this field is represented by quantum computing technologies such as coherent ising machines, hybrid quantum-classical algorithms, and quantum algorithms. The research problems in this field are categorized into three main types from a keyword perspective: graph theory problems, classical optimization problems, and open problems. The corresponding methods for solving these problems are also categorized into combinatorial optimization techniques, stochastic optimization methods, and machine learning-based optimization methods. The study highlights the need for interdisciplinary collaboration, more research on quantum computing technologies, advanced optimization techniques, and the exploration of emerging research frontiers in combination o

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Coverage Hole Detection with Image Analysis and Heuristic for healing

Coverage Hole Detection with Image Analysis and Heuristic fo...

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

作者： Anandakrishnan U Greeshma Sarath Department of Computer Science Amrita School of Computation Kollam India

Coverage holes in WAN are of significant issue. They reduces impact overall performance of WAN. Early detection of coverage holes in a ROI thus has great significant as it helps to plan a healing strategy. Various detection approaches has been proposed in earlier papers however they are all computationally intensive or require prior knowledge of the sensor location. In this paper we propose a image analysis based hole detection approach. The method has the advantage that it require minimum prior knowedge of the ROI and can be used in non homogenous ROI. The information extracted from the detection is used as heuristic to heal larger holes in ROI quickly. The algorithm has similar performance to existing algorithms as the no of sensors reaches 50 however has a better performance as the sensor count increase beyond 50.

关键词： Wide area networks Image analysis Heuristic algorithms data mining Optimization

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