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Diagnosis of COVID-19 Pneumonia via a Novel Deep Learning Architecture

Journal of computer Science & technology 2022年第2期37卷 330-343页

作者： Xin Zhang Siyuan Lu Shui-Hua Wang Xiang Yu Su-Jing Wang Lun Yao Yi Pan Yu-Dong Zhang Department of Medical Imaging The Fourth People's Hospital of Huai'anHuai'an 223002China School of Informatics University of LeicesterLeicesterLE17RHU.K. School of Architecture Building and Civil Engineering Loughborough UniversityLoughboroughLE113TUU.K. School of Mathematics and Actuarial Science University of LeicesterLeicesterLE17RHU.K. Key Laboratory of Behavior Sciences Institute of PsychologyChinese Academy of SciencesBeijing 100101China Department of Psychology University of the Chinese Academy of SciencesBeijing 100101China Department of Infection Diseases The Fourth People's Hospital of Huai'anHuaian 223002China Department of Computer Science Georgia State UniversityAtlanta 30302-5060U.S.A. Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz University Jeddah 21589Saudi Arabia

COVID-19 is a contagious infection that has severe effects on the global economy and our daily *** diagnosis of COVID-19 is of importance for consultants,patients,and *** this study,we use the deep learning network AlexNet as the backbone,and enhance it with the following two aspects:1)adding batch normalization to help accelerate the training,reducing the internal covariance shift;2)replacing the fully connected layer in AlexNet with three classifiers:SNN,ELM,and ***,we have three novel models from the deep COVID network(DC-Net)framework,which are named DC-Net-S,DC-Net-E,and DC-Net-R,*** comparison,we find the proposed DC-Net-R achieves an average accuracy of 90.91%on a private dataset(available upon email request)comprising of 296 images while the specificity reaches 96.13%,and has the best performance among all three proposed *** addition,we show that our DC-Net-R also performs much better than other existing algorithms in the literature.

关键词： pneumonia COVID-19 convolutional neural network AlexNet deep learning

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Enhancing Cybersecurity in Rural Areas - A Multilayered AI Approach to Combat Phishing Threats using Quantum Computing

Enhancing Cybersecurity in Rural Areas - A Multilayered AI A...

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Emerging Smart Computing and informatics (ESCI), Conference on

作者： A. Jaya Mabel Rani V Manoj Sethi Sashi Kiran Vuppala K. Thilagam Manimegalai L T. C. Manjunath Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Applications Analyst Avaya LLC Irving TX United States Java Technical Architect Hyderabad Telangana India Department of ECE Velammal Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Professor in Computer Science & Engineering Department Dean Research (R & D) Rajarajeswari College of Engineering Bangalore Karnataka India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Rapid advances in quantum computing have raised worries about the security of classical encryption systems, as quantum computers can break many cryptographic techniques. This research strengthens encryption algorithms against quantum-based cryptographic assaults at the convergence of cybersecurity and quantum computing. Quantum computers, using quantum parallelism and quantum algorithms like Shor's and Grover's, threaten the security of widely used cryptographic systems like RSA and ECC, which use integer factorization and discrete logarithms. Post-quantum cryptography (PQC) is needed because quantum assaults can break classical encryption systems. This work emphasizes the need for quantum-resistant encryption techniques by examining the theoretical and practical effects of quantum computing on cryptography. It discusses quantum-safe algorithms like lattice-based, hash-based, and code-based cryptography that could replace or improve encryption standards. The research also examines quantum key distribution (QKD), a new secure communication method that uses quantum physics to share keys. The computational expense, implementation difficulty, and cybersecurity infrastructure integration difficulties of switching to quantum-resistant encryption methods are also examined. Quantum computing is evolving, hence safe encryption algorithms that can withstand quantum-based cryptographic assaults are needed. This paper concludes by emphasizing the need to standardize quantum-resistant algorithms, accelerate quantum cryptography research, and promote international collaboration to protect sensitive data from quantum threats. Future communication systems will depend on post-quantum cryptography to protect digital data.

关键词： Resistance computers Quantum computing Quantum algorithm Switches Elliptic curve cryptography Encryption Quantum key distribution Cryptography computer security

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Machine Learning Techniques for Analysis of Mars Weather Data

Machine Learning Techniques for Analysis of Mars Weather Dat...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Piyush Pant Anand Singh Rajawat S. B. Goyal Baharu Bin Kemat Traian Candin Mihălţan Chaman Verma Maria Simona Răboacă School of Computer Sciences and Engineering Sandip University Nashik India Faculty of Information Technology City University Petaling Jaya Malaysia City Graduate School City University Petaling Jaya Malaysia Faculty of Building Services Technical University of Cluj-Napoca Cluj-Napoca Romania Department of Media and Educational Informatics Faculty of Informatics Eötvös Loránd University Budapest ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Romania

The exploration of Mars has provided vast amounts of weather data that present unique challenges for analysis and prediction. To address these challenges, this research paper focuses on the application of machine learning techniques for the analysis of Mars weather data. The objective is to develop models that can effectively extract patterns, uncover hidden relationships, and enable accurate predictions in the Martian weather system. The research begins with a comprehensive review of the available Mars weather data. The dataset, consisting of historical records, serves as the foundation for training and evaluating machine learning models. The analysis of the Mars weather dataset reveals the planet's icy and harsh climate, with average maximum temperatures of around -21°C and average minimum temperatures of -80°C. The temperature variations show that the minimum temperature fluctuates within a narrower range of 20-30°C over the course of Mars sols from 0 to 2000, while the maximum temperature experiences larger variations of about 40-50°C. During this time, the atmospheric pressure on Mars fluctuates between 720 Pa and 950 Pa. In addition, using the elbow method revealed that 3 clusters were the ideal number for identifying distinct patterns in the weather data. The linear regression model also attained an accuracy of 85%, demonstrating its efficacy in forecasting weather patterns on Mars.

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Unsupervised Foggy Scene Understanding via Self Spatial-Temporal Label Diffusion

arXiv

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arXiv 2022年

作者： Liao, Liang Chen, Wenyi Xiao, Jing Wang, Zheng Lin, Chia-Wen Satoh, Shin'ichi The Digital Content and Media Sciences Research Division National Institute of Informatics Tokyo101-8430 Japan The School of Computer Science Wuhan University Wuhan430072 China The Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan

Understanding foggy image sequence in driving scene is critical for autonomous driving, but it remains a challenging task due to the difficulty in collecting and annotating real-world images of adverse weather. Recently, self-training strategy has been considered as a powerful solution for unsupervised domain adaptation, which iteratively adapts the model from the source domain to the target domain by generating target pseudo labels and re-training the model. However, the selection of confident pseudo labels inevitably suffers from the conflict between sparsity and accuracy, both of which will lead to suboptimal models. To tackle this problem, we exploit the characteristics of the foggy image sequence of driving scenes to densify the confident pseudo labels. Specifically, based on the two discoveries of local spatial similarity and adjacent temporal correspondence of the sequential image data, we propose a novel Target-Domain driven pseudo label Diffusion (TDo-Dif) scheme. It employs superpixels and optical flows to identify the spatial similarity and temporal correspondence, respectively, and then diffuses the confident but sparse pseudo labels within a superpixel or a temporal corresponding pair linked by the flow. Moreover, to ensure the feature similarity of the diffused pixels, we introduce local spatial similarity loss and temporal contrastive loss in the model re-training stage. Experimental results show that our TDo-Dif scheme helps the adaptive model achieve 51.92% and 53.84% mean intersection-over-union (mIoU) on two publicly available natural foggy datasets (Foggy Zurich and Foggy Driving), which exceeds the state-of-the-art unsupervised domain adaptive semantic segmentation methods. The proposed method can also be applied to non-sequential images in the target domain by considering only spatial similarity. Models and data can be found at https://***/velor2012/TDo-Dif. Copyright © 2022, The Authors. All rights reserved.

关键词： Diffusion

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Integer Factorization through Func-QAOA

arXiv

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arXiv 2023年

作者： Atallah, Mostafa Velmurugan, Haemanth Sharma, Rohan Midha, Siddhant Mamun, Shamim Al Botelho, Ludmila Glos, Adam Salehi, Özlem Department of Physics Faculty of Science Cairo University Giza12613 Egypt Department of Computer Science and Engineering NIT Tiruchirappalli India Department of Applied Geophysics IIT Dhanbad India Department of Electrical Engineering IIT Bombay India Department of Electrical and Electronics Engineering Islamic University of Technology Dhaka Gazipur1704 Bangladesh Joint Doctoral School Silesian University of Technology Akademicka 2A Gliwice Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Poland QWorld Association Tallinn Estonia

Integer factorization is a significant problem, with implications for the security of widely-used cryptographic schemes. No efficient classical algorithm for polynomial-time integer factorization has been found despite extensive research. Although Peter Shor’s breakthrough quantum algorithm offers a viable solution, current limitations of noisy intermediate-scale quantum (NISQ) computers hinder its practical implementation. To address this, researchers have explored alternative methods for factorization suitable for NISQ devices. One such method is the Quantum Approximate Optimization Algorithm, which treats factoring as an optimization problem defined over binary bits, resulting in various problematic aspects. In this paper, we explore the Func-QAOA approach for factorization, which premises overcoming some of the limitations of previous approaches and allows the incorporation of more advanced factorization techniques. After reviewing the most promising quantum implementations for integer arithmetics, we present a few illustrative examples to demonstrate the efficacy of the Func-QAOA approach and discuss methods to reduce the search space to speed up the optimization process. Copyright © 2023, The Authors. All rights reserved.

关键词： Factorization

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Automatic facial expression recognition in an image sequence using conditional random field

Automatic facial expression recognition in an image sequence...

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International Symposium on Computational Intelligence and informatics

作者： Mohamad Roshanzamir Mahdi Roshanzamir Abdolreza Mirzaei Mohammad Tayarani Darbandy Afshin Shoeibi Roohallah Alizadehsani Fahime Khozeimeh Abbas Khosravi Department of Computer Engineering Fasa University Fasa Iran Department of Electrical and Computer Engineering University of Tabriz Tabriz Iran Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran School of Architecture Islamic Azad University Taft Taft Iran Internship in Health Data Analytics Program AI-enabled Processes (AIP) Research Centre Macquarie University Sydney Australia Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Victoria Australia

ISBN: (纸本)9798350398830

Facial expression recognition is one of the fields that nowadays has attracted the attention of many researchers. It is possible to automate facial expression recognition using artificial intelligence methods. This will be of great help to researchers, especially in areas such as psychology. Automatic facial recognition can be derived from a static image of facial expression, but a better and more efficient way to do this is through a sequence of images. In this paper, a new method is proposed to automatically detect facial expressions from a sequence of images. Each sequence of facial images begins with a face neutral state and ends with one of the six main emotions. Motion vectors are extracted from the sequence using optical flow algorithm. These vectors are then used to train the conditional random field and finally to automatically determine the emotion. In this paper, in addition to the basic conditional random field, the hidden dynamic conditional random field is also investigated. Additionally, the effect of changing some parameters of these algorithms such as different optimization methods has been investigated. Given that a facial expression is recognized during a sequence of images, random field-based methods can be used for efficient classification of facial expressions reaching accuracy (more than 90%) competitive with the best existing methods for facial expression recognition.

关键词： Image recognition Mechatronics Face recognition Heuristic algorithms Psychology Optimization methods Feature extraction

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An Artificial Intelligence based Design and Implementation for classifying the Missing Data in IoT Applications 5

An Artificial Intelligence based Design and Implementation f...

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5th International Conference on Contemporary Computing and informatics, IC3I 2022

作者： Bhamangol, Balasaheb Kaiwade, Amit Pant, Bhasker Rana, Arti Kaiwade, Abhijeet Shaikh, Atik Dr. D. Y. Patil Institute of MCA and Management MCA Pune Maharashtra Akurdi India Marathawada Mitra Mandal College of Commerce Abeda Inamdar Research Center Department of HSC Azam Campus Maharashtra Pune India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Uttaranchal Institute of Technology Uttaranchal University Uttarakhand Dehradun India Sadhu Vaswani Institute of Management Studies Department of MCA Maharshtra Pune India Allana Institute of Management Sciences Department of MBA Maharshtra Pune India

ISBN: (纸本)9798350398267

The Industrial Internet of Things (IIoT) has become one of the most rapidly developing innovative technologies in recent years, with the potential to digitize and connect numerous industries for enormous commercial prospects and growth of the global GDP. Even while the IIoT offers exciting potential for the creation of many industrial applications, these applications must meet more stringent security requirements and are vulnerable to cyberattacks. Since there are so many sensors in the IIoT network, a lot of data is produced, which has caught the attention of hackers all around the world. Network activity is monitored by the intrusion detection system (IDS), which recognizes network behavior is regarded as is a crucial security measure for protecting IIoT applications from threats. Deep learning and machine learning approaches have recently demonstrated their ability to improve intrusion detection performance and reduce a variety of security concerns. The research's ultimate goal is to pinpoint the shortcomings and difficulties of previous studies as well as potential remedies and directions in futures. © 2022 IEEE.

关键词： Intrusion detection

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Keeping data at the edge of smart irrigation networks: A case study in strawberry greenhouses

arXiv

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arXiv 2021年

作者： Angelopoulos, Constantinos Marios Filios, Gabriel Nikoletseas, Sotiris Raptis, Theofanis P. Bournemouth University Poole United Kingdom Department of Computer Engineering and Informatics University of Patras Greece Greece Institute of Informatics and Telematics National Research Council Pisa Italy

Strawberries are widely appreciated for their characteristic aroma, bright red color, juicy texture, and sweetness. They are, however, among the most sensitive fruits when it comes to the quality of the end product. The recent commercial trends show a rising number of farmers who directly sell their products in the market and are more interested in using smart solutions for a continuous control of the factors that affect the quality of the final product. Cloud-based approaches for smart irrigation have been widely used in the recent years. However, the network traffic, security and regulatory challenges, which come hand in hand with sharing the crop data with third parties outside the edge of the network, lead strawberry farmers and data owners to rely on global clouds and potentially lose control over their data, which are usually transferred to third party data centers. In this paper, we follow a three-step methodological approach in order to design, implement and validate a solution for smart strawberry irrigation in greenhouses, while keeping the corresponding data at the edge of the network: (i) we develop a small-scale smart irrigation prototype solution with off-the-shelf hardware and software equipment, which we test and evaluate on different kinds of plants in order to gain useful insights for larger scale deployments, (ii) we introduce a reference network architecture, specifically targeting smart irrigation and edge data distribution for strawberry greenhouses, and (iii) adopting the proposed reference architecture, we implement a full-scale system in an actual strawberry greenhouse environment in Greece, and we compare its performance against that of conventional strawberries irrigation. We show that our design significantly outperforms the conventional approach, both in terms of soil moisture variation and in terms of water consumption, and conclude by critically appraising the costs and benefits of our approach in the agricultural industry. © 2021, CC

关键词： Fruits

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FedMonitor: An Implementation Architecture for Monitoring of Resources and Events'Notifications on Federated Cloud Computing Infrastructures

FedMonitor: An Implementation Architecture for Monitoring of...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Emmanuel Adetiba Uche Nnaji John S. Wejin Festus Ojesanmi Matthew B. Akanle Priscilla O. Ajayi Faith O. Sweetwiliams Surendra C. Thakur Ezekiel F. Adebiyi Department of Electrical and Information Engineering and Covenant Applied Informatics & Communication African Center of Excellene(CApIC-ACE) Covenant University Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban South Africa Department of Computer and Information Science & CApIC-ACE Covenant University Ota Nigeria Department of Electrical and Information Engineering & CApIC-ACE Covenant University Jalingo Nigeria Department of Computer Science Tareba State University Jalingo Nigeria Covenant Applied Informatics & Communication African Center of Excellene(CApIC-ACE) Covenant University Ota Nigeria Department of Electrical and Information Engineering Covenant University & CApIC-ACE Covenant University Ota Nigeria Center for Systems and Information Services & CApIC-ACE Covenant University Ota Nigeria Department of Information Technology and KZN e-Skills Co-Laboratory Durban University of Technology Durban South Africa Covenant University Bioinformatics Research (CUBRe) Covenant University Ota Ogun State Nigeria African Center of Excellence in Bioinformatics & Data Intensive Science Makerere University Kampala Uganda

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

In the evolving landscape of cloud computing, federated cloud infrastructures present unique challenges and opportunities for resource and application monitoring. Monitoring the diverse array of cloud resources and applications within a federated cloud environment is crucial for delivering consistent Quality of Service (QoS) and continuous oversight across various cloud applications and infrastructure. Despite the various successes documented in the literature on multi-cloud monitoring, existing frameworks have not fully explored or provided enough insights into centralized monitoring of resources, applications, and network devices in a federated cloud environment. This paper introduces FedMonitor, an implementation architecture designed to enhance the monitoring and notification capabilities in federated cloud environments by building on the theoretical ideals in our earlier work (i.e. FEDARGOS-V1). FedMonitor leverages Prometheus and Grafana to crawl, manage and visualize metrics from diverse software services, hardware resources, and network devices within a federated cloud environment. The results demonstrate hat FedMonitor improves the effectiveness of resource and application monitoring in federated cloud environments.

关键词： Measurement Cloud computing Visualization computer architecture Quality of service Production Software Real-time systems Stakeholders Monitoring

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Development of Robust Automatic Speech Recognition System for Children's using Kaldi Toolkit 2

Development of Robust Automatic Speech Recognition System fo...

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2nd International Conference on Inventive research in Computing Applications, ICIRCA 2020

作者： Bhardwaj, Vivek Bala, Sashi Kadyan, Virender Kukreja, Vinay Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science University of Petroleum and Energy Studies Department of Informatics Dehradun India

ISBN: (纸本)9781728153742

In this paper, the Punjabi children speech recognition system is developed using Subspace Gaussian mixture models (SGMM) acoustic modeling techniques. Initially, the system is dependent upon Mel-frequency cepstral coefficients (MFCC) approach for controlling the temporal variations in the input speech signals. Here, SGMM is integrated with HMM to measure the efficiency of each state which carries the information of a short-windowed frame. For handling the children speaker acoustic variations speaker adaptive training (SAT), based on vocal-tract length normalization and feature space maximum likelihood linear regression is adopted. Kaldi and open-source speech recognition toolkit is used to develop the Robust Automatic Speech Recognition (ASR) System for Punjabi Children's speech. S GMM accumulate the frame coefficients and their posterior probabilities and pass these probabilities to HMM which systematically fit the frame and output have resulted from HMM states. Therefore, the achievement of SGMM has gotten a large performance margin in Punjabi children speech recognition. A remarkable depletion in the word error rate (WER) was noticed using SGMM by varying the feature dimensions. The developed children ASR system obtained a recognition accuracy of 83.66% while tested by varying the feature dimensions to 12. © 2020 IEEE.

关键词： Acoustic Modeling

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