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Deep Learning Approach for Analysis and Characterization of COVID-19

Computers, Materials & Continua 2022年第1期70卷 451-468页

作者： Indrajeet Kumar Sultan S.Alshamrani Abhishek Kumar Jyoti Rawat Kamred Udham Singh Mamoon Rashid Ahmed Saeed AlGhamdi School of Computing Graphic Era Hill UniversityDehradun(UK)India Department of Information Technology College of Computer and Information TechnologyTaif UniversityTaif21944Saudi Arabia School of Computer Science&IT JAIN(to be Deemed University)BangaloreIndia School of Computing Dehradun Institute of TechnologyUttarakhandIndia Department of Computer Engineering Faculty of Science and TechnologyVishwakarma UniversityPuneIndia Department of Computer Engineering College of Computer and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Early diagnosis of a pandemic disease like COVID-19 can help deal with a dire situation and help radiologists and other experts manage human resources more *** a recent pandemic,laboratories perform diagnostics manually,which requires a lot of time and expertise of the laboratorial technicians to yield accurate ***,the cost of kits is high,and well-equipped labs are needed to perform this ***,other means of diagnosis is highly *** is one of the existing methods that finds its use in the diagnosis of *** radiography observes change in Computed Tomography(CT)chest images of patients,developing a deep learning-based method to extract graphical features which are used for automated diagnosis of the disease ahead of laboratory-based *** proposed work suggests an Artificial Intelligence(AI)based technique for rapid diagnosis of COVID-19 from given volumetric chest CT images of patients by extracting its visual features and then using these features in the deep learning *** proposed convolutional neural network aims to classify the infectious and non-infectious SARS-COV2 *** proposed network utilizes 746 chests scanned CT images of 349 images belonging to COVID-19 positive cases,while 397 belong to negative cases of *** experiment resulted in an accuracy of 98.4%,sensitivity of 98.5%,specificity of 98.3%,precision of 97.1%,and F1-score of 97.8%.The additional parameters of classification error,mean absolute error(MAE),root-mean-square error(RMSE),and Matthew’s correlation coefficient(MCC)are used to evaluate our proposed *** obtained result shows the outstanding performance for the classification of infectious and non-infectious for COVID-19 cases.

关键词： Coronavirus covid-19 respiratory infection computed tomography deep neural network

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An Empirical Investigation on Employing Machine Learning for Balancing Home Agent Loads in Next Generation IP Mobility 2

An Empirical Investigation on Employing Machine Learning for...

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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Khatri, Anshu Mathi, Senthilkumar Ashiq Sheriff, A. Deepthika, R. Naveen Raaghavendran, G. Ramalingam, Venkadeshan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Faculty in Information Technology Department University of Technology and Applied Sciences-Shinas Oman

Mobile IPv6 is a key technology to enable the mobility of devices on next-generation internet protocol networks. Home agents provide simple services to registered mobile nodes. In addition, the use of multiple domestic operators for load-balancing devices faces problems of synchronization and signalling overhead. Thus, most of the existing load balancing methods present a significant delay in the transmission of data packets. The machine learning approach provides an efficient hardware storage solution for balancing the load of the home agent by improving the state-work schedule. It provides a method for dynamic load balancing based on the research-consumption system. Thus, the present paper investigates the use of machine learning, specifically reinforcement learning, to enhance home agent load balancing in next-generation IP traffic. The reinforcement learning-based home agent decisions, guided by mobile node speed and registration queries, reduce latency, improve network performance, and provide dynamic solutions. The proposed method significantly reduces the delay for packet delivery by balancing the home agent load with the incorporation of reward parameters. The empirical investigation found a remarkable improvement in network reliability, as manifested by a 2.31% increase in packet delivery ratio compared to systems with existing methods. © 2024 Elsevier B.V.. All rights reserved.

关键词： Reinforcement learning

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Image Captcha Blended with OTP for Secured Authentication 1

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7th International Conference on Intelligent computing and Optimization, ICO 2023

作者： Bazeer Ahamed, B. Krishnamoorthy, Murugan University of Technology and Applied Sciences College of Computing and Information Sciences Al Musannah Oman School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (数字)9783031733185

ISBN: (纸本)9783031733178

With the recent development in digital currency, the number of online transactions done every day is increasing exponentially. Along with the growing need for online transactions various attacks are developed to mimic an authenticated user, one of which is a phishing attack. The effort to access important materials such as credit card particulars, usernames, and passwords for mischievous reasons. We have proposed a unified model in which image CAPTCHA serves as the input for a hashing algorithm which serves as OTP generation, to avoid phishing attacks. This system is enhanced with more attack-handling mechanisms to intensify security in online application systems with better user authentication. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Phishing

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An Automated Model for Predicting Weekly Tea Auction Prices at Colombo Tea Auction for Specific Elevations and Grades 6

An Automated Model for Predicting Weekly Tea Auction Prices ...

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6th International Conference on Advancements in computing, ICAC 2024

作者： Ariyaratne, Chandika Arachchi, Samantha Mathara Pallewatta, Pandula Karunanayaka, Kasun Seneviratne, Gihan Halloluwa, Thilina University of Colombo School of Computing Colombo Sri Lanka Nanjing University of Information Science and Technology Nanjing China The University of Queensland School of Electrical Engineering and Computer Science Australia

ISBN: (纸本)9798331517878

Sri Lanka's tea business has existed since the 1800s and maintains a high standard worldwide. However, rivalry is increasing as more competitors enter the market, and tea exporters strive to make their operations more cost-effective. The discovery led to the development of a weekly tea price auction prediction model for a selected tea grade, Broken Orange Pekoe Fannings (BOPF), across different elevations. The existing research suggested using Box and Jenkins models over Machine Learning (ML) models such as Artificial Neural Networks (ANN) and Convolution Neural Networks (CNN). The research also suggested using external factors to improve accuracy. Supply, Demand, and Rainfall were the primary factors. This analysis required data from multiple sources to be combined into one dataset for model building. The tea auction data was from another source, but the rest was in the public domain. For the fourth elevation, the research group employed the SARIMAX variation of Box and Jenkins models to analyze them separately. The results ranged from a Mean Absolute Percentage Error (MAPE) of 0.26% to 6.03%, a manageable and acceptable level of risk. For structured Comma Separated (CSV) file inputs, a new function was created to easily extract predictions from the generated model, improving model usability. © 2024 IEEE.

关键词： Prediction models

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Trustworthy Sensor Fusion Against Inaudible Command Attacks in Advanced Driver-Assistance Systems

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IEEE Internet of Things Journal 2023年第19期10卷 17254-17264页

作者： Guan, Jiwei Pan, Lei Wang, Chen Yu, Shui Gao, Longxiang Zheng, Xi Macquarie University School of Computing SydneyNSW2109 Australia CSIRO Data61 SydneyNSW1710 Australia Deakin University School of Information Technology Waurn PondsVIC3125 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Qilu University of Technology Shangdong Computer Science Center Jinan250316 China

There are increasing concerns about malicious attacks on autonomous vehicles. In particular, inaudible voice command attacks pose a significant threat as voice commands become available in autonomous driving systems. How to empirically defend against these inaudible attacks remains an open question. Previous research investigates utilizing deep learning-based multimodal fusion for defense, without considering the model uncertainty in trustworthiness. As deep learning has been applied to increasingly sensitive tasks, uncertainty measurement is crucial in helping improve model robustness, especially in mission-critical scenarios. In this article, we propose the multimodal fusion framework (MFF) as an intelligent security system to defend against inaudible voice command attacks. MFF fuses heterogeneous audio-vision modalities using VGG family neural networks and achieves the detection accuracy of 92.25% in the comparative fusion method empirical study. Additionally, extensive experiments on audio-vision tasks reveal the model's uncertainty. Using expected calibration errors, we measure calibration errors and Monte Carlo Dropout to estimate the predictive distribution for the proposed models. Our findings show empirically to train robust multimodal models, improve standard accuracy and provide a further step toward interpretability. Finally, we discuss the pros and cons of our approach and its applicability for advanced driver assistance systems. © 2014 IEEE.

关键词： Speech recognition

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A New Approach to Road Incident Detection Leveraging Live Traffic Data: An Empirical Investigation 2

A New Approach to Road Incident Detection Leveraging Live Tr...

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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Kumar Gannina, Aswin Ram Jaffarullah, Aadhil Ahamed Reddy, Tiyyagura Mohit Subba Reddy, Sabbella Manoj Vikas, Ambati Sai Mathi, Senthilkumar Ramalingam, Venkadeshan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Faculty in Information Technology Department University of Technology and Applied Sciences-Shinas Oman

Accidents can have a significant impact on road safety and the efficiency of transport. With the ever-increasing number of vehicles on the road, it is crucial to quickly detect and respond to such events to decrease the impact. The current paper presents a road accident and incident detection solution utilizing traffic API monitoring and continuously analyzing real-time traffic data from various sources like GPS-enabled devices. Consequently, the proposed approach can identify abnormal patterns in the movement of vehicles, such as unexpected road closures or an accident. It numerically analyzes the traffic pattern and identifies incidents and accidents using live traffic data collected from the map service. When the approach identifies any abnormality, it can immediately let the user know, allowing them to act accordingly. It can facilitate authorities in identifying accident-prone areas and deploy safety measures. This can help law enforcement agencies to identify drivers at risk of causing accidents and implement education and enforcement programs targeted towards them. Hence, the proposed approach can aid in reducing the negative impact of traffic incidents, and the public can respond to these incidents more preparedly. © 2024 Elsevier B.V.. All rights reserved.

关键词： Accidents

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Lightweight Blockchain Prototype for Food Supply Chain Management 17

Lightweight Blockchain Prototype for Food Supply Chain Manag...

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17th International Conference on Security of information and Networks, SIN 2024

作者： Rusakov, Alexey Moradpoor, Naghmeh Akbarzadeh, Aida School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom Norwegian University of Science and Technology Faculty of Information Technology and Electrical Engineering Gjovik Norway

ISBN: (纸本)9798331509736

The modern food supply chain often involves multiple layers of participants spread across different countries and continents. This complex system offers significant benefits to businesses worldwide;however, it also presents several challenges. One major problem is the inability to trace the product flow back to its origin, a critical issue in many industries. Another issue is the lack of trust among supply chain participants. Blockchain technology can help address these and other challenges faced by the supply chain industry. However, it is surprising that, globally, there are still not many examples of the technology's adoption, with most projects remaining in the pilot stage. This paper explores the field of custom blockchain design tailored to specific applications, with a focus on supply chain operations in the food industry. It includes the development of a lightweight yet fully featured Python prototype for a decentralized blockchain system. In addition to common features like block validation and state updates, the prototype includes a newly designed type of transaction tailored specifically for supply chain operations. These transactions eliminate the need for smart contracts, making the system more lightweight compared to general-purpose blockchain platforms such as Ethereum and less prone to security vulnerabilities. The prototype is designed as a public blockchain network, with Proof of Work selected as the consensus algorithm. The novelty of this research work lies in advancing the concept of a custom blockchain solution for the food industry. The key elements of the prototype have been unit tested. The overall evaluation was completed using a Python script that simulates product flow through an example supply chain, allowing product provenance to be determined by tracing the product flow back to its origin. © 2024 IEEE.

关键词： Food industry

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Metamorphic Relation Patterns for Metamorphic Testing, Exploration and Robustness

Metamorphic Relation Patterns for Metamorphic Testing, Explo...

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作者： Ying, Zhihao Towey, Dave Bellotti, Anthony Chua, Caslon Quan Zhou, Zhi School of Computer Science University of Nottingham Ningbo China Zhejiang Ningbo China Department of Computer Science and Software Engineering Swinburne University of Technology HawthornVIC Australia School of Computing and Information Technology University of Wollongong WollongongNSW Australia

Metamorphic testing (MT) is an effective software quality assurance method;it uses metamorphic relations (MRs) to examine the inputs and outputs of multiple test cases. Metamorphic exploration (ME) and metamorphic robustness testing (MRT) are advanced additions to the MT literature: ME can enable better understanding, and thus testing, of systems;MRT can test software robustness without an oracle. Metamorphic relation patterns (MRPs) are abstractions or templates for multiple concrete MRs. The performance of MT/ME/MRT relies on the quality of MRs, but the identification of MRs is often a manual task requiring some knowledge of MRPs, creative thinking and a good understanding of the system. In other words, the effective identification of MRs remains a challenge and requires further study. In this paper, we propose formal definitions to describe the relationship between multiple MRPs in order to classify them. We also identify a series of new MRPs and propose a new MT framework that can guide the identification and application of MRPs. Based on our definitions, we present the notion of MRP family trees and further propose two trees by classifying previously published MRPs and our proposed MRPs, in order to make it easier for users to find their target MRPs for reuse, reference or inference. Finally, we report on case studies using the MRPs and our framework to guide the identification of MRs for MT/ME/MRT and successfully detect multiple violations. © 2025 John Wiley & Sons Ltd.

关键词： Software testing

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Digital Nudge Alerts: Fact-Checking Generative AI Responses 26th

Digital Nudge Alerts: Fact-Checking Generative AI Responses

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26th International Conference on Asia-Pacific Digital Libraries, ICADL 2024

作者： Lee, Chei Sian Lim, Kok Khiang Lee, Heechan Goh, Dion Hoe-Lian Wee Kim Wee School of Communication and Information Nanyang Technological University Singapore Singapore School of Computing Korea Advanced Institute of Science and Technology Daejeon Korea Republic of

ISBN: (纸本)9789819608645

Generative artificial intelligence (GenAI) chatbots have reshaped human-AI interaction behavior and transformed industries and educational sectors. Despite its advantages, GenAI presents several limitations and concerns. This study addresses fact-checking responses from GenAI to minimize the negative impacts of artificial hallucination. Artificial hallucination is a response generated by GenAI that contains false or misleading information. Addressing this problem necessitates appropriate interventions to remind users to ensure the accuracy of the GenAI-generated information, thus cultivating responsible and accountable usage. To address this concern, this study employed a behavioral economics approach using a digital nudge-based intervention to subtly remind users to fact-check the AI-generated output. To ensure cost efficiency, the proposed digital nudge intervention is delivered through a browser extension that automatically triggers popover alerts within the GenAI environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Testing the Phenomenological Interacting Dark Energy Model with Gamma-Ray Bursts and Pantheon+ type Ia Supernovae

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Research in Astronomy and Astrophysics 2024年第12期24卷 24-30页

作者： Xiao-Dong Nong Nan Liang Key Laboratory of Information and Computing Science Guizhou Province Guizhou Normal UniversityGuiyang 550025China School of Cyber Science and Technology Guizhou Normal UniversityGuiyang 550025China Joint Center for FAST Sciences Guizhou Normal University Node Guiyang 550025China

In this paper, we utilize recent observational data from gamma-ray bursts(GRBs) and Pantheon+ supernovae Ia(SNe Ia) samples to explore the interacting dark energy(IDE) model in a phenomenological scenario. Results from GRBs alone, SNe Ia and GRBs+SNe Ia indicate that the energy is transferred from dark energy to dark matter and the coincidence problem is alleviated. The value of H0from GRBs+SNe Ia in the IDE scenario shows agreement with the SH0ES measurement. Considering the age estimate of the quasar APM 08279+5255 at z = 3.91, we find that the phenomenological IDE scenario can predict a cosmic age greater than that of the ΛCDM model, thus the cosmic age problem can be alleviated.

关键词： cosmology observations-(cosmology) dark energy-(cosmology) cosmological parameters

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