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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Asmaa Shati Ghulam Mubashar Hassan Amitava Datta Department of Computer Science and Software Engineering University of Western Australia Australia Department of Information Systems King Khalid University Saudi Arabia

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

A wide range of respiratory diseases, such as cold and flu, asthma, and COVID-19, affect people’s daily lives worldwide. In medical practice, respiratory sounds are widely used in medical services to diagnose various respiratory illnesses and lung disorders. The traditional diagnosis of such sounds requires specialized knowledge, which can be costly and reliant on human expertise. Recently, cough audio recordings have been used to automate the process of detecting respiratory conditions. This research aims to examine various acoustic features that enhance the performance of machine learning (ML) models in detecting COVID-19 from cough signals. This study investigates the efficacy of three feature extraction techniques, including Mel Frequency Cepstral Coefficients (MFCC), Chroma, and Spectral Contrast features, on two ML algorithms, Support Vector Machine (SVM) and Multilayer Perceptron (MLP), and thus proposes an efficient COVID-19 detection system. The proposed system produces a practical solution and demonstrates higher state-of-the-art classification performance with an AUC of 0.843 on the COUGHVID dataset and 0.953 on the Virufy dataset for COVID-19 detection.

关键词： COVID-19 Support vector machines Training Computational modeling System performance Machine learning Feature extraction

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A Systematic Survey of Human Ear Biometrics Recognition by using 2D images

A Systematic Survey of Human Ear Biometrics Recognition by u...

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Sciences and Techniques of Automatic Control and computer engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Howayda Abedallah Elmarzaki Bothaina F. Gargoum Amina A Abdo Department of Software Engineering University of Benghazi Benghazi Libya Department of Computer Science University of Benghazi Benghazi Libya Department of Artificial Intelligence Collage of Computer Technology Benghazi Libya

Nowadays, one of the most serious issues is secure verification, especially with the advent of artificial intelligence and machine learning and deep learning algorithms. As a result, the research field of recognizing human biometrics has grown significantly, and various types of algorithms have been proposed to help with security. From the available literature, we noticed that researchers were focusing on various systematic surveys for the recognition of human biometrics, including faces, eyes, finger prints, palms, and etc. Despite the popularity of machine and deep learning methods in the field of human hearing prediction, surveys of such methods are rare. In this work, we present an up-to-date survey of the ear recognition field by using 2D images and display the most relevant papers from the most popular databases by applying different techniques based on several criteria. This review presents an overview of the similarities and differences between different works and provides a statistical analysis view of common techniques commonly used in ear recognition literature. The results presented in this report provide insight on the patterns of this field's research.

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Digital Twin Model Selection for Feature Accuracy in Wireless Edge Networks

Digital Twin Model Selection for Feature Accuracy in Wireles...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Hong Chen Terence D. Todd Dongmei Zhao George Karakostas Department of Electrical and Computer Engineering McMaster University Hamilton Ontario Canada Department of Computing and Software McMaster University Hamilton Ontario Canada

Digital twins (DTs) are virtual implementations of real physical systems (PSs) that interact with other objects on their behalf. Each PS periodically communicates with its digital twin so that the state of the DT is always sufficiently current. Using these updates, a DT can provide features that represent the real behavior of its PS using models that yield differing levels of system accuracy. In this paper, we study the DT model selection problem in wireless networks where the DTs of multiple PSs are hosted at an edge server (ES). The accuracy obtained from a given model is a function of its required amount of PS input data, the updating frequency, and the amount of computational capacity needed at the ES. The objective is to maximize the minimum achieved accuracy among the requested features by making appropriate model selections subject to wireless channel and ES resource availability. The problem is first formulated as an NP-complete integer program. The paper then uses relaxation and dependent rounding, and introduces a polynomial time approximation algorithm to obtain good solutions. A variety of simulation results are presented that demonstrate the excellent performance of the proposed solution.

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Enhancing Leukemia Detection: An Automated Approach Using Deep Learning and Ensemble Techniques

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SSRN 2024年

作者： Ahmed, Syed Saad Nisar, Humaira Jaffari, Rabeea Chai, Hum Yan Jen, Lee Yu Qaisar, Saeed Mian Kampar31900 Malaysia Sungai Long31900 Malaysia Department of Software Engineering Mehran University of Engineering and Technology Jamshoro Pakistan Kampar31900 Malaysia LINEACT CESI Lyon69100 France Electrical and Computer Engineering Department Effat University Jeddah22332 Saudi Arabia

As leukemia ranks high among the global causes of death, it's crucial to identify it early to enhance the prognosis for patients. The majority of diagnostic processes used today rely on medical experts inspecting samples manually. This is a laborious process that lacks an automated detection mechanism and takes a lot of time. With a focus on acute lymphoblastic leukemia (ALL), this work suggests an automated diagnostic method that uses Deep Learning (DL)-based ensembles to improve leukemia detection and prediction. We propose to utilize a combination of ten DL techniques (ResNet, ResNeXt, SE-ResNet, Inception V3, VGG, and its variants) and three ensemble techniques (Max voting, Averaging, and Stacking) to constitute the leukemia detection models and observe their performances. The ALL IDB benchmark leukemia dataset was evaluated using these techniques, with performances measured across several metrics namely: classification accuracy, F1 score, precision, recall (sensitivity), Kappa index, and ROC-AUC score. The findings from the experiments demonstrate a notable enhancement in leukemia detection performance when utilizing the proposed techniques. In particular, the proposed Ensemble Max Voting technique surpasses all other stateof-the-art detection models in the literature with an accuracy of 100.0% and an F1 score of 0.997. The main achievement of this study is the identification of the most effective method among various models and techniques for detecting leukemia. © 2024, The Authors. All rights reserved.

关键词： Diseases

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Investigation of availability,demand,targets,and development of renewable energy in 2017-2050:a case study in Indonesia

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International Journal of Coal Science & Technology 2021年第4期8卷 483-499页

作者： Erdiwansyah Erdiwansyah Mahidin Mahidin Husni Husin Nasaruddin Nasaruddin Khairil Khairil Muhammad Zaki Jalaluddin Jalaluddin Doctoral Program School of EngineeringUniversitas Syiah KualaBanda Aceh 23111Indonesia Department of Chemical Engineering Universitas Syiah KualaBanda Aceh 23111Indonesia Department of Electrical and Computer Engineering Syiah Kuala UniversityBanda Aceh 23111Indonesia Department of Mechanical Engineering Universitas Syiah KualaBanda Aceh 23111Indonesia Department of Accounting Universitas Syiah KualaBanda Aceh 23111Indonesia Faculty of Engineering Universitas Serambi MekkahBanda Aceh 23245Indonesia

Abundant potential of renewable energy(RE)in Indonesia is predicted to replace conventional energy which continues to experience depletion year by ***,until now,the use of RE has only reached 2%of the existing potential of 441.7 *** main overview of this work is to investigate the availability of RE that can be utilized for electricity generation in *** energy demand and targets in the long run during the 2017-2050 period are also ***,government policies in supporting RE development are considered in this *** results show that the potential of RE in Indonesia can be utilized and might replace conventional energy for *** use of RE for electricity generation can be achieved by employing a government policy that supports the investor as the executor of RE *** selling price of electricity generated from RE is cheaper than electricity generated from fossils;this makes economy is more affordable for ***,the target set by the government for utilizing RE as the main energy in Indonesia can be done by implementing several policies for the RE ***,greenhouse gas emissions and the use of petroleum fuels can be reduced.

关键词： Energy potential Energy demand Energy development Economic growth Energy policy Energy targets

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Service Function Chaining in LEO Satellite Networks via Multi-Agent Reinforcement Learning

Service Function Chaining in LEO Satellite Networks via Mult...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Khai Doan Marios Avgeris Aris Leivadeas Ioannis Lambadaris Wonjae Shin Department of Systems and Computer Engineering Carleton University Ottawa ON Canada Department of Software and IT Engineering École de Technologie Supérieure Montreal QC Canada Korea University School of Electrical Engineering Seoul South Korea

Low-earth-orbit satellite networks (LSNs) offer an enhanced global connectivity and a wide range of applications such as disaster response and military operations, among others. Each specific application can be represented by a service function chain (SFC) in which each function is considered as a task in the application. Our objective is to optimize the long-term system performance by minimizing the average end-to-end delay of SFC deployments in LSNs. To achieve this, we formulate a dynamic programming (DP) problem to derive an optimal placement policy. To overcome the computational intractability, the need for statistical knowledge of SFC requests, and centralized decision-making challenges, we present a multi-agent Q-learning approach where satellites act as independent agents. To facilitate performance convergence in non-stationary agents' environments, we let agents to collaborate by sharing designated learning parameters. In addition, agents update their Q-tables via two distinct rules depending on selected actions. Extensive experimentation shows that our approach achieves convergence and performance relatively close to the optimum obtained by solving the formulated DP equation.

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Evaluation of Large Tweet Dataset for Emotion Detection Model: A Comparative Study between Various ML and Transformer

Evaluation of Large Tweet Dataset for Emotion Detection Mode...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Sanghyub John Lee JongYoon Lim Leo Paas Ho Seok Ahn The Department of Marketing of the University of Auckland Auckland New Zealand Department of Electrical Computer and Software Engineering of the University of Auckland Auckland New Zealand

Specific emotion detection in written human language is a challenging problem in various research fields, including psychology, neuroscience, and computer science. Twitter is a suitable source for collecting large emotion datasets, as users have provided tweets with emotion hashtags (e.g., #fear, #anger, #sadness, #joy, #surprise, and #disgust) expressing their emotions. However, the criteria for data collection, i.e., the position of representative or synonymous emotion hashtags, remains unclear. Next to this unclarity, we assess the suitability of various machine learning (ML) algorithms for this purpose. In this study, we collected over five million tweets (n=5,645,139) with 24 emotion hashtags and investigated the efficacy of different criteria for collecting tweets. Contrary to previous research, we found that applying any position of representative emotion hashtags can achieve strong performance, rather than applying the last position of synonymous emotion hashtags. Our study shows that the RoBERTa-large transformer model outperforms deep learning algorithms and traditional ML algorithms in terms of specific emotion detection in tweets, especially when trained on a dataset with a balance between size and quality. We also found that larger datasets are more efficient for RoBERTa model training than smaller datasets. Along with these empirical contributions, we share the collected emotion dataset.

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Disclosure Privacy Information Social Media on TikTok 9

Disclosure Privacy Information Social Media on TikTok

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Syamsuar, Dedy Fahrezi, Fazri Favian, Ahnaf Irwansyah, Edy Irawan, Achmad Fakhri School of Information System Bina Nusantara University Faculty of Engineering Professional Engineer Program Dept. Information System Department Jakarta11480 Indonesia School of Information System Bina Nusantara University Information System Department Jakarta11480 Indonesia School of Information System Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

With the rapid development of technology, the use of social media by the public, especially among young people, is increasing. One of the social media platforms currently used by young people is the TikTok application. It is a video-based TikTok feature accompanied by music, writing, and pictures that are considered attractive, so teenagers like it to show their existence and self-disclosure. Therefore, this study aims to examine the intention of users to disclose their privacy. As the basis of the theory, this study deployed the privacy calculus theory, where the perceived benefits and perceived risks play crucial roles in the intention to disclose their privacy. The study successfully collected 369 responses. The empirical findings indicated that only the relationship between Perceived Personalization (PP) and Perceived Risk (PR) was not supported. However, all the remaining hypotheses were supported. These findings emphasize how several complicated variables influence social media users' intentions to disclose their privacy. When people feel in control of their privacy settings and see significant benefits, they are more likely to divulge personal information;nevertheless, perceived risks can greatly discourage users. © 2024 IEEE.

关键词： Differential privacy

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A Way to Protect the Surveillance Data Stored in the Cloud Using FC and SVM Methods 4

A Way to Protect the Surveillance Data Stored in the Cloud U...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Suganthi, D. Raju, M. Mahesh Kumar, N.B. Vasavi, M. Washeel, Oday Faris Abdtawfeeq, Tareq Hafdhi Saveetha College of Liberal Arts and Sciences Simats Department of Computer Science Chennai Tamilnadu Thandalam602105 India Sri Krishna Arts and Science College Department of Software Systems Coimbatore India Tamil Nadu Coimbatore641 032 India Cvr College of Engineering Et Department Hyderabad India National University of Science and Technology Collage of Nursing Dhi Qar64001 Iraq

ISBN: (纸本)9798350360165

Building trust between consumers and service providers is critical to the functioning of cloud computing, a technology that offers a range of services over the Internet. When consumers choose which cloud services to utilise for processing and storing sensitive data, trust values are a major factor. In addition, service providers can employ trust values as a means of granting access to resources, protecting against malevolent users. The subjective trust model category, which evaluates trust based on user and service provider behaviour, is the one that this research suggests for trust models. Because trust is intrinsically ambiguous in our method, trust levels for cloud consumers and service providers are calculated using fuzzy logic and support vector machines. Using triangle membership functions for defuzzification and Gauss membership functions for fuzzification, we use the Mamdani fuzzy approach. In order to determine how trustworthy a resource is, our paradigm for evaluating trust takes performance and flexibility into account. Performance evaluation includes measurements related to workload and reaction time, whereas elasticity evaluation takes into account elements like scalability, availability, security, and usability. We apply fuzzy C-means clustering on pertinent criteria to establish trust levels for users, including faulty requests, fake requests, unauthorised requests, and total requests. © 2024 IEEE.

关键词： Cloud platforms

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A connected components based layout analysis approach for educational documents 16

A connected components based layout analysis approach for ed...

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16th IEEE International Conference on computer Science and Education, ICCSE 2021

作者： Liu, Ruiying Yu, Shenbao Yang, Fan Pan, Yinghui Zeng, Yifeng Xiamen University Department of Automation China Shenzhen University College of Computer Science and Software Engineering China Northumbria University Department of Computer and Information Sciences

ISBN: (纸本)9781665414685

Layout analysis, which aims to detect and categorize areas of interest on document images, is an increasingly important part in document image processing. Existing researches have conducted layout analysis on various documents, but none has been proposed for documents yielded from teaching, i.e. exam papers and workbooks, which are worth studying. In this paper, we propose a novel layout analysis system to achieve two tasks for workbook pages and exam papers respectively. On one hand, we segment text and non-text areas of workbook pages. On the other hand, we extract regions of interest on exam papers. Our system is based on connected component (CC) analysis, specifically, it extracts geometric features and spatial information of CCs to recognize page elements. We carried out experiments on images collected from real-world scenarios, and promising results confirmed the applicability and effectiveness of our system. © 2021 IEEE.

关键词： Image analysis

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