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computer Science, computer engineering, & Applied Computing (CSCE), Congress in

作者： Bassam Zahran Fuad Abu Zahra dept of Computer Science and Software Engineering University of Wisconsin Platteville Wisconsin USA dept of Engineering and Applied Sciences University of Wisconsin Milwaukee Wisconsin USA

The convergence of Information Technology (IT) and Operational Technology (OT) systems is becoming a ne-cessity. In the context of IT/OT convergence, various protocols are widely employed to enable smooth communication and data exchange between IT and OT systems. It is crucial to gain a comprehensive understanding of the networking and data flow between these systems in order to effectively identify and address potential security risks. This study aims to explore several common protocols utilized in IT/OT convergence and examine their strengths and weaknesses. This initial exploration will serve as a foundation for further investigation into potential security breaches and the development of appropriate countermeasures in the context of IT/OT convergence.

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IoT Security: A Deep Learning-Based Approach for Intrusion Detection and Prevention

IoT Security: A Deep Learning-Based Approach for Intrusion D...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Bhanu Kauhsik Himanshu Nandanwar Rahul Katarya Dept. of Software Engineering Delhi Technological University Delhi India Dept. of Computer Science and Engineering Delhi Technological University Delhi India

The size and market worth of the Internet of Things (IoT) have expanded, but unfortunately, the likelihood of user data being compromised has also risen. This presents a notable danger that has the potential to create disorder. Consequently, the main aim of this paper is to recognize any weaknesses within existing security solutions and suggest an innovative approach to address them. In order to accomplish this, the paper puts forth four research inquiries concerning IoT security that leverage Machine Learning and Deep Learning techniques. Additionally, it conducts an extensive review of the latest publications and concludes that a combination of specific techniques can provide a potential solution to the security problems in IoT. Overall, the findings of this study suggest that this systematic literature review could help in significantly enhancing the security of IoT devices and applications, safeguarding user data from unauthorized access and potential misuse.

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Integrating Computational Thinking Into the Curricula to Bridge the Skill Gap in engineering Education

Integrating Computational Thinking Into the Curricula to Bri...

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Frontiers in Education (FIE) Conference

作者： Nasrin Dehbozorgi Maysam Nezafati Mehdi Roopaei Dept. of Software Engineering Kennesaw State University Marietta GA USA Dept. of Biomedical Engineering Georgia Institute of Technology Atlanta GA USA Dept. of Electrical and Computer Engineering) University of Wisconsin-Platteville Platteville WI USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This work-in-progress research-to-practice paper presents an intervention on integrating computational thinking modules into a software engineering course. The national consensus on the significance of computational thinking has prompted the expansion of related educational initiatives over the past decade. Since the definition of computational thinking by Wing in 2006, this concept has gained significant attention within the educational community. Particularly this surge of interest has led to extensive research into its conceptual foundations and subsequent integration into educational curricula since 2013. National initiatives have since emerged to incorporate computational thinking into the educational system. Furthermore, as artificial intelligence and computing systems become increasingly integrated into daily life, there is a growing demand from industries for a workforce and graduates adept at critical thinking and problem-solving. Aligned with this national movement, our study presents a two-year institutional initiative, aimed at integrating computational thinking into the software engineering program. The software engineering discipline extensively involves design thinking and problem-solving skills. However, we noticed that these higher-level skills are not imparted early in the program to teach students this method of thinking and approaching problems. To bridge this skill gap, we developed a set of computational thinking modules and integrated them into a gateway course in the software engineering program. Over two years, we implemented this intervention in an introductory-level course and evaluated its impact on students' computational thinking skills by analyzing their responses to a standard Computational Thinking Assessment survey. The results showed significant improvement in most components. These early findings underscore the effectiveness of integrating these computational thinking modules into the gateway courses, regardless of the specific co

关键词： Bridges Surveys Industries Logic gates Problem-solving Surges engineering education Artificial intelligence Standards software engineering

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GolfPoseNet: Golf-Specific 3D Human Pose Estimation Network

GolfPoseNet: Golf-Specific 3D Human Pose Estimation Network

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International Conference on Electronics, Information and Communications (ICEIC)

作者： BoJeong Park JongWoo Kim SeoYeong Mun Younglim Choi Hyunseok Kim dept. of Electric Engineering Dong-A University Busan South Korea dept. of Software Innovation Center Dong-A University Busan South Korea dept. of Computer Engineering Dong-A University Busan South Korea

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

This paper aims to develop a robotic golf trainer using a wheeled robot and create a model that accurately recognizes the user's golf motion. Since existing 3D pose models show limitations in golf motion recognition [1], [2], we propose an optimized recognition model by combining various methods and preprocessing golf datasets to overcome these problems. We used HRNet to estimate 2D pose from video frames and fed these data into a transformer-based model to generate three hypotheses through residual linking and embedding layers. These were integrated to finally design a structure to predict 3D pose, and batch normalization, dropout, and LeakyReLU were used to improve the stability and nonlinearity handling of the model. Experimental results show that the proposed model effectively learns temporal information from multi-view 2D pose data without 3D annotation and performs well even when 3D data is scarce. The proposed method can be used in various applications such as human-robot interaction and sports motion analysis including golf, and further research on multiple datasets and real-world environments is expected to improve further and generalize the model's performance.

关键词： Solid modeling Three-dimensional displays Annotations Human-robot interaction Transformers Data models Real-time systems Stability analysis Mobile robots Sports

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A Machine Learning-Enhanced IoT Wearable Device for Monitoring and Providing Feedback on Stuttering 3

A Machine Learning-Enhanced IoT Wearable Device for Monitori...

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3rd International Conference on Computational Modelling, Simulation and Optimization, ICCMSO 2024

作者： Suttarwala, Hussain Kumar, Ritesh Deorankar, Madhavi L. Daulat Sarpate, Dikshendra Shingade, Pallavi Kumar Sharma, Surender More, Vrushali Patkar, Uday Chandrakant Sharma, Deepak K. American Express Senior Software Engineer PhoenixAZ85054 United States Dr. Akhilesh Das Gupta Institute of Professional Studies Dept. of AI and Data Science Delhi New Delhi110053 India Marathwada Mitramandal College of Engineering Dept. of Electronics and Telecommunications Maharashtra Pune411052 India Zeal College of Engineering and Research Dept. of AI and Data Science MaharashtraNarhe Pune411041 India D Y Patil College of Engineering Dept. of AI and Data Science Akurdi Pune411041 India Poornima University Dept. of Electrical Engineering Jaipur India Genba Sopanrao Moze College of Engineering Dept. of AI and Data Science Balewadi Maharashtra Pune411045 India Bharati Vidyapeeth's College of Engineering Department of Computer Engineering Lavale Pune412115 India Savitribai Phule Pune University Dept. of AI and Data Science Maharashtra Pune India

ISBN: (纸本)9798350361391

This study introduces an innovative IoT-enabled wearable device tailored for monitoring and providing feedback on stuttering, leveraging advanced machine learning algorithms to enhance precision and real-time responsiveness. The device integrates an array of hardware and software elements, including sensors, microcontrollers, and communication protocols, to accurately capture speech patterns and movements. Utilizing machine learning techniques such as Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), the device performs real-time analysis to identify stuttering instances. Detailed discussions cover the criteria for selecting machine learning models, the training and testing procedures, and the metrics used for performance evaluation. The prototype underwent extensive testing and validation, showcasing high accuracy, sensitivity, and specificity in differentiating stuttered from fluent speech. User feedback emphasized the ergonomic design, user-friendly interface, and real-time feedback features, highlighting its potential to significantly enhance stuttering therapy outcomes. Comparative analysis with existing solutions revealed the device's superior performance, indicating strong potential for clinical application. The study discusses the implications for stuttering therapy, acknowledges the limitations, outlines future research directions, and underscores the device's contributions to personalized and effective stuttering interventions. This research advances the field of assistive technologies in speech therapy and demonstrates the promise of IoT-based wearable devices integrated with machine learning for improving the quality of life for individuals with stuttering disorders. © 2024 IEEE.

关键词： Convolutional neural networks

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The Early Detection of Dementia Disease Using Machine Learning Approach

The Early Detection of Dementia Disease Using Machine Learni...

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International Conference on computer Communication and Informatics (ICCCI)

作者： Md Shariar Kabir Jakia Khanom Md. Atik Bhuiyan Zerin Nasrin Tumpa Sk. Fazlee Rabby Saurabh Bilgaiyan Dept. of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Dept. of Software Engineering Daffodil International University Dhaka Bangladesh School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India

Early dementia detection is a crucial but challenging task in Bangladesh. Often, dementia is not recognized until it is too late to receive effective care. This results in part from a lack of knowledge about the illness and its signs and symptoms. Recent improvements in machine learning algorithms, however, may change this. In a recent study, we developed a model that can identify early dementia in Bangladesh using machine learning algorithms. This research paper proposed an efficient machine learning-based approach for early detection of dementia disease A dataset of 199 people with dementia and 175 healthy controls was used to develop the model. In 96% of the cases, the algorithm correctly identified dementia. This is a significant accomplishment that could revolutionize Bangladesh's dementia detection process. For patients to get the care they require, early dementia detection is essential. This study offers a proof-of-concept for the use of machine learning in dementia early detection & The results of this study suggest that machine learning models can be used as a powerful tool for early detection of dementia.

关键词： Surveys Machine learning algorithms Sociology Machine learning Predictive models Prediction algorithms Task analysis

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Exploiting TAS schemes to Enhance the PHY-security in Cooperative NOMA Networks: A Deep Learning Approach

Exploiting TAS schemes to Enhance the PHY-security in Cooper...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Yushintia Pramitarini Ridho Hendra Yoga Perdana Kyusung Shim Beongku An Dept. of Software and Communications Engineering in Graduate School Hongik University Republic of Korea School of Computer Engineering and Applied Mathematics Hankyong National University Republic of Korea Dept. of Software and Communications Engineering Hongik University Republic of Korea

In this paper, we propose a novel antenna selection scheme to enhance the secrecy performance in a relay-aided non-orthogonal multiple access (NOMA) network against an eavesdropper. Different from the conventional antenna selection schemes that does not use channel information, the proposed antenna selection scheme can employ each channel information to maximize the main channel capacity and minimize the eaves-dropper channel capacity, respectively. In order to evaluate the secrecy performance, we propose a deep learning (DL)-based framework that can do real-time configuration since the DL-based framework is based on a compact mapping function. In detail, the proposed min-max relay transmit antenna selection (MMRTAS) scheme can improve the secrecy performance compared to that of the benchmark scheme. Numerical results show that the proposed MMRTAS scheme improves the secrecy performance compared to that of the benchmark scheme. The proposed DL-based framework can estimate the main channel and eavesdropper channel capacities for the near user and far user with an accuracy of 99.79%, respectively.

关键词： Deep learning NOMA Channel capacity Transmitting antennas Receiving antennas Channel estimation Benchmark testing

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Multi-Criteria Client Selection and Scheduling with Fairness Guarantee for Federated Learning Service

arXiv

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arXiv 2023年

作者： Zhang, Meiying Zhao, Huan Ebron, Sheldon Xie, Ruitao Yang, Kan Dept. of Computer Science University of Memphis United States College of Computer Science and Software Engineering Shenzhen University China

Federated Learning (FL) enables multiple clients to train machine learning models collaboratively without sharing the raw training data. However, for a given FL task, how to select a group of appropriate clients fairly becomes a challenging problem due to budget restrictions and client heterogeneity. In this paper, we propose a multi-criteria client selection and scheduling scheme with a fairness guarantee, comprising two stages: 1) preliminary client pool selection, and 2) per-round client scheduling. Specifically, we first define a client selection metric informed by several criteria, such as client resources, data quality, and client behaviors. Then, we formulate the initial client pool selection problem into an optimization problem that aims to maximize the overall scores of selected clients within a given budget and propose a greedy algorithm to solve it. To guarantee fairness, we further formulate the per-round client scheduling problem and propose a heuristic algorithm to divide the client pool into several subsets such that every client is selected at least once while guaranteeing that the ‘integrated’ dataset in a subset is close to an independent and identical distribution (iid). Our experimental results show that our scheme can improve the model quality especially when data are non-iid. © 2023, CC BY.

关键词： Heuristic algorithms

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Using a Multi-Agent System to Better Simultaneous Collaboration software Systems 2

Using a Multi-Agent System to Better Simultaneous Collaborat...

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2nd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2022

作者： Abd Alati Elfargane, Rabeia N. Yousef, Khamisa. A. Saleh, Eiman M. Abd Elati, Naeima N Benghazi University Dept. Computer Science Benghazi Libya University of Tobruk Dept. Computer Science Tobruk Libya Benghazi University Dept. Software Engineering Benghazi Libya Benghazi University Dept. Statistics Benghazi Libya

ISBN: (数字)9781665479189

ISBN: (纸本)9781665479189

In each field, there is a need for better collaborative efforts among team members. The software development community is currently concentrating its efforts on developing tools that will make this endeavor easier. As a result, the two major purposes of a collaborative system are to solve the lack of community consciousness (since group members must be aware of one another) and to maintain flooring directive (i.e. provide means to manage access to shared resource in synchronous groupware systems). The goal of this study is to design a dynamic, extendable, and adaptable framework capable of handling challenges in synchronous community consciousness systems in order to solve these concerns. The major purpose of this study is to improve Simultaneous Collaboration software Systems utilizing the structure analysis real-Time approach (SART), which is based on a multiagent system (MAS)). © 2022 IEEE.

关键词： Multi agent systems

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Improved Residual U-Net for COVID-19 Lung Infection Multi-Class Segmentation in CT Image

Improved Residual U-Net for COVID-19 Lung Infection Multi-Cl...

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2022 International Conference of Advanced Technology in Electronic and Electrical engineering, ICATEEE 2022

作者： Khouloud, Sahib Ahlem, Melouah Fadel, Toure Amel, Slim LRI Badji-Mokhtar Annaba University Dept. Computer Science Annaba23000 Algeria Trois-Rivieres Laboratory of Software Engineering Dept. du Quebec Quebec Canada

ISBN: (纸本)9798350346749

COVID-19, the new coronavirus, is a threat to global public health. Today, there is an urgent need for automatic COVID-19 infection detection tools. This work proposes an automatic COVID-19 infection detection system based on CT image segmentation. A deep learning network developed from an improved Residual U-net architecture extracts infected areas from a CT lung image. We tested the system on COVID-19 public CT images. An evaluation using the F1 score, sensitivity, specificity and accuracy proved the effectiveness of the proposed network. Besides, experimental results showed that the proposed network performed well in extracting infection regions so, it can assist experts in COVID-19 infection detection. © 2022 IEEE.

关键词： Coronavirus

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