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arXiv 2022年

作者： Khan, Wali Ullah Lagunas, Eva Ali, Zain Javed, Muhammad Awais Ahmed, Manzoor Chatzinotas, Symeon Ottersten, Björn Popovski, Petar University of Luxembourg Luxembourg City1855 Luxembourg Department of Electrical and Computer Engineering University of California Santa Cruz United States The Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad45550 Pakistan The College of Computer Science and Technology Qingdao University Qingdao266071 China The Department of Electronic Systems Aalborg University Denmark

Unmanned Aerial Vehicles (UAVs) are an important component of next-generation wireless networks that can assist in high data rate communications and provide enhanced *** high mobility and aerial nature offer deployment flexibility and low-cost infrastructure support to existing cellular networks and provide many applications that rely on mobile wireless communications. However, security is a major challenge in UAV communications, and Physical Layer Security (PLS) is an important technique to improve the reliability and security of data shared with the assistance of UAVs. Recently, Intelligent Reflective Surfaces (IRS) have emerged as a novel technology to extend and/or enhance wireless coverage by re-configuring the propagation environment of communications. This paper provides an overview of how the IRS can improve the PLS of UAV networks. We discuss different use cases of PLS for IRS-enhanced UAV communications and briefly review the recent advances in this area. Then based on the recent advances, we also present a case study that utilizes alternate optimization to maximize the secrecy capacity for IRS-enhanced UAV scenario in the presence of multiple Eves. Finally, we highlight several open issues and research challenges to realize PLS in IRS-enhanced UAV communications. Copyright © 2022, The Authors. All rights reserved.

关键词： Antennas

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Evaluation of Performance Enhancement in Ethereum Fraud Detection Using Oversampling Techniques

SSRN

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SSRN 2023年

作者： Ravindranath, Vaishali Nallakaruppan, M.K. Shri, M. Lawanya Balusamy, Balamurugan Bhattacharyya, Siddhartha Department of Computer Science Avinashilingam Institute for Home Science and Higher Education for Women Tamilnadu Coimbatore641043 India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamilnadu Vellore632014 India Shiv Nadar University NCR Uttar Pradesh Delhi201314 India Rajnagar Mahavidyalaya West Bengal Birbhum731130 India Algebra University College Zagreb Croatia

With the growing popularity of cryptocurrencies and their decentralized nature, the risk of fraudulent activities within these ecosystems has become a pressing concern. This research paper focuses on Ethereum fraud detection using a dataset specifically curated for this purpose. The methodology encompasses essential steps, including data cleaning, correlation analysis, data splitting, and exploratory data analysis to understand the characteristics. Self-optimized machine learning models are trained with the Pycaret library while addressing the class imbalance using SMOTE (Synthetic Minority Over Sampling Technique), ADA-SYN (Adaptive Synthetic Algorithm), Borderline SMOTE (Borderline Synthetic Minority Over Sampling Technique), and SVMSMOTE (Support Vector Machine Assisted Synthetic Minority Over Sampling Technique) techniques. The performance of the various models is evaluated on test and validation datasets using metrics such as accuracy, precision, recall, and AUC. The study reveals that ensemble models, particularly CATBoost (Categorical Boost) and LGBM (Light Gradient Boost Method), have shown exceptional efficiency, with accuracy ranging from 97% to 98.5% after oversampling. Moreover, these models exhibit higher F1 scores and AUC (region of convergence) values, indicating their potential to detect fraud effectively. Notably enough, the validation metrics also lie in the same range, demonstrating that the models do not suffer from over-fitting. The experiment demonstrates the promise of ensemble models in Ethereum fraud detection, paving the way for deploying robust fraud detection systems in crypto-currency ecosystems. The results show that the SMOTE and Borderline SMOTE oversampling techniques improve the classification accuracy levels to 98.54% with an AUC of 99.57%. These methods provide a 50-50 class balance for Ethereum fraud detection. © 2023, The Authors. All rights reserved.

关键词： Support vector machines

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Security Analysis for Virtual Machine Allocation in Cloud Computing

Security Analysis for Virtual Machine Allocation in Cloud Co...

引用

Cyber Resilience (ICCR), International Conference on

作者： Tahir Alyas Karamath Ateeq Mohammed Alqahtani Saigeeta Kukunuru Nadia Tabassum Rukshanda Kamran Department of Computer Science Lahore Garrison University Lahore Pakistan School of Information Technology Skyline University College University City Sharjah Sharjah UAE Department of Computer Information Systems College of Computer Science and Information Technology Abdulrahman Bin Faisal University Dammam Saudi Arabia Assistant professor in management Faculty Swiss Business school. RAK. UAE Department of Computer Science Virtual University of Pakistan Pakistan Faculty of Compter science and Information Technology Universiti Kota Samarahan Sarawak East Malaysia

ISBN: (数字)9781665461221

ISBN: (纸本)9781665461238

A huge number of cloud users and cloud providers are threatened of security issues by cloud computing adoption. Cloud computing is a hub of virtualization that provides virtualization-based infrastructure over physically connected systems. With the rapid advancement of cloud computing technology, data protection is becoming increasingly necessary. It's important to weigh the advantages and disadvantages of moving to cloud computing when deciding whether to do so. As a result of security and other problems in the cloud, cloud clients need more time to consider transitioning to cloud environments. Cloud computing, like any other technology, faces numerous challenges, especially in terms of cloud security. Many future customers are wary of cloud adoption because of this. Virtualization Technologies facilitates the sharing of recourses among multiple users. Cloud services are protected using various models such as type-I and type-II hypervisors, OS-level, and unikernel virtualization but also offer a variety of security issues. Unfortunately, several attacks have been built in recent years to compromise the hypervisor and take control of all virtual machines running above it. It is extremely difficult to reduce the size of a hypervisor due to the functions it offers. It is not acceptable for a safe device design to include a large hypervisor in the Trusted Computing Base (TCB). Virtualization is used by cloud computing service providers to provide services. However, using these methods entails handing over complete ownership of data to a third party. This paper covers a variety of topics related to virtualization protection, including a summary of various solutions and risk mitigation in VMM (virtual machine monitor). In this paper, we will discuss issues possible with a malicious virtual machine. We will also discuss security precautions that are required to handle malicious behaviors. We notice the issues of investigating malicious behaviors in cloud computing, give t

关键词： Cloud computing Trusted computing Virtual machine monitors Memory Virtual machining Behavioral sciences Security

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VGG-16 based Deep Learning Approach for Cephalometric Landmark Detection

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Open Public Health Journal 2024年 17卷

作者： Mehra, Pranav Neeraja, R. Anbarasi L, Jani Ravi, Vinayakumar Al Mazroa, Alanoud School of Computer Science and Engineering Vellore Institute of Technology India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University (PNU) P.O. Box 84428 Riyadh 11671 Saudi Arabia

Aims: The aim of this research work is to compare the accuracy and precision of manual landmark identification versus automated methods using deep learning neural networks. Background: Cephalometric landmark detection is a critical task in orthodontics and maxillofacial surgery and accurate identification of landmarks is essential for treatment planning and precise diagnosis outcomes. It entails locating particular anatomical landmarks on lateral cephalometric radiographs of the skull that can be utilised to evaluate the relationships between the skeleton and the teeth as well as the soft tissue profiles. Many software tools and landmark identification approaches have been implemented over time to increase the precision and dependability of cephalometric analysis. Objective: The primary objective of this research is to evaluate the effectiveness of an automated deep learning-based VGG-16 algorithm for cephalometric landmark detection and to compare its performance against traditional manual identification methods in terms of accuracy and precision. Methods: The study employs a VGG16 transfer learning model on a dataset of skull X-ray images from the IEEE 2015 ISBI Challenge to automatically identify 19 cephalometric landmarks on lateral cephalometric radiographs. The model is fine-tuned to predict the precise XY coordinates of these landmarks enhancing the accuracy of cephalometric analysis by minimizing manual intervention and improving detection consistency. Results: The experimental findings indicate that the presented cephalometric landmark detection system has attained Successful Detection Rates (SDR) of 26.84%, 41.57%, 59.89% and 94.42% in the 2, 2.5, 3 and 4mm precision range respectively and a Mean Radial Error (MRE) of 2.67mm. Conclusion: This paper has presented an approach for cephalometric landmark detection using the VGG-16 model a widely used deep learning architecture in computer vision. Through the experiments it is shown that the VGG-16 model can ac

关键词： Cephalometric landmarks Clinical decision-making CNN Deep learning Orthodontics VGG16

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How are Websites Used During Development and What are the Implications for the Coding Process?

SSRN

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SSRN 2022年

作者： Alghamdi, Omar Clinch, Sarah Skeva, Rigina Jay, Caroline Department of Computer Science The University of Manchester M13 9PL United Kingdom College of Computing and Informatics Saudi Electronic University Riyadh6867 Saudi Arabia

Websites are frequently used to support the development process. This paper investigates how websites are used when writing code and programmers’ perceptions of the potential impact of this on their behaviour and the quality of the resulting software. We interviewed 18 programmers (13 students enrolled in undergraduate computer science courses, and 5 experienced professionals), and analysed the data thematically. The findings were used to develop a survey, which was distributed to 276 programmers (251 students, 25 experienced professionals). The results indicate that use of websites, especially Stack Overflow, is viewed as an essential part of programming by both students completing coursework and professionals developing code in *** also found that developers have experience of encountering a diverse set of problematic code snippets online, that copying code from websites without checking its quality or understanding how it worked is common, and that using online resources in this way had a potentially counter-productive effect on learning. Based on these findings, we make a number of recommendations, including better consideration of online code reuse in taught programmes, co-development and code-reuse practices in professional settings, and software licensing training for professional developers. © 2022, The Authors. All rights reserved.

关键词： Websites

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Using process mining algorithms for process improvement in healthcare

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Healthcare Analytics 2024年 5卷 100305-100305页

作者： Rabbi, Fazla Banik, Debapriya Hossain, Niamat Ullah Ibne Sokolov, Alexandr Engineering Management Department College of Engineering and Computer Science Arkansas State University Jonesboro 72467 AR United States Industrial Manufacturing & Systems Engineering The University of Texas at El Paso El Paso 79968 TX United States

Healthcare professionals must provide their patients with the best possible service and be well-informed and expert at carrying out complex surgical procedures to fulfill this responsibility. The aim of the medical treatments is fewer complications, shorter hospital stays, and a better patient experience. Through continuous learning and training, medical practitioners trained in up-to-date and state-of-the-art surgical techniques and technologies make productive and effective healthcare systems possible. Healthcare systems often report on problems with surgical processes, skipped procedures, unusual activities during operations, and lengthy transition times. This event log data allows implementing process mining methods to deliver medical professionals with simple and understandable findings using Petri nets for process analysis and enhancement. This study identifies the parallels and discrepancies between the pre-and post-stages and their respective frequency on each typical Central Venous Catheter (CVC) installation activity. The Process Mining for Python (PM4Py) frameworks used four major mining algorithms to view the event log (i.e., alpha miner, directly-follows graph (DFG), heuristic miner, and inductive miner). This study's findings indicate that medical residents are more susceptible to error during pre-operative procedures. © 2024 The Authors

关键词： Business process management Healthcare analytics Hospital information systems Process mining Process Mining for python

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Attention mechanisms in computer vision: A survey

arXiv

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arXiv 2021年

作者： Guo, Meng-Hao Xu, Tian-Xing Liu, Jiang-Jiang Liu, Zheng-Ning Jiang, Peng-Tao Mu, Tai-Jiang Zhang, Song-Hai Martin, Ralph R. Cheng, Ming-Ming Hu, Shi-Min The BNRist Department of Computer Science and Technology Tsinghua University Beijing100084 China TKLNDST College of Computer Science Nankai University Tianjin300350 China The School of Computer Science and Informatics Cardiff University United Kingdom

Humans can naturally and effectively find salient regions in complex scenes. Motivated by this observation, attention mechanisms were introduced into computer vision with the aim of imitating this aspect of the human visual system. Such an attention mechanism can be regarded as a dynamic weight adjustment process based on features of the input image. Attention mechanisms have achieved great success in many visual tasks, including image classification, object detection, semantic segmentation, video understanding, image generation, 3D vision, multi-modal tasks and self-supervised learning. In this survey, we provide a comprehensive review of various attention mechanisms in computer vision and categorize them according to approach, such as channel attention, spatial attention, temporal attention and branch attention;a related repository https://***/MenghaoGuo/Awesome-Vision-Attentions is dedicated to collecting related work. We also suggest future directions for attention mechanism research. Copyright © 2021, The Authors. All rights reserved.

关键词： Surveys

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A Practical Approach to Improve CS Curriculum Through Continuous Assessment to ABET Outcomes

Journal of Computing Sciences in Colleges

引用

Journal of Computing sciences in colleges 2024年第1期40卷

作者： Yi Liu Gongbing Hong Department of Information Systems and Computer Science Georgia College and State University Milledgeville GA

This paper introduces a practical methodology to improve our CS curriculum using the continuous assessments conducted over the past decade. This methodology not only ensures effective improvements to curriculum, but also aims to reduce faculty workload associated with assessments. The approach includes integrating ABET-enabled student outcomes and the online assessment system from the university, optimizing the frequency of assessments by rotating a subset of student learning outcomes, focusing on the important components of our curriculum, establishing a routine meeting at the beginning of each fall semester aligned with the university's schedule, and consistently applying the collected results to restructure and refine the CS undergraduate curriculum at our college. As a result, the prerequisites of CS courses have been clarified, and the connections among CS courses have been strengthened. These curriculum improvements also help to enhance the quality of our CS program, as shown by ABET re-accreditation and higher rank from U.S. News and World Report.

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Automated Defect Detection in Electronic Components using Convolutional Neural Networks

Automated Defect Detection in Electronic Components using Co...

引用

International Conference on science Technology Engineering and Management (ICONSTEM)

作者： A. Shobanadevi Sreekanth Kottu K R Senthil Kumar K. Amudha K. Praveena R. Venkatesh Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science & Engineering Mallareddy University Hyderabad Telangana India Department of Mechanical Engineering R.M.K. Engineering College Kavaraipettai Tamil Nadu India Department of Science and Humanities-Physics R.M.D. Engineering College (An Autonomous Institution) Kavaraipettai Tamil Nadu India Department of Electronics and Communication Engineering Mohan Babu University Erstwhile SreeVidyanikethan Engineering College Andhra Pradesh India Department of Physics PSNA College of Engineering and Technology (Autonomous) Dindigul Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

This exploration paper explores the operation of convolutional neural networks(CNNs) in automating the discovery of blights in electronic factors. With the rapid-fire advancement of technology, the demand for high- quality electronic factors has surged, emphasizing the need for effective disfigurement discovery styles. using the capabilities of CNNs, this study aims to develop a robust and accurate automated system able to relate colorful types of blights to electronic factors. By training the CNN model on a different dataset of disfigurement images, the system learns to fete subtle variations and anomalies reflective of faults, thereby enhancing examination delicacy and effectiveness. The proposed approach offers promising results in streamlining disfigurement discovery processes, reducing homemade intervention, and ameliorating overall quality control in electronics manufacturing. Results indicate the effectiveness and trustability of CNNs in automating disfigurement discovery, paving the way for enhanced productivity and product trustability in electronic diligence.

关键词： Training Productivity Process control Quality control Streaming media Mathematics Manufacturing

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Automatic Concrete Surface Crack Recognition Using EfficientNetV2 Variants

Automatic Concrete Surface Crack Recognition Using Efficient...

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： A. Rama Robertas Damaševičius S. Arunmozhi Mazin Abed Mohammed Ragheed Hussam V. Rajinikanth Department of CSE Saveetha School of Engineering SIMATS Chennai TN India Centre of Real Time Computer Systems Kaunas University of Technology Kaunas Lithuania Department of Electronics and Communication Engineering Manakula Vinayagar Institute of Technology Puducherry TN India Department of Artificial Intelligence College of Computer Science and Information Technology University of Anbar Anbar Iraq Medical instruments engineering techniques Al-farahidi University Baghdad Iraq Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai TN India

Building health and structural integrity are important to ensure occupant safety and long-term durability. Monitoring and assessing building condition regularly ensures that potential problems are identified early. This research aims to develop and implement a deep-learning (DL) supported tool to examine the concrete surface images collected using digital camera. The data gathering forms the first stage and the chosen DL algorithm is used for feature extraction followed by the feature selection and then fusion is done and finally a fivefold cross verification schema is applied for a binary classification. This work considered the EfficientNetV2 (ENV2) model with variants like small, medium and large for the examination. The investigation is implemented using individual and fused deep-features and the performance of the developed tool is confirmed using the achieved accuracy. The outcome of this research confirms that the proposed tool produces a betterment of >96% accuracy with individual-features and 100% accuracy with fused-features. These results confirm that the proposed scheme provides significant result during the crack detection.

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