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A High Temperature Operando Study of Epitaxial Graphene Growth on Cubic Silicon Carbide Using Neutron Reflectometry

Neutron News 2024年第2期35卷 9-11页

作者： Pradeepkumar, A. Cortie, D. Smyth, E. Le Brun, A.P. Iacopi, F. School of Electrical and Data Engineering Faculty of Engineering and Information Technology University of Technology Sydney Sydney 2007 NSW Australia ARC Centre of Excellence for Transformative Meta-Optical Systems University of Technology Sydney 2007 NSW Australia Australian Centre for Neutron Scattering Australian Nuclear Science and Technology Organization 2234 NSW Australia ARC Centre of Excellence in Future Low-Energy Electronics Technologies Melbourne 3800 VIC Australia

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Enhancing Convergence Efficiency in Federated Learning Models

Enhancing Convergence Efficiency in Federated Learning Model...

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information technology (ACIT)

作者： Ghassan Samara Ala'Mazen Elhilo Mohammad Aljaidi Amneh Alamleh Mohammad Rasmi Al-Mousa Radwan Batyha Raed Alazaidah Alaa Bani-Bakr Ali Elrashidi Department of Computer Science Faculty of IT Zarqa University Zarqa Jordan Faculty of Information Technology Zarqa University Zarqa Jordan Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan Department of Cybersecurity Zarqa University Zarqa Jordan Faculty of Information Technology Applied Science Private University Amman Jordan Department of Cyber Security Faculty of IT Zarqa University Zarqa Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

This study evaluates federated learning models' performance compared to centralized models using the MNIST and CIFAR-10 datasets. Four models were tested: two Federated Learning models and two centralized models. The evaluation focused on accuracy, training rounds, and CO2 emissions, to optimize convergence and reducing energy consumption while maintaining strong model performance. The results showed that Federated learning models outperformed centralized models, with the Federated Learning MNIST model achieving 98.79% accuracy compared to 87.93% for the centralized model. Also, the accuracy of the Federated Learning CIFAR-10 model achieved 91.20% accuracy, exceeding the centralized model’s 90.14%. The most accuracy improvements happened in the early training rounds. An adjusted hill-climbing technique was used to the centralized models, decreasing the number of iterations and CO2 emissions without significantly reducing accuracy.

关键词： Performance evaluation Training Energy consumption Accuracy Federated learning Scalability data models information technology Carbon emissions Convergence

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Leveraging Deep Learning for Precise Chronic Bronchitis Identification in X-Ray Modalities

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Computers, Materials & Continua 2025年第4期83卷 381-405页

作者： Fahad Ahmad Saad Awadh Alanazi Kashaf Junaid Maryam Shabbir Asim Ali School of Computing Faculty of TechnologyUniversity of PortsmouthWinston Churchill Ave SouthseaPortsmouthPO12UPUK Portsmouth Artificial Intelligence and Data Science Center University of PortsmouthWinston Churchill AveSouthseaPortsmouthPO12UPUK Department of Computer Science College of Computer and Information SciencesJouf UniversitySakakaAljouf72341Saudi Arabia School of Biological and Behavioural Sciences Queen Mary University of LondonLondonE14NSUK Department of Computer Sciences Bahria UniversityLahorePunjab54700Pakistan

Image processing plays a vital role in various fields such as autonomous systems,healthcare,and cataloging,especially when integrated with deep learning(DL).It is crucial in medical diagnostics,including the early detection of diseases like chronic obstructive pulmonary disease(COPD),which claimed 3.2 million lives in ***,a life-threatening condition often caused by prolonged exposure to lung irritants and smoking,progresses through *** diagnosis through image processing can significantly improve survival *** encompasses chronic bronchitis(CB)and emphysema;CB particularly increases in smokers and generally affects individuals between 50 and 70 years *** damages the lungs’air sacs,reducing oxygen transport and causing symptoms like coughing and shortness of *** such as beta-agonists and inhaled steroids are used to manage symptoms and prolong lung ***,COVID-19 poses an additional risk to individuals with CB due to its impact on the respiratory *** proposed system utilizes convolutional neural networks(CNN)to diagnose *** this system,CNN extracts essential and significant features from X-ray modalities,which are then fed into the neural *** network undergoes training to recognize patterns and make accurate predictions based on the learned *** leveraging DL techniques,the system aims to enhance the precision and reliability of CB *** research specifically focuses on a subset of 189 lung disease images,carefully selected for model *** further refine the training process,various data augmentation and noise removal techniques are *** techniques significantly enhance the quality of the training data,improving the model’s robustness and *** a result,the diagnostic accuracy has improved from 98.6%to 99.2%.This advancement not only validates the efficacy of our proposed model but also represents a significant improvement over existing *** h

关键词： Deep learning chronic obstructive pulmonary disease chronic bronchitis convolutional neural network X-ray images

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FFC-Stereo: Stereo matching of binocular endoscopic images by Fast Fourier Convolution

FFC-Stereo: Stereo matching of binocular endoscopic images b...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Wei Lian, Ruoqi Luo, Huoling Qu, Weikai Liu, Wenjian Jia, Fucang City University of Macau Faculty of Data Science China Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen China Shenzhen Institute of Information Technology School of Digital Media Shenzhen China Chinese Academy of Sciences Research Center for Medical Ai Shenzhen Institute of Advanced Technology Shenzhen China Guangdong University of Technology School of International Education Guangzhou China Chinese Academy of Sciences Key Laboratory of Biomedical Imaging Science and System Shenzhen China

ISBN: (纸本)9798350386226

Three-dimensional reconstruction of minimally invasive surgical scenes using binocular endoscopic images plays a pivotal role in developing surgical navigation systems. However, there is a significant scarcity of well-labeled endoscopic datasets, and current stereo matching algorithms often fall short in terms of generalization. Consequently, the accuracy of predicting unseen data remains inadequate for practical applications. To address this challenge, we propose a fast Fourier convolution-based stereo matching model, named FFC-Stereo. The FFC-Stereo model includes a downsampling module, a fast Fourier convolution residual module, and an upsampling module. Experimental results indicate that the incorporation of fast Fourier convolution markedly enhances the model's generalization performance while preserving a straightforward structure. Furthermore, FFC-Stereo demonstrates superior accuracy and faster inference on unseen datasets when compared to state-of-the-art methods. This advancement underscores the potential of FFC-Stereo in improving the efficacy and reliability of surgical navigation systems. The code is available at: https://***/Tobyzai/FFC-stereo. © 2024 IEEE.

关键词： Endoscopy

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A concise review on the Impact of Electric Vehicle Penetration on National Grid

A concise review on the Impact of Electric Vehicle Penetrati...

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2024 International Conference on science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Ikumapayi, Omolayo M. Adegoke, Olayinka Guwor-Niki, Ware-Ebi H. Jaiyesimi, Boluwaji G. Laseinde, Opeyeolu T. Ting, Tin T. University of Johannesburg Department of Mechanical and Industrial Engineering Technology South Africa Afe Babalola University Department of Mechanical and Mechatronics Engineering Ado-Ekiti Nigeria Afe Babalola University Department of Mathematical and Physics Sciences Ado-Ekiti Nigeria INTI International University Faculty of Data Science and Information Technology Negeri Sembilan Nilai71800 Malaysia

ISBN: (纸本)9798350358155

Over the past decade, climate change has become more severe as a result of greenhouse gas (GHG) emissions into the atmosphere. As the number of automobile users on the road has increased and the transportation sector has become a major contributor to greenhouse gas emissions, electrification of the sector is necessary to reduce or eliminate GHG emissions into the environment. Promising as the development of electric vehicles may be, their complete adoption is impeded by significant energy demands and policy obstacles. Numerous nations are unable to embrace electric vehicles as a result of unsanitary conditions that hinder their development. This paper examines the obstacles that may impede the widespread adoption of electric vehicles (EVs) in certain nations, taking into account significant factors including the potential repercussions on the power grid, viable strategies to meet the increased energy demand, and a comprehensive assessment of sustainable EV charging infrastructure. Additionally, policy recommendations to aid in the integration of electric vehicles are addressed in this paper. © 2024 IEEE.

关键词： Greenhouse gas emissions

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Internet of Things (IoT) Powered Child Rescue System from Open Borewells by Using WiFi Integrated ESP32 Controller and Smart Sensors Association

Internet of Things (IoT) Powered Child Rescue System from Op...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Vijayakumari, P. Mary, S. Jeya Divya, G. Vani, N. Narkhede, A. Sabri, M. Omar Sairam Engineering College Dept of Ece Chennai India Einstein College of Engineering Ece department Tirunelveli India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications TamilNadu Kattankulathur India R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Puduvoyal Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Faculty of Economic Department of Business Information Technology Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Currently, the negligence of individuals in society is the reason why children are falling into the Bore well. The current systems available to protect the child are both less effective and more expensive. Consequently, the society requires a novel approach that is both more effective and efficient. In the majority of cases that have been reported thus far, a parallel opening is excavated, and subsequently a horizontal path is established to access the infant. Not only is it a time-consuming procedure, but it also carries a variety of risks. The Borewells rescue system is capable of performing a variety of actions to rescue the child by moving within the same Borewells where the child has been confined. The purpose of this paper is to rescue children who have fallen into a bore well. It appears that abandoned bore wells are mortality traps for minors. The primary water framework requirements of cultivators in India are met by groundwater, as the country is primarily known for agriculture. The very significant requirement of bore wells is typically tunneled for the groundwater exploration requirement due to the increasing population, diminishing land assets, and the advancement of urbanization. In turn, these borewell have begun to claim the lives of numerous innocent individuals. The Dynamic Child Rescue Model (DCRM) is a novel rescue system that has been developed in this research. It is designed to easily protect children from the borewell and is cross-validated with the conventional approach, the Pipeline Navigation Model (PNM), to assess the efficacy of the proposed scheme. The system is supported by the Internet of Things (IoT). This work suggests a novel design that incorporates a sensor located at the top of the borewell opening to detect the presence of a child if they fall inside. © 2024 IEEE.

关键词： Abandoned wells

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A Brief Survey on Graph Anomaly Detection 11

A Brief Survey on Graph Anomaly Detection

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11th International Conference on information technology and Quantitative Management, ITQM 2024

作者： Song, Chengxi Niu, Lingfeng Lei, Minglong School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China School of Economic and Management University of Chinese Academy of Sciences Beijing100190 China Faculty of Information Technology Beijing University of Technology Beijing100124 China CAS Research Center on Fictitious Economy & Data Science University of Chinese Academy of Sciences Beijing100190 China

Graph anomaly detection (GAD) has been extensively studied in recent years. GAD aims to detect nodes, edges, and subgraphs that exhibit characteristics and distributions different from those of the majority of graph data. With the advancement of deep learning, many researchers have applied machine learning to address anomaly detection at various scales. In this paper, we classify GAD methods into detector-based and classifier-based approaches and provide a brief introduction and summary of relevant articles from the past three years. Finally, we analyze the challenges and future development directions in the field of GAD. © 2024 The Authors.

关键词： Adversarial machine learning

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Intelligent Predictive Model for Hepatitis C

Intelligent Predictive Model for Hepatitis C

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Artificial Intelligence (ICAI), International Conference on

作者： Mehreen Shahzadi Faisal Bukhari Numan Shafi Department of Data Science Faculty of Computing & Information Technology University of the Punjab Lahore Pakistan Department of Computer Science University of Engineering and Technology Lahore Pakistan

Hepatitis C is the liver's festering that can lead to severe liver damage, usually caused by the hepatitis C virus. Hepatitis C has different stages. It is tough to cure in it's last stages; at the same time, it is expensive and painful process. The current research, however, is an alternative precaution to this issue. Hepatitis C can be predicted early by using multiple factors. The dataset related to hepatitis C was not publicly available. To overcome this challenge, the healthy and HCV effected samples were collected from different hospitals in Punjab. A questionnaire based survey was taken including different HCV related factor i.e. gender, weight loss, hives/ rashes, swelling, jaundice, drug addiction history, hepatic encephalopa-thy (drowsiness, slurred speech), Ascites (fluid buildup in belly/ abdomen), spider angiomas (Spiderlike blood vessels), shared syringe usage, medical history, and severeness. Different cleaning, scaling, and feature selection techniques were applied to collect the best feature data. After selection, various machine learning algorithms were applied. Random forest, KNN, Decision Tree, SVC, and MLP were used, but MLP yielded optimal results in all classification algorithms. We have gained 95.9 % accuracy when tested on unknown data based on the MLP model. As the predictions' results were satisfactory, it would be helpful for the people and act as a critical awareness.

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Cloud Computing Malware Detection Using Feature Selection Based on Optimized White Shark Algorithm (WSO)

Cloud Computing Malware Detection Using Feature Selection Ba...

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Cyber Resilience (ICCR), International Conference on

作者： Hossam M. J. Mustafa Mohammad H. Al-Zyod Department of Data Science and Artificial Intelligence Faculty of Information Technology University of Petra Amman Jordan Computer Science Department Al-Balqa Applied University Salt Jordan

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

The majority of information technology organizations nowadays prefer cloud computing as their main choice due to improvements in information technology. However, as a result of cloud computing's vast and open architecture, which makes it vulnerable to hacker attacks, the security of data remains an important concern. As a consequence, ensuring the security and integrity of data has become crucial while constructing an abnormal intrusion detection system utilizing machine learning algorithms represents a method to potentially detect intrusions. Recurring clouding as well as prevention. Using machine learning algorithms and revised intrusion datasets is necessary to overcome the challenge of finding effective and optimal network intrusion detection systems. The White Shark Optimization approach works along with Support Vector Machines (SVM). The suggested algorithm's effectiveness is assessed using metrics including accuracy and recall. When results of previous research employing both of the two datasets (NSL-KDD and Kyoto) for predicting the detection of abnormal infiltration are compared to the proposed algorithm based on SVM, it is determined that the white shark optimization algorithm produced the best accuracy, at 99.8%.

关键词： Support vector machines Measurement Cloud computing Machine learning algorithms Sharks Organizations Prediction algorithms

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Automated Waste Classification using Convolutional Neural Network

Automated Waste Classification using Convolutional Neural Ne...

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International Conference on Software technology and Engineering (ICSTE)

作者： Rajermani Thinakaran J. Somasekar Vikram Neerugatti P. Guru Saran Faculty of Data Science and Information Technology INTI International University Negeri Sembilan Malaysia Department of Computer Science and Engineering Jain (Deemed-to-be University) Banglore Karnataka India

ISBN: (数字)9798350378955

ISBN: (纸本)9798350378962

Efficient waste management relies on precise waste classification, which facilitates recycling, composting, and appropriate disposal. Conventional waste classification methods often involve manual sorting or simple rule-based systems, which are labor intensive and prone to errors. This study investigates the application of Convolutional Neural Networks (CNNs) for automated waste classification using image data. CNNs have shown significant success in various image classification tasks due to their ability to learn hierarchical features directly from pixel values. The methodology outlined covers data collection, preprocessing, model architecture design, and training procedures for the CNN model. Experimental results demonstrate the effectiveness of the CNN approach in accurately categorizing various waste types. Additionally, we discuss the practical implications of CNN-based waste classification systems for real-world waste management applications. By leveraging CNNs, waste management processes can be optimized, leading to better resource utilization and environmental sustainability. The authors also highlight potential avenues for future research, including exploring advanced CNN architectures and integrating emerging technologies like Internet of Things (IoT) sensors for improved waste management solutions. Overall, this study emphasizes the potential of CNNs to transform waste classification practices and enhance the efficiency of waste management systems.

关键词： Waste management Training Computer architecture Transforms data models Software Sensors Convolutional neural networks Internet of Things Sorting

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