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39th International Conference on Information Networking, ICOIN 2025

作者： Hoffmann, Eduardo Freitas Machado, Caciano Westphall, Carla Merkle Federal University of Santa Catarina Dept. of Computer Science and Statistics Santa Catarina Brazil Federal University of Rio Grande do Sul Data Processing Center Porto Alegre Brazil

ISBN: (纸本)9798331506940

User authentication in web environments and Wi-Fi networks is crucial due to the increase in fraud and invasions. Traditional methods, such as WPA2-PSK, have vulnerabilities, making the use of more secure solutions necessary. RadChain Connect is a proposal that combines blockchain with FreeRADIUS and self-sovereign identity, aiming to provide an efficient authentication system. Initially, it focuses on web authentication in universities, enhancing security at login. In the future, there are plans to expand the solution to Wi-Fi networks, leveraging the versatility of FreeRADIUS and the security of the EAP protocol. © 2025 IEEE.

关键词： authentication blockchain decentralized identity eap radius self-sovereign identity

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Enhancing Brain Tumor Detection in MRI Images Through Fine-Tuning of VGG 16 Networks: A Precision Diagnostic Approach

Enhancing Brain Tumor Detection in MRI Images Through Fine-T...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Abhay Upadhyay Satya Prakash Yadav Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era Deemed to be University Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

This paper explores the impact of fine-tuning on brain tumour diagnosis using MRI data and a pre-trained VGG 16 model on ImageNet. Our objectives in fine-tuning the model were to improve its generalisation ability and diagnostic accuracy. Our results demonstrate a considerable improvement in the model's performance, with decreased validation loss and increased validation accuracy. The findings show that, despite the possibility of overfitting, fine-tuning is a helpful technique for deep learning model optimisation in medical image analysis. By enhancing diagnostic skills in healthcare, particularly in the early detection of brain tumours, this tactic may enhance patient outcomes.

关键词： Training Accuracy Magnetic resonance imaging Brain modeling data models Telecommunication computing Tuning Optimization Monitoring Overfitting

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Resource Virtualization with End-to-End Timing Guarantees for Multi-Hop Multi-Channel Real-Time Wireless Networks

Resource Virtualization with End-to-End Timing Guarantees fo...

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Real-Time Systems Symposium (RTSS)

作者： Jiachen Wang Tianyu Zhang Xiaobo Sharon Hu Song Han Dept. of Computer Science and Engineering University of Connecticut Storrs CT Dept. of Computer Science and Engineering University of Notre Dame Notre Dame IN

Resource virtualization is a promising technique that has been increasingly deployed in industrial automation systems to support multiple time-critical applications sharing the same physical resources. Extensive studies have been reported on how to perform real-time virtualization on computing resources. However, when applying virtualization techniques on network resources (especially for real-time wireless networks), node dependency among applications, wireless channel contention and stringent end-to-end timing requirements of the real-time flows in the network pose severe challenges. To address this problem, this paper formulates the network virtualization problem for multi-hop multi-channel real-time wireless networks (RTWNs). We first present a Satisfiability Modulo Theory (SMT)-based exact solution to capture the constraints posted by each application's resource interfaces and node dependency graphs. A novel supply graph (SG)-based partitioning framework, SGP, is then proposed to determine the resource partitions for individual applications. SGP uses supply graph to maintain compliance with the regularity constraints while efficiently allocating resources. Experimental results from both a real-world testbed and extensive simulations show that SGP can achieve comparable success ratio with the SMT-based exact solution but reduce the computational overhead significantly.

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Mastoidectomy Multi-View Synthesis from a Single Microscopy Image

arXiv

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arXiv 2024年

作者： Zhang, Yike Noble, Jack H. Dept. of Computer Science Vanderbilt University United States Dept. of Electrical and Computer Engineering Vanderbilt University United States

Cochlear Implant (CI) procedures involve performing an invasive mastoidectomy to insert an electrode array into the cochlea. In this paper, we introduce a novel pipeline that is capable of generating synthetic multi-view videos from a single CI microscope image. In our approach, we use a patient’s pre-operative CT scan to predict the post-mastoidectomy surface using a method designed for this purpose. We manually align the surface with a selected microscope frame to obtain an accurate initial pose of the reconstructed CT mesh relative to the microscope. We then perform UV projection to transfer the colors from the frame to surface textures. Novel views of the textured surface can be used to generate a large dataset of synthetic frames with ground truth poses. We evaluated the quality of synthetic views rendered using Pytorch3D and PyVista. We found both rendering engines lead to similarly high-quality synthetic novel-view frames compared to ground truth with a structural similarity index for both methods averaging ∼0.86. A large dataset of novel views with known poses is critical for ongoing training of a method to automatically estimate microscope pose for 2D to 3D registration with the pre-operative CT to facilitate augmented reality surgery. This dataset will empower various downstream tasks, such as integrating Augmented Reality (AR) in the OR, tracking surgical tools, and supporting other video analysis studies. Copyright © 2024, The Authors. All rights reserved.

关键词： Mesh generation

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Predicting Mobile Prices with Machine Learning Techniques

Predicting Mobile Prices with Machine Learning Techniques

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Computational Intelligence and Computing Applications (ICCICA), International Conference on

作者： Deepak Singh Rana Shiv Ashish Dhondiyal Sumeshwar Singh Sanjeev Kukreti Ashish Dhyani Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Dept. of Computer Science and Engineering Jaypee Institute of Information Technology Department of Hospitality Management Graphic Era Deemed to be University Dehradun Uttrakahnd

ISBN: (数字)9798350306446

ISBN: (纸本)9798350306453

Smartphones are increasingly vital to people on a daily basis. Telephones are utilized in all aspects of life, ranging from personal to professional, due to technological advancements. It serves a function beyond making phone calls. It enables internet connectivity and email reading. when not using the computer. The characteristics of a mobile phone are a crucial consideration when buying *** overall objective of this research is to find the best way to apply machine learning to estimate the retail pricing of smartphones based on their individual specs. Individuals who frequently use their phone are more attentive to selecting features. When purchasing a cell phone, a comparison is done based on the price-performance ratio. Phone features are regarded as performance. This research aims to forecast if mobile phones with certain features are considered economical or *** work is capable of being utilized in various marketing and business contexts to assist in making informed purchasing decisions by maximizing features while minimizing costs.

关键词： Support vector machines Machine learning algorithms Costs Machine learning Pricing Predictive models Prediction algorithms

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YOLOv8-Based Deep Learning Approach for Real-Time Skin Lesion Classification Using the HAM10000 dataset

YOLOv8-Based Deep Learning Approach for Real-Time Skin Lesio...

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International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Utsha Saha Imtiaj Uddin Ahamed Md Ashique Imran Imam Uddin Ahamed Al-Amin Hossain Ucchash Das Gupta Dept. of Computer Science North Dakota State University Fargo ND USA Agricultural Data Analytics Unit North Dakota State University Fargo ND USA Dept. of Computer Science University of South Dakota Vermillion SD USA Dept. of Applied Computer Science The University of Winnipeg Winnipeg Canada Dept. of Computer Science Liverpool John Moores University Liverpool UK

ISBN: (数字)9798350350548

ISBN: (纸本)9798350350555

Skin cancer is a prevalent and potentially fatal disease that requires early detection for effective treatment. We trained and evaluated five YOLOv8 classification model variants (YOLOv8n-cls, YOLOv8s-cls, YOLOv8m-cls, YOLOv8l-cls, and YOLOv8x-cls) on the HAM10000 dataset, which contains 10,015 dermatoscopic images of common pigmented skin lesions. The models were trained for 30 epochs using data augmentation techniques to enhance generalization. Performance was assessed using metrics including accuracy, precision, recall, F1-score, and inference time. The YOLOv8x-cls model achieved the highest accuracy of 86.2% and precision of 82.1%, while the YOLOv81-cIs model demonstrated the best balance with the highest F1-score of 77.0%. Compared to previous ensemble approaches, our single YOLOv8 models achieved superior performance with lower computational overhead. The YOLOv8n-cls variant showed the fastest inference time of 0.5 ms, making it suitable for real-time applications. Our results demonstrate the potential of YOLOv8-based models for accurate and efficient skin lesion classification, which could aid in early skin cancer detection and improve patient outcomes.

关键词： Measurement Deep learning Accuracy Computational modeling Skin Real-time systems Lesions Ensemble learning Skin cancer Diseases

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Impact on mental health due to COVID-19 pandemic: A cross-sectional study in Bangladesh

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Clinical eHealth 2023年第1期6卷 42-52页

作者： Pathan, Refat Khan Biswas, Munmun Yasmin, Suraiya Uddin, Mohammad Amaz Das, Anindya Khandaker, Mayeen Uddin Faruque, M.R.I. Sarker, Moniruzzaman Dept. of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway 47500 Malaysia Dept. of Computer Science and Engineering BGC Trust University Bangladesh Chittagong 4381 Bangladesh Department of Computer and Information Science Tokyo University of Agriculture and Technology Koganei - 184-0012 Tokyo Japan Dept. of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong 4349 Bangladesh Dept. of Electrical and Electronic Engineering Chittagong University of Engineering and Technology Chittagong 4349 Bangladesh Center for Applied Physics and Radiation Technologies School of Engineering and Technology Sunway University Selangor Bandar Sunway 47500 Malaysia Space Science Centre Universiti Kebangsaan Malaysia 43600 UKM Selangor Bangi Malaysia Department of Marketing Sunway University Business School Sunway University Selangor Bandar Sunway 47500 Malaysia Faculty of Graduate Studies Daffodil International University Daffodil Smart City Birulia Savar Dhaka – 1216 Bangladesh

The government of Bangladesh has implemented the “Stay Home” policy following the WHO recommendation to resist the community transmission of Covid-19. As a result, the routine activities of all government, semi-government establishments, including educational institutions, are severely affected, and the country's economic growth becomes slowed down. To overcome such a situation, the relevant authorities have introduced the “Work from Home” policy for the employees and “Remote Education” for students. However, due to the persistence of multi-dimensional socio-economic problems, many employees and students face big challenges in performing their regular jobs while adopting such a policy. Consequently, enormous psychological anxiety has been developed for all people, including students, parents, employees, etc., and concurrently created severe changes in their behavior. This study aims to analyze the reasons for the behavioral changes of the employees, students, academic staff, and family members of different ages due to psychological anxiety, stress, or physical issues. A comprehensive online-based survey has been carried out on people working in various sectors in Bangladesh. A modified Apriori Algorithm has been used to sort out the associations between the causes and types of behavioral changes. Analyzed data revealed a massive human behavioral change in most participants. This finding indicates that the negligence of those significant human behavioral changes may cause a higher risk of creating psychological imbalance. Therefore, there is a need to have a solid understanding of the reasons for the behavioral changes and set up standard guidelines to maintain “Work from Home” in this Covid-19 situation to avoid psychological imbalance. Based on this study, some suggestions have been given for implementation by the government on an urgent basis. © 2023

关键词： Apriori algorithm Behavioral changes Covid-19 outbreak Family crisis Work from home

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ML-Based Wildfire Prediction and Detection

ML-Based Wildfire Prediction and Detection

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Computing and Machine Intelligence (ICMI), International Conference on

作者： Chiragee C. Joshi Jaya S. S. K. Payyavula Soham Patel Yasser M. Alginahi Electrical and Computer Engineering Dept. University of Windsor Windsor Canada Computer Science Dept. Adrian College Michigan USA

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

This paper provides a comprehensive study on prediction and detection of wildfire using Machine Learning and Deep Learning algorithms. Due to the current environmental trends, wildfire possess a great threat to the ecosystem and human lives at a great cost. Multiple factors are the root cause for wildfires which include environmental factors like temperature, humidity, air pressure index, forest terranean, vegetation. Taking these factors into consideration, a Machine Learning model was built considering diverse algorithms to learn the previous trends and predict future wildfires instances. Based on the satellite imagery of previous wildfires, using CNN and AlexNet algorithms to detect wildfires that are currently taking place for early detection so to contain and control the fire without it causing any damage. Amalgamating these two algorithms, in a single graphical user interface, enhances user accessibility and convenience, providing an invaluable tool in wildfire management. The algorithm achieved an accuracy of average 96.33 % to predict wildfires and was able to detect them based on images at the rate of 93.66%.

关键词： Temperature sensors Wildfires Machine learning algorithms Accuracy Prediction algorithms Market research data models

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Fairness-driven link scheduling approach for heterogeneous gateways for digital twin enabled industry 4.0

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International Journal of Intelligent Networks 2023年第1期4卷 162-170页

作者： Patil, Suvarna Kaur, Mandeep Rogulj, Katarina Dr. D Y Patil Institute of Engineering Management and Research Pune India Dept. of Computer Science Savitribai Phule Pune University Pune India Permtech Research Solutions Pune India Faculty of Civil Engineering Architecture and Geodesy University of Split Split Croatia

The advent of Industry 4.0 has brought with it the integration of digital twin technology, which has enabled businesses to develop a virtual replica of their physical assets. This technology allows businesses to optimize their operations and improve their overall efficiency. However, the successful implementation of digital twin technology in Industry 4.0 heavily relies on the effective utilization of gateways. A significant challenge in gateway utilization is the fair allocation of resources, particularly in heterogeneous environments where gateways have different capabilities. Digital Twin is helping Industry 4.0 vision by connecting the authorized people to the exact data and processes to protect the data/assets from unauthorized access. It is accomplished by connecting sensing devices using a unique addressing system and transmitting their combined data to the Internet of Things (IoT) cloud. Massive volumes of heterogeneous data have resulted from the rapid growth of IoT applications and services. As a result, evaluation of data which affects the Digital Twin enabled industry is studied in this article which focuses on data traffic generated from different Industry 4.0 applications and protection of data along with the industry assets is looked by Digital Twin technology. IoT gateways are currently used to connect the devices from various technologies to the Digital Twins. In such networks, sudden increase in demand of IoT gateways will increase with the increase in IoT devices and the operational cost will also be increased. In the proposed system, low-cost specific gateways are proposed to minimize cost and maximize network performance for protecting assets of smart city through Digital Twin technology. In order to accomplish effective resource allocation in a Digital Twin based infrastructure, data transmission fairness at every gateway is accomplished in an IIoT network by considering link scheduling issues. To address these issues and provide fairness in he

关键词： Internet of things

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Texture Features Based Hybrid Multi Support Vector Machine Model for the diagnosis of Alzheimer's disease through Brain MRI Images

Texture Features Based Hybrid Multi Support Vector Machine M...

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2023 International Conference on Computational Intelligence, Networks and Security, ICCINS 2023

作者： Mallika, R.M. Rani, K Usha Hemalatha, K. Giri, M. Dept. of Cse Siddarth Institute of Engineering &technology Andhra Pradesh Puttur India Sri Padmavati Mahila Visva Vidyalayam Dept. of Computer Science Andhra Pradesh Tirupati India Madanapalle Institute of Technology & Science Dept. of CSE-AI Andhra Pradesh Madanapalle India Siddarth Institute of Engineering & Technology Department of Cse Andhra Pradesh Puttur India

ISBN: (纸本)9798350313796

Alzheimer's Disease (AD) is a complex neurodegenerative disorder that severely affects cognitive functions and poses significant challenges in early and accurate diagnosis. In recent years, machine learning techniques have shown remarkable potential in medical image analysis, particularly when applied to Brain MRI Images for AD diagnosis. This study proposes a novel approach utilizing a Texture Features based Hybrid Multi-Support Vector Machine Model to enhance the precision and early detection of Alzheimer's disease through Brain MRI Images. The proposed model combines the strength of texture features extracted from Brain MRI Images with a Hybrid Multi- Support Vector Machine framework. Texture features capture subtle patterns and structural variations within brain images, making them valuable in distinguishing different stages of Alzheimer's disease. By leveraging the optimization capabilities of the Hybrid Multi-Support Vector Machine, the model effectively handles high-dimensional data and complex patterns, contributing to improved diagnostic accuracy. To evaluate the model's performance, a comprehensive dataset of Brain MRI Images, encompassing AD patients, Mild Cognitive Impairment cases, and non-AD individuals, was employed. The experimental results demonstrate the effectiveness of the proposed Texture Features based Hybrid Multi-Support Vector Machine Model in accurately classifying Alzheimer's disease cases, outperforming traditional diagnostic methods. © 2023 IEEE.

关键词： Support vector machines

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