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Exploring Advancements in 6G Wireless Networks for the Future of High-Speed Connectivity 2

Exploring Advancements in 6G Wireless Networks for the Futur...

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2nd International Conference Computational and Characterization Techniques in Engineering and sciences, IC3TES 2024

作者： Suresh, J. Kumari, V Sheeja Pillai, N. Muthuvairavan Jayarin, P. Jesu David, D. Beulah Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Thandalam Tamil Nadu Chennai India R.M.D Engineering College Department of Computer Science and Business Systems Tamil Nadu India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Information Technology ThandalamTamil Nadu Chennai India

ISBN: (纸本)9798350364699

High-speed connectivity technologies that aim to be faster and more secure have been explored so that there will be a change of the very nature of wireless communication. This article presents advances concerning the high-speed connection component of the sixth-generation wireless networks, which is simply denoted as 6G. Different performance parameters of 6G are compared with 4G (LTE) and 5G (5G) through multiplicity of research analyses done across disciplines. Our findings suggested that 6G networks are incredibly dynamic and will change how the internet is used. These SixG networks are due to accompany massive data transmission rates of 100 Gbps, five times higher than 5G's 20 Gbps. Latency also approaches same level as 6G where on average 1 millisecond is noticed compared to 5G's 10 and 4G's 50. Server speed and its response time in milliseconds will expose room for a number of real-time applications from only augmented reality imaging to the world of driverless automobiles and interactive games. Our study outlines the major consequences of the coming health shift. 4G allowed only for slow/limited improvements in healthcare whereas the 5G made telemedicine possible. On the other hand, 6G reportedly offers "precision remote surgery", allowing surgeons to be located remotely of a patient while still performing a high precision surgery. Attitude of people becomes completely unaware when they are briefed about concepts of latest generation known as 6G. Before studying about 6G, the number of those are eagerly delighted are 20%, having no idea on how it will be is 50%, while the other 30% are skeptics. Meanwhile, once people are better cognizant of 6G, fanatics raise up from 60 percent and sceptics drop till 10 percent. The significant shift is a reflection of 6G technology in the stakeholders' mind. © 2024 IEEE.

关键词： 5G mobile communication systems

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Pay Self-Attention to Audio-Visual Navigation 33

Pay Self-Attention to Audio-Visual Navigation

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33rd British Machine Vision Conference Proceedings, BMVC 2022

作者： Yu, Yinfeng Cao, Lele Sun, Fuchun Liu, Xiaohong Wang, Liejun Department of Computer Science and Technology State Key Lab on Intelligent Technology and Systems Tsinghua University Beijing China Motherbrain EQT Stockholm Sweden College of Information Science and Engineering Xinjiang University Urumqi China

Audio-visual embodied navigation, as a hot research topic, aims training a robot to reach an audio target using egocentric visual (from the sensors mounted on the robot) and audio (emitted from the target) input. The audio-visual information fusion strategy is naturally important to the navigation performance, but the state-of-the-art methods still simply concatenate the visual and audio features, potentially ignoring the direct impact of context. Moreover, the existing approaches requires either phase-wise training or additional aid (e.g. topology graph and sound semantics). Up till this date, the work that deals with the more challenging setup with moving target(s) is still rare. As a result, we propose an end-to-end framework FSAAVN (feature self-attention audio-visual navigation) to learn chasing after a moving audio target using a context-aware audio-visual fusion strategy implemented as a self-attention module. Our thorough experiments validate the superior performance (both quantitatively and qualitatively) of FSAAVN in comparison with the state-of-the-arts, and also provide unique insights about the choice of visual modalities, visual/audio encoder backbones and fusion patterns. © 2022. The copyright of this document resides with its authors.

关键词： Robots

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COVID-19 Infection Detection in X-Ray Images Using Enhanced Image Segmentation and Machine Learning Models 25

COVID-19 Infection Detection in X-Ray Images Using Enhanced ...

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25th International Arab Conference on information technology, ACIT 2024

作者： Nahar, Khalid M.O. Ebrahim Megdad, Mohammad Al Mamlook, Rabia Emhamed Aljaidi, Mohammad Nahar, Besan K.M. Nahar, Mohammad K. M. Anbarkhan, Samar Hussni Khafajeh, Hayel Amer, Waleed Arab Open University Faculty of Computer Studies P.O. Box 84901 Riyadh11681 Saudi Arabia Yarmouk University Computer Science Department Irbid21163 Jordan University of Zawia Dept. of Mechanical and Industrial Engineering Zawia Libya Zarqa University Faculty of Information Technology Department of Computer Science Zarqa13110 Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Yarmouk University Faculty of Medicine Irbid Jordan Jordan University of Science and Technology Faculty of Applied Medical Science Irbid21163 Jordan Northern Border University Information Systems Department Rafha76322 Saudi Arabia Zarqa University Faculty of Information Technology Department of Artificial Intelligence Zarqa13110 Jordan Zarqa University Faculty of Information Technology Department of Cybersecurity Zarqa13110 Jordan

ISBN: (纸本)9798331540012

The COVID-19 pandemic has shown a significant challenge to healthcare associations worldwide demanding the progress of reliable diagnostic techniques. Chest X-ray imaging is an easily available and cost-effective tool for evaluating lung defects combined with COVID-19. This study aims to introduce a novel COVID-19 infection detection and recognition method in X-ray images through image segmentation. By employing machine learning (ML) methods, particularly CNN-based U-Net architectures, the lung sections are accurately segmented, and infection sections are classified based on specific radiological features. The model was trained on a large dataset of annotated X-ray images assisting it to distinguish between healthy lungs, COVID-19-affected lungs, and other lung pathologies. The proposed method apprehended a segmentation accuracy of 96.6% with a Dice coefficient of 94.36% offering a potential solution for mechanical detection and 98.4% with KNN analysis of COVID-19 infection in chest X-rays in that way assisting radiologists in making faster and more accurate diagnoses. © 2024 IEEE.

关键词： COVID-19

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Enhancing Cybersecurity in the Digital Age with Machine Learning for Threat Detection and Prevention

Enhancing Cybersecurity in the Digital Age with Machine Lear...

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2024 IEEE International Conference on Recent Advances in science and Engineering technology, ICRASET 2024

作者： Jayarin, P.Jesu David, D. Beulah Jenifa Kumari, V. Sheeja Pillai, N. Muthuvairavan Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Information Technology Thandalam Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Thandalam Tamil Nadu Chennai India R.M.D Engineering College Department of Computer Science and Business Systems Tamil Nadu Thiruvallur India

ISBN: (纸本)9798350388602

information security cannot be underemphasized today especially with the increased use of information Communication technology which requires improved threat identification techniques. In this study, we propose a novel methodology comprising four variants - CyberShield, SentinelGuard, ThreatHunter, and SecureNet - and evaluate their performance against existing approaches in terms of key performance metrics: which are efficiency, precision, recall, F1-score and AUC. In all variants above experiments that we had demonstrated, there is rather more distinctive feature towards threat identifiers guaranteed other than what is presently offered. Interestingly, CyberShield with +3.8% and ThreatHunter with +3.8%, both the highest, are more accurate in terms of classifications of cyper-attacks than other algorithms. Finally, all proposed variants are ranked higher than the current approaches in terms of recall with possible variation between 4.2% and 2.2 for TP rates, meaning these approaches have the potential to minimize false negatives. In addition, the advance variants actually cause slight increase in Precision and F1-Score but with aids to make Precision equivalent to Recall, the achieved performance is relatively higher. What this would do is expose the advantages of the new proposed variants in terms of the degree of influence human behavior and the rate of risk of cyber threats. © 2024 IEEE.

关键词： Adversarial machine learning

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Contrast as a Method of Image Processing in Increasing Diagnostic Efficiency When Studying Liver Fatty Tissue Levels 2

Contrast as a Method of Image Processing in Increasing Diagn...

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2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Abu-Jassar, Amer Al-Sharo, Yasser Boboyorov, Sardor Lyashenko, Vyacheslav Ajloun National University Faculty of Information Technology Department of Computer Science Ajloun Jordan Ajloun National University Faculty of Information Technology Department of Cyber Security Ajloun Jordan Tashkent Medical Academy Termiz Branch Termiz Uzbekistan Kharkiv National University of Radio Electronics Department of Media Systems and Technology Kharkiv Ukraine

ISBN: (纸本)9798350373363

The paper addresses several challenges associated with analyzing digital medical images, focusing on identifying and assessing fatty liver tissue levels. The methodology enhances the perception of transition zones delineating fatty lesions by employing a contrast technique on the input image. Classic histogram equalization is utilized for contrast, ensuring an even distribution of brightness levels. The evaluation of processed image quality relies on indicators such as entropy, NIQE, and BRISQUE. The paper explores diverse strategies for enhancing the original image based on individual or combined values of quality indicators, which effectively improves the identification of transition zones in fatty lesions. The findings are illustrated using real digital images of fatty liver lesions. © 2023 IEEE.

关键词： Image enhancement

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Mechanical Evaluation of Bone Scaffolds' Behavior under Compression 46

Mechanical Evaluation of Bone Scaffolds' Behavior under Comp...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Ntousi, Ourania A. Pleouras, Dimitrios S. Siogkas, Panagiotis K. Roumpi, Maria Kontaxis, Lykourgos C. Bourantas, George Deligianni, Despoina Kostopoulos, Vassilis Pakos, Emilios E. Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering IoanninaGR45110 Greece University of Patras Laboratory of Biomechanics and Biomedical Engineering Dept. of Mechanical Engineering and Aeronautics RionGR26500 Greece University of Patras Dept of Agriculture MessolonghiGR30200 Greece University of Patras Dept. of Mechanical Engineering and Aeronautics Rio Achaia26504 Greece University of Ioannina School of Medicine Department of Orthopaedic Surgery and Traumatology Epirus Ioannina45221 Greece University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Biomedical Research Institute-FORTH GR45110 Greece

ISBN: (纸本)9798350371499

This study aims to comprehensively evaluate the mechanical performance of bone scaffolds, focusing on a compression test displacement rate 0.5mm/s and 2mm/min. The research employs the Yeoh 3rd order model for the hyperelastic PLA scaffolds to simulate the compression tests, which are validated using experimental results. More specifically, the stress- strain curves derived from the in-silico analysis exhibit strong agreement with the experimental results, highlighting the reliability of the computational approach. Two distinct scaffold geometries were employed and a comparative analysis of their responses to specific loading conditions had been performed. The investigation reveals that the PCL-50 scaffold demonstrates promising mechanical behavior in terms of better endurance in such loading conditions. © 2024 IEEE.

关键词： Stress-strain curves

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Mobility and Behavior based Trustable Routing in Mobile Wireless Sensor Network 7

Mobility and Behavior based Trustable Routing in Mobile Wire...

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Praveen Kumar, B. Umamageswaran, J. Kalpana, A.V. Dhanalakshmi, R. Department of Computer Science and Engineering KL University Andhra Pradesh Vijayawada India Department of Information Technology RMK Engineering College Tamil Nadu Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9781665464086

Mobile wireless sensor networks (MWSNs) allow the sensor nodes to move freely and transmit to each other without needing a fixed infrastructure. Usually, the routing process is very complex, and it becomes even more complicated as the sensor nodes are low-power, cost-effective mobile devices with minimum resources. MWSNs raises deployed in security-critical applications due to their resource-constrained features;they are prone to several security attacks that critically affect data transmission. The most significant innovation of trust is that it evades untrustable nodes by activating several detection routes to detect and obtain node trust, enhancing security quickly. Mobility and Behavior based Trustable Routing (MBTR) is proposed to solve these issues. This approach measures sensor node mobility based on the Received Signal Strength (RSS) between the two sensor nodes. The trust manager computes the node trust ability by the node behavior. Here, the node behavior is checked through the node energy, supportive account, and received packet account. The trust manager selects the security algorithm based on distance during data transmission. The simulation examination demonstrates lesser delay and lesser packet loss in the network. Furthermore, it improves the network throughput in the MWSN. © 2023 IEEE.

关键词： Sensor nodes

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ReScore Disease Groups Based on Multiple Machine Learnings Utilizing the Grouping-Scoring-Modeling Approach

ReScore Disease Groups Based on Multiple Machine Learnings U...

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17th International Joint Conference on Biomedical Engineering systems and Technologies, BIOSTEC 2024

作者： Qumsiyeh, Emma Yousef, Miar Yousef, Malik Department of Computer Science and Information Technology Al-Quds University Palestine Lady Davis Carmel Medical Center Haifa Israel Department of Information Systems Zefat Academic College Zefat Israel Galilee Digital Health Research Center Zefat Academic College Zefat Israel

The integrating of biological prior knowledge for disease gene associations has shown significant promise in discovering new biomarkers with potential translational applications. GediNET is a recent tool that is considered an integrative approach. In this research paper, we aim to enhance the functionality of GediNET by incorporating ten different machine learning algorithms. A critical element of this study involves utilizing the Robust Rank Aggregation method to aggregate all the ranked lists over the cross-validations, suggesting the final ranked significant list of disease groups. The Robust Rank Aggregation is used to re-score disease groups based on multiple machine learning. Moreover, a comprehensive comparative analysis of these ten machine learning algorithms has revealed insights regarding their intrinsic qualities. This facilitates researchers in determining which algorithm is most effective in the context of disease grouping and classification. © 2024 by SCITEPRESS–science and technology Publications, Lda.

关键词： Adversarial machine learning

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HARNESSING OUT-OF-DISTRIBUTION EXAMPLES VIA AUGMENTING CONTENT AND STYLE 11

HARNESSING OUT-OF-DISTRIBUTION EXAMPLES VIA AUGMENTING CONTE...

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11th International Conference on Learning Representations, ICLR 2023

作者： Huang, Zhuo Xia, Xiaobo Shen, Li Han, Bo Gong, Mingming Gong, Chen Liu, Tongliang Syndey AI Centre The University of Sydney Australia JD Explore Academy Department of Computer Science Hong Kong Baptist University Hong Kong The University of Melbourne Australia Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China

Machine learning models are vulnerable to Out-Of-Distribution (OOD) examples, and such a problem has drawn much attention. However, current methods lack a full understanding of different types of OOD data: there are benign OOD data that can be properly adapted to enhance the learning performance, while other malign OOD data would severely degenerate the classification result. To Harness OOD data, this paper proposes a HOOD method that can leverage the content and style from each image instance to identify benign and malign OOD data. Particularly, we design a variational inference framework to causally disentangle content and style features by constructing a structural causal model. Subsequently, we augment the content and style through an intervention process to produce malign and benign OOD data, respectively. The benign OOD data contain novel styles but hold our interested contents, and they can be leveraged to help train a style-invariant model. In contrast, the malign OOD data inherit unknown contents but carry familiar styles, by detecting them can improve model robustness against deceiving anomalies. Thanks to the proposed novel disentanglement and data augmentation techniques, HOOD can effectively deal with OOD examples in unknown and open environments, whose effectiveness is empirically validated in three typical OOD applications including OOD detection, open-set semi-supervised learning, and open-set domain adaptation. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Learning systems

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QoS Improvement using Federated Learning-based Position Prediction for mobile IoT 10

QoS Improvement using Federated Learning-based Position Pred...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Zafar, Saniya Alshehri, Mohammed S. Shah, Syed Aziz Ahmad, Jawad Zafar, Adnan Khattak, Aizaz Ahmad Institute of Space Technology Department of Electrical Engineering Islamabad Pakistan Najran University College of Computer Science and Information Systems Department of Computer Science Saudi Arabia Coventry University Research Centre for Intelligent Healthcare United Kingdom Prince Mohammad Bin Fahd University Cybersecurity Center Alkhobar Saudi Arabia Edinburgh Napier University School of Computing Engineering and the Built Environment United Kingdom

ISBN: (纸本)9798350373011

The rapid advancement in mobile broadband technology has evolved wireless infrastructure influencing internet-of-things (IoTs). This evolution has made mobile IoTs (mIoTs) as centre of attention due to its potential of providing seamless connectivity to enable intelligent transportation systems with huge data demands of vehicular users. To address the ever-escalating data demands of vehicular mobile users, vehicular edge computing (VEC) and moving small cells (mSCs) play a pivotal role. mSCs together with VEC are capable to provide improved service quality for mIoTs. However, incorporating the mSCs in vehicles for mIoTs presents challenges while assigning the resources in mIoT-mSC network. This is due to the existence of time varying interference among mSCs. This work explores the distributed resource assignment mechanism for RSU assisted mIoT-mSC network formed in vehicular environment. In the proposed work, location prediction of city buses deployed with mSCs is done using federated learning (FL) which is subsequently utilized for efficient assignment of resources in mIoT-mSC network. Numerical results are provided to validate the proposed resource assignment mechanism. © 2024 IEEE.

关键词： intelligent systems

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