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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Pachar, Sunita Reddy, Satti Sudha Mohan Kompally, Gouthami Srinivasagopalan, Lakshmi Narasimhan Sangore, Rohidas Balu Katta, Sivakoteswararao GLA University IBM Department India SRKR Engineering College Department of ECE India Department of CSE-IOT India Software Development Enterprise Architecture PlanoTX United States Computer Science and Engineering MIT Art Design Technology University India KIETW Department of Mechanical Kakinada India

ISBN: (纸本)9798331530013

The evolution of wireless communication is poised for a major shift with the transition from 5G to 6G, a leap characterized by breakthroughs in speed, ultra-reliable low-latency communication, and AI-driven network optimization. This paper examines these advancements, focusing on sub-millisecond latency, terahertz spectrum utilization, and enhanced network capabilities tailored to industries such as smart infrastructure, healthcare, and autonomous vehicles. While 6G promises revolutionary improvements, it also presents new challenges, including increased computational complexity and energy demands, data privacy concerns, and efficient spectrum allocation. The proposed model aims to address these issues by integrating AI and machine learning for optimized resource management, resulting in a high-performance, cost-effective solution for real-time, data-intensive applications. To assess the model's effectiveness, comprehensive simulations and real-world trials will be conducted, measuring critical metrics such as network throughput, latency, reliability, and energy efficiency. This study offers an all-encompassing guide to understanding and harnessing the transformative potential of 6G technology, laying the groundwork for future wireless communication systems. © 2024 IEEE.

关键词： Terahertz spectroscopy

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Co-design of virtual reality exergames for balance rehabilitation

Co-design of virtual reality exergames for balance rehabilit...

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2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025

作者： Henriksen, Signe Toftgaard Adjorlu, Ali Serafin, Stefania Onofrei, Marius George Fontana, Federico Søndergaard, Kasper Aalborg University Department of Architecture Design and Media Technology Copenhagen Denmark University of Udine Department of Mathematics Computer Science and Physics Udine Italy University Hospital Herlev and Gentofte Department of Occupational and Physical Therapy Hellerup Denmark

ISBN: (纸本)9798331514846

Balance disorders, particularly in the elderly population, pose a significant public health challenge due to the associated risk of falls. Exercise-based therapies are effective for reducing this risk but face limitations like cost, accessibility, and sustaining patient engagement. By enabling personalized rehabilitation through engaging exergames, Virtual Reality (VR) offers a promising, cost-effective solution. This paper presents the collaborative design and preliminary evaluation of two VR-based games targeting specific balance skills. Developed in collaboration with a physiotherapist with extensive experience with patients with balance disorders, the games align with a rehabilitation protocol used by the therapist that categorizes training based on individual balance skills. Inspired by traditional physiotherapy exercises, the gameplay aims to train the same skills. Six health professionals agreed on the games' therapeutic potential, while recommending improvements in usability, difficulty adjustment and monitoring of patient progress. © 2025 IEEE.

关键词： Patient monitoring

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OntoBlock: a novel ontological-based and blockchain enabled spectrum sensing framework for detection of malicious users in cognitive radio internet of things (CR-IoT) networks

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International Journal of Information technology (Singapore) 2024年第6期16卷 3913-3921页

作者： Marriwala, Nikhil Kumar Shukla, Vinod Kumar Raju, Ayalapogu Ratna Panda, Sunita S, Suma Purad, Hemavati C. Electronics and Communication Engineering Department University Institute of Engineering and Technology Kurukshetra University Haryana Kurukshetra India Information Technology Head of Academics–School of Engineering Architecture Interior Design Amity University Dubai Dubai International Academic City Dubai United Arab Emirates Department of Computer Science and Engineering Mahatma Gandhi Institute of Technology Hyderabad India Department of Electrical Electronics and Communication Engineering GITAM School of Technology Bengaluru Campus Karnataka Bengaluru India CMR Technical Campus Hyderabad India Department of AIML BITM Karnataka Ballari India

CR-IoT networks can benefit from the idea of blockchain technology to achieve accurate decisions in order to provide very high-accuracy spectrum sensing in the network. It allows for multi-user cooperation as the sensing readings are being exchanged in the spectrum. It leads to security issues in CR-IoT networks. Thus, in order to increase the quality of decision-making in spectrum sensing, the given paper introduces an ontological framework with a blockchain-enabled system to detect malicious users in networks. The proposed framework first captures network packets from available Wi-Fi, followed by the creation of blocks with private, public, and hash keys. The keys are used to encode information about the nodes, such as confidential information and their location, data relative to users, their position where packets are delivered, etc. This is subsequently followed by the generation of network ontology, which includes parsing, computation of similarity, and reduction in clustered packets. Finally, the performance of the suggested mechanism is validated using a set of evaluation measures such as authentication rate, intrusion rate, average network throughput time consumption, and energy efficiency. As seen from the findings, it is evident that the proposed framework performs better than existing recent studies, thereby improving the performance of CR-IoT networks. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Blockchain Cognitive radio Internet of things (IoT) Ontology Spectrum sensing

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Deep Learning based vehicle image detection using Yolo V5 with Region-Based Convolutional Neural Network 3

Deep Learning based vehicle image detection using Yolo V5 wi...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Rani, S. Swapna Mudigonda, Aditya Hemanth, S.V. Sundararajan Reddy, G. Vinoda Amirthayogam, G. Engineering College Department of Electronics and Communication Engineering Hyderabad India JNIAS School of Planning and Architecture Telangana Hyderabad500034 India Hyderabad Institute of Technology and Management Department of Computer Science and Engineering Telangana Hyderabad501401 India PSNA College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Dindigul624622 India Telangana Hyderabad501401 India Hindustan Institute of Technology and Science Department of Information Technology Tamilnadu Chennai603103 India

ISBN: (纸本)9798350372120

Detecting and classifying vehicles is a modern technology with numerous uses. Administration and regulation of traffic is one of the primary uses. Projects utilizing image processing to prevent traffic accidents heavily rely on vehicle monitoring and detection. Monitoring and recording human movement in surveillance situations require the ability to follow moving objects. Considering its significance, it provides a valuable image processing-based vehicle detection technique - a vehicle tracking and detection system based on images. The recently released high-resolution road vehicle dataset supports deep learning-based vehicle detection and monitoring on the Python platform. It includes over 100 well-defined pictures taken from movies from various locations. This data is produced using a range of image processing methods. The most recent iteration of the YOLO model, the v5 model, was employed for detection. R-CNN is additionally utilized for model detection. The four stages in the Region-Based Convolutional Neural Network (RCNN) process are preprocessing, segmentation, feature selection, and classification. The first step is identifying moving vehicles accurately. Preprocessing the original image is necessary. This enhances the image's quality, valuable information extracted, or unwanted areas cropped out. Segmentation is the second step. The region's borders are established, and the entire image is divided into numerous smaller areas using a threshold. The vehicle image's most pertinent aspects or characteristics (features) that aid in precise detection are found in the third stage, feature selection. A region-based convolutional neural network (RCNN) is employed to determine the categorization. The Yolov5 method can be used to identify and categorize items. The Region-based Convolutional Neural Network (RCNN) approach enhances accuracy and temporal complexity over current segmentation algorithms. © 2024 IEEE.

关键词： Vehicles

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Zero-day Android botnet detection using neural networks

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Neural Computing and Applications 2024年 1-11页

作者： Seraj, Saeed Pimenidis, Elias Trovati, Marcello Polatidis, Nikolaos Department of Computer Science Royal Holloway University EghamTW20 0EX United Kingdom School of Computing and Creative Technologies University of the West of England BristolBS16 1QY United Kingdom Department of Computer Science Edge Hill University OrmskirkL39 4QP United Kingdom School of Architecture Technology and Engineering University of Brighton BrightonBN2 4GJ United Kingdom

Android devices have evolved to offer a diverse array of services, spanning applications related to banking, business, health, and entertainment. The widespread adoption of Android devices, coupled with the open-source architecture of the Android operating system, has rendered them a prime target for malicious actors. Among the most perilous threats are Android botnets, which enable malicious actors, often referred to as botmasters, to exert remote control for the execution of destructive attacks. Android botnets have huge potential to be an emerging threat to mobile device security. In this paper, we focus on detecting evolving Android botnets and introduce a new dataset of 3458 apps, represented by 455 permission-based features. We propose an improved multilayer perceptron neural network for zero-day botnet detection. Our methodology, in this way, achieves an accuracy of 98.5%, thus outperforming traditional classifiers. It has a lot of functionality and is based on the neural network approach, making it able to identify slight botnet behaviours in order to improve Android security. © The Author(s) 2024.

关键词： Bot (Internet)

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An enhanced scheme for mutual authentication for healthcare services

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Digital Communications and Networks 2022年第2期8卷 150-161页

作者： Salman Shamshad Muhammad Faizan Ayub Khalid Mahmood Saru Kumari Shehzad Ashraf Chaudhry Chien-Ming Chen Department of Computer Science COMSATS University IslamabadSahiwal Campus57000Pakistan Department of Mathematics Ch.Charan Singh UniversityMeerutUttar Pradesh250004India Department of Computer Engineering Faculty of Engineering and ArchitectureIstanbul Gelisim UniversityIstanbulTurkey College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Riphah School of Computing&Innovation(RSCI) Riphah International UniversityLahore CampusLahore 55150Pakistan Department of Mathematics University of Padua35131 PaduaItaly

With the advent of state-of-art technologies,the Telecare Medicine Information System(TMIS)now offers fast and convenient healthcare services to patients at their ***,this architecture engenders new risks and challenges to patients'and the server's confidentiality,integrity and *** order to avoid any resource abuse and malicious attack,employing an authentication scheme is widely considered as the most effective approach for the TMIS to verify the legitimacy of patients and the ***,several authentication protocols have been proposed to this *** recently,Chaudhry et *** that there are vulnerabilities of impersonation attacks in Islam et al.'s ***,they introduced an improved protocol to mitigate those security ***,Qiu et *** that these schemes are vulnerable to the man-in-the-middle,impersonation and offline password guessing ***,they introduced an improved scheme based on the fuzzy verifier techniques,which overcome all the security flaws of Chaudhry et al.'s ***,there are still some security flaws in Qiu et al.'s *** this article,we prove that Qiu et al.'s protocol has an incorrect notion of perfect user anonymity and is vulnerable to user impersonation ***,we introduce an improved protocol for authentication,which reduces all the security flaws of Qiu et al.'s *** also make a comparison of our protocol with related protocols,which shows that our introduced protocol is more secure and efficient than previous protocols.

关键词： Authentication protocol Security protocol Anonymous protocol Impersonation attack TMIS

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Safety Analysis of Riding at Intersection Entrance Using Video Recognition technology

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computers, Materials & Continua 2022年第9期72卷 5135-5148页

作者： Xingjian Xue Linjuan Ge Longxin Zeng Weiran Li Rui Song Neal N.Xiong College of Landscape Architecture Central South University of Forestry and TechnologyChangshaHunan410004China College of Logistics and Transportation Central South University of Forestry and TechnologyChangshaHunan410004China Department of Mathematics and Computer Science Northeastern State UniversityTahlequahOK74464United States

To study riding safety at intersection entrance,video recognition technology is used to build vehicle-bicycle conflict models based on the Bayesian *** is analyzed the relationship among the width of nonmotorized lanes at the entrance lane of the intersection,the vehicle-bicycle soft isolation form of the entrance lane of intersection,the traffic volume of right-turning motor vehicles and straight-going non-motor vehicles,the speed of right-turning motor vehicles,and straight-going non-motor vehicles,and the conflict between right-turning motor vehicles and straight-going nonmotor *** to the traditional statistical methods,to overcome the discreteness of vehicle-bicycle conflict data and the differences of influencing factors,the Bayesian random effect Poisson-log-normal model and random effect negative binomial regression model are *** results show that the random effect Poisson-log-normal model is better than the negative binomial distribution of random effects;The width of non-motorized lanes,the form of vehicle-bicycle soft isolation,the traffic volume of right-turning motor vehicles,and the coefficients of straight traffic volume obey a normal *** them,the type of vehicle-bicycle soft isolation facilities and the vehicle-bicycle traffic volumes are significantly positively correlated with the number of vehicle-bicycle *** width of non-motorized lanes is significantly negatively correlated with the number of vehicle-bicycle *** periods and flat periods,the average speed of right-turning motor vehicles,and the average speed of straight-going non-motor vehicles have no significant influence on the number of vehicle-bicycle conflicts.

关键词： Video recognition technology vehicle-bicycle conflict intersection entrance random effect poisson-log-normal model random effect negative binomial regression model

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A novel Cross-Layer Protocols for Resource Allocation in Mobile Networks 15

A novel Cross-Layer Protocols for Resource Allocation in Mob...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Giri, Rakesh Kumar Vittapu, Mani Sarma Dev, Anoop Shewale, Chaitali R. Majid, Mohd Aliff Abdul Kulkarni, Nandini Sunrise University Alwar Department of Computer Science and Engineering India Mallareddy Engineering College and Management Sciences Department of Computer Science and Engineering Kistapur Village Medchal Telangana Hyderabad India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura India Vishwakarma Institute of Technology Department of Information Technology Pune India University Teknology MARA Faculty of Hotel and Tourism Management Department of Foodservice Management Malaysia Symbiosis International University Department of Symbiosis School of Planning Architecture and Design Nagpur Nagpur India

ISBN: (纸本)9798350370249

Cross-layer Protocols for proper resource Allocation in Mobile Networks are a singular technique to enhance the efficiency of wireless networks. The method utilizes multiple layers of the radio protocol stack better to coordinate cell node scheduling and resource allocation. Using combining communication, routing, and scheduling functionalities in an unmarried protocol, pass-Layer Protocols for proper resource Allocation can offer a sizable will increase in the number of facts that may be transferred and the overall latency and throughput of the community. This technique can also enhance the radio interface's operation by lowering interference and encouraging collaborative communications. Eventually, by using facts and managing records from the communique and routing layers, go-Layer Protocols for proper resource Allocation can reduce the complexity of the community, even offering greater scalability. This paper affords unique cross-Layer Protocols for resource Allocation in Cellular Networks. The proposed protocol permits integrated resource allocation in the cell network environment, considering communication and computation layers. © 2024 IEEE.

关键词： Network layers

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Mapping Quantum Circuits to 2-Dimensional Quantum architectures

Mapping Quantum Circuits to 2-Dimensional Quantum Architectu...

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2022 Informatik in den Naturwissenschaften, INFORMATIK 2022 - 2022 computer Science in the Natural Sciences, INFORMATIK 2022

作者： Datta, Kamalika Kole, Abhoy Sengupta, Indranil Drechsler, Rolf Cyber Physical System Bremen Germany JIS University Department of Computer Science and Engineering Kolkata India Indian Institute of Technology Kharagpur Department of Computer Science and Engineering India University of Bremen Group of Computer Architecture Bremen Germany

ISBN: (纸本)9783885797203

We have been witnessing a rapid growth in quantum computing research over the years, with the emergence of demonstrable quantum computers of moderate size. The major issues that are faced to run a quantum algorithm reliably on these systems are: (i) lower qubit coherence period, (ii) noisy primitive gate operations, (iii) limited number of available physical qubits, and (iv) support of restricted set of 2-qubit operations. Overcoming these issues mandates physical resources that exceeds the capabilities of these noisy intermediate scale quantum (NISQ) systems. On the other hand, computation using bare qubits get further disturbed due to the inclusion of additional gates to mitigate the nearest neighbor constraints. In the present work, the 2-dimensional square, heavy-hex and fully hexagonal qubit coupling lattices are considered for mapping quantum circuits. The benefits are assessed in terms of minimal additional gates needed to satisfy the nearest neighbor (NN) constraint and the compilation complexity of mapping circuits on these architectures. From the experiments by mapping benchmark circuits on 16-qubit square and 65-qubit heavy-hex architectures from IBM as well as on a 64-qubit fully hexagonal architecture, it is observed that none of the square or heavy-hex lattice architecture provides uniform compilation advantage compared to the fully hexagonal architecture. It is expected that beyond the NISQ era, strongly connected lattices like hexagonal will become practically feasible. © 2022 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Qubits

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Deep Learning-Based Cellular Nuclei Segmentation Using Transformer Model 5th

Deep Learning-Based Cellular Nuclei Segmentation Using Trans...

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5th Workshop on Intelligent Data - From Data to Knowledge, DOING 2024, 3rd Workshop on Knowledge Graphs Analysis on a Large Scale, K-GALS 2024, 6th Workshop on Modern Approaches in Data Engineering and Information System Design, MADEISD 2024 and 3rd Workshop on Personalization and Recommender Systems, PERS 2024 held in conjunction with 28th European Conference on Advances in Databases and Information Systems, ADBIS 2024

作者： Erezman, Mateusz Dziubich, Tomasz Computer Vision and Artificial Intelligence Laboratory Department of Computer Architecture Faculty of Electronics Telecommunications and Informatics Gdańsk University of Technology Gdańsk Poland

ISBN: (纸本)9783031704208

Accurate segmentation of cellular nuclei is imperative for various biological and medical applications, such as cancer diagnosis and drug discovery. Histopathology, a discipline employing microscopic examination of bodily tissues, serves as a cornerstone for cancer diagnosis. Nonetheless, the conventional histopathological diagnosis process is frequently marred by time constraints and potential inaccuracies. Consequently, there arises a pressing need for automated image analysis tools to augment medical practitioners’ efforts. In this paper, we present a novel approach utilising Transformer model, originally designed for natural language processing tasks, for automated cellular nuclei segmentation in whole-slide microscopic images. Specifically targeting cell nuclei, this methodology holds significance as the initial phase in diagnosing various illnesses, streamlining the analysis and quantification process. The study introduces a novel model that combines a U-Net architecture with a Transformer-based network functioning as a parallel encoder. This model was compared against three other popular architectures in the literature: U-Net, ResU-Net, and LinkNet-34. The impact of augmentation and colour normalisation techniques was investigated. The average Dice similarity coefficient for the considered images was found to be 0.8041. The obtained results seem to be clinically relevant. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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