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Medical Technologies National Conference (TIPTEKNO)

作者： Mohamad Humam Alzaeim Kevser Banu Köse Şenol Pişkin Wael Shamsi Basha Fatma Zeynep Gungoren Ömer Faruk Şahin Js Femilda Josephin Master’s Student in Biomedical Engineering & Bioinformatics Istanbul Medipol University Istanbul Turkey Assistant Professor in Biomedical Engineering department Istanbul Medipol University Istanbul Turkey Assistant Professor in Mechanical Engineering department İstinye University Istanbul Turkey Department of Radiology The Anadolu Medical Center Istanbul Turkey Bachelor of Science in Molecula Biotechnology İstinye University Istanbul Turkey Assistant Professor in Computer Engineering Department İstinye University Istanbul Turkey

ISBN: (数字)9798331529819

ISBN: (纸本)9798331529826

In the realm of congenital cardiovascular diseases, the understanding, modeling, and visualization of such diseases as they develop during the neonatal growth period continues to be a challenge despite the current technological advancements. Moreover, such diseases as vascular abnormalities pose further risks due to their implications for a proper standardized method for three-dimensional visualization for planning and growth monitoring. This study uses imaging and computational techniques to create multiple models that relate the diseases as they progress from the initial phase toward the late growth stage for newborns’ cardiovascular systems. Three-dimensional modeling of patients would be created from patient-specific scans coupled with image processing algorithms. This study also aims to use the working principles of already existing tools for diagnosing aortic & brain aneurysms and pulmonary hypertension for cardiovascular development. To comprehensively understand such diseases and how their physical morphology may be standardized.

关键词： Visualization Solid modeling Pediatrics Computational modeling Morphology Standardization Brain modeling Planning Monitoring Medical diagnostic imaging

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Green innovation ecosystem evolution: Diffusion of positive green innovation game strategies on complex networks

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Journal of Innovation and Knowledge 2024年第3期9卷

作者： Zhang, Ren-Jie Tai, Hsing-Wei Cao, Zheng-Xu Wei, Chia-Chen Cheng, Kuo-Tai Weifang University of Science and Technology Weifang 262700 China Professor School of Civil and Architectural Engineering Weifang University of Science and Technology Weifang 262700 China Professor Department of Engineering and Management International College Krirk University Bangkok 10220 Thailand Management college Ocean University of China Qingdao 266100 China Associate Professor Department of Civil Engineering Pingtung University of Science and Technology Pingtung 912301 Taiwan Professor Department of Environmental and Cultural Resources National Tsing Hua University Hsinchu 300044 Taiwan

The essence of the evolution of the green innovation ecosystem is the process by which the positive strategy of green innovation spreads in a complex network. In this study, the "Object-Technology-Environment-Management-Culture (OETMC)" structure paradigm of innovation ecosystems is presented, and a causal analysis framework of green innovation ecosystem evolution based on green innovation behavior strategy, supported by green innovation efficiency and focused on green innovation ecosystems is developed. Based on this framework, the structure of the green innovation ecosystem and the diffusion process of positive green innovation in the network are discussed. The results reveal the following. (1) The complexity of the green innovation ecological network has visibly improved, but numerous blank connections remain. These are the key constraints that hinder structural evolution. (2) The appearance of a large number of long-range connections weakens the constraint of geographical distance but also hinders edge nodes from joining the network. (3) The existence of preference attachment and power law distribution of the green innovation ecological network leads to the incomplete convergence of positive green innovation strategy at any initial strategy ratio. (4) Default cost and pollution tax rate ensure the enthusiasm of green innovation actors to seek innovation cooperation by increasing opportunity costs, thus accelerating the formation and reconstruction of the green innovation ecological network. These conclusions provide a decision-making reference for governments to encourage green innovation subjects to formulate targeted policies. © 2024 The Author(s)

关键词： Complex network Evolutionary game Green innovation Innovative ecosystem Temporal and spatial distribution pattern

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AI-Based Cybersecurity Frameworks for 7G-Enabled Virtual Therapy Platforms

Cyber Security and Applications

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Cyber Security and Applications 2025年

作者： A. Jayanthiladevi Jayapandian Natarajan KP Arjun L. Godlin Atlas M. Arvindhan Daniel Arockiam Professor Department of Computer Engineering Marwadi University Rajkot Gujarat India Associate Professor Department of Computer Science and Engineering School of Engineering and Technology Christ University Kengeri Campus Bangalore Karnataka India Department of Computer Science and Engineering GITAM University Bengaluru Campus Karnataka India Associate Professor Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai Tamil Nadu Assistant Professor Amity School of Engineering & Technology Computer Science and Engineering Gwalior Madhya Pradesh India Associate Professor CSE Amity University Madhya Pradesh Gwalior 474005 India

The modern technology network plays an important role. Artificial intelligence to provide a higher solution for network security. As 7G networks emerge, virtual therapy platforms have begun to take center stage, offering fast, real-time interaction and high-definition immersive experiences. While the convergence of security in health care and mobility is in many ways even more integrated into our lives, it is fraught with cybersecurity risks. This article proposes an AI-based cybersecurity framework for a 7 G-based virtual therapy platform. The framework employs advanced machine learning (ML) algorithms, predictive analytics, and adaptive threat intelligence to protect the confidentiality, integrity, and availability of data. The article covers the core building blocks like anomaly detection, secure 7 G-based virtual therapy platform authentication protocols, and privacy-preserving techniques. AI-GN gives an overview of attack mitigation using AI with a comparative analysis of existing 5G/6G frameworks and solutions and their unique aspects needed for 7G environments. This article mainly focuses on threat detection using a deep learning method, also it aims to provide a continuous authentication process. The proposed model response time is decreased by nearly 30 percent, at the same time detection rate is increased to 98.5 percent in a 7G network.

关键词： AI-based cyber security framework Adaptive threat intelligence Anomaly detection Predictive analytics Threat detection

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SparseGTN: Human Trajectory Forecasting with Sparsely Represented Scene and Incomplete Trajectories

SparseGTN: Human Trajectory Forecasting with Sparsely Repres...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Jianbang Liu Guangyang Li Xinyu Mao Fei Meng Jie Mei Max Q.-H. Meng Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Department of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen Key Laboratory of Robotics Perception and Intelligence and the Department of Electronic and Electrical Engineering Southern University of Science and Technology in Shenzhen China Professor Emeritus in the Department of Electronic Engineering The Chinese University of Hong Kong in Hong Kong Professor in the Department of Electrical and Computer Engineering University of Alberta in Canada

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In recent years, great progress has been made in forecasting human motion in crowded scenes. However, current methods are far from practical applications due to the unbearable high computation costs, especially for encoding scene context. In addition, neglecting the partially detected trajectories makes the predicted outcome deviate from the real trajectory distribution. To handle the aforementioned concerns, we propose to represent the scene context and partially observed trajectories with sparse graphs. Customized for this special data structure, we design a hierarchical Graph Transformer Network model SparseGTN to predict multiple possible future trajectories of the target pedestrian by digesting the sparsely represented inputs. Our approach exhibits superiority over the state-of-the-art (SOTA) methods, utilizing a mere 3.42% of the number of floating point operations (FLOPs) and 0.53% of the number of model parameters. The code will be available online ⋆ .

关键词： Pedestrians Switches Predictive models Transformers Encoding Trajectory Mobile robots Forecasting Collision avoidance Intelligent robots

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An improved SSH optimization model for wireless security in Complex networks

An improved SSH optimization model for wireless security in ...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Sheetal Temara Devesh Tiwari Vivek Kumar. M Aditya Verma Rishi Prakash Shukla Jeyanthi P Department of Computer & Information Sciences University of the Cumberlands Williamsburg KY Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Department of ECE Sri Eshwar College of Engineering Coimbatore India Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Professor Marketing Jaipuriya Institute of Management Jaipur India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The SSH (Secure Shell) protocol has been widely utilized for secure and remote access of devices over a network. However, as wireless networks become more complicated, there is a requirement for an enhanced optimization model that can take care of the security challenges that these networks impose. This paper extends the SSH optimization to accommodate characteristics of complex wireless networks and puts forward a novel framework for its application. Second, our model is based on SSH communication with complex algorithms for key exchange and authentication, making your data more secure. The model also works with the present security mechanisms on wireless networks, like encryption methods and intrusion detection systems, to offer an allinclusive solution for enhanced security. Our model is also designed to be robust and resourceful against adversary behavior by utilizing learned dynamic parameters from wireless network environments. We evaluate the performance of our model through experiments and simulations in different complex wireless networks. Our optimization model can be optimal in terms of security, efficiency, and adaptability by comparing it with existing models. The model can be deployed to different Wireless Networks and offers a reliable solution for securing remote access & management.

关键词： Adaptation models Protocols Wireless networks Optimization models Computational modeling Scalability Complex networks Data models Security Communication system security

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Secure Localization Based Authentication (SLA) Strategy for Data Integrity in WNS

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computers, Materials & Continua 2021年第6期67卷 4005-4018页

作者： V.Manikandan M.Sivaram Amin Salih Mohammed V.Porkodi K.Shankar Assistant Professor Research Research CenterLebanese French UniversityErbil44001Iraq Vice President Lebanese French UniversityErbil44001Iraq Department of Information Technology College of Engineering and Computer ScienceLebanese French UniversityErbil44001Iraq Department of Computer Applications Alagappa UniversityKaraikudi630003India

Wireless Sensor Networks(WSN)has been extensively utilized as a communication model in Internet of Things(IoT).As well,to offer service,numerous IoT based applications need effective transmission over unstable *** ensure reliability,prevailing investigations exploit multiple candidate forwarders over geographic opportunistic routing in ***,these models are affected by crucial denial of service(DoS)attacks,where huge amount of invalid data are delivered intentionally to the receivers to disturb the functionality of ***,secure localization based authentication(SLA)is presented to fight against DoS attack,and to fulfil the need of reliability and *** examining state information,SLA projects a trust model to enhance efficacy of data ***,of the prevailing opportunistic protocols,SLA guarantees data integrity by modelling a trust based authentication,providing protection against DoS attackers and diminishing computational ***,this model acts as a verification strategy to accelerate?attackers and to handle *** strategy helps SLA in eliminating duplicate transmission and by continuous verification that results from conventional opportunistic *** is performed in a MATLAB environment that offers authentic and reliable delivery by consuming approximately 50%of the cost in contrast to other *** anticipated model shows better trade off in comparison to the prevailing ones.

关键词： Wireless sensor networks opportunistic routing secure localization based authentication denial of service computational cost

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Development of Autonomous Robots for Industrial and Domestic Applications 5

Development of Autonomous Robots for Industrial and Domestic...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Ramadevi, P. Sadu, Vijaya Bhaskar Fuskele, Veeresh Bolleddu, Siva Nagaiah Anil Kumar, T.C. Kalyan Chakravarthi, M. Telangana Hyderabad500043 India Jawaharlal Nehru Technological University Department of Mechanical Engineering Andhra Pradesh Kakinada533003 India BabulalTarabai Institute of Research and Technology Department of Mechanical Engineering Madhya Pradesh Sironja470228 India Koneru Lakshmaiah Education Foundation - Deemed to Be University Associate Professor of English and Coordinator KL-CDOE Andhra Pradesh Guntur522502 India Vignan's Foundation for Science Technology and Research Department of Mechanical Engineering Andhra Pradesh Guntur522213 India School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravathi522237 India

ISBN: (纸本)9798350321425

This research study investigates the potential design of autonomous robots for developing the domestic and industrial applications. The system underwent rigorous testing in both commercial and residential settings, demonstrating exceptional performance in navigation and achieving near-perfect scores in manipulation. Autonomous robots possess the capacity to enhance productivity, reduce expenses, and enhance safety in various scenarios, as emphasized in the literature. The report also outlines potential avenues for future research, which encompass the development of enhanced sensors and perceptual systems, collaborative systems, and advanced decision-making algorithms. In general, this study constitutes a crucial contribution to the expanding corpus of information pertaining to self-governing robots and will facilitate their prospective conception, advancement, and integration. © 2023 IEEE.

关键词： Accident prevention

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The Dimensional Analysis of Data Flow Programs That Include Multidimensional and User-Defined Functions

The Dimensional Analysis of Data Flow Programs That Include ...

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IEEE International Conference on Big Data

作者： Abdulmonem I Shennat William W Wadge Alex Kuo Alumnus of the Department of Engineering and Computer Science University of Victoria Victoria Canada Emeritus Professor in the Department of Engineering and Computer Science University of Victoria Victoria Health Information Science School University of Victoria Victoria

ISBN: (纸本)9781665480468

This paper is to design Dimensional Analysis (DA) algorithms for the multidimensional Lucid, the equational data flow language, which also includes user-defined functions. The significance is that the DA is indispensable for an efficient implementation of multidimensional Lucid and should aid the implementation of other data flow systems, such as Google’s TensorFlow. Data flow is a form of computation in which components of Multidimensional Data-sets (MDDs) travel on communication lines in a network of processing stations. Each processing station incrementally transforms its input MDDs to its output, another (possibly very different) MDD. MDDs are very common in Health Information Systems and data science in general. An important concept is that of a relevant dimension. A dimension is relevant if the coordinate of that dimension is required to extract a value. It is essential that in calculating with MDDs we avoid non-relevant dimensions, otherwise, we duplicate entries (say, in a cache) and waste time and *** example, if X is the MDD of raw rain measurements, its dimensionality is {location, day, hour}, and that of Y is {location, day}. Note that the dimensionality is more than just the rank, which is simply the number of dimensions. Previously, there was extensive research on data-flow itself, which we summarize. Nevertheless, an exhaustive literature search uncovered no relevant previous DA work. Our methodology is that we proceeded incrementally, solving increasingly difficult instances of DA corresponding to increasingly sophisticated language features. However, in this paper, we solved the DA of multidimensional Data Flow (DF) programs. We also solved the difficult problem (which the GLU (Granular Lucid) team never solved) of determining the dimensionality of the DF programs that include user-defined functions, including recursively defined functions. We do this by adapting the PyLucid interpreter (to produce the DAM interpreter) to evaluate the entire p

关键词： Analytical models Upper bound Rain Dams Redundancy Transforms Big Data

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A Method for Identifying Parkinson’s Disease from Spiral Images (Graphs) Created by Hand

A Method for Identifying Parkinson’s Disease from Spiral Im...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： Amit Kumar Mishra Vivek Deshpande D. Anandhasilambarasan Neelesh Singh Vijayalakshmi G Rekha M M Computer Science and Engineering Graphic Era Hill University Dehradun Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Engineering Vishwakarma Institute of Technology Pune India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai School of Sciences Chemistry and Biochemistry JAIN (Deemed to be University) Bangalore Karnataka

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

A chronic brain ailment known as Parkinson’s disease (PD) affects millions of people around the globe. The breakdown or death of brain cells responsible for producing dopamine, a neurotransmitter that regulates movement, causes this condition. Issues with mobility, equilibrium, and posture are brought on by PD, which develops as a result of this. To lessen the disease’s course and boost patients’ quality of life, early diagnosis is essential. A cosine annealing scheduler and deep transfer learning are presented in this study as a method for handwriting-based prediction. It uses handwriting samples from people with and without Parkinson’s disease, found in the NIATS collection. With the use of spiral sketching and CNN, this research hopes to provide a PD detection method. The main concept is to determine whether a person is healthy or has Parkinson’s disease based on their spiral drawings. Spiral drawings made by individuals in good health resemble conventional spiral forms. People with Parkinson’s disease have sluggish movement and poor hand-brain coordination, therefore their spiral drawings seem deformed because they stray greatly from the correct curve. This research presents the results of using a Convolutional Neural Network to diagnose Parkinson’s disease; the classification accuracy achieved was 83.6%.

关键词： Hands Spirals Accuracy Neurotransmitters Transfer learning Information and communication technology Convolutional neural networks Diseases Overfitting Image classification

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ML-Aided Dynamic Clustering and Classification of UEs as VBs in D2D Communication Networks

ML-Aided Dynamic Clustering and Classification of UEs as VBs...

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International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)

作者： Iacovos Ioannou Prabagarane Nagaradjane Ala' Khalifeh Christophoros Christophorou Vasos Vassiliou Gundepudi V. Surya Sashank Charu Jain Andreas Pitsillides Department of Computer Science University of Cyprus CYENS - Centre of Excellence Cyprus Department of ECE Sri Sivasubramaniya Nadar College of Engineering Chennai India Department of Electrical Engineering and Information Technology German Jordanian University Department of Electrical & Electronic Engineering Science University of Johannesburg (Visiting Professor)

With the next generation of mobile devices and streaming services such as Virtual Reality, Augmented Reality, and Meta being available worldwide, the network's data rate, latency, and connectivity must be improved. Even though 5G provides the user with the required Quality of Service (QoS) in terms of data rate and reduced delays by transmitting signals in the higher frequencies (called New Radio (NR)), it faces a lot of attenuation, leading to a short communication range. However, to meet goals set by utilising 6G requirements, many technological advancements and components must be incorporated into the network. The dense disposition of small cells will help reduce the network traffic in hotspot areas and increase the coverage and spectral efficiency. Nonetheless, the current deployment of 5G Base Stations (BSs) and small cells is static and cannot move around even though they are deployed in hot spot areas, leading to high operational costs. Furthermore, more than these static deployments of base stations can be required in an unpredictable scenario of extreme crowd movement. To overcome these issues, Device to Device (D2D) Communication with the dynamic deployment of Virtual Base stations (VBSs) can be called upon, which can be achieved by using User Equipment (UE) such as phones or laptops to mimic the functions of a Base Station (BS). Therefore, in this paper, a User Equipment based Virtual Base Station (UE-VBS) is studied, which will act as a secondary base station and, in turn, help alleviate the traffic load in the network. Specifically, as one UE cannot relieve the entire network traffic load, the network area is split into different clusters by using an unsupervised Machine Learning (ML) clustering technique(i.e., K-Means with Mean Shift Clustering), and a single UE is selected to act as a VBS for that cluster with the utilisation of supervised ML classification techniques (i.e., Decision Trees, Logistic Regression, Linear Discriminant Analysis And Quadrat

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