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10th IEEE Smart World Congress, SWC 2024

作者： Huang, Luwen Kapteyn, Michael Willcox, Karen E. University of Texas at Austin Department of Computer Science United States University of Texas at Austin Oden Institute for Computational Engineering and Sciences United States

ISBN: (纸本)9798331520861

This paper articulates the importance of graphical models and graph-based methods as fundamental enablers of Digital Twins. Graph-based representations are well known to be suited for describing complex systems where the connections between entities are as important as the entities themselves. The interconnections within and across data, models, and decisions are central to a Digital Twin's value. Not only does a graph emphasize the scalable representation of such interrelationships, it also provides a natural mathematical setting for addressing uncertainty and complexity - arguably the two biggest barriers to scalable deployment and adoption of Digital Twins. We discuss how recent advances in theory and algorithms for large-scale knowledge graphs and graphical models can be combined in a multi-layered formulation to provide a powerful foundation for achieving scalable Digital Twins for complex systems. © 2024 IEEE.

关键词： Knowledge graph

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A Detailed Structured Monitoting System for ICU Patient Mortality rate by using AI Algorithm 5

A Detailed Structured Monitoting System for ICU Patient Mort...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Joshi, Shivani Kumar, Rajiv Rai, Vipin Dwivedi, Avinash Tripathi, Vikash Chandra G L Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India G L Bajaj Institute of Technology and Management Nvidia Ai and Learning & Research Center Department of Applied Computational Science and Engineering Greater Noida India Galgotias University Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350383522

The complexity and seriousness of diseases in ICU patients, though, make it difficult for medical practitioners to identify high-risk patients who may face irreversible harm. With ANN (Artificial Neural Network), this study will provide an ICU admission prediction model to help identify high-risk patients at an earlier stage. After collecting and analyzing extensive data from various perspectives including demographics, medical history, vital signs, test results and treatment modalities, exploratory data analysis has been conducted. These are used as markers on how statistical techniques and machine learning algorithms can be used to predict mortality among critically ill patients. Consequently, the parameters are designed within the ANN model to create a predictive model that will be able to effectively classify those at higher risk. Advanced machine learning algorithms together with modern tools such as deep learning techniques will aid in the prompt analysis of complex information for accurate forecasts by this predictive model. The ANN model's training set will include a large dataset of ICU patients so that it can become reliable and valid. This makes the predictive model developed capable of accurately identifying high risk cases expeditiously © 2024 IEEE.

关键词： accuracy Artificial Neural Networks (ANN) exploratory data analysis high-risk patients ICU patient admission medical technology mortality outcomes periodic updates prediction model

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Detection analysis of malicious cyber attacks using machine learning algorithms

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Materials Today: Proceedings 2022年 68卷 26-34页

作者： Karthika, R.A. Maheswari, M. Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India Department of Computational Intelligence SRM Institute of Science and Technology KTR Chennai 603203 India

Cybersecurity is the practice of safeguarding information and the systems that store or process information. Cybersecurity violations are the foremost persecution instigated by cyber attackers through one or more systems on single or several networks or systems. This kind of cyber threats can ruthlessly trigger data loss or theft besides halting the network systems partially or fully. Among various cybersecurity attacks, the Denial of Service (DoS) attack is one of the most dominant hacking modus operandi over the internet. The hackers use the weapons at their disposal to disrupt traffic in and around the network surroundings or the target system by bombarding it with a lot of malicious requests. This paper introduced machine learning algorithms such as Gaussian Naïve Bayes and Nearest Centroid to identify the attacks via classifying the given dataset into normal and malicious traffic. Furthermore, metrics such as accuracy, f-score, FNR, precision, and prediction time etc., were calculated to identify the best performing model. © 2022

关键词： Denial-of-service attack

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Brain Tumor Detection Using ADARN Optimizer 2

Brain Tumor Detection Using ADARN Optimizer

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Kumar, Rajiv Dwivedi, Avinash Joshi, Shivani Tripathi, Vikas Chandra Nvidia Ai Learning & Research Center G L Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Greater Noida India Galgotias University Department of Computer Science & Engineering Greater Noida India G L Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350371055

The use of MRI scans to identify and categorise brain tumors is the main focus of this study. The goal is to develop a precise and reliable method for early identification and accurate categorisation of brain tumours, including pituitary neoplasms, meningiomas, and gliomas. Deep learning methods were utilized to analyze MRI image databases, resulting in a 96% identification accuracy for brain tumors and a 98% categorization accuracy for the three types. This research demonstrates the potential of deep learning techniques for accurate brain tumor identification and categorization. The early identification and precise categorization of brain tumors can assist medical professionals in making informed decisions about the best treatment options for patients, leading to better outcomes and survival rates. The article provides an extensive review of current methods for identifying and classifying brain tumors using MRI data. It emphasizes the critical importance of this field of study in improving patient outcomes and reducing unnecessary treatments. © 2024 IEEE.

关键词： Brain Tumor Classification Convolutional Neural Network identification Image Augmentation MRI Scans

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A Novel Transfer Learning Approach for Eye Tumour Detection using Pre-Trained CNN Models 3

A Novel Transfer Learning Approach for Eye Tumour Detection ...

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3rd International Conference on Smart Technologies, Communication and Robotics, STCR 2023

作者： Prabaharan, G. Rajasekar, Vani Sarveshwaran, Velliangiri B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Narasapur India Kongu Engineering College Department of Computer Science and Engineering Tamilnadu Erode India Srm Institute of Science and Technology Department of Computational Intelligence TamilNadu Kattankulathur India

ISBN: (纸本)9798350370867

According to a recent study, using retinal imaging to identify the tumour in the eye is difficult. When an image is analyzed, the affected region of the lesion impacted retinal picture is not discovered in the conventional system due to uneven illumination, low contrast, and blurring, which are symptoms of an eye disease. The proposed methodology was created to identify the eye tumour in the earlier stage with enhanced image quality and increased accuracy. The conventional system has a few drawbacks such as lower accuracy and more computational time to analyze the image. Convolutional neural networks (CNN) have recently demonstrated its result of their capacity to learn various category image denotions that aid in image classification in various sectors. These networks have a tremendous ability to extract the proper features from input images with training on millions of images, which produces accurate categorization. The deep learning-based transfer learning approach is suggested for earlier detection of eye tumours. The five pre-trained models such as AlexNet, ResNet, DensNet, VGG 19 and Inception model were used for efficient eye tumour classification. The performance analysis of the proposed approach has shown that higher accuracy of 98.63% and this model shows enhanced performance in better classification. © 2023 IEEE.

关键词： Tumors

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On the Attractive and Repulsive Forces of Generalized Stochastic Neighbor Embedding With Alpha-Divergence

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IEEE Access 2024年 12卷 90380-90394页

作者： Lin, Hsin-Yi Tseng, Huan-Hsin Chien, Jen-Tzung Seton Hall University Department Of Mathematics And Computer Science South OrangeNJ07079 United States Brookhaven National Laboratory Computational Science Initiative UptonNY11973 United States Institute Of Electrical And Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan

Stochastic neighbor embedding (SNE) performs nonlinear transformation from high-dimensional observation space to low-dimensional latent space which preserves neighbor affinities. Data pairs in latent space tend to be crowded due to the dimensionality reduction. To mitigate the crowding problem, certain characteristics are favorable in the design of the SNE setting. This study presents a fundamental analysis of SNE that not only generalizes the previous SNEs but also provides a systematic way to understand the intrinsic properties. From the perspective of theoretical connection, we are able to conceive a new generalized SNE (g-SNE) by introducing a regularized power-law distribution with the α-divergence for manifold learning. The proposed method generalizes and incorporates various favorable features for the clustering process. In addition, the proposed method provides high flexibility, admitting tailored realizations to properly reflect the similarity between original and dimension-reduced samples. Experiments are performed to analyze the proposed method, and its effectiveness is demonstrated with several learning tasks. © 2024 The Authors.

关键词： Data visualization

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Multiobjective adaptive car-following control of an intelligent vehicle based on receding horizon optimization

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science China(Information sciences) 2023年第4期66卷 301-302页

作者： Hongbo GAO Juping ZHU Fei ZHANG Ruidong YAN Junjie ZHOU Lei JIANG Jianqiang WANG Keqiang LI Department of Automation University of Science and Technology of China Institute of Advanced Technology University of Science and Technology of China Department of Computational Mathematics School of Mathematical Sciences Anhui University School of Traffic and Transportation Beijing Jiaotong University Autonomous Driving Business Unit Chery Automobile School of College of Computer Science and Technology Zhejiang University China North Vehicle Research Institute School of Automotive Engineering Tsinghua University

Dear editor, Vehicle control is one of the key steps of intelligent driving [1]. Algorithms based on receding horizon optimization(RHO) can predict future trajectories and handle multiobjective constraint conditions; therefore, RHO-based methods have attracted considerable attention in the field of vehicle control. Although existing methods use RHO to design controllers, they do not simultaneously meet the multiobjective optimal control performance requirements on tracking, fuel economy, and ride comfort.

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NTFS: Introduction and Analysis from Forensics Point of View 2

NTFS: Introduction and Analysis from Forensics Point of View

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2nd International Conference for Advancement in Technology, ICONAT 2023

作者： Hermon, Rahul Singh, Upasna Singh, Bhupendra Defense Institute of Advanced Technology School of Computer Engineering and Mathematical Sciences Pune India Indian Institute of Information Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9781665475174

New Technology File System (NTFS) is a file system being used by Windows Operating Systems since 1993. NTFS is a file system used by the Windows Operating Systems for storing, cataloguing and discovering/finding files. In this work we will learn about the basics of NTFS to include its file structure and other important attributes. Then carry out analysis of NTFS file system using WinHex and Active@ Disk Editor tools from a forensics point of view, compare the results of both tools. Lastly, we carried out comparison of NTFS in Windows 10 and 11 Operating System to include forensics analysis using WinHex and Active@ Disk Editor tools. © 2023 IEEE.

关键词： Windows operating system

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Cloud Based Smart Medical Recommendation System Using Pattern Recognition 1

Cloud Based Smart Medical Recommendation System Using Patter...

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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Sujatha, E. Shanthasheela, S. Gopirajan, P.V. Department of Computer Science and Engineering Thandalam Chennai602105 India Velammal Institute of Technology Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai603203 India

ISBN: (纸本)9798350336009

To build smart medical recommendation system the data needs to be collected from laboratories. The system is intended to provide services as control and manage data samples, industry standards, real test results, reports, human resources in the laboratories, machines, tiny instruments, and automation of the workflow. Combining the data from the laboratories and medical institutions enable ease of communication without time delay. The challenging task is that identifying appropriate hardware and software components which provides compatibility in collecting data and their processing steps, repository storage, secured distribution, and the policies used for information representation in the results. In order to automate the entire process of medical recommendation system the metric parameters are used for determining the efficiency and productivity of the lab to enhance the quality of control systems. The vulnerability lies in maintaining traditional way of records in paper and error in the data. The purpose of this project is to develop the process of daily tasks will be computed through the database using pattern recognition techniques. © 2023 IEEE.

关键词： Recommender systems

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Parameterized Complexity of Generalizations of Edge Dominating Set 50th

Parameterized Complexity of Generalizations of Edge Domina...

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50th International Conference on Current Trends in Theory and Practice of computer science, SOFSEM 2025

作者： Aute, Shubhada Panolan, Fahad Saha, Souvik Saurabh, Saket Upasana, Anannya Department of Computer Science and Engineering IIT Hyderabad Kandi India School of Computer Science University of Leeds Leeds United Kingdom The Institute of Mathematical Sciences HBNI Chennai India University of Bergen Bergen Norway

ISBN: (纸本)9783031826696

The objective of this article is to propose two natural generalizations of covering edges by edges (Edge Dominating Set) and study these problems from the multivariate lens. The first is simply considering Edge Dominating Set on hypergraphs, called Hyperedge Dominating Set. Given a hypergraph H=(U,F), a set F⊆F is called a hyperedge dominating set if all hyperedges intersect with at least one hyperedge e∈F. The objective of the Hyperedge Dominating Set problem is to determine whether a hyperedge dominating set of size at most k exists. We find it quite surprising that such generalization is missing from the literature. The second extension we consider is the t-Path Edge Dominating Set problem. In this problem, the input consists of a graph G and an integer k, and the goal is to find a set P of at most k paths, each of length at most t, such that for every edge in G, at least one of its endpoints belongs to the vertex set V(P) for some P∈P. We show the following results and add to the literature on Edge Dominating Set. Hyperedge Dominating Set is FPT parameterized by k+d, where d is the maximum size of a hyperedge in the input hypergraph.A kernel of size O(kd) can be obtained for the Hyperedge Dominating Set problem, where d is the maximum size of a hyperedge in the input *** problem of finding a Hyperedge Dominating Set is computationally difficult;specifically it is W[2]-hard when parameterized by k. This hardness result holds even when each vertex is contained in at most 2 hyperedges and the intersection between any two hyperedges is at most 1.t-Path Edge Dominating Set is FPT when parameterized by k+t. Additionally, it has a kernel of size O(k3t3). Hyperedge Dominating Set is FPT parameterized by k+d, where d is the maximum size of a hyperedge in the input hypergraph. A kernel of size O(kd) can be obtained for the Hyperedge Dominating Set problem, where d is the maximum size of a hyperedge in the input hypergraph. The problem of finding a Hyperedge Dom

关键词： Parameterization

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