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Predictive modeling of Lung Cancer using Support Vector Machine

Predictive Modeling of Lung Cancer using Support Vector Mach...

引用

international conference on Inventive Computation Technologies (ICICT)

作者： R. Arunkumar K. Gowtham R.G. Pavin E. Kamalesh Department of Information Technology K S Rangasamy College of Technology Tiruchengode Tamilnadu

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

The segmentation of lung cancer regions in medical images is a crucial part of computer-aided diagnosis systems. Describe a technique in this paper that achieves this goal by employing the Support Vector Machine (SVM) algorithm. Training an SVM on a split dataset involves using feature vectors generated from the retrieved features. The testing set's regions are subsequently classified using the trained SVM, and the segmentation outcomes are then improved by using post-processing techniques. The performance of the method is evaluated using metrics including dice coefficient, precision, recall, and F1 score. The proposed SVM-based algorithm serves as a fundamental tool for lung cancer segmentation, contributing to the ongoing advancements in computer-aided diagnosis and medical image analysis.

关键词： Support vector machines measurement Image segmentation Computational modeling Lung cancer Predictive models Prediction algorithms

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Network Intrusion Detection with Feature Elimination and Selection Using Deep Learning

Network Intrusion Detection with Feature Elimination and Sel...

引用

Emerging Technologies in computer Science for Interdisciplinary Applications (ICETCS), international conference on

作者： Aryam Mann M Vamsi Kiran B. Nithya Rishav Das Department of Computer Science and Engineering National Institute of Technology Trichy Tiruchirappalli Tamil Nadu India

ISBN: (数字)9798350372502

ISBN: (纸本)9798350372519

In the contemporary digital landscape, marked by an ever-increasing number of individuals seeking to exploit vulnerabilities in systems and cause immense damage to users, it becomes imperative for users to safeguard their systems, detect threats correctly, and carry out an appropriate response. Implementing Intrusion Detection systems (IDS) is vital for monitoring network traffic, spotting threats, and swiftly alerting users to suspicious activities. It is categorized into two parts: Network-based (NIDS) and Host-based (HIDS). NIDS is strategically placed and monitors data flow across devices. A major NIDS challenge is the prevalence of false positives and, more critically, false negatives, potentially allowing undetected threats to cause significant harm. To improve intrusion detection, a new approach using Feature Selection and Deep Learning framework is used for Network Intrusion Detection System. The study analyzes metrics like accuracy, precision, F1 score, and confusion matrix.

关键词： Deep learning Training measurement Computational modeling Telecommunication traffic Predictive models Network security

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modeling and Analysis of Coupling Mechanism Loss in Wireless Power Transfer systems Based on Partitioned Computation

Modeling and Analysis of Coupling Mechanism Loss in Wireless...

引用

Data Science and computer Application (ICDSCA), IEEE international conference on

作者： Long Ling Zhanli Wu Tao Shen Renlong Luo Guanlin Chen Jinxi Dong Liuzhou Power Supply Bureau of Guangxi Power Grid Co. Ltd Liuzhou China College of Automation Chongqing University Chongqing China

ISBN: (数字)9798350368239

ISBN: (纸本)9798350368246

In a magnetically coupled wireless power transfer (WPT) system, the coupling mechanism experiences coil losses, core losses, and aluminum plate losses due to high-frequency currents during operation. Establishing an accurate loss model can guide the design of the coupling mechanism and help reduce energy losses. This paper employs finite element simulation software and utilizes a partitioned computation method to optimize the loss model of the coupling mechanism. The optimized model offers higher precision, with simulation results more closely aligning with the actual system losses.

关键词： Couplings Wireless communication Analytical models Accuracy Computational modeling Wireless power transfer Loss measurement Data models Software Finite element analysis

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Dynamic Equivalent modeling Method for Distribution Grids Containing A High Percentage of Distributed PV

Dynamic Equivalent Modeling Method for Distribution Grids Co...

引用

Energy, Power and Electrical Technology (ICEPET), international conference on

作者： Yuetong Liu Min Xu Hao Bai Chengxi Liu Ruotian Yao Weichen Yang Leizhen He School of Electrical Engineering and Automation Wuhan University Wuhan China Electric Power Research Institute of CSG Guangzhou China

ISBN: (数字)9798350352658

ISBN: (纸本)9798350352665

The high proportion of distributed power sources connected to distribution networks exacerbates the uncertainty and complexity of these networks, making dynamic equivalency studies of the whole system significantly important. This paper aims to perform dynamic equivalent modeling on distribution grids containing a high proportion of distributed photovoltaic (PV) and proposes a methodology for equivalencing such networks. The methodology is based on ARMAX models that use only boundary variables measured at the distribution network point of common coupling. Therefore, no previous knowledge about the system is required. The effectiveness of the proposed method is evaluated through joint simulation with DIgSILENT-MATLAB. The experimental results show that the proposed method achieves dynamic equivalency for high-proportion distributed PV systems and significantly simplifies the description of dynamic processes in the simulation.

关键词： Photovoltaic systems Low voltage Uncertainty Energy measurement Distribution networks Predictive models Dynamic scheduling

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Empirical Time Complexity Analysis of Tractrix-Based Inverse Kinematics Algorithms

Empirical Time Complexity Analysis of Tractrix-Based Inverse...

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IEEE/RAS-EMBS international conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Marek Sierotowicz Aparna A. Ritwik Chattaraj Institute of Assistive Intelligent Robotics Friedrich-Alexander Universität Erlangen-Nürnberg Erlangen Germany Deparment of Mechatronics Manipal Institute of Technology Commonwealth Bank of Australia

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

In soft robotics, hyper-redundant kinematic chains can be an effective way of modeling compliant objects and structures. Unfortunately, increasing the number of joints, the computational complexity of the calculations needed to solve the inverse kinematics problem for such systems increases sharply, especially when adopting traditional algorithms for IK solving, such as the Jacobian pseudo-inverse method. A computational procedure with a potentially lower complexity as a function of joint amount is one based on the tractrix curve. More recently, this same algorithm was formalized to function in three dimensions, but its time complexity was not adequately evaluated for cases where additional constraints are added to the kinematic solver, such as the chain being bound to a mechanical ground. Here we present the results of an empirical evaluation of the time requirements for convergence.

关键词： Jacobian matrices Computational modeling Kinematics Biomedical measurement Soft robotics Real-time systems Complexity theory Trajectory Time complexity Convergence

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N Observation Photon Counting Imaging With Preprocessing

N Observation Photon Counting Imaging With Preprocessing

引用

international joint conference on computer Science and Software Engineering (JCSSE)

作者： Gilsu Yeo Hyun-Woo Kim Myungjin Cho Min-Chul Lee Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka City Fukuoka School of ICT Robotics and Mechanical Engineering IITC Hankyoung National University Anseong Kyonggi-do Republic of Korea

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

In this paper, we explain our method to enhance the conventional integral imaging method. The photon-counting imaging is used to obtain the image including objects or scene under photon-lacked environments. And the conventional $N$ observation photon counting imaging obtains three-dimensional (3D) image by repeating the estimation to obtain more accurate information of object or scene than conventional photon-counting imaging under almost photon-lacked environments. To enhance the visual quality and information of 3D objects in the conventional $\mathrm{N}$ observation photon counting imaging, the volumetric computational reconstruction (VCR) is utilized as preprocessing and combined $N$ observation photon counting integral imaging in this paper.

关键词： measurement Visualization Three-dimensional displays Accuracy Imaging Lakes Video recording

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Simulating measurement Attacks in a Scada System Testbed 15th

Simulating Measurement Attacks in a Scada System Testbed

引用

15th IFIP WG 11.10 international conference on Critical Infrastructure Protection, ICCIP 2021

作者： Reutimann, Brandt Ray, Indrakshi Computer Science Colorado State University Fort CollinsCO United States

ISBN: (纸本)9783030935108

Industrial control systems are target-rich environments for cyber criminals, terrorists and advanced persistent threats. Researchers have investigated various types of industrial control systems in smart grids, gas pipelines and manufacturing facilities to understand how they can be compromised by cyber threats. However, the manner in which industrial control systems are attacked is domain-dependent. Testbeds are a necessary tool to model specific domains and understand potential attacks. This chapter discusses the development of a virtual supervisory control and data acquisition system testbed for gas systems and how it is used to simulate the impacts of measurement attacks. The testbed provides opportunities for researchers and domain experts to model, simulate and understand the behavior of a real-world gas system and respond to cyber attacks. © 2022, IFIP international Federation for Information Processing.

关键词： SCADA systems

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Ensuring the Resilience of Cloud Platforms Based on Cyber Immunity

Ensuring the Resilience of Cloud Platforms Based on Cyber Im...

引用

international conference on Soft Computing and measurements (SCM)

作者： Artyom A. Balyabin Information Security Department Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russia

ISBN: (数字)9798350363708

ISBN: (纸本)9798350363715

This paper examines the problem of ensuring the resilience of cloud platforms under cyberattacks. It is proposed to use the principles of cyber immunity to provide information systems with the ability to detect both known and previously unknown malicious effects, counteract them and carry out self-healing by analogy with the human immune system. An indicator, metric, and measure of the resilience were defined. A model of a cloud platform with cyber immunity was developed on the example of the cloud platform of the Russian Federation “GosTech”. The experimental results showed that the model ensures the required resilience of cloud platforms under cyberattacks.

关键词： measurement Computational modeling computer crime Resilience Immune system Information systems

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Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?

Self-supervised Benchmark Lottery on ImageNet: Do Marginal I...

引用

international joint conference on Neural Networks (IJCNN)

作者： Utku Ozbulak Esla Timothy Anzaku Solha Kang Wesley De Neve Joris Vankerschaver Center for Biosystems and Biotech Data Science Ghent University Global Campus Republic of Korea Department of Electronics and Information Systems Ghent University Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Machine learning (ML) research strongly relies on benchmarks in order to determine the relative effectiveness of newly proposed models. Recently, a number of prominent research effort argued that a number of models that improve the state-of-the-art by a small margin tend to do so by winning what they call a "benchmark lottery". An important benchmark in the field of machine learning and computer vision is the ImageNet where newly proposed models are often showcased based on their performance on this dataset. Given the large number of self-supervised learning (SSL) frameworks that has been proposed in the past couple of years each coming with marginal improvements on the ImageNet dataset, in this work, we evaluate whether those marginal improvements on ImageNet translate to improvements on similar datasets or not. To do so, we investigate twelve popular SSL frameworks on five ImageNet variants and discover that models that seem to perform well on ImageNet may experience significant performance declines on similar datasets. Specifically, state-of-the-art frameworks such as DINO and Swav, which are praised for their performance, exhibit substantial drops in performance while MoCo and Barlow Twins displays comparatively good results. As a result, we argue that otherwise good and desirable properties of models remain hidden when benchmarking is only performed on the ImageNet validation set, making us call for more adequate benchmarking. To avoid the "benchmark lottery" on ImageNet and to ensure a fair benchmarking process, we investigate the usage of a unified metric that takes into account the performance of models on other ImageNet variant datasets.

关键词： measurement computer vision Computational modeling Neural networks Machine learning Self-supervised learning Benchmark testing

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Multi-feature and Multi-branch Action Segmentation Framework for modeling Long-Short-Term Dependencies

Multi-feature and Multi-branch Action Segmentation Framework...

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IEEE international conference on Multimedia and Expo (ICME)

作者： Junkun Hong Yitian Long Yueyi Luo Qianqian Qi Jun Long School of Computer Science and Engineering Central South University Changsha China Data Science Institute Vanderbilt University Nashville Tennessee United States School of Mathematics and Statistics Central South University Changsha China Big Data Institution Central South University Changsha China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Pioneer efforts have been dedicated to action segmentation that predicts what step is occurring in a video frame. Existing studies focus on improving the accuracy of video segmentation, but neglect the temporal continuity of intersegments and semantic consistency of intra-segments, which are necessary for developing computer-assisted systems. Meanwhile, Temporal Convolutional Networks have shown good performance in action segmentation tasks, but their high layers tend to lose fine-grained information and impact the results. Toward this end, we devise a multi-feature and multi-branch action segmentation framework for modeling long-term and short-term dependencies. Specifically, we present a multi-feature fusion to enhance temporal video representation and design a multi-branch predictor for extracting both segment-level and frame-level information. We justify our framework over three datasets and experimental results demonstrate its superiority, especially in Edit and F1 metrics, which means our framework is more applicable to computer-assisted systems.

关键词： measurement Accuracy Computational modeling Aggregates Semantics Feature extraction Data mining

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