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

作者： Kaushik, Harshita Rajendran, Naveen Kumar Kumar, Ashwini Vivekananda Global University Department of Computer Science& Application Jaipur India Department of Aerospace Engineering Karnataka Bangalore India Arka Jain University Department of Mechnical Engineering Jharkhand Jamshedpur India

ISBN: (纸本)9798350370249

Contingency-based learning strategies primarily based on synthetic intelligence (AI) are being hired within the area of precision remedy to enable greater powerful selection-making when treating patients. The overall performance of such learning strategies should be evaluated to make sure they are presenting accurate and dependable outcomes. This paper aims to assess contingency-based learning strategies for precision medication to help clinicians and researchers appropriately assess the efficacy of these learning techniques and decide the first-rate method for given software., a scientific evaluation of the current literature was undertaken to identify existing assessment strategies and analyze their strengths and weaknesses. The assessment criteria used in this analysis are accuracy, interpretability, complexity, scalability, and transferability, which had been selected as they affect the performance of AI-primarily based approaches in real-international settings. Furthermore, a discussion of current equipment and frameworks that could be used to assess AI-based techniques in the area of precision remedy was performed. From this assessment, it has been discovered that most of the present assessment techniques lack the necessary accuracy and transferability essential to appropriately check the efficacy of AI-based strategies for precision medicinal drugs. But, sure, evaluation metrics, together with explain ability, may be employed to degree the overall performance of AI-primarily based techniques in order to decide the nice approach for a given application. © 2024 IEEE.

关键词： Contingency Intelligence Interpretability Scalability Software Strategies

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A Collaborative Framework for Intrusion Detection in Cloud Computing Based on HLA-CNN-BiLSTM-SVM Model

A Collaborative Framework for Intrusion Detection in Cloud C...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： Amit K. Mogal Vaibhav P. Sonaje Department of Computer Science & Application School of Computer Science and Engineering Sandip University Nashik India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

In addition to describing a way to make better use of preexisting computer infrastructures, the phrase “cloud computing” describes a technology that facilitates the sale of access to shared computer resources and services. The flip side is that these dispersed and convoluted infrastructures are attractive targets for attackers. Because cyberattacks can reduce the quality of service offered to clients, the system constitute a substantial risk. This proposed method consists of three steps like preprocessing, feature selection, and model training. Data preprocessing, which includes both feature discretization and feature normalization, is a critical step in any knowledge discovery process. In this proposed to evaluate the impact of feature selection on improving classifier algorithm accuracy by comparing two feature selection methods: one that takes linear correlation into account, and another that takes mutual information into account. When training the model, an HLA-CNN-BiLSTM-SVM was utilized. With a 92.35 percent average accuracy, the suggested method surpasses CNN-SVM and BiLSTM.

关键词： Training Cloud computing Accuracy Correlation Computational modeling Intrusion detection Bidirectional long short term memory Feature extraction Classification algorithms Mutual information

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Automatic Image Acquisition and Matching in Control System of Near-Field Beam Shaping

Automatic Image Acquisition and Matching in Control System o...

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International Symposium on computer science and Intelligent Controls (ISCSIC)

作者： Suicheng Li Wenwen Shen Zizhou He Chen Zhou Li Li Jun Luo Department of Computer Engineering and Science Institute of Computer Application CAEP Mianyang China

ISBN: (数字)9798350380286

ISBN: (纸本)9798350380293

In the high power laser system of inertial Confinement fusion (ICF), in order to improve the safe operation flux of the system, near-field beam shaping is necessary to output the laser beam with uniform and smooth intensity distribution. Therefore, how to use optical control systems to automatically and efficiently accomplish near-field beamforming is an important issue. The optical control system based on liquid crystal spatial light modulator can control the beam shape and intensity distribution in real time. The control system of near-field beam shaping algorithm mainly includes the modules of light path control, image loading on liquid crystal spatial light modulator, Charge Coupled Device (CCD) nearfield light spot perception and image processing. There is a lot of data exchange between those modules. In the laser system, the manual operation mainly involves two parts: one is the interaction and data exchange between different modules. In the past work, the image data is collected by the operator on different devices, and then the compensation image is calculated on the computing device, and finally loaded into the display device. The whole process is complex, tedious and repetitive. The other is the image matching of input image on spatial light modulator and near-field light spot. This work requires staff to manually match feature points on computing equipment, and the accuracy of matching is difficult to guarantee, which is very time-consuming and labor-intensive. This paper will address the above problems and propose a distributed network architecture to connect different devices in near-field beam shaping and carry out image acquisition from them. In addition, a new image matching algorithm is proposed to extract and match the feature points of input image on spatial light modulator and near-field light spot. It enables the compensation algorithm to be completed automatically to avoid manual operation.

关键词： Shape Image matching Power lasers Modulation Network architecture Feature extraction Real-time systems Laser beams Laser fusion Optical control

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Comparative Analysis of Node-Dependent and Node-Independent Graph Matrices for Brain Connectivity Network 1

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4th International Conference on Machine Intelligence and Signal Processing, MISP 2022

作者： Kose, Mangesh Ramaji Ahirwal, Mitul Kumar Atulkar, Mithilesh Department of Computer Application NIT C.G. Raipur492010 India Department of Computer Science and Engineering MANIT M.P. Bhopal462003 India

ISBN: (数字)9789819900855

ISBN: (纸本)9789819900848

Brain connectivity network (BCN) represents an advanced approach for brain functionality analysis. In BCN, the human brain is modeled as a graph, containing vertices and connectivity links. The electroencephalogram (EEG) signals recorded from the diseased as well as healthy subjects are used to construct a BCN. The graph matrices are a quantitative approach for comparing the network of diseased and healthy subjects. The selection of graph matrices to be extracted from the network depends on whether all the networks have the same or a different number of nodes. In this paper, analysis of matrices dependent to numbers of nodes (DNN) and matrices independent to numbers of nodes (INN) has been performed. These matrices are calculated as features from schizophrenia disease EEG datasets having 16 and 19 channels. Analysis of these matrices has been done based on distance and similarity measures. From the results, it is observed that the INN features show less variability as compared to DNN. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electroencephalography

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Utilizing Machine Learning to Aid in Type 2 Diabetes Diagnosis 4

Utilizing Machine Learning to Aid in Type 2 Diabetes Diagnos...

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4th IEEE International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2024

作者： Pattnayak, Parthasarathi Mishra, Debaswapna Patnaik, Sanghamitra Mohanty, Arpeeta School of Computer Applications KIIT Deemed to Be University Bhubaneswar India OUAT Department of Computer Science and Application Bhubaneswar India School of Law KIIT Deemed to Be University Bhubaneswar India School of Computer Engineering KIIT Deemed to Be University Bhubaneswar India

ISBN: (纸本)9798331528171

High blood glucose levels are a defining characteristic of diabetes, with type 2 diabetes being the most prevalent form. Early detection and treatment can prevent or delay complications. To precisely anticipate type 2 diabetes risk, we applied machine learning techniques to identify unidentified correlations with a person's health condition. In order to find potential nonlinear correlations between the beginning of type 2 diabetes with an ensemble of health factors, this research used a binary classifier that was trained from scratch. Three datasets - MIMIC-III, MIMIC-IV, and NHANES - were used. The equal number of people who had or did not have type 2 diabetes have been included in one data set that was created by combining the two sets of data. Reliability was the key metric, given the balanced dataset. The model achieved ROC AUC score of 0.934 with accuracy ratings up to 86%, which were positive results of this investigation. To increase the model's dependability, more research is required to take into account multiple readings taken throughout period for an identical person. © 2024 IEEE.

关键词： Adversarial machine learning

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Surface flashover in 50 years:Theoretical models and competing mechanisms

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High Voltage 2023年第5期8卷 853-877页

作者： Zhen Li Ji Liu Yoshimichi Ohki George Chen He Gao Shengtao Li Key Laboratory of Engineering Dielectrics and Its Application Ministry of EducationHarbin University of Science and TechnologyHarbinChina State Key Laboratory of Electrical Insulation and Power Equipment Xi'an Jiaotong UniversityXi'anChina Research Institute for Materials Science and Technology Waseda UniversityShinjuku‐kuJapan School of Electronics and Computer Science University of SouthamptonSouthamptonUK

Surface flashover is a devastating electronic avalanche along the gas–solid interface when a high electric field is applied,which is a potential issue that threatens the safe operations of advanced power electronic,electrical,and spacecraft ***,the underlying physical mechanisms for surface flashover development are still under investigation owing to the complex charge transport processes through the gas phase,solid phase,and gas–solid *** this study,the history of surface flashover theory in the last 50 years is introduced,and several key questions are reviewed from the perspective of the competing mechanisms of charge transport:the role of each phase in a surface flashover,the origin of surface charging,and effects of traps in solid on surface ***,some suggestions involve charge transport processes in each phase,and their correlations are put forward,and a predictable‘charge transport competitive flashover model’is proposed by clarifying the competing mechanisms of charge transport processes through multiple *** study summarises the history and hot topics of physical mechanisms of surface flashover proposed based on classic and recent progress and offers promising routes for developing a more precise surface flashover theory and improving surface flashover performances.

关键词： flash transport charge

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Dynamic performance and energy efficiency of reflective and insulative composite coating on building exterior wall

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Building Simulation 2023年第12期16卷 2245-2259页

作者： Guangpeng Zhang Huijun Wu Jia Liu Jianming Yang Huakun Huang Yujie Ding Lei Xie School of Civil Engineering Guangzhou UniversityGuangzhou 510006China Guangdong Provincial Key Laboratory of Building Energy Efficiency and Application Technologies Guangzhou 510006China School of Automation Guangdong Polytechnic Normal UniversityGuangzhou 510665China School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou 510006China

Reflective and insulative composite coatings are a new energy-saving material with high solar reflectance and extremely low thermal conductivity for *** optimization and impact of high solar reflectance and low thermal conductivity on the insulating capacity of walls remain *** work investigates the dynamic thermal performance and energy efficiency of a reflective and insulative composite coating in regions with hot summer and warm winter.A simplified thermal resistance-heat capacitance model of an exterior building wall is established to predict thermal *** dynamic temperature and heat flow of the wall are predicted to reduce heat loss through the interior surface of the wall and compared to the conventional *** specific impact of the thermal conductivity and solar reflectance of the coating on the heat loss is further investigated to minimize heat loss of the *** research shows that the composite coating shows better performance on adjusting outdoor climate change than the other *** with cement,it reduces the maximum temperature of the exterior surface of the wall by 7.45°C,and the heat loss through the interior surface of the wall by 38%.The heat loss is reduced with the increase of solar reflectance and the reduction of thermal *** results can provide a useful reference and guidance for the application of reflective and insulative composite coating on building exterior wall to promote their energy-saving use on building envelopes.

关键词： reflective and insulative composite coating exterior wall building insulation solar reflectance thermal conductivity

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Using Hybrid Penalty and Gated Linear Units to Improve Wasserstein Generative Adversarial Networks for Single-Channel Speech Enhancement

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computer Modeling in engineering & sciences 2023年第6期135卷 2155-2172页

作者： Xiaojun Zhu Heming Huang School of Computer Science Qinghai Normal UniversityXining810008China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810008China School of Electronic and Information Engineering Lanzhou City UniversityLanzhou730000China

Recently,speech enhancement methods based on Generative Adversarial Networks have achieved good performance in time-domain noisy ***,the training of Generative Adversarial Networks has such problems as convergence difficulty,model collapse,*** this work,an end-to-end speech enhancement model based on Wasserstein Generative Adversarial Networks is proposed,and some improvements have been made in order to get faster convergence speed and better generated speech ***,in the generator coding part,each convolution layer adopts different convolution kernel sizes to conduct convolution operations for obtaining speech coding information from multiple scales;a gated linear unit is introduced to alleviate the vanishing gradient problem with the increase of network depth;the gradient penalty of the discriminator is replaced with spectral normalization to accelerate the convergence rate of themodel;a hybrid penalty termcomposed of L1 regularization and a scale-invariant signal-to-distortion ratio is introduced into the loss function of the generator to improve the quality of generated *** experimental results on both TIMIT corpus and Tibetan corpus show that the proposed model improves the speech quality significantly and accelerates the convergence speed of the model.

关键词： Speech enhancement generative adversarial networks hybrid penalty gated linear units multi-scale convolution

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Inculcating Algebra Properties in the Context of Image Processing

Inculcating Algebra Properties in the Context of Image Proce...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Agarwal, Trapty Raja Praveen, K.N. Asif Iqbal, Mohammad Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Computer Science Department Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350357769

Strong detection with mitigation systems are crucial given the growing danger of computer cluster breaches and worldwide assaults. Given the rising frequency and magnitude of network assaults and abnormalities, the intrusion detection systems (IDS) is an essential tool for recognizing them. In this study, we provide a novel method for security breach detection using three different DNN (deep neural network) vision-based approaches. The classification process uses each of the four parts (Red, Colored, Aquamarine's and Alpha) or circuit-generated pictures as a foundation, making it easier to train and validate the reputable ResN et50 visual analysis framework. We assess the performance of our suggested approach on two well-known datasets: UNSW-NB15 and Regularly Utilize Management Defeat of Service (DoS). The results demonstrate our method's outstanding effectiveness, as it identified broad exploits in the UB test with a 99.8% success rate. Moreover, our method achieves 99.7% accuracy in detecting devices unauthorized to enter restricted areas and diagnosing comparable intrusion data pertaining to cars. With this effort, cybersecurity defenses against emerging attacks have taken a major step forward. © 2023 IEEE.

关键词： DLN Firmware and Image Classification IDS NTF ResNet 5.0 UB Test UNSW-NB15 Zip Files

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Exploring the Power of Deep Learning in Anomaly Detection: A Comprehensive Review and Analysis 12th

Exploring the Power of Deep Learning in Anomaly Detection: A...

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12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Kumar, Gaurav Kumar, Ankit Pandy, Saroj Kumar Singh, Kamred Udham Singh, Teekam Kumar, Lalan Choudhury, Tanupriya Department of Computer Engineering and Applications GLA University UP Mathura India School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Master of Computer Application GL Bajaj Institute of Technology and Management Greater Noida India Department of Computer Science and Engineering University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9789819732913

This paper aims to provide a comprehensive overview of existing and state-of-the-art fraud and intrusion detection strategies, specifically focusing on incorporating neural networks. We begin by discussing the fundamental concept of data mining and its significance in detecting anomalies. By leveraging advanced machine learning and neural network techniques, we can effectively uncover subtle patterns and irregularities within complex datasets. We explore how the development of anomalies has enhanced our ability to identify and mitigate various threats, such as malware attacks and unlawful practices. Traditional detection strategies have proven effective, but as deep learning progresses, we uncover new opportunities and insights that can significantly enhance the accuracy and efficiency of anomaly detection systems. Throughout this study, we delve into the most cutting-edge approaches, ranging from broad neural networks to shallower architectures, and their applicability to fraud and intrusion detection. We analyze the strengths and limitations of these strategies, shedding light on their performance characteristics, scalability, and interpretability. By addressing these key issues, we aim to provide a comprehensive summary of the latest fraud and intrusion detection advancements driven by deep learning methodologies. This research contributes to the broader understanding of data mining's potential in uncovering anomalies and bolstering security measures across diverse domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intrusion detection

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