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Classification Model for IDS Using Auto Cryptographic Denoising Technique

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Computer Systems Science & Engineering 2023年第4期45卷 671-685页

作者： N.Karthikeyan P.Sivaprakash S.Karthik Department of Computer Science&Engineering PPG Institute of TechnologyCoimbatoreTamilnaduIndia Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia

Intrusion detection systems(IDS)are one of the most promising ways for securing data and networks;In recent decades,IDS has used a variety of categorization algorithms.These classifiers,on the other hand,do not work effectively unless they are combined with additional algorithms that can alter the classifier’s parameters or select the optimal sub-set of features for the problem.optimizers are used in tandem with classifiers to increase the stability and with efficiency of the classifiers in detecting invasion.These algorithms,on the other hand,have a number of limitations,particularly when used to detect new types of threats.In this paper,the NSL KDD dataset and KDD Cup 99 is used to find the performance of the proposed classifier model and compared;These two IDS dataset is preprocessed,then Auto Cryptographic Denoising(ACD)adopted to remove noise in the feature of the IDS dataset;the classifier algorithms,K-Means and Neural network classifies the dataset with adam optimizer.IDS classifier is evaluated by measuring performance measures like f-measure,recall,precision,detection rate and accuracy.The neural network obtained the highest classifying accuracy as 91.12%with drop-out function that shows the efficiency of the classifier model with drop-out function for KDD Cup99 dataset.Explaining their power and limitations in the proposed methodology that could be used in future works in the IDS area.

关键词： Auto cryptographic denoising(ACD) classifier intrusion detection system(IDS) optimizer performance measures

Deep Learning Based Automated Diagnosis of Skin Diseases Using Dermoscopy

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Computers, Materials & Continua 2022年第5期71卷 3145-3160页

作者： Vatsala Anand Sheifali Gupta Deepika Koundal Shubham Mahajan Amit Kant Pandit Atef Zaguia Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Department of Systemics School of Computer ScienceUniversity of Petroleum&Energy StudiesDehradunUttrakhandIndia School of Electronics&Communication Shri Mata Vaishno Devi UniversityKatra182320India Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Biomedical image analysis has been exploited considerably by recent technology involvements,carrying about a pattern shift towards‘automation’and‘error free diagnosis’classification methods with markedly improved accurate diagnosis productivity and cost effectiveness.This paper proposes an automated deep learning model to diagnose skin disease at an early stage by using Dermoscopy images.The proposed model has four convolutional layers,two maxpool layers,one fully connected layer and three dense layers.All the convolutional layers are using the kernel size of 3∗3 whereas the maxpool layer is using the kernel size of 2∗2.The dermoscopy images are taken from the HAM10000 dataset.The proposed model is compared with the three different models of ResNet that are ResNet18,ResNet50 and ResNet101.The models are simulated with 32 batch size and Adadelta optimizer.The proposed model has obtained the best accuracy value of 0.96 whereas the ResNet101 model has obtained 0.90,the ResNet50 has obtained 0.89 and the ResNet18 model has obtained value as 0.86.Therefore,features obtained from the proposed model are more capable for improving the classification performance of multiple skin disease classes.This model can be used for early diagnosis of skin disease and can also act as a second opinion tool for dermatologists.

关键词： Dermoscopy images CNN deep learning classification optimizer ResNet diagnosis skin disease

Ordinal Outcome Modeling: The Application of the Adaptive Moment Estimation optimizer to the Elastic Net Penalized Stereotype Logit

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Journal of Data Analysis and Information Processing 2019年第1期7卷 14-27页

作者： André A. A. Williams Center for Healthcare Delivery Science Nemours Children’s Specialty Care Jacksonville USA

Penalized ordinal outcome models were developed to model high dimensional data with ordinal outcomes. One option is the penalized stereotype logit, which includes nonlinear combinations of parameter estimates. Optimization algorithms assuming linearity and function convexity were applied to fit this model. In this study the application of the adaptive moment estimation (Adam) optimizer, suited for nonlinear optimization, to the elastic net penalized stereotype logit model is proposed. The proposed model is compared to the L1 penalized ordinalgmifs stereotype model. Both methods were applied to simulated and real data, with non-Hodgkin lymphoma (NHL) cancer subtypes as the outcome, with results presented and discussed.

关键词： Stereotype Logit Elastic Net Penalty Adam optimizer Non-Hodgkin Lymphoma

借助Press optimizer软件实现印刷标准化的研究

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包装工程 2011年第21期32卷 15-18页

作者：严岩白卫国胡媛南京林业大学江苏省制浆造纸科学与技术重点实验室南京210037 天津科技大学天津300222

在借助Press optimizer软件的基础上,进行了商业环境下的印刷标准化测试,并对比ISO 12647标准数据对测试结果进行了系统性分析,包括最佳密度、网点扩大补偿值、灰平衡情况,验证了Press optimizer软件可以有效地帮助印刷厂家快速实现标准... 详细信息

在借助Press optimizer软件的基础上,进行了商业环境下的印刷标准化测试,并对比ISO 12647标准数据对测试结果进行了系统性分析,包括最佳密度、网点扩大补偿值、灰平衡情况,验证了Press optimizer软件可以有效地帮助印刷厂家快速实现标准化,从而提高印刷产品质量。

关键词： Press optimizer 印刷标准化网点扩大灰平衡

OpenGL optimizer及其在大规模地形场景漫游中的应用

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计算机应用与软件 2004年第1期21卷 14-15,96页

作者：王源刘建永江南南京大学城市与资源学系南京210093 解放军理工大学工程兵工程学院南京210007 中国科学院南京地理与湖泊研究所南京210008

本文介绍了OpenGLoptimizer的强大功能和体系结构 ,针对组成地形场景地貌和点、线、面三类地物 ,阐明了运用该三维引擎创建大规模地形场景的方法 ,并论述了应用程序的性能优化策略。实例表明应用OpenGLoptimizer在保持场景真实感的情况... 详细信息

本文介绍了OpenGLoptimizer的强大功能和体系结构 ,针对组成地形场景地貌和点、线、面三类地物 ,阐明了运用该三维引擎创建大规模地形场景的方法 ,并论述了应用程序的性能优化策略。实例表明应用OpenGLoptimizer在保持场景真实感的情况下大大提高了场景的渲染速度。

关键词：三维地理信息系统 OpenGL optimizer 地形场景实时漫游

Advancing COVID-19 Diagnosis with CNNs: An Empirical Study of Learning Rates and Optimization Strategies

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Intelligent Control and Automation 2023年第4期14卷 45-78页

作者： Mainak Mitra Soumit Roy Product & Program (Data Platform) Conviva Foster City USA Presales Enngineering (Analytics) Jade Global San Jose USA

The rapid spread of the novel Coronavirus (COVID-19) has emphasized the necessity for advanced diagnostic tools to enhance the detection and management of the virus. This study investigates the effectiveness of Convolutional Neural Networks (CNNs) in the diagnosis of COVID-19 from chest X-ray and CT images, focusing on the impact of varying learning rates and optimization strategies. Despite the abundance of chest X-ray datasets from various institutions, the lack of a dedicated COVID-19 dataset for computational analysis presents a significant challenge. Our work introduces an empirical analysis across four distinct learning rate policies—Cyclic, Step Based, Time-Based, and Epoch Based—each tested with four different optimizers: Adam, Adagrad, RMSprop, and Stochastic Gradient Descent (SGD). The performance of these configurations was evaluated in terms of training and validation accuracy over 100 epochs. Our results demonstrate significant differences in model performance, with the Cyclic learning rate policy combined with SGD optimizer achieving the highest validation accuracy of 83.33%. This study contributes to the existing body of knowledge by outlining effective CNN configurations for COVID-19 image dataset analysis, offering insights into the optimization of machine learning models for the diagnosis of infectious diseases. Our findings underscore the potential of CNNs in supplementing traditional PCR tests, providing a computational approach to identify patterns in chest X-rays and CT scans indicative of COVID-19, thereby aiding in the swift and accurate diagnosis of the virus.

关键词： Learning Rate AI optimizer Deep Learning CNN Multi Class Classification

基于MeanShift与Group Search optimizer的视频目标跟踪方法

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工业控制计算机 2015年第8期28卷 101-103页

作者：张银虞旦上海海事大学物流工程学院上海201306

针对传统的Meanshift方法在目标快速运动的情况下,目标跟踪易丢失问题,提出了一种将Meanshift与Group Search optimizer融合的视频目标跟踪方法。该方法可以对运动目标位置进行全局搜索,根据Meanshift算法的跟踪结果判断是否要进行Group... 详细信息

针对传统的Meanshift方法在目标快速运动的情况下,目标跟踪易丢失问题,提出了一种将Meanshift与Group Search optimizer融合的视频目标跟踪方法。该方法可以对运动目标位置进行全局搜索,根据Meanshift算法的跟踪结果判断是否要进行Group Search optimizer算法优化,并且在目标跟踪丢失时可以快速重新找回原目标进行跟踪,提高了目标跟踪的鲁棒性。实验结果表明该方法可以有效提高对快速运动目标跟踪的准确性,具有较好的鲁棒性。

关键词：视频运动目标跟踪 Meanshift跟踪 Group Search optimizer

WEB图像压缩和优化的利器──JPEG optimizer

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计算机时代 2000年第7期 30-32页

作者：向坚持湖南师范大学计算机教学部 410081

突破WinASO Registry optimizer注册防线

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黑客防线 2006年第12期 109-111页

作者： TC-XB

WinASO Registry optimizer是一个Windows优化工具和高级注册表清理工具，它可以使大家以简单的鼠标单击来安全的清理及修复注册表故障，通过修复陈旧信息及调整Windows注册表参数，是维护注册表的一款利器。软件的功能虽然很强大，但... 详细信息

WinASO Registry optimizer是一个Windows优化工具和高级注册表清理工具，它可以使大家以简单的鼠标单击来安全的清理及修复注册表故障，通过修复陈旧信息及调整Windows注册表参数，是维护注册表的一款利器。软件的功能虽然很强大，但每次运行都会看到注册提示窗口．感觉很不方便，于是我最终选择了Crack这条路。

关键词： optimizer 注册表清理工具 Registry Windows注册表防线优化工具提示窗口修复