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intelligent classification of lung malignancies using deep learning techniques

International Journal of intelligent Computing and Cybernetics 2022年第3期15卷 345-362页

作者： Priyanka Yadlapalli D.Bhavana Suryanarayana Gunnam Department of Electronics and Communication Engineering Koneru Lakshmaiah Education FoundationGunturIndia Department of Electronics and Communications Gokaraju Rangaraju Institute of Engineering and TechnologyHyderabadIndia Department of Electronics and Communications VRSiddhartha Engineering CollegeVijayawadaIndia Shanghai Jiao Tong University-Minhang Campus ShanghaiChina

Purpose-Computed tomography(CT)scan can provide valuable information in the diagnosis of lung *** detect the location of the cancerous lung nodules,this work uses novel deep learning *** majority of the early investigations used CT,magnetic resonance and mammography *** appropriate procedures,the professional doctor in this sector analyses these images to discover and diagnose the various degrees of lung *** of the methods used to discover and detect cancer illnesses are time-consuming,expensive and stressful for the *** address all of these issues,appropriate deep learning approaches for analyzing these medical images,which included CT scan images,were ***/methodology/approach-Radiologists currently employ chest CT scans to detect lung cancer at an early *** certain situations,radiologists’perception plays a critical role in identifying lung melanoma which is incorrectly *** learning is a new,capable and influential approach for predicting medical *** this paper,the authors employed deep transfer learning algorithms for intelligent classification of lung *** neural networks(VGG16,VGG19,MobileNet and DenseNet169)are used to constrain the input and output layers of a chest CT scan image ***-The collection includes normal chest CT scan pictures as well as images from two kinds of lung cancer,squamous and adenocarcinoma impacted chest CT scan *** to the confusion matrix results,the VGG16 transfer learning technique has the highest accuracy in lung cancer classification with 91.28% accuracy,followed by VGG19 with 89.39%,MobileNet with 85.60% and DenseNet169 with 83.71% accuracy,which is analyzed using Google ***/value-The proposed approach using VGG16 maximizes the classification accuracy when compared to VGG19,MobileNet and *** results are validated by computing the confusion matrix for each network type.

关键词： Computed tomography scans Convolution neural networks Transfer learning intelligent classification State-of-the-art(SOTA)accuracy

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intelligent classification model of surrounding rock of tunnel using drilling and blasting method

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Underground Space 2021年第5期6卷 539-550页

作者： Mingnian Wang Siguang Zhao Jianjun Tong Zhilong Wang Meng Yao Jiawang Li Wenhao Yi Key Laboratory of Transportation Tunnel Engineering Ministry of EducationSouthwest Jiaotong UniversityChengdu 610031China School of Civil Engineering Southwest Jiaotong UniversityChengdu 610031China

classification of surrounding rock is the cornerstone of tunnel design and *** traditional methods are mainly qualitative and manual and require extensive professional knowledge and engineering *** minimize the effect of the empirical judgment on the accuracy of surrounding rock classification,it is necessary to reduce human *** intelligent classification technique based on information technology and artificial intelligence could overcome these *** this regard,using 299 groups of drilling parameters collected automatically using intelligent drill jumbos in tunnels for the Zhengzhou-Wanzhou high-speed railway in China,an intelligent-classification surrounding-rock database is constructed in this *** on a machine learning algorithm,an intelligent classification model is then developed,which has an overall accuracy of 91.9%.Finally,using the core of the model,the intelligent classification system for the surrounding rock of drilled and blasted tunnels is integrated,and the system is carried by intelligent jumbos to perform automatic recording and transmission of drilling parameters and intelligent classification of the surrounding *** approach provides a foundation for the dynamic design and construction(both conventional and intelligent)of tunnels.

关键词： Drilled and blasted tunnel Drilling parameter Machine learning intelligent classification Surrounding rock

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A Review on intelligent Detection and classification of Power Quality Disturbances:Trends,Methodologies,and Prospects

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Computer Modeling in Engineering & Sciences 2023年第11期137卷 1345-1379页

作者： Yanjun Yan Kai Chen Hang Geng Wenqian Fan Xinrui Zhou School of Automation Engineering University of Electronic Science and Technology of ChinaChengdu611731China

With increasing global concerns about clean energy in smart grids,the detection of power quality disturbances(PQDs)caused by energy instability is becoming more and more *** is well acknowledged that the PQD effects on power grid equipment are destructive and hazardous,which causes irreversible damage to underlying electrical/electronic equipment of the concerned intelligent *** order to ensure safe and reliable equipment implementation,appropriate PQDdetection technologiesmust be adopted to avoid such adverse *** paper summarizes the newly proposed and traditional PQD detection techniques in order to give a quick start to new researchers in the related field,where specific scenarios and events for which each technique is applicable are also clearly ***,comments on the future evolution of PQD detection techniques are *** the published review articles,this paper focuses on the new techniques from the last five years while providing a brief recap on traditional PQD detection techniques so as to supply researchers with a systematic and state-of-the-art review for PQD detection.

关键词： Power quality disturbance renewable energy feature extraction and optimization intelligent classification signal processing smart grids

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基于机器学习的阿尔兹海默症智能分类研究

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建模与仿真 2023年第2期12卷 1321-1330页

作者：郑耀杨亭亭周志鹏福建江夏学院电子信息科学学院，福建福州

阿尔兹海默症(AD)是一种使人记忆力衰退、大脑功能缓慢且逐渐变异的智力丧失表现的疾病。而早期诊断和预测AD可以延缓患者的发病，可能延长患者的寿命，对整个社会具有重要的科学意义。本文通过机器学习等方法对AD数据进行分析和建模，... 详细信息

阿尔兹海默症(AD)是一种使人记忆力衰退、大脑功能缓慢且逐渐变异的智力丧失表现的疾病。而早期诊断和预测AD可以延缓患者的发病，可能延长患者的寿命，对整个社会具有重要的科学意义。本文通过机器学习等方法对AD数据进行分析和建模，增添大脑特征指标，进行数据预处理，利用成对分类法的SVM实现AD的智能诊断，通过SVM得到的五分类的准确度达到0.71，高于随机森林的0.69与XGBoost提升树的0.706。并通过绘制ROC曲线，对于判断CN有64%准确率，判断AD有80%准确率，判断LMCI有68%准确率，判断SMC有58%准确率，判断EMCI有76%准确率，具有较好的测试效果。提取和发现影响AD诊断和预测的关键症状，并对患者类别进行分类，能够有效、快速地得到早期诊断和预测AD的患病概率，以达到预防AD的有效手段。基于成对分类法的SVM实现AD的智能诊断，并且能够用推广于更加多分类的问题。

关键词：随机森林支持向量机阿尔兹海默智能分类 Random Forest Support Vector Machines Alzheimer’s Disease intelligent classification

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