检索结果-南通市图书馆

Application of support vector machine algorithm for early differential diagnosis of prostate cancer

Data Science and Management 2023年第1期6卷 1-12页

作者： Boluwaji A.Akinnuwesi Kehinde A.Olayanju Benjamin S.Aribisala stephen g.fashoto Elliot Mbunge Moses Okpeku Patrick Owate Department of Computer Science Faculty of Science and EngineeringUniversity of EswatiniKwaluseniM201Swaziland Department of Computer Science Education Federal College of Education(Technology)AkokaLagos State100213Nigeria Department of Computer Science Faculty of ScienceLagos State UniversityOjoLagos State102101Nigeria Department of genetics University of KwaZulu-NatalDurban4041South Africa

Prostate cancer(PCa)symptoms are commonly confused with benign prostate hyperplasia(BPH),particularly in the early stages due to similarities between symptoms,and in some instances,*** methods have been utilized to diagnose PCa;however,at the full-blown stage,clinical methods usually present high risks of complicated side ***,we proposed the use of support vector machine for early differential diagnosis of PCa(SVM-PCa-EDD).SVM was used to classify persons with and without *** used the PCa dataset from the Kaggle Healthcare repository to develop and validate SVM model for *** PCa dataset consisted of 250 features and one class of *** considered in this study were age,body mass index(BMI),race,family history,obesity,trouble urinating,urine stream force,blood in semen,bone pain,and erectile *** SVM-PCa-EDD was used for preprocessing the PCa dataset,specifically dealing with class imbalance,and for dimensionality *** eliminating class imbalance,the area under the receiver operating characteristic(ROC)curve(AUC)of the logistic regression(LR)model trained with the downsampled dataset was 58.4%,whereas that of the AUC-ROC of LR trained with the class imbalance dataset was 54.3%.The SVM-PCa-EDD achieved 90%accuracy,80%sensitivity,and 80%*** validation of SVM-PCa-EDD using random forest and LR showed that SVM-PCa-EDD performed better in early differential diagnosis of *** proposed model can assist medical experts in early diagnosis of PCa,particularly in resource-constrained healthcare settings and making further recommendations for PCa testing and treatment.

关键词： Confusable diseases Computational intelligence Early differential diagnosis Logistic regression Prostate cancer Support vector machine

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Application of intelligence-based computational techniques for classification and early differential diagnosis of COVID-19 disease

引用

Data Science and Management 2021年第4期4卷 10-18页

作者： Boluwaji A.Akinnuwesi stephen g.fashoto Elliot Mbunge Adedoyin Odumabo Andile S.Metfula Petros Mashwama Faith-Michael Uzoka Olumide Owolabi Moses Okpeku Oluwaseun O.Amusa Department of Computer Science Faculty of Science and EngineeringUniversity of Eswatini(Formerly University of Swaziland)Kwaluseni M201ManziniEswatiniSwaziland Department of Computer Science Faculty of ScienceLagos State UniversityOjoLagos State102105Nigeria Department of Computer Science and Information Systems Mount Royal UniversityCalgaryAlbertaT3E6K6Canada Department of Computer Science University of Abuja900105Nigeria Department of genetics University of KwaZulu-Natal4041South Africa Department of English Studies Faculty of ArtsAdekunle Ajasin UniversityAkungba-AkokoOndo State342111Nigeria

Clinical methods are used for diagnosing COVID-19 infected patients,but reports posit that,several people who were initially tested positive of COVID-19,and who had some underlying diseases,turned out having negative results after further ***,the performance of clinical methods is not always ***,chest X-ray image data of COVID-19 infected patients are mostly used in the computational models for COVID-19 diagnosis,while the use of common symptoms,such as fever,cough,fatigue,muscle aches,headache,*** computational models is not yet *** this study,we employed seven classification algorithms to empirically test and verify their efficacy when applied to diagnose COVID-19 using the aforementioned *** experimented with Logistic Regression(LR),Support Vector Machine(SVM),Naïve Byes(NB),Decision Tree(DT),Multilayer Perceptron(MLP),Fuzzy Cognitive Map(FCM)and Deep Neural Network(DNN)*** techniques were subjected to random undersampling and *** results showed that with class imbalance,MLP and DNN outperform ***,without class imbalance,MLP,FCM and DNN outperform others with the use of random undersampling,but DNN has the best performance by utilizing random *** study identified MLP,FCM and DNN as better classifiers over LR,NB,DT and SVM,so that healthcare software system developers can adopt them to develop intelligence-based expert systems which both medical personnel and patients can use for differential diagnosis of COVID-19 based on the aforementioned ***,the test of performance must not be limited to the traditional performance metrics.

关键词： COVID-19 Coronavirus Machine Learning Intelligent classifier Early differential diagnosis Deep Learning

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Securing a Scalable E-Voting System Using the RSA Algorithm: The Case of a group Voting Process in a Tertiary School

引用

Computer Technology and Application 2016年第1期7卷 11-27页

作者： stephen gbenga fashoto Michael Adeyeye Faith-Michael Emeka Uzoka Fatima Hamza Usthman Department of Computer Science Kampala International University Kasanga Kampala Uganda Faculty of Mathematics Physical Sciences and Life Sciences Sheridan College Perth Australia Department of Computer Science and Information Systems Mount Royal University Calgary Canada Department of Computer Science Alhikmah University Ilorin Kwara State Nigeria

E-voting (electronic voting) is a significant part of an E-election (electronic election), which refers to the use of computers or computerized voting equipment to cast ballots in an election. Due to the rapid growth of computer technologies and advances in cryptographic techniques, E-voting is now an applicable alternative for many non-governmental elections. However, security demands are paramount to electoral process in political arena. It was revealed that researchers show little interest in robustness of E-voting system compared to other E-voting requirements [1]. This paper shows that RSA (Ron Rivest, Adi Shamir and Leonard Adleman) cryptography algorithm can be incorporated into E-voting process as a whole. The RSA cryptography algorithm ensures that votes casted are secured, thus maintaining the privacy of votes. The performance of the cryptography algorithm is tested on a university E-voting system over a public network. The E-voting process is initiated by a server system that other computer nodes are connected to. The system is such that when the votes are cast on the nodes, the RSA technique encrypts the vote that is sent to the server system using both node and vote identity number. The system performs consistently and reliably which in return gives good level of confidence of votes count.

关键词： Cryptography RSA algorithm E-voting paper ballot

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：