machine learning algorithms using (256 risultati)

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Taschenbuch. Condizione: Neu. Neuware -In the world, there are many rare and incurable diseases that need to be detected. The manual detection of those diseases requires more cost and time. Autism is one of the main diseases that are increasing at a higher rate and its detection techniques are very challenging. But the early detection of autism can save a patient or increase their lifetime. So this work concentrates on developing machine learning algorithms to detect autism. Our project¿s main aim is to detect whether the patient is having autism or not. The appropriate dataset is used with eight machine learning algorithm and their performance are evaluated to find the best algorithm for autism detection.Books on Demand GmbH, Überseering 33, 22297 Hamburg 52 pp. Englisch.

Taschenbuch. Condizione: Neu. Neuware -The Data mining technique can be used in collaboration With a K-NN classifiers, Naïve Bayes, Random Forest, Support Vector Machine algorithms which used in diagnosing Swine flu disease virus affected across different persons based on their Symptoms. This proposed approach showed some Promising results which may lead to further attempts to utilize information technology for diagnosing virus from which patients are suffering from Swine flu. Here we used K-NN Classification, Naïve Bayes, Random Forest, SVM rules which are easy to interpret. In future, we will try to get more the accuracy results for the swine flu disease which helps to find different parameters using different data mining techniques as per suggested by doctors.Books on Demand GmbH, Überseering 33, 22297 Hamburg 76 pp. Englisch.

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Taschenbuch. Condizione: Neu. Neuware -Breast cancer is the most common type of cancer in women. When cancers are found early, they can often be cured. There are some devices that detect the breast cancer but many times they lead to false positives, which results in patients undergoing painful, expensive surgeries that were not even necessary. These type of cancers are called benign which do not require surgeries and we can reduce these unnecessary surgeries by using Machine Learning. We take a dataset of the previous breast cancer patients and train the model to predict whether the cancer is benign or malignant. The objective of these predictions is to assign patients to either a benign group that is noncancerous or a malignant group that is cancerous. These predictions will help doctors to do surgeries only when the cancer is malignant, thus reducing the unnecessary surgeries for woman.Books on Demand GmbH, Überseering 33, 22297 Hamburg 68 pp. Englisch.

Taschenbuch. Condizione: Neu. Neuware -Every year, a huge number of children are missing in India. The number of missing cases is untraced. This Book proposes an innovative usage of machine learning techniques for the identification of the missing children. The Public can take the photograph of the suspicious child and upload in the common portal by specifying their location and contact information. The uploaded image was automatically compared to the recorded images of the missing child in the database. The supervisor stores the details of the missing child in the repository with the details of the child. Classification is carried out with input image and the best match is chosen from a registry of missing children. A predictive model is trained to recognize the missing child from the repository.Books on Demand GmbH, Überseering 33, 22297 Hamburg 60 pp. Englisch.

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Doctoral Thesis / Dissertation from the year 2017 in the subject Engineering - Computer Engineering, grade: 100.00/100.00, Çukurova University, language: English, abstract: The purpose of this thesis is twofold. The first purpose is to develop new hybrid feature selection-based maximal oxygen uptake (VO2max) prediction models using for the first time the double and triple combinations of maximal, submaximal and questionnaire variables. Several machine learning methods including Support Vector Machine, artificial neural network-based and tree-structured methods combined individually with three feature selectors Relief-F, minimum redundancy maximum relevance (mRMR) and maximum-likelihood feature selector (MLFS) have been applied for model development. The second purpose is to design a new ensemble feature selector, which aggregates the consensus properties of Relief-F, mRMR and MLFS to produce more robust decisions about the set of relevantly identified VO2max predictors and to create more accurate prediction models. Using 10-fold cross validation on three different datasets, the performance of prediction models has been evaluated by calculating their multiple correlation coefficients (R¿s) and root mean squared errors (RMSE¿s). The results show that compared with the results of the other regular feature selection-based models in literature, the reported values of R and RMSE of the hybrid models in this thesis are considerably more accurate. Furthermore, prediction models based on the proposed ensemble feature selector outperform the models created by individually using the Relief-F, mRMR or MLFS, achieving similar or ideally up to 12.46% lower error rates on the average.

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Bachelor Thesis from the year 2024 in the subject Computer Science - Commercial Information Technology, grade: 1.0, Frankfurt School of Finance & Management, language: English, abstract: In a world characterized by increasingly complex financial markets, the prediction of financial crises is a constant challenge. This bachelor thesis investigates the use of machine learning, in particular regression algorithms, to analyze and predict financial crises based on macroeconomic data. By building six different regression models and optimizing them using cross-validation and GridSearch, the feasibility of using these technologies for accurate predictions is discussed. Although traditional models show limited effectiveness, the integration of machine learning, especially kNN algorithms, reveals significant potential for improving prediction accuracy. The paper highlights the importance of classification algorithms and provides crucial insights for application in real-world scenarios to provide valuable tools for policy and business decision makers.

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Taschenbuch. Condizione: Neu. Neuware -Several machine learning algorithms have been contributed to this thesis to overcome a forgery image issue and some appropriate datasets have played a vital role to provide good outcomes. In the first chapter of this thesis, a brief overview of the literature reviews, the structure of the thesis, the research questions, and aims are going to be presented. The main objective behind writing this thesis is to examine a number of machine learning algorithms and assess their performance on suitable datasets that serve the main goal of the thesis. In order to examine these datasets to fulfill the objective, we will have to use one of the programming languages such as Python which is very suitable for the purpose of the thesis as it has a number of libraries that make it easy to implement and examine.Books on Demand GmbH, Überseering 33, 22297 Hamburg 104 pp. Englisch.

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Taschenbuch. Condizione: Neu. Neuware -The goal of this book is to provide a more effective way to extract features with highly important information to a specific disease, i.e. informative features, using correlation based rough set feature extraction method (RSs), rough set, genetic algorithms (GAs) and its variants, fuzzy-rough set, nearest neighbor, decision tree algorithms and partial least square method and some adaptive neural networks due to their learning abilities to construct hypotheses that can explain complex relationships in the data. This research explores the effectiveness of integrated and hybrid feature extraction methods proposed in the following chapters, in analyzing gene expression activities, based on a specific tumor disease and identifying the informative genes that underlie different precision levels in the extraction process. The identified gene subset may give an enhanced insight on the gene-gene interaction in response to different stages of abnormal cell growth which could be vital in designing treatment strategies to prevent any progression of abnormal cells.Books on Demand GmbH, Überseering 33, 22297 Hamburg 192 pp. Englisch.

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Taschenbuch. Condizione: Neu. Neuware -Education Data Mining is vividly advancing in the field of research due to the increasing amount of data on a daily basis. EDM deals with extracting meaningful information from the education setting. To analyse and understand the ever-growing education data, Machine learning algorithms are employed to classify and cluster the datasets. Research in this field focuses on understanding the behaviour analysis of students, classifying the students to predict the academic outcome, clustering the students based on various factors that can influence the performance and many more. Many researchers have recommended a recommender system to achieve the goal of identifying and classifying the students based on their performance.Books on Demand GmbH, Überseering 33, 22297 Hamburg 148 pp. Englisch.