scholarly journals Malaria Outbreak Detection with Machine Learning Methods

2020 ◽  
Author(s):  
Gurcan Comert ◽  
Negash Begashaw ◽  
Ayse Turhan-Comert
Keyword(s):  
Machine Learning ◽  
Binary Classification ◽  
False Negative ◽  
Machine Learning Techniques ◽  
Outbreak Detection ◽  
Maximum Temperature ◽  
Testing Dataset ◽  
Learning Techniques ◽  
Sample Data ◽  
Malaria Outbreak

AbstractIn this paper, we utilized and compared selected machine learning techniques to detect malaria out-break using observed variables of maximum temperature, minimum temperature, humidity, rainfall amount, positive case, and Plasmodium Falciparum rate. Random decision tree, logistic regression, and Gaussian processes are specially analyzed and adopted to be applied for malaria outbreak detection. The problem is a binary classification with outcomes of outbreak or no outbreak. Sample data provided in the literature from Maharashtra, India is used. Performance of the models are compared with the results from similar studies. Based on the sample data used, we were able to detect the malaria outbreak without any false positive or false negative errors in the testing dataset.

scholarly journals Predicting Takeover Success Using Machine Learning Techniques

2012 ◽  
Vol 10 (10) ◽  
pp. 547
Author(s):  
Mei Zhang ◽  
Gregory Johnson ◽  
Jia Wang
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Binary Classification ◽  
Classification Problem ◽  
Machine Learning Techniques ◽  
Success Prediction ◽  
Support Vector ◽  
Font Size ◽  
Network Support ◽  
Learning Techniques

<span style="font-family: Times New Roman; font-size: small;"> </span><p style="margin: 0in 0.5in 0pt; text-align: justify; mso-pagination: none; mso-layout-grid-align: none;" class="MsoNormal"><span style="color: black; font-size: 10pt; mso-themecolor: text1;"><span style="font-family: Times New Roman;">A takeover success prediction model aims at predicting the probability that a takeover attempt will succeed by using publicly available information at the time of the announcement.<span style="mso-spacerun: yes;"> </span>We perform a thorough study using machine learning techniques to predict takeover success.<span style="mso-spacerun: yes;"> </span>Specifically, we model takeover success prediction as a binary classification problem, which has been widely studied in the machine learning community.<span style="mso-spacerun: yes;"> </span>Motivated by the recent advance in machine learning, we empirically evaluate and analyze many state-of-the-art classifiers, including logistic regression, artificial neural network, support vector machines with different kernels, decision trees, random forest, and Adaboost.<span style="mso-spacerun: yes;"> </span>The experiments validate the effectiveness of applying machine learning in takeover success prediction, and we found that the support vector machine with linear kernel and the Adaboost with stump weak classifiers perform the best for the task.<span style="mso-spacerun: yes;"> </span>The result is consistent with the general observations of these two approaches.</span></span></p><span style="font-family: Times New Roman; font-size: small;"> </span>

scholarly journals Machine learning techniques as an efficient alternative diagnostic tool for COVID-19 cases

2021 ◽  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sensitivity And Specificity ◽  
Predictive Power ◽  
Characteristic Curve ◽  
False Negative ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
X Ray ◽  
Learning Techniques

Background: The SARS-CoV-2 virus has demonstrated the weakness of many health systems worldwide, creating a saturation and lack of access to treatments. A bottleneck to fight this pandemic relates to the lack of diagnostic infrastructure for early detection of positive cases, particularly in rural and impoverished areas of developing countries. In this context, less costly and fast machine learning (ML) diagnosis-based systems are helpful. However, most of the research has focused on deep-learning techniques for diagnosis, which are computationally and technologically expensive. ML models have been mainly used as a benchmark and are not entirely explored in the existing literature on the topic of this paper. Objective: To analyze the capabilities of ML techniques (compared to deep learning) to diagnose COVID-19 cases based on X-ray images, assessing the performance of these techniques and using their predictive power for such a diagnosis. Methods: A factorial experiment was designed to establish this power with X-ray chest images of healthy, pneumonia, and COVID-19 infected patients. This design considers data-balancing methods, feature extraction approaches, different algorithms, and hyper-parameter optimization. The ML techniques were evaluated based on classification metrics, including accuracy, the area under the receiver operating characteristic curve (AUROC), F1-score, sensitivity, and specificity. Results: The design of experiment provided the mean and its confidence intervals for the predictive capability of different ML techniques, which reached AUROC values as high as 90% with suitable sensitivity and specificity. Among the learning algorithms, support vector machines and random forest performed best. The down-sampling method for unbalanced data improved the predictive power significantly for the images used in this study. Conclusions: Our investigation demonstrated that ML techniques are able to identify COVID-19 infected patients. The results provided suitable values of sensitivity and specificity, minimizing the false-positive or false-negative rates. The models were trained with significantly low computational resources, which helps to provide access and deployment in rural and impoverished areas.

Thwarting Spam on Facebook

2018 ◽  
pp. 51-70
Author(s):  
Arti Jain ◽  
Reetika Gairola ◽  
Shikha Jain ◽  
Anuja Arora
Keyword(s):  
Machine Learning ◽  
Online Social Networks ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Testing Dataset ◽  
Learning Techniques ◽  
Textual Image ◽  
Combined Feature ◽  
Entire Dataset ◽  
F Measure

Spam on the online social networks (OSNs) is evolving as a prominent problem for the users of these networks. Spammers often use certain techniques to deceive the OSN users for their own benefit. Facebook, one of the leading OSNs, is experiencing such crucial problems at an alarming rate. This chapter presents a methodology to segregate spam from legitimate posts using machine learning techniques: naïve Bayes (NB), support vector machine (SVM), and random forest (RF). The textual, image, and video features are used together, which wasn't considered by the earlier researchers. Then, 1.5 million posts and comments are extracted from archival and real-time Facebook data, which is then pre-processed using RStudio. A total of 30 features are identified, out of which 10 are the best informative for identification of spam vs. ham posts. The entire dataset is shuffled and divided into three ratios, out of which 80:20 ratio of training and testing dataset provides the best result. Also, RF classifier outperforms NB and SVM by achieving overall F-measure 89.4% on the combined feature set.

scholarly journals Predictive Models of Student College Commitment Decisions Using Machine Learning

Data ◽  
2019 ◽  
Vol 4 (2) ◽  
pp. 65 ◽  
Author(s):  
Kanadpriya Basu ◽  
Treena Basu ◽  
Ron Buckmire ◽  
Nishu Lal
Keyword(s):  
Machine Learning ◽  
Liberal Arts ◽  
Optimal Allocation ◽  
Binary Classification ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Student College

Every year, academic institutions invest considerable effort and substantial resources to influence, predict and understand the decision-making choices of applicants who have been offered admission. In this study, we applied several supervised machine learning techniques to four years of data on 11,001 students, each with 35 associated features, admitted to a small liberal arts college in California to predict student college commitment decisions. By treating the question of whether a student offered admission will accept it as a binary classification problem, we implemented a number of different classifiers and then evaluated the performance of these algorithms using the metrics of accuracy, precision, recall, F-measure and area under the receiver operator curve. The results from this study indicate that the logistic regression classifier performed best in modeling the student college commitment decision problem, i.e., predicting whether a student will accept an admission offer, with an AUC score of 79.6%. The significance of this research is that it demonstrates that many institutions could use machine learning algorithms to improve the accuracy of their estimates of entering class sizes, thus allowing more optimal allocation of resources and better control over net tuition revenue.

Machine learning-based approaches for disease gene prediction

2020 ◽  
Vol 19 (5-6) ◽  
pp. 350-363
Author(s):  
Duc-Hau Le
Keyword(s):  
Machine Learning ◽  
Disease Gene ◽  
Gene Prediction ◽  
Binary Classification ◽  
Training Sample ◽  
Machine Learning Techniques ◽  
Disease Genes ◽  
Disease Gene Prediction ◽  
Learning Techniques ◽  
In The Beginning

Abstract Disease gene prediction is an essential issue in biomedical research. In the early days, annotation-based approaches were proposed for this problem. With the development of high-throughput technologies, interaction data between genes/proteins have grown quickly and covered almost genome and proteome; thus, network-based methods for the problem become prominent. In parallel, machine learning techniques, which formulate the problem as a classification, have also been proposed. Here, we firstly show a roadmap of the machine learning-based methods for the disease gene prediction. In the beginning, the problem was usually approached using a binary classification, where positive and negative training sample sets are comprised of disease genes and non-disease genes, respectively. The disease genes are ones known to be associated with diseases; meanwhile, non-disease genes were randomly selected from those not yet known to be associated with diseases. However, the later may contain unknown disease genes. To overcome this uncertainty of defining the non-disease genes, more realistic approaches have been proposed for the problem, such as unary and semi-supervised classification. Recently, more advanced methods, including ensemble learning, matrix factorization and deep learning, have been proposed for the problem. Secondly, 12 representative machine learning-based methods for the disease gene prediction were examined and compared in terms of prediction performance and running time. Finally, their advantages, disadvantages, interpretability and trust were also analyzed and discussed.

scholarly journals Efficient Cognitive Fog Computing for Classification of Network Cyberattacks Using Machine Learning

2020 ◽  
pp. 176-184
Author(s):  
A. V. Deorankar ◽  
Shiwani S. Thakare
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Binary Classification ◽  
Fog Computing ◽  
Machine Learning Techniques ◽  
Machine Learning Classification ◽  
Detection Techniques ◽  
Learning Techniques ◽  
Iot Devices ◽  
Cloud Servers

IoT is the network which connects and communicates with billions of devices through the internet and due to the massive use of IoT devices, the shared data between the devices or over the network is not confidential because of increasing growth of cyberattacks. The network traffic via loT systems is growing widely and introducing new cybersecurity challenges since these loT devices are connected to sensors that are directly connected to large-scale cloud servers. In order to reduce these cyberattacks, the developers need to raise new techniques for detecting infected loT devices. In this work, to control over this cyberattacks, the fog layer is introduced, to maintain the security of data on a cloud. Also the working of fog layer and different anomaly detection techniques to prevent the cyberattacks has been studied. The proposed AD-IoT can significantly detect malicious behavior using anomalies based on machine learning classification before distributing on a cloud layer. This work discusses the role of machine learning techniques for identifying the type of Cyberattacks. There are two ML techniques i.e. RF and MLP evaluated on the USNW-NB15 dataset. The accuracy and false alarm rate of the techniques are assessed, and the results revealed the superiority of the RF compared with MLP. The Accuracy measures by classifiers are 98 and 53 of RF and MLP respectively, which shows a huge difference and prove the RF as most efficient algorithm with binary classification as well as multi- classification.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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