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This research employs a novel approach to headcounting by comparing the performance of the ANN algorithm with that of the SVM algorithm. To determine how many individuals are moving under an overhead monitoring system, researchers have developed a revolutionary paper counting approach based on head recognition and tracking. Substances and Techniques: Twenty samples in all were taken from an electronic resource. We maintained a significance level of 0.05, a confidence interval of 95% (95% CI), an enrollment ratio of 1, and a significance level of 80% (G power). The precision value is calculated using the SPSS software. Result: The results of two separate t-tests show that the ANN algorithm outperformed the SVM method, although the difference is not statistically significant (p=0.721; p>0.05). In summary, it seems that the ANN algorithm outperforms the SVM method on the analysed datasets. [ABSTRACT FROM AUTHOR]

Recently, researchers and clinicians have been searching for new technologies to slow down or stop COVID-19 pandemic. The utility of Data Mining (DM) algorithms to suggests new opportunities to combat the spread of the new Coronavirus. This paper suggests a comparative study on data mining approaches to predict COVID19. We used common classification algorithms like the Support Vector Machines, Random Forest, Logistic Regression, K-Nearest Neighbor and Artificial Neural Network with Python simulation to compare it in metrics accuracy, recall, precision and AUC; results showed that Random Forest model had a 98.43% accuracy – which is a higher accuracy than many other previous studies known COVID-19 data mining algorithms. [ABSTRACT FROM AUTHOR]

Malignancy is one of the dangerous sicknesses across numerous nations. In any case, malignant growth can be restored whenever recognized at a beginning phase. Analysts are dealing with medical care for early identification and avoidance of malignant growth. Clinical information has arrived at its most revolutionary potential by giving specialists enormous informational indexes gathered from everywhere the globe. In the current situation, Machine Learning has been broadly utilized in malignancy analysis and guess space. Endurance examination might help the expectation of the beginning stage of sickness, backslide, pre-event of infections, and biomarker recognizable proof. Uses of ML and data mining strategies in the clinical field are the broadest in disease recognition and endurance examination. In this paper, various approaches to distinguish and foresee cellular breakdown in the lungs utilize hybrid Machine learning calculations that incorporate Support Vector Machine and ANN (Artificial Neural Networks). Near investigation of different ML procedures and advances has been done over various kinds of information like clinical information, omics information, picture information, and so forth. [ABSTRACT FROM AUTHOR]

Machine learning has helped a lot in the field of cancer research. For the detection and diagnosis of cancer various algorithms like artificial neural networks, Support Vector Machine, Decision Tree, Bayesian Networks, Naive Bayes and K-Nearest Neighbor etc. have been used. Today Machine learning techniques are widely used in distinguishing and characterizing diseases by means of X-ray and CRT (Cathode Ray Tube) images. Using these techniques, one is attempting to anticipate the probability of building up of cancer growth before the disease occur. In this paper, a review has been done on different approaches used to detect different type of cancer using various machine learning techniques. Further the efficiency of these methods is compared on the basis of various parameters like Accuracy, Sensitivity, Specificity, F-measure and ROC (Receiver Operating Characteristics) Curve. [ABSTRACT FROM AUTHOR]

The water vapor content in atmosphere plays very important role in climate. In this paper the application of GPS signal in meteorology is discussed, which is useful technique that is used to estimate the perceptible water vapor of atmosphere. In this paper various algorithms like artificial neural network, support vector machine and multiple linear regression are use to predict perceptible water vapor. The comparative studies in terms of root mean square error and mean absolute errors are also carried out for all the algorithms. [ABSTRACT FROM AUTHOR]

The aim of statistical modeling is to predict with good accuracy the variable of interest and sometimes to determine the contribution of the independent variables towards the dependent variable. Over the years, researches have always tried to produce better models in order to increase the accuracy of their predictions. This paper will discuss some statistical techniques that were used frequently in environmental research as well as recent techniques and advances in statistical modelling. The first technique that will be discussed is the multiple linear regression (MLR) models. Some other regression models will also be given. Next probability distributions will be discussed. This is to obtain statistical distributions that best fit the variable of interest. This will enable the researcher to predict the return period of an event. The application of Bayesian statistics has increased over the years. The used of least squares method in MLR and hypothesis testing is known as classical statistical techniques. The Bayesian statistics is based on the probability distributions of the prior and likelihood of the Bayes theorem. The results of using classical and Bayesian statistics will be discussed. Machine learning method will be deliberated next. Machine learning is the application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being programmed explicitly. Some machine learning methods that will be discussed are boosted regression trees, artificial neural networks and support vector machines. Finally, hybrid models that are the combination of two statistical models will be deliberated next. It is hoped that by combining two statistical models, a more accurate and improved model will be obtained. [ABSTRACT FROM AUTHOR]

In view of the problem of low detection accuracy and the long detection time of support vector machine, which directly applied to the network intrusion detection system. Optimization of SVM parameters can greatly improve the detection accuracy, but it can not be applied to high-speed network because of the long detection time, a method based on Kohonen neural network feature selection is proposed to reduce the optimization time of support vector machine parameters. Firstly, this paper is to calculate the weights of the KDD99 network intrusion data by Kohonen network and select feature by weight. Then, after the feature selection is completed, genetic algorithm (GA) and grid search method are used for parameter optimization to find the appropriate parameters and classify them by support vector machines. By comparing experiments, it is concluded that feature selection can reduce the time of parameter optimization, which has little influence on the accuracy of classification. Trie experiments suggest that the support vector machine can be used in the network intrusion detection system and reduce the missing rate. [ABSTRACT FROM AUTHOR]

In this paper we make a comprehensive review regarding the use of neural networks in automated medical diagnosis, with a special emphasis in Support Vector Machines (SVMs), which are specialized types of neural functions. Through the study, we see that, in many cases, symptoms and diseases can be efficiently predicted by neural systems, while SVMs are increasingly used in medical diagnosis due to their accurate classification characteristics. [ABSTRACT FROM AUTHOR]