Proceedings of the Nineteenth International Conference on Machine Learning

Objective: Skin diseases are a major global health problem associated with high number of people. With the rapid development of technologies and the application of various data mining techniques in recent years, the progress of dermatological predictive classification has become more and more predictive and accurate. Therefore, development of machine learning techniques, which can effectively differentiate skin disease classification, is of vast importance. The machine learning techniques applied to skin disease prediction so far, no techniques outperforms over all the others. Methods: In this research paper, we present a new method, which applies five different data mining techniques and then developed an ensemble approach that consists all the five different data mining techniques as a single unit. We use informative Dermatology data to analysis different data mining techniques to classify the skin disease and then, an ensemble machine learning method is applied. Results: The proposed ensemble method, which is based on machine learning was tested on Dermatology datasets and classify the type of skin disease in six different classes like include C1: psoriasis, C2: seborrheic dermatitis, C3: lichen planus, C4: pityriasis rosea, C5: chronic dermatitis, C6: pityriasis rubra. The results show that the dermatological prediction accuracy of the test data set is increased compared to a single classifier. Conclusion: The ensemble method used on Dermatology datasets give better performance as compared to different classifier algorithms. Ensemble method gives more accurate and effective skin disease prediction.

http://journal.waocp.org/article_88612_94f63c68180b160947e217ca62901f12.pdf

Classification of Skin Disease using Ensemble Data Mining Techniques.

Comparison of skin disease prediction by feature selection using ensemble data mining techniques

Nowadays, skin disease is a major problem among peoples worldwide. Different machine learning techniques are applied to predict the various classes of skin disease. In this research paper, we have applied six different machine learning algorithm to categorize different classes of skin disease using three ensemble techniques and then a feature selection method to compare the results obtained from different machine learning techniques. In the proposed study, we present a new method, which applies six different data mining classification techniques and then developed an ensemble approach using bagging, AdaBoost, and gradient boosting classifiers techniques to predict the different classes of skin disease. Further, the feature importance method is used to select important 15 features which play a major role in prediction. A subset of the original dataset is obtained after selecting only 15 features to compare the results of used six machine learning techniques and ensemble approach as on the whole dataset. The ensemble method used on skin disease dataset is compared with the new subset of the original dataset obtained from feature selection method. The outcome shows that the dermatological prediction accuracy of the test dataset is increased compared with an individual classifier and a better accuracy is obtained as compared with subset obtained from feature selection method. The ensemble method and feature selection used on dermatology datasets give better performance as compared with individual classifier algorithms. Ensemble method gives more accurate and effective skin disease prediction.

Prediction of Skin Disease Using Ensemble Data Mining Techniques and Feature Selection Method—a Comparative Study

The goal of robust parameter estimation is developing a model which can properly fit to data. Parameter estimation of a geometric model, in presence of noise and error, is an important step in many image processing and computer vision applications. As the random sample consensus (RANSAC) algorithm is one of the most well-known algorithms in this field, there have been several attempts to improve its performance. In this paper, after giving a short review on existing methods, a robust and efficient method that detects the gross outliers to increase the inlier to outlier ratio in a reduced set of corresponding image points is proposed. It has a new hypothesis and verification scheme which utilizes spatial relations between extracted corresponding points in two images. It can also be considered as a preprocessing step for RANSAC to improve the accuracy as well as the runtime of RANSAC in estimating the parameters of a geometric model (such as fundamental and homography matrices). Obviously, like almost all previous works for enhancing RANSAC's runtime, the proposed method does not use heavy and compilicated processes. Performance analysis is performed on a variety of standard challenging datasets for estimating the homography and fundamental matrix (as an applicable case used in the literature, especially in the state-of-the-art methods). The performance is also compared quantitatively to RANSAC, PROSAC, and SCRAMSAC robust estimators to demonstrate its superiority. Experimental results show that the proposed method removes about 50% of outliers in most cases and hence extremely reduces the required runtime of RANSAC, while improving its accuracy.

SC-RANSAC: Spatial consistency on RANSAC

With the advancement in the field of medical research, there has been a copious increase in the data that is stored in the various public and private hospitals, clinics and other places of medical practice. This large store of data needs to be administered in a proper manner so that we can derive useful insights and conclusions using a proper analysis system. Such large amount of data is aptly handled through analyzing the unstructured or structured data by utilizing machine learning algorithms. Predictive analytics, a key component of machine learning algorithms, helps users make enhanced and supervised decisions. Visual Analytics is a tool to cost-effectively sort the exuberant and rapidly incrementing data in the field of medical research. It helps us cope with the assorted data in an organized manner, which the human brain would be able to visualize easily. This would in turn provide new innovative and potential results. This analytics not only provides structured data but also initiates structured thoughts in the mind of humans. As practitioners analyze certain anomalous situations, the process of visual analytics process would provide sorted relevant data related to it. This would in turn decrease the cost of maintaining huge amount of data.

https://priyaltrivedi.com/assets/srs_final_draft1.10.pdf

Docaid: Predictive Healthcare Analytics Using Na\"{i}ve Bayes Classification

RANSAC is a repeating hypothesize-and-verify procedure for parameter estimation and filtering of noise or outlier data. In the traditional approach, this algorithm is evaluated without any prior information on the set of data points which leads to an increase in the number of iterations and compute time. In this paper, we present a GPU based RANSAC algorithm with pre-processing of the assumed sample set of hypothetical inliers by Monte Carlo method. Based on our implementation and results using the Point Cloud Library and NVIDIA CUDA framework for data intensive tasks we obtain significant improvement in the performance of plane segmentation algorithm over the randomly sampled subset of hypothetical inliers. The final consensus set is formed with less number of iterations using our pre-processing model. We can conclude that a pre-processed sample set of hypothetical inliers results in a faster determination of the consensus set consisting of maximum inliers.

/pdf/mc-ransac-a-pre-processing-model-for-ransac-using-monte-3bgnz3xvia.pdf

MC-RANSAC: A Pre-processing Model for RANSAC using Monte Carlo method implemented on a GPU

This paper presents an automated video surveillance system which deals with content monitoring and activity change in the environment. We use Viewpoint Feature Histogram, an image descriptor for object recognition and pose estimation for the purpose of monitoring in the surveillance system. In order to enhance the performance of the system, we exploit the GPU architecture to perform data intensive task of surveillance system and implement it on CUDA-enabled devices. The experimental evaluation on the static data sets and live scenes captured from Microsoft Kinect show that Viewpoint Feature Histogram can be successfully used as an image descriptor in surveillance systems. We also test the performance of the Viewpoint Feature Histogram generation for different data sets on GPU and CPU to conclude that GPU clearly outperforms CPU for larger datasets.

An automated video surveillance system using Viewpoint Feature Histogram and CUDA-enabled GPUs

Querying forms a central part of dealing with data. Hence, it becomes imperative to have efficient data structures and query-search algorithms for data retrieval. Among the different types of data structures, in this paper, we have focused on 4 tree data structures. These are B, Kd, Range and Quad trees. We have made a comparative study of their build processes. Traditionally, the median of dataelements is used for the building of Kd trees. This gives a time complexity of O (k*nlogn) where n is the number of points to be sorted and k is the dimensionality of the points. We propose the building of Kd trees on the basis of mean of the data elements. The proposed algorithm has been shown to reduce the number of computations in the build process. The Kd trees produced by this methodology are balanced. Hence, this provides an improvement over the traditional building mechanism.

/pdf/an-improvement-in-the-build-algorithm-for-kd-trees-using-4uw7pfpuxr.pdf

An improvement in the build algorithm for Kd-trees using mathematical mean

This research aims to provide a novel approach for analysing road crash severity ranking by integrating all injury classified crash types. The road crash data of all Indian states (i.e., Andhrapradesh, Arunachal Pradesh, Bihar, etc.) for 2019 were incorporated to analyse severity rankings by using multi-criteria decision-making (MCDM) methods. Two of these methods – the Analytical Hierarchy Process (AHP) and Technique for Order Performance by Similarity to an Ideal Solution (TOPSIS) – were applied. The application of MCDM methods easily incorporated the injury classified crash data and provided clear rankings. Further, the correlation analysis of rankings provided by both MCDM methods proved the validity of the proposed research. Therefore, this approach is considered to have great potential to reform conventional severity ranking practices.

/pdf/identification-of-road-crash-severity-ranking-by-integrating-md6evwf8.pdf

Priyank Trivedi

Papers

Docaid: Predictive Healthcare Analytics Using Na\"{i}ve Bayes Classification

MC-RANSAC: A Pre-processing Model for RANSAC using Monte Carlo method implemented on a GPU

An automated video surveillance system using Viewpoint Feature Histogram and CUDA-enabled GPUs

An improvement in the build algorithm for Kd-trees using mathematical mean

Identification of Road Crash Severity Ranking by Integrating the Multi-Criteria Decision-Making Approach