scholarly journals Skin Cancer Diagnostic using Machine Learning Techniques - Stationary Wavelet Transform and Random Forest Classifier

2019 ◽  
Vol 9 (2) ◽  
pp. 4705-4708
Keyword(s):  
Wavelet Transform ◽  
Skin Cancer ◽  
Random Forest ◽  
Sensitivity And Specificity ◽  
Image Features ◽  
The Body ◽  
Machine Learning Techniques ◽  
Stationary Wavelet Transform ◽  
Abnormal Growth ◽  
Learning Techniques

The abnormal growth of skin cells in the skin is known as skin cancer. It develops in skin due to exposure of the sun. Skin cancer spreads into other parts of the body easily. The death rate of skin cancer is increasing day by day, so, early diagnosis of skin cancer is necessary. In this paper, an efficient method for skin image classification using Stationary Wavelet Transform (SWT) based entropy features and Random Forest (RF) classifier is presented. The input skin images are decomposed by SWT. The skin image features are extracted by the entropy of decomposed skin images and classified using RF classifier. The performance of the system is evaluated in terms of accuracy, sensitivity and specificity. The results show the better classification accuracy of 91.5% at the 3rd level of SWT decomposition based on entropy features and RF classifier, and also their sensitivity and specificity are 90 % and 93 %..

Novel similarity measure-based random forest for fingerprint recognition using dual-tree complex wavelet transform and ring projection

2019 ◽  
Vol 34 (02) ◽  
pp. 2050022
Author(s):  
Harinder Kaur ◽  
Gaganpreet Kaur ◽  
Husanbir Singh Pannu
Keyword(s):  
Machine Learning ◽  
Wavelet Transform ◽  
Random Forest ◽  
Similarity Measure ◽  
Fingerprint Recognition ◽  
Machine Learning Techniques ◽  
Complex Wavelet Transform ◽  
Learning Techniques ◽  
Computational Speed ◽  
Complex Wavelet

Designing an efficient fingerprint recognition technique is an ill-posed problem. Recently, many researchers have utilized machine learning techniques to improve the fingerprint recognition rate. The random forest (RF) is found to be one of the extensively utilized machine learning techniques for fingerprint recognition. Although it provides good recognition results at significant computational speed, still there is room for improvement. RF is not so-effective for high-dimensional features and also when features contain both discrete and continuous values at the same time. Therefore, in this paper, a novel similarity measure-based random forest (NRF) is proposed. The proposed technique, initially, computes both mutual information and conditional entropy. Thereafter, it uses three designed if-then rules to obtain final information measure. Additionally, to obtain feature set for fingerprint dataset, dual-tree complex wavelet transform is used to evaluate complex detail coefficients. Thereafter, ring project is considered to compute significant moments from these complex detail coefficients. Finally, information gain-based feature selection technique is used to select potential features. To prevent over-fitting, 20-fold cross validation is also used. Extensive experiments are considered to evaluate the effectiveness of the proposed technique. The comparative analyses reveal that the proposed technique outperforms the existing techniques in terms of accuracy, f-measure, sensitivity, specificity, kappa statistics and computational speed.

A Comprehensive Review on Nanotechnology-Based Innovations in Topical Drug Delivery for the Treatment of Skin Cancer

2020 ◽  
Vol 26 (44) ◽  
pp. 5720-5731 ◽  
Author(s):  
Arun Singh Lalotra ◽  
Vishesh Singh ◽  
Bharat Khurana ◽  
Shelly Agrawal ◽  
Shubham Shrestha ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Cell Carcinoma ◽  
Systemic Exposure ◽  
Chemotherapeutic Agents ◽  
The Body ◽  
Toxic Substances ◽  
Abnormal Growth ◽  
Dermal Layer ◽  
Therapeutic Benefits ◽  
Carrier Systems

Background: Skin is the largest organ of the body and helps to regulate several physiological functions. It acts as a barrier that protects the body against UV-radiation, toxic substances, infections, etc. The abnormal growth of the skin cells is called skin cancer. Different types of skin cancer can be classified as Basal Cell Carcinoma (BCC) and Squamous Cell Carcinoma (SCC); which mainly occur due to chronic exposure to UV- sunlight and pollution. Methods: The conventional topical treatments of skin cancer such as cream, gel, ointment, etc., are more occlusive and thus they do not penetrate deep into the skin (dermal layer) and remain at the upper part of the skin (epidermal layer). The stratum corneum acts as a physiological barrier for the drug-loaded in the conventional formulation. The novel carrier systems have the potential to facilitate the penetration of the drug deep into the skin (dermal layer) because these have less size and higher flexibility than conventional treatment. Conclusion: In the present review, we have discussed various novel carrier systems being investigated for the topical application of chemotherapeutic agents for efficient skin targeting and better dermatological as well as therapeutic benefits with minimal systemic exposure and toxicity.

scholarly journals Learning from Imbalanced Educational Data Using Ensemble Machine Learning Algorithms

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 01) ◽  
pp. 183-195
Author(s):  
Thingbaijam Lenin ◽  
N. Chandrasekaran
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Missing Values ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Adaptive Boosting ◽  
Stochastic Gradient Boosting ◽  
Ensemble Machine Learning ◽  
Learning Techniques ◽  
Student’S Performance

Student’s academic performance is one of the most important parameters for evaluating the standard of any institute. It has become a paramount importance for any institute to identify the student at risk of underperforming or failing or even drop out from the course. Machine Learning techniques may be used to develop a model for predicting student’s performance as early as at the time of admission. The task however is challenging as the educational data required to explore for modelling are usually imbalanced. We explore ensemble machine learning techniques namely bagging algorithm like random forest (rf) and boosting algorithms like adaptive boosting (adaboost), stochastic gradient boosting (gbm), extreme gradient boosting (xgbTree) in an attempt to develop a model for predicting the student’s performance of a private university at Meghalaya using three categories of data namely demographic, prior academic record, personality. The collected data are found to be highly imbalanced and also consists of missing values. We employ k-nearest neighbor (knn) data imputation technique to tackle the missing values. The models are developed on the imputed data with 10 fold cross validation technique and are evaluated using precision, specificity, recall, kappa metrics. As the data are imbalanced, we avoid using accuracy as the metrics of evaluating the model and instead use balanced accuracy and F-score. We compare the ensemble technique with single classifier C4.5. The best result is provided by random forest and adaboost with F-score of 66.67%, balanced accuracy of 75%, and accuracy of 96.94%.

scholarly journals Heart Disease Prediction using Machine Learning Techniques

2021 ◽  
pp. 42-47
Author(s):  
Ramesh Ponnala ◽  
K. Sai Sowjanya
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Linear Model ◽  
Machine Learning Techniques ◽  
Disease Prediction ◽  
Huge Amount ◽  
Healthcare Enterprise ◽  
Learning Techniques ◽  
Accuracy Level

Prediction of Cardiovascular ailment is an important task inside the vicinity of clinical facts evaluation. Machine learning knowledge of has been proven to be effective in helping in making selections and predicting from the huge amount of facts produced by using the healthcare enterprise. on this paper, we advocate a unique technique that pursuits via finding good sized functions by means of applying ML strategies ensuing in improving the accuracy inside the prediction of heart ailment. The severity of the heart disease is classified primarily based on diverse methods like KNN, choice timber and so on. The prediction version is added with special combos of capabilities and several known classification techniques. We produce a stronger performance level with an accuracy level of a 100% through the prediction version for heart ailment with the Hybrid Random forest area with a linear model (HRFLM).

scholarly journals CNN-based Framework using Spatial Dropping for Enhanced Interpretation of Neural Activity in Motor Imagery Classification

2020 ◽  
Author(s):  
Diego Fabian Collazos Huertas ◽  
Andres Marino Alvarez Meza ◽  
German Castellanos Dominguez
Keyword(s):  
Wavelet Transform ◽  
Motor Imagery ◽  
Continuous Wavelet Transform ◽  
Brain Activity ◽  
Machine Learning Techniques ◽  
Continuous Wavelet ◽  
Brain Response ◽  
Power Spectral ◽  
Learning Techniques ◽  
Eeg Data

Abstract Interpretation of brain activity responses using Motor Imagery (MI) paradigms is vital for medical diagnosis and monitoring. Assessed by machine learning techniques, identification of imagined actions is hindered by substantial intra and inter subject variability. Here, we develop an architecture of Convolutional Neural Networks (CNN) with enhanced interpretation of the spatial brain neural patterns that mainly contribute to the classification of MI tasks. Two methods of 2D-feature extraction from EEG data are contrasted: Power Spectral Density and Continuous Wavelet Transform. For preserving the spatial interpretation of extracting EEG patterns, we project the multi-channel data using a topographic interpolation. Besides, we include a spatial dropping algorithm to remove the learned weights that reflect the localities not engaged with the elicited brain response. Obtained results in a bi-task MI database show that the thresholding strategy in combination with Continuous Wavelet Transform improves the accuracy and enhances the interpretability of CNN architecture, showing that the highest contribution clusters over the sensorimotor cortex with differentiated behavior between μ and β rhythms.

Classification study of solvation free energies of organic molecules using machine learning techniques

RSC Advances ◽  
2014 ◽  
Vol 4 (106) ◽  
pp. 61624-61630 ◽  
Author(s):  
N. S. Hari Narayana Moorthy ◽  
Silvia A. Martins ◽  
Sergio F. Sousa ◽  
Maria J. Ramos ◽  
Pedro A. Fernandes
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Organic Molecules ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Models ◽  
Free Energies ◽  
Learning Techniques ◽  
Solvation Free Energies

Classification models to predict the solvation free energies of organic molecules were developed using decision tree, random forest and support vector machine approaches and with MACCS fingerprints, MOE and PaDEL descriptors.

Enhancing alpine glacial lakes detection and mapping using multi-source data and machine learning techniques

2020 ◽  
Author(s):  
Sonam Wangchuk ◽  
Tobias Bolch
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Satellite Images ◽  
Random Forest Classifier ◽  
Machine Learning Techniques ◽  
Glacial Lake ◽  
Glacial Lakes ◽  
Alpine Regions ◽  
Learning Techniques ◽  
Source Data

<p>An accurate detection and mapping of glacial lakes in the Alpine regions such as the Himalayas, the Alps and the Andes are challenged by many factors. These factors include 1) a small size of glacial lakes, 2) cloud cover in optical satellite images, 3) cast shadows from mountains and clouds, 4) seasonal snow in satellite images, 5) varying degree of turbidity amongst glacial lakes, and 6) frozen glacial lake surface. In our study, we propose a fully automated approach, that overcomes most of the above mentioned challenges, to detect and map glacial lakes accurately using multi-source data and machine learning techniques such as the random forest classifier algorithm. The multi-source data are from the Sentinel-1 Synthetic Aperture Radar data (radar backscatter), the Sentinel-2 multispectral instrument data (NDWI), and the SRTM digital elevation model (slope). We use these data as inputs for the rule-based segmentation of potential glacial lakes, where decision rules are implemented from the expert system. The potential glacial lake polygons are then classified either as glacial lakes or non-glacial lakes by the trained and tested random forest classifier algorithm. The performance of the method was assessed in eight test sites located across the Alpine regions (e.g. the Boshula mountain range and Koshi basin in the Himalayas, the Tajiks Pamirs, the Swiss Alps and the Peruvian Andes) of the word. We show that the proposed method performs efficiently irrespective of geographic, geologic, climatic, and glacial lake conditions.</p>

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