Computer Vision System for Mango Fruit Defect Detection Using Deep Convolutional Neural Network.

Machine learning techniques play a significant role in agricultural applications for computerized grading and quality evaluation of fruits. In the agricultural domain, automation improves the quality, productivity, and economic growth of a country. The quality grading of fruits is an essential measure in the export market, especially defect detection of a fruit's surface.

Precise prediction of multiple anticancer drug efficacy using multi target regression and support vector regression analysis.

Background And Objectives: The prediction of multiple drug efficacies using machine learning prediction techniques based on clinical and molecular attributes of tumors is a new approach in the field of precision medicine of oncology. The selection of suitable and effective therapeutic drugs among the potential drugs is performed computationally considering the tumor features. In this study, we developed and validated machine learning models to predict the efficacy of five anti-cancer drugs according to the clinical and molecular attributes of 30 oral squamous cell carcinoma (OSCC) cohorts.

Hemisection-A Challenge for Perio-endo Lesions: A Case Report.

The perio-endo lesions confront the clinician as far as diagnosis and prognosis of the affected teeth is concerned. Hemisection is the treatment choice for perio-endo lesions to preserve the remaining part of the molar having sound periodontium. It is a resection of periodontally involved root along with the associated crown portion.

Computational models for predicting anticancer drug efficacy: A multi linear regression analysis based on molecular, cellular and clinical data of oral squamous cell carcinoma cohort.

Background And Objectives: The computational prediction of drug responses based on the analysis of multiple clinical features of the tumor will be a novel strategy for accomplishing the long-term goal of precision medicine in oncology. The cancer patients will be benefitted if we computationally account all the tumor characteristics (data) for the selection of most effective and precise therapeutic drug. In this study, we developed and validated few computational models to predict anticancer drug efficacy based on molecular, cellular and clinical features of 31 oral squamous cell carcinoma (OSCC) cohort using computational methods.

Electroencephalogram Signal Classification for Automated Epileptic Seizure Detection Using Genetic Algorithm.

Background: Epilepsy causes when the repeated seizure occurs in the brain. Electroencephalogram (EEG) test provides valuable information about the brain functions and can be useful to detect brain disorder, especially for epilepsy. In this study, application for an automated seizure detection model has been introduced successfully.