Chongwei Shi Advances Biostatistical Methods and Computational Biology Through Deep Learning and Statistical Shape Analysis

A deep learning framework combines convolutional and bidirectional recurrent networks to improve protein function prediction from genomic ...
Frontiers

Comparative evaluation of fast-learning classification algorithms for urban forest tree species identification using EO-1 hyperion hyperspectral imagery

Introduction: Accurate identification of forest tree species is essential for sustainable forest management, biodiversity assessment, and environmental monitoring. Urban forests, in particular, ...
IEEE

Facilitating Seamless Communication through Voice-Enhanced Sign Language and Gesture Recognition with Random Forest Classifier and Graph Convolution Networks

Abstract: An innovative system designed to enhance communication for individuals using sign language. Developed in Python, the system leverages a Random Forest classifier to accurately interpret hand ...
Frontiers

A hybrid fuzzy logic–Random Forest model to predict psychiatric treatment order outcomes: an interpretable tool for legal decision support

Background: Decisions surrounding involuntary psychiatric treatment orders often involve complex clinical, legal, and ethical considerations, especially when patients lack decisional capacity and ...
IEEE

Enhancing Early Detection of Parkinson's Disease with Machine Learning: Insights from Xception Architecture and Random Forest Classifier

Abstract: The degenerative neurological disorder known as Parkinson's disease impacts millions of people around the globe. Lack of dopamine, a substance that helps brain neurons communicate, causes ...
EurekAlert!

Smarter skies: A new AI model turns street cameras into rainfall sensors

A novel framework integrates urban surveillance video data with a two-stage AI pipeline: an enhanced random forest classifier detects rain streaks and selects key image regions, while a hybrid deep ...
Scientific Research Publishing

Kirasich, K., Smith, T. and Sadler, B. (2018) Random Forest vs Logistic Regression: Binary Classification for Heterogeneous Datasets. SMU Data Science Review, 1, Article 9.

ABSTRACT: The Efficient Market Hypothesis postulates that stock prices are unpredictable and complex, so they are challenging to forecast. However, this study demonstrates that it is possible to ...
Scientific Research Publishing

Pazhanikumar, K. and KuzhalVoiMozhi, S.N. (2023) Remote Sensing Image Classification Using Modified Random Forest with Empirical Loss Function through Crowd-Sourced Data ...

ABSTRACT: In this study, multi-source remote sensing data and machine learning algorithms were used to delineate the prospect area of remote sensing geological prospecting in eastern Botswana. Landsat ...
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Assam villagers rescue 12-foot-long python in Boko, forest dept faces flak for apathy

A 12-foot-long python weighing approximately 20 kilograms was rescued by locals of Chandra Village in the Nagarbera Riverine Range under the West Kamrup Division on Friday. India slams Bangladesh for ...