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Assessing grass water use efficiency through smartphone imaging and ImageJ analysis

Shen et al. | Jul 27, 2022

Assessing grass water use efficiency through smartphone imaging and ImageJ analysis

Overwatering and underwatering grass are widespread issues with environmental and financial consequences. This study developed an accessible method to assess grass water use efficiency (WUE) combining smartphone imaging with open access color unmixing analysis. The method can be applied in automated irrigation systems or apps, providing grass WUE assessment for regular consumer use.

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The Role of a Mask - Understanding the Performance of Deep Neural Networks to Detect, Segment, and Extract Cellular Nuclei from Microscopy Images

Dasgupta et al. | Jul 06, 2021

The Role of a Mask - Understanding the Performance of Deep Neural Networks to Detect, Segment, and Extract Cellular Nuclei from Microscopy Images

Cell segmentation is the task of identifying cell nuclei instances in fluorescence microscopy images. The goal of this paper is to benchmark the performance of representative deep learning techniques for cell nuclei segmentation using standard datasets and common evaluation criteria. This research establishes an important baseline for cell nuclei segmentation, enabling researchers to continually refine and deploy neural models for real-world clinical applications.

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Reimagize – a digital card-based roleplaying game to improve adolescent girls’ body image

Kumar et al. | Oct 04, 2021

Reimagize – a digital card-based roleplaying game to improve adolescent girls’ body image

Reimagize, a role-playing with decision-making, was conjured, implementing social psychological concepts like counter-stereotyping and perspective-taking. As the game works implicitly to influence body image, it even counters image issues beyond personal body dissatisfaction. This study explored whether a digital role-playing card game, incorporating some of the most common prejudices of body image (like size prejudice, prejudices from the media, etc.) as identified by a digital survey/questionnaire completed by Indian girls aged 11-21, could counter these issues and reduce personal body dissatisfaction.

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Predicting asthma-related emergency department visits and hospitalizations with machine learning techniques

Chatterjee et al. | Oct 25, 2021

Predicting asthma-related emergency department visits and hospitalizations with machine learning techniques

Seeking to investigate the effects of ambient pollutants on human respiratory health, here the authors used machine learning to examine asthma in Lost Angeles County, an area with substantial pollution. By using machine learning models and classification techniques, the authors identified that nitrogen dioxide and ozone levels were significantly correlated with asthma hospitalizations. Based on an identified seasonal surge in asthma hospitalizations, the authors suggest future directions to improve machine learning modeling to investigate these relationships.

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Motion tracking and analysis of spray water droplets studied by high-speed photography using an iPhone X

Geng et al. | Sep 11, 2021

Motion tracking and analysis of spray water droplets  studied by high-speed photography using an iPhone X

Smartphones are not only becoming an inseparable part of our daily lives, but also a low-cost, powerful optical imaging tool for more and more scientific research applications. In this work, smartphones were used as a low-cost, high-speed, photographic alternative to expensive equipment, such as those typically found in scientific research labs, to accurately perform motion tracking and analysis of fast-moving objects. By analyzing consecutive images, the speed and flight trajectory of water droplets in the air were obtained, thereby enabling us to estimate the area of the water droplets landing on the ground.

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A novel approach for early detection of Alzheimer’s disease using deep neural networks with magnetic resonance imaging

Ganesh et al. | Mar 20, 2022

A novel approach for early detection of Alzheimer’s disease using deep neural networks with magnetic resonance imaging

In the battle against Alzheimer's disease, early detection is critical to mitigating symptoms in patients. Here, the authors use a collection of MRI scans, layering with deep learning computer modeling, to investigate early stages of AD which can be hard to catch by human eye. Their model is successful, able to outperform previous models, and detected regions of interest in the brain for further consideration.

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Augmented Reality Chess Analyzer (ARChessAnalyzer): In-Device Inference of Physical Chess Game Positions through Board Segmentation and Piece Recognition using Convolutional Neural Networks

Mehta et al. | Jul 17, 2020

Augmented Reality Chess Analyzer (ARChessAnalyzer): In-Device Inference of Physical Chess Game Positions through Board Segmentation and Piece Recognition using Convolutional Neural Networks

In this study the authors develop an app for faster chess game entry method to help chess learners improve their game. This culminated in the Augmented Reality Chess Analyzer (ARChessAnalyzer) which uses traditional image and vision techniques for chess board recognition and Convolutional Neural Networks (CNN) for chess piece recognition.

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A comparative analysis of machine learning approaches for prediction of breast cancer

Nag et al. | May 11, 2021

A comparative analysis of machine learning approaches for prediction of breast cancer

Machine learning and deep learning techniques can be used to predict the early onset of breast cancer. The main objective of this analysis was to determine whether machine learning algorithms can be used to predict the onset of breast cancer with more than 90% accuracy. Based on research with supervised machine learning algorithms, Gaussian Naïve Bayes, K Nearest Algorithm, Random Forest, and Logistic Regression were considered because they offer a wide variety of classification methods and also provide high accuracy and performance. We hypothesized that all these algorithms would provide accurate results, and Random Forest and Logistic Regression would provide better accuracy and performance than Naïve Bayes and K Nearest Neighbor.

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