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A machine learning approach for abstraction and reasoning problems without large amounts of data

Isik et al. | Jun 25, 2022

A machine learning approach for abstraction and reasoning problems without large amounts of data

While remarkable in its ability to mirror human cognition, machine learning and its associated algorithms often require extensive data to prove effective in completing tasks. However, data is not always plentiful, with unpredictable events occurring throughout our daily lives that require flexibility by artificial intelligence utilized in technology such as personal assistants and self-driving vehicles. Driven by the need for AI to complete tasks without extensive training, the researchers in this article use fluid intelligence assessments to develop an algorithm capable of generalization and abstraction. By forgoing prioritization on skill-based training, this article demonstrates the potential of focusing on a more generalized cognitive ability for artificial intelligence, proving more flexible and thus human-like in solving unique tasks than skill-focused algorithms.

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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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The Effects of Ultraviolet Light on Escherichia coli

Kodoth et al. | Sep 07, 2015

The Effects of Ultraviolet Light on <em>Escherichia coli</em>

In this study E. coli bacteria was exposed to small UV lights currently used in school laboratories to see the effect on colony growth. This project explores how UV radiation methods could be applied in common households to inhibit bacterial growth.

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A juxtaposition of the effects of natural and chemical fertilizers on Ocimum basilicum

Wilson et al. | Jun 03, 2024

A juxtaposition of the effects of natural and chemical fertilizers on <i>Ocimum basilicum</i>
Image credit: The authors

Agricultural fertilizer application is a key innovation in providing enough food to feed the world. Fertilizers come in various types and farmers must choose which fertilizer is the best for their applications. To learn more about the effectiveness of various fertilizers, Wilson and Rasmus studied the effects of natural and chemical fertilizers on growth of basil plants.

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A novel approach for predicting Alzheimer’s disease using machine learning on DNA methylation in blood

Adami et al. | Sep 20, 2023

A novel approach for predicting Alzheimer’s disease using machine learning on DNA methylation in blood
Image credit: National Cancer Institute

Here, recognizing the difficulty associated with tracking the progression of dementia, the authors used machine learning models to predict between the presence of cognitive normalcy, mild cognitive impairment, and Alzheimer's Disease, based on blood DNA methylation levels, sex, and age. With four machine learning models and two dataset dimensionality reduction methods they achieved an accuracy of 53.33%.

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Efficacy of electrolytic treatment on degrading microplastics in tap water

Schroder et al. | Apr 23, 2023

Efficacy of electrolytic treatment on degrading microplastics in tap water
Image credit: Imani

Here seeking to identify a method to remove harmful microplastics from water, the authors investigated the viability of using electrolysis to degrade microplastics in tap water. Compared to control samples, they found electrolysis treatment to significantly the number of net microplastics, suggesting that this treatment could potentially implemented into homes or drinking water treatment facilities.

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Decolorization of textile dyes by edible white rot fungi

Lin et al. | Apr 29, 2022

Decolorization of textile dyes by edible white rot fungi

As fast fashion explodes in popularity, the fashion industry remains one of the most prominent industries responsible for pollution. This pollution includes a lack of treatment for textile dyes that remain toxic or carcinogenic as they persist in wastewater. To resolve this, the authors of this study set out to determine the efficacy of using edible white rot fungi for cell-based biodegradation of textile dyes into harmless chemicals. This method takes advantage of fungi found in excess from the fungi industry, decreasing food waste while addressing textile waste in tandem.

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The effects of algaecides on Spirulina major and non-target organism Daphnia magna

Halepete et al. | Oct 09, 2023

The effects of algaecides on <i>Spirulina major</i> and non-target organism <i>Daphnia magna</i>
Image credit: The authors

Algal blooms pose a threat to ecosystems, but the methods used to combat these blooms might harm more than just the algae. Halepete, Graham, and Lowe-Schmahl demonstrate negative effects of anti-algae treatments on a cyanobacterium (Spirulina major), and the water fleas (Daphnia magna) that live alongside these cyanobacteria.

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