Study of Evolution By Automating Hardy Weinberg Equilibrium With Machine Learning Techniques In TensorFlow And Keras

Balan, Karthika; Santora, Michael; Faied, Mariam; Carmona-Galindo, Víctor D.

doi:10.1109/accthpa49271.2020.9213202

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…Future studies could furthermore explore applications of drone imagery (e.g., collected during delivery routes) coupled with machine learning algorithms as novel opportunities to monitor microplastic contamination in aquatic features in agricultural or urban landscapes (e.g., waterways, canals, bioswales, etc.) [54].…”

Section: Discussionmentioning

confidence: 99%

Detection of Secondary Microplastics in an Aquatic Mesocosm by Means of Object-Based Image Analysis

Carmona-Valdivieso,

Valdivieso,

Carmona-Galindo

2023

Microplastics

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When plastics are discarded, they do not biodegrade and instead break down over time into progressively smaller particles, termed secondary microplastics, which adversely impact biota and human health as well as persist in the environment for centuries. Our research objective was to evaluate the capabilities of object-based image analyses in detecting compositionally varied microplastics suspended in an aquatic mesocosm under no-slip and turbulent water conditions. We found that the presence of polypropylene, polyethylene terephthalate, and low-density polyethylene microplastic pollution in both single-type and mixed-type suspensions was not detectable by either average red (R), average blue (B), average green (G), or average RBG pixel intensities, but was significantly detectable by means of total RBG pixel intensity from digital imagery of the surface-water. Our findings suggest that object-based image analyses of surface waters to quantify pixel information is better suited for monitoring the presence and absence of suspended microplastics, rather than for the stepwise determination of microplastic concentrations. We propose the development of a smartphone application to facilitate citizen-science monitoring of microplastic contamination as well as comment on future applications utilizing drone imagery to boost cloud-based mapping spatiotemporal plumes.

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Section: Discussionmentioning

confidence: 99%

Detection of Secondary Microplastics in an Aquatic Mesocosm by Means of Object-Based Image Analysis

Carmona-Valdivieso,

Valdivieso,

Carmona-Galindo

2023

Microplastics

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“…Balan et al [36] developed a novel approach to combining Ecology and Engineering using machine learning algorithms. They used the traditional allele frequency database "ALFRED" to demonstrate the efficiency of the built model.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comprehensive Survey of Deep Learning Models Based on Keras Framework

Chicho

Sallow

2021

JSCDM

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Python is one of the most widely adopted programming languages, having replaced a number of those in the field. Python is popular with developers for a variety of reasons, one of which is because it has an incredibly diverse collection of libraries that users can run. The most compelling reasons for adopting Keras come from its guiding principles, particularly those related to usability. Aside from the simplicity of learning and model construction, Keras has a wide variety of production deployment options and robust support for multiple GPUs and distributed training. A strong and easy-to-use free, open-source Python library is the most important tool for developing and evaluating deep learning models. The aim of this paper is to provide the most current survey of Keras in different aspects, which is a Python-based deep learning Application Programming Interface (API) that runs on top of the machine learning framework, TensorFlow. The mentioned library is used in conjunction with TensorFlow, PyTorch, CODEEPNEATM, and Pygame to allow integration of deep learning models such as cardiovascular disease diagnostics, graph neural networks, identifying health issues, COVID-19 recognition, skin tumors, image detection, and so on, in the applied area. Furthermore, the author used Keras's details, goals, challenges, significant outcomes, and the findings obtained using this method.

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Hardy Weinberg Principle Automation : A Comparative study of Neural network and Fuzzy based accessibility model for the study of Evolution

Balan

Santora

Faied

et al. 2022

2022 Second International Conference on Next Generation Intelligent Systems (ICNGIS)

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Study of Evolution By Automating Hardy Weinberg Equilibrium With Machine Learning Techniques In TensorFlow And Keras

Cited by 5 publications

References 16 publications

Detection of Secondary Microplastics in an Aquatic Mesocosm by Means of Object-Based Image Analysis

Detection of Secondary Microplastics in an Aquatic Mesocosm by Means of Object-Based Image Analysis

A Comprehensive Survey of Deep Learning Models Based on Keras Framework

Hardy Weinberg Principle Automation : A Comparative study of Neural network and Fuzzy based accessibility model for the study of Evolution

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