Prediction of the Probabilities of the Transmission of Genetic Traits within Bayesian Logical Inference

Cevri, Mehmet; Üstündağ, Dursun

doi:10.12693/aphyspola.130.45

Cited by 6 publications

(6 citation statements)

References 33 publications

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“…Students are introduced to the fundamental equation underlying Bayesian probabilities and then asked to apply it to the pedigree. A more recent development of this pedigree problem is available in Mathematica from Cevri and Üstündağ (2016).…”

Section: Mendelian Geneticsmentioning

confidence: 99%

Biological Models for Finite Mathematics

Jungck¹

2021

PRIMUS

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Finite Mathematics has become an enormously rich and productive area of contemporary mathematical biology. Fortunately, educators have developed educational modules based upon many of the models that have used Finite Mathematics in mathematical biology research. A sufficient variety of computer modules that employ graph theory (phylogenetic trees, food webs, networks), cellular automata (pattern formation, diffusion limited aggregation), fractals (both measurement and generation of self-similar structures), finite difference equations and deterministic chaos (logistic growth, predator-prey, SIR epidemiology), combinatorics and probability (genetics and evolution), information theory (biodiversity, sequence logos), and Boolean logic (operons) are available to adopt, adapt, and implement. An emphasis has been placed on modules that are freely available, that have been educationally vetted, and that run on a variety of operating systems. Most modules are easy to use, graphically visual, and amenable to modification. In this paper, two different approaches are stressed: (1) "glass box models" that allow students to see equations associated with each cell in a spreadsheet and to modify/extend those models with minimal effort; and, (2) agent-based models that emphasize "bottom-up" modeling and that instantiate the power of massively parallel simulation and address the misconceptions of a "centralized mind-set."

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Section: Mendelian Geneticsmentioning

confidence: 99%

Biological Models for Finite Mathematics

Jungck¹

2021

PRIMUS

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“…They provide a more straightforward interpretation of predictions and have been employed in many areas and have been proven very effective for text categorization [20,21]. NBC classifies the sentences using the frequencies of the words in each sentence.…”

Section: Classification Phasementioning

confidence: 99%

Sentiment Analysis: an Application to Anadolu University

2017

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Social media is a Web 2.0 platform that allows to share content and information without the limitations of time and space. Social media networks have managed to become a part of today's lifestyle and are increasingly gaining importance when viewed from a state perspective. Sentiment analysis refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in source materials. In this study, we focus on social media mining and sentiment analysis for students of an open and distance education system. Anadolu University which has approximately two million students and more than two million graduates, is a well-known institution in Turkey, that offers higher education through contemporary distance education model. Firstly, we have fetched Tweets related to Anadolu University open and distance education system. To perform sentiment analysis, these tweets were analysed by statistical and data mining techniques. Finally, results were visualized.

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“…This method has been applied in many fields for predicting the outcomes of various events [13][14][15]. It applies advanced statistical techniques such as generalized linear models [16] or Monte Carlo simulation [17] and attempts to answer the question: "What might happen in the future?"…”

Section: Predictive Analytics Phasementioning

confidence: 99%

Design of a Machine Learning Based Predictive Analytics System for Spam Problem

2017

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Spamming is the act of abusing an electronic messaging system by sending unsolicited bulk messages. Filtering of these messages is merely another line of defence and does not prevent spam messages from circulating in email systems. This problem causes users to distrust email systems, suspect even legitimate emails and leads to substantial investment in technologies to counter the spam problem. Spammers threaten users by abusing the lack of accountability and verification features of communicating entities. To contribute to the fight against spamming, a cloud-based system that analyses the email server logs and uses predictive analytics with machine learning to build trust identities that model the email messaging behavior of spamming and legitimate servers has been designed. The system constructs trust models for servers, updating them regularly to tune the models. This study proposed that this approach will not only minimize the circulation of spam in email messaging systems, but will also be a novel step in the direction of trust identities and accountability in email infrastructure.

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Prediction of the Probabilities of the Transmission of Genetic Traits within Bayesian Logical Inference

Cited by 6 publications

References 33 publications

Biological Models for Finite Mathematics

Biological Models for Finite Mathematics

Sentiment Analysis: an Application to Anadolu University

Design of a Machine Learning Based Predictive Analytics System for Spam Problem

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