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Project targeted at understanding any relationship between the availability of mental health care service providers and how this might impact the local incidence of crime. The study is a statistical exploration of USA healthcare shortage areas and reported crime data from the FBI Crime database.
Project targeted at understanding any relationship between the availability of mental health care service providers and how this might impact the local incidence of crime. The study is a statistical exploration of USA healthcare shortage areas and reported crime data from the FBI Crime database.
This project explores the use of machine learning to extract simple logical rules for distinguishing edible and poisonous mushrooms. The dataset consists of hypothetical samples corresponding to 23 species of gilled mushrooms from the Audubon Field Guide. The objective is to investigate how well machine learning algorithms can extract simple logic, such as logical disjunction (inclusive OR). The project demonstrates that by using the features 'odor' and 'spore print color', it is possible to distinguish if a mushroom is poisonous. Data treatment includes boht non-ordinal and ordinal encoding with the 'odor' feature.database.
This project explores the use of machine learning to extract simple logical rules for distinguishing edible and poisonous mushrooms. The dataset consists of hypothetical samples corresponding to 23 species of gilled mushrooms from the Audubon Field Guide. The objective is to investigate how well machine learning algorithms can extract simple logic, such as logical disjunction (inclusive OR). The project demonstrates that by using the features 'odor' and 'spore print color', it is possible to distinguish if a mushroom is poisonous. Data treatment includes boht non-ordinal and ordinal encoding with the 'odor' feature.database.
NLP has emerged as a significant area of research in mental health classification. A narrative review of 399 studies from 10,467 records revealed an upward trend in research focused on mental illness detection using NLP. Deep learning methods have demonstrated superior performance compared to traditional machine learning approaches. Topic models have been used to understand differences in language usage between individuals with depression and those without. Data cleansing techniques such as tokenization, stop word removal, handling null entries, punctuation removal, and lemmatization are commonly employed.
NLP has emerged as a significant area of research in mental health classification. A narrative review of 399 studies from 10,467 records revealed an upward trend in research focused on mental illness detection using NLP. Deep learning methods have demonstrated superior performance compared to traditional machine learning approaches. Topic models have been used to understand differences in language usage between individuals with depression and those without. Data cleansing techniques such as tokenization, stop word removal, handling null entries, punctuation removal, and lemmatization are commonly employed.
(IOD Data Science and AI Certification: Capstone Project) This project revolves around the development and understanding of why crystalline materials exhibit particular physical properties. This knowledge is necessary specifically in the areas of design, manufacturing, and quality control. The primary goal is to enable the swift interpretation of X-ray images, which are crucial for understanding microscopic structure and optimizing material properties. Industries such as semiconductors, energy storage, pharmaceuticals, ceramics, agrochemicals, and thin-film materials can benefit from this project, as it addresses research, manufacturing, engineering, quality control, and patent-related aspects. The challenge lies in automating the interpretation of X-ray images, which currently relies on physical modelling and analysis expertise. The objective is to streamline the development cycle by training a convolutional neural network (CNN) with simulated X-ray images, taking inspiration from similar CNN applications in analysing biological samples.
(IOD Data Science and AI Certification: Capstone Project) This project revolves around the development and understanding of why crystalline materials exhibit particular physical properties. This knowledge is necessary specifically in the areas of design, manufacturing, and quality control. The primary goal is to enable the swift interpretation of X-ray images, which are crucial for understanding microscopic structure and optimizing material properties. Industries such as semiconductors, energy storage, pharmaceuticals, ceramics, agrochemicals, and thin-film materials can benefit from this project, as it addresses research, manufacturing, engineering, quality control, and patent-related aspects. The challenge lies in automating the interpretation of X-ray images, which currently relies on physical modelling and analysis expertise. The objective is to streamline the development cycle by training a convolutional neural network (CNN) with simulated X-ray images, taking inspiration from similar CNN applications in analysing biological samples.
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