Demographic

A craniometry study was undertaken to obtain anthropometric measurements of three hundred and five (305) medical staff within Trinidad & Tobago which is a twin island republic situated in the Caribbean. A non-contact measurement method was used involving 3D scanning equipment to record the geometry of each subject’s head as a digital file. The digital files were then processed using CAD software to obtain measurements for twenty-two (22) facial points of interest. In addition, the gender of each staff member was recorded.

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189 Views

This study investigates whether the ingredients listed on restaurant menus can provide insights into a city's socioeconomic status. Using data from an online food delivery system, the study compares menu items with local education rates and rental prices. A machine learning model is developed to predict menu prices based on ingredients and socioeconomic factors. An efficiency metric is proposed to cluster restaurants to address autocorrelation, comparing ingredient averages to socioeconomic indicators.

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193 Views

This survey dataset delves into the diverse experiences and perspectives of individuals, focusing on key aspects of their educational journey and subsequent career choices. Comprising more than 60 questions or attributes,respondents were asked to share insights into their personal background, educational history, university preferences, and current professional status. The questionnaire covers a range of topics, including high school experiences, university decision-making criteria, major selection influences, and post-graduation outcomes.

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848 Views

Seven years of water consumption data, along with population data, were manually collected in collaboration with the local municipality office. This data was then combined with climatic data to model the proposed machine learning algorithm. The weather data was recorded for a period of 7 years using precise meteorological instruments installed in Islamabad at coordinates 33.64° N and 72.98° E, with an elevation of 500 meters above sea level.

 

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685 Views

The dataset analyzed in this study is the result of a systematic literature review and a crowdsourced mini-project that aimed to identify and validate metrics relevant to maternal and neonatal healthcare examinations. The study involved a diverse group of participants, including 193 registered medical personnel from reputable institutions and 161 non-medical individuals who were active on various social media platforms related to maternal and neonatal healthcare.

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620 Views

Data Set is about a Paper on the Impulse Buying Behaviour of the consumers.

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11 Views

One of the most consequential creations in the human evolution phase is handwriting. Due to writing, today we are conveying our reflections, making business pacts, rendering an understandable world and making hitherto tasks austerer. Determining gender using offline handwriting is an applied research problem in forensics, psychology, and security applications, and with technological evolution, the need is growing. The general problem of gender detection from handwriting poses many difficulties resulting from interpersonal and intrapersonal differences.

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1062 Views

Gestational diabetes is a type of high blood sugar that develops during pregnancy. It can occur at any stage of pregnancy and cause problems for both the mother and the baby, during and after birth. The risks can be reduced if they are early detected and managed, especially in areas where only periodic tests of pregnant women are available. Intelligent systems designed by machine learning algorithms are remodelling all fields of our lives, including the healthcare system. This study proposes a combined prediction model to diagnose gestational diabetes.

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3371 Views

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