scholarly journals GENRE PREDICTION FOR MUSIC RECOMMENDATION USING MACHINE LEARNING

2020 ◽  
pp. 206-210
Author(s):  
Arpit Seth
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Music Recommendation ◽  
Age And Gender ◽  
Vast Number ◽  
The World ◽  
And Gender

Music applications are one of the most used applications in the world. Consumers can hear the song they like but difficult for them to find songs from the vast number of songs list. The flow of this paper is to increase the efficiency of music recommendation in terms of the genre based on the decision-tree which helps the users to get the music according to their preferences. This model uses age and gender as an input set and genre as output. The model will predict the genre according to age and gender and the decision tree helps to reduce the complexity of the model.

scholarly journals Exploring Impact of Age and Gender on Sentiment Analysis Using Machine Learning

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 374 ◽  
Author(s):  
Sudhanshu Kumar ◽  
Monika Gahalawat ◽  
Partha Pratim Roy ◽  
Debi Prosad Dogra ◽  
Byung-Gyu Kim
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Age Groups ◽  
Modern World ◽  
Support Vector ◽  
Digital Information ◽  
Age And Gender ◽  
And Gender ◽  
The Impact

Sentiment analysis is a rapidly growing field of research due to the explosive growth in digital information. In the modern world of artificial intelligence, sentiment analysis is one of the essential tools to extract emotion information from massive data. Sentiment analysis is applied to a variety of user data from customer reviews to social network posts. To the best of our knowledge, there is less work on sentiment analysis based on the categorization of users by demographics. Demographics play an important role in deciding the marketing strategies for different products. In this study, we explore the impact of age and gender in sentiment analysis, as this can help e-commerce retailers to market their products based on specific demographics. The dataset is created by collecting reviews on books from Facebook users by asking them to answer a questionnaire containing questions about their preferences in books, along with their age groups and gender information. Next, the paper analyzes the segmented data for sentiments based on each age group and gender. Finally, sentiment analysis is done using different Machine Learning (ML) approaches including maximum entropy, support vector machine, convolutional neural network, and long short term memory to study the impact of age and gender on user reviews. Experiments have been conducted to identify new insights into the effect of age and gender for sentiment analysis.

scholarly journals Hubungan antara Demografi Konsumen dengan Kepuasan terhadap Layanan Paket Perawatan Spa di GY Spa Jakartal

2010 ◽  
Vol 1 (2) ◽  
pp. 561
Author(s):  
Hera Oktadiana ◽  
Yuliana Yuliana
Keyword(s):  
Consumer Satisfaction ◽  
Age And Gender ◽  
Related Services ◽  
The World ◽  
And Gender

Spa industry has been growing quite fast in many parts of the world, including Indonesia. The prospect of spa business is still very positive and wide open. There are many varieties of spas offered, such as day spas, hotel-based spas, resort spas and destination spas. Regardless of the type of spa, most spa operators provide massage and nutrition related services. GY Spa, one of the best spa operators in Jakarta offers several spa packages which include body massage, body treatment, body scrub, face massage and aromatheraphy. This research aims to find the correlation between the demographic segmentation and the consumer satisfaction towards the spa packages in GY Spa. Based on the research, it is found that education, occupation and nationality have no correlation toward the consumer satisfaction, while age and gender shown to have correlation. Female and the younger customers are more satisfied with the spa packages offered by GY Spa. 

scholarly journals Signal Processing and Analysis of a Person on the Basis of Time, Age and Gender

2020 ◽  
Vol 9 (5) ◽  
pp. 1135-1139
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Constant Time ◽  
Age Group ◽  
Learning Tools ◽  
Age And Gender ◽  
Human Speech ◽  
And Gender ◽  
Two Parameters ◽  
Google Search

Nowadays, signal processing is very common in machine learning. Tools like MATLAB and PRAAT made this really easy. Best example is google search using voice, this can be mostly beneficial for illiterate people and children. I have collected the sample of speech of 160 people of different age group, different genders, and at different and constant time as databases and then these databases are analysed.. Each database consists of samples of human speech uttering the same first 10 alphabets of hindi varnamala .I have used two parameters i.e. formants and intensity for analysing the results of database.I have also characterized it into the form of constant time and variant time. Our aim is to identify the voices of a person on the basis of age and gender through different paremeters like frequency and formants

scholarly journals Machine Learning Techniques for Human Age and Gender Identification Based on Teeth X-Ray Images

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
K. C. Santosh ◽  
Nijalingappa Pradeep ◽  
Vikas Goel ◽  
Raju Ranjan ◽  
Ekta Pandey ◽  
...  
Keyword(s):  
Machine Learning ◽  
Digital Images ◽  
Automated System ◽  
Machine Learning Techniques ◽  
Human Beings ◽  
Age And Gender ◽  
X Ray ◽  
Learning Techniques ◽  
Gender And Age ◽  
And Gender

The use of digital medical images is increasing with advanced computational power that has immensely contributed to developing more sophisticated machine learning techniques. Determination of age and gender of individuals was manually performed by forensic experts by their professional skills, which may take a few days to generate results. A fully automated system was developed that identifies the gender of humans and age based on digital images of teeth. Since teeth are a strong and unique part of the human body that exhibits least subject to risk in natural structure and remains unchanged for a longer duration, the process of identification of gender- and age-related information from human beings is systematically carried out by analyzing OPG (orthopantomogram) images. A total of 1142 digital X-ray images of teeth were obtained from dental colleges from the population of the middle-east part of Karnataka state in India. 80% of the digital images were considered for training purposes, and the remaining 20% of teeth images were for the testing cases. The proposed gender and age determination system finds its application widely in the forensic field to predict results quickly and accurately. The prediction system was carried out using Multiclass SVM (MSVM) classifier algorithm for age estimation and LIBSVM classifier for gender prediction, and 96% of accuracy was achieved from the system.

scholarly journals Air Quality in Indoor and Characteristics of Workers with Sick Building Syndrome at Mall

2020 ◽  
Vol 16 (1) ◽  
pp. 121-129
Author(s):  
Putri Damayanty Simatupang ◽  
Devi Nuraini Santi ◽  
Irnawati Marsaulina
Keyword(s):  
Air Quality ◽  
Indoor Air ◽  
Sick Building Syndrome ◽  
Public Place ◽  
Cross Sectional ◽  
Age And Gender ◽  
The World ◽  
Sick Building ◽  
Cross Sectional Design ◽  
And Gender

Sick Building Syndrome (SBS) was a healthproblem that caused by air polution, indoor air quality and a bad ventilation. In around the world,2.7 milions of people were dead because air polution in the room and this air polution source from ventilation (52%), tools in the room (17 %), outside the room (11%), the building material (3%), microorganism (5%) and others (12%). Mall was a public place with a close ventilation so it can influence the air quality and being the risk of SBS.The aim of this research was looking for correlation between air quality in the room and the characteristics of workerswith sick building syndrome (SBS).This research was an analytic with cross sectional design. The population was all of the workerwith 36 sample workers.Data analysis used univariat and bivariat.Result of this research showed the variabelswhich had correlationwith sick building syndrome are humidity, wind velocity, light intensity, age, and gender. The variables that had no correlation with sick building syndrome are temperature, microorganism quantity, duration of work and period of work.

scholarly journals Creating Own Histories: Adolescent Girls In Mumbai’s Bastis Use Photography As A Tool Of Documentation And Advocacy

2015 ◽  
Vol 3 (1) ◽  
pp. 30
Author(s):  
Amrita De
Keyword(s):  
Adolescent Girls ◽  
The Self ◽  
Collective Consciousness ◽  
Age And Gender ◽  
Reference Framework ◽  
Marginalised Groups ◽  
The World ◽  
Revolutionary Movements ◽  
And Gender ◽  
International Conventions

Expressing oneself is a fundamental right as enunciated in many international conventions and national constitutions. Expressing oneself, however, is subject to other factors, namely, one’s access to language, and means and platforms of expression. Marginalised groups have historically been kept away from gaining, as well as creating, knowledge and language.Breaking out of deprivation for marginalised groups requires having a reference framework of their own stories that are accessible to their own people, as well as visible to the rest of the world as legitimate history. As Sheila Rowbotham (1973) says, “in order to create an alternative, an oppressed group must at once shatter the self-reflecting world which encircles it and, at the same time, project its own image onto history.... All revolutionary movements create their own ways of seeing.” (p. 27)This paper seeks to show Vacha’s work with adolescent girls who purport to express themselves and document their perspectives through photography. Girls form one of the most marginalised sections of society, due to age and gender. Girls from deprived backgrounds contend with further disabilities of caste and class. Their perspectives are seldom part of the collective consciousness of their own communities, let alone enter mainstream discourses. Vacha uses photography as a useful tool for deprived girls to express and document their stories. Public exhibitions are used to take these images to a wider audience. 

scholarly journals Influence of age and gender specifics of social network users in Russia on marketing and advertising methods

2020 ◽  
Vol 3 (3) ◽  
pp. 21-28
Author(s):  
L. G. Akhmaeva
Keyword(s):  
Social Networks ◽  
Social Network ◽  
User Profile ◽  
The Internet ◽  
Age And Gender ◽  
External Resources ◽  
New Information ◽  
The World ◽  
And Gender ◽  
Using Data

A brief overview of the history of the Internet and social networks in the world and in Russia in particular has been provided. The concepts of social network, user profile and properties inherent in any social network – virtuality, interactivity and multimedia have been сharacterized and revealed. Dynamic data on the state of digital technologies for 2019 in the world and in Russia in particular have been analysed. The history and prospects for further development of social networks have been considered. Statistical data on the number of users of the 9 most popular social networks in Russia, namely: their activity, the amount of time spent on the Internet and in social networks, age and gender specifics and preferences of the technical devices used and types of Internet connection have been adduced. General recommendations to marketers on accurate targeting of ads placed in social networks have been given. To do this, companies should work with groups of users, that are united by a number of parameters, as well as create communities in social networks by companies that convey new information to users. Using data on the age, gender and other attributes of the target audience of social networks, marketers will be able to successfully solve the problems of increasing brand awareness and loyalty, attracting new customers, influencing the search promotion of external resources (sites and communities) containing information about the brand, products and services, and using them as effective tools for attracting potential customers.

scholarly journals Identifying Factors Associated with Neonatal Mortality in Sub-Saharan Africa using Machine Learning

2020 ◽  
Author(s):  
William Ogallo ◽  
Skyler Speakman ◽  
Victor Akinwande ◽  
Kush R. Varshney ◽  
Aisha Walcott-Bryant ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neonatal Mortality ◽  
Neonatal Death ◽  
Household Size ◽  
Sub Saharan Africa ◽  
Age And Gender ◽  
Factors Associated ◽  
Sub Saharan ◽  
And Gender ◽  
Machine Learning Models

AbstractThis study aimed at identifying the factors associated with neonatal mortality. We analyzed the Demographic and Health Survey (DHS) datasets from 10 Sub-Saharan countries. For each survey, we trained machine learning models to identify women who had experienced a neonatal death within the 5 years prior to the survey being administered. We then inspected the models by visualizing the features that were important for each model, and how, on average, changing the values of the features affected the risk of neonatal mortality. We confirmed the known positive correlation between birth frequency and neonatal mortality and identified an unexpected negative correlation between household size and neonatal mortality. We further established that mothers living in smaller households have a higher risk of neonatal mortality compared to mothers living in larger households; and that factors such as the age and gender of the head of the household may influence the association between household size and neonatal mortality.

scholarly journals Machine learning techniques to predict different levels of hospital care of CoVid-19

2021 ◽  
Author(s):  
Elena Hernández-Pereira ◽  
Oscar Fontenla-Romero ◽  
Verónica Bolón-Canedo ◽  
Brais Cancela-Barizo ◽  
Bertha Guijarro-Berdiñas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Clinical Data ◽  
Hospital Care ◽  
Data Set ◽  
Age And Gender ◽  
And Gender ◽  
Prediction Problems ◽  
Different Levels

AbstractIn this study, we analyze the capability of several state of the art machine learning methods to predict whether patients diagnosed with CoVid-19 (CoronaVirus disease 2019) will need different levels of hospital care assistance (regular hospital admission or intensive care unit admission), during the course of their illness, using only demographic and clinical data. For this research, a data set of 10,454 patients from 14 hospitals in Galicia (Spain) was used. Each patient is characterized by 833 variables, two of which are age and gender and the other are records of diseases or conditions in their medical history. In addition, for each patient, his/her history of hospital or intensive care unit (ICU) admissions due to CoVid-19 is available. This clinical history will serve to label each patient and thus being able to assess the predictions of the model. Our aim is to identify which model delivers the best accuracies for both hospital and ICU admissions only using demographic variables and some structured clinical data, as well as identifying which of those are more relevant in both cases. The results obtained in the experimental study show that the best models are those based on oversampling as a preprocessing phase to balance the distribution of classes. Using these models and all the available features, we achieved an area under the curve (AUC) of 76.1% and 80.4% for predicting the need of hospital and ICU admissions, respectively. Furthermore, feature selection and oversampling techniques were applied and it has been experimentally verified that the relevant variables for the classification are age and gender, since only using these two features the performance of the models is not degraded for the two mentioned prediction problems.

scholarly journals Machine Learning Model and Statistical Methods for COVID-19 Evolution Prediction

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
M. D. Alsulami ◽  
Hanaa Abu-Zinadah ◽  
Anwar Hassan Ibrahim
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Statistical Processing ◽  
Economic Consequences ◽  
World Health ◽  
Population Densities ◽  
Tree Model ◽  
The United Kingdom ◽  
Machine Learning Model ◽  
The World

In this paper, we discuss the statistical processing of COVID-19 data. COVID-19 was initially recognized in Wuhan, China, on December 31, 2019. It then spread to other parts of the world, so it became known as a pandemic. It has received interest due to its sudden emergence as a deadly human pathogen. The effect is not only confined to morbidity and mortality but also extends to social and economic consequences. Statistical analysis is required to measure the damage done to humans and take the necessary measures to limit this damage. The objective of the work was to examine the effects of various factors on the deaths due to COVID-19. To achieve this goal, we applied a logistic regression (LR) model, as a statistical method, and a decision tree model, as a machine learning method, to model the deaths due to COVID-19 in France, Germany, Italy, and Spain. The predictive abilities of these two models were compared. The overall accuracies of the decision tree and LR were 94.1% and 93.9%, respectively. It was also observed that countries with high population densities tended to have more cases than those with smaller population densities. There were more female deaths than male deaths in the United Kingdom, and more deaths occurred for those aged 65 years and older. The data were collected from the World Health Organization’s official website from January 11, 2020, to May 29, 2020. The results obtained were in agreement with the previous results obtained by others.

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