scholarly journals Novel approach to determine age and gender from wrist x-ray images

2021 ◽  
Vol 12 (3) ◽  
pp. 5215-5229
Author(s):  
Santosh K C Et. al.
Keyword(s):  
Random Forest ◽  
Decision Tree ◽  
Classification Algorithm ◽  
Automated System ◽  
Growth Stages ◽  
Age And Gender ◽  
X Ray ◽  
Gender And Age ◽  
And Gender ◽  
Time Accuracy

Human gender and age prediction in field of forensic department is a very important and crucial stage in means of criminal and judicial law. Human identification is essential when it required for recognising a body in case of mass disaster and natural disaster like earth quake, floods, tsunamis, hurricanes and other geological process that causes huge damage for mankind and loss of life. Human bones during the growth stages undergo few substantial changes of size and shapes. In diagnosing growth of bones, x ray images are frequently used. Hand x ray images in particular has been chosen as a part of x ray imaging, since hand has more unique features and more number of parts. Manual technique of identification is also attainable, but this process can be adopted when medical practitioners, assistants and basic tools are available. Manual method can be carried out based on the availability of bone like skull, long bones, short bones, hand, pelvis bone etc. It requires ample time to process the accurate outcome of the available samples. Hence hand operated technique is not feasible for identification. A machine driven automated system for gender and age identification is essential to overcome the flaws occurred in manual technique. This would facilitate better outcome in lesser time, without intervention of labour and also enables quantitative and accurate assessments. In the proposed system, we have identified most important features from wrist bone which contributes in age and gender identification. Main aim of our study is to identify gender and estimation of age of Middle East population of Karnataka state in India by analysing digital images of wrist bone. Random forest classification algorithm is used to deploy this system by considering 76 male samples and 50 female samples in total 126 wrist radiographs of age group between 06 to 78 years old. Random forest classifier belongs to decision tree family, each decision tree when executed may tends to overfit that training data, but random forest avoids this over fitting problems and it will try to capture maximum amount of pattern. Since multiple decision trees are implemented in RFC, this makes it a power full classification algorithm that will predict results with higher accuracy most of the time. Accuracy of 97% is achieved in the present work for age and gender prediction.

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 Age and Gender as Cyber Attribution Features in Keystroke Dynamic-Based User Classification Processes

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 835
Author(s):  
Ioannis Tsimperidis ◽  
Cagatay Yucel ◽  
Vasilios Katos
Keyword(s):  
High Accuracy ◽  
Keystroke Dynamics ◽  
Time Intervals ◽  
Age And Gender ◽  
Common Features ◽  
User Classification ◽  
Gender And Age ◽  
And Gender ◽  
Keystroke Dynamic

Keystroke dynamics are used to authenticate users, to reveal some of their inherent or acquired characteristics and to assess their mental and physical states. The most common features utilized are the time intervals that the keys remain pressed and the time intervals that are required to use two consecutive keys. This paper examines which of these features are the most important and how utilization of these features can lead to better classification results. To achieve this, an existing dataset consisting of 387 logfiles is used, five classifiers are exploited and users are classified by gender and age. The results, while demonstrating the application of these two characteristics jointly on classifiers with high accuracy, answer the question of which keystroke dynamics features are more appropriate for classification with common classifiers.

scholarly journals No Self Without Salience: Affective and Self-relevance Ratings of 552 Emotionally Valenced and Neutral Dutch Words

2021 ◽  
Author(s):  
Lora I. Dimitrova ◽  
Eline M. Vissia ◽  
Hanneke Geugies ◽  
Hedwig Hofstetter ◽  
Sima Chalavi ◽  
...  
Keyword(s):  
Repeated Measures ◽  
Emotional Valence ◽  
Age And Gender ◽  
Gender And Age ◽  
Main Effect ◽  
Emotional Salience ◽  
And Gender ◽  
The Relationship

AbstractIt is unknown how self-relevance is dependent on emotional salience. Emotional salience encompasses an individual's degree of attraction or aversion to emotionally-valenced information. The current study investigated the interconnection between self and salience through the evaluation of emotional valence and self-relevance. 56 native Dutch participants completed a questionnaire assessing valence, intensity, and self-relevance of 552 Dutch nouns and verbs. One-way repeated-measures ANCOVA investigated the relationship between valence and self, age and gender. Repeated-measures ANCOVA also tested the relationship between valence and self with intensity ratings and effects of gender and age. Results showed a significant main effect of valence for self-relevant words. Intensity analyses showed a main effect of valence but not of self-relevance. There were no significant effects of gender and age. The most important finding presents that self-relevance is dependent on valence. These findings concerning the relationship between self and salience opens avenues to study an individual's self-definition.

scholarly journals Soft Biometrics for a Socially Assistive Robotic Platform

2015 ◽  
Vol 6 (1) ◽  
Author(s):  
Pierluigi Carcagnì ◽  
Dario Cazzato ◽  
Marco Del Coco ◽  
Pier Luigi Mazzeo ◽  
Marco Leo ◽  
...  
Keyword(s):  
Real Time ◽  
Humanoid Robot ◽  
Field Of View ◽  
Robotic Platform ◽  
Time System ◽  
Real Time System ◽  
Age And Gender ◽  
Gender And Age ◽  
And Gender ◽  
Assistive Robotic

AbstractIn thiswork, a real-time system able to automatically recognize soft-biometric traits is introduced and used to improve the capability of a humanoid robot to interact with humans. In particular the proposed system is able to estimate gender and age of humans in images acquired from the embedded camera of the robot. This knowledge allows the robot to properly react with customized behaviors related to the gender/age of the interacting individuals. The system is able to handle multiple persons in the same acquired image, recognizing the age and gender of each person in the robot’s field of view. These features make the robot particularly suitable to be used in socially assistive applications.

scholarly journals Drinkers in the micro-social environment of the inhabitants of Ukraine. Age and gender characteristics

2021 ◽  
pp. 4-11
Author(s):  
Igor Linskiy ◽  
Valerii Kuzminov ◽  
Oleksandr Minko ◽  
Hanna Kozhyna ◽  
Yevheniia Grynevych ◽  
...  
Keyword(s):  
Alcohol Problems ◽  
Subjective Perception ◽  
Age And Gender ◽  
Main Research ◽  
Gender And Age ◽  
Comparison Groups ◽  
Gender Characteristics ◽  
And Gender ◽  
Research Consortium ◽  
Ukrainian Society

The purpose of the work is to determine in the Ukrainian society the scale of harm inflicted by drinkers to other persons, as well as the gender and age characteristics of this harm. In four regions of Ukraine (Kharkiv, Lugansk and Zaporizhzhia regions, Kyiv), during 2018-2020, 1,742 people were examined from three qualitatively different comparison groups: patients with alcohol dependence (393 people); their healthy relatives (274 people) and representatives of the general population (1075 people). The main research tool was the questionnaire of the international research consortium GENAHTO (Gender, Alcohol, and Harms to Others). It was shown that, in general, drinkers are present in the microsocial environment of 27.62 % of the surveyed healthy respondents. Extrapolation of this indicator to the entire population of Ukraine suggests that the total number of people who suffer in one way or another due to the drinkers in their environment is about 11.6 million people. In addition, the drinkers frequency in the environment of respondents can be used to determine the real number of people with alcohol problems in our country. Preliminary calculations indicate that this number is from 1.5 to 2.2 million people. The drinkers frequency in the environment of the respondents significantly depends on the age of the latter. The highest values of this indicator are characteristic of the respondents in the age category 40—59 years old” (31.43 % for men and 41.27 % for women). The subjective perception of harm from drinkers in the environment grows steadily with increasing age of the respondents, while this growth is most pronounced in women.

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.

Changes regarding Age and Correlations between Serum Lipids and Body Mass Index in Humankind

2002 ◽  
Vol 41 (03) ◽  
pp. 202-208 ◽  
Author(s):  
I. Yamasawa ◽  
S. Kamohara ◽  
M. Shiota ◽  
T. Komori ◽  
Y. Watanabe ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Serum Lipids ◽  
Density Lipoprotein ◽  
Atherogenic Index ◽  
Health Examination ◽  
Age And Gender ◽  
Gender And Age ◽  
Specialized Care ◽  
And Gender

Summary Objectives: To improve insight into age and gender related distributions of serum lipids and their correlation with body mass index (BMI). Methods: Serum lipids embracing atherogenic index (AI) and BMI were analyzed from the results obtained in 19,823 men and 14,788 women undergoing a health examination between 1986 and 1996. Results: The changes in total cholesterol (TC), triglyceride (TG), AI and BMI differed regarding gender. Although high-density lipoprotein-cholesterol (HDL-C) showed a flat pattern for all ages in both genders, its level in women was higher than in men. The ratio of the number in the unsuitable range to those in the suitable range increased with age as to TC in both sexes, then more than half of the population have an unsuitable level in the sixth decade. As for the correlation between serum lipids and BMI: TC, TG and AI correlated positively, but HDL-C correlated negatively. There were significant gaps between both age and gender. Conclusions: We suggest that the normal range of values of serum lipids needs to be revised according to gender and age to evaluate the risk status for a cardio-cerebrovascular disease more precisely in the field of preventive medicine. Simpler guidelines are preferable in specialized care as well as in general practice, particularly since computer technology is not yet universally adapted. In the near future, when computed information technology will be as common as the electricity and the telephone are current on the whole earth, all guidelines will have to be computed on the spot and personally.

scholarly journals Role of pharmacogenetics in rifampicin pharmacokinetics and the potential effect on TB–rifampicin sensitivity among Ugandan patients

2019 ◽  
Vol 114 (2) ◽  
pp. 107-114 ◽  
Author(s):  
Jackson K Mukonzo ◽  
Allan Kengo ◽  
Bisaso Kutesa ◽  
Sarah Nanzigu ◽  
Anton Pohanka ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Steady State ◽  
Multidrug Resistant ◽  
Potential Effect ◽  
Age And Gender ◽  
Gender And Age ◽  
Subsequent Effect ◽  
Variant Alleles ◽  
And Gender

Abstract Background Suboptimal anti-TB drugs exposure may cause multidrug-resistant TB. The role of African predominant SLCO1B1 variant alleles on rifampicin pharmacokinetics and the subsequent effect on the occurrence of Mycobacterium tuberculosis–rifampicin sensitivity needs to be defined. We describe the rifampicin population pharmacokinetics profile and investigate the relevance of SLCO1B1 genotypes to rifampicin pharmacokinetics and rifampicin-TB sensitivity status. Methods Fifty patients with TB (n=25 with rifampicin-resistant TB and n=25 with rifampicin-susceptible TB) were genotyped for SLOC1B1 rs4149032 (g.38664C>T), SLOC1B1*1B (c.388A>G) and SLOC1B1*5 (c.521 T>C). Steady state plasma rifampicin levels were determined among patients infected with rifampicin-sensitive TB. Data were analysed using NONMEM to estimate population rifampicin pharmacokinetics as well as the effect of SLOC1B1 genotypes on rifampicin pharmacokinetics and on rifampicin-TB sensitivity status. Results Overall allele frequencies of SLOC1B1 rs4149032, *1B and *5 were 0.66, 0.90 and 0.01, respectively. Median (IQR) Cmax and Tmax were 10.2 (8.1–12.5) mg/L and 1.7 (1.125–2.218) h, respectively. Twenty-four percent of patients exhibited Cmax below the recommended 8–24 mg/L range. SLOC1B1 genotypes, gender and age did not influence rifampicin pharmacokinetics or TB-rifampicin sensitivity. Conclusions Although SLOC1B1 genotype, age and gender do not influence either rifampicin pharmacokinetics or rifampicin-TB sensitivity status, one in every four Ugandan TB patients achieve subtherapeutic plasma rifampicin concentrations.

scholarly journals Trend-Based Categories Recommendations and Age-Gender Prediction for Pinterest and Twitter Users

2020 ◽  
Vol 10 (17) ◽  
pp. 5957 ◽  
Author(s):  
Roberto Garcia-Guzman ◽  
Yair A. Andrade-Ambriz ◽  
Mario-Alberto Ibarra-Manzano ◽  
Sergio Ledesma ◽  
Juan Carlos Gomez ◽  
...  
Keyword(s):  
Text Analysis ◽  
State Of The Art ◽  
Essential Feature ◽  
User Profile ◽  
Age And Gender ◽  
Category Information ◽  
Gender And Age ◽  
Twitter Users ◽  
And Gender ◽  
Number Of Publications

Category suggestions or recommendations for customers or users have become an essential feature for commerce or leisure websites. This is a growing topic that follows users’ activity in social networks generating a huge quantity of information about their interests, contacts, among many others. These data are usually collected to analyze people’s behavior, trends, and integrate a complete user profile. In this sense, we analyze a dataset collected from Pinterest to predict the gender and age by processing input images using a Convolutional Neural Network. Our method is based on the meaning of the image rather than the visual content. Additionally, we propose a heuristic-based approach for text analysis to predict users’ age and gender from Twitter. Both of the classifiers are based on text and images and they are compared with various similar approaches in the state of the art. Suggested categories are based on association rules conformed by the activity of thousands of users in order to estimate trends. Computer simulations showed that our approach can recommend interesting categories for a user analyzing his current interest and comparing this interest with similar users’ profiles or trends and, therefore, achieve an improved user profile. The proposed method is capable of predicting the user’s age with high accuracy, and at the same time, it is able to predict gender and category information from the user. The certainty that one or more suggested categories be interesting to people is higher for those users with a large number of publications.

Symptom CheckList-90-R: Proposed Scales in a Probability Sample of Adolescents from the General Population

2014 ◽  
Vol 17 ◽  
Author(s):  
Elena Felipe-Castaño ◽  
Benito León-del-Barco ◽  
José Antonio López-Pina
Keyword(s):  
Normative Data ◽  
Sampling Error ◽  
Effect Sizes ◽  
Cluster Sampling ◽  
Age Group ◽  
Age And Gender ◽  
Symptom Dimensions ◽  
Probability Sample ◽  
Gender And Age ◽  
And Gender

AbstractTo provide questionnaires for clinical assessment with scales adapted for adolescents would benefit clinical practice as well as research. The aim of this paper is to report normative data for adolescents on the SCL-90-R using a probability sample from the community. The participants were 1,663 adolescents, 845 girls and 818 boys, with an average age of 14.26 (SD = 1.36). They were selected through stratified cluster sampling with groups randomly selected from schools. Sampling error was estimated at 4% with a 95.5% confidence level. Cohen´s d effect sizes are reported for age-group. We found significant differences across participants according to gender and age on SCL-90-R Global Scores and Symptom Dimensions. Thus, we provide normative data, divided according to age and gender.

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