Improving gender classification with feature selection in forensic anthropology

Gender classification has been one of the most vital tasks in a real world problem especially when it comes to death investigations. Developing a biological profile of an individual is a crucial step in forensic anthropology process as for the identification of gender. Forensic anthropologists employ the principle of skeleton remains to produce a biological profile. Different parts of skeleton contains different features that will contribute to gender classification. However, not all the features could contribute to gender classification and affect to a low accuracy of gender classification. Therefore, feature selection method is applied to identify the most significant features for gender classification. This paper presents the implementation of feature selection approaches which are Particle Swarm Optimization (PSO) and Harmony Search (HS) algorithm using three different dataset from Goldman Osteometric Dataset, Osteological Collection and George Murray Black Collection. All three dataset contains 4081 samples of metrics measurement and have gone through the process of classification by using Back Propagation Neural Network (BPNN) and Naïve Bayes classifier. The main scope of this paper is to identify the effect of feature selection towards gender classification. The result shows that the accuracy of gender classification for every dataset increased when feature selection is applied to the dataset. Among all the skeleton parts in this experiment, clavicle part achieved the highest increment of accuracy rate which is from 89.76% to 96.06% for PSO algorithm and 96.32% for HS

A method for detecting the profile of an author

This paper presents a method for detecting an author’s profile using the following two elements: gender and age. This is based on a set of dialogues, written in two languages: English and Spanish, provided for Author Profiling competence within the evaluation forum "Uncovering Plagiarism, Authorship, and Social Software Misuse" (PAN2018). Counts of lexical, semantic, and syntactic characteristics are used to generate a two-phase classification system, which first classifies gender and then age. The results obtained show that, with the amount of data available, it is possible to characterize both the age and gender of an author with an accuracy greater than 50%. However, these values could be improved by having more evidence of information in the training data

Inferring Social Media Users’ Demographics from Profile Pictures: A Face++ Analysis on Twitter Users

In this research, we evaluate the applicability of using facial recognition of social media account profile pictures to infer the demographic attributes of gender, race, and age of the account owners leveraging a commercial and well-known image service, specifically Face++. Our goal is to determine the feasibility of this approach for actual system implementation. Using a dataset of approximately 10,000 Twitter profile pictures, we use Face++ to classify this set of images for gender, race, and age. We determine that about 30% of these profile pictures contain identifiable images of people using the current state-of-the-art automated means. We then employ human evaluations to manually tag both the set of images that were determined to contain faces and the set that was determined not to contain faces, comparing the results to Face++. Of the thirty percent that Face++ identified as containing a face, about 80% are more likely than not the account holder based on our manual classification, with a variety of issues in the remaining 20%. Of the images that Face++ was unable to detect a face, we isolate a variety of likely issues preventing this detection, when a face actually appeared in the image. Overall, we find the applicability of automatic facial recognition to infer demographics for system development to be problematic, despite the reported high accuracy achieved for image test collection

Differentiating the behavioural profile in autism and mental retardation and testing of a screener

Abstract. : In order to differentiate the behavioural profiles in autism and mental retardation and to cross-validate a behavioural autism screen, 84 subjects with autism (64 males and 20 females) with a mean age of 10 years selected from a Swiss national survey were compared to a control group of 84 subjects matched by age and gender with mental retardation, but without autistic features. The behavioural profile was assessed using the Developmental Behaviour Checklist (DBC). The behavioural profile in autism, in contrast to mental retardation, was marked by higher scores in the domains of disruptive, self-absorbed, communication disturbed, anxious and autistic behaviour, and a higher total DBC score. Furthermore, a higher vulnerability for behavioural abnormalities became evident for females with autism. A recently proposed DBC-Autism Screen was cross-validated, and a slight extension of the screen led to even higher correct classification rates. It was concluded that the DBC is a suitable instrument for the assessment of the behavioural profile and for screening in autis

Morbidity Pattern Among Out-Patients Attending Urban Health Training Centre in Srinagar

The current study was designed to identify the morbidity pattern of out-patients attending Urban Health Training Centre in an urban area of a medical college in Srinagar, Pauri Garhwal district, Uttarakhand, North India. The present study record-based retrospective study was conducted among the out-patients attending the regular clinic at the Urban Health Training Centre, of a medical college in Srinagar city of Uttarakhand State of North India during the study period of one year in 2014. Data was retrieved from the OPD registers maintained at the clinic. Data was collected pertaining to socio-demographic profile, morbidity details and treatment pattern. Diseases were identified using the International Classification of Diseases (ICD-10) code. Descriptive analysis was done. During the study period, a total of 9343 subjects attended the OPD. Among them, majority of them (60%) were females. More than half (56 %) belonged to the age group of 35-65 year age-group. The association of disease classification was found to be statistically significant with respect to gender. The leading morbidity of communicable disease was found to be certain infectious and parasitic diseases especially Typhoid whereas musculoskeletal system and connective tissue disorders were the most common cause among morbidity due to NCDs. Out of all, typhoid was found to cause maximum of morbidity among the subjects. The present study highlights the morbidity pattern of communicable and NCDs among the population of hilly areas of Garhwal, Uttarakhand India. Priority should be preferred for the regular tracking of diseases in terms of preventive and promotive aspects. Morbidity in the out- door clinics reflects the emerging trend of mixed disease spectrum burden comprising communicable and non-communicable diseases

Data Mining Menggunakan Algoritma Na�ve Bayes Untuk Klasifikasi Kelulusan Mahasiswa Universitas Dian Nuswantoro. ( Studi Kasus: Fakultas Ilmu Komputer Angkatan 2009 ).

Student data and data Dian Nuswantoro University student graduation produce data that is very abundant in the form of student profile data and academic data. This happens repeatedly and cause a build up of the student data that affect information retrieval to the data. This study aims to perform the classification of student data Dian Nuswantoro University of Computer Science faculty class of 2009 tiered Diploma and S1 by using data mining process using classification techniques. The method used is the CRISP-DM with a through understanding of business processes, understanding data, the data preparation, modeling, evaluation and deployment. The algorithm used for graduation classification is Naive Bayes algorithm. Naïve Bayes is a simple probabilistic based prediction technique on the application of Bayes theorem or rule with a strong independence assumption on feature, meaning that a feature is not data relating to the presence or absence of other features in the same data. Implementation using RapidMiner 5.3 is used to help find an accurate value. Attributes used is NIM, Name, Qualification, courses, Province of Origin, Gender, credits, GPA, and Graduation Year. The results of this study are used as one basis for determining policy decisions by the computer sciene faculty

Wearing Many (Social) Hats: How Different are Your Different Social Network Personae?

This paper investigates when users create profiles in different social networks, whether they are redundant expressions of the same persona, or they are adapted to each platform. Using the personal webpages of 116,998 users on About.me, we identify and extract matched user profiles on several major social networks including Facebook, Twitter, LinkedIn, and Instagram. We find evidence for distinct site-specific norms, such as differences in the language used in the text of the profile self-description, and the kind of picture used as profile image. By learning a model that robustly identifies the platform given a user's profile image (0.657--0.829 AUC) or self-description (0.608--0.847 AUC), we confirm that users do adapt their behaviour to individual platforms in an identifiable and learnable manner. However, different genders and age groups adapt their behaviour differently from each other, and these differences are, in general, consistent across different platforms. We show that differences in social profile construction correspond to differences in how formal or informal the platform is.Comment: Accepted at the 11th International AAAI Conference on Web and Social Media (ICWSM17