Gender recognition from unconstrained selfie images: a convolutional neural network approach

Human gender recognition is an essential demographic tool. This is reflected in forensic science, surveillance systems and targeted marketing applications. This research was always driven using standard face images and hand-crafted features. Such way has achieved good results, however, the reliability of the facial images had a great effect on the robustness of extracted features, where any small change in the query facial image could change the results. Nevertheless, the performance of current techniques in unconstrained environments is still inefficient, especially when contrasted against recent breakthroughs in different computer vision research. This paper introduces a novel technique for human gender recognition from non-standard selfie images using deep learning approaches. Selfie photos are uncontrolled partial or full-frontal body images that are usually taken by people themselves in real-life environment. As far as we know this is the first paper of its kind to identify gender from selfie photos, using deep learning approach. The experimental results on the selfie dataset emphasizes the proposed technique effectiveness in recognizing gender from such images with 89% accuracy. The performance is further consolidated by testing on numerous benchmark datasets that are widely used in the field, namely: Adience, LFW, FERET, NIVE, Caltech WebFaces andCAS-PEAL-R1

Intelligent word-based spam filter detection using multi-neural networks

Abstract SPAM e-mails have a direct cost in terms of time, server storage space, network bandwidth consumptions and indirect costs to protect privacy and security breaches. Efforts have been done to create new filters techniques to block SPAM, however spammers have developed tactics to avoid these filters. A constant update to these techniques is required. This paper proposes a novel approach which is a characters-word-based technique. This approach uses a multi-neural networks classifier. Each neural network is trained based on a normalized weight obtained from the ASCII value of the word characters. Results of the experiment show high false positive and low true negative percentages

An Android Augmented Reality Application for Retail Fashion Shopping

this report will exhibit an outline of the basic features of Mobile Augmented Reality (MAR) and the key concepts of this technology in the Fashion Retail Industry. Several obstacles such as the necessity of putting on the garments to experience its actual view have been tackled by customers throughout the process of clothes shopping. The goal is to constringe these obstacles by creating an Augmented Reality based application that will directly solve these problems. The methodology was conducted by questioning shoppers about the different holdbacks faced during shopping. Solutions for the issues are expected to be achieved by developing an easily utilizable MAR application for targeted customers. The design of this paper involved the utilization of handed questionnaire and semi structured interviews with several store managers to gather sufficient about their sales strategy and proposing the solution of Augmented Reality utilization within the market. Augmented Reality Technology have a great potential to be utilized efficiently inside the fashion market as it will meritoriously improve the shopping experience of consumers across multiple fashion related industry channels

Mobile Applications and Semantic-Web – A case study on Automated Course Management

Different types of e-assessment systems that are recognized at universities and based on the campus wireless have been developed. These systems help the students to use their Mobile Phones as learning media to access the information more easily from anywhere and at any time.  Seppala and Alamaki developed a mobile learning project for teacher training. Their study compared the effectiveness of internet, face-to-face and mobile based instructions.        Al Masri has proposed a study to compare the effective strategy in paper-based assessment with mobile-based assessment for assessing university students in English literature. It has been found that students gained better scores in mobile phone-based test than in paper-based test.  This paper aims to determine and measure the effects of mobile-based assessments on the perception, achievement levels and performance of the students in internet-assisted courses. The main functionalities and features of this paper are: Knowledge evaluation, automatic generation of exams, exam grading, communication, course management, and questions-bank database