Criminal Detection: Study of Activation Functions and Optimizers

Face is the primary means of recognizing a person, transmitting information, communicating with others, and inferring people’s feelings, among others. Our faces will reveal more than we think. A facial image may show personal characteristics such as ethnicity, gender, age, fitness, emotion, psychology, and occupation. In addition to the recent specialisation of deep learning models, the exponential output and memory space growth of computer machines has greatly increased the role of images in recognising semantic patterns. Facial photographs can reveal those personality features in the same way as a textual message on social media reveals the author's individual characteristics. We investigate a new degree of image comprehension by using deep learning to infer a criminal proclivity from facial images. A convolutional neural network (CNN) deep learning model is used to differentiate between criminal and non-criminal facial images. Using tenfold cross-validation on a set of 5500 face pictures, the model's confusion matrix, training, and test accuracies are registered. In learning to achieve the highest test accuracy, CNN was more reliable than the SNN, which was 8% better than the SNN's test accuracy. Finally, CNN's dissection and visualization of convolutional layers showed that CNN distinguished the two sets of images based on the shape of the face, eyebrows, top of the eye, pupils, nostrils, and lips. In this project we focus on Activation functions and optimizers. Activation functions are of two types Saturated and Non-Saturated. Here we use non saturated activation functions like ReLU, SELU and SOFTMAX. When we combine ReLU and SOFTMAX, we get 99.3 percentages as test accuracy. By combining SELU and SOFTMAX we get 99.6 as test accuracy. Therefore, SELU and SOFTMAX combination give the better accuracy

Comparative Study of the Effectiveness of Matrix Training and tDCS Treatments on Positive and Negative Affects and Craving in Substance Abusers

Background: Substance dependence is acknowledged as one of the major social and health issues inflicting severe and profound physical and psychological harm, as well as numerous social damages, such as divorce and unemployment. The present study aimed to make a comparison between the effectiveness of Matrix training and transcranial direct current stimulation (tDCS) treatments on positive and negative affects and craving in substance abusers who referred to Ahwaz addiction treatment centers within 2018-2019. Materials and Methods: The present semi-experimental study was conducted using a pre-test post-test control group design with a two-month follow-up. The study population consisted of all substance abusers who referred to Ahwaz addiction treatment centers within 2018-2019. A total of 60 volunteers were selected by voluntary sampling method and randomly assigned to Matrix training (n=20), tDCS (n=20), and control (n=20) groups. Data were collected by The Positive and Negative Affect Schedule (PANAS) and Desire For Drug Questionnaire and were analyzed in SPSS software (version 22). Results: Based on the obtained results, Matrix, tDCS, and control groups were significantly different in terms of positive and negative affects and craving (P<0.001). Moreover, it was found that Matrix training and tDCS were effective on positive and negative affects and cravings (P<0.001); nonetheless, no significant difference was observed between the matrix and tDCS groups (P>0.05). Conclusion: Generally speaking, it can be concluded that Matrix training and tDCS methods are equally effective in emotions and craving.