Long-Term Physical and Mental Health Effects of Domestic Violence

Domestic violence is an issue affecting people of all ages, races, genders, and sexual orientations. Violence against men and same-sex domestic violence are often considered less of a threat to society and to the people involved, but it is important to understand that male-on-female violence, female-on-male violence, and same-sex violence all involve serious consequences to the victim’s and batterer’s short- and long-term health. This paper determines whether men or women suffer from more long-term health problems caused by domestic violence by comparing the currently published statistics on the prevalence of domestic violence in heterosexual and homosexual relationships, and analyzing the results of existing studies on the short- and long-term health effects of domestic violence. The findings indicate that although men and women sustain many of the same injuries, women suffer from more long-term health problems caused by domestic violence

The CSI Effect: Fact or Fiction?

The CSI effect has been a subject undergoing intense scrutiny in recent years. With the ever-increasing number of television shows, such as CSI and all of its spinoffs, that poorly represent the field of forensic science, there has also been a growing concern over the effects that media has on the legal system. Prosecutors argue that the CSI effect raises their burden of proof and makes jurors more likely to acquit in cases involving little or no forensic evidence, while defense lawyers claim that jurors are more inclined to wrongfully convict based on their unrealistic perceptions of forensic evidence. This paper aims to determine if the CSI effect exists by exploring the effects that crime-show-related media has on the community, analyzing jurors’ perceptions of forensic evidence, and comparing the currently published statistics on pre- and post-CSI acquittal rates

Recipe Helper System Mobile Application with Voice Recognition

This project features research done on the current technology of mobile application on an android platform as well as performing integration of the mobile app to existing Voice recognition systems and software’s. With the current growth of the mobile application technologies in handheld devices, the workload of human is eased in many ways. Integrating voice recognition abilities that has grown vastly since 1963 will enhance these technologies taking it into another level and spectrum. Voice Recognition not only allows human interaction with computers but also brings in the edge of using such technologies in our daily lives. This project forms a purpose to develop a mobile application that is able to ease the workload in households. The mobile application developed on an Android platform that is integrated with Voice Recognition abilities to allow the user to communicate with the device without the need of using their hands while cooking. The mobile application is designed to contain cooking recipes from various countries and having the ability to view and interact with users during the cooking process. The project research is done in phases and the research and planning stages are completed in FYP 1 followed by the designing and testing of the application in FYP 2. The project follows a thorough method of throwaway prototyping and the system is developed accordingly. Various tests has been performed to measure the accuracy and performance upon a different groups of people and the results have indicated the benefits and necessity of this mobile application and the function of it has proven to accomplish the initial project objectives

The Role of Women?s Empowerment and Domestic Violence in Child Growth and Undernutrition in a Tribal and Rural Community in South India

Moderate undernutrition continues to affect 46 per cent of children under 5 years of age and 47 per cent of rural women in India. Women?s lack of empowerment is believed to be an important factor in the persistent prevalence of undernutrition. In India, women?s empowerment often varies by community, with tribes sometimes being the most progressive. This paper explores the relationship between women?s empowerment, domestic violence, maternal nutritional status, and the nutritional status and growth over six months in children aged 6 to 24 months in a rural and tribal community. This longitudinal observational study undertaken in rural Karnataka, India included tribal and rural subjects. Structured interviews with mothers were conducted and anthropometric measurements were obtained for 820 mother-child pairs, the follow-up rate after 6 months was 82 per cent. The data were analysed by multivariate regression. Some degree of undernutrition was seen in 83.5 per cent of children and 72.4 per cent of mothers in the sample, moreover the prevalence of undernutrition increased among children at follow-up. Domestic violence was experienced by 34 per cent of mothers in the sample. In multivariate analysis, biological variables explained most of the variance in nutritional status and child growth, followed by health-care seeking and women?s empowerment variables; socio-economic variables explained the least variance. Women?s empowerment variables were significantly associated with child nutrition on enrolment and child growth at follow-up. At follow-up, mother?s prior lifetime experience of physical violence significantly undermined child growth in terms of weight-for-age, and older age at marriage and high mobility of mothers predicted less stunting in their children. In addition to the known investments needed to reduce undernutrition, improving women?s nutrition, promoting gender equality, empowering women, and ending violence against women could further reduce the prevalence of undernutrition in this segment of the Indian population.child nutrition, child growth, domestic violence, nutritional status, women?s empowerment, maternal nutritional status

Human Face Recognition and Age Estimation with Machine Learning: A Critical Review and Future Perspective

Face Recognition (FR) applications are becoming more and more common these days. Face recognition, techniques, tools, and performance are all shown in this work, along with a literature review and gaps in many areas. Some of the most common uses of the FR include medical and government sectors as well as educational institutions. The FR technique can identify an appropriate individual through a camera. Online courses, online FDPs, and Webinars are becoming more interactive nowadays. Using Machine Learning, it is possible to quickly and securely determine a student\u27s unique id to administer virtual online tests. The paper is an analysis of Machine learning and deep learning algorithms as well as tools such as Matlab and Python. The paper covers a survey of different aspects such as face detection, face recognition, face expressions, and age estimation. Hence, this is helpful for researchers to choose the right direction for their research. Future face recognition research is also considered in the paper which is now trending in face recognition systems. Data from recent years are used to evaluate the performance

RESDEN: A Novel Deep Unified Model for Face Recognition System

The Face Recognition technology plays a significant role in the field of Computer Vision in contemporary times. The research article is centered on a Facial attendance system that utilizes a deep learning technique to recognize face photos. To execute face identification and classification via the use of deep learning processes, many Convolutional Neural Network (CNN) models are taken into account. Previous studies have mostly focused on either the ResNet or DenseNet-based convolutional neural network model. The present research utilizes the merging of ResNet and DenseNet to propose a hybrid model. The proposed work is expected to provide enhanced efficiency and accuracy. In the training and testing stages of the simulation, considerations are made for both binary and category classifications. The current research focuses on the use of the LFW dataset. The pictures undergo an initial step of the noise reduction process. The evaluation of picture quality is conducted by taking into account metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM). After the proposed model has undergone training, it generates photographs of superior quality. Finally, the proposed system incorporates the RESDEN framework, which integrates DenseNet with a noise reduction technique, a segmentation mechanism, and a CNN based on ResNet. A comparative analysis has been conducted to evaluate the accuracy of several filtered picture sets across different convolutional neural network (CNN) models. The simulation results indicate that the suggested model exhibited a good level of performance and accuracy