Home Automation and RFID-Based Internet of Things Security: Challenges and Issues

Internet of Things (IoT) protection refers to the software field related to securing the Internet of Things and associated linked devices and systems. The IoT is a system of interconnected computers, sensors, actuators, or people on the World Wide Web (WWW). All these different devices have a unique identity in the IoT and must convey data across the network automatically. If computers are not adequately secured, allowing them to connect to the Internet exposes them to a range of serious vulnerabilities. Because the consequences of IoT failures are severe, it is necessary to observe and analyze security issues related to IoT. The prime goal of IoT security is to protect personal safety, while also guaranteeing and ensuring accessibility. In the context of IoT technology, the present study conducts a systematic literature review that analyzes the security problems associated with commercial and educational applications of home automation and details the technical possibilities of IoT with respect to the network layer. In this systematic review, we discuss how current contexts result in the inability of designers of IoT devices to enhance their cyber-security initiatives. Typically, application developers are responsible for training themselves to understand recent security advancements. As a result, active participation on the ridge scale with passive improvement can be achieved. A comparative analysis of the literature was conducted. The main objective of this research is to provide an overview of current IoT security research in home automation, particularly those using authentication methods in different devices, and related technologies in radio frequency identification (RFID) on network layers. IoT security issues are addressed, and various security problems in each layer are analyzed. We describe cross-layer heterogeneous integration as a domain of IoT and demonstrate how it can provide some promising solutions.Qatar University High Impact Grant (QUHI-CBE-21/22-1)

Evaluation of Mental Health Services Intervention for Refugees/Immigrant/Migrant (RIM) Population in Clarkston, Ga.

The prevalence of mental health-related illnesses in refugee/immigrant/migrant (RIM) population is reportedly as high as 86% compared to 8% in the general population. This population faces significant barriers to accessing mental health services due to affordability, language barriers, limited health literacy, cultural stigma, and lack of transportation. Clarkston, Georgia has hosted more than 60,000 RIM individuals from 40 different countries. More than a half of Clarkston population (~51%) of about 13,000 are foreign born. Unfortunately, though 40% of those screened at Clarkston Community Health Center being reported as having at least one psychiatric diagnosis, mental health care in Clarkston is scarce. Therefore, in response to this overwhelming need, the Mental Health Alliance (MHA) aims to provide culturally- and linguistically responsive, and trauma-sensitive care by integrating mental health services within the pre-existing infrastructure at a large refugee resettlement organization [the International Rescue Committee (IRC)]. METHODS: This research evaluated the effectiveness of MHA by documenting the number of clients served, client satisfaction with mental health services, and changes in clients\u27 mental health outcomes collected through semi-structured interviews. These interviews (N=9) explored participants\u27 mental health and experience, changes in self-reported health over the course of their treatment, and extent to which the program addressed barriers to accessing mental health care. Data was analyzed using rapid qualitative analysis using a thematic approach. RESULTS/ DISCUSSION: This study found that the settlement-integrated approach to mental health care significantly improved mental health outcomes, quality of life, and increased social support for study participants from RIM population in Clarkston GA. The program was well-received by participants and demonstrated the effectiveness of addressing barriers (transportation, language, stigma, affordability) and facilitators (cultural competence and trauma sensitive mental health care) to accessing mental health services. The study\u27s findings suggest that this approach is a promising strategy for providing mental health services to refugees

Lessons learnt from COVID-19 to reduce mortality and morbidity in the Global South: addressing global vaccine equity for future pandemics

COVID-19, which killed more than 6 million people, will not be the last pandemic. Vaccines are key to preventing and ending pandemics. Therefore, it is critical to move now, before the next pandemic, towards global vaccine equity with shared goals, intermediate steps and long-term advocacy goals. Scientific integrity, ethical development, transparency, accountability and communication are critical. Countries can draw on lessons learnt from their response to the HIV pandemics, which has been at the vanguard of ensuring equitable access to rights-based services, to create shared goals and engage communities to increase access to and delivery of safe, quality vaccines. Access can be increased by: fostering the spread of mRNA intellectual property (IP) rights, with mRNA vaccine manufacturing on more continents; creating price transparency for vaccines; creating easily understandable, accessible and transparent data on vaccines; creating demand for a new international legal framework that allows IP rights to be waived quickly once a global pandemic is identified; and drawing on scientific expertise from around the world. Delivery can be improved by: creating strong public health systems that can deliver vaccines through the lifespan; creating or strengthening national regulatory agencies and independent national scientific advisory committees for vaccines; disseminating information from reliable, transparent national and subnational surveillance systems; improving global understanding that as more scientific data become available, this may result in changes to public health guidance; prioritising access to vaccines based on scientific criteria during an epidemic; and developing strategies to vaccinate those at highest risk with available vaccines

Deep Learning Based Multi Pose Human Face Matching System

Current techniques for multi-pose human face matching yield suboptimal outcomes because of the intricate nature of pose equalization and face rotation. Deep learning models, such as YOLO-V5, etc., that have been proposed to tackle these complexities, suffer from slow frame matching speeds and therefore exhibit low face recognition accuracy. Concerning this, certain literature investigated multi-pose human face detection systems; however, those studies are of elementary level and do not adequately analyze the utility of those systems. To fill this research gap, we propose a real-time face matching algorithm based on YOLO-V5. Our algorithm utilizes multi-pose human patterns and considers various face orientations, including organizational faces and left, right, top, and bottom alignments, to recognize multiple aspects of people. Using face poses, the algorithm identifies face positions in a dataset of images obtained from mixed pattern live streams, and compares faces with a specific piece of the face that has a relatively similar spectrum for matching with a given dataset. Once a match is found, the algorithm displays the face on Google Colab, collected during the learning phase with the Robo-flow key, and tracks it using the YOLO-V5 face monitor. Alignment variations are broken up into different positions, where each type of face is uniquely learned to have its own study demonstrated. This method offers several benefits for identifying and monitoring humans using their labeling tag as a pattern name, including high face-matching accuracy and minimum speed of owing face-to-pose variations. Furthermore, the algorithm addresses the face rotation issue by introducing a mixture of error functions for execution time, accuracy loss, frame-wise failure, and identity loss, attempting to guide the authenticity of the produced image frame. Experimental results confirm effectiveness of the algorithm in terms of improved accuracy and reduced delay in the face-matching paradigm