The advancements in modern day computing and architectures focus on harnessing parallelism and achieve high performance computing resulting in generation of massive amounts of data. The information produced needs to be represented and analyzed to address various challenges in technology and business domains. Radical expansion and integration of digital devices, networking, data storage and computation systems are generating more data than ever. Data sets are massive and complex, hence traditional learning methods fail to rescue the researchers and have in turn resulted in adoption of machine learning techniques to provide possible solutions to mine the information hidden in unseen data. Interestingly, deep learning finds its place in big data applications. One of major advantages of deep learning is that it is not human engineered. In this paper, we look at various machine learning algorithms that have already been applied to big data related problems and have shown promising results. We also look at deep learning as a rescue and solution to big data issues that are not efficiently addressed using traditional methods. Deep learning is finding its place in most applications where we come across critical and dominating 5Vs of big data and is expected to perform better.
The goal for collaborative robots has always driven advancements in robotic technology, especially in the manufacturing sector. However, this is not the case in service sectors, especially in the health sector. Thus, this lack of focus has now opened more room for the design and development of service robots that can be used in the health sector to help patients with ailments, cognitive problems, and disabilities. There is currently a global effort towards the development of new products and the use of robotic medical devices and computer-assisted systems. However, the major problem has been the lack of a thorough and systematic review of robotic research into disease and epidemiology, especially from a technology perspective. Also, medical robots are increasingly being used in healthcare to perform a variety of functions that improve patient care. This scoping review is aimed at discovering the types of robots used in healthcare and where they are deployed. Moreover, the current study is an overview of various forms of robotic technology and its uses the healthcare industry. The considered technologies are the products of a partnership between the healthcare sector and academia. They demonstrate the research and testing that are necessary for the service of robot development before they can be employed in practical applications and service scenarios. The discussion also focused on the upcoming research areas in robotic systems as well as some important technologies necessary for human-robot collaboration, such as wireless sensor networks, big data, and artificial intelligence.
Real-time detection and recognition systems for vehicle license plates present a significant design and implementation challenge, arising from factors such as low image resolution, data noise, and various weather and lighting conditions.This study presents an efficient automated system for the identification and classification of vehicle license plates, utilizing deep learning techniques. The system is specifically designed for Iraqi vehicle license plates, adapting to various backgrounds, different font sizes, and non-standard formats. The proposed system has been designed to be integrated into an automated entrance gate security system. The system’s framework encompasses two primary phases: license plate detection (LPD) and character recognition (CR). The utilization of the advanced deep learning technique YOLOv4 has been implemented for both phases owing to its adeptness in real-time data processing and its remarkable precision in identifying diminutive entities like characters on license plates. In the LPD phase, the focal point is on the identification and isolation of license plates from images, whereas the CR phase is dedicated to the identification and extraction of characters from the identified license plates. A substantial dataset comprising Iraqi vehicle images captured under various lighting and weather circumstances has been amassed for the intention of both training and testing. The system attained a noteworthy accuracy level of 95.07%, coupled with an average processing time of 118.63 milliseconds for complete end-to-end operations on a specified dataset, thus highlighting its suitability for real-time applications. The results suggest that the proposed system has the capability to significantly enhance the efficiency and reliability of vehicle license plate recognition in various environmental conditions, thus making it suitable for implementation in security and traffic management contexts.