People with hearing impairments are found worldwide; therefore, the development of effective local level sign language recognition (SLR) tools is essential. We conducted a comprehensive review of automated sign language recognition based on machine/deep learning methods and techniques published between 2014 and 2021 and concluded that the current methods require conceptual classification to interpret all available data correctly. Thus, we turned our attention to elements that are common to almost all sign language recognition methodologies. This paper discusses their relative strengths and weaknesses, and we propose a general framework for researchers. This study also indicates that input modalities bear great significance in this field; it appears that recognition based on a combination of data sources, including vision-based and sensor-based channels, is superior to a unimodal analysis. In addition, recent advances have allowed researchers to move from simple recognition of sign language characters and words towards the capacity to translate continuous sign language communication with minimal delay. Many of the presented models are relatively effective for a range of tasks, but none currently possess the necessary generalization potential for commercial deployment. However, the pace of research is encouraging, and further progress is expected if specific difficulties are resolved.

The Saudi Cancer Registry reported in 2007 the 5-year observed survival for the most common cancer sites for the years 1994–2004. In this report we looked at the cancer survival in the period 2005–2009 and evaluated the trend over the 15 years period from 1994 to 2009. Cases of the top 14 cancer sites reported by the population based Saudi Cancer Registry from 1 January 2005 to December 31, 2009, were submitted for survival analysis. The vital status of those patients was collected. Analysis of survival for the above period was compared with the prior reported 2 periods (1994–1999, 2000–2004). In addition, analysis was done according to age, sex, disease stage and the province. Data of 25,969 patients of the commonest cancer sites were submitted. Of those 14,146 patients (54%) had complete demographic data available and vital status was reported. Thyroid cancer had the highest 5- year observed survival of 94% (95% confidence interval (CI) 93–95%)), followed by Breast (72%, 95% CI 71–74%). In hematological malignancies, Hodgkin’s Lymphoma had the highest 5-year survival of 86% (95% CI 84–88%). Survival rates has improved in most of the cancers sites for the studied periods except for lung, uterine and Hodgkin’s lymphoma which plateaued. Our study confirms a steady improvement in the 5-year observed survival over time for the majority of cancers. Our survival data were comparable to western countries. This data should be used by policy makers to improve on cancer care in the kingdom.

The fact that almost every person owns a smartphone device that can be precisely located is both empowering and worrying. If methods for accurate tracking of devices (and their owners) via WiFi probing are developed in a responsible way, they could be applied in many different fields, from data security to urban planning. Numerous approaches to data collection and analysis have been covered, some of which use active sensing equipment, while others rely on passive probing, which takes advantage of nearly universal smartphone usage and WiFi network coverage. In this study, we introduce a system that uses WiFi probing technologies aimed at tracking user locations and understanding individual behavior. We built our own devices to passively capture WiFi request probe packets from smartphones, without the phones being connected to the network. The devices were tested at the headquarters of the research sector of the Elm Company. The results of the analyses carried out to estimate the crowd density in offices and the flows of the crowd from one place to another are promising and illustrate the importance of such solutions in indoor and closed spaces.

Automated assessment of car damage is a major challenge in the auto repair and damage assessment industries. The domain has several application areas, ranging from car assessment companies, such as car rentals and body shops, to accidental damage assessment for car insurance companies. In vehicle assessment, the damage can take many forms, from scratches, minor dents, and major dents to missing parts. Often, the assessment area has a significant level of noise, such as dirt, grease, oil, or rush, which makes accurate identification challenging. Moreover, in the repair industry, identifying a particular part is the first step in obtaining an accurate labor and part assessment, where the presence of different car models, shapes, and sizes makes the task even more challenging for a machine-learning model to perform well. To address these challenges, this study explores and applies various instance segmentation methodologies to determine the best-performing models. This study focuses on two genres of real-time instance segmentation models, namely, SipMask and YOLACT, owing to their industrial significance. These methodologies were evaluated against a previously reported car parts dataset (DSMLR) as well as an internally curated dataset extracted from local car repair workshops. The YOLACT-based part localization and segmentation method outperformed other real-time instance mechanisms with an mAP of 66.5. For the workshop repair dataset, SipMask++ reported better accuracy for object detection with a mAP of 57.0, with outcomes for A P I o U = . 50 and A P I o U = . 75 reporting 72.0 and 67.0, respectively, whereas YOLACT was observed to be a better performer for A P s with 44.0 and 2.6 for object detection and segmentation categories, respectively.