ABSTRACT

No abstract

Internet of Things (IoT) with deep learning (DL) is drastically growing and plays a significant role in many applications, including medical and healthcare systems. It can help users in this field get an advantage in terms of enhanced touchless authentication, especially in spreading infectious diseases like coronavirus disease 2019 (COVID-19). Even though there is a number of available security systems, they suffer from one or more of issues, such as identity fraud, loss of keys and passwords, or spreading diseases through touch authentication tools. To overcome these issues, IoT-based intelligent control medical authentication systems using DL models are proposed to enhance the security factor of medical and healthcare places effectively. This work applies IoT with DL models to recognize human faces for authentication in smart control medical systems. We use Raspberry Pi (RPi) because it has low cost and acts as the main controller in this system. The installation of a smart control system using general-purpose input/output (GPIO) pins of RPi also enhanced the antitheft for smart locks, and the RPi is connected to smart doors. For user authentication, a camera module is used to capture the face image and compare them with database images for getting access. The proposed approach performs face detection using the Haar cascade techniques, while for face recognition, the system comprises the following steps. The first step is the facial feature extraction step, which is done using the pretrained CNN models (ResNet-50 and VGG-16) along with linear binary pattern histogram (LBPH) algorithm. The second step is the classification step which can be done using a support vector machine (SVM) classifier. Only classified face as genuine leads to unlock the door; otherwise, the door is locked, and the system sends a notification email to the home/medical place with detected face images and stores the detected person name and time information on the SQL database. The comparative study of this work shows that the approach achieved 99.56% accuracy compared with some different related methods.

Background: An adequate closure of the appendiceal stump is vital to minimize intra-abdominal and surgical site infections. There are various techniques for the closure of base of appendix while performing a laparoscopic appendectomy like endoloops, knotting, clips and staplers. Objective: To compare the extracorporeal knot-tying suture with metallic endoclips in laparoscopic appendiceal stump closure in terms of complications, operative time, hospital stay and cost. Methodology: This study was conducted as a single-blinded randomized controlled trial. Patients undergoing laparoscopic appendicectomies in three tertiary care hospitals of Peshawar, i.e. Khyber Teaching Hospital, Lady Reading Hospital and Hayatabad Medical Complex from June 1, 2013 to June 1, 2014 were included in the study and randomized into two groups – extra-corporeal knotting group and the metallic endoclip group. Data on demographics, complications, operative time, hospital stay and cost for the two techniques were collected and analyzed. Statistics analyses were done with IBM SPSS v19 (IBM Corp., Armonk, NY, USA). T-test was use for comparison of continuous data; Chi-square test was used for comparison of categorical data. P < 0.05 was considered statistically significant. Results: A total of 68 patients were enrolled in the study and randomized into two groups: metallic endoclip group n = 32 (47.1%), extracorporeal knot group n = 36 (52.9%). The two groups didn't significantly differ in age (P = 0.9). There were no statistically significant differences between the two groups in terms of complication rates (P > 0.05) and hospital stay (P > 0.05). The mean operative time for the endoclip group was shorter (mean 42.1 ± 7.4 min) as compared to the extracorporeal knot group (mean 48.3 ± 8.4 min) (P = 0.002). The cost of endoclip group was higher (800PKR = 8.10US$) as compared to the extracorporeal knot group (220PKR = 2.23US$). Conclusion: The use of metallic endoclip for appendix stump closure is safe and less time consuming but costs higher. Because of the simplicity of the technique it's a useful alternative to the extracorporeal knotting especially for learners. Highlights:

Blind deconvolution or deblurring is a challenging problem in many signal processing applications as signals and images often suffer from blurring or point spreading with unknown blurring kernels or point-spread functions as well as noise corruption. Most existing methods require certain knowledge about both the signal and the kernel and their performance depends on the amount of prior information regarding the both. Independent component analysis (ICA) has emerged as a useful method for recovering signals from their mixtures. However, ICA usually requires a number of different input signals to uncover the mixing mechanism. In this paper, a blind deconvolution and deblurring method is proposed based on the nongaussianity measure of ICA as well as a genetic algorithm. The method is simple and does not require prior knowledge regarding either the image or the blurring process, but is able to estimate or approximate the blurring kernel from a single blurred image. Various blurring functions are described and discussed. The proposed method has been tested on images degraded by different blurring kernels and the results are compared to those of existing methods such as Wiener filter, regularization filter, and the Richardson-Lucy method. Experimental results show that the proposed method outperform these methods.