Machine Learning with Quantum Seagull Optimization Model for COVID-19 Chest X-Ray Image Classification.

Early and accurate detection of COVID-19 is an essential process to curb the spread of this deadly disease and its mortality rate. Chest radiology scan is a significant tool for early management and diagnosis of COVID-19 since the virus targets the respiratory system. Chest X-ray (CXR) images are highly useful in the effective detection of COVID-19, thanks to its availability, cost-effective means, and rapid outcomes. In addition, Artificial Intelligence (AI) techniques such as deep learning (DL) models play a significant role in designing automated diagnostic processes using CXR images. With this motivation, the current study presents a new Quantum Seagull Optimization Algorithm with DL-based COVID-19 diagnosis model, named QSGOA-DL technique. The proposed QSGOA-DL technique intends to detect and classify COVID-19 with the help of CXR images. In this regard, the QSGOA-DL technique involves the design of EfficientNet-B4 as a feature extractor, whereas hyperparameter optimization is carried out with the help of QSGOA technique. Moreover, the classification process is performed by a multilayer extreme learning machine (MELM) model. The novelty of the study lies in the designing of QSGOA for hyperparameter optimization of the EfficientNet-B4 model. An extensive series of simulations was carried out on the benchmark test CXR dataset, and the results were assessed under different aspects. The simulation results demonstrate the promising performance of the proposed QSGOA-DL technique compared to recent approaches.

A Smart Surveillance System for Uncooperative Gait Recognition Using Cycle Consistent Generative Adversarial Networks (CCGANs).

Surveillance remains an important research area, and it has many applications. Smart surveillance requires a high level of accuracy even when persons are uncooperative. Gait Recognition is the study of recognizing people by the way they walk even when they are unwilling to cooperate. It is another form of a behavioral biometric system in which unique attributes of an individual's gait are analyzed to determine their identity. On the other hand, one of the big limitations of the gait recognition system is uncooperative environments in which both gallery and probe sets are made under different and unknown walking conditions. In order to tackle this problem, we propose a deep learning-based method that is trained on individuals with the normal walking condition, and to deal with an uncooperative environment and recognize the individual with any dynamic walking conditions, a cycle consistent generative adversarial network is used. This method translates a GEI disturbed from different covariate factors to a normal GEI. It works like unsupervised learning, and during its training, a GEI disrupts from different covariate factors of each individual and acts as a source domain while the normal walking conditions of individuals are our target domain to which translation is required. The cycle consistent GANs automatically find an individual pair with the help of the Cycle Loss function and generate the required GEI, which is tested by the CNN model to predict the person ID. The proposed system is evaluated over a publicly available data set named CASIA-B, and it achieved excellent results. Moreover, this system can be implemented in sensitive areas, like banks, seminar halls (events), airports, embassies, shopping malls, police stations, military areas, and other public service areas for security purposes.

Reported Benefits of Insulin Therapy for Better Glycemic Control in Type 2 Diabetic Patients-Is This Applicable in Saudi Patients?

AIM: To compare the effect of different treatment regimens (oral hypoglycemic agents [OHGs], insulin therapy, and combination of both) on glycemic control and other cardiometabolic risk factors in type 2 diabetes mellitus (T2DM) patients in Saudi. SUBJECTS AND METHODS: Patients with T2DM, but no serious diabetic complications, were randomly recruited from the diabetes clinics at two large hospitals in Jeddah, Saudi Arabia, during June 2013 to July 2014. Only those without change in treatment modality for the last 18 months were included. Blood pressure and anthropometric measurements were measured. Treatment plan was recorded from the patients' files. Fasting blood sample was obtained to measure glucose, HbA1c, and lipid profile. RESULTS: A total of 197 patients were recruited; 41.1% were men and 58.9% were women. The mean (±SD) age was 58.5 ± 10.5 years. Most patients (60.7%) were on OHGs, 11.5% on insulin therapy, and 27.7% were using a combination of insulin and OHGs. The mean HbA1c was lower in patients using OHGs only, compared with means in those using insulin, or combined therapy in patients with disease duration of ≤10 years (P = 0.001) and also in those with a longer duration of the disease (P < 0.001). A lower mean diastolic and systolic blood pressure was found among patients on insulin alone (P < 0.01). No significant differences were found in lipid profiles among the groups. CONCLUSION: Insulin therapy, without adequate diabetes education, fails to control hyperglycemia adequately in Saudi T2DM patients. There is a challenge to find out reasons for poor control and the ways as to how to improve glycemic control in T2DM.