A comprehensive review of vehicle detection using computer vision

A crucial step in designing intelligent transport systems (ITS) is vehicle detection. The challenges of vehicle detection in urban roads arise because of camera position, background variations, occlusion, multiple foreground objects as well as vehicle pose. The current study provides a synopsis of state-of-the-art vehicle detection techniques, which are categorized according to motion and appearance-based techniques starting with frame differencing and background subtraction until feature extraction, a more complicated model in comparison. The advantages and disadvantages among the techniques are also highlighted with a conclusion as to the most accurate one for vehicle detection

Breast cancer diagnosis using the fast learning network algorithm

The use of machine learning (ML) and data mining algorithms in the diagnosis of breast cancer (BC) has recently received a lot of attention. The majority of these efforts, however, still require improvement since either they were not statistically evaluated or they were evaluated using insufficient assessment metrics, or both. One of the most recent and effective ML algorithms, fast learning network (FLN), may be seen as a reputable and efficient approach for classifying data; however, it has not been applied to the problem of BC diagnosis. Therefore, this study proposes the FLN algorithm in order to improve the accuracy of the BC diagnosis. The FLN algorithm has the capability to a) eliminate overfitting, b) solve the issues of both binary and multiclass classification, and c) perform like a kernel-based support vector machine with a structure of the neural network. In this study, two BC databases (Wisconsin Breast Cancer Database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC)) were used to assess the performance of the FLN algorithm. The results of the experiment demonstrated the great performance of the suggested FLN method, which achieved an average of accuracy 98.37%, precision 95.94%, recall 99.40%, F-measure 97.64%, G-mean 97.65%, MCC 96.44%, and specificity 97.85% using the WBCD, as well as achieved an average of accuracy 96.88%, precision 94.84%, recall 96.81%, F-measure 95.80%, G-mean 95.81%, MCC 93.35%, and specificity 96.96% using the WDBC database. This suggests that the FLN algorithm is a reliable classifier for diagnosing BC and may be useful for resolving other application-related problems in the healthcare sector

A review of forest fire surveillance technologies: Mobile ad-hoc network routing protocols perspective

Mobile Ad-Hoc Network (MANET) is a type of structure-less wireless mobile network, in which each node plays the role of the router and host at the same time. MANET has gained increased interest from researchers and developers for various applications such as forest fire detection. Forest fires require continuous monitoring and effective communication, technology, due to the big losses are brought about by this event. As such, disaster response and rescue applications are considered to be a key application of the MANET. This paper gives an extensive review of the modern techniques used in the forest fire detection based on recent MANET routing protocols such as reactive Location-Aided Routing (LAR), proactive Optimized Link State Routing (OLSR) and LAR-Based Reliable Routing Protocol (LARRR)

Effect of Garden Cress Seeds Powder and Its Alcoholic Extract on the Metabolic Activity of CYP2D6 and CYP3A4

The powder and alcoholic extract of dried seeds of garden cress were investigated for their effect on metabolic activity of CYP2D6 and CYP3A4 enzymes. In vitro and clinical studies were conducted on human liver microsomes and healthy human subjects, respectively. Dextromethorphan was used as a common marker for measuring metabolic activity of CYP2D6 and CYP3A4 enzymes. In in vitro studies, microsomes were incubated with NADPH in presence and absence of different concentrations of seeds extract. Clinical investigations were performed in two phases. In phase I, six healthy female volunteers were administered a single dose of dextromethorphan and in phase II volunteers were treated with seeds powder for seven days and dextromethorphan was administered with last dose. The O-demethylated and N-demethylated metabolites of dextromethorphan were measured as dextrorphan (DOR) and 3-methoxymorphinan (3-MM), respectively. Observations suggested that garden cress inhibits the formation of DOR and 3-MM metabolites. This inhibition of metabolite level was attributed to the inhibition of CYP2D6 and CYP3A4 activity. Garden cress decreases the level of DOR and 3-MM in urine and significantly increases the urinary metabolic ratio of DEX/DOR and DEX/3-MM. The findings suggested that garden cress seeds powder and ethanolic extract have the potential to interact with CYP2D6 and CYP3A4 substrates

Forest Fire Detection Using New Routing Protocol

The Mobile Ad-Hoc Network (MANET) has received significant interest from researchers for several applications. In spite of developing and proposing numerous routing protocols for MANET, there are still routing protocols that are too inefficient in terms of sending data and energy consumption, which limits the lifetime of the network for forest fire monitoring. Therefore, this paper presents the development of a Location Aided Routing (LAR) protocol in forest fire detection. The new routing protocol is named the LAR-Based Reliable Routing Protocol (LARRR), which is used to detect a forest fire based on three criteria: the route length between nodes, the temperature sensing, and the number of packets within node buffers (i.e., route busyness). The performance of the LARRR protocol is evaluated by using widely known evaluation measurements, which are the Packet Delivery Ratio (PDR), Energy Consumption (EC), End-to-End Delay (E2E Delay), and Routing Overhead (RO). The simulation results show that the proposed LARRR protocol achieves 70% PDR, 403 joules of EC, 2.733 s of E2E delay, and 43.04 RO. In addition, the performance of the proposed LARRR protocol outperforms its competitors and is able to detect forest fires efficiently

Clinical, epidemiological, and laboratory characteristics of mild-to-moderate COVID-19 patients in Saudi Arabia: an observational cohort study

Background Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) emerged from China in December 2019 and has presented as a substantial and serious threat to global health. We aimed to describe the clinical, epidemiological, and laboratory findings of patients in Saudi Arabia infected with SARS-CoV-2 to direct us in helping prevent and treat coronavirus disease 2019 (COVID-19) across Saudi Arabia and around the world. Materials and methods Clinical, epidemiological, laboratory, and radiological characteristics, treatment, and outcomes of pediatric and adult patients in five hospitals in Riyadh, Saudi Arabia, were surveyed in this study. Results 401 patients (mean age 38.16 ± 13.43 years) were identified to be SARS-CoV-2 positive and 80% of cases were male. 160 patients had moderate severity and 241 were mild in severity. The most common signs and symptoms at presentation were cough, fever, fatigue, and shortness of breath. Neutrophil and lymphocyte counts, aspartate aminotransferase, C-reactive protein, and ferritin were higher in the COVID-19 moderate severity patient group. Mild severity patients spent a shorter duration hospitalized and had slightly higher percentages of abnormal CT scans and X-ray imaging. Conclusions This study provides an understanding of the features of non-ICU COVID-19 patients in Saudi Arabia. Further national collaborative studies are needed to streamline screening and treatment procedures for COVID-19

Clinical Characteristics of Non-Intensive Care Unit COVID-19 Patients in Saudi Arabia: A Descriptive Cross-sectional Study

Introduction: The ongoing pandemic of the coronavirus disease 2019 (COVID-19) is a global health concern. It has affected more than 5 million patients worldwide and resulted in an alarming number of deaths globally. While clinical characteristics have been reported elsewhere, data from our region is scarce. We investigated the clinical characteristics of mild to moderate cases of COVID-19 in Saudi Arabia. Methods: This is a descriptive, cross-sectional study. Data of 401 confirmed COVID-19 patients were collected from 22 April 2020 to 21 May 2020 at five tertiary care hospitals in Riyadh, Saudi Arabia. The patients were divided into four groups according to age, Group 1: 0-60 years; and their clinical symptoms were compared. Results: The median (IQR) age in years was 10.5 (1.5-16) in group I, 34 (29-41) in group II, 53 (51-56) in group III, and 66 (61-76) in group IV. Most patients were male (80%, n = 322) and of Arabian or Asian descent. The median length of stay in the hospital was 10 (8-17) days (range 3-42 days). The most common symptoms were cough (53.6%), fever (36.2%), fatigue (26.4%), dyspnea (21.9%), and sore throat (21.9%). Hypertension was the most common underlying comorbidity (14.7%), followed by obesity (11.5%), and diabetes (10%). Hypertensive patients were less likely to present with shortness of breath, cough, sputum, diarrhea, and fever. Conclusion: There was no significant difference in the symptoms among different age groups and comorbidities were mostly seen in the older age group. Interestingly, hypertensive patients were found to have milder symptoms and a shorter length of stay. Further larger collaborative national studies are required to effectively understand clinical characteristics in our part of the world to efficiently manage and control the spread of SARS-CoV-2

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care