With many successful stories, machine learning (ML) and deep learning (DL) have been widely used in our everyday lives in a number of ways. They have also been instrumental in tackling the outbreak of Coronavirus (COVID-19), which has been happening around the world. The SARS-CoV-2 virus-induced COVID-19 epidemic has spread rapidly across the world, leading to international outbreaks. The COVID-19 fight to curb the spread of the disease involves most states, companies, and scientific research institutions. In this research, we look at the Artificial Intelligence (AI)-based ML and DL methods for COVID-19 diagnosis and treatment. Furthermore, in the battle against COVID-19, we summarize the AI-based ML and DL methods and the available datasets, tools, and performance. This survey offers a detailed overview of the existing state-of-the-art methodologies for ML and DL researchers and the wider health community with descriptions of how ML and DL and data can improve the status of COVID-19, and more studies in order to avoid the outbreak of COVID-19. Details of challenges and future directions are also provided.
In public vehicles, one of the major concerns is driver's level of expertise for its direct proportionality to safety of passengers. So before a driver is subjected to certain type of vehicle, he should be thoroughly evaluated and categorized with respect to certain parameters instead of only one-time metric of having driving license. These aspects may be driver's expertise, vigilance, aptitude, experience years, cognition, driving style, formal education, terrain, region, minor violations, major accidents and age group etc. The purpose of this categorization is to ascertain suitability of a driver for certain vehicle type(s) to ensure passengers' safety. Currently, no driver categorization technique fully comprehends the implicit as well as explicit characteristics of drivers dynamically. In this paper, machine learning based dynamic and adaptive technique named D-CHAIT (Driver Categorization based on Hybrid Artificial Intelligence Techniques) is proposed for driver categorization with an objective focus on driver's attributes modeled in DriverOntology. A supervised mode of learning has been employed on a labeled dataset, having diverse profiles of drivers with attributes pertinent to drivers' perspectives of demographics, behaviors, expertise and inclinations. A comparative analysis of D-CHAIT with three other machine learning techniques (Fuzzy Logic, Case Based Reasoning, and Artificial Neural Networks) is also presented. The efficacy of all techniques was empirically measured while categorizing the drivers based on their profiles through metrics of accuracy, precision, recall, f-measure performance and associated costs. These empirical quantifications assert D-CHAIT as a better technique than contemporary ones. The novelty of proposed technique is signified through preprocessing of feature attributes, quality of data, training of machine learning model on more relevant data and adaptivity.
Detection of benign and malignant liver masses is very important for the treatment. Objectives: To determine the diagnostic accuracy of ultrasound for hepatic masses taking computed tomography as gold standard Methods: It was cross a sectional analytical study to.it involves 266 patients suffering from hepatocellular cell carcinoma age group 45 to 65 years visiting Department of Radiology THQ Hospital Hazro, both genders were included. Consecutive sampling method was used. The collection of data was done through questionnaire and analysis by using SPSS version 25. Results: This study enlisted the participation of 261 patients. The average age of all patients was 59.28 14 years, with a range of 45 to 65 years. It describes that the total number of true positive disease were 228 which was also detected on ultrasound. However, the occurrence of HCC is highest on CT scan when compared with adenoma and hemangioma. There were 28 patients with multiple lesions, with 71.4 % being malignant and 28.6 % being benign. On the other hand, 22 individuals had a single lesion, of which 36.4% were malignant and 63.6 % were benign (p 0.001). CT had a sensitivity of 96 % to diagnose a malignant lesion, a specificity of 88.4 %, an accuracy of 95.78 %, a positive predictive value of 98.70 %, and a negative predictive value of 73.33 %. Conclusions: The results of the present study therefore concluded that CT is a useful modality for the diagnosis of malignant liver masses.Ultrasound had high sensitivity, specificity for the hepatic masses. females were more effected than males. Among hepatic masses, HCC is the commonest.
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