Optimal Kernel Extreme Learning Machine for COVID-19 Classification on Epidemiology Dataset

Artificial Intelligence (AI) encompasses various domains such as Machine Learning (ML), Deep Learning (DL), and other cognitive technologies which have been widely applied in healthcare sector. AI models are utilized in healthcare sector in which the machines are used to investigate and make decisions based on prediction and classification of input data. With this motivation, the current study involves the design of Metaheuristic Optimization with Kernel Extreme Learning Machine for COVID19 Prediction Model on Epidemiology Dataset, named MOKELM-CPED technique. The primary aim of the presented MOKELM-CPED model is to accomplish effectual COVID-19 classification outcomes using epidemiology dataset. In the proposed MOKELM-CPED model, the data first undergoes pre-processing to transform the medical data into useful format. Followed by, data classification process is performed by following Kernel Extreme (SOS) optimization algorithm is utilized to fine tune the KELM parameters which consequently helps in achieving high detection efficiency. In order to investigate the improved classifier outcomes of MOKELM-CPED model in an effectual manner, a comprehensive experimental analysis was conducted and the results were inspected under diverse aspects. The outcome of the experiments infer the enhanced performance of the proposed method over recent approaches under distinct measures.

Optimal Hybrid Feature Extraction with Deep Learning for COVID-19 Classifications

Novel coronavirus 2019 (COVID-19) has affected the people's health, their lifestyle and economical status across the globe. The application of advanced Artificial Intelligence (AI) methods in combination with radiological imaging is useful in accurate detection of the disease. It also assists the physicians to take care of remote villages too. The current research paper proposes a novel automated COVID-19 analysis method with the help of Optimal Hybrid Feature Extraction (OHFE) and Optimal Deep Neural Network (ODNN) called OHFE-ODNN from chest x-ray images. The objective of the presented technique is for performing binary and multi-class classification of COVID-19 analysis from chest X-ray image. The presented OHFE-ODNN method includes a sequence of procedures such as Median Filtering (MF)based pre-processed, feature extraction and finally, binary (COVID/Non-COVID) and multiclass (Normal, COVID, SARS) classification. Besides, in OHFE-based feature extraction, Gray Level Co-occurrence Matrix (GLCM) and Histogram of Gradients (HOG) are integrated together. The presented OHFE-ODNN model includes Squirrel Search Algorithm (SSA) for fine-tuning the parameters of DNN. The performance of the presented OHFE-ODNN technique is conducted using chest x-rays dataset. The presented OHFE-ODNN method classified the binary classes effectively with a maximum precision of 95.82%, accuracy of 94.01% and F-score of 96.61%. Besides, multiple classes were classified proficiently by OHFE-ODNN model with a precision of 95.63%, accuracy of 95.60% and an F-score of 95.73%. © 2022 Tech Science Press. All rights reserved.

The impact of covid-19 on medical students: A cross sectional survey

Aim: To understand the psychological impact of COVID - 19 on Medical Students of a private sector Medical University in Karachi, Pakistan. Method: This cross-sectional study was conducted among medical students studying at Hamdard College of Medicine and Dentistry, Karachi, Pakistan. The data collection was done through online survey from July 2020 to December 2020. The study aimed to gather data from many medical students. A total number of 420 students were participated from Hamdard College of Medicine and Dentistry in Karachi, Pakistan. The participants were selected from all years of MBBS and BDS programs . Results: Out of 420 participants, 236 (56.2%) were male and 184 (43.8%) female, with a male:female ration of 1.28:1. Majority of participants were single as 411 (97.9%), of 224 (53.3%) students living with their family, 150 (35.7%) in hostel and 46 (11%) living with friends. In our sample 369 (87.9%) students studying in MBBS program while only 51 (12.1%) BDS, among those 80 (19%) medical students were in first year, followed by 122 (29%) second year, 65 (15.5%) third year, 54 (12.9%) fourth year and 99 (23.6%) studying in final year. IES-R scale and results shows 75 (17.9%) reported that PTSD is a clinical concern, probable diagnosis of PTSD 28 (6.7%) and majority rated as high enough to PTSD 133 (31.7%). Impact of event (revised) scale shows significant association with age and year of study with p value 0.026 and 0.002 respectively. Based on the PHQ9 scale, Gender, Living arrangements and the program enrolled in were reported significant association with depression p values 0.059, 0.008 and 0.006 respectively. Conclusion: Findings suggests high rate of anxiety, depression, and signs of PTSD in medical students due to COVID-19 which needs pressing attention and provision of professional help from mental health practitioners.

Artificial intelligence based sentiment analysis for health crisis management in smart cities

Smart city promotes the unification of conventional urban infrastructure and information technology (IT) to improve the quality of living and sustainable urban services in the city. To accomplish this, smart cities necessitate collaboration among the public as well as private sectors to install IT platforms to collect and examinemassive quantities of data. At the same time, it is essential to design effective artificial intelligence (AI) based tools to handle healthcare crisis situations in smart cities. To offer proficient services to people during healthcare crisis time, the authorities need to look closer towards them. Sentiment analysis (SA) in social networking can provide valuable information regarding public opinion towards government actions. With this motivation, this paper presents a new AI based SA tool for healthcare crisis management (AISA-HCM) in smart cities. The AISA-HCM technique aims to determine the emotions of the people during the healthcare crisis time, such as COVID-19. The proposed AISA-HCM technique involves distinct operations such as pre-processing, feature extraction, and classification. Besides, brain stormoptimization (BSO) with deep belief network (DBN), called BSODBN model is employed for feature extraction. Moreover, beetle antenna search with extreme learning machine (BAS-ELM) method was utilized for classifying the sentiments as to various classes. The use of BSO and BAS algorithms helps to effectively modify the parameters involved in the DBN andELMmodels respectively. The performance validation of the AISA-HCM technique takes place using Twitter data and the outcomes are examined with respect to various measures. The experimental outcomes highlighted the enhanced performance of the AISA-HCM technique over the recent state of art SA approaches with the maximum precision of 0.89, recall of 0.88, Fmeasure of 0.89, and accuracy of 0.94. © 2022 Tech Science Press. All rights reserved.

Machine Learning Based Depression, Anxiety, and Stress Predictive Model During COVID-19 Crisis

Corona Virus Disease-2019 (COVID-19) was reported at first in Wuhan city, China by December 2019. World Health Organization (WHO) declared COVID-19 as a pandemic i.e., global health crisis on March 11, 2020. The outbreak of COVID-19 pandemic and subsequent lockdowns to curb the spread, not only affected the economic status of a number of countries, but it also resulted in increased levels of Depression, Anxiety, and Stress (DAS) among people. Therefore, there is a need exists to comprehend the relationship among psycho-social factors in a country that is hypothetically affected by high levels of stress and fear;with tremendously-limiting measures of social distancing and lockdown in force;and with high rates of new cases and mortalities. With this motivation, the current study aims at investigating the DAS levels among college students during COVID-19 lockdown since they are identified as a highly-susceptible population. The current study proposes to develop Intelligent Feature Subset Selection with Machine Learning-based DAS predictive (IFSSML-DAS) model. The presented IFSSML-DAS model involves data preprocessing, Feature Subset Selection (FSS), classification, and parameter tuning. Besides, IFSSML-DAS model uses Group Gray Wolf Optimization based FSS (GGWO-FSS) technique to reduce the curse of dimensionality. In addition, Beetle Swarm Optimization based Least Square Support Vector Machine (BSO-LSSVM) model is also employed for classification in which the weight and bias parameters of the LSSVM model are optimally adjusted using BSO algorithm. The performance of the proposed IFSSML-DAS model was tested using a benchmark DASS-21 dataset and the results were investigated under different measures. The outcome of the study suggests the development of specialized programs to handle DAS among population so as to overcome COVID-19 crisis.