Comparative Study of VGG-13, AlexNet, MobileNet and Modified-DarkCovidNet for Chest X-Ray Classification

Convolutional neural network (CNN) is the most widely used structure-building technique for deep learning models. In order to classify chest x-ray pictures, this study examines a number of models, including VGG-13, AlexN ct, MobileNet, and Modified-DarkCovidNet, using both segmented image datasets and regular image datasets. Four types of chest X- images: normal chest image, Covid-19, pneumonia, and tuberculosis are used for classification. The experimental results demonstrate that the VGG offers the highest accuracy for segmented pictures and Modified Dark CovidN et performs best for multi class classification on segmented images. © 2023 Bharati Vidyapeeth, New Delhi.

A Comparative Study of Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps on Covid Variants

The pandemic situation created by COVID'19 is ridiculous. It has made even the blood relations hide themselves from the infected person. The whole world was stunned by this situation. This is because of the uncertainty in the way in which this disease is spread. As an advancement of this disease, a few other variants like delta, omicron etc. also got spread. It is essential to find a solution to this situation. The variants Omicron and Delta are taken into consideration here. Though both the vibrant colours look alike, the symptoms and prevention methods changes for each of these vibrants. This work aims to make a study of the parameters responsible for these variants. As a result of this study, the parameters involved in the spread of these diseases are identified, and the prevention parameters are concluded. The major benefit of this comparatively study is to identify the parameters that are inconclusive, applying the concepts of fuzzy cognitive maps and neutrosophic cognitive maps is applied to bring out the result © 2023, Neutrosophic Sets and Systems.All Rights Reserved.

Comparative study of Deep Learning Models for Covid 19 Diagnosis

The Covid 19 pandemic that started a couple of years ago has had a devastating effect on mankind across the globe. The disease had no known treatment. Early detection and prevention was very important to curtail the effects of the Pandemic. In this work two deep learning models the RestNet and the models are proposed for diagnosing Corona from chest X-rays and CT scans. The models were trained with publicly available data sets of covid and non covid images. It has been found that Inception V3 performs better than ResNet for chest x-rays and RestNet performs better for CT Scans. The performance of the RestNet is found to be similar for both the chest x-rays and CT scans datasets. © 2023 IEEE.

Measuring Health Systems Resilience: A Comparative Study of Turkey's Health System during COVID-19 Pandemic

The recent outbreak of the COVID-19 pandemic has drawn significant attention to the topic of health-system resilience. Many countries have taken certain measures to deal with the negative outcomes of the pandemic and to improve their health systems. Having a resilient health system during pandemics ensures the continuity and success of healthcare services. Resilience, as a concept, represents a proactive rather than a reactive approach to overcoming the negative outcomes of disasters. Understanding the characteristics of a resilient health system will help to strengthen the health systems for future pandemics or any other disasters. In this research project, characteristics of resilient health systems are investigated using a framework based on three main dimensions of systems resilience: (1) a system's capability to decrease its level of vulnerability to expected and unexpected disruptive events, (2) its ability to change itself and adapt to the changing environment;(3) its ability to recover in the least possible time in case of a disruptive event. Based on this framework, four attributes of resilience are identified, namely agility, adaptability, flexibility, and vulnerability. Further, these attributes of resilience are evaluated using country-specific COVID-19-related qualitative and quantitative data from Turkey and compared with several other countries. Suggestions and further recommendations are provided on how to measure and improve the resiliency of health systems for future pandemics. © 2022 IEEE.

Incremental Donor Approach for Immune Plasma Algorithm on Solving Path Planning Problem of UCAV

Immune Plasma algorithm (IP algorithm or IPA) that models the implementation details of a medical method popularized with the COVID-19 pandemic again known as the immune or convalescent plasma has been introduced recently and used successfully for solving different engineering optimization problems. In this study, incremental donor (ID) approach was first developed for controlling how many donor individuals will be chosen before the treatment of receivers representing the poor solutions of the population and then a promising IPA variant called ID-IPA was developed as a new path planner. For analyzing the contribution of the ID approach on the solving capabilities of the IPA, a set of experimental studies was carried out and results of the ID-IPA were compared with different well-known meta-heuristic algorithms. Comparative studies showed that controlling the incrementation of donor individuals as described in the ID approach increases the qualities of the final solutions and improves the stability of the IP algorithm. © 2022 IEEE.

Effect of Elevator Design on the Risk Associated with COVID Transmission – A Comparative Study between a Sliding Door and a Collapsible Gate Elevator

The ominous spread of the COVID-19 pandemic is attributed to the droplets respired during coughing, sneezing or speaking. These droplets undergo evaporation to become aerosols, which, along with the larger droplets, are believed to ultimately spread the virus. In this current work, a small, enclosed region like an elevator (containing a COVID infected passenger) is considered where the risk of infection is high as the commonly practiced norm of social distancing is not possible. Numerical simulations are performed using OpenFOAM. Two different types of elevators – one equipped with a sliding door and the other with a collapsible gate, are considered and the change in droplet behavior is examined. Certain parameters pertaining to the risk of virus transmission have been quantified and assessed thoroughly, such as the percentage of droplets floating in the height range from a person's waist height to his mouth height, the radial span of the floating droplets from the infected passenger's mouth. From these parameters, the safety measures to be adopted by other copassengers can be determined. After an extensive study, it has been found that the collapsible gate elevator is safer than the sliding door elevator along with added advantages in the context of disease transmission. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Streaming Big Data with Open-source: A Comparative Study and Architectural Recommendations

This study focusses on providing state-of-the-art infrastructure for data pipelines in e-Commerce sector, especially for online stores. With people going digital and also latest impact of Covid-19, daily e-Commerce companies are dealing with large amount of data (terabytes to petabytes). With growing Internet of Things, systems of computing devices which are interrelated. The inter-relation may be between mechanical and digital machines, objects or people. The interrelated objects will be provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. Growth of big data poses several challenges and opportunities in every field of its usage. Realtime analysis of data and its inference gives a competitive edge over its partners in every business field especially in e-commerce. Recent advances in technology and tools have exposed new opportunities to get actionable insights from historical data like market data, customer demographics, along with real-time data. Advancement in distributed streaming technology makes it important to investigate existing streaming data pipeline capabilities in eCommerce sector with a focus on online stores. This study analyzes the published research works on streaming data pipelines in e-commerce sector also to facilitate e-commerce's variety of data streaming applications requirement. A state-of-the-art lambda architecture for streaming is proposed completely based on open-source technologies. Challenge in proprietary owned streaming platforms are vendor lock-in, limited ability to customize, cost, limited innovation & support. Proposed reference architecture will address many streaming use cases compared to its competitors, it has support of large open-source community in providing the inter-operability between streaming & related technologies like connectors, apart from providing better performance apart from other open-source based product advantages. © 2023 IEEE.

Augmented Reality for medical practice: A comparative study of Deep learning models for Ct-scan segmentation

The coronavirus disease has hardly affected medical healthcare systems worldwide. Physicians use radiological examinations as a primary clinical tool for diagnosing patients with suspected COVID-19 infection. Recently, deep learning approaches have further enhanced medical image processing and analysis, reduced the workload of radiologists, and improved the performance of radiology systems. This paper addresses medical image segmentation;we present a comparative performance study of four neural networks 'NN' models, U-Net, 3D-Unet, KiU-Net and SegNet, for aid diagnosis. Additionally, we present his 3D reconstruction of COVID-19 lesions and lungs and his AR platform with augmented reality, including AR visualization and interaction. Quantitative and qualitative assessments are provided for both contributions. The NN model performed well in the AI-COVID-19 diagnostic process. The AR-COVID-19 platform can be viewed as an ancillary diagnostic tool for medical practice. It serves as a tool to support radiologist visualization and reading. © 2022 IEEE.

Learning Techniques for Depression Detection: A Comparative Studies

Depression is a common mental problem that can fundamentally affect individuals' emotional wellness as well as their everyday lives. After COVID-19 other pandemics and subsequent social isolation this issue is more potent than ever. Numerous research works have been going on searching for methods that effectively recognize depression in order to detect depression. In this regard, a number of studies have been proposed. In this study, it examines a number of previous ones utilizing various Machine Learning (ML) and Artificial Intelligence (AI) methods for depression detection. In addition, various methods for determining an individual's mood and emotion are discussed. This study also discusses how facial expression, voice, gesture can be understood by chatbot and classified it as a depressed person or not. Addition to this, it reviews all the related research works and evaluates their methods to detect depression. © 2023 IEEE.

Predictive Analytics of Stock Market as a Time Series

This paper presents a new methodology and a comparative study using past stock market data that can help businesses take investing or divesting decisions in critical situations in the future. These may be like the COVID-19 pandemic, where market volatility is extremely high, thus creating an urgent need for better decision support systems to minimise loss and ensure better profits. The results of the study are based on the comparison of different configurations of ARIMAX, Prophet, LSTM and Bidirectional LSTM Models trained on historical NSE data. By understanding the correlation and variations in the data processing and model training parameters, we have successfully proposed a LSTM neural network model training and optimising method which could successfully help businesses take both long and short term profitable decisions before and after big financial and market crises with a respective accuracy of 98.60 percent and 96.97 percent. © 2022 IEEE.

Detect misinformation of COVID-19 using deep learning: A comparative study based on word embedding

Since its emergence in December 2019, there have been numerous news of COVID-19 pandemic shared on social media, which contain information from both reliable and unreliable medical sources. News and misleading information spread quickly on social media, which can lead to anxiety, unwanted exposure to medical remedies, etc. Rapid detection of fake news can reduce their spread. In this paper, we aim to create an intelligent system to detect misleading information about COVID-19 using deep learning techniques based on LSTM and BLSTM architectures. Data used to construct the DL models are text type and need to be transformed to numbers. We test, in this paper the efficiency of three vectorization techniques: Bag of words, Word2Vec and Bert. The experimental study showed that the best performance was given by LSTM model with BERT by achieving an accuracy of 91% of the test set. © 2023 IEEE.

Comparative Study of Various Lightweight Mask Recognition Algorithms

As an important line of defense against novel coronavirus, masks can effectively reduce the risk of novel coronavirus infection. In this paper, three algorithms were used for mask wear detection, respectively using the opencv native library, MTCNN+MobileNet, and pyramidbox_lite_mobile_mask in paddlehub. Finally, the test results of the three algorithms were analyzed and compared, and the experimental results are that the pyramidbox_lite_mobile_mask model in paddlehub has the most sensitive face recognition and mask detection ability, which can identify the blurred face and judge whether to wear a mask, followed by MTCNN + MobileNet. © 2022 IEEE.

Comparative Study of Molecular Docking, Structural, Electronic, and Fukui Function Studies on Favipiravir and its Newly Designed Derivatives (Potential Inhibitors) for COVID-19 Protease

Favipiravir is an antiviral medication currently being trialed as a COVID-19 treatment. These results motivate us to develop new species (possibly drugs) from favipiravir, perform comparative molecular docking, and reexamine their biological and pharmacological activities. Detailed quantum chemical research on favipiravir and its newly designed derivatives has been carried out with the help of DFT/B3LYP/6–311 + + G (d, p). In the present work, the structure of favipiravir has been modified and 12 new species have been modeled (all species are inherently stable because no virtual frequency is found during the vibration analysis). Reactivity of all species using various descriptors (local) such as Fukui function, local softness, electrophilicity, and global, i.e., electronegativity, hardness, HOMO–LUMO gap, etc. of the same are calculated and discussed. In silico studies such as molecular docking of all species and complete quantum chemistry studies suggest that four of them may mitigate the effects of the COVID-19 protease. © 2023 Wiley-VCH GmbH.

On the Use of Conventional Neural Networks for COVID-19 Detection in CT-Scan Images: A Comparative Study and Performance Analysis

This paper presents a comprehensive study on deep learning for COVID-19 detection using CT-scan images. The proposed study investigates several Conventional Neural Networks (CNN) architectures such as AlexNet, ZFNet, VGGNet, and ResNet, and thus proposed a hybrid methodology base on merging the relevant optimized architectures considered for detecting COVID-19 from CT-scan images. The proposed methods have been assessed on real datasets, and the experimental results conducted have shown the effectiveness of the proposed methods, allowing achieving a higher accuracy up to 99%. © 2022 IEEE.

Text Based Diagnosis of COVID-19 Using Data Mining Techniques: A Comparative Study

In the course of the recent pandemic, we have witnessed non-clinical approaches such as data mining and artificial intelligence techniques being exceedingly utilized to restrain and combat the increase of COVID-19 across the globe. The emergence of artificial intelligence in the medical field has helped in reducing the immense burden on medical systems by providing the best means for diagnosis and prognosis of COVID-19. This work attempts to analyze & evaluate superlative models on robust data resources on symptoms of COVID-19, consisting of age, gender, demographic information, pre-existing medical conditions, and symptoms experienced by patients. This study establishes paradigmatic pipeline of supervised learning algorithms coupled with feature extraction techniques and surpassing the current state-of-the-art results by achieving an accuracy of 93.360. The optimal score was found by performing feature extraction on the data using principal component analysis (PCA) followed by binary classification using the AdaBoost classifier. In addition, the present study also establishes the contribution of various symptoms in the diagnosis of the malady. © 2022 IEEE.

Rate Insight: A Comparative Study on Different Machine Learning and Deep Learning Approaches for Product Review Rating Prediction in Bengali Language

In this contemporary era of digital marketing, ecommerce has emerged as one of the most preferred methods for day-to-day shopping. Ever since the COVID-19 pandemic, online shopping behavior has forever changed to less or no human-to-human interaction. As a result, it is getting more difficult for e-commerce enterprises to observe and evaluate market trends, particularly when done through consumer behavior analysis. To identify behavioral patterns and customer review-rating discrepancies, extensive analysis of product reviews is a substantial research field. Lack of benchmark corpora and language processing techniques, predicting review ratings in Bengali has become increasingly problematic. This paper thoroughly analyzes the approach to product review rating prediction for Bengali text reviews exploiting our own constructed dataset that was collected from an e-commerce website called DarazBD1. We acquired product reviews with labels known as ratings of five sentiment classes, from "1"to "5". It is noteworthy that we established a well-balanced dataset using our automated scraping system and a significant amount of time and effort is spent to maintain quality standards through the human annotation process. Exploration of multiple approaches to machine learning models such as logistic regression, random forest, multinomial naïve Bayes, and support vector machine, the best classification accuracy score of 78.63% is achieved by SVM. Subsequently, using Word2Vec, FastText, and GloVe embeddings with three deep neural network(DNN) architectures: CNN, Bi-LSTM, and a combination of CNN and Bi-LSTM, CNN+Bi-LSTM gave the highest accuracy score of 75.25% among the DNN architectures. © 2022 IEEE.

A Comparative Study of Supervised Machine Learning Algorithms for Fake Review Detection

The behavior of shopping has shifted into online shopping. Especially after Coronavirus Disease of 2019 (COVID-19), people choose online shopping rather than going to the market for economic and hygienic reasons. Reviews help the seller to make customers trust their products, but since some sellers are not honest, they use fake reviews to help boost their products. Fake reviews are commonly generated randomly by a computer bot or someone not using the product. Some researchers are already working on fake review detection to help this problem using many methods. In this paper, we compared three supervised machine learning algorithms: Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF). By preprocessing the data and using the Term Frequency-Inverse Document Frequency (TF-IDF) feature, we begin the experiment process without tuning. We apply the tuning parameters to each algorithm for the other experiments using 5-fold cross-validation. The result showed that SVM algorithms outperform the best algorithms of the three before and after tuning, with 88.89% and 89.77%, respectively. © 2022 IEEE.

New trends in digital marketing emergence during pandemic times

Purpose: This study relies on an integrated model to study the role of instant mobile messaging apps in the pandemic. The COVID-19 pandemic is reshaping different forms of businesses;one of them is digital marketing. Many aspects of digital marketing augmented in response to the consequences of the virus. A comparative study between Pakistan and Iraq is conducted to investigate the resistors of innovation with the mediation of intention toward actual system usage. It examines the behavioral intentions and actual behavior of individuals in response to the resistance toward innovation. Design/methodology/approach: A total of 800 responses were collected through a convenient sampling method from individuals residing in Pakistan and Iraq in the first wave of COVID-19. The data was analyzed through covariance-based structural equation modeling;SPSS and Smart PLS 3.0 were used as efficient data analysis tools in the study. Findings: The results inferred that individuals are faced with resistance to innovation when they adopt innovative technology. It was inferred that technology adoption is not poised through image both in Pakistan and Iraq. Intention toward actual behavior was determined to be a potential mediator, which enhances the stature of the integrated model. Originality/value: The significance of this study considering practical and theoretical implications is incorporated for marketer's policymakers and consumers, along with recommendations for future research. © 2022, Emerald Publishing Limited.

Sentiment Analysis Using CatBoost Algorithm on COVID-19 Tweets

Sentimental analysis is a study of emotions or analysis of text as an approach to machine learning. It is the most well-known message characterization device that investigates an approaching message and tells whether the fundamental feeling is positive or negative. Sentimental analysis is best when utilized as an instrument to resolve the predominant difficulties while solving a problem. Our main objective is to identify the emotional tone and classify the tweets on COVID-19 data. This paper represents an approach that is evaluated using an algorithm namely—CatBoost and measures the effectiveness of the model. We have performed a comparative study on various machine learning algorithms and illustrated the performance metrics using a Bar-graph. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Adaptive Fuzzy Neural Network vs. Convolution Neural Network in Classifying COVID-19 from Chest X-rays

Detecting COVID-19 in the early time can save lives and reduce the cost of huge pressure on healthcare centers. Many machine and deep learning models have been proposed by researchers to detect and diagnose COVID-19 based on chest X-rays. However, we need to know which of those models is more effective and efficient. This paper presents a comparative study between adaptive fuzzy neural network (AFNN) and convolutional neural network (CNN) in classifying COVID-19 using chest X-rays. We present the experimental results showing the comparative performance measures with respect to the size of available dataset. We also present the relative advantage of each family of neural network in accuracy, precision, recall, F1score, and the computation time. © 2022 IEEE.