Study on Dynamic Change of Land Usage in Hongta District Based on Sentinel-2

This work utilizes Sentinel-2A L1C remote sensing photographs from the years 2018, 2020, and 2022 to identify the different land use categories in the study area using the support vector machine (SVM) technique. The accuracy of categorization is greater than 90%. This research explores four factors of the dynamic change in land use in Hongta District from 2018 to 2022: the proportion of various types of land;the extent of something like the changing land usage;land use transfer;and the dynamic degree of the change in land use. According to the study's results, the proportion of cultivated and grassland land grew, while the quantity of barren and construction land fell by 1.90 percent, 0.03 percent, and 0.69 percent, respectively. The water system land portion of total area increased by 2.58 percent and 0.13 percent, respectively. After comparing the two research periods, the entire dynamic degree of the second stage is determined to be 3.5 percent lower than that of the first stage, and the pace of land use change is quite sluggish, which may be associated with the worldwide COVID-19 outbreak in 2020. The outcomes of the research may give the natural resources department the knowledge it needs to manage land resources properly. © 2023 SPIE.

Artificial Intelligence driven security model for Internet of Medical Things (IoMT)

The Internet of Medical Things (IoMT) has been applied to provide health care facilities for elders and parents. Remote health care is essential for providing scarce resources and facilities to coronavirus patients. Ongoing IoMT communication is susceptible to potential security attacks. In this research, an artificial intelligence-driven security model of the IoMT is also proposed to simulate and analyses the results. Under the proposed plan, only authorized users will be able to access private and sensitive patient information, and unauthorized users will be unable to access a secure healthcare network. The various phases for implementing artificial intelligence (AI) techniques in the IoMT system have been discussed. AI-driven IoMT is implemented using decision trees, logistic regression, support vector machines (SVM), and k-nearest neighbours (KNN) techniques. The KNN learning models are recommended for IoMT applications due to their low consumption time with high accuracy and effective prediction. © 2023 IEEE.

Multi-operator Variant of Differential Evolution and its Application in Classification of COVID-19 CT-scan Images

Novel coronavirus disease (COVID-19), caused by the virus (SARS-CoV-2), has drastically impacted human beings' lives since early 2020. The virus is constantly changing, and with mutations, it becomes diverse and spreads more easily. Several automatic COVID-19 diag-nostic tools are proposed that emphasize feature extraction mechanism from radiographical images using modern deep learning technology. The general idea is to leverage smart solutions of pre-trained networks for deep-feature processing. However, all the extracted features may not essentially contribute to the performance of the COVID-19 diag-nostic model, and hence an optimal subset of features must be discov-ered. Motivated by this, we propose a novel feature selection method based on multi-operator differential evolution (MODE), which helps to acquire optimal feature-subset. To show the efficacy of the proposed algorithm, we focus on applying the COVID-19 classification model through medical imaging. Eight advanced pre-trained architectures have been selected for COVID-19 feature extraction from CT-scan medi-cal imaging. After that, the proposed feature selection technique based on MODE is applied. A customized SVM kernel is implemented that supports feature classification. The result analysis shows the perfor-mance of the existing COVID-19 designs with the proposed feature selection technique, MODE, integrated with a customized SVM kernel. It even beats the existing state-of-the-art frameworks carried forward for COVID-19 diagnosis. We have observed that MODE feature selec-tion is suitable for fast COVID-19 detection, having overall accuracy of 85.27%.

Machine learning-based diffusion model for prediction of coronavirus-19 outbreak.

The coronavirus pandemic has been globally impacting the health and prosperity of people. A persistent increase in the number of positive cases has boost the stress among governments across the globe. There is a need of approach which gives more accurate predictions of outbreak. This paper presents a novel approach called diffusion prediction model for prediction of number of coronavirus cases in four countries: India, France, China and Nepal. Diffusion prediction model works on the diffusion process of the human contact. Model considers two forms of spread: when the spread takes time after infecting one person and when the spread is immediate after infecting one person. It makes the proposed model different over other state-of-the art models. It is giving more accurate results than other state-of-the art models. The proposed diffusion prediction model forecasts the number of new cases expected to occur in next 4 weeks. The model has predicted the number of confirmed cases, recovered cases, deaths and active cases. The model can facilitate government to be well prepared for any abrupt rise in this pandemic. The performance is evaluated in terms of accuracy and error rate and compared with the prediction results of support vector machine, logistic regression model and convolution neural network. The results prove the efficiency of the proposed model.

Predicting Stock Price Movement for the Inefficient Market: Case of Hanoi Stock Exchange (HNX)

If the market is efficient, with stock prices accurately reflecting the true risk of an investment, then the issue becomes simpler. While this is true, investors may have a window of opportunity to discover a successful investing strategy if the market is inefficient. The primary goal of this research is to use the Support Vector Machine (SVM) algorithm to predict daily cycles of price increases for the ten largest-cap companies trading on the Hanoi Stock Exchange (HNX) over the Covid-19 timeframe (January 1st, 2019, to December 1st, 2022). Study how the model performs when trained and tested with a moving window. The outcome was an impressive average accuracy of 81.68 percent for the predicting model. © 2023 IEEE.

Performance of COVID Face Mask Detection Based on SVM Algorithm Using OpenCV DNN as Preprocessing Model

As COVID-19 became a pandemic in the world, wearing a mask has become one of the best measures to prevent the spread of the epidemic, so face mask recognition in public places has become a very important part of controlling the epidemic. This paper mainly tests the performance of the OpenCV DNN preprocessing model (OpenCV DNN + SVM) based on the SVM algorithm model in the face mask recognition dataset. The dataset I use is from Kaggle called COVID Face Mask Detection Dataset. This dataset contains 503 face images with masks and 503 face images without masks. I test the performance of using OpenCV DNN + SVM and using only the SVM algorithm to evaluate this study by setting a control experimental group. In this study, it was found that using OpenCV DNN + SVM, the accuracy of ROI parameters and SVM parameters can reach 93.06% and F1score can also reach 93.06% without a lot of adjustment. The accuracy rate can only reach 68.31%, and the F1score reaches 68.31%. Findings suggest that the method using OpenCV DNN + SVM can achieve slightly better results in the COVID Face Mask Detection Dataset, and can perform better than only using the SVM algorithm. In addition, using OpenCV DNN preprocessing model based on the SVM algorithm plays an important role in feature extraction in face mask recognition. If the developer does enough parameters tuning, the accuracy will also increase. © 2022 SPIE.

COVID-19 vaccination perception and outcome: society sentiment analysis on twitter data in India.

This study examines the perceptions and results of COVID-19 immunization using sentiment analysis of Twitter data from India. The tweets were collected from January 2021 to March 2023 using relevant hashtags and keywords. The dataset was pre-processed and cleaned before conducting sentiment analysis using Natural Language Processing techniques. Our results show that the overall sentiment toward COVID-19 vaccination in India has been positive, with a majority of tweets expressing support for vaccination and encouraging others to get vaccinated. However, we also identified some negative sentiments related to vaccine hesitancy, side effects, and mistrust in the government and pharmaceutical companies. We further analyzed the sentiment based on demographic factors such as gender, age, and location. The analysis revealed that the sentiment varied across different demographics, with some groups expressing more positive or negative sentiments than others. This study provides insights into the perception and outcomes of COVID-19 vaccination in India and highlights the need for targeted communication strategies to address vaccine hesitancy and increase vaccine uptake in specific demographics.

Automatic detection of novel corona virus (SARS-CoV-2) infection in computed tomography scan based on local adaptive thresholding and kernel-support vectors

The recent studies have indicated the requisite of computed tomography scan analysis by radiologists extensively to find out the suspected patients of SARS-CoV-2 (COVID-19). The existing deep learning methods distribute one or more of the subsequent bottlenecks. Therefore, a straight forward method for detecting COVID-19 infection using real-world computed tomography scans is presented. The detection process consists of image processing techniques such as segmentation of lung parenchyma and extraction of effective texture features. The kernel-based support vector machine is employed over feature vectors for classification. The performance parameters of the proposed method are calculated and compared with the existing methodology on the same dataset. The classification results are found outperforming and the method is less probabilistic which can be further exploited for developing more realistic detection system.Copyright © 2023 Inderscience Enterprises Ltd.

Automatic Diagnosis of COVID-19 Based on Machine Learning

The lives and health of individuals are significantly threatened by the extremely infectious and dangerous Corona Virus Disease 2019 (COVID-19). For the containment of the epidemic, quick and precise COVID-19 detection and diagnosis are essential. Currently, artificial diagnosis based on medical imaging and nucleic acid detection are the major approaches used for COVID-19 detection and diagnosis. However, nucleic acid detection takes a long time and requires a dedicated test box, while manual diagnosis based on medical images relies too much on professional knowledge, and analysis takes a long time, and it is difficult to find hidden lesions. Thanks to the rapid development of pattern recognition algorithms, building a COVID-19 diagnostic model based on machine learning and clinical symptoms has become a feasible rapid detection solution. In this paper, support vector machines and random forest algorithms are used to build a COVID-19 diagnostic model, respectively. Based on the quantitative comparison of the performance of the two methods, the future development trends in this field are discussed. © 2023 IEEE.

Large‐scaled detection of COVID‐19 from X‐ray using transfer learning

The pandemic cause as a result of the outbreak of COVID‐19 disease continues to burden the healthcare system despite several interventions using vaccines and other preventive measures. Healthcare settings adopted the use of reverse transcription‐polymerase chain reaction (RT‐PCR) which is hampered by so many challenges such as miss‐diagnosis, false positive results, high cost, especially for those in remote and rural areas, the need for trained medical pathologists, the use of chemicals, and a lack of point‐of‐care detection. The use of radiographic images as an alternative or confirmatory approach has offered medical experts another option, but has some limitations, such as misinterpretation, and can be tedious for analyzing thousands of cases. In order to bridge this gap, we applied two AlexNet models for the classification of different types of pneumonia, including COVID‐19 using X‐ray. Considering the fact that the majority of articles in the literature reported binary classifications of radiographic images. This article utilizes X‐ray images for classification of COVID‐19, non‐COVID‐19 viral pneumonia, bacterial pneumonia, and normal cases using the AlexNet‐SoftMax classifier and the AlexNet‐SVM classifier. The research also evaluates performance based on 5k‐fold and 10k fold cross validation (CV). The results achieved in terms of accuracy, sensitivity, and specificity based on 70:30 partition, 5k, and 10k CV have shown that the models outperformed the majority of the state‐of‐the‐art deep learning architectures. [ FROM AUTHOR] Copyright of International Journal of Imaging Systems & Technology is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Utilization of Transfer Learning Model in Detecting COVID-19 Cases From Chest X-Ray Images

Diagnosis of COVID-19 pneumonia using patients' chest x-ray images is new but yet important task in the field of medicine. Researchers from different parts of the globe have developed many deep learning models to classify COVID-19. The performance of feature extraction and classifier plays a vital role in the recognizing the different patterns in the image. The pivotal process is the extraction of optimum features from the chest x-ray images. The main goal of this study is to design an efficient hybrid algorithm that integrates the robustness of MobileNet (using transfer learning approach) to extract features and support vector machine (SVM) to classify COVID-19. Experiments were conducted to test the proposed algorithm, and it was found to have a high classification accuracy of 95%.

HuDA_COVID Human Disposition Analysis During COVID-19 Using Machine Learning

Coronavirus has greatly impacted various aspects of human life, including human psychology and human disposition. In this paper, the authors analyzed the impact of the COVID-19 pandemic on human health. In the proposed work, human disposition analysis during COVID-19 using machine learning (HuDA_COVID), where factors such as age, employment, addiction, stress level are studied. A mass survey is conducted on individuals of various age groups, regions, and professions, and the methodology achieved varied accuracy ranges from 87.5% to 98%. The study shows people are worried about lockdown, work, and relationships. Furthermore, 23% of the respondents have not had any effect. Forty-five percent and 32% have had positive and negative effects, respectively. HuDA_COVID is a novel study in human disposition analysis in COVID-19 where a weighted assignment indicating the health status is also proposed. HuDA_COVID clearly indicates a need for a methodical approach towards the human psychological needs to help the social organizations formulating holistic interventions for affected individuals.

Mucormycosis Detection using Hybrid Convolutional Neural Network with Support Vector Machine and Compare the performance with Support Vector Machine

Based on the patient's underlying condition, mucormycosis, often known as a black fungus illness, is an uncommon but severe disease with a high fatality rate. The large second wave of the COVID-19 epidemic has presented a challenge for the Indian healthcare system from this life-threatening powerful threat. The fungus family Mucorales causes mucormycosis, which affects numerous bodily organs. This fungal opportunistic illness spreads quickly. Recently, this unusual fungus has been infecting covid sufferers in India at greater rates than before. In India, the frequency of this black fungus illness amongst covid-19 as well as post-covid-19 patients is now on the rise. Finding a solution for this potentially fatal fungal infection requires the aid of modern technologies, including artificial intelligence and data learning. In this article, we present a unique hybrid model for black fungus identification that combines support vector machine classifier and convolutional learning network. Under the proper circumstances, the CNNSVM model is made simpler by minimizing the amount of variables because it is not important to constantly the weighting factors in a back propagation cycle. Additionally, it was shown that the SVM classifier was the best merging equivalent when the CNN was employed as a feature extractor, offering the highest accuracy-related synergy effect. On testing data, the mean classification performance was 99.3%, which is a significant improvement over current techniques. © 2023 IEEE.

Black Fungus Classification using Adaboost with SVM-based classifier and Compare accuracy with Support Vector Machine

India recognize a severe public health issue in addition to the COVID-19 outbreak and the growing percentage of patients with related mucormycosis from 2021. An uncommon condition known as mucormycosis is brought on by fungus in the family Mucorales. Mucormycosis is a fairly uncommon illness that is caused by common environmental moulds that may be found in soil and decomposing organic materials. Spores develop into hyphae in a susceptible individual, which subsequently infect nearby tissue, including blood vessels, leading to hemorrhagic infarction. Doctors have offered many hypotheses on this. The issue is if black fungus is present in other countries given how uncontrolled it is growing in India. Patients in India with weakened immune systems are more susceptible to illnesses other than corona virus infection. The revised machine learning strategy which will be created in this work is Adaboost with an Support Vector Machine-based classifier (ASVM). Due of the difficulties in learning SVM and the differential in variety as well as efficiency over straightforward SVM classifiers, ASVM classifier is frequently believed to violate the Boosting principle. The Adaboost classifier used in the study gradually replaces SVM as the primary classifier when the weight value of the training sample changes. On testing data, the mean accuracy of the classification was 97.1%, which was much higher than that of SVM classifiers without Adaboost. © 2022 IEEE.

Covid-19 and Diabetes Risk Prediction for Diabetic Patient using Advance Machine Learning Techniques and Fuzzy Inference System

Diabetes has become a common and critical disease which generally occurs due to the presence of high sugar in blood for long time. A diabetic patient has to follow different rules and restrictions where he/she has to be under proper attention by measuring diabetes level frequently to avoid unexpected risk. The risk become more when patient even doesn't know that he/she is already having diabetes and doesn't follow those restrictions. To prevent this risk, everyone should check the diabetes status to be sure. With the same target different system using machine learning techniques have been introduced which can predict the diabetes status of a patient. But the challenging fact is that the performances and accuracy of those models are questionable where there may be a huge risk of patient's life. The conventional systems are not able to show that which level of diabetes a patient can have using the previous records. To solve this issue, through this paper an efficient system has been proposed with which the diabetes status can be predicted correctly. The proposed system can also show the complexity of diabetes as well as the Covid-19 risk percentage that can also be possible to measure. After comparing several machine learning techniques, the suitable model has been selected where high level of accuracy has been ensured in term of predicting the disease. © 2022 IEEE.

Diagnosis of Pulmonary Edema and Covid-19 from CT slices using Squirrel Search Algorithm, Support Vector Machine and Back Propagation Neural Network

A Computer Aided Diagnosis (CAD) framework to diagnose Pulmonary Edema (PE) and covid-19 from the chest Computed Tomography (CT) slices were developed and implemented in this work. The lung tissues have been segmented using Otsu's thresholding method. The Regions of Interest (ROI) considered in this work were edema lesions and covid-19 lesions. For each ROI, the edema lesions and covid-19 lesions were elucidated by an expert radiologist, followed by texture and shape extraction. The extracted features were stored as feature vectors. The feature vectors were split into train and test set in the ratio of 80 : 20. A wrapper based feature selection approach using Squirrel Search Algorithm (SSA) with the Support Vector Machine (SVM) classifier's accuracy as the fitness function was used to select the optimal features. The selected features were trained using the Back Propagation Neural Network (BPNN) classifier. This framework was tested on a real-time PE and covid-19 dataset. The BPNN classifier's accuracy with SSA yielded 88.02%, whereas, without SSA it yielded 83.80%. Statistical analysis, namely Wilcoxon's test, Kendall's Rank Correlation Coefficient test and Mann Whitney U test were performed, which indicates that the proposed method has a significant impact on the accuracy, sensitivity and specificity of the novel dataset considered. Comparative experimentations of the proposed system with existing benchmark ML classifiers, namely Cat Boost, Ada Boost, XGBoost, RBF SVM, Poly SVM, Sigmoid SVM and Linear SVM classifiers demonstrate that the proposed system outperforms the benchmark classifiers' results. [ FROM AUTHOR] Copyright of Journal of Intelligent & Fuzzy Systems is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Evaluation of AI Techniques for Detecting Deceptive Reviews in Cyberspace: A Study of Pre- and Post-COVID-19 Trends

With cyberspace's continuous evolution, online reviews play a crucial role in determining business success in various sectors, ranging from restaurants and hotels to e-commerce applications. Typically, a favorable review for a specific product draws in more consumers and results in a significant boost in sales. Unfortunately, a few businesses are using deceptive methods to improve their online reputation by using fake reviews of competitors. As a result, detecting fake reviews has become a difficult and ever-changing research field. Verbal characteristics extracted from review text, as well as nonverbal features such as the reviewer's engagement metrics, the IP address of the device, and so on, play an important role in detecting fake reviews. This article examines and compares various machine learning techniques for detecting deceptive reviews on various online platforms such as e-commerce websites such as Amazon and online review websites such as Yelp, among others. © 2023 IEEE.

Career Path Prediction System Using Supervised Learning Based on Users' Profile

In the past few years, technology has changed drastically and due to COVID-19 pandemic, people spend more time on screen. The use of social media platforms has also been increased and this affects the human mind and decision taking ability. Online career counseling is largely supported these days and hence this paper proposes an online career prediction system using supervised machine learning based on the user's profile. This research attempted to develop a model for the user which predicts the career path in a precise manner and gives actionable feedback and career recommendations to encourage them to make significant career judgments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

Comparative Analysis of Swarm Algorithms to Classification of covid19 on X-Rays

The use of Chest radiograph (CXR) images in the examination and monitoring of different lung disorders like infiltration, tuberculosis, pneumonia, atelectasis, and hernia has long been known. The detection of COVID-19 can also be done with CXR images. COVID-19, a virus that results in an infection of the upper respiratory tract and lungs, was initially detected in late 2019 in China's Wuhan province and is considered to majorly damage the airway and, thus, the lungs of people afflicted. From that time, the virus has quickly spread over the world, with the number of mortalities and cases increasing daily. The COVID-19 effects on lung tissue can be monitored via CXR. As a result, This paper provides a comparison regarding k-nearest neighbors (KNN), Support-vector machine (SVM), and Extreme Gradient Boosting (XGboost) classification techniques depending on Harris Hawks optimization algorithm (HHO), Salp swarm optimization algorithm (SSA), Whale optimization algorithm (WOA), and Gray wolf optimizer (GWO) utilized in this domain and utilized for feature selection in the presented work. The dataset used in this analysis consists of 9000 2D X-ray images in Poster anterior chest view, which has been categorized by using valid tests into two categories: 5500 images of Normal lungs and 4044 images of COVID-19 patients. All of the image sizes were set to 200 × 200 pixels. this analysis used several quantitative evaluation metrics like precision, recall, and F1-score. © 2022 IEEE.

Automated speech emotion polarization for a distance education system based on orbital local binary pattern and an appropriate sub-band selection technique.

The distance education system was widely adopted during the Covid-19 pandemic by many institutions of learning. To measure the effectiveness of this system, it is essential to evaluate the performance of the lecturers. To this end, an automated speech emotion recognition model is a solution. This research aims to develop an accurate speech emotion recognition model that will check the lecturers/instructors' emotional state during lecture presentations. A new speech emotion dataset is collected, and an automated speech emotion recognition (SER) model is proposed to achieve this aim. The presented SER model contains three main phases, which are (i) feature extraction using multi-level discrete wavelet transform (DWT) and one-dimensional orbital local binary pattern (1D-OLBP), (ii) feature selection using neighborhood component analysis (NCA), (iii) classification using support vector machine (SVM) with ten-fold cross-validation. The proposed 1D-OLBP and NCA-based model is tested on the collected dataset, containing three emotional states with 7101 sound segments. The presented 1D-OLBP and NCA-based technique achieved a 93.40% classification accuracy using the proposed model on the new dataset. Moreover, the proposed architecture has been tested on the three publicly available speech emotion recognition datasets to highlight the general classification ability of this self-organized model. We reached over 70% classification accuracies for all three public datasets, and these results demonstrated the success of this model.