Abstract Online learning is a very important means of study, and has been adopted in many countries worldwide. However, only recently are researchers able to collect and analyze massive online learning datasets due to the COVID-19 epidemic .In this article, we analyze the difference between online learner groups by using an unsupervised machine learning technique, i.e., k-prototypes clustering. Specifically, we use a questionnaire designed by domain experts to collect various online learning data, and investigate students’ online learning behavior and learn ing outcomes through analyzing the collected questionnaire data. Our analysis results suggest that students with better learning media generally have bet ter online learning behavior and learning results than those with poor online learning media. In addition, both in economically developed or undeveloped regions, the number of students with better learning media is less than the number of students with poor learning media. Finally, the results presented here show that whether in an economically developed or an economically undeveloped region, the number of students who are enriched with learning media available is an important factor that affects online learning behavior and learning outcomes.

Abstract In our study, we chose python as the programming platform for finding an Automatic Bengali Document Summarizer. English has sufficient tools to process and receive summarized records. However, there is no specifically applicable to Bengali since Bengali has a lot of ambiguity, it differs from English in terms of grammar. Afterward, this language holds an important place because this language is spoken by 26 core people all over the world. As a result, it has taken a new method to summarize Bengali documents. The proposed system has been designed by using the following stages: pre-processing the sample doc/input doc, word tagging, pronoun replacement, sentence ranking, as well as summary. Pronoun replacement has been used to reduce the incidence of swinging pronouns in the performance review. We ranked sentences based on sentence frequency, numerical figures, and pronoun replacement. Checking the similarity between two sentences in order to exclude one since it has less duplication. Hereby, we’ve taken 3000 data as input from newspaper and book documents and learned the words to be appropriate with syntax. In addition, to evaluate the performance of the designed summarizer, the design system looked at the different documents. According to the assessment method, the recall, precision, and F-score were 0.70, 0.82 and 0.74, respectively, representing 70%, 82% and 74% recall, precision, and F-score. It has been found that the proper pronoun replacement was 72%.

Abstract This paper presented the new sanitary and health protocols implemented by cruise companies in order to internationally resuming the industry. The perception of Brazilians regarding these new protocols was identified through a quantitative survey with a sample of 412 Brazilian respondents, carried out between May and June 2021. As main results, sanitary and health protocols that do not affect their experiences on board were identified, as well as those protocols that compromised the experience perceived by the future traveler. The respondents’ propensity to travel on cruise ships and their perceptions about the influence of the ship size, the number of ports of call, nationalities on board, the number of guests, among other aspects, were also analyzed. Finally, in the final section of this paper, we presented an estimation of the expected number of cruisers for the 21/22 cruising season in Brazil, based on a kind Bayesian argument, considering different scenarios for the forthcoming season .

Abstract COVID-19 virus is certainly considered as one of the harmful viruses amongst all the illnesses in biological science. COVID-19 symptoms are fever, cough, sore throat, and headache. The paper gave a singular function for the prediction of most of the COVID-19 virus diseases and presented with the Convolutional Neural Networks and Logistic Regression which might be the supervised learning and gaining knowledge of strategies for most of COVID-19 virus diseases detection. The proposed system makes use of an 8-fold pass determination to get a correct result. The COVID-19 virus analysis dataset is taken from Microsoft Database, Kaggle, and UCI websites gaining knowledge of the repository. The proposed studies investigate Convolutional Neural Networks (CNN) and Logistic Regression (LR) about the usage of the UCI database, Kaggle, and Google Database Datasets. This paper proposed a hybrid method for COVID-19 virus, most disease analyses through reducing the dimensionality of capabilities the usage of Logistic Regression (LR), after which making use of the brand new decreased function dataset to Convolutional Neural Networks and Logistic regression. The proposed method received the accuracy of 78.82%, sensitiveness of 97.41%, and specialness of 98.73%. The overall performance of the proposed system is appraised thinking about performance, accuracy, error rate, sensitiveness, particularity, correlation and coefficient. The proposed strategies achieved the accuracy of 78.82% and 97.41% respectively through Convolutional Neural Networks and Logistic Regression.

Abstract The identification of objects in binary images is a fundamental task in image analysis and pattern recognition tasks. The Euler number of a binary image is an important topological measure which is used as a feature in image analysis. In this paper, a very fast algorithm for the detection and localization of the objects and the computation of the Euler number of a binary image is proposed. The proposed algorithm operates in one scan of the image and is based on the Image Block Representation (IBR) scheme. The proposed algorithm is more efficient than conventional pixel based algorithms in terms of execution speed and representation of the extracted information.

Abstract Artificial intelligence has significantly altered many job workflows, hence expanding earlier notions of limitations, outcomes, size, and prices. GeoAI is a multidisciplinary field that encompasses computer science, engineering, statistics, and spatial science. Because this subject focuses on real-world issues, it has a significant impact on society and the economy. A broad context incorporating fundamental questions of theory, epistemology, and the scientific method is used to bring artificial intelligence (Al) and geography together. This connection has the potential to have far-reaching implications for the geographic study. GeoAI, or the combination of geography with artificial intelligence, offers unique solutions to a variety of smart city issues. This paper provides an overview of GeoAI technology, including the definition of GeoAI and the differences between GeoAI and traditional AI. Key steps to successful geographic data analysis include integrating AI with GIS and using GeoAI tools and technologies. Also shown are key areas of applications and models in GeoAI, likewise challenges to adopt GeoAI methods and technology as well as benefits. This article also included a case study on the use of GeoAI in Kuwait, as well as a number of recommendations.

Abstract The purpose of the article is to develop a methodology for automating the detection and selection of moving objects. The detection and separation of moving objects based on impulse and recurrence neural networks simulation. The result of the work is a developed motion detector based on impulse and recurrence neural networks and an automated system developed on the basis of this detector for detecting and separating moving objects and is ready for practical application. The feasibility of integrating the developed motion detector with Emgu CV (OpenCV) image processing package, multimedia framework functions, and DirectShow application programming interface were investigated. The proposed approach and software for the detection and separating of moving objects in video images using neural networks can be integrated into more sophisticated specialized computer-aided video surveillance systems, IoT (Internet of Things), IoV (Internet of Vehicles), etc.

Abstract Clinical assessment of fluid volume status in children during malaria can be taxing and often inaccurate. During malaria, changes in fluid volume are rather multifarious and estimating this parameter, especially in sick children is very challenging for clinicians who frequently rely on indices such as long capillary refill times, tachycardia, central venous pressure and decreased urine volume as guides. Here, we present the UHAS-MIDA, an open-source software tool that calculates the red blood cell (RBC) concentration and blood volume during malaria in children determined using a stable isotope of chromium (53Cr as the label) by gas chromatography-mass spectrometry in selective ion monitoring (GC/MS-SIM) analysis. A key component involves the determination of the compositions of the most abundant naturally occurring isotopes of Cr (50Cr, 52Cr, 53Cr), and converting the proportions into a 3 × 3 matrix. To estimate unknown proportions of chromium isotopic mixtures from the measured abundances of three ions, an inverse matrix was calculated. The inverse together with several inputs is then used to calculate the corrected MS ion abundances. Thus, we constructed the software tool UHASMIDA using HTML, CSS/Bootstrap, JavaScript, and PHP scripting languages. The tool enables the user to efficiently determine RBC concentration and fluid volume. The source code, binary packages and associated materials for UHAS-MIDA are freely available at at https://github.com/bentil078/Abaye-et-al_UHASmida.