Introduction to the Special Issue on Artificial Intelligence for Smart Cities and Industries

Smart Cities and Artificial Intelligence offers an intensive evaluation of how the smart city establishments are made at different scales through automated thinking headways, for instance, geospatial information, data examination, data portrayal, clever related things, and quick natural frameworks handiness. Progressing propels in electronic thinking attract us closer to making a persistent reproduced model of human-made and trademark structures, from urban regions to transportation establishments to utility frameworks. This continuous living model empowers us to all the bound to manage and improve these working structures, making them dynamically watchful. Keen Cities and Artificial Intelligence gives a multidisciplinary, joined procedure, using speculative and applied bits of information, for the evaluation of savvy city situations. This special issue shows how the mechanized and physical universes are associated inside this organic framework, and how nonstop data arrangement is changing the possibility of our urban as well as industrial condition. It gives a fresh sweeping perspective on the natural framework designing, advances, and parts that include the masterminding and execution of sharp city and industry establishments. This special issue also shows how the computerized and physical universes are connected inside this biological system, and how continuous information assortment is changing the idea of our urban and industry condition. It gives a crisp all-encompassing viewpoint on the biological system engineering, advances, and parts that involve the arranging and execution of keen city and industry foundations. After following double blind peer review for all the submitted manuscripts across the globe, and after the rigorous review process, revision and based on final recommendations of the reviewers and editorial team, finally 17 manuscripts have been accepted for publication.

Development and Validation of Predictors for the Survival of Patients With COVID-19 Based on Machine Learning

Background: The outbreak of COVID-19 attracted the attention of the whole world. Our study aimed to explore the predictors for the survival of patients with COVID-19 by machine learning.Methods: We conducted a retrospective analysis and used the idea of machine learning to train the data of COVID-19 patients in Leishenshan Hospital through the logical regression algorithm provided by scikit-learn.Results: Of 2010 patients, 42 deaths were recorded until March 29, 2020. The mortality rate was 2.09%. There were 6,812 records after data features combination and data arrangement, 3,025 records with high-quality after deleting incomplete data by manual checking, and 5,738 records after data balancing finally by the method of Borderline-1 Smote. The results of 10 times of data training by logistic regression model showed that albumin, saturation of pulse oxygen at admission, alanine aminotransferase, and percentage of neutrophils were possibly associated with the survival of patients. The results of 10 times of data training including age, sex, and height beyond the laboratory measurements showed that percentage of neutrophils, saturation of pulse oxygen at admission, alanine aminotransferase, sex, and albumin were possibly associated with the survival of patients. The rates of precision, recall, and f1-score of the two training models were all higher than 0.9 and relatively stable.Conclusions: We demonstrated that percentage of neutrophils, saturation of pulse oxygen at admission, alanine aminotransferase, sex, and albumin were possibly associated with the survival of patients with COVID-19.

Satellite Orbit Prediction Using Big Data and Soft Computing Techniques to Avoid Space Collisions

The number of satellites and debris in space is dangerously increasing through the years. For that reason, it is mandatory to design techniques to approach the position of a given object at a given time. In this paper, we present a system to do so based on a database of satellite positions according to their coordinates (x,y,z) for one month. We have paid special emphasis on the preliminary stage of data arrangement, since if we do not have consistent data, the results we will obtain will be useless, so the first stage of this work is a full study of the information gathered locating the missing gaps of data and covering them with a prediction. With that information, we are able to calculate an orbit error which will estimate the position of a satellite in time, even when the information is not accurate, by means of prediction of the satellite’s position. The comparison of two satellites over 26 days will serve to highlight the importance of the accuracy in the data, provoking in some cases an estimated error of 4% if the data are not well measured.

Development of Hankel Singular-Hypergraph Feature Extraction Technique for Acoustic Partial Discharge Pattern Classification

Different types of classifiers for acoustic partial discharge (PD) pattern classification have been widely discussed in the literature. The classifier performance mainly depends on the measurement conditions (location and type of the PD, acoustic sensor position and frequency response) as well as extracted features. Recent research posits that features extracted by singular value decomposition (SVD) can exhibit the natural characteristics and energy contained in the signal. Though the technique by itself is not novel, in this paper, SVD is employed for PD classification in a revised way starting from data arrangement in Hankel form, to embedding the hypergraph-based features and finally to extracting the required set of optimal features. The algorithm is tested for various measurement conditions that include the influences of various PD locations and oil temperatures. The robustness of the algorithm is also tested using noisy PD signals. Experimental results show the proposed feature extraction method supremacy.

A Critical Comparison between Manufacturing and Service Firms’ Mission Statements: Manage Your Firms’ Mission Statements with Care Using Readability and Lexical Techniques

This paper aims to investigate the Readability and Lexical Density of the mission statements of the large service and manufacturing firms. In respect of mission statements, a comparison between the service and manufacturing firms is drawn. For initial data arrangement, the mission statements of all selected service firms are grouped together, same was also done with the manufacturing firms separately. The mission statements are processed through the software for Readability (Gunning Fog, Flesch-Kincaid, SMOG, Coleman-Liau, Automated, Flesch Reading Ease score) and Lexical Density analysis in two categories, service firms and manufacturing firms. Result show that Service firms’ mission statements are more Lexically dense and possess higher average level than those of the manufacturing firms. This study contributes to the Strategic Management literature and practical implications to the service and manufacturing firms and their stakeholders. Also comes out of the ole ways of data analysis in management studies. Future researchers may carry out similar research in a specific industry.

An efficient clustering mechanism in big data framework for data preprocessing and management

An efficient data handling mechanism has been applied based on epoch-based k-means associated fuzzy clustering (EKFC). In the first phase weights have been assigned to individual data segment presented based on the classification key metrics. It has been assigned automatically. Then weight preprocessing has been done in such manner to prune the unwanted weights. It has been pruned in such way to filter the weights which are not scalable. Then epoch-based k-means associated fuzzy clustering (EKFC) approach has been applied for data arrangement. First different epochs have been considered for the calculation of initial seeds values. These seeds have been considered after considering 100 epochs. After 100 epochs seeds have been determined. These seeds values have been used as the initial centroid for the k-means clustering. After the complete validation similar clusters from the two clustering approaches have been considered. In the next phase operational clustering has been performed. In the final phase threshold ranking has been performed. It has been performed for the final classification based on the above clusters. It will arrange in the order of threshold values. It will be used for the determination of the priority of the task in the big data environment. The results are found to be prominent in terms of classification accuracy.

Documentary analysis in the study of architectural designs of a hospital in the city of São Paulo

ABSTRACT Objectives: to report the experience of using architectural designs of a hospital for a historical documentary research. Methods: report of the experience of the methodological route of using architectural designs of a model hospital from 1974 to 2002. Results: after being spread on a worksheet, the projects of interest were selected, enabling the data arrangement, where the analytical chart was applied, containing: context; authorship; authenticity/ reliability; nature of the text and preliminary analysis. The findings were grouped by pertinence and similarity, resulting in the construction of categories of analysis. Final Considerations: architectural design is a challenging source, both for its pursuit, since it took two and a half years until it was legally licensed, as well as for involving specific terminologies and symbology of its own. A special attention should be given to the selection criteria, organization and analysis of the document, and sharing the access of unusual sources with the health area, like this one, so as to stimulate the development of research.