Demand Forecasting in SCM for Automobile Industry based on Time-series Modeling

In supply chain management (SCM), product classification and demand forecasting are crucial pillars to ensure companies to have production in the right category and quantity for long-term profitability. Due to COVID-19 from 2019, the automobile industry has been seriously negatively affected as the demand dropped dramatically. Therefore, it is necessary to make reasonable product classification and accurate demand forecasting to facilitate automobile companies in SCM to reduce unpopular product manufacture and unnecessary storage costs. In this paper, the Canada automobile market has been chosen with the period from 1946 to 2022. To classify a number of different types of motor vehicles into several categories with general characteristics, K-means Clustering method is applied. With the seasonal patterns and random generated features for auto sales, the time series models ARIMA and SARIMA are adopted for demand forecasting. According to the analysis, the automobiles fitting in the category with high demand and low price are valuable for further production. In addition, SARIMA Model is more accurate and fits better than ARIMA Model for both the training and test datasets for long-term prediction. The classification and forecasting results shed light on guiding manufacturers to adjust production schemes and ensuring auto dealers to predict more accurate sales in order to optimize the strategic planning. © 2023 SPIE.

Intelligent Methods and Models for Assessing Level of Student Adaptation to Online Learning

The problem of introducing online learning is becoming more and more popular in our society. Due to COVID-19 and the war in Ukraine, there is an urgent need for the transition of educational institutions to online learning, so this paper will help people not make mistakes in the process and afterward. The paper's primary purpose is to investigate the effectiveness of machine learning tools that can solve the problem of assessing student adaptation to online learning. These tools include intelligent methods and models, such as classification techniques and neural networks. This work uses data from an online survey of students at different levels: school, college, and university. The survey consists of questions such as gender, age, level of education, whether the student is in the city, class duration, quality of Internet connection, government/non-government educational institution, availability of virtual learning environment, whether the student is familiar with IT, financial conditions, type of Internet connection, a device used for studying, etc. To obtain the results on the effectiveness of online education were used the following machine learning algorithms and models: Random Forest (RF), Extra Trees (ET), Extreme, Light, and Simple Gradient Boosting (GB), Decision Trees (DT), K-neighbors (K-mean), Logistic Regression (LR), Support Vector Machine (SVM), Naїve Bayes (NB) classifier and others. An intelligent neural network model (NNM) was built to address the main issue. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)

Criteria Clustering and Supplier Segmentation Based on Sustainable Shared Value Using BWM and PROMETHEE

With the advent of healthy visions, two of the trends that have become extremely important in the supply chain in recent decades are corporate social responsibility (CSR) and sustainability, which have affected the activities of buyers and suppliers. The next trend that is emerging is the vision of creating shared value (CSV), which wants to move the supply chain toward solving social problems in a completely strategic way. This research intends to develop a step-by-step framework for evaluating and segmenting suppliers based on CSV criteria in the supply chain. In the first stage, the criteria for creating sustainable shared value (CSSV) are obtained through existing activities in the field of CSR. The obtained criteria are then divided into two categories, strategic and critical, and then the weight of each criterion is obtained using the best–worst method (BWM). In the next step, based on the Kraljic model, the suppliers are divided into four clusters using the preference ranking organization method for enrichment evaluation (PROMETHEE) technique. This framework helps the buyer to conclude and select purchasing decisions and relationships with suppliers through the lenses of CSV and sustainability.

The Relationship Between COVID-19 Cases and ArcGIS Technology to Present in Indonesia

Giving the COVID-19 vaccine has many benefits, including increasing immunity from exposure to COVID-19 and preventing new mutations from COVID-19. In addition, the COVID-19 vaccine that has been injected into the community has gone through a series of strict tests, so that it is guaranteed to be safe, quality and efficacious. The research aims to cluster the spread of the corona virus in DKI Jakarta province which is displayed on a visual map using ArcGIS Technology. Based on the data on the spread of the corona virus which has been grouped using K-means clustering, it is hoped that it can help make the right decisions in vaccination and the priority of COVID-19 assistance that is determined and directed based on information cluster, so this research is expected to help the government in tackling the COVID-19 pandemic in Indonesia, especially DKI Jakarta. In addition, this research also aims to see the correlation between the COVID-19 vaccine and the number of positive cases of Covid-19. © 2022 IEEE.

Chemoinformatics using dimensionality reduction and clustering for enzyme commission number prediction in organic synthesis

The outbreak of COVID-19 has increased the demand for new drug development. That has led to a growing interest in chemoinformatics, which is valuable information technology to predict chemical reactions. The use of enzymes as catalysts is gaining importance in terms of the environment and reaction efficiency. In order to predict the best enzyme to obtain the desired product, the target chemical equation is compared with typical chemical equations of enzymes classified by Enzyme Commission number (EC number) using clustering. The EC number of the chemical equation that is evaluated to have the highest similarity is predicted. © 2023, ICIC International. All rights reserved.

Identifying Clinical Subtypes of Long COVID: An Unsupervised Machine Learning Approach

Objectives: The symptoms of patients with post-acute COVID-19 syndrome are heterogenous, impact multiple systems, and are often non-specific. To better understand the symptomatic profile of this population, this study used real-world data and unsupervised machine learning techniques to identify distinct groupings of long COVID patients. Method(s): Children/adolescents (age 0-17) and adults (age 18-64 and >=65) with >=2 primary diagnoses for U09.9 "Post COVID-19 condition" from 10/01/2021 (ICD-10 code introduction) until 03/31/2022 were selected from Optum's de-identified Clinformatics Data Mart Database, with the first diagnosis deemed index. Included patients had >=1 diagnosis for COVID-19 at least 4 weeks before index and continuous enrollment during the 12 months prior to index. Diagnoses recorded +/-2 weeks from index that were not present prior to the initial COVID-19 diagnosis were captured and used as patient features for k-means clustering. Final cluster assignments were selected based on silhouette coefficient and clinical relevancy of groupings. Result(s): 3,587 patients met eligibility criteria, yielding three clusters. Concurrent symptom domains surrounding index included breathing, fatigue, pain, cognitive, and cardiovascular diagnoses. The first cluster (N=2,578, 71.8%) was characterized by patients with only a single symptom domain (33% breathing, 33% cardiovascular, 20% fatigue, 11% cognitive). The second cluster (N=651, 18.1%) all presented with breathing symptoms accompanied by one additional domain (cardiovascular 40%, fatigue 28%, pain 18%). The final cluster (N=358, 9.9%) experienced breathing symptoms accompanied by two additional domains (fatigue and cardiovascular 34%, cardiovascular and cognitive 34%). Cluster 3 was slightly older than clusters 1 or 2 (mean age 66 vs. 58 years, respectively). Conclusion(s): Unsupervised machine learning identified distinct groups of long COVID patients, which may help inform multidisciplinary care needs. Our analysis suggests that many patients with long COVID may experience symptoms from only a single domain, and multi-system illness may generally include breathing complications accompanied by fatigue and/or cardiovascular complications.Copyright © 2023

An optimisation approach for determining the efficiency of vital medical devices in intensive care units with COVID-19 patients using Apriori algorithm

Improving the process of strategic management in hospitals preparation and equipping the intensive care units (ICUs) and the availability of medical devices plays an important role for knowing consumer behaviour and need. This cross-sectional study was performed in the ICU of Farhikhtegan Hospital, Tehran, Iran for a period of six months. During these months, ten medical devices have been used 5,497 times. These devices include: ventilator, oxygen cylinder, infusion pump, electrocardiography machine, vital signs monitor, oxygen flowmeter, wavy mattress, ultrasound sonography machine, ultrasound echocardiography machine, and dialysis machine. The Apriori algorithm showed that four devices: ventilator, oxygen cylinder, vital signs monitoring device, oxygen flowmeter are the most used ones by patients. These devices are positively correlated with each other and their confidence is over 80% and their support is 73%. For validating the results, we have used equivalence class clustering and bottom-up lattice traversal (ECLAT) algorithm in our dataset.

Developing a resilient healthcare supply chain to prevent disruption in the wake of emergency health crisis

PurposeThe study aims to identify and analyse the drivers of resilient healthcare supply chain (HCSC) preparedness in emergency health outbreaks to prevent disruption in healthcare services delivery in the context of India.Design/methodology/approachThe present study has opted for the grey clustering method to identify and analyse the drivers of resilient HCSC preparedness during health outbreaks into high, moderate and low important grey classes based on Grey-Delphi, analytic hierarchy process (AHP) and Shannon's information entropy (IE) theory.FindingsThe drivers of the resilient HCSC are scrutinised using the Grey-Delphi technique. By implementing AHP and Shannon's IE theory and depending upon structure, process and outcome measures of HCSC, eleven drivers of a resilient HCSC preparedness are clustered as highly important, three drivers into moderately important, and two drivers into a low important group.Originality/valueThe analysis and insights developed in the present study would help to plan and execute a viable, resilient emergency HCSC preparedness during the emergence of any health outbreak along with the stakeholders' coordination. The results of the study offer information, rationality, constructiveness, and universality that enable the wider application of AHP-IE/Grey clustering analysis to HCSC resilience in the wake of pandemics.

Who's minding the children: Gender equity in the first 2 years of the pandemic

The wholesale changes brought about by the COVID‐19 pandemic to men and women's paid work arrangements and work–family balance provide a natural experiment for testing the common elements of two theories, needs exposure (Schafer et al. Canadian Review of Sociology/Revue Canadienne De Sociologie, 57(4);2020:523–549) and parental proximity (Sullivan et al. Family Theory & Review, 2018;10(1):263–279) against a third theory also suggested by Schafer et al. (2020), and labelled in this article, entrenchment/exacerbation of gender inequality. Both needs exposure and parental proximity suggest that by being home because of the pandemic, in proximity to their children, fathers are exposed to new and enduring family needs, which may move them toward more equal sharing in childcare and other domestic responsibilities. By contrast to studies that have tested such theories using retrospective, self‐report survey data over a 2‐year period, we analyse more than a decade of time‐use diary data from the American Time Use Survey (ATUS) that covers the first 2 years of the pandemic. We model the secular and quarterly trends to predict what would have occurred in the absence of the pandemic, contrasting this to what indeed happened. Our analyses consider aggregate and individual impacts, using methods of sequence analysis, clustering, and matching. Among our results, we find that the division of childcare responsibilities did not become more equitable during the pandemic. Suggestions for future research are provided as are suggestions for the implementation of social policies that could influence greater gender equity in unpaid work and childcare. [ FROM AUTHOR] Copyright of International Journal of Social Welfare is the property of Wiley-Blackwell 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.)

A New Approach to Extracting Tourism Focus Points from Chinese Inbound Tourist Reviews after COVID-19

The number of inbound tourists in Japan has been increasing steadily in recent years. However, due to the COVID-19 pandemic, the number of inbound tourists decreased in 2020. This is particularly worrisome for Japan, as the number of inbound tourists is expected to reach 60 million per year by 2030. In order to help Japan's tourism industry to recover from the pandemic, we propose a method of identifying elements that attract the attention of inbound tourists (focus points) by analyzing reviews on tourist sites. We focus on Hokkaido, a popular area in Japan for tourists from China. Our proposed method extracts high-frequency n-gram patterns from reviews written by Chinese inbound tourists, showing which aspects are mentioned most often. We then use seven types of motivational factors for tourists and principal component analysis to quantify the focus points of each tourist destination. Finally, we estimate the focus points by clustering the n-gram patterns extracted from the tourists' reviews. The results show that our method successfully identifies the features and focus points of each tourist spot.

An Improved Soft Subspace Clustering Algorithm for Brain MR Image Segmentation

In recent years, the soft subspace clustering algorithm has shown good results for high-dimensional data, which can assign different weights to each cluster class and use weights to measure the contribution of each dimension in various features. The enhanced soft subspace clustering algorithm combines interclass separation and intraclass tightness information, which has strong results for image segmentation, but the clustering algorithm is vulnerable to noisy data and dependence on the initialized clustering center. However, the clustering algorithm is susceptible to the influence of noisy data and reliance on initialized clustering centers and falls into a local optimum;the clustering effect is poor for brain MR images with unclear boundaries and noise effects. To address these problems, a soft subspace clustering algorithm for brain MR images based on genetic algorithm optimization is proposed, which combines the generalized noise technique, relaxes the equational weight constraint in the objective function as the boundary constraint, and uses a genetic algorithm as a method to optimize the initialized clustering center. The genetic algorithm finds the best clustering center and reduces the algorithm's dependence on the initial clustering center. The experiment verifies the robustness of the algorithm, as well as the noise immunity in various ways and shows good results on the common dataset and the brain MR images provided by the Changshu First People's Hospital with specific high accuracy for clinical medicine.

The Impact of Socioeconomic and Demographic Factors on COVID-19 Forecasting Model

Background: COVID-19 has become a primary public health issue in various countries across the world. The main difficulty in managing outbreaks of infectious diseases is due to the difference in geographical, demographic, economic inequalities and people's behavior in each region. The spread of disease acts like a series of diverse regional outbreaks;each part has its disease transmission pattern. Objective: This study aims to assess the association of socioeconomic and demographic factors to COVID-19 cases through cluster analysis and forecast the daily cases of COVID-19 in each cluster using a predictive modeling technique. Methods: This study applies a hierarchical clustering approach to group regencies and cities based on their socioeconomic and demographic similarities. After that, a time-series forecasting model, Facebook Prophet, is developed in each cluster to assess the transmissibility risk of COVID-19 over a short period of time. Results: A high incidence of COVID-19 was found in clusters with better socioeconomic conditions and densely populated. The Prophet model forecasted the daily cases of COVID-19 in each cluster, with Mean Absolute Percentage Error (MAPE) of 0.0869;0.1513;and 0.1040, respectively, for cluster 1, cluster 2, and cluster 3. Conclusion: Socioeconomic and demographic factors were associated with different COVID-19 waves in a region. From the study, we found that considering socioeconomic and demographic factors to forecast COVID-19 cases played a crucial role in determining the risk in that area. © 2023 The Authors. Published by Universitas Airlangga.

Joint Transmission Scheme and Coded Content Placement in Cluster-Centric UAV-Aided Cellular Networks

Recently, as a consequence of the COVID-19 pandemic, dependence on telecommunication for remote learning/working and telemedicine has significantly increased. In this context, preserving high Quality of Service (QoS) and maintaining low-latency communication are of paramount importance. In cellular networks, the incorporation of unmanned aerial vehicles (UAVs) can result in enhanced connectivity for outdoor users due to the high probability of establishing Line of Sight (LoS) links. The UAV's limited battery life and its signal attenuation in indoor areas, however, make it inefficient to manage users' requests in indoor environments. Referred to as the cluster-centric and coded UAV-aided femtocaching (CCUF) framework, the network's coverage in both indoor and outdoor environments increases by considering a two-phase clustering framework for Femto access points (FAPs)' formation and UAVs' deployment. Our first objective is to increase the content diversity. In this context, we propose a coded content placement in a cluster-centric cellular network, which is integrated with the coordinated multipoint (CoMP) approach to mitigate the intercell interference in edge areas. Then, we compute, experimentally, the number of coded contents to be stored in each caching node to increase the cache-hit-ratio, signal-to-interference-plus-noise ratio (SINR), and cache diversity and decrease the users' access delay and cache redundancy for different content popularity profiles. Capitalizing on clustering, our second objective is to assign the best caching node to indoor/outdoor users for managing their requests. In this regard, we define the movement speed of ground users as the decision metric of the transmission scheme for serving outdoor users' requests to avoid frequent handovers between FAPs and increase the battery life of UAVs. Simulation results illustrate that the proposed CCUF implementation increases the cache-hit-ratio, SINR, and cache diversity and decrease the users' access delay, cache redundancy, and UAVs' energy consumption.

Analysis of the Omicron virus cases using data mining methods in rapid miner applications

Background: Omicron has respiratory problems and pneumonia in general and specific terms. This pandemic was ravaging all countries in the world. This virus outbreak had new types to appear or so-called new variants that are still being studied by experts. Computer-assisted methods (includes smart intelligence systems, algorithms, and data mining) is key solution for detecting variants of virus. Methods: In present study, it discussed and analyzed the omicron variant which is one of the variants of the Coronavirus 2019 (COVID-19). It's a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The emergence of this Omicron variant of COVID-19, raised more concern in the world because of its dangerous ability and the high level of spread of omicron cases. Analysis using the k-means algorithm in order to determine the level of distribution of the virus variant. Result: From the results and outputs found in this method, it is concluded that this method is used to divide the data into 3 clusters of case distribution of the Omicron variant which has been understood as a level in the distribution of cases where cluster 0 is low level, cluster 1 is high level, and cluster 2 is medium level. Conclusion: Therefore, this data mining method with special clustering and data-mining techniques give the highest number of virus distributions in which countries and divide some countries into several clusters. © 2020 The author (s).

Artificial Intelligence-based Speech Signal for COVID-19 Diagnostics

The speech signal has numerous features that represent the characteristics of a specific language and recognize emotions. It also contains information that can be used to identify the mental, psychological, and physical states of the speaker. Recently, the acoustic analysis of speech signals offers a practical, automated, and scalable method for medical diagnosis and monitoring symptoms of many diseases. In this paper, we explore the deep acoustic features from confirmed positive and negative cases of COVID-19 and compare the performance of the acoustic features and COVID-19 symptoms in terms of their ability to diagnose COVID-19. The proposed methodology consists of the pre-trained Visual Geometry Group (VGG-16) model based on Mel spectrogram images to extract deep audio features. In addition to the K-means algorithm that determines effective features, followed by a Genetic Algorithm-Support Vector Machine (GA-SVM) classifier to classify cases. The experimental findings indicate the proposed methodology's capability to classify COVID-19 and NOT COVID-19 from acoustic features compared to COVID-19 symptoms, achieving an accuracy of 97%. The experimental results show that the proposed method remarkably improves the accuracy of COVID-19 detection over the handcrafted features used in previous studies. © 2022 ACM.

Determination of households benefits from subsidies by using data mining approaches

Poverty, known as a widespread economic and political challenge (specifically at the times of crisis, like COVID-19), is a very complicated problem, which many countries have been trying for a long time to eradicate. Cash-subsidy allocation procedure using traditional statistical vision is the famous approach, which articles have targeted. Inefficiency of these solutions besides the fact that a pair of households with exact same situation will not be existing leads us to inadequacy and inaccuracy of these methods. This study, by putting data mining and machine learning (as well-known majors in IT and computer Science) visions together, draws a path to overcome this challenge. For this aim, the social, income and expenditure dimensions of a dataset are surveyed from 18885 households considered to measure the population poverty ratio (a fuzzy look at on their eligibility). In respect to the different experimental mode, the effective features are being filtered to use in FCM algorithm in order to determine to what extend the households in the poor or wealthy. Moreover, Genetic Algorithm displays its efficiency in the role of optimizer. Finally, the evaluation results show more accurate outcomes from the feature selection technique (on normalized data) and get the optimized clusters. [ FROM AUTHOR] Copyright of Journal of Information Technology & Politics is the property of Taylor & Francis Ltd 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.)

A crowd clustering prediction and captioning technique for public health emergencies.

The COVID-19 pandemic has come to the end. People have started to consider how quickly different industries can respond to disasters due to this public health emergency. The most noticeable aspect of the epidemic regarding news text generation and social issues is detecting and identifying abnormal crowd gatherings. We suggest a crowd clustering prediction and captioning technique based on a global neural network to detect and caption these scenes rapidly and effectively. We superimpose two long convolution lines for the residual structure, which may produce a broad sensing region and apply our model's fewer parameters to ensure a wide sensing region, less computation, and increased efficiency of our method. After that, we can travel to the areas where people are congregating. So, to produce news material about the present occurrence, we suggest a double-LSTM model. We train and test our upgraded crowds-gathering model using the ShanghaiTech dataset and assess our captioning model on the MSCOCO dataset. The results of the experiment demonstrate that using our strategy can significantly increase the accuracy of the crowd clustering model, as well as minimize MAE and MSE. Our model can produce competitive results for scene captioning compared to previous approaches.

Erratum: Unsupervised clustering reveals phenotypes of AKI in ICU COVID-19 patients.

[This corrects the article DOI: 10.3389/fmed.2022.980160.].

Comorbidity Patterns and Mortality Among Hospitalized Patients with Psychiatric Disorders and COVID-19.

OBJECTIVE: To examine the association between psychiatric and non-psychiatric comorbidity and 28-day mortality among patients with psychiatric disorders and COVID-19. METHODS: We performed a multicenter observational retrospective cohort study of adult patients with psychiatric disorders hospitalized with laboratory-confirmed COVID-19 at 36 Greater Paris University hospitals (January 2020-May 2021) (N=3,768). First, we searched for different subgroups of patients according to their psychiatric and non-psychiatric comorbidities through cluster analysis. Next, we compared 28-day all-cause mortality rates across the identified clusters, while taking into account sex, age, and the number of medical conditions. RESULTS: We found 5 clusters of patients with distinct psychiatric and non-psychiatric comorbidity patterns. Twenty-eight-day mortality in the cluster of patients with mood disorders was significantly lower than in other clusters. There were no significant differences in mortality across other clusters. CONCLUSIONS: All psychiatric and non-psychiatric conditions may be associated with increased mortality in patients with psychiatric disorders and COVID-19. The lower risk of death among patients with mood disorders might be in line with the potential beneficial effect of certain antidepressants in COVID-19, but requires further research. These findings help identify at-risk patients with psychiatric disorders who should benefit from vaccine booster prioritization and other prevention measures.

Using a manifold-based approach to extract clinical codes associated with winter respiratory viruses at an emergency department

Every winter, respiratory viruses put most Emergency Departments (ED) around the world under intense pressure. To reduce the consequent stress for hospitals, anticipation of the massive increase of intakes for illness-based symptoms is essential. As the Covid-19 2020 pandemic clearly illustrates, patients are not systematically tested. The ED staff therefore has no real-time knowledge of the presence of the virus in the patients flow. To address this issue, we propose here to use the hospital's laboratory-confirmed database as an attractor for the manifold-based approach for clustering the clinical codes associated with respiratory viruses. We propose a new framework based on the embedding of time series onto the Stiefel manifold, coupled with a density-based clustering algorithm (HDBSCAN) enhanced by a reduction of dimension (UMAP) for the clustering on that manifold. In particular, we show, based on real data sets of two academic hospitals in France, the significant benefits of using geometrical approaches for time series clustering as compared to traditional methods.