Evaluation of Consumer Reviews for adidas Sports Brands Using Data Mining Tools and Twitter APIs

The sportswear industry has become prominent, popular, and a very obvious category among various age groups in India. Big sports brands like adidas, seeing the Indian market potential, have extended their businesses across the country. To increase sales, online purchase has become one the most effective, easy, cheap, and quickest solution for the end customers as it provides the end consumers a variety of products, their designs, and color combinations on clicks. A large number of consumers express what exactly the end customer thinks about a particular product's preference level of brand, quality of service, quality of product, or stylish nature of the product. The current study does an evaluation of such online comments and reviews giving their feedback on their public Twitter accounts, flipkart.com, or amazon.in about an adidas sports brand in India. The research also provides a basic flow of Java program in the form of an algorithm that is used to collect the dataset from Twitter, process it, and export it into an Excel sheet for further investigation.

Toward Reducing Construction Project Delivery Time under Limited Resources

One of the most vital construction project aspects is to complete a project in minimum time restricted to the time–cost trade-off. Overlapping activities’ planning and their impact on the project under limited resource constraints should be considered. This study aims to develop a model for optimizing the project schedule and cost regarding overlap activities and their impacts. This study reviews previous studies on changes in past activities likely to produce additional reworking of subsequent activities. In addition, an AHP model is developed to assess the reworking time of subsequent activities based on possible changes in previous activities. In addition, five realistic construction projects are applied. Finally, an optimizing model is developed for optimizing project time and cost using overlapping techniques by using the Java program. The results indicate that the proposed model can be used by project managers easily for solving time and cost optimization problems. In addition, it can be updated to continuously improve its functionality. Finally, it can be updated later to support AI for finding better solutions.

SEIMR/R-S. General Epidemic Simulation Model Multi-Infected States - Multi Sociodemographic Segments - Multi-Region Mobility. Theory

SEIMR/R-S corresponds to a generalized mathematical model of pandemics that enhances traditional, aggregated simulation models when considering inter-regional impacts in a macro region (conurbed); SEIMR/R-S also considers the impact of modeling the population divided into sociodemographic segments based on age and economic stratum (it is possible to include other dimensions, for example: ethnics, gender, … ). SEIMR/R-S is the core of the SEIMR/R-S/OPT epidemic management optimization model that determines optimal policies (mitigation and confinement) considering the spatial distribution of the population, segmented sociodemographically and multiple type of vaccines. The formulation of SEIMR/R-S/OPT is presented by Velasquez-Bermudez (2021a) that includes the modeling of the vaccination process. SEIMR/R-S can be understood and used by any epidemiologist, and/or physician, working with SIR, SEIR or similar simulation models, and by professionals working on the issue of public policies for epidemic control. Following the theory presented in this document, ITCM (Instituto Tecnologico de Ciudad Madero, México) implemented the SEIMR/R-S epidemic model in a JAVA program (Velasquez-Bermudez et. al, 2021). This program may be used by the organizations that considers the SEIMR/R-S will be useful for management the COVID-19 pandemic, it is presented by VelasquezBermudez et al. (2021).

Transforming the German ICD-10 (ICD-10-GM) into Injury Severity Score (ISS)—Introducing a new method for automated re-coding

Background While potentially timesaving, there is no program to automatically transform diagnosis codes of the ICD-10 German modification (ICD-10-GM) into the injury severity score (ISS). Objective To develop a mapping method from ICD-10-GM into ICD-10 clinical modification (ICD-10-CM) to calculate the abbreviated injury scale (AIS) and ISS of each patient using the ICDPIC-R and to compare the manually and automatically calculated scores. Methods Between January 2019 and June 2021, the most severe AIS of each body region and the ISS were manually calculated using medical documentation and radiology reports of all major trauma patients of a German level I trauma centre. The ICD-10-GM codes of these patients were exported from the electronic medical data system SAP, and a Java program was written to transform these into ICD-10-CM codes. Afterwards, the ICDPIC-R was used to automatically generate the most severe AIS of each body region and the ISS. The automatically and manually determined ISS and AIS scores were then tested for equivalence. Results Statistical analysis revealed that the manually and automatically calculated ISS were significantly equivalent over the entire patient cohort. Further sub-group analysis, however, showed that equivalence could only be demonstrated for patients with an ISS between 16 and 24. Likewise, the highest AIS scores of each body region were not equal in the manually and automatically calculated group. Conclusion Though achieving mapping results highly comparable to previous mapping methods of ICD-10-CM diagnosis codes, it is not unrestrictedly possible to automatically calculate the AIS and ISS using ICD-10-GM codes.

Implementation Of Bubble Sort Algorithm On 2 Fruit Models Of Data Selection Using The Java Program Language

There are various kinds of data sorting methods that we know of which are the Bubble Sort, Selection Sort, Insertion Sort, Quick Sort, Shell Sort, Heap Sort, and Radix Sort methods. All of these methods have advantages and disadvantages of each, whose use is determined based on needs. Each method has a different algorithm, where different algorithms affect the execution time. One interesting algorithm to be implemented on 2 variant models of data sorting is the Bubble Sort algorithm, the reason is that this algorithm has a fairly long and detailed process flow to produce an ordered data sequence from a previously unordered data sequence. Two (2) data sorting variant models that will be implemented using the Bubble Sort algorithm are: Ascending data sorting variants moving from left to right, and Descending data sorting variants moving from left to right. And the device used in implementing the Bubble Sort algorithm is the Java programming language.

Risk-taking and risk aversion in the period of before and after the 2008 crisis: evaluation with bibliometric analysis

PurposeThis study aims to quantitatively classify the articles with risk-taking and risk aversion keywords and to investigate whether there is a similar emphasis in articles as parallel to the change in risk appetite in the market in the period before the crisis (bubble period) and after the crisis.Design/methodology/approachIn this study, a bibliometric analysis of the articles in which the keywords risk-taking and risk aversion are mentioned together with the word finance in the journals scanned in the Web of Science between 2004 and 2012 was performed. In this context, 936 articles were specified. Analyses were made using the CiteSpace Java program.FindingsThe three journals with the most articles with these characteristics are Journal of Banking and Finance, Journal of Financial Economics and Strategic Management Journal. Along with these two main keywords, the other two most used keywords were “model” and “performance”. In addition, the keywords “attitude”, “corporate governance”, “choice” and “determinant” were used more in the post-crisis period. On the other hand, concepts such as investor sentiment or emotions were not amongst the 10 most frequently used keywords during the nine years. This can be considered as an indicator that risk is being modelled, but emotions are relatively neglected. As a result, the findings of this study show that academic papers do not develop in connection with the mood and excitement in the market.Originality/valueThis study is one of the first studies to examine the reflection of risk appetite in the market on academic papers on financial risk-taking and aversion and to investigate whether the situation in the market and the development in publications are related.

Abstraction of Object Diagram from Input Java Program

The Unified Modelling Language (UML) is a design tool used to design the software applications or software systems. It comprises thirteen different diagrams which represent the static behavior and dynamic behavior of the software systems. This paper presents an automated methodology to abstract the UML object diagram features from the input restructured java program. The methodology proposed here takes the restructured java program as an input and then abstracts the class name, attributes, operations, visibility, relationships and stores in the form a table. Further by using the restructured java program and the class table, the relevant details of the object diagram is abstracted and is stored in the form a table for further processing FULL TEXT PDF

Classification and Automatic Feature-Based Extraction Approach for Cylindrical and Milling Parts

This paper reports classification and automatic extraction of various cylindrical and milling features in conventional machining process parts. In this work, various algorithms like hole recognition algorithm (HRA) and milling feature recognition algorithm (MFRA) have been used for identification of different cylindrical and milling features. A cylindrical feature is identified based on specific logical rules, and milling feature is identified based on the concept of concave decomposition of edges. In-house developed JAVA program is used to write algorithm, and then validation of the algorithm is done through two case studies. The HRA and MFRA algorithms extract the cylindrical features (through holes, blind holes, taper holes, and bosses) and milling features (slot, blind slot, step, blind step, pockets) precisely. The current work is well suitable to extract the features in conventional machining parts and thereby improve the downstream applications likes process planning, CAPP, CAM, etc.

Currency Exchange Forecasting Using Sample Mean Estimator and Multiple Linear Regression Machine Learning Models

In recent time, there is an increasing growth in the amount of trading taking place in the currency exchange market. However, effective analysis and simulation tools for performing accurate prediction of these exchange rates are lacking. To alleviate this challenge, this work presents an hybrid machine learning and prediction model by suitably combining the Sample Mean Estimator (SME) simulation architecture with the multiple linear regression technique based training of feed-forward parameters. The developed model has the capability to overcome prediction inaccuracy, inconsistent forecasting, slow response due to computational complexity and scalability problems. The SME method is used to overcome the problems of uncertainty and non-linearity nature of the predictive variable as it’s always affected by economic and political factors. The implementation of the proposed currency exchange rate forecasting system is achieved through the use of a developed in-house Java program with Net Beans as the editor and compiler. Performance comparison between the present system and two baseline methods which are the Autoregressive Moving Average and the Deep Belief network techniques demonstrates that the present forecasting model out-performed the baseline methods studied. The experimental result shows that the precision rate of the present system are equal to or greater than 70%. Therefore, the present foreign exchange predictive system is capable of providing usable, consistent, efficient, faster and accurate prediction to the users consistently at any-time.Keywords- currency exchange,, feed-forward. Forecasting, Sample Mean Estimator, multiple linear regressions, prediction