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Featured ApplicationThe research has a potential application in the field of fake news detection. By using the feature extraction technique, TwIdw, proposed in this paper, more relevant and informative features can be extracted from the text data, which can lead to an enhancement in the accuracy of the classification models employed in these tasks.This research proposes a novel technique for fake news classification using natural language processing (NLP) methods. The proposed technique, TwIdw (Term weight–inverse document weight), is used for feature extraction and is based on TfIdf, with the term frequencies replaced by the depth of the words in documents. The effectiveness of the TwIdw technique is compared to another feature extraction method—basic TfIdf. Classification models were created using the random forest and feedforward neural networks, and within those, three different datasets were used. The feedforward neural network method with the KaiDMML dataset showed an increase in accuracy of up to 3.9%. The random forest method with TwIdw was not as successful as the neural network method and only showed an increase in accuracy with the KaiDMML dataset (1%). The feedforward neural network, on the other hand, showed an increase in accuracy with the TwIdw technique for all datasets. Precision and recall measures also confirmed good results, particularly for the neural network method. The TwIdw technique has the potential to be used in various NLP applications, including fake news classification and other NLP classification problems.
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Sustainable development integrates business, environmental, and social objectives into a unified effort to achieve a common goal. Sustainable customer relationship management (CRM) combines company strategy, customer-focused business processes, and computer technologies. From the consumer's perspective, it lowers psychological, energy, time, and other costs;from the company's perspective, it offers a means of engaging with customers to build lasting and reliable relationships. The sustainable CRM program provides advantages to businesses in various industries, particularly online commerce. It alludes to a comprehensive strategy that promotes solid interactions between buyers and sellers of goods and services. Since current customer retention is less costly than new customer attraction in competitive markets, especially online shopping, identifying the factors affecting relationship management with stable customers is essential. This investigation intends to evaluate the effect of the use of management information systems (MIS), as well as insights on employee behavior and knowledge, and customer behavior (satisfaction and loyalty), on the effectiveness of sustainable CRM in online shopping. The model is validated using the PLS–SEM technique, and study sample of 293 employees and managers from private organizations. According to the results, the MIS, employee behavior and knowledge, customer satisfaction, and customer loyalty influence the effectiveness of sustainable CRM in online shopping. Furthermore, employee behavior and knowledge positively moderate the relationship between customer loyalty and the effectiveness of sustainable CRM. However, the moderating role of employee behavior and knowledge on customer satisfaction and the effectiveness of sustainable CRM is not confirmed. Overall, taking these characteristics into account might help organizations to take significant steps toward increasing the efficacy of sustainable CRM.
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Nowadays, all kinds of service-based organizations open online feedback possibilities for customers to share their opinion. Swiss National Railways (SBB) uses Facebook to collect commuters' feedback and opinions. These customer feedbacks are highly valuable to make public transportation option more robust and gain trust of the customer. The objective of this study was to find interesting association rules about SBB's commuters pain points. We extracted the publicly available FB visitor comments and applied manual text mining by building categories and subcategories on the extracted data. We then applied Apriori algorithm and built multiple frequent item sets satisfying the minsup criteria. Interesting association rules were found. These rules have shown that late trains during rush hours, deleted but not replaced connections on the timetable due to SBB's timetable optimization, inflexibility of fines due to unsuccessful ticket purchase, led to highly customer discontent. Additionally, a considerable amount of dissatisfaction was related to the policy of SBB during the initial lockdown of the Covid-19 pandemic. Commuters were often complaining about lack of efficient and effective measurements from SBB when other passengers were not following Covid-19 rules like public distancing and were not wearing protective masks. Such rules are extremely useful for SBB to better adjust its service and to be better prepared by future pandemics. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
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Purpose>This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.Design/methodology/approach>This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.Findings>The research provides a framework for operational practices and Industry 4.0 technologies. They identify the relative importance of practices and performance metrics.Originality/value>The briefing saves busy executives, strategists, and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.
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The Internet has changed everyone's life. Social networks may have only been initiated with the help of the Internet. Today's generation is using the internet, which means social media like Snap-chat, Twitter, Facebook, etc., is increasing. Hence, the purpose of this paper is to understand the positive and negative outcomes of social media. In addition, this paper discusses the current situation, the previous situation, and the situation where people do not have social networks. Finally, the influence of social networks on different stages of a person's life is described.
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In order to cope with the changing era of the innovative management paradigm of the manufacturing industry, it is necessary to advance the construction of smart factories in the domestic manufacturing industry, and in particular, the 3D design and manufacturing content sector is highly growthable. In particular, the core technologies that enable digital transformation VR (Virtual Reality)/AR (Augmented Reality) technologies have developed rapidly in recent years, but have not yet achieved any particular results in industrial engineering. In the manufacturing industry, digital threads and collaboration systems are needed to reduce design costs that change over and over again due to the inability to respond to various problems and demands that should be considered when designing products. To this end, we propose a VR/AR collaboration model that increases efficiency of manufacturing environments such as inspection and maintenance as well as design simultaneously with participants through 3D rendering virtualization of facilities or robot 3D designs in VR/AR. We implemented converting programs and middleware CPS (Cyber Physical System) servers that convert to BOM (Bill of Material)-based 3D graphics models and CPS models to test the accuracy of data and optimization of 3D modeling and study their performance through robotic arms in real factories.
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Furthermore, as David says, people with lived experience need to be equal partners in the design, production and decision-making processes, such as about funding and staffing - 'nothing about us without us' (p. 237).2 In reality this means, patient leaders need to occupy executive leadership positions in for-profit, not-for-profit and charitable organisations and companies in the aged and home care sectors. [...]as aged and home care is part of the wider health care system, the new federal government health and aged care ministers and state and territory health ministers must work together to reduce the backlog of patients in acute care waiting for weeks and months for a home care package or a place in a residential care home. [...]the election of a new federal government has provided hope that the current situation can be fixed.
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Artificial intelligence in E-Business, on the pharmaceutical side, is used by large companies producing such products, and in the case of vaccines for the provision of chatbot services, analysis of customer feedback and the provision of personalized services related to the required volume, the regions with high vaccine demand, as well as the evolution of pandemic eradication following the implementation of vaccination or specific medication. The proper functioning of the pharmaceutical system has become a crucial value for every nation, in the sense of rapidly producing the medication needed to treat and prevent disease. The present study aims to analyze how the artificial intelligence can help the pharmaceutical industry to give faster and efficient prevention and treatment in crisis situations in the health system. The paper underlines how financing of this particular industry involves more than money and how artificial intelligence can help the humanity to fight against COVID-19. The research was conducted through successive interviews with representatives of public institutions, of the chemical, pharmaceutical, and medical industries and Artificial intelligence developers. © 2021 ACM.
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Purpose>The purpose of this paper is to develop a framework for benchmarking the service quality of amusement parks.Design/methodology/approach>A hybrid approach, which is a combination of AHP (analytic hierarchy process) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), is applied for service quality benchmarking.Findings>Amusement parks are centers of attraction at various tourist destinations across the world. Their service quality is constituted by the attainment of certain quality attributes that varies with different parks. For sustaining in the industry, the managers of the parks need to have a good overview of the practices followed by them and their competitors that necessitate benchmarking of the service quality.Practical implications>The developed framework using the hybrid methodology of AHP and TOPSIS can be applied for comparing different amusement parks based on quality attributes, which will help the organizers in improving their service quality.Originality/value>The paper identifies various service quality attributes of amusement parks and an evaluation scheme for those attributes had been developed. Based on these, a framework had been developed for benchmarking of service quality of different amusement parks.
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In today’s environment, as the complexity of actual events develops, products become increasingly complicated. As a result, companies should collaborate to integrate disparate technologies while developing a product or service. Additionally, collaborating with the right supplier helps a company increase the flexibility, competitiveness, and profitability of its goods or services. The goal of this study is to look into the factors that influence supplier selection for speech recognition. Twelve sub-criteria for quality, affordability, maintenance, and adaptability are used to evaluate prospective providers. Two separate hybrid methodologies for finding the best supplier of an information technology product are presented. intuitionistic Fuzzy Due to the uncertainty of the data, VIKOR operates as the decision-making matrix and solves the issue by determining the ideal alternative for group utility using VIKOR. The second technique, Q-ROF TOPSIS, selects suppliers by utilizing q-rung orthopair fuzzy sets, which provides decision makers with greater expression flexibility than the majority of uncertainty-related strategies. To demonstrate the effectiveness of the recommended measures, a case study is conducted. The outcomes of various strategies are compared, as well as the associated advantages.
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Sentiment analysis is a task of identifying the sentiments in text which is often applied to analyzing text in social media, customer feedbacks, and product reviews. Various studies have explored how sentiment analysis can automatically done by using machine learning techniques. However, there has been few attempts in implementing sentiment analysis on multilingual text. Furthermore, most of the existing works uses labelled data to train and develop machine learning models for sentiment analysis. Using labelled data are often expensive and time consuming. In this study, a sentiment analysis model for multilingual text using semi-supervised machine learning was explored. The data used is composed of 50,788 tweets about COVID-19, these are cleaned by removing unnecessary characters, stop words, and emojis. After cleaning, the language of each tweet was identified, all tweets that are not written in Filipino or English were removed from the dataset. Afterwards, the tweets were all translated in English in preparation for the annotation phase. This study used an open-source tool, TextBlob, in annotating the tweets. TextBlob outputs the polarity of the text in vector representation. The TextBlob annotation were then validated by human experts through an inter-rater agreement. The level of agreement between the human annotations and TextBlob annotations have a substantial agreement with 0.78 Fleiss' Kappa value. Classifier models were developed using various machine learning algorithms. Based on the results of the experiment, SVC is the best performing model with count vectorizer as feature with an accuracy, precision, recall, and F1-score of 95%. For future work, fine tuning hyperparameters to optimize the models can be considered. © 2021 ACM.