Conceptualizing corporate identity in a dynamic environment

Purpose – The study revisits the meaning of Corporate Identity (CI) in practice to identify its key dimensions and the interrelationships between them, and to provide insights on how to operationalize the construct. Design/methodology/approach – This study is based on a comprehensive literature review and qualitative research consisting of 22 semi-structured interviews with senior managers from 11 UK-leading companies, and three in-depth interviews with corporate brand consultants who worked closely with these firms in cognate areas. Findings – The study identifies six key dimensions of CI in UK industry: communication, visual identity, behavior, organizational culture, stakeholder management, and founder value-based leadership. Research limitations/implications – The focus on UK leading companies limits the generalizability of the results. Further studies should be conducted in other sectors and country settings to examine the relationships identified in the current study. Originality/value – This study identifies the salient dimensions of CI and, for the first time, the role of founder transformational leadership, employee identification and top management behavioral leadership as key dimensions and sub-dimensions of CI. The study also provides novel insights about the measurements for these dimensions. Additionally, this study introduces a model for the interrelationships between CI dimensions and their influence on corporate image, based on rigorous theoretical underpinnings, which lays the foundation for future empirical testing

Artificial Intelligence Chatbots: A Survey of Classical versus Deep Machine Learning Techniques

Artificial Intelligence (AI) enables machines to be intelligent, most importantly using Machine Learning (ML) in which machines are trained to be able to make better decisions and predictions. In particular, ML-based chatbot systems have been developed to simulate chats with people using Natural Language Processing (NLP) techniques. The adoption of chatbots has increased rapidly in many sectors, including, Education, Health Care, Cultural Heritage, Supporting Systems and Marketing, and Entertainment. Chatbots have the potential to improve human interaction with machines, and NLP helps them understand human language more clearly and thus create proper and intelligent responses. In addition to classical ML techniques, Deep Learning (DL) has attracted many researchers to develop chatbots using more sophisticated and accurate techniques. However, research has paid chatbots have widely been developed for English, there is relatively less research on Arabic, which is mainly due to its complexity and lack of proper corpora compared to English. Though there have been several survey studies that reviewed the state-of-the-art of chatbot systems, these studies (a) did not give a comprehensive overview of how different the techniques used for Arabic chatbots in comparison with English chatbots; and (b) paid little attention to the application of ANN for developing chatbots. Therefore, in this paper, we conduct a literature survey of chatbot studies to highlight differences between (1) classical and deep ML techniques for chatbots; and (2) techniques employed for Arabic chatbots versus those for other languages. To this end, we propose various comparison criteria of the techniques, extract data from collected studies accordingly, and provide insights on the progress of chatbot development for Arabic and what still needs to be done in the future

Arabic Educational Neural Network Chatbot

Chatbots (machine-based conversational systems) have grown in popularity in recent years. Chatbots powered by artificial intelligence (AI) are sophisticated technologies that replicate human communication in a range of natural languages. A chatbot’s primary purpose is to interpret user inquiries and give relevant, contextual responses. Chatbot success has been extensively reported in a number of widely spoken languages; nonetheless, chatbots have not yet reached the predicted degree of success in Arabic. In recent years, several academics have worked to solve the challenges of creating Arabic chatbots. Furthermore, the development of Arabic chatbots is critical to our attempts to increase the use of the language in academic contexts. Our objective is to install and create an Arabic chatbot that will help the Arabic language in the area of education. To begin implementing the chabot, we collected datasets from Arabic educational websites and had to prepare these data using the NLP methods. We then used this data to train the system using a neural network model to create an Arabic neural network chabot. Furthermore, we found relevant research, conducted earlier investigations, and compared their findings by searching Google scholar and looking through the linked references. Data was gathered and saved in a json file. Finally, we programmed the chabot and the models in Python. As a consequence, an Arabic chatbot answers all questions about educational regulations in the United Arab Emirates

Nonexistence of nonzero derivations on some classes of zero-symmetric 3-prime near-rings

We give some classes of zero-symmetric 3-prime near-rings such that every member of these classes has no nonzero derivation. Moreover, we extend the concept of “3-prime” to subsets of near-rings and use it to generalize Theorem 1.1 due to Fong, Ke, and Wang concerning the transformation near-rings M o (G) by using a different technique and a simpler proof.Наведено дєякі класи 3-простих майже-кілець з нульовою симєтрією таких, що будь-який елемент цих класів не має ненульової похідної. Крім того, поняття „3-простих" узагальнено на підмножини майже-кілець i застосовано, щоб узагальнити теорему 1.1 Фонга, Ке i Ванга про трансформацію майже-кілець M o (G) за допомогою іншої техніки та більш простого доведення

A systematic review on sequence-to-sequence learning with neural network and its models

We develop a precise writing survey on sequence-to-sequence learning with neural network and its models. The primary aim of this report is to enhance the knowledge of the sequence-to-sequence neural network and to locate the best way to deal with executing it. Three models are mostly used in sequence-to-sequence neural network applications, namely: recurrent neural networks (RNN), connectionist temporal classification (CTC), and attention model. The evidence we adopted in conducting this survey included utilizing the examination inquiries or research questions to determine keywords, which were used to search for bits of peer-reviewed papers, articles, or books at scholastic directories. Through introductory hunts, 790 papers, and scholarly works were found, and with the assistance of choice criteria and PRISMA methodology, the number of papers reviewed decreased to 16. Every one of the 16 articles was categorized by their contribution to each examination question, and they were broken down. At last, the examination papers experienced a quality appraisal where the subsequent range was from 83.3% to 100%. The proposed systematic review enabled us to collect, evaluate, analyze, and explore different approaches of implementing sequence-to-sequence neural network models and pointed out the most common use in machine learning. We followed a methodology that shows the potential of applying these models to real-world applications