Sanjay Misra

Bio: Sanjay Misra is an academic researcher from Covenant University. The author has contributed to research in topics: Computer science & Cloud computing. The author has an hindex of 29, co-authored 499 publications receiving 4100 citations. Previous affiliations of Sanjay Misra include Pontifical Catholic University of Valparaíso & Federal University of Technology Minna.

Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges

Abstract: Cloud computing plays a critical role in modern society and enables a range of applications from infrastructure to social media. Such system must cope with varying load and evolving usage reflecting societies’ interaction and dependency on automated computing systems whilst satisfying Quality of Service (QoS) guarantees. Enabling these systems are a cohort of conceptual technologies, synthesized to meet demand of evolving computing applications. In order to understand current and future challenges of such system, there is a need to identify key technologies enabling future applications. In this study, we aim to explore how three emerging paradigms (Blockchain, IoT and Artificial Intelligence) will influence future cloud computing systems. Further, we identify several technologies driving these paradigms and invite international experts to discuss the current status and future directions of cloud computing. Finally, we proposed a conceptual model for cloud futurology to explore the influence of emerging paradigms and technologies on evolution of cloud computing.

Co-LSTM: Convolutional LSTM model for sentiment analysis in social big data

Abstract: Analysis of consumer reviews posted on social media is found to be essential for several business applications. Consumer reviews posted in social media are increasing at an exponential rate both in terms of number and relevance, which leads to big data. In this paper, a hybrid approach of two deep learning architectures namely Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) (RNN with memory) is suggested for sentiment classification of reviews posted at diverse domains. Deep convolutional networks have been highly effective in local feature selection, while recurrent networks (LSTM) often yield good results in the sequential analysis of a long text. The proposed Co-LSTM model is mainly aimed at two objectives in sentiment analysis. First, it is highly adaptable in examining big social data, keeping scalability in mind, and secondly, unlike the conventional machine learning approaches, it is free from any particular domain. The experiment has been carried out on four review datasets from diverse domains to train the model which can handle all kinds of dependencies that usually arises in a post. The experimental results show that the proposed ensemble model outperforms other machine learning approaches in terms of accuracy and other parameters.

Relationship between Convenience, Perceived Value, and Repurchase Intention in Online Shopping in Vietnam

Abstract: Electronic commerce (e-commerce) is an increasingly popular trend in modern economy concomitant with the development of the Internet. E-commerce has developed considerably, making Vietnam one of the fastest growing markets in the world. However, its growth rate has not matched its potential, leading to the question how online retailers could improve their practices and thus contribute to the sustainable development of emerging markets such as Vietnam. Therefore, with the goal of providing online retailers with many methods to improve their online shopping service, this study examined the direct and indirect influence of the dimensions of online shopping convenience on repurchase intention through customer-perceived value. A survey of 230 Vietnamese customers was conducted to test the theoretical model. A structural equation model was used for data analysis. The results determined that the five dimensions of online shopping convenience are: access, search, evaluation, transaction, and possession/post-purchase convenience. All dimensions have a direct impact on perceived value and repurchase intention. The results also show the important role of perceived value when a factor both directly influences repurchase intention and mediates the relationship between convenience and repurchase intention.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

The knowledge-creating company

Abstract: How have Japanese companies become world leaders in the automotive and electronics industries, among others? What is the secret of their success? Two leading Japanese business experts, Ikujiro Nonaka and Hirotaka Takeuchi, are the first to tie the success of Japanese companies to their ability to create new knowledge and use it to produce successful products and technologies. In The Knowledge-Creating Company, Nonaka and Takeuchi provide an inside look at how Japanese companies go about creating this new knowledge organizationally. The authors point out that there are two types of knowledge: explicit knowledge, contained in manuals and procedures, and tacit knowledge, learned only by experience, and communicated only indirectly, through metaphor and analogy. U.S. managers focus on explicit knowledge. The Japanese, on the other hand, focus on tacit knowledge. And this, the authors argue, is the key to their success--the Japanese have learned how to transform tacit into explicit knowledge. To explain how this is done--and illuminate Japanese business practices as they do so--the authors range from Greek philosophy to Zen Buddhism, from classical economists to modern management gurus, illustrating the theory of organizational knowledge creation with case studies drawn from such firms as Honda, Canon, Matsushita, NEC, Nissan, 3M, GE, and even the U.S. Marines. For instance, using Matsushita's development of the Home Bakery (the world's first fully automated bread-baking machine for home use), they show how tacit knowledge can be converted to explicit knowledge: when the designers couldn't perfect the dough kneading mechanism, a software programmer apprenticed herself withthe master baker at Osaka International Hotel, gained a tacit understanding of kneading, and then conveyed this information to the engineers. In addition, the authors show that, to create knowledge, the best management style is neither top-down nor bottom-up, but rather what they call "middle-up-down," in which the middle managers form a bridge between the ideals of top management and the chaotic realities of the frontline. As we make the turn into the 21st century, a new society is emerging. Peter Drucker calls it the "knowledge society," one that is drastically different from the "industrial society," and one in which acquiring and applying knowledge will become key competitive factors. Nonaka and Takeuchi go a step further, arguing that creating knowledge will become the key to sustaining a competitive advantage in the future. Because the competitive environment and customer preferences changes constantly, knowledge perishes quickly. With The Knowledge-Creating Company, managers have at their fingertips years of insight from Japanese firms that reveal how to create knowledge continuously, and how to exploit it to make successful new products, services, and systems.