The behavioural intention to adopt Blockchain for supply chain management (BCSCM) is studied in this paper. The research framework adopted considers how Performance Expectancy (PE), Effort Expectan...

Unearthing the determinants of Blockchain adoption in supply chain management

Industry 4.0 ten years on: A bibliometric and systematic review of concepts, sustainability value drivers, and success determinants

Anonymization is a practical solution for preserving user’s privacy in data publishing. Data owners such as hospitals, banks, social network (SN) service providers, and insurance companies anonymize their user’s data before publishing it to protect the privacy of users whereas anonymous data remains useful for legitimate information consumers. Many anonymization models, algorithms, frameworks, and prototypes have been proposed/developed for privacy preserving data publishing (PPDP). These models/algorithms anonymize users’ data which is mainly in the form of tables or graphs depending upon the data owners. It is of paramount importance to provide good perspectives of the whole information privacy area involving both tabular and SN data, and recent anonymization researches. In this paper, we presents a comprehensive survey about SN (i.e., graphs) and relational (i.e., tabular) data anonymization techniques used in the PPDP. We systematically categorize the existing anonymization techniques into relational and structural anonymization, and present an up to date thorough review on existing anonymization techniques and metrics used for their evaluation. Our aim is to provide deeper insights about the PPDP problem involving both graphs and tabular data, possible attacks that can be launched on the sanitized published data, different actors involved in the anonymization scenario, and major differences in amount of private information contained in graphs and relational data, respectively. We present various representative anonymization methods that have been proposed to solve privacy problems in application-specific scenarios of the SNs. Furthermore, we highlight the user’s re-identification methods used by malevolent adversaries to re-identify people uniquely from the privacy preserved published data. Additionally, we discuss the challenges of anonymizing both graphs and tabular data, and elaborate promising research directions. To the best of our knowledge, this is the first work to systematically cover recent PPDP techniques involving both SN and relational data, and it provides a solid foundation for future studies in the PPDP field.

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

Exploring the role of personality, trust, and privacy in customer experience performance during voice shopping: Evidence from SEM and fuzzy set qualitative comparative analysis

Financial institutions, investors, mining companies and related firms need an effective accurate forecasting model to examine gold price fluctuations in order to make correct decisions. This paper proposes an innovative approach to accurately forecast gold price movements and to interpret predictions. First, it compares six machine learning models. These models include two very recent methods: the eXtreme Gradient Boosting (XGBoost) and CatBoost. The empirical findings indicate the superiority of XGBoost over other advanced machine learning models. Second, it proposes Shapley additive explanations (SHAP) in order to help policy makers to interpret the predictions of complex machine learning models and to examine the importance of various features that affect gold prices. Our results illustrate that the utilization of XGBoost along with SHAP approach could provide a significant boost in increasing the gold price forecasting performance.

Forecasting gold price with the XGBoost algorithm and SHAP interaction values

This study aims to contribute in the area of foreign exchange forecasting. Exchange rate plays an essential role for the economic policy of a country. Due to the floating exchange rate regime, and ever-changing economic conditions, analysts have observed significant volatility in the exchange rates. However, exchange rate forecasting has been a challenging task before the analysts over the years. Various stakeholders such as the central bank, government, and investors try to maximize the returns and minimize the risk in their decision-making using exchange rate forecasting. The study aims to propose a novel ensemble technique to forecast daily exchange rates for the three most traded currency pairs (EUR/USD, GBP/USD, and JPY/USD). The ensemble technique combines the linear and non-linear time-series forecasting techniques (mean forecast, ARIMA, and neural network) with their most optimal weights. We have taken the data of more than seven years, and the results indicate that the proposed methodology could be an effective technique to forecast better as compared to the component models separately. The study has crucial economic and academic implications. The results derived from this study would be useful for policymakers, regulators, investors, speculators, and arbitrageurs.

Exchange Rate Forecasting Using Ensemble Modeling for Better Policy Implications

The Indian motion picture industry has experienced phenomenal growth during the last few decades and plays an important role in emerging economy of India. This paper integrates three analytical models in order to address the intriguing problem of revenue prediction of movies in Indian film industry. The paper attempts to investigate the determinants leading to the success of indigenous movies in Indian context. Ensemble model has been constructed by integrating the three analytical models (Neural Network, Classification and Regression Tree and Robust Regression) using linear optimization approach. Further, a four-way comparative analysis of these three models along with Ensemble model has been carried out. The predictive power of the models has been evaluated using four performance metrics namely root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and large prediction error (LPE). Analyzing novel and original data of 120 Indian movies released during the period August’06-October’15, this paper inspects the nitty-gritties of Indian film industry and seeks to explain the nuances. The study revealed that factors like hype generated on web by a movie, screens on which the movie is released, rating garnered by movie and its genre are the most influential variables in deciding the box-office performance of a movie. Further we observed, that the neural network model closely competes with ensemble model in terms of predictive accuracy. The ensemble model considerably reduces the predictive errors and yields better results on two of the performance metrics.

Evaluating the predictive power of an ensemble model for economic success of indian movies

Decision-making in human beings is affected by emotions and sentiments. The affective computing takes this into account, intending to tailor decision support to the emotional states of people. However, the representation and classification of emotions is a very challenging task. The study used customized methods of deep learning models to aid in the accurate classification of emotions and sentiments.,The present study presents affective computing model using both text and image data. The text-based affective computing was conducted on four standard datasets using three deep learning customized models, namely LSTM, GRU and CNN. The study used four variants of deep learning including the LSTM model, LSTM model with GloVe embeddings, Bi-directional LSTM model and LSTM model with attention layer.,The result suggests that the proposed method outperforms the earlier methods. For image-based affective computing, the data was extracted from Instagram, and Facial emotion recognition was carried out using three deep learning models, namely CNN, transfer learning with VGG-19 model and transfer learning with ResNet-18 model. The results suggest that the proposed methods for both text and image can be used for affective computing and aid in decision-making.,The study used deep learning for affective computing. Earlier studies have used machine learning algorithms for affective computing. However, the present study uses deep learning for affective computing.

Deep learning based affective computing

This project attempts to investigate the effect of the introduction of Futures trading in the National Stock Exchange, India (NSE) and get insights into the effect upon the volatility of the NSE. The underlying spot market volatility is estimated using symmetric GARCH methods. Any increase in stock market volatility that has followed the onset of futures trading has generally been taken as justifying the traditional view that the introduction of futures markets induces destabilizing speculation. This has led to calls for greater regulation to minimise any detrimental effects. An alternative view is that futures markets provide an additional route by which information can be transmitted, and, therefore, increased spot market volatility may simply be a consequence of the more frequent arrival, and more rapid processing of information. Thus, futures trading may be fully consistent with efficiently functioning markets. This paper attempts to investigate the change, if any, in the volatility observed in the Indian stock market due to the introduction of futures trading. The change in the volatility is compared not only in absolute levels of volatility but also in terms of the structure of the volatility. This is done to give insights into the way the futures market is influencing the Indian spot market's volatility.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID333520_code021028550.pdf?abstractid=333520

Understanding Volatility - The Case of the Introduction of Futures Trading in the National Stock Exchange, India

Privacy preserving graph publishing is gaining importance in recent times mainly because of inherent privacy issues existing in publishing graph and social network data. Therefore, graph structure needs to be anonymized before publishing. This study proposes anonymization of a graph using fuzzy sets to preserve the graph's privacy while maintaining the utility that can be derived from the graph. We have conducted the experiments on four different datasets, and the results suggest that the proposed approach would not only help in protecting the privacy of data but also in maintaining the quality of data for analysis. To check the robustness of the proposed approach, we have validated the effectiveness of the approach on five key community detection algorithms on three performance measures.

Saurabh Kumar

Papers

Exchange Rate Forecasting Using Ensemble Modeling for Better Policy Implications

Evaluating the predictive power of an ensemble model for economic success of indian movies

Deep learning based affective computing

Understanding Volatility - The Case of the Introduction of Futures Trading in the National Stock Exchange, India

Privacy preserving graph publishing using fuzzy set