Prediction of well-grounded market information, particularly short-term forecast of prices of agricultural commodities, is the essential requirement for the sustainable development of the farming community. Such predictions are mostly performed with the help of time series models. In this study, the soft computing method is used for short-term forecasting of agriculture commodity price based on time series data using the artificial neural network (ANN). The time series data for sunflower seed and soybean seed are considered as the agriculture commodities. The soybean seed time series data were collected for the period of five years (Jan 2014–Dec 2018), for Akola district market, Maharashtra, India. The sunflower time series data were collected for the period of six years (Jan 2011–Dec 2016), for Kadari district market, Andhra Pradesh, India. The dataset is available at the Indian government website taken from the website www.data.gov.in. For forecasting, the ANN model is used on the abovementioned datasets. The performance of the model is compared with the result of the traditional ARIMA model. The mean absolute percentage error (MAPE) and root mean square percentage error (RMSPE) are considered as the performance parameters for the forecasting model. It is observed that the ANN is a better forecasting model than the ARIMA model by considering the two forecasting performance parameters MAPE and RMSPE.

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The benefits of open data were realised worldwide since the past decades, and the efforts to move more data under the license of open data intensified. There was a steep rise of open data in government repositories. In our study, we point out that privacy is one of the consistent and leading barriers among others. Strong privacy laws restrict data owners from opening the data freely. In this paper, we attempted to study the applied solutions and to the best of our knowledge, we found that anonymity-preserving algorithms did a substantial job to protect privacy in the release of the structured microdata. Such anonymity-preserving algorithms argue and compete in objectivethat not only could the released anonymized data preserve privacy but also the anonymized data preserve the required level of quality. K-anonymity algorithm was the foundation of many of its successor algorithms of all privacy-preserving algorithms. l-diversity claims to add another dimension of privacy protection. Both these algorithms used together are known to provide a good balance between privacy and quality control of the dataset as a whole entity. In this research, we have used the K-anonymity algorithm and compared the results with the addon of l-diversity. We discussed the gap and reported the benefits and loss with various combinations of K and l values, taken in combination with released data quality from an analyst’s perspective. We first used dummy fictitious data to explain the general expectations and then concluded the contrast in the findings with the real data from the food technology domain. The work contradicts the general assumptions with a specific set of evaluation parameters for data quality assessment. Additionally, it is intended to argue in favour of pushing for research contributions in the field of anonymity preservation and intensify the effort for major trends of research, considering its importance and potential to benefit people.

Software engineering is comparatively a new addition in the vocabulary of traditional engineering discipline. Being a late joiner, software engineering obtained many of its process foundation from traditional engineering domains. But the ever-changing business needs and the growing complexity that are required to be addressed in a software application, have kept software engineers on their toes to continuously improve the development process to meet and to manage the challenges in it. Agile project management has been the most significant development in IT industry to manage software development process that could deliver quality software product at an extremely high speed compared to any of the predecessor methods. The key abstraction of all the flavors of agile methods is adaptability towards change. This adaptability is achieved by the use of quality practices and practitioners in a closely integrated working environment that also involves the customers in the development process more than ever before. IT industry has acknowledged the significant success of the agile process and has been a buzz-word for a decade in the IT industry. The paper is built upon a comparative study of the application of Agile project management in both IT and non-IT industries. It further discusses the adaptability of agile methods and its potential to benefit the Non-IT industry in managing the quality of deliverables while maintaining high delivery speed. The discussion extends its boundaries to cover the reason for less acceptance of Agile process in non-IT industry and put forth an argument against the suitability of some of the success-factors in the case of non-IT industries, while they enabled a high acceptance of the Agile process in IT-industry.