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As an emerging and promising approach, crowdsourcing-based software development has become popular in many domains due to the participation of talented pool of developers in the contests, and to promote the ability of requesters (or customers) to choose the ‘wining’ solution with respect to their desired quality levels. However, due to lack of a central mechanism for team formation, continuity in the developer’s work on consecutive tasks and risk of noise in submissions of a contest, there is a gap between the requesters of a domain and their quality concerns related to the adaptation of a crowdsourcing-based software development platform. In order to address concerns and aid requesters, we describe three measures; Quality of Registrant Developers (QRD), Quality of Contest (QC) and Quality of Support (QS) to compute and predict the quality of a crowdsourcing-based platform through historical information on its completed tasks. We evaluate the capacity of the QRD, QC and QS as assessors to predict the quality. Subsequently, we implement a crawler to mine the information of completed development tasks from the TopCoder platform to inspect the proposed measures. The promising results of our QRD, QC, and QS measures suggest to use the proposed measures to the requesters and researchers of other domains such as pharmaceutical research and development, in order to investigate and predict the quality of crowdsourcing-based software development platforms.

A well representing feature set that has enough differentiated power plays an important role in the classification. The existing techniques for feature set selection are mostly statistical. They are not flexible to incorporate the human reasoning and the changing requirements and preferences of the real-life systems. They only make a decision between a feature inclusion or exclusion. The fuzziness of human reasoning and thinking are not considered at all that may improve the feature selection and hence the accuracy of the classifier. Also, the selection of overlapping features in case of Local Feature Selection (LFS) methods is an important issue that negatively impacts classification accuracy. For example, in case of Odd Ratio (OR), the selection may contain overlapping features of multiple classes. In this paper, a Fuzzy Set Theory (FST) based feature selection method has been proposed. The approach aims to tackle both above mentioned issues efficiently. The selected final feature set is used to train the well-known classification algorithms and the results are compared with Global Feature Selection (GFS) and LFS methods. The comparison shows that the proposed method has improved the accuracy of the classifiers and also extract comparatively small feature set that ultimately reduces the time complexity of the system.

Cryptocurrency is one of the emerging online currency of the modern era. Big companies are investing in this technology. However, some established companies still hesitate to use it. According to them, it is a volatile trend which will fade up eventually. There is no such authority which will provide them feasibility information. So, investors can be helped by providing them feasibility information about locations for cryptocurrency investment around the world. This paper aims to provide the aforementioned information to the investors. The proposed methodology is based on Topic modeling along with public opinion mining about cryptocurrencies, blockchain network, bitcoin, litecoin, and ethereum. The crawled data for other cryptocurrencies are much insufficient, so that are excluded from the study. In the proposed methodology, the top locations where cryptocurrency is widely used are identified, then in that particular locations' users concerns along with their sentiment analyses are investigated. Top locations are identified such as Australia, Denmark, Netherlands, and the USA etc. Almost 83.7% tweets of Sweden show positive sentiment for cryptocurrency investment which ranks as the highest having friendly environment for cryptocurrency investment. Similarly, the UK shows the least positive perception of cryptocurrency and blockchain technology usage. Some of the noteworthy terms found are legitimacy, authorization rules, volatility, profit, investment, and fluctuations. Which describe the users’ concerns/ interests’ about cryptocurrency. Investors can focus on all these areas during business. These subtopics can help business experts to evolve their businesses’ and to make them more sustainable on the basis of public perception.

Researchers have widely used statistical and machine learning techniques to construct prediction models in several domains such as prediction of software faults, spam detection, disease diagnosis, and financial fraud identification. The prediction of patients prone to lung cancer can help doctors in their decision making regarding their treatments. In this regard, this research paper attempts to evaluate the discriminative power of several predictors in the study to increase the efficiency of lung cancer detection through their symptoms. A number of classifiers including Support Vector Machine (SVM), C4.5 Decision tree, Multi-Layer Perceptron, Neural Network, and Naive Bayes (NB) are evaluated on a benchmark dataset obtained from UCI repository. The performance is also compared with well-known ensembles such as Random Forest and Majority Voting. Based on performance evaluations, it is observed that Gradient-boosted Tree outperformed all other individual as well as ensemble classifiers and achieved 90% accuracy.

As an emerging and promising approach, crowdsourcing-based software development becomes popular in many domains due to the participation of talented pool of developers in the contests, and to promote the ability of requesters (or customers) to choose the 'wining' solution with respect to their desire quality levels. However, due to lack of a central mechanism for team formation, continuity in the developer's work on consecutive tasks and risk of noise in submissions of a contest, requesters of a domain have quality concerns to adopt a crowdsourcing-based software development platform. In order to address this concern, we proposed a measure Quality of Contest (QoC) to analyze and predict the quality of a crowdsourcing-based platform through historical information on its completed tasks. We evaluate the capacity of QoC as assessor to predict the quality. Subsequently, we implement a crawler to mine the information of completed development tasks from the TopCoder platform of Tech Platform Inc (TPI) to empirically investigate the proposed measures. The promising results of QoC measure suggest the applicability of the proposed measure across the other crowdsourcing-based platforms.