The advent of the World Wide Web and the rapid adoption of social media platforms (such as Facebook and Twitter) paved the way for information dissemination that has never been witnessed in the human history before. With the current usage of social media platforms, consumers are creating and sharing more information than ever before, some of which are misleading with no relevance to reality. Automated classification of a text article as misinformation or disinformation is a challenging task. Even an expert in a particular domain has to explore multiple aspects before giving a verdict on the truthfulness of an article. In this work, we propose to use machine learning ensemble approach for automated classification of news articles. Our study explores different textual properties that can be used to distinguish fake contents from real. By using those properties, we train a combination of different machine learning algorithms using various ensemble methods and evaluate their performance on 4 real world datasets. Experimental evaluation confirms the superior performance of our proposed ensemble learner approach in comparison to individual learners.

No abstract

No abstract

Following a well-established track record of success in other domains such as manufacturing, Kanban is increasingly used to achieve continuous development and delivery of value in the software industry. However, while research on Kanban in software is growing, these articles are largely descriptive, and there is limited rigorous research on its application and with little cohesive building of cumulative knowledge. As a result, it is extremely difficult to determine the true value of Kanban in software engineering. This study investigates the scientific evidence to date regarding Kanban by conducting a systematic mapping of Kanban literature in software engineering between 2006 and 2016. The search strategy resulted in 382 studies, of which 23 were identified as primary papers relevant to this research. This study is unique as it compares the findings of these primary papers with insights from a review of 23 Kanban experience reports during the same period. This study makes four important contributions, (i) a state-of-the-art of Kanban research is provided, (ii) the reported benefits and challenges are identified in both the primary papers and experience reports, (iii) recommended practices from both the primary papers and experience reports are listed and (iv) opportunities for future Kanban research are identified.

Vegetable and fruit plants facilitate around 7.5 billion people around the globe, playing a crucial role in sustaining life on the planet. The rapid increase in the use of chemicals such as fungicides and bactericides to curtail plant diseases is causing negative effects on the agro-ecosystem. The high scale prevalence of diseases in crops affects the production quantity and quality. Solving the problem of early identification/diagnosis of diseases by exploiting a quick and consistent reliable method will benefit the farmers. In this context, our research work focuses on classification and identification of tomato leaf diseases using convolutional neural network (CNN) techniques. We consider four CNN architectures, namely, VGG-16, VGG-19, ResNet, and Inception V3, and use feature extraction and parameter-tuning to identify and classify tomato leaf diseases. We test the underlying models on two datasets, a laboratory-based dataset and self-collected data from the field. We observe that all architectures perform better on the laboratory-based dataset than on field-based data, with performance on various metrics showing variance in the range 10%–15%. Inception V3 is identified as the best performing algorithm on both datasets.