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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.

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.

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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.

Industry needs graduates from universities having knowledge and skills to tackle the practical issues of real life software development. To facilitate software engineering students and fulfill industry need, the Department of Information Processing Science, University of Oulu, Finland, built a Software Factory laboratory (SWF) in 2012 based on Lean concept. This study examines factors in the SWF learning environment that affect learning of a SWF course by the students. It employs amended Computer laboratory Environment Inventory (CLEI) and Attitude towards Computers and Computing Courses Questionnaire instrument (ACCC) with two additional constructs: 1) Kanban board 2) Collaborative learning. The general findings indicate that SWF learning environment, collaborative learning and Kanban board play important role in software engineering students learning, academic achievements and professional skills gaining. The findings are helpful to develop a better understanding about learning environments. The information gathered in this study can also be used to improve the software engineering learning environment. Keywords-software engineering education; software factory; computer laboratory learning enviroment; smart classroom collaborative learning; teaching and learning I.

PurposeThis paper seeks to examine how expectations from business analytics (BA) by members of agile information systems development (ISD) teams affect their perceptions and continuous use of BA in ISD projects.Design/methodology/approachData was collected from 153 respondents working in agile ISD projects and analysed using partial least squares structural equation modelling techniques (PLS-SEM).FindingsPerceived usefulness and technological compatibility are the most salient factors that affect BA continuance intention in agile ISD projects. The proposed model explains 48.4% of the variance for BA continuance intention, 50.6% of the variance in satisfaction, 36.7% of the variance in perceived usefulness and 31.9% of the variance in technological compatibility.Research limitations/implicationsFirst, this study advances understanding of the factors that affect the continuous use of BA in agile ISD projects; second, it contextualizes the expectation-confirmation model by integrating technological compatibility in the context of agile ISD projects.Originality/valueThis is the first study to investigate BA continuance intention from an employee perspective in the context of agile ISD projects.