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.

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.

Using of Kanban in software development is an emerging topic. This systematic literature review was conducted in order to analyze the current trend of Kanban usage in software development and to identify the obtained benefits and involved challenges. The search strategy resulted in 492 papers, of which 19 were identified as primary studies relevant to our research. The main reported benefits of using the Kanban method were improved lead time to deliver software, improved quality of software, improved communication and coordination, increased consistency of delivery, and decreased customer reported defects. The reported challenges included lack of knowledge and specialized training as well as various organizational issues. Additionally, suggested practices were extracted from the primary studies and summarized for guiding the practitioners interested in adopting Kanban. The findings of this literature review are intended for helping researchers and practitioners to gain a better understanding of the current state of Kanban usage in software development.

Purpose: The purpose of this paper is to explore the factors that enable end‐user adoption of e‐government services in Pakistan, where these facilities are at a rudimentary stage.Design/methodology/approach: Following previous research on e‐government services adoption, this study employs the Unified Theory of Acceptance and Use of Technology (UTAUT) model to examine the influential factors of the adoption and use of e‐government services in Pakistan from a citizen perspective. An online survey was conducted and a statistical descriptive analysis was performed on the responses received from 115 Pakistani citizens.Findings: In line with previous research, the findings show that performance expectancy, effort expectancy, facilitating conditions and social influence are the factors that affect the user's adoption of e‐government services in Pakistan. Furthermore, the results show that lack of awareness, user data privacy, lack of appropriate support and assistance hamper the process.Practical implications: The findings are useful for policy‐makers and decision‐makers to develop a better understanding of citizens' needs. The adopted model can be used as a guideline for the implementation of e‐government services in Pakistan. This study suggests that government should run extensive advertising campaigns to ensure that people are aware of the services and use them. This implies that government should place emphasis on increasing awareness of the services, show the benefits of citizens, and encouraging confidence in the system.Originality/value: This study is one of the few to examine what influences citizens adoption of e‐government services in South Asia. This paper is the first step exploring end‐user adoption of the e‐government services of Pakistan using UTAUT model. Corresponding to previous research, this study enforces the significance of particular factors that need to be considered when the goal is to increase e‐government services adoption in developing countries, particularly South Asian.