Internet of things (IoT) is playing a pivotal role in bringing comfort and ease in human lives by connecting billions of devices across the globe. Innovation and development in IoT have established new ways to analyze patient data in healthcare systems. Currently, IoT and cloud-based solutions are available to process and analyze patient data, but cloud computing causes large end-to-end delay and network usage problems while processing huge amounts of data. Efficiency and security are still a challenge in IoT-based healthcare systems. Fog computing is introduced to overcome the issues of cloud computing by providing computing and storage services at the edge of the network. In this article, first we present a fog based efficient architecture for IoT-based healthcare systems. Then we present the user authentication method through identity management to prevent security breaches. The fog based architecture for healthcare includes the idea of a virtual machine (VM) partitioning in fog node to smoothly run the body sensor network and medical IoT devices data. The Elliptic Curve Cryptography technique is used to generate the output token for the user authentication method.

Context: Recently, research community of certain domain showing their eagerness towards the use of social media networks to gain constructive knowledge in decision making and automation, such as aid to perform software development activities, crypto-currencies usage, network community detection and recommendation and so on. Recently, besides other domains of eHealth, the use of social media and big data analytics has become hot topic to predict the patient of mental illness involved in either depression, schizophrenia, eating disorders, anxiety or addictive behaviors. Problem: Traditional methods either need enough historic data or to keep the regular monitoring on patient activities for identification of a patient associated with a mental illness disease. Method: In order to address this issue, we propose a methodology to classify the patients associated with chronic mental illness diseases (i.e. Anxiety, Depression, Bipolar, and ADHD (Attention Deficit Hyperactivity Disorder) based on the data extracted from the Reddit, a well-known network community platform. The proposed method is employed through Co-training (type of semi-supervised learning approach) technique by incorporating the discriminative power of widely used classifiers namely Random Forrest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB). We used Reddit API to download posts and top five associated comments for construction of a feature space. Results: The experimental results indicate the effectiveness of Co-training based classification rather than the state of the art classifiers by a margin of 3% on average in par with every state of art technique. In future, the proposed method could be employed to investigate any classification problem of any domain by extracting date from the social media.

Presently, global software development (GSD) is growing very rapidly. However, it is not an easy and straightforward process. Requirements engineering (RE) and requirements change management (RCM) both are considered as very challenging activities due to demanding rich communications. Because it is necessary to address geographical and cultural differences in GSD, this requirement makes RE and RCM challenging. This study investigates the importance of project management in RE and RCM processes. First, the frameworks with the phenomena of specialized project management are proposed for RE and RCM. Then, a survey and blind interviews of the experts are conducted to validate the proposed frameworks. Finally, statistical tools are applied to analyze the collected data. By utilizing the analyzed data, our results show the significant impacts of both frameworks (i.e., RE and RCM) in the GSD environment.

Traditionally, software development organizations relied on heavyweight development methodologies, such as waterfall, V-model, and others. Later, agile development methodologies known as lightweight methodologies were introduced. Many considered these to be more flexible and more effective than heavyweight methodologies. Both methodologies are equally important for a software development life cycle. The purpose of adopting software development methodologies is to optimize the process model to achieve milestones while concurrently and effectively managing time, budget, and quality. The literature review reveals that there is a lack of statistical evidence for determining the effect of both methodologies on the six-pointed star model (schedule, scope, budget, risk, resource, and quality). In this paper, statistical comparisons were performed for the effects of both methodologies on each factor of the six-pointed star model and the interdependency among factors. Numerical analyses were conducted based on survey responses collected from the experienced users of both methodologies. After examining the results of all the factors of both methodologies, it was determined that lightweight methodologies are suitable for small-scale projects and that heavyweight methodologies perform better for mediumand large-scale projects.