Ashir Ahmed

ROLE OF GIS, RFID AND HANDHELD COMPUTERS IN EMERGENCY MANAGEMENT: AN EXPLORATORY CASE STUDY ANALYSIS

This paper underlines the task characteristics of the emergency management life cycle. Moreover, the characteristics of three ubiquitous technologies including RFID, handheld computers and GIS are discussed and further used as a criterion to evaluate their potential for emergency management tasks. Built on a rather loose interpretation of Task-technology Fit model, a conceptual model presented in this paper advocates that a technology that offers better features for task characteristics is more likely to be adopted in emergency management. Empirical findings presented in this paper reveal the significance of task characteristics and their role in evaluating the suitability of three ubiquitous technologies before their actual adoption in emergency management.

Potential of RFID in Emergency Management: Task-Technology Fit Perspective

This study aims to explore the potential of adopting radio frequency identification (RFID) in emergency management. In discovering the relevance and practicality of RFID in emergency management, a conceptual framework based on TTF model has been proposed and discussed in this paper. Multiple case study method has been employed to validate the proposed framework. The empirical findings in this study indicate that five key factors influencing the decision to adopt are privacy, compatibility, standardization, implementation and locatability. It is hoped that the findings of this research will inform emergency management organizations to better plan the adoption of RFID for emergency operations.

Depression detection from social network data using machine learning techniques

PurposeSocial networks have been developed as a great point for its users to communicate with their interested friends and share their opinions, photos, and videos reflecting their moods, feelings and sentiments. This creates an opportunity to analyze social network data for user’s feelings and sentiments to investigate their moods and attitudes when they are communicating via these online tools.MethodsAlthough diagnosis of depression using social networks data has picked an established position globally, there are several dimensions that are yet to be detected. In this study, we aim to perform depression analysis on Facebook data collected from an online public source. To investigate the effect of depression detection, we propose machine learning technique as an efficient and scalable method.ResultsWe report an implementation of the proposed method. We have evaluated the efficiency of our proposed method using a set of various psycholinguistic features. We show that our proposed method can significantly improve the accuracy and classification error rate. In addition, the result shows that in different experiments Decision Tree (DT) gives the highest accuracy than other ML approaches to find the depression.ConclusionsMachine learning techniques identify high quality solutions of mental health problems among Facebook users.

What Is IT for Social Impact?: A Review of Literature and Practices

Information and communication technologies (ICTs) have incredible power to improve peoples lives [6], [21], [33], [35] and foster economic growth [22], [24], [29]. In doing so, ICTs create opportunities for individuals, companies, and nations around the globe [37], [46]. While, the idea that technology has the potential to positively change a persons life is not new, a catchphrase has emerged in recent years, technology for good [11], [13], [17], [36], [50], whereby leading technology companies such as Google, Microsoft, and Ericsson have embraced this concept.