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The term "personality" can be defined as the mixture of features and qualities that built an individual's distinctive characters, including thinking, feeling and behaviour. Nowadays, it is hard to select the right employees due to the vast pool of candidates. Traditionally, a company will arrange interview sessions with prospective candidates to know their personalities. However, this procedure sometimes demands extra time because the total number of interviewers is lesser than the total number of job seekers. Since technology has evolved rapidly, personality computing has become a popular research field that provides personalisation to users. Currently, researchers have utilised social media data for auto-predicting personality. However, it is complex to mine the social media data as they are noisy, come in various formats and lengths. This paper proposes a machine learning technique using Random Forest classifier to automatically predict people's personality based on Myers-Briggs Type Indicator® (MBTI). Researchers compared the performance of the proposed method in this study with other popular machine learning algorithms. Experimental evaluation demonstrates that Random Forest classifier performs better than the different three machine learning algorithms in terms of accuracy, thus capable in assisting employers in identifying personality types for selecting suitable candidates.

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The continuously increasing cost of managing IT systems has led many companies to outsource their commercial services to external hosting centers. Cloud computing has emerged as one of the enabling technologies that allow such external hosting efficiently. Like any IT environment, a Cloud Computing environment requires high level of maintenance to be able to provide services to its customers. Replacing defective items (hardware/software), applying security patches, or upgrading firmware are just a few examples of the typical maintenance procedures needed in such environments. While taking resources down for maintenance, applying efficient change management techniques is a key factor to the success of the cloud. As energy has become a precious resource, research has been conducted towards devising protocols that minimize energy consumption in IT systems. In this paper, we propose a pro-active energy efficient technique for change management in cloud computing environments. We formulate the management problem into an optimization problem that aims at minimizing the total energy consumption of the cloud. Our proposed approach is pro-active in the sense that it takes prior SLA (Service Level Agreement) requests into account while determining time slots in which changes should take place.

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We investigate how to maximize network lifetime by devising a task assignment protocol that balances task load on sensors based on their remaining energy. First, we show that energy-unaware protocols can excessively assign tasks to a set of sensors more than others. Consequently, the energy of those heavily loaded sensors will be depleted much faster than lightly loaded ones. This results in reducing network density around heavily loaded sensors and eventually creates energy holes that isolate the network into separated islands. This kind of behavior has its negative impacts on network durability and reliability. To avoid these problems, we propose a management protocol that assigns tasks to sensors based on their remaining energy so that energy consumption is almost even among network sensors.

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