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Automatic moving object detection has gained increased research interest due to its widespread applications like security provision, traffic monitoring, and various types of anomalies detection, etc. In the video surveillance system, the video is processed for the detection of motion objects in a step-by-step process. However, the detection has become complex and less effective due to various complex constraints. To obtain an effective performance in the detection of motion objects, this research work focuses to develop an automatic motion object detection system based on the statistical properties of video and supervised learning. In this paper, a novel Background Modeling mechanism is proposed with the help of a Biased Illumination Field Fuzzy C-means algorithm to detect the moving objects more accurately. Here, the non-stationary pixels are separated from stationary pixels through the Background Subtraction. Afterward, the Biased Illumination Field Fuzzy C-means approach has accomplished to improve the segmentation accuracy through clustering under noise and varying illumination conditions. The performance of the proposed algorithm compared with conventional methods in terms of accuracy, precision, recall, and F- measure.

The ultimate goal of any software developer seeking a competitive edge is to meet stakeholders' needs and expectations. To achieve this, it is necessary to effectively and accurately manage stakeholders' system requirements. The paper proposes a systematic way of classifying stakeholders and then describes a novel method for calculating stakeholder priority taking into consideration the fact that different stakeholders will have different importance level and different requirement preference. Finally the requirement preference calculation is done where stakeholders choose the best requirements based on two factors, value and urgency of the requirement. The proposed method actively involves stakeholders in the requirement elicitation process (Abstract)

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