Digital image processing is a rapidly growing area of computer science since it was introduced and developed in the 1960's. In the case of flower classification, image processing is a crucial step for computer-aided plant species identification. Colour of the flower plays very important role in image classification since it gives additional information in terms of segmentation and recognition. On the other hand, Texture can be used to facilitate image-based retrieval system normally and it is encoded by a number of descriptors, which represented by a set of statistical measures such as gray-level co-occurrence matrix (GLCM) and Law's Order approach. This study addresses the application of NN and on image processing particularly for understanding flower image features. For predictive analysis, two techniques have been used namely, Neural Network (NN) and Logistic regression. The study shows that NN obtains the higher percentage of accuracy among two techniques. The MLP is trained by 1800 flower's dataset to classify 30 kinds of flower's type.

The recent trend towards developing a new generation of robots capable of operating in human-centered environments, and participating in and assisting our daily lives has introduced the need for robotic systems capable to communicate and to react to their users in a social and engaging way. This type of robot could play essential roles to help individuals with severe cognitive problems. In this paper, several core components to design a robotic assisted therapy to support individuals with anxiety traits and states are presented.

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The oil palm industry has been well known as the backbone in Malaysian agriculture and still maintain as the main commodity exports. The research on oil palm industry has been growing gradually by utilizing various methods and technology to solve the problem in managing oil palm plantation. The aim of this paper is to review the technique in managing oil palm plantation towards a digitalized online 3D application. Various problems and techniques on managing oil palm plantation has been reviewed which involving various technology such as GIS, GPS, DBMS and hyperspectral. It was found that monitoring the characteristic of oil palm plantation is beneficial and important to oil palm planters. The new online 3D application for oil palm plantation management has a potential of assisting oil palm managers in making a decision, visualizing their plantation in online 3D environment, and managing their plantation effectively.

This paper presents a temporal dynamic model of anxiety states and traits for an individual. Anxiety is a natural part of life, and most of us experience it from time to time. But for some people, anxiety can be extreme. Based on several personal characteristics, traits, and a representation of events (i.e. psychological and physiological stressors), the formal model can represent whether a human that experience certain scenarios will fall into an anxiety states condition. A number of well-known relations between events and the course of chronic fatigue are summarized from the literature and it is shown that the model exhibits those patterns. In addition, the agent model has been mathematically analyzed to find out which stable situations exist. Finally, it is pointed out how this model can be used in therapy, supported by a software agent.