The procedure can be (1) algorithm, a predetermined sequence of actions that will lead to the correct answer when properly executed, or (2) actions that may need to be appropriately structured to solve a given problem (example equation is complete). This knowledge is developed through practice problem-solving, and thus dependent on the type of problem. Furthermore, 'It is clear the nature of the procedures that may set them apart from most other sciences' (Hiebert & Lefevre,
Thus, the purpose of this report is to analyse the positive and negative impacts of deforestation in Malaysia. In addition, the report will also explore the question of sustainable logging in Malaysia. 1.1 Significance & Relevance of the Study Nowadays, deforestation has become the main topic of debate in our daily lives. This is significance because deforestation has been conducted to make more lands available for the residents but this also brings an issue which is flora and fauna might losses its habitat, certain pollutions may occurs after deforestation. The objective of this report is to explore the question of sustainable logging in Malaysia.
In  white box classification techniques are used to predict the dropouts. Decision trees and rules induction algorithms and evolutionary algorithms are mainly used as the “white box” classification techniques. White box classification algorithms obtain models that can explain their predictions at a higher level of abstraction by IF-THEN rules. A decision tree is a set of conditions organized in a hierarchical structure. An instance can be classified by following the path of satisfied conditions from the root of the tree until a leaf is reached, which corresponds to a class label.
In contrast some plant species which are considered as invasive plants are still using in many forest management practises. So it is important to investigate whether they are really invasive, or whether they really negatively impact on ecosystem and to understand what are their impacts on ecosystem, in order to have a
A Globally Optimal Solution is the probably best solution which meets all Constraints. The Simplex LP Solver regularly finds the Globally Optimal Solution at the point where 2 or more Constraints intersect. 3.2. NONLINEAR PROGRAMMING (NLP) A model in which the objective function and the greater part of the constraints (except for integer constraints) are smooth nonlinear functions of the decision variables is known as a nonlinear programming (NLP) or a nonlinear optimization problem. Such problems are inherently harder to understand than linear programming (LP) problems.
Case-based reasoning Case-based reasoning is a problem solving paradigm that in many respects is fundamentally different from other major AI approaches. Instead of re-lying solely on general knowledge of a problem domain, or making associations along generalized relationships between problem descriptors and conclusions, CBR is able to utilize the specific knowledge of previously experienced, concrete problem situations (cases). A new problem is solved by finding a similar past case, and reusing it in the new problem situation. A second important difference is that CBR also is an approach to incremental, sustained learning, since a new experience is retained each time a problem has been solved, making it immediately available for future problems
The pathway may be, a simple one or a difficult one. Vervaeke explains this phenomenon by formulating, (Vervaeke, Lillicrap & Richards, 2009). ‘F’ is as the number of operators which can be performed at any given time; ‘D’ is the number of steps performed to reach a goal state. Blackmore (2002) and Vervaeke (2012) argued against this assumption. Besides this, RR is grounded in the cognitive function of consciousness just as the GWT.
This is the major problem of opinion mining but the results are more accurate as the data is more authentic. There are three classification techniques used for solving this purpose i.e. Naïve Bayes classification, Support Vector Machine and Maximum Entropy. In Naive Bayes, models that assign class label to problem instances, represented as vectors of feature values, where the class labels are drawn from some finite set. Whereas Support Vector Machine(SVM) is a machine learning tool that is based on the idea of large margin data classiﬁcation and Maximum Entropy is rooted in information theory, the mem seeks to extract as much information from a measurement as is justified by the data's signal-to-noise