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Market research shows that one of the most intolerable issues in the pack of cigarettes is the cigarette missing. This issue makes substantial adverse effects on a company which needs to be avoided completely. Existing research uses a weight detection method to identity packages with issues. However, the accuracy of weight detection methods is low due to instrument error and complex workshop environment...
An intelligent system uses machine learning algorithms to provide outputs to every input provided. The introduction of emotions in intelligent systems is required to create systems that are more similar to human beings and thus more reliable. In this paper, the idea of introducing the emotion ‘uncertainty’ in Intelligent Systems is proposed. A Semi-Automated Intelligent System is introduced in this...
Inside the sets of data, hidden knowledge can be acquired by using neural network. These knowledge are described within topology, using activation function and connection weight at hidden neurons and output neurons. Is hardly to be understanding since neural networks act as a black box. The black box problem can be solved by extracting knowledge (rule) from trained neural network. Thus, the aim of...
In this work, we present a new multiple channel feature called Deep Compact Channel Feature (DCCF), which generates a compact, discriminative feature representation by a pre-trained deep encoder-decoder. With the combination of DCCF and boosted decision trees, a new object detector is proposed which achieved outstanding performance on standard pedestrian dataset INRIA and Caltech. Furthermore, a large...
An open world turn based monster battle game was developed in Java using the popular LibGDX game framework applying multiple machine learning algorithms for its mechanics consisting of an ID3 decision tree, perceptron, naïve Bayes classifier and A∗ pathfinding in an attempt to imitate ‘machine intelligence’. A tiled map was used as the game area containing multiple AI agents with different personalities...
Inspired on decision trees and evolutionary algorithms, this paper proposes a learning algorithm of constructive neural networks that relies on three principles: to layout the neurons in a tree-like structure; to train each neuron individually; and, to optimize all the weights using an evolutionary approach. This way, it is expected to advance in two main questions concerning multilayer perceptrons...
Today in data mining research we are daily confronted with large amount of data. Most of the time, these data contain redundant and irrelevant data that it is important to extract before a learning task in order to get good accuracy. The fact that today's computers are more powerful does not solves the problems of this ever-growing data. It is therefore crucial to find techniques which allow handling...
With introduction of online transaction the fraudulent activities through World Wide Web have increased rapidly. It's not only affecting common people but also making them lose huge amount of money. Online transaction basically takes place between merchant and customer, and in this case neither customer nor the card needs to be present at the time of transaction so merchant does not know that whether...
Software Testing is a very important phase in the cycle of software development. It is the only phase which ensures the reliability on the software. Generally 40–50% of the software development cost is spent on this phase. Though many automatic testing tools are present, but still most research is required in this field to reduce cost and time allotted for this phase. Test Oracle is a process which...
This work proposes a new approach based on Machine Learning to predict astigmatism in patients with kera-toconus (KC) after ring implantation. KC is a non-inflamatory, progressive thinning disorder of the cornea, resulting in a protusion, myopia and irregular astigmatism. The intracorneal ring implantation surgery has become a suitable technique to deal with keratoconus without the need of a corneal...
This paper analyzes and compares different machine learning methods such as decision trees, SOMs, MLPs and rough sets for the classification of the operation condition of a power transformer. The purpose is to construct a classification model able to estimate the hot-spot temperature as a function of other external input variables. The classifier would then be used to detect anomalous operation conditions...
Prediction of Dengue presents great challenge as the clinical symptoms overlaps with other conventional fever. Dengue viral infection has been reported in more than 100 countries, with total of 2.5 billion people. Testing of prognosis, the stages of dengue such as DF, Dengue Hemorrhagic Fever (DHF) and Dengue Shock Syndrome (DSS) under conventional methods needs frequent blood test information. The...
An NNTree is a decision tree with each non-terminal node containing a neural network (NN). Our previous researches show that compared with neural networks, the NN-tree can classify given data in a hierarchical structure which has very small system scale can can be applied to many PORTABLE DEVICE applications. However, for text data, the high dimensionality is a serious problem for induction of NNTrees...
One of the most important issues in fuzzy decision tree learning is the fuzzification of input data. This paper proposes a self-adaptive data fuzzification algorithm based on the self-organizing map (SOM) technology, which can automatically determine the number and coordinates of centers in triangular membership functions. Then the membership degree of each sample to all fuzzy subsets can be calculated...
Neural networks are a powerful classification technique that are capable of discovering complex relationships between inputs and outputs. This powerful classification ability has meant that neural networks have been widely applied to medical domains. However, neural networks suffer from the so-called blackbox problem: they predict accurately but offer no explanation of how the decision has been derived...
In this paper we present data mining and its utilization for childhood obesity prediction. Data mining was widely used in many childhood obesity prediction systems. Predicting obesity at an early age is both useful and important because the number of obese patients is increasing while its main cause cannot yet be defined. The ability to predict childhood obesity will help early prevention. The purpose...
The paper deals with the problem of the detection of rare patterns in an unbalanced dataset related to an industrial problem concerning the identification of manufactured defective metal products on the basis of product and process parameters. Within this work several approaches have been attempted for the development of a classifier whose performance are able to meet the industrial requirements,...
Neural network tree (NNTree) is a hybrid model for machine learning. Compared with single model fully connected neural networks, NNTrees are more suitable for structural learning, and faster for decision making. To increase the realizability of the NNTrees, we have tried to induce more compact NNTrees through dimensionality reduction. So far, we have used principal component analysis (PCA) and linear...
Linguistic Decision Trees based on label semantics have been used as a classifier or predictor in many areas. A linguistic decision tree presents information propagation from input attributes to a goal variable based on transparent linguistic rules. The relationship between input attributes and the goal variable is often highly nonlinear. Cerebellar Model Articulation Controller (CMAC) belongs to...
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