Klasifikasi Penentuan Pengajuan Kartu Kredit Menggunakan K-Nearest Neighbor

A credit card is a device payment issued by the bank certain made of plastic and useful as a tool payment on credit carried out by the owner of the card or in accordance with the name of listed in a credit card is on when making purchases goods or services. The problems facing in giving a credit cards to customers bank that have signed up is difficult to determine the category of a credit cards in accordance with the customer bank. By doing this research is expected to facilitate the bank or the analysis to determine the category of a credit card to customers bank right. The research used is by applying methods K-Nearest Neighbor to classify prospective customers in the making a credit card in accordance with the category of  customers by using data customers at the Bank BNI Syariah Surabaya. A method K-Nearest Neighbor used to seek patterns on the data customers so established variable as factors supporters in the form of gender, the status of the house, the status, the number of dependants (children), a profession and revenue annually. The results of this research shows that an average of the value of precision of 92%, the value of recall of 83%, and the value of accuracy of 93%. Thus, this application is effective to help analyst credit cards in classifying customers to get credit cards that appropriate criteria

Determination of the wear condition of the cutting tool through vibration monitoring and artificial intelligence

One of the most important consequence in machining process is the tool wear. Thus, monitoring the wear of cutting tools becomes essential to ensure product success, increase productivity and avoid catastrophic damages to the equipment. Since wear is related to the vibrations of the process the vibration signal can be used to monitor it. This work presents a new approach for identification of wear condition of a tool during turning operation of VC 131 steel (AISI D6) using well known techniques with low computational cost which can provide a future industrial application to identify the ideal moment of tool change. To achieve this purpose, the vibration signals were measured during each turning step. Then, an artificial classification intelligence method (W-kNN) with features extracted from the vibration signals was used to identify the wear stage. Tests were performed with tools under different wear conditions, which were measured before and after each turning step. The results show that the combination of artificial classification intelligence method with vibratory features can successfully predict the lifespan of cutting tools which can certainly be used as an industrial tool wear-monitoring system.CNPq - Conselho Nacional de Desenvolvimento Científico e TecnológicoDissertação (Mestrado)Uma das consequências mais importantes no processo de usinagem é o desgaste da ferramenta. Assim, monitorar o desgaste das ferramentas de corte torna-se essencial para garantir o sucesso do produto, aumentar a produtividade e evitar danos catastróficos ao equipamento. Como o desgaste está relacionado às vibrações do processo, o sinal de vibração pode ser usado para monitorá-lo. Este trabalho apresenta uma nova abordagem para a identificação do estado de desgaste de uma ferramenta durante o torneamento do aço VC 131 AISI D6, utilizando técnicas conhecidas com baixo custo computacional, que podem fornecer uma futura aplicação industrial para identificação do momento ideal de troca da ferramenta. Para atingir este objetivo, os sinais de vibração foram medidos durante cada etapa de torneamento. Em seguida, utilizou-se um método de inteligência artificial de classificação (W-kNN) com parâmetros de entrada extraídas dos sinais de vibração para identificar o estágio de desgaste da ferramenta de corte. Os testes foram realizados com ferramentas sob diferentes condições de desgaste, medidas antes e depois de cada etapa de torneamento. Os resultados mostram que a combinação da técnica de inteligência artificial utilizada com os parâmetros vibratórios extraídos através de técnicas estatísticas clássicas, pode prever com sucesso a condição de desgaste das ferramentas de corte, e certamente pode ser usada como um sistema de monitoramento no meio industrial

An automated classification system to determine malignant grades of brain tumour (glioma) in magnetic resonance images based on meta-trainable multiple classifier schemes

The accurate classification of malignant grades of brain tumours is crucial for therapeutic planning as it impacts on the tumour’s prognosis, where the higher the malignancy levels of the brain tumour are, the higher the mortality rate is. It is also essential to provide patients with appropriate clinical management that may prolong survival and improve their quality of life. Determining the malignant grade of a brain tumour is a critical challenge because different malignant grades of brain tumours, in some cases, have inconsistent and mixed morphological characteristics. Consequently, the visual diagnosis using only the naked eye is a very complex and challenging task. The most common type of brain tumour is glioma. According to the World Health Organisation, low-grade glioma, which includes grade I and grade II are the least malignant, slow growing, and respond well to treatment. While, high-grade gliomas, which include grade III and grade IV are extremely malignant, have a poor prognosis and may lead to a high mortality rate. Hence, the motivation to develop an automated classification system to predict the malignant grade of glioma is the aim of this research. To achieve this aim, several novel methods were developed and this includes new methods for the extraction of statistical measures, selection of the dominant predictors, and the fusion of multi-classification models. The integration of these stages generates an accurate and automated decision system to determine the malignant grade of glioma. The feature extraction starts from the viewpoint that the objective measure of the brain tumour descriptors in MR images lead to an accurate classification of malignant brain tumours. This work starts from the standpoint that meta-trainable fusion of multiple classifier models can offer a better classification accuracy to recognise the malignant grade of glioma in MR images. This study developed a novel strategy based on two stages of multiple classifier systems for glioma grades. In the first stage, different machine learning algorithms were used. In the second stage, a systematic trainable combiner was designed based on deep neural networks. This research was validated using four benchmark datasets of MR images, which are publicly available and confirmed with the histopathological diagnosis. The proposed system was also evaluated and compared against different traditional algorithms; the experimental results showed that the proposed system has successfully achieved better and optimal discrimination in glioma grades on all dataset