Predictive Framework for Imbalance Dataset

The purpose of this research is to seek and propose a new predictive maintenance framework which can be used to generate a prediction model for deterioration of process materials. Real yield data which was obtained from Fuji Electric Malaysia has been used in this research. The existing data pre-processing and classification methodologies have been adapted in this research. Properties of the proposed framework include; developing an approach to correlate materials defects, developing an approach to represent data attributes features, analyzing various ratio and types of data re-sampling, analyzing the impact of data dimension reduction for various data size, and partitioning data size and algorithmic schemes against the prediction performance. Experimental results suggested that the class probability distribution function of a prediction model has to be closer to a training dataset; less skewed environment enable learning schemes to discover better function F in a bigger Fall space within a higher dimensional feature space, data sampling and partition size is appear to proportionally improve the precision and recall if class distribution ratios are balanced. A comparative study was also conducted and showed that the proposed approaches have performed better. This research was conducted based on limited number of datasets, test sets and variables. Thus, the obtained results are applicable only to the study domain with selected datasets. This research has introduced a new predictive maintenance framework which can be used in manufacturing industries to generate a prediction model based on the deterioration of process materials. Consequently, this may allow manufactures to conduct predictive maintenance not only for equipments but also process materials. The major contribution of this research is a step by step guideline which consists of methods/approaches in generating a prediction for process materials

Predictive maintenance framework for hard disk media production

Manufacturing yield control for hard disk media is getting tougher.This is proportional to the tremendous increases of its bit/inch2 storage capacity.With the significantly difficult lithography process will be involved and drastically increase in number of product total output volume that will be required in near future, conventional maintenance type is no longer feasible. This paper proposes a novel framework for the implementation of predictive maintenance in hard disk media production.A novel technique to visualize the temporal data into pattern that can be trained with machine learning algorithm is introduced. Predictive models were produced after dealing with imbalance datasets issue, ensemble datasets and data cleaning.Experimental results have indicated that the proposed framework is successful

Improving F-Score of the imbalance visualized pattern dataset for yield prediction robustness

In a non closed loop manufacturing process, a prediction model of the yield outcome can be achieved by visualizing the temporal historical data pattern generated from the inspection machine, discretize to visualized data patterns, and map them into machine learning algorithm.Our previous study shows that combination of under-sampling and over sampling techniques unabel wider range of data sets where SMOTE+VDM and random under-sampling produced robust classifier performance of handling better with different batches of prediction test data.In this paper, the integration of K* entropy base similarity distance function with SMOTE, CNN+Tomek Links and the introduction of SMOTE and SMaRT (Synthetic Majority Replacement Technique)combination, has improved the classifiers F-Score robustness

Handling imbalance visualized pattern dataset for yield prediction

The prediction of the yield outcome in a non close loop manufacturing process can be achieved by visualizing the historical data pattern generated from the inspection machine, transform the data pattern and map it into machine learning algorithm for training, in order to automatically generate a prediction model without the visual interpretation needs to be done by human. Anyhow, the nature of manufacturing process dataset for the bad yield outcome is highly skewed where the majority class of good yield extremely outnumbers the minority class of bad yield. Comparison between the undersampling, over- sampling and SMOTE + VDM sampling technique indicates that the combination of SMOTE + VDM and undersampled dataset produced a robust classifier performance capable of handling better with different batches of prediction test data sets. Furtherance, suitable distance function for SMOTE is needed to improve class recall and minimize overfitting whilst different approach on the majority class sampling is required to improve the class precision due to information loss by the undersampling

An Overview of Multi-Attribute Decision Making (MADM) Vertical Handover Using Systematic Mapping

The evolution of infotainment industries yet the advancement of cellular gadgets such as smartphones, tablets, and laptop had increased the request on cellular traffic demands. As a result, a Heterogeneous Wireless Network (HWN) has been introduced to fulfil users requests in having seamless mobility and better Quality of Services (QoS) for the users. A lot of research works have been done in order to provide a seamless connection to the users. Even though a lot of methods have been proposed, a Multi-Attribute Decision Making (MADM) has been seemed like a promising way due to its ability to evaluate many attributes simultaneously. Previously, many reviews based on MADM methods in a Heterogeneous Wireless Network provides a details review which required researchers time in order to determine the possible potential areas to be explored. Therefore, in this study, we present an overview of the MADM method in performing vertical handover via a systematic mapping method. This will enable future researchers to identify the trends and research opportunities within this area. This mapping study analysed 30 papers. Results from the study show eight main potential research issues can be explored by researchers, including normalisation, criteria weighting, ranking abnormality, network selection, and performance comparison between MADM algorithms, network selection for a group of calls, mobility patterns and handover triggering

Machine Learning for Clustering Regencies-Cities Based on Inflation and Poverty Rates in Indonesia

The COVID-19 pandemic has increased inflation and poverty rates in many cities, thus requiring considerable attention from the government as a policymaker. Therefore, this study aims to cluster regencies/cities that need mitigation priorities from the Indonesian government based on inflation and poverty rates in 2021. Four machine learning methods, namely k-Means (KM), Partitioning around medoids (PAM), Ward, and Divisive analysis (Diana) are utilized and compared to achieve that purpose. Clustering 90 regencies/cities in Indonesia produced five optimal clusters. Furthermore, the clustering results were validated using the Silhouette width (SW) and Dunn index (DI). The results showed that the k-means method produced the most compact cluster. Hence, this study's results can be utilized as a reference for the government in determining the steps and priorities of economic policy in Indonesia

Effect of rain induced scintillation on Ku-band satellite link in tropical region

Scintillation data was collected from MEASAT3 for one year. This paper presents the scintillation statistics for dry (non-rain) and wet (rainy) conditions. Scintillation statistics are represented by scintillation intensity and amplitude distributions. Wet scintillation or scintillation during rain is obviously higher than dry scintillation (clear-sky scintillation)