A wafer map contains a graphical representation of the locations about defect pattern on the semiconductor wafer, which can provide useful information for quality engineers. Various defect patterns occur due to increasing wafer sizes and decreasing features sizes, which makes it very complex and unreliable process to identify them. In this paper, we propose a voting ensemble classifier with multi-types features to identify wafer map defect patterns in semiconductor manufacturing. Our research contents can be summarized as follows. First, three distinctive features such as density-, geometry-, and radon-based features were extracted from raw wafer images. Then, we applied four machine learning classifiers namely logistic regression (LR), random forests (RF), gradient boosting machine (GBM), and artificial neural network (ANN), and trained them using extracted features of original data set. Then their results were combined with a soft voting ensemble (SVE) technique which assigns higher weights to the classifiers with respect to their prediction accuracy. Consequently, we got performance measures with accuracy, precision, recall, F-measure, and AUC score of 95.8616%, 96.9326%, 96.9326%, 96.7124%, and 99.9114%, respectively. These results show that the SVE classifier with proposed multi-types features outperformed regular machine learning-based classifiers for wafer maps defect detection.

Wafer maps contain information about various defect patterns on the wafer surface and automatic classification of these defects plays a vital role to find their root causes. Semiconductor engineers apply various methods for wafer defect classification such as manual visual inspection or machine learning-based algorithms by manually extracting useful features. However, these methods are unreliable, and their classification performance is also poor. Therefore, this paper proposes a deep learning-based convolutional neural network for automatic wafer defect identification (CNN-WDI). We applied a data augmentation technique to overcome the class-imbalance issue. The proposed model uses convolution layers to extract valuable features instead of manual feature extraction. Moreover, state-of-the-art regularization methods such as batch normalization and spatial dropout are used to improve the classification performance of the CNN-WDI model. The experimental results comparison using a real wafer dataset shows that our model outperformed all previously proposed machine learning-based wafer defect classification models. The average classification accuracy of the CNN-WDI model with nine different wafer map defects is 96.2%, which is an increment of 6.4% from the last highest average accuracy using the same dataset.

The Internet of Things (IoT) is the global network of interrelated physical devices such as sensors, actuators, smart applications, objects, computing devices, mechanical machines, and people that are becoming an essential part of the internet. In an industrial environment, these devices are the source of data which provide abundant information in manufacturing processes. Nevertheless, the massive, heterogeneous, and time-sensitive nature of the data brings substantial challenges to the real-time collection, processing, and decision making. Therefore, this paper presents a framework of an IoT-based Industrial Data Management System (IDMS) which can manage the huge industrial data, support online monitoring, and control smart manufacturing. The framework contains five basic layers such as physical, network, middleware, database, and application layers to provide a service-oriented architecture for the end users. Experimental results from a smart factory case study demonstrate that the framework can manage the regular data and urgent events generated from various factory devices in the distributed industrial environment through state-of-the-art communication protocols. The collected data is converted into useful information which improves productivity and the prognosis of production lines.

Preventing exacerbation and seeking to determine the severity of the disease during the hospitalization of chronic obstructive pulmonary disease (COPD) patients is a crucial global initiative for chronic obstructive lung disease (GOLD); this option is available only for stable-phase patients. Recently, the assessment and prediction techniques that are used have been determined to be inadequate for acute exacerbation of chronic obstructive pulmonary disease patients. To magnify the monitoring and treatment of acute exacerbation COPD patients, we need to rely on the AI system, because traditional methods take a long time for the prognosis of the disease. Machine-learning techniques have shown the capacity to be effectively used in crucial healthcare applications. In this paper, we propose a voting ensemble classifier with 24 features to identify the severity of chronic obstructive pulmonary disease patients. In our study, we applied five machine-learning classifiers, namely random forests (RF), support vector machine (SVM), gradient boosting machine (GBM), XGboost (XGB), and K-nearest neighbor (KNN). These classifiers were trained with a set of 24 features. After that, we combined their results with a soft voting ensemble (SVE) method. Consequently, we found performance measures with an accuracy of 91.0849%, a precision of 90.7725%, a recall of 91.3607%, an F-measure of 91.0656%, and an AUC score of 96.8656%, respectively. Our result shows that the SVE classifier with the proposed twenty-four features outperformed regular machine-learning-based methods for chronic obstructive pulmonary disease (COPD) patients. The SVE classifier helps respiratory physicians to estimate the severity of COPD patients in the early stage, consequently guiding the cure strategy and helps the prognosis of COPD patients.

Heart Failure (HF) occurrence is increasing day by day and is the leading death cause disease in our society. HF is among the most expensive diseases as well. Social and individual burden of this disease can be reduced by early detection of HF. This would provide the means that may helpful to slow progression of the disease as well as to recover patient to good health. In this research study, we have applied data mining techniques to get useful information from medical reports of patients and using machine learning classification algorithm, we propose a risk model to predict 1-year or more survival for HF diagnosed patients. To perform multi-class classification we use multi-nominal Naïve Bayes (NB) classification algorithm. We got our required data from the Armed Forces Institute of Cardiology (AFIC), Pakistan, in the form of medical reports of patients which are available in the structured and unstructured format. Unfortunately, a lot of information is buried in unstructured data format. Our proposed model achieved an accuracy and Area under the Curve (AUC) of 86.7% and 92.4%, respectively.