The Dissolve Gas Analysis (DGA) to determine the ageing and degradation of the transformer is standard and routine periodic maintenance. In general, there are two DGA analysis methods which are conventional (lab-based) and online monitoring. DGA monitoring will be able to access to detect incipient fault and transformer failure. Several techniques are available to analyse, interpret and diagnose the DGA result, such as IEEE standard, IEC 60599 standard, Key Gas Method, and Duval methods. There are several Machine Learning (ML) techniques has been explored such as Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Neural Neighbours (KNN), Random Neural Network (RNN), and Fuzzy Logic for determining the transformer condition, including fault diagnostic and fault detection. However, there are unexplored studies to combine the commercial device to determine the Health Index (HI) of Transformer. In this study, an ML method with the available input feature from the commercial device to the network is trained to determine the HI. In general, the benchmark dataset from the existing work is employed to validate the proposed investigation. There are 730 datasets comprising five different classes; 1) Very Good, 2) Good, 3) Fair, 4) Poor, 5) Very Poor in determining the HI of a transformer. Conventional rule to partition the train and testing dataset with a 70:30 ratio is employed in this study. The maximum accuracy results and method for 1) M1 is 66.67% for ANN, 2) M2 is 68.49% for ANN, 3) M3 is 76.71% for KNN, 4) M5 is 76.26% for ANN, 5) M6 is 79.00% for ANN and 6) M7 is 86.30% for ANN. In conclusion, the multi-gas device will have a good accuracy performance and provide a good HI indicator to classify the condition of the transformer, which can be used for preventive maintenance.

Alzheimer’s disease (AD) is a progressive and irreversible neurodegenerative disease that requires attentive medical evaluation. Therefore, diagnosing of AD accurately is crucial to provide the patients with appropriate treatment to slow down the progression of AD as well to facilitate the treatment interventions. To date, deep learning by means of convolutional neural networks (CNNs) has been widely used in diagnosing of AD. There are several well-established CNNs architectures that have been used in the image classification domain for magnetic resonance imaging (MRI) images analysis such as LeNet-5, Inception-V4, VGG-16 and Residual Network. However, these existing deep learning-based methods have lack of ability to be spatial invariance to the input data, due to overlooking some salient local features of the region of interest (ROI) (i.e., hippocampal). In medical image analysis, local features of MRI images are hard to exploit due to the small pixel size of ROI. On the other hand, CNNs requires large dataset sample to perform well, but we have limited number of MRI images to train, thus, leading to overfitting. Therefore, we propose a novel deep learning-based model without pre-processing techniques by incorporating attention mechanism and global average pooling (GAP) layer to VGG-16 architecture to capture the salient features of the MRI image for subtle discriminating of AD and normal control (NC). Also, we utilize transfer learning to surpass the overfitting issue. Experiment is performed on data collected from Open Access Series of Imaging Studies (OASIS) database. The accuracy performance of binary classification (AD vs NC) using proposed method significantly outperforms the existing methods, 12-layered CNNs (trained from scratch) and Inception-V4 (transfer learning) by increasing 1.93% and 3.43% of the accuracy. In conclusion, Attention-GAP model capable of improving and achieving notable classification accuracy in diagnosing AD.

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