Analysis on vowel /E/ in Malay language recognition via Convolution Neural Network (CNN)

In recent years, the silent killer disease, defined as a non-communicable disease, has become a frequent topic discussed in many academic discussions. Although this disease is not transferable from one to another, starting from 1990, the increment trend was annually published by the world statistic data for this disease, e.g., heart attack and stroke. The more significant consequence of these two diseases is to disable one or more human capabilities. One of the stroke disease effects is becoming disabled from hearing. Speech disabilities are the focus of this proposed study in this paper. Since the person diagnosed as a stroke patient requires attending the recovery session or rehabilitation session, the rehabilitation center must prepare and provide a sound module and system to help the patient regain their capability. Rehabilitation is an alternative path to gradually giving routine practice to the patient to improve their capability back. For this purpose, the rehab center requires a quantity of time to provide the patient to attend the training session. The training, however, is conducted in two ways, physically and virtually. For the Malaysia stroke patient, the training for pronouncing the vowel in the Malay language is crucial in getting back the speaking capability. Since the Malay language has 6 types of vowels, which are/a/,/e/,/ê/,/i/,/u/, and/o/. Here, there is a limitation to smartly recognizing the difference between the two/e/vowels. Malay's/e/vowel is crucial as the similar spelling vocabulary conveys two different meanings. This study analyzed the differences in recognizing the two/e/vowels using Convolution Neural Network (CNN) with the help of the existing sound-image dataset

A Hybrid Compression Method for Medical Images Based on Region of Interest Using Artificial Neural Networks

The number of medical images being stored and communicated daily is rapidly increasing, according to the need for these images in medical diagnoses. Hence, the storage space and bandwidths required to store and communicate these images are exponentially increasing, which has brought attention toward compressing these images. In this study, a new compression method is proposed for medical images based on convolutional neural networks. The proposed neural network consists of two main stages: a segmentation stage and an autoencoder. The segmentation stage is used to recognize the Region of Interest (ROI) in the image and provide it to the autoencoder stage, so more emphasis on the information of the ROI is applied. The autoencoder part of the neural network contains a bottleneck layer that has one-eighth of the dimensions of the input image. The values in this layer are used to represent the image, while the following layers are used to decompress the images, after training the neural network. The proposed method is evaluated using the CLEF MED 2009 dataset, where the evaluation results show that the method has significantly better performance, compared to the existing state-of-the-art methods, by providing more visually similar images using less data

Application of Rhetorical Relations Between Sentences to Cluster-Based Text Summarization

Many of previous research have proven that the usage of rhetorical relations is capable to enhance many applications such as text summarization, question answering and natural language generation. This work proposes an approach that expands the benefit of rhetorical relations to address redundancy problem in text summarization. We first examined and redefined the type of rhetorical relations that is useful to retrieve sentences with identical content and performed the identification of those relations using SVMs. By exploiting the rhetorical relations exist between sentences, we generate clusters of similar sentences from document sets. Then, cluster-based text summarization is performed using Conditional Markov Random Walk Model to measure the saliency scores of candidates summary. We evaluated our method by measuring the cohesion and separation of the clusters and ROUGE score of generated summaries. The experimental result shows that our method performed well which shows promising potential of applying rhetorical relation in cluster-based text summarization

Potential of Nanocellulose Composite for Electromagnetic Shielding

Nowadays, most people rely on the electronic devices for work, communicating with friends and family, school and personal enjoyment. As a result, more new equipment or devices operates in higher frequency were rapidly developed to accommodate the consumers need. However, the demand of using wireless technology and higher frequency in new devices also brings the need to shield the unwanted electromagnetic signals from those devices for both proper operation and human health concerns. This paper highlights the potential of nanocellulose for electromagnetic shielding using the organic environmental nanocellulose composite materials. In addition, the theory of electromagnetic shielding and recent development of green and organic material in electromagnetic shielding application has also been reviewed in this paper. The use of the natural fibers which is nanocelllose instead of traditional reinforcement materials provides several advantages including the natural fibers are renewable, abundant and low cost. Furthermore, added with other advantages such as lightweight and high electromagnetic shielding ability, nanocellulose has a great potential as an alternative material for electromagnetic shielding application

A Hybrid SCA Inspired BBO for Feature Selection Problems

Recent trend of research is to hybridize two and more metaheuristics algorithms to obtain superior solution in the field of optimization problems. This paper proposes a newly developed wrapper-based feature selection method based on the hybridization of Biogeography Based Optimization (BBO) and Sine Cosine Algorithm (SCA) for handling feature selection problems. The position update mechanism of SCA algorithm is introduced into the BBO algorithm to enhance the diversity among the habitats. In BBO, the mutation operator is got rid of and instead of it, a position update mechanism of SCA algorithm is applied after the migration operator, to enhance the global search ability of Basic BBO. This mechanism tends to produce the highly fit solutions in the upcoming iterations, which results in the improved diversity of habitats. The performance of this Improved BBO (IBBO) algorithm is investigated using fourteen benchmark datasets. Experimental results of IBBO are compared with eight other search algorithms. The results show that IBBO is able to outperform the other algorithms in majority of the datasets. Furthermore, the strength of IBBO is proved through various numerical experiments like statistical analysis, convergence curves, ranking methods, and test functions. The results of the simulation have revealed that IBBO has produced very competitive and promising results, compared to the other search algorithms

A Comprehensive Performance Analysis of Transfer Learning Optimization in Visual Field Defect Classification

Numerous research have demonstrated that Convolutional Neural Network (CNN) models are capable of classifying visual field (VF) defects with great accuracy. In this study, we evaluated the performance of different pre-trained models (VGG-Net, MobileNet, ResNet, and DenseNet) in classifying VF defects and produced a comprehensive comparative analysis to compare the performance of different CNN models before and after hyperparameter tuning and fine-tuning. Using 32 batch sizes, 50 epochs, and ADAM as the optimizer to optimize weight, bias, and learning rate, VGG-16 obtained the highest accuracy of 97.63 percent, according to experimental findings. Subsequently, Bayesian optimization was utilized to execute automated hyperparameter tuning and automated fine-tuning layers of the pre-trained models to determine the optimal hyperparameter and fine-tuning layer for classifying many VF defect with the highest accuracy. We found that the combination of different hyperparameters and fine-tuning of the pre-trained models significantly impact the performance of deep learning models for this classification task. In addition, we also discovered that the automated selection of optimal hyperparameters and fine-tuning by Bayesian has significantly enhanced the performance of the pre-trained models. The results observed the best performance for the DenseNet-121 model with a validation accuracy of 98.46% and a test accuracy of 99.57% for the tested datasets

Potential of Nanocellulose Composite for Electromagnetic Shielding

Nowadays, most people rely on the electronic devices for work, communicating with friends and family, school and personal enjoyment. As a result, more new equipment or devices operates in higher frequency were rapidly developed to accommodate the consumers need. However, the demand of using wireless technology and higher frequency in new devices also brings the need to shield the unwanted electromagnetic signals from those devices for both proper operation and human health concerns. This paper highlights the potential of nanocellulose for electromagnetic shielding using the organic environmental nanocellulose composite materials. In addition, the theory of electromagnetic shielding and recent development of green and organic material in electromagnetic shielding application has also been reviewed in this paper. The use of the natural fibers which is nanocelllose instead of traditional reinforcement materials provides several advantages including the natural fibers are renewable, abundant and low cost. Furthermore, added with other advantages such as lightweight and high electromagnetic shielding ability, nanocellulose has a great potential as an alternative material for electromagnetic shielding application