Showing papers in "International Journal of Machine Learning and Computing in 2020"

Digital Watermarking Using Deep Neural Network

Abstract: Recently in the vast advancement of Artificial Intelligence, Machine learning and Deep Neural Network (DNN) driven us to the robust applications. Such as Image processing, speech recognition, and natural language processing, DNN Algorithms has succeeded in many drawbacks; especially the trained DNN models have made easy to the researchers to produce state-of-art results. However, sharing these trained models are always a challenging task, i.e. security, and protection. We performed extensive experiments to present some analysis of watermark in DNN. We proposed a DNN model for Digital watermarking which investigate the intellectual property of Deep Neural Network, Embedding watermarks, and owner verification. This model can generate the watermarks to deal with possible attacks (fine-tuning and train to embed). This approach is tested on the standard dataset. Hence this model is robust to above counter-watermark attacks. Our model accurately and instantly verifies the ownership of all the remotely expanded deep learning models without affecting the model accuracy for standard information data.

Keystroke dynamics based user authentication using deep multilayer perceptron

Abstract: User authentication is an essential factor to protect digital service and prevent malicious users from gaining access to the system. As Single Factor Authentication (SFA) is less secure, organizations started to utilize Multi-Factor Authentication (MFA) to provide reliable protection by using two or more identification measures. Keystroke dynamics is a behavioral biometric, which analyses users typing rhythm to identify the legitimacy of the subject accessing the system. Keystroke dynamics that have a low implementation cost and does not require additional hardware in the authentication process since the collection of typing data is relatively simple as it does not require extra effort from the user. This study aims to propose deep learning model using Multilayer Perceptron (MLP) in keystroke dynamics for user authentication on CMU benchmark dataset. The user typing rhythm from 51 subjects collected based on the static password (.tie5Roanl) typed 400 times over 8 sessions and 50 repetitions per session. The MLP achieved optimum EER of 4.45% compared to original benchmark classifiers such as 9.6% (scaled Manhattan), 9.96% (Mahalanobis Nearest Neighbor), 10.22% (Outlier Count), 10.25% and 16.14% (Neural Network Auto-Assoc). © 2020 by the authors.

Enhanced Numeral Recognition for Handwritten Multi-language Numerals Using Fuzzy Set-Based Decision Mechanism

Abstract: Handwritten character and numeral recognition have gained interest in the research community as part of the big picture of Machine Learning. Writer independent recognition systems are still in the working and the research is geared towards an optimized technique that can achieve this. In this paper, we propose a numeral recognition system that forms fuzzy sets of the features extracted using modified structural features for English, Arabic, Persian, and Devanagari Numerals. The structural features extract the geometrical primitives that distinguish each image. After the feature extraction phase, the results are input into a classifier, we test two different classifiers namely Neural Network and Naïve Base. To further enhance the recognition process with low overhead the erroneously recognized numerals (confusion matrix) are processed through the fuzzy set-based decision mechanism to enhance the numeral recognition process. Results indicate that recognition is enhanced by applying the fuzzy set-based decision mechanism for both classifer.

Improved Fast R-CNN with Fusion of Optical and 3D Data for Robust Palm Tree Detection in High Resolution UAV Images

Abstract: Palm density records are crucial for fertilizer, yield and biomass estimation. Traditionally, workers have to count the number of standing palms on the ground, which is physically arduous and costly. Remote sensing imageries such as unmanned aerial vehicle (UAV) data provide an efficient way to partially or completely eliminate the needs of physical counting. This paper proposes to fuse 3D digital surface model (DSM) and Red, Green and Blue (RGB) image to detect region of interest (ROIs) and subsequently classify palm trees based on Fast Region-based Convolutional Neural Network (Fast R-CNN) architecture. The proposed method reduced computation time by passing the ROI extracted from DSM using local maximum filtering (LM) to the convolutional feature map of the RGB image for bounding box regression and classification. Results showed that the proposed method detected palm trees in high resolution UAV images 5 times faster and 2.5 to 4.5% more accurate than the state-of-the-art Faster R-CNN. It successfully achieved 99.8%, 100% and 91.4% average accuracy in young, mature and mixed vegetation areas, respectively. Results also showed that unlike Faster R-CNN and YOLO V2, the accuracy of the proposed method was not affected by the input image size.

A General Chinese Chatbot Based on Deep Learning and Its’ Application for Children with ASD

Abstract: Commercial chatbots such as Apple’s Siri, Microsoft’s XiaoIce, Amazon’s Alexa, Jingdong’s JIMI, and Alibaba’s Alime, have some great prospective in applications such as hosting programs, writing poetry, providing pre-sale consulting and after-sales service in E-commerce, and providing virtual shopping guidance. However, in most cases, existed chatbots in the world are neither designed specifically for children, nor suitable for children, especially for children with ASD (autism spectrum disorder). In order to develop chatbots that are suitable for children with ASD, the present study firstly adopted an open source chatting corpus containing more than 1.7 million question-and-answer Chinese sentences of chatting histories involving children in many cases, and screened out more than 400,000 ideal chatting sentences for model training. Then a generative-based method combing Bi-LSTM and attention mechanism with word embedding based on deep neural network was adopted to build a general Chinese chatbot. The quality evaluation results indicated that our chatbot can successfully intrigue participants’ interest and made them understand it well. The chatbot also showed its’ great potential for using in the conversation-mediated intervention for Chinese children with ASD.

A Web-Based Dataset for Garbage Classification Based on Shanghai’s Rule

Abstract: In 2019, Shanghai has published a new regulation of trash management, which obtains a series of achievements in managing trash. In order to implement this regulation further and help citizens understand clearer about trash classification, we decided to develop a deep learning dataset and models to classify waste automatically. The object of this study is to take an image as input and identify the category of trash. We write a web crawler to capture images. After pre-processing, we gain about 14,000 images in total. We compare models including CNN, a ResNet50 model, and a VGG16 model on the dataset. Our experiments show that the ResNet50 model perform better than the others.

An Efficient Robust Blind Watermarking Method Based on Convolution Neural Networks in Wavelet Transform Domain

Abstract: Digital watermarking is one of the most widely used techniques for the protection of ownership rights of digital audio, images, and videos. One of the desirable properties of a digital watermarking scheme is its robustness against attacks aiming at removing or destroying the watermark from the host data. Different from the common watermarking techniques based on the spatial domain or transform domain, in this paper, a novel scheme of digital image blind watermarking based on the combination of the discrete wavelet transform (DWT) and the convolutional neural network (CNN) is proposed. Firstly, the host images are decomposed by the DWT with 4 levels and, then, the low frequency sub-bands of the first level and the high frequency sub-bands of the fourth level are used as the input data and the output target data to train the CNN model for embedding and extracting the watermark. Experimental results show that the proposed scheme has superior performance against common attacks of JPEG compression, mean and median filtering, salt and pepper noise, Gaussian noise, speckle noise, brightness modification, scaling, cropping, rotation, and shearing operations.

AB-SMOTE: An Affinitive Borderline SMOTE Approach for Imbalanced Data Binary Classification

Abstract: SMOTE is an oversampling approach previously proposed to solve the imbalanced data binary classification problem. SMOTE managed to improve the classification accuracy, however it needs to generate large number of synthetic instances, which is not efficient in terms of memory and time. To overcome such drawbacks, the Borderline-SMOTE (BSMOTE) is previously proposed to minimize the number of generated synthetic instances by generating such instances based on the borderline between the majority and minority classes. Unfortunately, BSMOTE could not provide big savings regarding the number of generated instances, trading to the classification accuracy. To improve BSMOTE accuracy, this paper proposes an Affinitive Borderline SMOTE (AB-SMOTE) that leverages the BSMOTE, and improves the quality of the generated synthetic data by taking into consideration the affinity of the borderline instances. Experiments’ results show the AB-SOMTE, when compared with BSMOTE, managed to produce the most accurate results in the majority of the test cases adopted in our study.

Particle Filter for Hector SLAM to Improve the Performance of Robot Positioning by Image Processing Based

Abstract: Abstract—Today, robot navigation uses navigation from maps that have been collected from laser sensors. The quality and usability of the received environmental grid map. This has an evident impact on robot navigation. The more accurate the map is a direct effect on the autonomous navigation. The morphological operation was used to an improved map by using opening and closing operations to reduce noise from the original map. This paper presents an image processing technique. In order to, robots be able to reduce the time required for the navigation to the destination due to inaccuracy maps.

A Comparative Study of Learning Techniques with Convolutional Neural Network Based on HPC-Workload Dataset

Abstract: High-Performance Computing or HPC is a computer system that has high computing power. The HPC supports various computational domains. A huge amount of jobs from a large group of users prefer to complete their jobs in this kind of system. Therefore, managing the jobs or job scheduling is very important since it involves the overall system efficiency. The analysis of an HPC-workload log file is a solution to improve system efficiency. Because some information may appear in the log file, this information can help the system scheduler to make an appropriate decision for job scheduling in the HPC system. This research proposed predictive models for predicting the job status at the finishing state in the HPC system. The model can be used as a tool for monitoring the jobs in the HPC system. We develop and build the three models including HPC-CNN, HPC-AlexNet, and HPC-VGG16 based on the two different learning techniques, which comprise Initial and Transfer Learning of Convolutional Neural Network based on the HPC-work load dataset. Moreover, the three state-of-the-art Machine Learning methods: Classification and Regression Tree (CART), Artificial Neural Network (ANN), and Support Vector Machine (SVM) are used as the baseline models for performance comparison. The results show that the model that performs the best predictive performance is the proposed HPC-CNN model. It archives 76.48% accuracy of the prediction followed with the CART model (75.60%), while the SVM model performs lowest the accuracy at 66.80%.

Identification of Skin Disease Using K-Means Clustering, Discrete Wavelet Transform, Color Moments and Support Vector Machine

Abstract: Skin disease is one of disease that is often found in tropical countries, such as Indonesia. People who suffered from skin disease in Indonesia were still relatively high, the prevalence could range between 20% 80%. Therefore, the help of computer technology was expected to detect the disease earlier that attacked the skin in the human’s body and it could reduce the possibility of the occurrence for other dangerous diseases. This study proposed the making of an application of identification image for skin disease by using one of the machines learning method, called Support Vector Machine (SVM) which was done by processing the image and machine learning processes that could perform early detection of skin diseases. This study aimed to determine the classification of skin diseases in humans into four classes, such as the class Benign Keratosis, Melanoma, Nevus, and Vascular. The segmentation method used was K-Means Clustering, while the feature extraction method that used was feature extraction of the Discrete Wavelet Transform (DWT) and Color Moments. Based on the results of the test that had conducted, the sensitivity was 95%, the specificity was 97.9% and the accuracy was 97.1% by using SVM parameters, that was kernel Radial Basis Function (RBF), Box Constraint = 1.5, RBF_Sigma (σ) = 1, and iterations = 1000.

Deep Convolutional Neural Networks for Emotion Recognition of Vietnamese

Abstract: Human emotions play a very important role in communication. Emotional speech recognition research brings human–machine communication closer to human-to-human communication. This paper presents the evaluation using ANOVA and T-test for the Vietnamese emotional corpus and using deep convolutional neural networks to recognize four basic emotions of Vietnamese based on this corpus: neutrality, sadness, anger, and happiness. Five sets of characteristic parameters were used as inputs of the deep convolutional neural network in which the mel spectral images were taken and attention was paid to the fundamental frequency, F0, and its variants. Experiments were conducted for these five sets of parameters and for four cases, depending on dependent or independent content and dependent or independent speakers. On average, the maximum recognition accuracy achieved was 97.86% under speaker-dependent and content-dependent conditions. The results of the experiments also show that F0 and its variants contribute significantly to the increased accuracy of Vietnamese emotional recognition.

Gender Classification of Thai Facebook Usernames

Abstract: Abstract—This paper presents an application of machine learning to classify Facebook users’ gender based on their username alone. User profile information on social networks is important in many studies, but occasionally no information is publicly available online, such as age or gender. Most studies only use textual information from the web page. Instead, we opted to study gender classification by username, in which the gender is inferred from the users first name and alias name. We focused only on Thai names which may have certain patterns that reveal the owner’s gender. A combination of different models is proposed to classify gender based on Thai Facebook usernames. Each model was trained using a supervised learning approach. Furthermore, all the classification results were combined into a final model. Using this method, the model achieved 91.75% level of accuracy.

Implementation of HOG Feature Extraction with Tuned Parameters for Human Face Detection

Abstract: Extracting and tracking face in image sequences is a required first step in many applications such as face recognition facial expression classification and face tracking, it is a challenging problem in computer vision field because of many factors that effects on the image, some of these factors are luminosity, different face colors, background patterns, face orientation and variability in size, shape, and expression. The objective of this paper is to Experiment wide range of parameters for HOG face detector and setting up the most suitable kernel for Support Vector Machine (SVM) and then, comparing this method with some well-known methods for face detection and identifying the most reliable one. The aim of this study is not providing the best face detector method rather than a try to find out the performance of HOG feature for detecting a face, experimenting different kernels and eventually finding the tuned parameters for HOG descriptors for detecting a face, in this study based on experimental results as shown in Table IV. The HOG + SVM scores the highest value of precision, accuracy, and sensitivity. As 0.8824, 0.9986 and 0.75 respectively compared to Viola-Jones method which scores 0.6512, 0.9973 and 0.7 finally skin color method which scores 0.3968, 0.9947 and 0.625.

How Different Are Full and Short Papers in Word-Usage?

Abstract: Academic papers submitted to a conference are assessed by reviewers and judged if they deserve to be presented at the conference. The accepted papers are often classified into full papers, short papers, and other types, according mainly to the reviewers’ assessment. The major aim of the study presented in this paper is to find tips which are effective for a paper to be improved so that a paper supposed to be classified as a short paper becomes a full paper. In this study, we investigate a scenario for finding the differences between full and short papers on the usage of words/terms. Then, we extract words which are characteristic for either full or short papers through an experimental study. In order to find these words, we introduce a couple of indexes of a word. The results inspire that we can obtain practical tips in this approach by refining this method.

Towards Machine Learning Based Analysis of Quality of User Experience (QoUE)

Abstract: Industries use various platforms to receive feedback from users of their products. In this paper, there is an overview of the potentials of using natural language processing system (NLP) in classifying the quality of user experience. The user experience is captured using google form. To test the efficacy of the platform, sentiments of users were analysed using hotels.ng as the source of data. The natural processing of electronic word of mouth (e-WOM) can be applied to any feedback platforms to classify and predict customers' sentiments and provide a veritable opportunity for companies to capture the quality of users' experiences and improve service delivery. The feature or sentiments extraction was done using opinion mining and data cleaning tools on heterogeneous data sources to judge the decision-making process of users. Using charts and correlations, with an average performance level of the willingness to recommend and degree of review helpfulness, the platform showed that the Quality of User Experience (QoUE) of the customers are 7.31 and 7.03 respectively. Finally, an improved logistic regression classifier was developed to test, train and classify the user experiences. Comparing the improved logistic regression classifier with standard logistic regression classifier shows that the training accuracy of the proposed improved logistic regression gave 97.67% as against the standard logistic regression which had accuracy of 86.01%