Dynamically-adaptive Weight in Batch Back Propagation Algorithm via Dynamic Training Rate for Speedup and Accuracy Training, Journal of Telecommunications and Information Technology, 2017, nr 4

The main problem of batch back propagation (BBP) algorithm is slow training and there are several parameters need to be adjusted manually, such as learning rate. In addition, the BBP algorithm suffers from saturation training. The objective of this study is to improve the speed up training of the BBP algorithm and to remove the saturation training. The training rate is the most significant parameter for increasing the efficiency of the BBP. In this study, a new dynamic training rate is created to speed the training of the BBP algorithm. The dynamic batch back propagation (DBBPLR) algorithm is presented, which trains with adynamic training rate. This technique was implemented with a sigmoid function. Several data sets were used as benchmarks for testing the effects of the created dynamic training rate that we created. All the experiments were performed on Matlab. From the experimental results, the DBBPLR algorithm provides superior performance in terms of training, faster training with higher accuracy compared to the BBP algorithm and existing works

Implementation of Sub-Grid-Federation Model for Performance Improvement in Federated Data Grid

In this work, a new model for federation data grid system called Sub-Grid-Federation was designed to improve access latency by accessing data from the nearest possible sites. The strategy in optimising data access was based on the process of searching into the area identified as ‘Network Core Area’ (NCA). The performance of access latency in Sub-Grid-Federation was tested based on the mathematical proving and simulated using OptorSim simulator. Four case studies were carried out and tested in Optimal Downloading Replication Strategy (ODRS) and the Sub-Grid-Federation. The results show that Sub-Grid-Federation is 20% better in terms of access latency and 21% better in terms of reducing remotes sites access compared to ODRS. The results indicate that the Sub-Grid-Federation is a better alternative for the implementation of collaboration and data sharing in data grid system.                                                                                    Keywords: Data grid, replication, scheduling, access latenc

Facial Landmark Detection and Estimation under Various Expressions and Occlusions

Landmark localization is one of the fundamental approaches to facial expressions recognition, occlusions detection and face alignments. It plays a vital role in many applications in image processing and computer vision. The acquisition conditions such as expression, occlusion and background complexity affect the landmark localization performance, which subsequently lead to system failure. In this paper, the writers bestowed the challenges of various landmark detection techniques, number of landmark points and dataset types been employed from the existing literatures. However, advance technique for facial landmark detection under various expressions and occlusions was presented. This was carried out using Point Distribution Model (PDM) to estimate the occluded part of the facial regions and detect the face. The proposed method was evaluated using University Milano Bicocca Database (UMB). This approach gave more promising result when compared to several previous works. In conclusion, the technique detected images despite varieties of occlusions and expressions. It can further be applied on images with different poses and illumination variations

Heuristic Evaluation Of i-Dyslex Tool for Dyslexia Screening

Early detection for dyslexia is crucial in order for children to receive early as well as proper treatment. There are various studies that have focused on early detection of dyslexia, however the results remain limited. Therefore, an easy and user-friendly dyslexia screening tool called i-Dyslex was developed. In order to make sure the tool is free from design and interface problems, heuristic evaluation has been carried out. This paper discusses the heuristic evaluation of i-Dyslex tool for dyslexia screening among expert evaluators. This study adopted ten Usability Heuristics to be included in the questionnaire. Overall result derived from the evaluation is above average mean score, which are neutral (3.00) in one domain. Several comments and feedback from the experts. Both the experts’ evaluation and the feedback were essentials for further improvement of the i-Dyslex tool to ensure meets the user requirement and expectation

Improved statistical recognition algorithms for oil palm ripeness identification

Awareness for high quality crude oil is crucial in oil palm production. Proper grading process is important to ensure only the ripe fruits are taken into consideration for the maximum level of oil content. Currently, researchers focus mainly on providing an automatic grading system using various techniques such as producing digital numbers, oil palm colorimeter, photogrammetric grading, fuzzy or neuro-fuzzy technique and so on. Even though some of them have more than 85% accuracy, it is only valid in controlled environment. However, when they are applied in real situation with uncontrolled environment, the accuracy can drop to less than 50%. So far, there is limited study on suitable colour model conducted on oil palm ripeness identification. Most researchers use RGB colour model to determine an oil palm ripeness. This research looks into the suitability and performance of HSV colour model in classifying an oil palm ripeness. Distance Measurement and Linear Discriminant Analysis are chosen as methods to classify an oil palm ripeness in this study. Histogram is used as a feature vector for feature extraction method while colour as a feature to be analysed. Images of oil palm were captured by an expert in the form of JPEG images. Preprocessing is then performed to remove noise and background from the images. Subsequently, images are transformed into histogram and mean value are extracted. Selected Distance Measurement such as Euclidean Distance, Nearest Neighbour, Furthest Neighbour and Mean Distance are then used for feature matching process. An Oil Palm Ripeness Identification algorithm is proposed, wherein an elimination technique is also introduced in the process. In addition, a Multiple Features Technique is also proposed to find the best feature which brings a very good recognition rate for selected Distance Measurement. The results show that 98% accuracy have been obtained in comparison with other researchers’ work

Offline signature verification using long short-term memory and histogram orientation gradient

The signing process is a critical step that organizations take to ensure the confidentiality of their data and to safeguard it against unauthorized penetration or access. Within the last decade, offline handwritten signature research has grown in popularity as a common method for human authentication via biometric features. It is not an easy task, despite the importance of this method; the struggle in such a system stem from the inability of any individual to sign the same signature each and every time. Additionally, we are indeed interested in the dataset’s features that could affect the model's performance; thus, from extracted features from the signature images using the histogram orientation gradient (HOG) technique. In this paper, we suggested a long short-term memory (LSTM) neural network model for signature verification, with input data from the USTig and CEDAR datasets. Our model’s predictive ability is quite outstanding: The classification accuracy efficiency LSTM for USTig was 92.4% with a run-time of 1.67 seconds and 87.7% for CEDAR with a run-time of 2.98 seconds. Our proposed method outperforms other offline signature verification approaches such as K-nearest neighbour (KNN), support vector machine (SVM), convolution neural network (CNN), speeded-up robust features (SURF), and Harris in terms of accuracy

AUTO-ADAPTIVE THE WEIGHT IN BATCH BACK PROPAGATION ALGORITHM VIA DYNAMIC LEARNING RATE

Abstract Batch back propagation (BBP) algorithm is commonly used in many applications, including robotics, automation, and global positioning systems. The man drawbacks of batch back propagation (BBP) algorithm is slow training, and there are several parameters needs to be adjusted manually, also suffers from saturation training. The objective of this study is to improve the speed uptraining of the BBP algorithm and to remove the saturation training. To overcome these problems, we have created a new dynamic learning rate to escape the local minimum, which enables a faster training time. We presented dynamic batch backpropagation algorithm (DBBPLR) which training with dynamic learning rate. This technique was implemented using a sigmoid function. The XOR problem, the Balance dataset, and the Iris dataset were used as benchmarks with different structures to test the efficiency of the dynamic learning rate. The real datasets were divided into a training set and a testing set, and 75 experiments were carried out using Matlab software2016a. From the experimental results, it can be shown that the DBBPLR algorithm provides superior performance over the existing BBP algorithm in terms of training, the speed of training, time training, number of epochs and accuracy training and also with existing work

Patient Data Hiding and Integrity Control Using Prediction-Based Watermarking for Brain MRI and CT Scan Images

The increase of using e-Health services enables remote access, communication, and analysis of medical images and data to facilitate medical diagnostics. This has introduced potential security threats to the medical images and data. Therefore, the security of private patient information and medical images becomes an important issue that should be considered. In such a framework, reversible watermarking has been presented to enhance medical image security. Reversible watermarking is an image watermarking category, that has the capability of completely restoring the original image after the hidden data is extracted. In this paper, a reversible watermarking scheme based on adaptive prediction error is introduced, in order to ensure the confidentiality of the patient's information, verify the authenticity of the host image, and localize and recover tampered areas if the image has been modified. This scheme segments the medical image into two segments namely: Region of Interest (ROI) and Region of Non-Interest (RONI). The Patient data and the hash value of the ROI are hidden in ROI. The hash value of the RONI, tamper detection and tamper recovery data are hidden in RONI. To reduce the distortion of ROI, the most diagnostically significant region, each embeddable pixel of ROI is able to conceal one bit and each embeddable pixel of RONI could conceal two bits. From the experimental results conducted on brain MRI and CT scan images, the proposed scheme is capable to accurately locate tempered regions and recover them. Moreover, the reversibility and the high hiding capacity with very good perceptibility are proven