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The aim of optimization methods is to identify the best results in the search area. In this research, we focused on a mixture of the interior point method, opposite gradient method, and mean-variance mapping optimization, named IPOG-MVMO, where the solutions can be obtained from the gradient field of the cost function on the constraint manifold. The process was divided into three main phases. In the first phase, the interior point method was applied for local searching. Secondly, the opposite gradient method was used to generate a population of candidate solutions. The last phase involved updating the population according to the mean and variance of the solutions. In the experiments on real parameter optimization problems, three types of functions, which were unimodal, multimodal, and continuous composition functions, were considered and used to compare our proposed method with other meta-heuristics techniques. The results showed that our proposed algorithms outperformed other algorithms in terms of finding the optimal solution. [ABSTRACT FROM AUTHOR]

Big streaming data environment concerns a complicated scenario where data to be processed continuously flow into a processing unit and certainly cause a memory overflow problem. This obstructs the adaptation of deploying all existing classic sorting algorithms because the data to be sorted must be entirely stored inside the fixed-size storage including the space in internal and external storage devices. Generally, it is always assumed that the size of each data chunk is not larger than the size of storage (M) but in fact the size of the entire stream (n) is usually much larger than M. In this paper, a new fast continuous streaming sorting is proposed to cope with the constraint of storage overflow. The algorithm was tested with various real data sets consisting of 10,000 to 17,000,000 numbers and different storage sizes ranging from 0.01n to 0.50n. It was found that the feasible lower bound of storage size is 0.35n with 100% sorting accuracy. The sorting time outperforms bubble sort, quick sort, insertion sort, and merge sort when data size is greater than 1,000,000 numbers. Remarkably, the sorting time of the proposed algorithm is 1,452 times less than the sorting time of external merge sort and 28.1767 times less than the sorting time of streaming data sort. The time complexity of proposed algorithm is O(n) while the space complexity is O(M). [ABSTRACT FROM AUTHOR]

Due to the fast speed of data generation and collection from advanced equipment, the amount of data obviously overflows the limit of available memory space and causes difficulties achieving high learning accuracy. Several methods based on discard-after-learn concept have been proposed. Some methods were designed to cope with a single incoming datum but some were designed for a chunk of incoming data. Although the results of these approaches are rather impressive, most of them are based on temporally adding more neurons to learn new incoming data without any neuron merging process which can obviously increase the computational time and space complexities. Only online versatile elliptic basis function (VEBF) introduced neuron merging to reduce the space-time complexity of learning only a single incoming datum. This paper proposed a method for further enhancing the capability of discard-after-learn concept for streaming data-chunk environment in terms of low computational time and neural space complexities. A set of recursive functions for computing the relevant parameters of a new neuron, based on statistical confidence interval, was introduced. The newly proposed method, named streaming chunk incremental learning (SCIL), increases the plasticity and the adaptabilty of the network structure according to the distribution of incoming data and their classes. When being compared to the others in incremental-like manner, based on 11 benchmarked data sets of 150 to 581,012 samples with attributes ranging from 4 to 1,558 formed as streaming data, the proposed SCIL gave better accuracy and time in most data sets. [ABSTRACT FROM AUTHOR]

Throughout recent times, cybersecurity problems have occurred in various business applications. Although previous researchers proposed to cope with the occurrence of cybersecurity issues, their methods repeatedly replicated the training processes for several times to classify datasets of these problems in streaming non-stationary environments. In dynamic environments, the conventional methods possibly deteriorate the adaptive solution to prevent these issues. This research proposes a one-pass-throw-away learning using the dynamical structure of the network to solve these problems in dynamic environments. Furthermore, to speed up the computational time and to maintain a minimum space complexity for streaming data, the new concepts of learning in forms of recursive functions were introduced. The information gain-based feature selection was also applied to reduce the learning time during the training process. The experimental results signified that the proposed algorithm outperformed the others in incremental-like and online ensemble learning algorithms in terms of classification accuracy, space complexity, and computational time. [ABSTRACT FROM AUTHOR]

Modern biometric identification methods combine interdisciplinary approaches to enhance person identification and classification accuracy. One popular technique for this purpose is Brain-Computer Interface (BCI). The signal so obtained from BCI will be further processed by the Autoregressive (AR) Model for feature extraction. Many researches in the area find that for more accurate results, the signal must be cleaned before extracting any useful feature information. This study proposes Independent Component Analysis (ICA), k-NN classifier, and AR as the combined techniques for electroencephalogram (EEG) biometrics to achieve the highest personal identification and classification accuracy. However, there is a classification gap between using the combined ICA with the AR model and AR model alone. Therefore, this study takes one step further by modifying the feature extraction of AR and comparing the outcome with the proposed approaches in lieu of prior researches. The experiment based on four relevant locations shows that the combined ICA and AR can achieve higher accuracy than the modified AR. More combinations of channels and subjects are required in future research to explore the significance of channel effects and to enhance the identification accuracy. [ABSTRACT FROM AUTHOR]

Objective: This paper proposed a new mathematical model of within-host population dynamics of bacteria after cell wall synthesis inhibitors administration for practically predicting treatment duration and drug dosage. The aim of this paper is to predict the duration of antibacterial treatment with cell wall synthesis inhibitors using mathematical models. Materials and Methods: Our model deployed various concepts from different fields of probability, biology, physics, chemistry, pharmacology (pharmacokinetics and pharmacodynamics), and medical sciences. The following assumptions and hypotheses were established: (i) Binding or collision rate between drug molecule and bacteria depends on the relative velocity between drug molecule and bacteria, (ii) ability or probability of binding or capturing between drug molecule and bacteria can be evaluated using four probability factors, based on the principal of physics and chemistry, (iii) the number of bacteria dying from antibiotics is equal to the number of drug molecules binding bacteria. Thus, the bacterial death rate is equal to rate of drug molecules binding and killing bacteria (amount of drug molecules per second), and (iv) plasma drug concentration is constant and time-independent. In this paper, Neisseria meningitidis and Pseudomonas aeruginosa are selected to demonstrate the numerical results. Conclusion: In this result, duration of treatment are 2-7 days, which is nearly the same as course of antibacterial therapy. [ABSTRACT FROM AUTHOR]

This paper modified the method of three-dimensional (3-dimensional) face reconstruction from a single two-dimensional (2-dimensional) image based on the Lambertian model consisting of height estimation, normal surface estimation, albedo calculation and image normalization, normal surface calculation, actual height calculation, and error correction. In height estimation, the facial height of each input image is estimated from the average facial height of face images in the training data set. The estimated height is used for estimating the normal surface by applying our proposed pattern morphing method (PMM). To calculate the actual normal surface, the albedo of the input face image is calculated to normalize the image first. Then, the actual normal surface is derived by using our proposed Y-ratio calculation with improved computational time. Finally, the actual height of input face image is computed afterwards to construct the 3-dimensional face. Two face databases of 110 human face images containing 10 real and 100 synthetic images were tested by our proposed method with uniform reflectance and high level of heterogeneous reflectance abilities. The experimental results were compared with the results obtained from other existing methods, such as the traditional minimization, shape propagation, local, and linear approaches. Our method can accurately reconstruct 3-dimensional face from a single 2-dimensional face image with the error less than 6%. [ABSTRACT FROM AUTHOR]

Background: Human Papillomavirus (HPV) genotyping is an important approach to fight cervical cancer due to the relevant information regarding risk stratification for diagnosis and the better understanding of the relationship of HPV with carcinogenesis. This paper proposed two new feature extraction techniques, i.e. ChaosCentroid and ChaosFrequency, for predicting HPV genotypes associated with the cancer. The additional diversified 12 HPV genotypes, i.e. types 6, 11, 16, 18, 31, 33, 35, 45, 52, 53, 58, and 66, were studied in this paper. In our proposed techniques, a partitioned Chaos Game Representation (CGR) is deployed to represent HPV genomes. ChaosCentroid captures the structure of sequences in terms of centroid of each sub-region with Euclidean distances among the centroids and the center of CGR as the relations of all sub-regions. ChaosFrequency extracts the statistical distribution of mono-, di-, or higher order nucleotides along HPV genomes and forms a matrix of frequency of dots in each sub-region. For performance evaluation, four different types of classifiers, i.e. Multi-layer Perceptron, Radial Basis Function, K-Nearest Neighbor, and Fuzzy K-Nearest Neighbor Techniques were deployed, and our best results from each classifier were compared with the NCBI genotyping tool. Results: The experimental results obtained by four different classifiers are in the same trend. ChaosCentroid gave considerably higher performance than ChaosFrequency when the input length is one but it was moderately lower than ChaosFrequency when the input length is two. Both proposed techniques yielded almost or exactly the best performance when the input length is more than three. But there is no significance between our proposed techniques and the comparative alignment method. Conclusions: Our proposed alignment-free and scale-independent method can successfully transform HPV genomes with 7,000 - 10,000 base pairs into features of 1 - 11 dimensions. This signifies that our ChaosCentroid and ChaosFrequency can be served as the effective feature extraction techniques for predicting the HPV genotypes. [ABSTRACT FROM AUTHOR]

Sport type classification and posture identification based on visual meaning of posture semantic in still images are challenging tasks. The difficulty of these tasks comes from the complex image content consisting of a player's posture, the color and texture of a player's clothes as well as complexity of the background. Player detection is one of the most important tasks in posture identification. For sport type classification without object segmentation, the new set of features, based on 64-bins color histogram, DCT coefficients, and Cb and Cr components, is introduced. To achieve high accuracy, an appropriate feature extraction technique should be also realized. For posture identification, three algorithms, concerning player region detection and suitable features for posture identification, are proposed namely blurred background elimination, irrelevant region elimination, and trimming players region. The DFT coefficients, based on image resizing and slicing techniques, are used as significant features in posture identification. Our proposed features were compared with Edge Histogram and Region-based Shape (EH and RS), two of MPEG-7 descriptors. The experimental results showed that our proposed features yielded better performance with 85.76% of accuracy in sport classification and 86.66% of accuracy in posture identification. [ABSTRACT FROM AUTHOR]