Entropy in Image Analysis III

Image analysis can be applied to rich and assorted scenarios; therefore, the aim of this recent research field is not only to mimic the human vision system. Image analysis is the main methods that computers are using today, and there is body of knowledge that they will be able to manage in a totally unsupervised manner in future, thanks to their artificial intelligence. The articles published in the book clearly show such a future

The Performance of Binary Artificial Bee Colony (BABC) in Structure Selection of Polynomial NARX and NARMAX Models

This paper explores the capability of the Binary Artificial Bee Colony (BABC) algorithm for feature selection of Nonlinear Autoregressive Moving Average with Exogenous Inputs (NARMAX) model, and compares its implementation with the Binary Particle Swarm Optimization (BPSO) algorithm. A binarized modification of the BABC algorithm was used to perform structure selection of the NARMAX model on a Flexible Robot Arm (FRA) dataset. The solution quality and convergence was compared with the BPSO optimization algorithm. Fitting and validation tests were performed using the One-Step Ahead (OSA), correlation and histogram tests. BABC was able to outperform BPSO in terms of convergence consistency with equal solution quality. Additionally, it was discovered that BABC was less prone to converge to local minima while BPSO was able to converge faster. Results from this study showed that BABC was better-suited for structure selection in huge dataset and the convergence has been proven to be more consistent relative to BPSO

Large-Batch, Neural Multi-Objective Bayesian Optimization

Bayesian optimization provides a powerful framework for global optimization of black-box, expensive-to-evaluate functions. However, it has a limited capacity in handling data-intensive problems, especially in multi-objective settings, due to the poor scalability of default Gaussian Process surrogates. We present a novel Bayesian optimization framework specifically tailored to address these limitations. Our method leverages a Bayesian neural networks approach for surrogate modeling. This enables efficient handling of large batches of data, modeling complex problems, and generating the uncertainty of the predictions. In addition, our method incorporates a scalable, uncertainty-aware acquisition strategy based on the well-known, easy-to-deploy NSGA-II. This fully parallelizable strategy promotes efficient exploration of uncharted regions. Our framework allows for effective optimization in data-intensive environments with a minimum number of iterations. We demonstrate the superiority of our method by comparing it with state-of-the-art multi-objective optimizations. We perform our evaluation on two real-world problems - airfoil design and color printing - showcasing the applicability and efficiency of our approach. Code is available at: https://github.com/an-on-ym-ous/lbn\_mob

Study of Anti-proliferative Activity of Cucurbitacins Inspired Estrone Analogs on Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is considered the third leading cause of death from cancer. Overall survival rate is significantly low, due to the emerging resistance to chemotherapeutic agents and lack of selectivity. Recent studies have demonstrated that epidermal growth factor receptor (EGFR) is a promising molecular target for cancer therapy, especially HCC. Current studies showed that cucurbitacins are potent anticancer compounds which target EGFR. This prompted us to investigate the antiproliferative activity of novel cucurbitacins inspired estrone analogs (CIEA) against sensitive and resistant HepG2 cell lines. Anti-proliferation activity of 20 CIEA analogs were examined against HepG2 using MTT assay and showed that antiproliferative activity of analogs MMA132, and MMA102 IC50 are 2μM, and 3 μM respectively in comparison to Erlotinib 25 μM. Study of the mechanism of anti-proliferation effects of these novel analogs was elucidated. Western blot analysis showed that MMA132, and MMA102 significantly inhibit EGFR/pEGFR, RAF/pRAF, MEK/pMEK, and ERK/PERK. Cell cycle analysis on HepG2 cell line revealed that MMA132 and MMA102 arrested the cells at G1 phase and inhibited the HepG2 cell migration after 24 hr. MMA132 induced apoptosis through activation of caspase 3,9 and inhibition of PARP. Treatment of HepG2-R (Erlotinib resistant) with MMA132 and MMA102 showed that these two novel drug candidates still possessing potent anti-proliferation activities against HepG2-R. Further characterization of the anti-proliferation of these lead compounds was demonstrated through mapping the change in EGFR signaling pathway (ERK, pERK, RAS, AKT and MEK) by western blot, cell cycle analysis, demonstrated that MMA132 and MMA102 stop the cell cycle of HepG2-R at G2 phase and inhibited cell migration after 48hrs. HepG2-R cell line significantly expressed MRP2 in comparing to sensitive cells. Moreover, MK571(MRP2 inhibitor) showed an inhibitory effect on resistant HepG2-R cancer cell lines. Combination of MMA132 with MK571 (13 μM and 15 μM respectively) showed a significant increase in the cytotoxicity of MK571 from 18.5 μM to 10 μM. In conclusion, our study documented the discovery of novel estrone analogs as potential drug candidates for treatment of HCC and promising chemotherapeutic agent toward HepG2 resistant to erlotinib

Three layer wavelet based modeling for river flow

All existing methods regarding time series forecasting have always been challenged by the continuous climatic change taking place in the world. These climatic changes influence many unpredictable indefinite factors. This alarming situation requires a robust forecasting method that could efficiently work with incomplete and multivariate data. Most of the existing methods tend to trap into local minimum or encounter over fitting problems that mostly lead to an inappropriate outcome. The complexity of data regarding time series forecasting does not allow any one single method to yield results suitable in all situations as claimed by most researchers. To deal with the problem, a technique that uses hybrid models has also been devised and tested. The applied hybrid methods did bring some improvement compared to the individual model performance. However, most of these available hybrid models exploit univariate data that requires huge historical data to achieve precise forecasting results. Therefore, this study introduces a new hybrid model based on three layered architecture: Least Square Support Vector Machine (LSSVM), Discrete Wavelet Transform (DWT), correlation (R) and Kernel Principle Components Analyses (KPCA). The three-staged architecture of the proposed hybrid model includes Wavelet-LSSVM and Wavelet-KPCA-LSSVM enabling the model to present itself as a well-established alternative application to predict the future of river flow. The proposed model has been applied to four different data sets of time series, taking into account different time series behavior and data scale. The performance of the proposed model is compared against the existing individual models and then a comparison is also drawn with the existing hybrid models. The results of WKPLSSVM obtained from Coefficient of Efficiency (CE) performance measuring methods confirmed that proposed model has encouraging data of 0.98%, 0.99%, 0.94% and 0.99% for Jhelum River, Chenab River, Bernam River and Tualang River, respectively. It is more robust for all datasets regardless of the sample sizes and data behavior. These results are further verified using diverse data sets in order to check the stability and adaptability. The results have demonstrated that the proposed hybrid model is a better alternative tool for time series forecasting. The proposed hybrid model proves to be one of the best available solutions considering the time series forecasting issues

Assessment of paint appearance quality in the automotive industry

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.In the modem automotive industry, more and more manufacturers recognise that vehicle paint appearance makes an important contribution to customer satisfaction. Attractive appearance has become one of the important factors for customers in making a decision to purchase a car. Objective measurement of the quality of autobody paint appearance, as perceived by the customer, in a repeatable, reproducible, continuous scale manner is an important requirement for improving the paint appearance. It can provide car manufacturers a standard reference to evaluate the quality of the paint appearance. This thesis mainly deals with the measurement of paint appearance quality in the automotive industry by investigating, identifying and developing measurement methods in this area. First of all, the 'state of the art' in the area of paint appearance measurement was presented, which summarised the concept of appearance, models, attributes and definitions. To further identify the parameters and instruments used in the automotive industry, a round robin test was launched to perform visual assessment and instrument measurements on a set of panels in some European car manufacturers. A summary of the correlation found between measurable parameters and visual assessment provided the basis of the further work. Based on the literature survey and round robin test results, the next work is mainly concentrated on the two most important parameters, 'orange peel' and 'metal texture effect', how to separate and evaluate them. Digital signal processing technique, FFT and Filtering, have been employed to separate them and a set of measures have been provided for evaluation. At the same time, the technique for texture pattern recognition was introduced to evaluate the texture effect when a fine texture comparison was needed. A set of computable textural parameters based on grey-tone spatial-dependence matrices gives good correlation directly corresponding to visual perception. To resolve the overall appearance modelling problem, two novel and more powerful modelling tools, artificial neural networks and fuzzy logic, are introduced to model the overall appearance. The test results showed that both of them are able to reflect the correlation between overall appearance and the major parameters measured from a painted surface. Finally, an integrated measurement system, 'Smart Appearance', was developed using the image processing techniques and the artificial neural network model. The implement results show that this system can measure the major attributes of paint appearance and provide an overall appearance index corresponding to human visual perception. This system is helpful to product quality control on car body paint. It also could be used on the paint production line for dynamic measurement.European Union BRITE EURAM Projec

Spatio-Chromatic Adaptation via Higher-Order Canonical Correlation Analysis of Natural Images

Peer reviewe