New Structural Evolving Algorithms For Fuzzy Systems

Recently, the issue of accuracy and interpretability trade-off has been getting more attention when designing new fuzzy systems. In this thesis, three evolving fuzzy models, namely enhancement of fuzzy term identification (EFTI), structure identification method (SIM) and structural evolving approach (SEA) are proposed to spot the best trade-off between accuracy and interpretability. EFTI, SIM and SEA are designed based on error reducing methods. EFTI is developed to fit with single input single output (SISO) problems (i.e. one dimension), while SIM and SEA are developed to fit with multi input single output (MISO) (medium and high dimension). EFTI begins with a simple fuzzy structure that is composed of two fuzzy terms in the input space. Then EFTI continues evolving by identifying splitting points of the input space that are compatible with the consequent parameters. On the other hand, SIM and SEA start with one fuzzy rule that has no fuzzy term in the input space regardless of the degree level of input dimension. Then they evolve on the basis of either closure or split processes for the selected input attribute of the selected subregion. If the selected attribute has no fuzzy terms, closure is performed, otherwise split is done. The evolving continues until a satisfactory accuracy is fulfilled or maximum number of subregion is reached. A partitioning technique based on the similarity feature and a static partition-selection technique are developed for SIM. While, a partitioning technique based on splitting the selected subregion into two subregions with maximum and minimum average error and a dynamic partition-selection technique are developed for SEA. Furthermore, a pruning technique based on the importance level of the fuzzy rules is proposed to shrink the rule-base of SEA. Compared with SISO models and using three datasets, EFTI produces the lowest RMSE with lowest number of rules. For MISO models and using nine benchmark datasets, SIM achieves the lowest RMSE with the smallest size of rule-base systems. Similarly, for MISO state-of-the-art models and using six benchmark datasets, SEA also produces the lowest RMSE with the smallest size of rule-base systems. In conclusion, the results proved that EFTI, SIM and SEA are able to produce a significant trade-off between accuracy and interpretabilit

High-Speed Implementations Of Fractal Image Compression For Low And High Resolution Images

Fractal Image Compression (FIC) is a very popular coding technique that is used in image/video applications due to its simplicity and superior performance. The major drawback of FIC is that it is a time consuming algorithm, especially when a full search is attempted. Hence, it is very challenging to achieve a real-time operation especially when this algorithm is run on a general or graphic processor unit. Therefore, in this research new hardware implementations of FIC are proposed for accelerating the encoding process by means of parallelism and pipelining. Various approaches have been investigated for achieving high speed performance. The computational complexity of fractal operations are first investigated in order to select the minimum and efficient bit sizes that can provide similar or nearly similar encoding quality. This has resulted in a relatively new FIC hardware which is referred in this thesis as Design I. In this design, a full-search approach was adopted in order to enable reconstruction at highest possible quality. However, full-search scheme is not suitable for encoding larger images since the encoding time is increased dramatically when processing high-resolution images. This problem is solved in Design II which used a partial-search based scheme in order to achieve high-speed operation. This method exploits the inherently high degree of correlation between pixels in the neighbourhood areas in digital image to restrict the search space to those areas. By fixing these areas for each group of range blocks and partitioning an image in which each domain block contains four range blocks, enabled two matching operations be performed simultaneously. This reduced the memory access by half, thereby, doubling the speed by a factor of 2. This design was extended to encode RGB image, resulting in another new design referred to as Design III. In this design, the strong cross-correlation between the image components was exploited so that only the G component was encoded using the same approach as in Design II, while the R and B components were encoded by searchless-based scheme with direct mapping between overlapped blocks. All three designs were examined in terms of runtime, peak-signal-to-noise-ratio (PSNR), and compression rate. The experimental results of Design I when implemented in Altera Cyclone II FPGA, showed speedup of 3 times, on average, while the PSNR was not significantly affected. Empirical results demonstrated that this firmware is competitive when compared to other existing full-search hardware with PSNR averaging at 30 dB, 5.82 % compression rate and a runtime of 9.8 ms. On the other hand, Design II was synthesised on Altera Stratix IV FPGA and showed an ability to encode a 1024×1024 image at 395 MHz in 10.8 ms with PSNR averaging at 27 dB and compression rate of 34. These results suggest that the proposed approach enables colour images be encoded at approximately same speed as grayscale images. Also the proposed architectures have achieved better performance compared to the state-of-the-art designs, with speed averaging at 100, 92 and 83 fps for Design I, II and III respectively

Hematological Study of Infants Amoebiasis in Duhok City

Out of 180 children, 60 (33.3%) have Amoebiasis infection as diagnosed by direct wet smear and Saturated Salt Solution (SSS). SSS method is more significant (P=0.001) in diagnosis of the disease. Number of children infected with Amoebiasis infection is higher in infants aged 1-6 months, but without any significant difference to ages 6-12 or 12-18 months. In contrast, infants aged 18-24 months are significantly differant (P=0.01) as the infection rate is 16.6%. Gender also is seen to be reduced in significance (P= 0.001) for females aged 18-24 months. Blood profile of the involved infants has shown a significant variation (P=0. 01) for all blood profile parameters (RBC (P=0.05), WBC (P=0.001), Lymphocytes (P=0.05), Granulated WBC (P=0.05), Hb (P=0.01) and Platelets counts (P=0.001). Many medicinal regimes are dependent in the treatment of Amoebiasis, Metronidazole (Flagyl) in significant variation (P=0.01), combination of Metronidazole and Bactri

A Copula Based Approach for Design of Multivariate Random Forests for Drug Sensitivity Prediction

Modeling sensitivity to drugs based on genetic characterizations is a significant challenge in the area of systems medicine. Ensemble based approaches such as Random Forests have been shown to perform well in both individual sensitivity prediction studies and team science based prediction challenges. However, Random Forests generate a deterministic predictive model for each drug based on the genetic characterization of the cell lines and ignores the relationship between different drug sensitivities during model generation. This application motivates the need for generation of multivariate ensemble learning techniques that can increase prediction accuracy and improve variable importance ranking by incorporating the relationships between different output responses. In this article, we propose a novel cost criterion that captures the dissimilarity in the output response structure between the training data and node samples as the difference in the two empirical copulas. We illus- trate that copulas are suitable for capturing the multivariate structure of output responses independent of the marginal distributions and the copula based multivariate random forest framework can provide higher accuracy prediction and improved variable selection. The proposed framework has been validated on genomics of drug sensitivity for cancer and cancer cell line encyclopedia database

S1: Supplementary Information for Article: A copula based approach for design of multivariate random forests for drug sensitivity prediction

Changes in performance with prior feature selection Random forest (RF) is designed to create uncorrelated trees using random subsets of features in each node of each tree. RF by itself is a great tool for feature selection from a high dimensional set of features. But we observed that the prediction accuracy is improved when a prior feature selection (RELIEFF) [1] approach is implemented. Table A shows the performance of RF, VMRF and CMRF with and without RELIEFF feature selection in 2 drug sets of GDSC. Performance Analysis for drugsets consisting of more 8 than two drugs We have generated empirical copulas for the bivariate cases as they are able to capture all forms of dependency structures. However, generation of empirical copulas has high computational complexity along with the need for a significant number of training samples at each node. Thus for more than two drug responses, we have considered parametric copulas and the difference between Gaussian copula parameters generated using root node and split node samples instead of the integral difference between empirical copulas is used. To test our hypothesis that VMRF and CMRF will perform better than RF, we considered a drug set with 4 different drugs from CCLE with single common target between them and a drug set with 3 different drugs in GDSC with a common target between them. The CCLE set has 482 cell lines and the GDSC set has 308 cell lines. RELIEFF was used to reduce the feature space prior to random forest application. For simplicity, in this case, we’ve used 30% of the sample cell lines as training data and 70% of them as testing data

Design of Probabilistic Random Forests with Applications to Anticancer Drug Sensitivity Prediction- 2016

Random forests consisting of an ensemble of regression trees with equal weights are frequently used for design of predictive models. In this article, we consider an extension of the methodology by representing the regression trees in the form of probabilistic trees and analyzing the nature of heteroscedasticity. The probabilistic tree representation allows for analytical computation of confidence intervals (CIs), and the tree weight optimization is expected to provide stricter CIs with comparable performance in mean error. We approached the ensemble of probabilistic trees’ prediction from the perspectives of a mixture distribution and as a weighted sum of correlated random variables. We applied our methodology to the drug sensitivity predic- tion problem on synthetic and cancer cell line encyclopedia dataset and illustrated that tree weights can be selected to reduce the average length of the CI without increase in mean error

A Copula Based Approach for Design of Multivariate Random Forests for Drug Sensitivity Prediction

Modeling sensitivity to drugs based on genetic characterizations is a significant challenge in the area of systems medicine. Ensemble based approaches such as Random Forests have been shown to perform well in both individual sensitivity prediction studies and team science based prediction challenges. However, Random Forests generate a deterministic predictive model for each drug based on the genetic characterization of the cell lines and ignores the relationship between different drug sensitivities during model generation. This application motivates the need for generation of multivariate ensemble learning techniques that can increase prediction accuracy and improve variable importance ranking by incorporating the relationships between different output responses. In this article, we propose a novel cost criterion that captures the dissimilarity in the output response structure between the training data and node samples as the difference in the two empirical copulas. We illus- trate that copulas are suitable for capturing the multivariate structure of output responses independent of the marginal distributions and the copula based multivariate random forest framework can provide higher accuracy prediction and improved variable selection. The proposed framework has been validated on genomics of drug sensitivity for cancer and cancer cell line encyclopedia database

Prevalence of body-focused repetitive behaviors in three large medical colleges of Karachi: a cross-sectional study.

Background: Body-focused repetitive behaviors (BFRBs) that include skin picking (dermatillomania), hair pulling (trichotillomania) and nail biting (onychophagia), lead to harmful physical and psychological sequelae. The objective was to determine the prevalence of BFRBs among students attending three large medical colleges of Karachi. It is imperative to come up with frequency to design strategies to decrease the burden and adverse effects associated with BFRBs among medical students. Methods: A cross-sectional study was conducted among 210 students attending Aga Khan University, Dow Medical College and Sind Medical College, Karachi, in equal proportion. Data were collected using a pre tested tool, “Habit Questionnaire”. Diagnoses were made on the criteria that a student must be involved in an activity 5 times or more per day for 4 weeks or more. Convenience sampling was done to recruit the participants aged 18 years and above after getting written informed consent. Results: The overall prevalence of BFRBs was found to be 46 (22%). For those positive for BFRBs, gender distribution was as follows: females 29 (13.9%) and males 17 (8.1%). Among these students, 19 (9.0%) were engaged in dermatillomania, 28 (13.3%) in trichotillomania and 13 (6.2%) in onychophagia. Conclusions: High proportions of BFRBs are reported among medical students of Karachi. Key health messages and interventions to reduce stress and anxiety among students may help in curtailing the burden of this disease which has serious adverse consequences

Portal biliopathy

Portal biliopathy (PB) is a rare disorder, which mostly presents as sub-clinically. It occurs most commonly due to idiopathic extrahepatic portal vein obstruction. We present three cases having features of portal biliopathy secondary to portal hypertension. Our first case did not have a prior history of chronic liver disease while next two patients had previous history of chronic liver disease resulting in portal hypertension. Cavernous transformation of the portal vein due to extrahepatic portal vein obstruction is not infrequent but biliary obstruction in association with this disorder is distinctly uncommon. Proper case management is very important as prolonged biliary duct obstruction can lead to the development of ascending cholangitis or later on secondary biliary cirrhosis