Combining group method of data handling models using artificial bee colony algorithm for time series forecasting

Time series forecasting which uses models to predict future values based on some historical data is an important area of forecasting, and has gained the attention of researchers from various related fields of study. In line with its popularity, various models have been introduced for producing accurate time series forecasts. However, to produce an accurate forecast is not an easy feat especially when dealing with nonlinear data due to the abstract nature of the data. In this study, a model for accurate time series forecasting based on Artificial Bee Colony (ABC) algorithm and Group Method of Data Handling (GMDH) models with variant transfer functions, namely polynomial, sigmoid, radial basis function and tangent was developed. Initially, in this research, the GMDH models were used to forecast the time series data followed by each forecast that was combined using ABC. Then, the ABC produced the weight for each forecast before aggregating the forecasts. To evaluate the performance of the developed GMDH-ABC model, input data on tourism arrivals (Singapore and Indonesia) and airline passengers’ data were processed using the model to produce reliable forecast on the time series data. To validate the evaluation, the performance of the model was compared against benchmark models such as the individual GMDH models, Artificial Neural Network (ANN) model and combined GMDH using simple averaging (GMDH-SA) model. Experimental results showed that the GMDH-ABC model had the highest accuracy compared to the other models, where it managed to reduce the Root Mean Square Error (RMSE) of the conventional GMDH model by 15.78% for Singapore data, 28.2% for Indonesia data and 30.89% for airline data. As a conclusion, these results demonstrated the reliability of the GMDH-ABC model in time series forecasting, and its superiority when compared to the other existing models

a convolutional autoencoder approach for feature extraction in virtual metrology

Abstract Exploiting the huge amount of data collected by industries is definitely one of the main challenges of the so-called Big Data era. In this sense, Machine Learning has gained growing attention in the scientific community, as it allows to extract valuable information by means of statistical predictive models trained on historical process data. In Semiconductor Manufacturing, one of the most extensively employed data-driven applications is Virtual Metrology, where a costly or unmeasurable variable is estimated by means of cheap and easy to obtain measures that are already available in the system. Often, these measures are multi-dimensional, so traditional Machine Learning algorithms cannot handle them directly. Instead, they require feature extraction, that is a preliminary step where relevant information is extracted from raw data and converted into a design matrix. Features are often hand-engineered and based on specific domain knowledge. Moreover, they may be difficult to scale and prone to information loss, affecting the effectiveness and maintainability of machine learning procedures. In this paper, we present a Deep Learning method for semi-supervised feature extraction based on Convolutional Autoencoders that is able to overcome the aforementioned problems. The proposed method is tested on a real dataset for Etch rate estimation. Optical Emission Spectrometry data, that exhibit a complex bi-dimensional time and wavelength evolution, are used as input

Selected Computing Research Papers Volume 2 June 2013

An Evaluation of Current Innovations for Solving Hard Disk Drive Vibration Problems (Isiaq Adeola) ........................................................................................................ 1 A Critical Evaluation of the Current User Interface Systems Used By the Blind and Visually Impaired (Amneet Ahluwalia) ................................................................................ 7 Current Research Aimed At Improving Bot Detection In Massive Multiplayer Online Games (Jamie Burnip) ........................................................................................................ 13 Evaluation Of Methods For Improving Network Security Against SIP Based DoS Attacks On VoIP Network Infrastructures (David Carney) ................................................ 21 An Evaluation of Current Database Encryption Security Research (Ohale Chidiebere) .... 29 A Critical Appreciation of Current SQL Injection Detection Methods (Lee David Glynn) .............................................................................................................. 37 An Analysis of Current Research into Music Piracy Prevention (Steven Hodgson) .......... 43 Real Time On-line Analytical Processing: Applicability Of Parallel Processing Techniques (Kushatha Kelebeng) ....................................................................................... 49 Evaluating Authentication And Authorisation Method Implementations To Create A More Secure System Within Cloud Computing Technologies (Josh Mallery) ................... 55 A Detailed Analysis Of Current Computing Research Aimed At Improving Facial Recognition Systems (Gary Adam Morrissey) ................................................................... 61 A Critical Analysis Of Current Research Into Stock Market Forecasting Using Artificial Neural Networks (Chris Olsen) ........................................................................... 69 Evaluation of User Authentication Schemes (Sukhdev Singh) .......................................... 77 An Evaluation of Biometric Security Methods for Use on Mobile Devices (Joe van de Bilt) .................................................................................................................. 8

THE ACTIVATION, RECEPTOR COMPLEXING AND ENDOGENOUS REGULATION OF THE TYPE-I INTERFERON RESPONSE AS IT PERTAINS TO INNATE IMMUNITY

To defend against pathogen challenge, multi-cellular organisms mount an immune response that recognizes, sequesters and eradicates invading infectious agents. Critical to this safeguard is the receptor-mediated detection of pathogens. Pathogen recognition then initiates a variety of signaling cascades that lead to the modulation of genes orchestrating an immune response. Toll-like receptor 3 (TLR3), a transmembrane receptor found in endosomes, is vital to the innate immune response against viruses. Double-stranded RNA (dsRNA) stimulation of TLR3 initiates a signaling cascade that leads to the production of type-I interferons and proinflammatory cytokines necessary to trigger the protective defenses of the immune system. Critical to this pathway is the activation of a kinase, TANK binding kinase 1 (TBK1), which phosphorylates the downstream transcription factors, IRF3 and IRF7, and leads to the production of IFN-beta. Interestingly, TBK1 function has been implicated in a number of other signaling cascades ranging from the insulin response and vesicle transport to xenophagy and anti-viral immunity. Increasingly, however, TBK1 dysregulation has been linked to autoimmune disorders and cancers, heightening the need to understand regulatory controls of TBK1. As a result, this dissertation investigates three components of the TLR3 signaling cascade in an attempt to further advance our understanding of the innate immune response. First, investigations into the adjuvant potential of dsRNA reveal that a 139bp dsRNA molecule is a viable candidate for vaccine adjuvant studies. Next, structural and functional studies of TLR3 with neutralizing antibodies provide evidence for a new TLR-signaling model in which dsRNA:TLR3 signaling units laterally cluster to achieve efficient signaling. Finally, cell-based assays, biophysical experiments and kinetic investigations into the mechanism by which an endogenous regulator of the TLR3 response, SIKE, functions, reveal that SIKE not only inhibits TBK1-mediated IRF3 phosphorylation, but is also a high affinity substrate. Findings from this study further suggest that SIKE regulates a critical catalytic hub not by direct repression of activity, but by redirection of catalysis through substrate affinity. Taken together, the results presented in this dissertation establish a foundation for building long-term studies on the function, regulation and viral subversion of the innate immune response

ALL classification using neural ensemble and memetic deep feature optimization

Acute lymphoblastic leukemia (ALL) is a fatal blood disorder characterized by the excessive proliferation of immature white blood cells, originating in the bone marrow. An effective prognosis and treatment of ALL calls for its accurate and timely detection. Deep convolutional neural networks (CNNs) have shown promising results in digital pathology. However, they face challenges in classifying different subtypes of leukemia due to their subtle morphological differences. This study proposes an improved pipeline for binary detection and sub-type classification of ALL from blood smear images. At first, a customized, 88 layers deep CNN is proposed and trained using transfer learning along with GoogleNet CNN to create an ensemble of features. Furthermore, this study models the feature selection problem as a combinatorial optimization problem and proposes a memetic version of binary whale optimization algorithm, incorporating Differential Evolution-based local search method to enhance the exploration and exploitation of feature search space. The proposed approach is validated using publicly available standard datasets containing peripheral blood smear images of various classes of ALL. An overall best average accuracy of 99.15% is achieved for binary classification of ALL with an 85% decrease in the feature vector, together with 99% precision and 98.8% sensitivity. For B-ALL sub-type classification, the best accuracy of 98.69% is attained with 98.7% precision and 99.57% specificity. The proposed methodology shows better performance metrics as compared with several existing studies

Research on Risk Prediction and Early Warning of Human Resource Management Based on Machine Learning and Ontology Reasoning

Talent is the first resource, the development of the enterprise to retain key talent is essential, the main research is based on machine learning and ontological reasoning, human resources analysis and management risk prediction and early warning methods, first of all, according to the specific situation and the target case, through the calculation of the similarity of the concept name and attribute of the similarity assessment of the source case in the case library, the matching of knowledge-based employees of the company\u27s case for the similarity prediction and human resources management risk prediction research. Then, according to the evaluation results, we can find out the most suitable job matches in specific risk problems and situations. This is a solution to the target cases and criteria for companies to evaluate candidates. Second, we have successfully developed and implemented a prediction model that applies machine learning to the early warning study of risk prediction for HR management. The model is optimized with a cross-validation function, and the convergence of the model training is accelerated by the regularization of Newton\u27s iterative method. Finally, our prediction model achieved 82% yield. Ontological reasoning and machine learning are promising in human resource management risk prediction and warning, which is proved by the high accuracy rate verified by examples. Finally, we analyze the proposed results of HRM risk prediction and early warning to contribute to the improvement of risk control and suggest measures for possible risks

Data science for engineering design: State of the art and future directions

Abstract Engineering design (ED) is the process of solving technical problems within requirements and constraints to create new artifacts. Data science (DS) is the inter-disciplinary field that uses computational systems to extract knowledge from structured and unstructured data. The synergies between these two fields have a long story and throughout the past decades, ED has increasingly benefited from an integration with DS. We present a literature review at the intersection between ED and DS, identifying the tools, algorithms and data sources that show the most potential in contributing to ED, and identifying a set of challenges that future data scientists and designers should tackle, to maximize the potential of DS in supporting effective and efficient designs. A rigorous scoping review approach has been supported by Natural Language Processing techniques, in order to offer a review of research across two fuzzy-confining disciplines. The paper identifies challenges related to the two fields of research and to their interfaces. The main gaps in the literature revolve around the adaptation of computational techniques to be applied in the peculiar context of design, the identification of data sources to boost design research and a proper featurization of this data. The challenges have been classified considering their impacts on ED phases and applicability of DS methods, giving a map for future research across the fields. The scoping review shows that to fully take advantage of DS tools there must be an increase in the collaboration between design practitioners and researchers in order to open new data driven opportunities

One-class Classification: An Approach to Handle Class Imbalance in Multimodal Biometric Authentication

Biometric verification is the process of authenticating a person‟s identity using his/her physiological and behavioural characteristics. It is well-known that multimodal biometric systems can further improve the authentication accuracy by combining information from multiple biometric traits at various levels, namely sensor, feature, match score and decision levels. Fusion at match score level is generally preferred due to the trade-off between information availability and fusion complexity. However, combining match scores poses a number of challenges, when treated as a two-class classification problem due to the highly imbalanced class distributions. Most conventional classifiers assume equally balanced classes. They do not work well when samples of one class vastly outnumber the samples of the other class. These challenges become even more significant, when the fusion is based on user-specific processing due to the limited availability of the genuine samples per user. This thesis aims at exploring the paradigm of one-class classification to advance the classification performance of imbalanced biometric data sets. The contributions of the research can be enumerated as follows. Firstly, a thorough investigation of the various one-class classifiers, including Gaussian Mixture Model, k-Nearest Neighbour, K-means clustering and Support Vector Data Description, has been provided. These classifiers are applied in learning the user-specific and user-independent descriptions for the biometric decision inference. It is demonstrated that the one-class classifiers are particularly useful in handling the imbalanced learning problem in multimodal biometric authentication. User-specific approach is a better alternative with respect to user-independent counterpart because it is able to overcome the so-called within-class sub-concepts problem, which arises very often in multimodal biometric systems due to the existence of user variation. Secondly, a novel adapted score fusion scheme that consists of one-class classifiers and is trained using both the genuine user and impostor samples has been proposed. This method also replaces user-independent by user-specific description to learn the characteristics of the impostor class, and thus, reducing the degree of imbalanced proportion of data for different classes. Extensive experiments are conducted on the BioSecure DS2 and XM2VTS databases to illustrate the potential of the proposed adapted score fusion scheme, which provides a relative improvement in terms of Equal Error Rate of 32% and 20% as compared to the standard sum of scores and likelihood ratio based score fusion, respectively. Thirdly, a hybrid boosting algorithm, called r-ABOC has been developed, which is capable of exploiting the natural capabilities of both the well-known Real AdaBoost and one-class classification to further improve the system performance without causing overfitting. However, unlike the conventional Real AdaBoost, the individual classifiers in the proposed schema are trained on the same data set, but with different parameter choices. This does not only generate a high diversity, which is vital to the success of r-ABOC, but also reduces the number of user-specified parameters. A comprehensive empirical study using the BioSecure DS2 and XM2VTS databases demonstrates that r-ABOC may achieve a performance gain in terms of Half Total Error Rate of up to 28% with respect to other state-of-the-art biometric score fusion techniques. Finally, a Robust Imputation based on Group Method of Data Handling (RIBG) has been proposed to handle the missing data problem in the BioSecure DS2 database. RIBG is able to provide accurate predictions of incomplete score vectors. It is observed to achieve a better performance with respect to the state-of-the-art imputation techniques, including mean, median and k-NN imputations. An important feature of RIBG is that it does not require any parameter fine-tuning, and hence, is amendable to immediate applications