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Aldhaifallah M.,Prince Sattam bin Abdulaziz University Prince | Nisar K.S.,Sattam bin Abdulaziz University
13th International Multi-Conference on Systems, Signals and Devices, SSD 2016 | Year: 2016

In this paper we develop a new algorithm to identify Auto-Regressive Exogenous (ARX) input Hammerstein Models based on Twin Support Vector Machine Regression (TSVR). The model is determined by minimizing two ϵ-insensitive loss functions. One of them determines the ϵ1-insensitive down bound regressor while the other determines the ϵ1-insensitive up bound regressor. The algorithm is compared to Support Vector Machine (SVM) and Least Square Support Vector Machine (LSSVM) based algorithms using simulation. © 2016 IEEE. Source

Chaieb M.,Sattam bin Abdulaziz University | Chaieb M.,University of Tunis | Jemai J.,University of Bahrain | Mellouli K.,University of Tunis
Procedia Computer Science | Year: 2015

Complex Optimization Problems has existed in many fields of science, including economics, healthcare, logistics and finance where a complex problem has to be solved. Thus, modeling a complex problem is a fundamental step to relax its complexity and achieve to a final solution of the master problem. Hierarchical optimization is a main step in optimization problems handling process. It consists of decomposing an optimization problem into two or more sub-problems; each sub-problem has its own objectives and constraints. It will help to prove the correct understanding and represent the problem in a different form that facilitates its solving. In this work, we stipulate that a hierarchical decomposition of complex problems can yield to more effective solutions. The proposed framework will contain four possible strategies which will be detailed through this paper; objective based decomposition; constraints based decomposition, semantic decomposition and data partitioning strategy. Each strategy will be argued by a set of examples from the literature to validate our framework. However, some conditions shall be verified to model the problem using such conditions are problems' characteristics that will help to identify if a combinatorial optimization problem can be modeled within the proposed framework and they are detailed in the following subsections. © 2015 The Authors. Published by Elsevier B.V. Source

Gebali F.,University of Victoria | Ibrahim A.,University of Victoria | Ibrahim A.,Sattam bin Abdulaziz University | Ibrahim A.,Electronics Research Institute of Egypt
Microprocessors and Microsystems | Year: 2016

This paper proposes a three bit-serial and digit-serial semi-systolic GF(2m) multipliers using Progressive Product Reduction (PPR) technique. These architectures are obtained by converting the GF(2m) multiplication algorithm into an iterative algorithm using systematic techniques for scheduling the computational tasks and mapping them to Processing Elements (PE). Three different semi systolic arrays were obtained. ASIC implementation of the proposed designs and previously published schemes were used to verify the performance of the proposed designs. One proposed design has at least 29% lower area compared to previously published bit/digit serial multipliers. This design has also at least 70% lower power compared to previously published bit/digit serial multipliers. Another proposed design has at least 12% lower power-delay product (PDP) compared to previously published bit/digit serial multipliers. This makes the proposed designs more suited to resource-constrained embedded applications. © 2015 Elsevier B.V. All rights reserved. Source

Al Kafri A.S.,Liverpool John Moores University | Sudirman S.,Liverpool John Moores University | Hussain A.J.,Liverpool John Moores University | Fergus P.,Liverpool John Moores University | And 3 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2016

Back pain is one of the major musculoskeletal pain problems that can affect many people and is considered as one of the main causes of disability all over the world. Lower back pain, which is the most common type of back pain, is estimated to affect at least 60% to 80% of the adult population in the United Kingdom at some time in their lives. Some of those patients develop a more serious condition namely Chronic Lower Back Pain in which physicians must carry out a more involved diagnostic procedure to determine its cause. In most cases, this procedure involves a long and laborious task by the physicians to visually identify abnormalities from the patient’s Magnetic Resonance Images. Limited technological advances have been made in the past decades to support this process. This paper presents a comprehensive literature review on these technological advances and presents a framework of a methodology for diagnosing and predicting Chronic Lower Back Pain. This framework will combine current state-of-the-art computing technologies including those in the area of artificial intelligence, physics modelling, and computer graphics, and is argued to be able to improve the diagnosis process. © Springer International Publishing Switzerland 2016. Source

Aday Curbelo Montanez C.,Liverpool John Moores University | Fergus P.,Liverpool John Moores University | Hussain A.,Liverpool John Moores University | Al-Jumeily D.,Liverpool John Moores University | And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2016

Obesity is a growing epidemic that has increased steadily over the past several decades. It affects significant parts of the global population and this has resulted in obesity being high on the political agenda in many countries. It represents one of the most difficult clinical and public health challenges worldwide. While eating healthy and exercising regularly are obvious ways to combat obesity, there is a need to understand the underlying genetic constructs and pathways that lead to the manifestation of obesity and their susceptibility metrics in specific individuals. In particular, the interpretation of genetic profiles will allow for the identification of Deoxyribonucleic Acid variations, known as Single Nucleotide Polymorphism, associated with traits directly linked to obesity and validated with Genome-Wide Association Studies. Using a robust data science methodology, this paper uses a subset of the TwinsUK dataset that contains genetic data from extremely obese individuals with a BMI ≥ 40, to identify significant obesity traits for potential use in genetic screening for disease risk prediction. The paper posits an approach for methodical risk variant identification to support intervention strategies that will help mitigate long-term adverse health outcomes in people susceptible to obesity and overweight. © Springer International Publishing Switzerland 2016. Source

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