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Musbah M. Aqel

Bio: Musbah M. Aqel is an academic researcher. The author has contributed to research in topics: Soft computing & Fuzzy logic. The author has an hindex of 1, co-authored 2 publications receiving 36 citations.

Papers
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01 Jan 2009
TL;DR: This paper will focus on soft computing paradigm in bioinformatics with particular emphasis on integrative research.
Abstract: Bioinformatics is a promising and innovative research field in 21st century. Despite of a high number of techniques specifically dedicated to bioinformatics problems as well as many successful applications, we are in the beginning of a process to massively integrate the aspects and experiences in the different core subjects such as biology, medicine, computer science, engineering, chemistry, physics, and mathematics. Recently the use of soft computing tools for solving bioinformatics problems have been gaining the attention of researchers because of their ability to handle imprecision, uncertainty in large and complex search spaces. The paper will focus on soft computing paradigm in bioinformatics with particular emphasis on integrative research.

37 citations

Journal ArticleDOI
TL;DR: A simulation model for analyzing artificial emotion injected to design the game characters and a pheromone distribution or labeling is presented mimicking the behavior of social insects.
Abstract: This paper describes a simulation model for analyzing artificial emotion injected to design the game characters. Most of the game storyboard is interactive in nature and the virtual characters of the game are equipped with an individual personality and dynamic emotion value which is similar to real life emotion and behavior. The uncertainty in real expression, mood and behavior is also exhibited in game paradigm and this is focused in the present paper through a fuzzy logic based agent and storyboard. Subsequently, a pheromone distribution or labeling is presented mimicking the behavior of social insects. Keywords— Artificial Emotion, Fuzzy logic, Game character, Pheromone label

1 citations


Cited by
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Proceedings ArticleDOI
01 Jun 2018
TL;DR: This paper first study the system conditions that trigger GPU errors using six-month trace data collected from a large-scale, operational HPC system, then uses machine learning to predict the occurrence of GPU errors, by taking advantage of temporal and spatial dependencies of the trace data.
Abstract: GPUs are widely deployed on large-scale HPC systems to provide powerful computational capability for scientific applications from various domains. As those applications are normally long-running, investigating the characteristics of GPU errors becomes imperative for reliability. In this paper, we first study the system conditions that trigger GPU errors using six-month trace data collected from a large-scale, operational HPC system. Then, we use machine learning to predict the occurrence of GPU errors, by taking advantage of temporal and spatial dependencies of the trace data. The resulting machine learning prediction framework is robust and accurate under different workloads.

69 citations

Proceedings ArticleDOI
01 Sep 2017
TL;DR: How power consumption and temperature characteristics affect reliability is explored, and insights into what are the implications of such understanding are provided, and how to exploit these insights toward predicting GPU errors using neural networks are provided.
Abstract: GPUs have become part of the mainstream high performance computing facilities that increasingly require more computational power to simulate physical phenomena quickly and accurately. However, GPU nodes also consume significantly more power than traditional CPU nodes, and high power consumption introduces new system operation challenges, including increased temperature, power/cooling cost, and lower system reliability. This paper explores how power consumption and temperature characteristics affect reliability, provides insights into what are the implications of such understanding, and how to exploit these insights toward predicting GPU errors using neural networks.

48 citations

Journal ArticleDOI
TL;DR: This paper focuses on IRIS plant classification using Neural Network, and Multilayer feed- forward networks are trained using back propagation learning algorithm.
Abstract: Classification is a machine learning technique used to predict group membership for data instances. To simplify the problem of classification neural networks are being introduced. This paper focuses on IRIS plant classification using Neural Network. The problem concerns the identification of IRIS plant species on the basis of plant attribute measurements. Classification of IRIS data set would be discovering patterns from examining petal and sepal size of the IRIS plant and how the prediction was made from analyzing the pattern to form the class of IRIS plant. By using this pattern and classification, in future upcoming years the unknown data can be predicted more precisely. Artificial neural networks have been successfully applied to problems in pattern classification, function approximations, optimization, and associative memories. In this work, Multilayer feed- forward networks are trained using back propagation learning algorithm.

44 citations