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Predicting Stock Prices Using Polynomial Classifiers: The Case of Dubai Financial Market

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Journal of Intelligent Learning Systems and Applications 2011年第2期3卷 82-89页

作者： khaled assaleh Hazim El-Baz Saeed Al-Salkhadi 不详

Predicting stock price movements is a challenging task for academicians and practitioners. In particular, forecasting price movements in emerging markets seems to be more elusive because they are usually more volatile often accompa-nied by thin trading-volumes and they are susceptible to more manipulation compared to mature markets. Technical analysis of stocks and commodities has become a science on its own;quantitative methods and techniques have been applied by many practitioners to forecast price movements. Lagging and sometimes leading technical indicators pro-vide rich quantitative tools for traders and investors in their attempt to gain advantage when making investment or trading decisions. Artificial Neural Networks (ANN) have been used widely in predicting stock prices because of their capability in capturing the non-linearity that often exists in price movements. Recently, Polynomial Classifiers (PC) have been applied to various recognition and classification application and showed favorable results in terms of recog-nition rates and computational complexity as compared to ANN. In this paper, we present two prediction models for predicting securities’ prices. The first model was developed using back propagation feed forward neural networks. The second model was developed using polynomial classifiers (PC), as a first time application for PC to be used in stock prices prediction. The inputs to both models were identical, and both models were trained and tested on the same data. The study was conducted on Dubai Financial Market as an emerging market and applied to two of the market’s leading stocks. In general, both models achieved very good results in terms of mean absolute error percentage. Both models show an average error around 1.5% predicting the next day price, an average error of 2.5% when predicting second day price, and an average error of 4% when predicted the third day price.

关键词： Dubai Financial Market Polynomial Classifiers Stock Market Neural Networks

Novel algorithms for accurate DNA base-calling

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Journal of Biomedical Science and Engineering 2013年第2期6卷 165-174页

作者： Omniyah G. Mohammed khaled T. assaleh Ghaleb A. Husseini Amin F. Majdalawieh Scott R. Woodward Department of Biology Chemistry and Environmental Sciences American University of Sharjah Sharjah UAE Department of Chemical Engineering American University of Sharjah Sharjah UAE Department of Electrical Engineering American University of Sharjah Sharjah UAE Sorenson Molecular Genealogy Foundation Salt Lake City USA

The ability to decipher the genetic code of different species would lead to significant future scientific achievements in important areas, including medicine and agriculture. The importance of DNA sequencing necessitated a need for efficient automation of identification of base sequences from traces generated by existing sequencing machines, a process referred to as DNA base-calling. In this paper, a pattern recognition technique was adopted to minimize the inaccuracy in DNA base-calling. Two new frameworks using Artificial Neural Networks and Polynomial Classifiers are proposed to model electropherogram traces belonging to Homo sapiens, Saccharomyces mikatae and Drosophila melanogaster. De-correlation, de-convolution and normalization were implemented as part of the pre-processing stage employed to minimize data imperfections attributed to the nature of the chemical reactions involved in DNA sequencing. Discriminative features that characterize each chromatogram trace were subsequently extracted and subjected to the chosen classifiers to categorize the events to their respective base classes. The models are trained such that they are not restricted to a specific species or to a specific chemical procedure of sequencing. The base- calling accuracy achieved is compared with the exist- ing standards, PHRED (Phil’s Read Editor) and ABI (Applied Biosystems, version2.1.1) KB base-callers in terms of deletion, insertion and substitution errors. Experimental evidence indicates that the proposed models achieve a higher base-calling accuracy when compared to PHRED and a comparable performance when compared to ABI. The results obtained demon- strate the potential of the proposed models for efficient and accurate DNA base-calling.

关键词： Artificial Neural Network (ANN) Base-Calling Electropherogram Polynomial Classifier (PC) Sequencing

In vitro–in vivo studies of the quantitative effect of calcium,multivitamins and milk on single dose ciprofloxacin bioavailability

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Journal of Pharmaceutical Analysis 2015年第6期5卷 389-395页

作者： Baishakhi Dey Prakash Katakam Fathi H.assaleh Babu Rao Chandu Shanta Kumari Adiki Analava Mitra School of Medical Science and Technology Faculty of Pharmacy University of Zawia Department of Pharmaceutical Analysis Nirmala College of Pharmacy

Ciprofioxacin, commonly used in India as an anti-microbial for prolonged use in chronic and non-specific indications, may affect the bioavailability of the drug. The drug prescribed is commonly taken with multivitamins, calcium and milk. A simple and reliable analytical methodology obtaining a correlation with in vivo urinary excretion studies using UV and HPLC and in vitro dissolution studies （IVIVC） has shown a significant increase in elimination rate of ciprofloxacin co-administered with multivitamins, calcium and milk. Appreciable IVlVC results proved that dissolution studies could serve as an alternative to in vivo bioavailability and also support bio-waivers.

关键词： CiprofloxacinAnalytical methodologyBioavailabilityBio-waiverDissolution study

Continuous Arabic Sign Language Recognition in User Dependent Mode

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Journal of Intelligent Learning Systems and Applications 2010年第1期2卷 19-27页

作者： K. assaleh T. Shanableh M. Fanaswala F. Amin H. Bajaj 不详

Arabic Sign Language recognition is an emerging field of research. Previous attempts at automatic vision-based recog-nition of Arabic Sign Language mainly focused on finger spelling and recognizing isolated gestures. In this paper we report the first continuous Arabic Sign Language by building on existing research in feature extraction and pattern recognition. The development of the presented work required collecting a continuous Arabic Sign Language database which we designed and recorded in cooperation with a sign language expert. We intend to make the collected database available for the research community. Our system which we based on spatio-temporal feature extraction and hidden Markov models has resulted in an average word recognition rate of 94%, keeping in the mind the use of a high perplex-ity vocabulary and unrestrictive grammar. We compare our proposed work against existing sign language techniques based on accumulated image difference and motion estimation. The experimental results section shows that the pro-posed work outperforms existing solutions in terms of recognition accuracy.

关键词： Pattern Recognition Motion Analysis Image/ Video Processing and Sign Language