Browsing by Author "Alhajj, Reda S."

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    Computational Drug Repositioning Based on Integrated Similarity Measures and Deep Learning
    (2020-09-11) Jarada, Tamer N R; Rokne, Jon G.; Alhajj, Reda S.; Özyer, Tansel; Helaoui, Mohamed; Sadaoui, Samira
    Drug repositioning is an emerging approach in pharmaceutical research for identifying novel therapeutic potentials for approved drugs and discover therapies for untreated diseases. Due to its time and cost efficiency, drug repositioning plays an instrumental role in optimizing the drug development process compared to the traditional \textit{de novo} drug discovery process. Advances in the genomics, together with the enormous growth of large-scale publicly available data and the availability of high-performance computing capabilities, have further motivated the development of computational drug repositioning approaches. Numerous attempts have been carried out, with different degrees of efficiency and success, to computationally study the potential of identifying alternative drug indications, which slow, stop, or reverse the courses of incurable diseases. More recently, the rise of machine learning techniques, together with the availability of powerful computers, has made the area of computational drug repositioning an area of intense activities. In this thesis, the integration of various biological and biomedical data from different sources to improve the quality of biomedical knowledge in the computational drug repositioning field is addressed. The main contribution of this thesis is four-fold. First, it provides a comprehensive review of drug repositioning strategies, resources, and computational approaches. Second, it develops an approach for identifying disease-specific gene associations, which can be further used as a resource for computational drug repositioning methods. Third, it proposes a robust framework that utilizes known drug-disease interactions and drug-related similarity information to predict new drug-disease interactions. Fourth, it introduces a novel integrative framework for predicting drug-disease interactions using known drug-disease interactions, drug-related similarity information, and disease-related similarity information. The two proposed frameworks leverage advanced similarity calculation, selection, and integration to understand the functional and behavioural correlation between drugs and diseases. Furthermore, they employ the most advanced machine learning tools in predicting hidden or indirect drug-disease interactions for potential drug repositioning applications.
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    Design of Multi-band/Wide-band High Efficiency Power Amplifiers
    (2018-04-18) Li, Xiang; Helaoui, Mohamed; Belostotski, Leonid; Kim, Bumman; Ghannouchi, F. M.; Fear, Elise C.; Alhajj, Reda S.
    As an important component of the RF front-end in the transmitter, the power amplifier (PA) is used to convert the DC supply power into RF power. As the PA consumes most of the power in the transmitter, minimizing power dissipation of the PA would have a significant effect on the efficiency of the whole system. During recent decades, several works have been done to enhance the PA efficiency. However, there must be a trade-off between efficiency, linearity, gain, output power and bandwidth, which are five crucial attributes of the PA. With the development of the wireless communication system, multiple standards at different frequency bands are highly demanded to be integrated into one system while the increasing data rate requires a wide frequency spectrum and therefore broadband components. The multi-band/wide-band signals also lead to high peak-to-average power ratio. Thus, PAs are required to operate at multi-band/wide-band with high efficiency within a wide output power back-off (OPBO) range. For this reason, this thesis focuses on the PA theory and design method for multi-band, wide-band and wide-OPBO purpose. For the first time, a quad-band impedance inverter with arbitrary frequency ratio is proposed. Based on this impedance inverter, a concurrent quad-band Doherty has been designed at 0.75GHz, 1.75GHz, 2.65GHz and 3.55GHz with efficiency up to 50% at 6dB OPBO. For the first time, this thesis present a new theory for harmonically tuned PAs with maximally flat waveform, named class X PAs. The theory was developed for arbitrary harmonic tuning with arbitrary number of harmonics. The analytic close-form formulas for the voltage and current waveforms are provided. Design space for the Class-X PA with only first three harmonics is derived. To validate the theory, a wide-band PA has been designed with output power above 38dBm, efficiency higher than 70% over an octave bandwidth. The harmonic tuning method is also applied to the outphasing system in order to improve the efficiency. A harmonically tuned class-F outphasing system at 27.1MHz is designed with three third harmonic tuning cases to validate the theory. A harmonically tuned class-F-1 outphasing system at 2.14GHz is designed with efficiency of 68% at 6dB OPBO
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    Fuzzy Logic Classification in Review Spam Detection
    (2019-05-21) Rachdi, Btissam; Rokne, Jon G.; Alhajj, Reda S.; Moshirpour, Mohammad
    With the recent popularity of e-commerce, customers publish reviews about the products or services they purchased or utilized and these reviews in turn serve as the means for the potential customers to make a better choice based on the experiences of others. These pieces of opinion information are not only important for individual users but also benefit the business organizations, as they can monitor the customers’ opinions, and accordingly adjust their business strategies. However, many of the reviewing systems exploit this motivation for some people to enter their fake reviews to promote some products or defame some others. Hence, in recent years, review analysis has gained a lot of importance and by using opinion-mining detection; I could locate and eliminate potential spam reviews. In this thesis, I have introduced fuzzy logic in the review spam detection and combined two others data mining techniques, periodicity of frequent pattern and the outlier detection to study the behavior of the reviewer towards the reviewed product and classify the users using the fuzzy logic classification model. Thus, the proposed analysis have been proposed and examined over a sample of dataset.
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    Intelligent Data Analysis for Early Warning: From Multiple Sources to Multiple Perspectives
    (2019-09-12) Afra, Salim; Alhajj, Reda S.; Moussavi, Mahmood; Alhajj, Reda; Rokne, Jon G.; Moshirpour, Mohammad; Tavli, Bülent
    Misusing and benefiting from the development in technology for communication, criminal and terror groups have recently expanded and spread into global organizations and activities. Fortunately, it is possible to benefit from the technology to fight against terror and criminal groups by tracing, identifying, surrendering, and preventing them from executing their bloodily plans. Indeed, it is very affordable to capture various kinds of data which could be analyzed to predict potential criminals and terrorists. Data comes in various formats from text to images, and may become available incrementally due to dynamic sources. This leads to what has been recently classified as big data which has attracted considerable attention from the industry and the research community. Researchers and developers involved in this domain are trying to adapt and integrate existing techniques into customized solutions which could successfully and effectively handle big data with all its distinguishing characteristics. Alternatively, tremendous effort has been invested in developing new techniques to cope with big data for situations where existing techniques neither individually nor as an integrated group could address the shortcomings in this domain. Realizing the need for effective solutions capable of dealing with criminal and terror groups could be mentioned as the main motivation to undertake the study described in this thesis. The main contribution of this thesis is an early warning system that uses different sources of data to identify potential criminals and terrorists (hereafter both criminals and terrorists will be meant when any of them is mentioned in the text). The process works as follows. Criminal profiles are analyzed and their corresponding criminal networks are derived. This automates and facilitates the work of crime analysts in predicting events that may lead to disaster. We used face images as a data source and performed different studies to determine the accuracy and effectiveness of current face recognition and clustering algorithms in identifying people in uncontrolled environments, which are actually the environments encountered in real situations when dealing with criminals and terrorists. We trained our own face recognition algorithm using convolutional neural networks (CNN) by pre-processing the input images for better recognition rates. We showed how this is more effective than frontalized profile face images. We designed a queuing system for surveillance camera monitoring to raise an alarm when unknown people who pass through a monitored area turn into potential suspects. We also integrated different data sources such as social media, news, and official criminal documents to extract criminal names. We then generate a criminal profile which includes the activities that a given criminal is involved in. We also linked criminals together to build a criminal network by expanding the coverage and analyzing the collected data. We then proposed several unique criminal network analysis techniques to provide better understanding and knowledge for crime analysts. To achieve this, we added more functions related to criminal network analysis to NetDriller which is a powerful social network analysis tool developed by our research group. We also designed an algorithm for link prediction which better detects if a link between two nodes will exist in the future. All these functionalities have been well integrated into the monitoring system which has been developed and well tested to demonstrate its applicability and effectiveness.
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    Offline and Online Interactive Frameworks for MRI and CT Image Analysis in the Healthcare Domain : The Case of COVID-19, Brain Tumors and Pancreatic Tumors
    (2023-08) Sailunaz, Kashfia; Alhajj, Reda S.; Alhajj, Reda S.; Rokne, Jon George; Ozyer, Tansel; Kawash, Jalal Yusef; Agarwal, Nitin
    Medical imaging represents the organs, tissues and structures underneath the outer layers of skin and bones etc. and stores information on normal anatomical structures for abnormality detection and diagnosis. In this thesis, tools and techniques are used to automate the analysis of medical images, emphasizing the detection of brain tumor anomalies from brain MRIs, Covid infections from lung CT images and pancreatic tumor from pancreatic CT images. Image processing methods such as filtering and thresholding models, geometry models, graph models, region-based analysis, connected component analysis, machine learning models, and recent deep learning models are used. The following problems for medical images : abnormality detection, abnormal region segmentation, interactive user interface to represent the results of detection and segmentation while receiving feedbacks from healthcare professionals to improve the analysis procedure, and finally report generation, are considered in this research. Complete interactive systems containing conventional models, machine learning, and deep learning methods for different types of medical abnormalities have been proposed and developed in this thesis. The experimental results show promising outcomes that has led to the incorporation of the methods for the proposed solutions based on the observations of the performance metrics and their comparisons. Although currently separate systems have been developed for brain tumor, Covid and pancreatic cancer, the success of the developed systems show a promising potential to combine them to form a generalized system for analyzing medical imaging of different types collected from any organs to detect any type of abnormalities.
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    Predictive Analysis and Recommendation for Managing Risk and Avoiding Hazard in Chemical and Oil & Gas Industrial Infrastructures
    (2018-12-07) Polat, Serhan; Rokne, Jon G.; Alhajj, Reda S.; Moshirpour, Mohammad
    Chemical processing industrial infrastructures such as oil & gas plants are operated with the risk of hazardous events which may lead to casualties, economic and/or environmental consequences. Fortunately, a variety of devices and mechanisms are already available or rapidly emerging to capture data which may be used to develop techniques that may assist in issuing timely hazard alerts. This would help to avoid or prevent the hazard and hence save lives, the environment and the economy. Thus, the aim of this thesis is to develop an approach capable of analyzing the reports data captured after operations of infrastructure which can be used to guide domain experts in handling various causes and consequences of hazards. Available data may be publicly available or may exist in private repositories of processing companies. The latter data may not be accessible outside the company premises. However, the data available for this thesis has been crawled from publicly available data which exists as reports in various formats varying from plain text, semi-structured to structured. The crawled reports have been preprocessed using natural language processing techniques. Domain ontology has been used to guide the whole processes of clustering, and classification and a multiagent system have been integrated into the developed approach. Utilizing a multiagent system in the process allows for multiple perspectives to be incorporated into the process. These aspects are represented by independent agents who collaborate and negotiate to reach a consensus. The developed approach has been successfully applied to some publicly available gas and oil infrastructure hazard related data. The reported results may be used to issue recommendations to use certain safeguards to reduce the risk level in the processes.