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You searched for +publisher:"University of Windsor" +contributor:("Mehdi Kargar"). Showing records 1 – 2 of 2 total matches.

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University of Windsor

1. PATEL, PARTH ATULKUMAR. Productive Cluster Hire.

Degree: M.C.Sc., Computer Science, 2019, University of Windsor

Discovering a group of experts to complete a set of tasks that require various skills is known as Cluster Hire Problem. Each expert has a set of skills which he/she can offer and charges a monetary cost to offer their expertise. We are given a set of projects that need to be completed and on completion of each project, the organization gets a Profit. For performing a subset of given projects, we are given a predetermined budget. This budget is spent on hiring experts. We extend this problem by introducing the productivity and capacity of experts. We want to hire experts that are more productive, and this factor is determined on the basis of their past experience. We also want to make sure that no expert is overworked as it is not possible for a single expert to provide his/her expertise for unlimited times. Our goal is to hire as many experts as possible in which the sum of their hiring costs (i.e., salary) is under the given budget as we are interested to maximize the profit and also maximize the productivity of the group of experts, our problem is a bi-objective optimization problem. To achieve this, we propose two different approaches that maximize our Profit and Productivity. Advisors/Committee Members: Ziad Kobti, Mehdi Kargar.

Subjects/Keywords: Bi-objective maximization; Cluster Hire; Data Mining; Greedy Algorithm; Productivity; Team Formation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

PATEL, P. A. (2019). Productive Cluster Hire. (Masters Thesis). University of Windsor. Retrieved from https://scholar.uwindsor.ca/etd/7652

Chicago Manual of Style (16th Edition):

PATEL, PARTH ATULKUMAR. “Productive Cluster Hire.” 2019. Masters Thesis, University of Windsor. Accessed November 14, 2019. https://scholar.uwindsor.ca/etd/7652.

MLA Handbook (7th Edition):

PATEL, PARTH ATULKUMAR. “Productive Cluster Hire.” 2019. Web. 14 Nov 2019.

Vancouver:

PATEL PA. Productive Cluster Hire. [Internet] [Masters thesis]. University of Windsor; 2019. [cited 2019 Nov 14]. Available from: https://scholar.uwindsor.ca/etd/7652.

Council of Science Editors:

PATEL PA. Productive Cluster Hire. [Masters Thesis]. University of Windsor; 2019. Available from: https://scholar.uwindsor.ca/etd/7652


University of Windsor

2. Gutha, Sindhuja. A deep learning approach to real-time short-term traffic speed prediction with spatial-temporal features.

Degree: MS, Computer Science, 2019, University of Windsor

In the realm of Intelligent Transportation Systems (ITS), accurate traffic speed prediction plays an important role in traffic control and management. The study on the prediction of traffic speed has attracted considerable attention from many researchers in this field in the past three decades. In recent years, deep learning-based methods have demonstrated their competitiveness to the time series analysis which is an essential part of traffic prediction. These methods can efficiently capture the complex spatial dependency on road networks and non-linear traffic conditions. We have adopted the convolutional neural network-based deep learning approach to traffic speed prediction in our setting, based on its capability of handling multi-dimensional data efficiently. In practice,the traffic data may not be recorded with a regular interval, due to many factors, like power failure, transmission errors,etc.,that could have an impact on the data collection. Given that some part of our dataset contains a large amount of missing values, we study the effectiveness of a multi-view approach to imputing the missing values so that various prediction models can apply. Experimental results showed that the performance of the traffic speed prediction model improved significantly after imputing the missing values with a multi-view approach, where the missing ratio is up to 50%. Advisors/Committee Members: Jessica Chen, Mehdi Kargar.

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Gutha, S. (2019). A deep learning approach to real-time short-term traffic speed prediction with spatial-temporal features. (Masters Thesis). University of Windsor. Retrieved from https://scholar.uwindsor.ca/etd/7704

Chicago Manual of Style (16th Edition):

Gutha, Sindhuja. “A deep learning approach to real-time short-term traffic speed prediction with spatial-temporal features.” 2019. Masters Thesis, University of Windsor. Accessed November 14, 2019. https://scholar.uwindsor.ca/etd/7704.

MLA Handbook (7th Edition):

Gutha, Sindhuja. “A deep learning approach to real-time short-term traffic speed prediction with spatial-temporal features.” 2019. Web. 14 Nov 2019.

Vancouver:

Gutha S. A deep learning approach to real-time short-term traffic speed prediction with spatial-temporal features. [Internet] [Masters thesis]. University of Windsor; 2019. [cited 2019 Nov 14]. Available from: https://scholar.uwindsor.ca/etd/7704.

Council of Science Editors:

Gutha S. A deep learning approach to real-time short-term traffic speed prediction with spatial-temporal features. [Masters Thesis]. University of Windsor; 2019. Available from: https://scholar.uwindsor.ca/etd/7704

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