M
Mansoor Alam
Researcher at University of Toledo
Publications - 17
Citations - 901
Mansoor Alam is an academic researcher from University of Toledo. The author has contributed to research in topics: Reliability (statistics) & Software quality. The author has an hindex of 5, co-authored 14 publications receiving 800 citations.
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Journal ArticleDOI
The impact of plug-in hybrid electric vehicles on distribution networks: A review and outlook
TL;DR: In this article, the impact of plug-in hybrid electric vehicles (PHEVs) on the distribution network can be determined using the following aspects of PHEVs: driving patterns, charging characteristics, charge timing, and vehicle penetration.
Proceedings ArticleDOI
The impact of plug-in hybrid electric vehicles on distribution networks: a review and outlook
TL;DR: In this paper, the impact of plug-in hybrid electric vehicles (PHEVs) on the distribution network can be determined using the following aspects of PHEVs: driving patterns, charging characteristics, charge timing, and vehicle penetration.
Proceedings ArticleDOI
Reliability analysis of phased-mission systems: a practical approach
TL;DR: In this article, the authors present a technique to transform a phase-mission system into several non-phased-mission systems, and thus allow each phase to be modeled separately.
Journal ArticleDOI
TobSet: A New Tobacco Crop and Weeds Image Dataset and Its Utilization for Vision-Based Spraying by Agricultural Robots
Muhammad Shahab Alam,Mansoor Alam,Muhammad Tufail,Muhammad Umer Khan,Ahmet Güneş,Bashir Salah,Fazal Nasir,Waqas Saleem,Muhammad Tahir Khan +8 more
TL;DR: The results demonstrate that Faster R-CNN and YOLOv5-based vision systems can analyze plants at 10 and 16 frames per second (fps) with a classification accuracy of 98% and 94%, respectively, and the precise smart application of pesticides with the proposed system offered a 52% reduction in pesticide usage by spotting the targets only.
Proceedings ArticleDOI
A Data Science Approach to Football Team Player Selection
TL;DR: A data science approach to minimize the time taken in selecting a player for a team by considering the cost and player's skills as constraints is presented and shows that it leads to improved business profits through a systematic enhancement to football data sets.