Aisha Batool
Iqra University
8 Papers
19 Citations
Aisha Batool is an academic researcher from Iqra University. The author has contributed to research in topics: Computer science & Big data. The author has an hindex of 3, co-authored 6 publications.
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Papers
Big data reduction framework for value creation in sustainable enterprises
TL;DR: A novel concept of big data reduction at the customer end is presented in which early data reduction operations are performed to achieve multiple objectives, such as lowering the service utilization cost, enhancing the trust between customers and enterprises, and preserving privacy of customers.
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iELMNet: Integrating Novel Improved Extreme Learning Machine and Convolutional Neural Network Model for Traffic Sign Detection
Aisha Batool,Muhammad Wasif Nisar,Jamal Hussain Shah,Muhammad Attique Khan,Ahmed A. Abd El-Latif +4 more
- 06 Jan 2022
TL;DR: Li et al. as discussed by the authors proposed an improved extreme learning machine network (ELM), convolutional neural network (CNN), and scale transformation (ST)-based model to detect traffic signs in real-time environment.
13
Big Data Analytics in Mobile and Cloud Computing Environments
Muhammad Habib ur Rehman,Atta ur Rehman Khan,Aisha Batool +2 more
- 01 Jan 2016
TL;DR: This chapter presents a thorough discussion about mobile computing systems and their implication for big data analytics with different perspectives involving descriptive, predictive, and prescriptive analytical methods.
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Pattern-based Data Sharing in Big Data Environments
Muhammad Habib ur Rehman,Aisha Batool +1 more
- 11 Aug 2015
TL;DR: The proposed methodology enables local data processing near the data sources and transforms the raw data streams into actionable knowledge patterns that have dual utility of availability of local knowledge patterns for immediate actions as well as for participatory data sharing in big data environments.
Traffic sign recognition using proposed lightweight twig-net with linear discriminant classifier for biometric application
Aisha Batool,Muhammad Wasif Nisar,Muhammad Attique Khan,Jamal Hussain Shah,Usman Tariq,Robertas Damaševičius +5 more
TL;DR: In this article , a 30-layered deep Convolutional Neural Network (CNN) model is proposed to recognize traffic signs robustly and efficiently in challenging environmental conditions, keeping the diversity of the problem in mind, a novel 30-layer deep CNN model was proposed.
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