Author
Ibrahima Faye
Other affiliations: Applied Science Private University, Petronas, Paul Sabatier University ...read more
Bio: Ibrahima Faye is an academic researcher from Universiti Teknologi Petronas. The author has contributed to research in topics: Curvelet & Feature extraction. The author has an hindex of 20, co-authored 182 publications receiving 2079 citations. Previous affiliations of Ibrahima Faye include Applied Science Private University & Petronas.
Papers published on a yearly basis
Papers
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TL;DR: The main purpose of this study is to address the issues like data forwarding, deployment and localization in UWSNs under different conditions, and presents a review and comparison of different algorithms proposed recently in order to fulfill this requirement.
305 citations
TL;DR: The results show that DVRP has better performance than other existing delay efficient routing protocols, in term of end-to-end delays, energy consumption, and data delivery ratios.
164 citations
TL;DR: In this survey, EEG inverse problem is discussed with its primary to most developed and recent solutions, the introduction to the field along with the categorization of different solutions and the relative advantages and limitations for each method are discussed.
144 citations
TL;DR: This paper presents a method for breast cancer diagnosis in digital mammogram images that depends on extracting the features that can maximize the ability to discriminate between different classes and is validated using 5-fold cross validation.
127 citations
TL;DR: A comparative study between wavelet and curvelet transform for breast cancer diagnosis in digital mammogram suggests that curvelettransform outperforms wavelet transform and the difference is statistically significant.
120 citations
Cited by
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01 Jan 2016
TL;DR: This is an introduction to the event related potential technique, which can help people facing with some malicious bugs inside their laptop to read a good book with a cup of tea in the afternoon.
Abstract: Thank you for downloading an introduction to the event related potential technique. Maybe you have knowledge that, people have look hundreds times for their favorite readings like this an introduction to the event related potential technique, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they are facing with some malicious bugs inside their laptop.
2,445 citations
01 Jan 1979
TL;DR: This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis and addressing interesting real-world computer Vision and multimedia applications.
Abstract: In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to: • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information. Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.
1,758 citations
TL;DR: Computer and Robot Vision Vol.
Abstract: Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.
1,426 citations
TL;DR: This paper presents the key features and the driver technologies of IoT, and identifies the application scenarios and the correspondent potential applications, and focuses on research challenges and open issues to be faced for the IoT realization in the real world.
1,178 citations
TL;DR: The psychology of fear and stress is also a way as one of the collective books that gives many advantages as discussed by the authors, and the advantages are not only for you, but for the other peoples with those meaningful benefits.
766 citations