J
Jawad Nagi
Researcher at Dalle Molle Institute for Artificial Intelligence Research
Publications - 38
Citations - 2191
Jawad Nagi is an academic researcher from Dalle Molle Institute for Artificial Intelligence Research. The author has contributed to research in topics: Gesture recognition & Swarm behaviour. The author has an hindex of 19, co-authored 38 publications receiving 1872 citations. Previous affiliations of Jawad Nagi include University of Lugano & Information Technology University.
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Proceedings ArticleDOI
Max-pooling convolutional neural networks for vision-based hand gesture recognition
Jawad Nagi,Frederick Ducatelle,Gianni A. Di Caro,Dan Ciresan,Ueli Meier,Alessandro Giusti,Farrukh Nagi,Jürgen Schmidhuber,Luca Maria Gambardella +8 more
TL;DR: This work uses a state-of-the-art big and deep neural network combining convolution and max-pooling for supervised feature learning and classification of hand gestures given by humans to mobile robots using colored gloves.
Journal ArticleDOI
Nontechnical Loss Detection for Metered Customers in Power Utility Using Support Vector Machines
TL;DR: In this article, the authors presented a new approach towards nontechnical loss (NTL) detection in power utilities using an artificial intelligence based technique, support vector machine (SVM), which provided a method of data mining, which involves feature extraction from historical customer consumption data.
Journal ArticleDOI
Improving SVM-Based Nontechnical Loss Detection in Power Utility Using the Fuzzy Inference System
TL;DR: The inclusion of human knowledge and expertise is presented into the SVM-based fraud detection model (FDM) with the introduction of a fuzzy inference system (FIS), in the form of fuzzy IF-THEN rules.
Proceedings ArticleDOI
Detection of abnormalities and electricity theft using genetic Support Vector Machines
TL;DR: This paper presents a hybrid approach towards non-technical loss analysis for electric utilities using genetic algorithm (GA) and support vector machine (SVM) and proves the proposed method is more effective compared to the current actions taken by TNB in order to reduce NTL activities.
Proceedings ArticleDOI
Automated breast profile segmentation for ROI detection using digital mammograms
TL;DR: An automated technique for mammogram segmentation that uses morphological preprocessing and seeded region growing (SRG) algorithm in order to remove digitization noises, suppress radiopaque artifacts, and remove the pectoral muscle, for accentuating the breast profile region is explored.