34 citations · 97 across the 12 of their papers we have counts for
12 papers
Data-driven Residual Generation for Early Fault Detection with Limited Data
Hamed Khorasgani, Ahmed Farahat, Chetan Gupta
Traditionally, fault detection and isolation community has used system dynamic equations to generate diagnosers and to analyze detectability and isolability of the dynamic systems.…
Deep Time Series Models for Scarce Data
Qiyao Wang, Ahmed Farahat, Chetan Gupta +1
Time series data have grown at an explosive rate in numerous domains and have stimulated a surge of time series modeling research. A comprehensive comparison of different time seri…
Wisdom of the Ensemble: Improving Consistency of Deep Learning Models
Lijing Wang, Dipanjan Ghosh, Maria Teresa Gonzalez Diaz +5
Deep learning classifiers are assisting humans in making decisions and hence the user's trust in these models is of paramount importance. Trust is often a function of constant beha…
Health Indicator Forecasting for Improving Remaining Useful Life Estimation
Qiyao Wang, Ahmed Farahat, Chetan Gupta +1
Prognostics is concerned with predicting the future health of the equipment and any potential failures. With the advances in the Internet of Things (IoT), data-driven approaches fo…
Generative Adversarial Networks for Failure Prediction
Shuai Zheng, Ahmed Farahat, Chetan Gupta
Prognostics and Health Management (PHM) is an emerging engineering discipline which is concerned with the analysis and prediction of equipment health and performance. One of the ke…
Remaining Useful Life Estimation Using Functional Data Analysis
Qiyao Wang, Shuai Zheng, Ahmed Farahat +2
Remaining Useful Life (RUL) of an equipment or one of its components is defined as the time left until the equipment or component reaches its end of useful life. Accurate RUL estim…
Two Birds with One Network: Unifying Failure Event Prediction and Time-to-failure Modeling
Karan Aggarwal, Onur Atan, Ahmed Farahat +3
One of the key challenges in predictive maintenance is to predict the impending downtime of an equipment with a reasonable prediction horizon so that countermeasures can be put in…
Supervised Dictionary Learning and Sparse Representation-A Review
Mehrdad J. Gangeh, Ahmed K. Farahat, Ali Ghodsi +1
Dictionary learning and sparse representation (DLSR) is a recent and successful mathematical model for data representation that achieves state-of-the-art performance in various fie…
Greedy Column Subset Selection for Large-scale Data Sets
Ahmed K. Farahat, Ahmed Elgohary, Ali Ghodsi +1
In today's information systems, the availability of massive amounts of data necessitates the development of fast and accurate algorithms to summarize these data and represent them…
A Fast Greedy Algorithm for Generalized Column Subset Selection
Ahmed K. Farahat, Ali Ghodsi, Mohamed S. Kamel
This paper defines a generalized column subset selection problem which is concerned with the selection of a few columns from a source matrix A that best approximate the span of a t…
Discriminative Density-ratio Estimation
Yun-Qian Miao, Ahmed K. Farahat, Mohamed S. Kamel
The covariate shift is a challenging problem in supervised learning that results from the discrepancy between the training and test distributions. An effective approach which recen…
Embed and Conquer: Scalable Embeddings for Kernel k-Means on MapReduce
Ahmed Elgohary, Ahmed K. Farahat, Mohamed S. Kamel +1
The kernel -means is an effective method for data clustering which extends the commonly-used -means algorithm to work on a similarity matrix over complex data structures. The…