Vasu Jindal

Workload Estimation for Improving Resource Management Decisions in the Cloud

In cloud computing, good resource management can benefit both cloud users as well as cloud providers. Workload prediction is a crucial step towards achieving good resource management. While it is possible to estimate the workloads of long-running tasks based on the periodicity in their historical workloads, it is difficult to do so for tasks which do not have such recurring workload patterns. In this paper, we present an innovative clustering based resource estimation approach which groups tasks that have similar characteristics into the same cluster. The historical workload data for tasks in a cluster are used to estimate the resources needed by new tasks based on the cluster(s) to which they belong. In particular, for a new task T, we measure Ts initial workload and predict to which cluster(s) it may belong. Then, the workload information of the cluster(s) is used to estimate the workload of T. The approach is experimentally evaluated using Google dataset, including resource usage data of over half a million tasks. We develop a workload model based on the dataset which is then used to estimate the workload patterns of several randomly selected tasks from the trace log. The results confirm the effectiveness of this cluster-based method for estimating the resources required by each task.

A two-stage clustering technique for automatic biaxial gating of flow cytometry data

Measurement of various markers of single cells using flow cytometry has several biological applications. These applications include improving our understanding of behavior of cellular systems, identifying rare cell populations and personalized medication. A common critical issue in the existing methods is approximation of the number of cellular populations which heavily affects the accuracy of results. In this work, we propose a novel technique to estimate the number of dominant subtypes and identify them in flow cytometry datasets. Our experimentation on 42 flow cytometry datasets indicates high performance and accurate clustering (F-measure > 91%) in identifying the main cellular populations.

Integrating mobile and cloud for PPG signal selection to monitor heart rate during intensive physical exercise

Heart rate monitoring has become increasingly popular in the industry through mobile phones and wearable devices. However current determination of heart rate through mobile applications suffer from high corruption of signals during intensive physical exercise. In this paper, we present a novel technique for accurately determining heart rate during intensive motion by classifying PPG signals obtained from smartphones or wearable devices combined with motion data obtained from accelerometer sensors. Our approach utilizes the Internet of Things (IoT) cloud connectivity of smartphones for PPG signals selection using deep learning. The technique is validated using the TROIKA dataset and is accurately able to predict heart rate with a 10-fold cross validation error margin of 4.88%.

An adaptive deep learning approach for PPG-based identification

Wearable biosensors have become increasingly popular in healthcare due to their capabilities for low cost and long term biosignal monitoring. This paper presents a novel two-stage technique to offer biometric identification using these biosensors through Deep Belief Networks and Restricted Boltzman Machines. Our identification approach improves robustness in current monitoring procedures within clinical, e-health and fitness environments using Photoplethysmography (PPG) signals through deep learning classification models. The approach is tested on TROIKA dataset using 10-fold cross validation and achieved an accuracy of 96.1%.Wearable biosensors have become increasingly popular in healthcare due to their capabilities for low cost and long term biosignal monitoring. This paper presents a novel two-stage technique to offer biometric identification using these biosensors through Deep Belief Networks and Restricted Boltzman Machines. Our identification approach improves robustness in current monitoring procedures within clinical, e-health and fitness environments using Photoplethysmography (PPG) signals through deep learning classification models. The approach is tested on TROIKA dataset using 10-fold cross validation and achieved an accuracy of 96.1%.

Single and multi-subject clustering of flow cytometry data for cell-type identification and anomaly detection

BackgroundMeasurement of various markers of single cells using flow cytometry has several biological applications. These applications include improving our understanding of behavior of cellular systems, identifying rare cell populations and personalized medication. A common critical issue in the existing methods is identification of the number of cellular populations which heavily affects the accuracy of results. Furthermore, anomaly detection is crucial in flow cytometry experiments. In this work, we propose a two-stage clustering technique for cell type identification in single subject flow cytometry data and extend it for anomaly detection among multiple subjects.ResultsOur experimentation on 42 flow cytometry datasets indicates high performance and accurate clustering (F-measure > 91 %) in identifying main cellular populations. Furthermore, our anomaly detection technique evaluated on Acute Myeloid Leukemia dataset results in only <2 % false positives.

A Personalized Markov Clustering and Deep Learning Approach for Arabic Text Categorization.

Text categorization has become a key research field in the NLP community. However, most works in this area are focused on Western languages ignoring other Semitic languages like Arabic. These languages are of immense political and social importance necessitating robust categorization techniques. In this paper, we present a novel three-stage technique to efficiently classify Arabic documents into different categories based on the words they contain. We leverage the significance of root-words in Arabic and incorporate a combination of Markov clustering and Deep Belief Networks to classify Arabic words into separate groups (clusters). Our approach is tested on two public datasets giving a F-Measure of 91.02%.

Integrating Clustering and Learning for Improved Workload Prediction in the Cloud

Good resource management is very important in the cloud and workload prediction is a crucial step towards achieving good resource management. While it is possible to predict the workloads of long-running tasks based on the seasonality in their historical workloads, it is difficult to do so for tasks which do not have such recurring workload patterns. In this paper, we consider a different solution for task workload prediction. Instead of using the historical workload of a task to predict the future workload of the same task, we use the knowledge about the workloads of a pool of tasks to help predict the workloads of new tasks. In this paper, we develop a clustering and learning based approach to realize this concept. First, the workloads of existing tasks are grouped into multiple clusters. Then, neural network is used to learn the characteristics of the workloads of each cluster. For each new task, we collect its initial workload, determine its cluster, and use the trained neural network of its cluster to predict its future workload. Our approach is experimentally evaluated using Google dataset. The results confirm the effectiveness of our integrated scheme.

Towards an intelligent fault prediction code editor to improve software quality using deep learning

Software quality assurance has become the pillar for success in software companies. High quality, low maintenance programs can be achieved if fault-prone modules can be identified early in the development lifecycle. In this paper, we propose a new intelligent Integrated Development Environment (IDE) which seamlessly allow programmers to test their code for faults using prior source code databases. Our IDE is built upon deep learning models for making recommendations. The editor also gives scores to programmers on their program design. We evaluate and validate our approach using famous NASA code repositories.

A deep learning framework for identification of microRNA regulatory modules: student research abstract

MicroRNAs (miRNAs) belong to the family of RNAs and are known to repress the expressions of their targets. They work cooperatively with genes in post-transcriptional gene regulation called miRNA regulatory modules (MRMs). Furthermore, understanding the functional roles of miRNAs is essential to explain their combinatorial effects in complex cellular processes like cancer. In this paper, we present a novel Markov graph clustering and deep learning based approach to identify these MRMs. We leverage miRNA-target binding information, ii) gene expression data iii) miRNA expression profiles, iv) Protein- Protein and predict the regulatory modules using Deep Belief Networks and Restricted Boltzmann Machines. Our experiments on breast cancer dataset results in 34 identified MRMs which are significantly enriched in known functional sets and consist of highly-related miRNAs and their target genes with respect to underlying biological processes.