Kotagiri Ramamohanarao

University of Melbourne, Melbourne, Victoria, Australia

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Publications (305)109.95 Total impact

  • Deepak Poola, Kotagiri Ramamohanarao, Rajkumar Buyya
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    ABSTRACT: Scientific workflows are used to model applications of high throughput computation and complex large scale data analysis. In recent years, Cloud computing is fast evolving as the target platform for such applications among researchers. Furthermore, new pricing models have been pioneered by Cloud providers that allow users to provision resources and to use them in an efficient manner with significant cost reductions. In this paper, we propose a scheduling algorithm that schedules tasks on Cloud resources using two different pricing models (spot and on-demand instances) to reduce the cost of execution whilst meeting the workflow deadline. The proposed algorithm is fault tolerant against the premature termination of spot instances and also robust against performance variations of Cloud resources. Experimental results demonstrate that our heuristic reduces up to 70% execution cost as against using only on-demand instances.
    Procedia Computer Science 12/2014; 29:523-533. DOI:10.1016/j.procs.2014.05.047
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    ABSTRACT: Altered expression profiles of microRNAs (miRNAs) are linked to many diseases including lung cancer. miRNA expression profiling is reproducible and miRNAs are very stable. These characteristics of miRNAs make them ideal biomarker candidates. This work is aimed to detect 2-and 3-miRNA groups, together with specific expression ranges of these miRNAs, to form simple linear discriminant rules for biomarker identification and biological interpretation. Our method is based on a novel committee of decision trees to derive 2-and 3-miRNA 100%-frequency rules. This method is applied to a data set of lung miRNA expression profiles of 61 squamous cell carcinoma (SCC) samples and 10 normal tissue samples. A distance separation technique is used to select the most reliable rules which are then evaluated on a large independent data set. We obtained four 2-miRNA and three 3-miRNA top-ranked rules. One important rule is that: If the expression level of miR-98 is above 7.356 and the expression level of miR-205 is below 9.601 (log2 quantile normalized MirVan miRNA Bioarray signals), then the sample is normal rather than cancerous with specificity and sensitivity both 100%. The classification performance of our best miRNA rules remarkably outperformed that by randomly selected miRNA rules. Our data analysis also showed that miR-98 and miR-205 have two common predicted target genes FZD3 and RPS6KA3, which are actually genes associated with carcinoma according to the Online Mendelian Inheritance in Man (OMIM) database. We also found that most of the chromosomal loci of these miRNAs have a high frequency of genomic alteration in lung cancer. On the independent data set (with balanced controls), the three miRNAs miR-126, miR-205 and miR-182 from our best rule can separate the two classes of samples at the accuracy of 84.49%, sensitivity of 91.40% and specificity of 77.14%. Our results indicate that rule discovery followed by distance separation is a powerful computational method to identify reliable miRNA biomarkers. The visualization of the rules and the clear separation between the normal and cancer samples by our rules will help biology experts for their analysis and biological interpretation.
    BMC Genomics 12/2014; 15(Suppl 9):S16. DOI:10.1186/1471-2164-15-S9-S16 · 4.04 Impact Factor
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    ABSTRACT: It is well known that processing big graph data can be costly on Cloud. Processing big graph data introduces complex and multiple iterations that raise challenges such as parallel memory bottlenecks, deadlocks, and inefficiency. To tackle the challenges, we propose a novel technique for effectively processing big graph data on Cloud. Specifically, the big data will be compressed with its spatiotemporal features on Cloud. By exploring spatial data correlation, we partition a graph data set into clusters. In a cluster, the workload can be shared by the inference based on time series similarity. By exploiting temporal correlation, in each time series or a single graph edge, temporal data compression is conducted. A novel data driven scheduling is also developed for data processing optimization. The experiment results demonstrate that the spatiotemporal compression and scheduling achieve significant performance gains in terms of data size and data fidelity loss.
    Journal of Computer and System Sciences 12/2014; 80(8). DOI:10.1016/j.jcss.2014.04.022 · 1.09 Impact Factor
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    ABSTRACT: Retinal arteriovenous (AV) nicking is a precursor for hypertension, stroke and other cardiovascular diseases. In this paper, an effective method is proposed for the analysis of retinal venular widths to automatically classify the severity level of AV nicking. We use combination of intensity and edge information of the vein to compute its widths. The widths at various sections of the vein near the crossover point are then utilized to train a random forest classifier to classify the severity of AV nicking. We analyzed 47 color retinal images obtained from two population based studies for quantitative evaluation of the proposed method. We compare the detection accuracy of our method with a recently published four class AV nicking classification method. Our proposed method shows 64.51% classification accuracy in-contrast to the reported classification accuracy of 49.46% by the state of the art method.
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    ABSTRACT: Dynamic resource provisioning and the notion of seemingly unlimited resources are attracting scientific workflows rapidly into Cloud computing. Existing works on workflow scheduling in the context of Clouds are either on deadline or cost optimization, ignoring the necessity for robustness. Robust scheduling that handles performance variations of Cloud resources and failures in the environment is essential in the context of Clouds. In this paper, we present a robust scheduling algorithm with resource allocation policies that schedule workflow tasks on heterogeneous Cloud resources while trying to minimize the total elapsed time (make span) and the cost. Our results show that the proposed resource allocation policies provide robust and fault-tolerant schedule while minimizing make span. The results also show that with the increase in budget, our policies increase the robustness of the schedule.
    2014 IEEE 28th International Conference on Advanced Information Networking and Applications (AINA); 05/2014
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    ABSTRACT: Retinal imaging can facilitate the measurement and quantification of subtle variations and abnormalities in retinal vasculature. Retinal vascular imaging may thus offer potential as a noninvasive research tool to probe the role and pathophysiology of the microvasculature, and as a cardiovascular risk prediction tool. In order to perform this, an accurate method must be provided that is statistically sound and repeatable. This paper presents the methodology of such a system that assists physicians in measuring vessel caliber (i.e., diameters or width) from digitized fundus photographs. The system involves texture and edge information to measure and quantify vessel caliber. The graphical user interfaces are developed to allow retinal image graders to select individual vessel area that automatically returns the vessel calibers for noisy images. The accuracy of the method is validated using the measured caliber from graders and an existing method. The system provides very high accuracy vessel caliber measurement which is also reproducible with high consistency.
    Computers in Biology and Medicine 01/2014; 44:1–9. DOI:10.1016/j.compbiomed.2013.07.018 · 1.90 Impact Factor
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    IEEE Transactions on Cloud Computing 01/2014; DOI:10.1109/TCC.2014.2382119
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    ABSTRACT: We discuss a new formulation of a fuzzy validity index that generalizes the Newman-Girvan (NG) modularity function. The NG function serves as a cluster validity functional in community detection studies. The input data is an undirected weighted graph that represents, e.g., a social network. Clusters correspond to socially similar substructures in the network. We compare our fuzzy modularity with two existing modularity functions using the well-studied Karate Club and American College Football datasets.
    IEEE Transactions on Fuzzy Systems 12/2013; 21(6). DOI:10.1109/TFUZZ.2013.2245135 · 6.31 Impact Factor
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    ABSTRACT: Blockmodelling is an important technique in social network analysis for discovering the latent structure in graphs. A blockmodel partitions the set of vertices in a graph into groups, where there are either many edges or few edges between any two groups. For example, in the reply graph of a question and answer forum, blockmodelling can identify the group of experts by their many replies to questioners, and the group of questioners by their lack of replies among themselves but many replies from experts. Non-negative matrix factorisation has been successfully applied to many problems, including blockmodelling. However, these existing approaches can fail to discover the true latent structure when the graphs have strong background noise or are sparse, which is typical of most real graphs. In this paper, we propose a new non-negative matrix factorisation approach that can discover blockmodels in sparse and noisy graphs. We use synthetic and real datasets to show that our approaches have much higher accuracy and comparable running times.
    Proceedings of the 22nd ACM international conference on Conference on information & knowledge management; 10/2013
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    ABSTRACT: Retinal vascular landmark points such as branching points and crossovers are important features for automatic retinal image matching and vascular abnormality detection. These landmark points can enable automatic screening of large dataset through the detection of vascular network abnormalities (i.e., arteriovenous nicking, retinal vein occlusion) which are important for hypertension and cardiovascular disease prediction. Existing methods for crossover point detection use only local information at each image pixel without considering vascular features to detect crossover positions. This leads to the misclassification of very acute crossovers which are represented by two bifurcation points in the skeleton image. In this article, we propose a robust method that utilizes both local information and vascular geometrical features at the crossing to distinguish crossover from non-crossover points in a retinal image. The proposed method was validated on fifteen high resolution retinal images and the results show that our method achieves higher accuracy than any existing methods. In particular, the proposed method can discover more than 74% (recall) of crossovers with a detection accuracy (fraction of detected crossover points that are correct) of 83% (precision). The detected crossovers provide essential results for the automatic detection of vascular network abnormalities, such as arteriovenous nicking, neovascularization, and retinal vein occlusion.
    2013 20th IEEE International Conference on Image Processing (ICIP); 09/2013
  • Pallab Kanti Roy, Alauddin Bhuiyan, Kotagiri Ramamohanarao
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    ABSTRACT: In this paper, a fully automated segmentation method is proposed to identify Multiple Sclerosis (MS) related white matter lesions from brain magnetic resonance imaging (MRI) data. The main contribution of this paper is to obtain a new texture feature set for MS Lesion segmentation that is a combination of local and global neighbourhood information. The proposed method adopts a robust intensity normalization technique and lesion contrast enhancementfilter for enhancing the region of interest. We use a Support Vector Machine (SVM) to classify lesion pixels and level set based active contour and morphological filtering to achieve higher accuracy on lesion pixel identification. Quantitative evaluation of the proposed method is carried on real MRI data set provided by MS Lesion Challenge 2008. The results obtained from our method indicate significant improvement in performance compare to three state of the art methods that shows the proposed method's high suitability for assisting the neurologist to detect the MS in clinical practice.
    2013 20th IEEE International Conference on Image Processing (ICIP); 09/2013
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    Pallab Kanti Roy, Alauddin Bhuiyan, Kotagiri Ramamohanarao
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    ABSTRACT: In this paper an automated method is presented for the detection of Focal Arteriolar Narrowing (FAN), a precursor for hypertension, stroke and other cardiovascular diseases. Our contribution in this paper is that, we have proposed a novel retinal blood vessel tracing and vessel width measurement algorithm, which is fully automated. We developed a novel method to detect FAN affected vessel segments by analysing their width distribution pattern. For initial results and quantitative evaluation of the proposed method, we evaluate our method on 30 color retinal images which are randomly selected by an experienced grader from SiMES dataset. We achieved the sensitivity of 75% and specificity of 98% in detecting FAN affected vessel segments and sensitivity of 80% and specificity of 86% in identifying healthy and FAN affected images. The acquired result shows the potential suitability of the proposed method for assisting the ophthalmologist to detect the FAN in clinical practice.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 07/2013; 2013:7376-7379. DOI:10.1109/EMBC.2013.6611262
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    ABSTRACT: Age-related macular degeneration (AMD) is a major cause of visual impairment in the elderly and identifying people with the early stages of AMD is important when considering the design and implementation of preventative strategies for late AMD. Quantification of drusen size and total area covered by drusen is an important risk factor for progression. In this paper, we propose a method to detect drusen and quantify drusen size along with the area covered with drusen in macular region from standard color retinal images. We used combined local intensity distribution, adaptive intensity thresholding and edge information to detect potential drusen areas. The proposed method detected the presence of any drusen with 100% accuracy (50/50 images). For drusen detection accuracy (DDA), the segmentations produced by the automated method on individual images achieved mean sensitivity and specificity values of 74.94% and 81.17%, respectively.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 07/2013; 2013:7392-7395. DOI:10.1109/EMBC.2013.6611266
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    ABSTRACT: Retinal arteriovenous nicking (AV nicking) is the phenomenon where the venule is compressed or decreases in its caliber at both sides of an arteriovenous crossing. Recent research suggests that retinal AVN is associated with hypertension and cardiovascular diseases such as stroke. In this article, we propose a computer method for assessing the severity level of AV nicking of an artery-vein (AV) crossing in color retinal images. The vascular network is first extracted using a method based on multi-scale line detection. A trimming process is then performed to isolate the main vessels from unnecessary structures such as small branches or imaging artefact. Individual segments of each vessel are then identified and the vein is recognized through an artery-vein identification process. A vessel width measurement method is devised to measure the venular caliber along its two segments. The vessel width measurements of each venular segment is then analyzed and assessed separately and the final AVN index of a crossover is computed as the most severity of its two segments. The proposed technique was validated on 69 AV crossover points of varying AV nicking levels extracted from retinal images of the Singapore Malay Eye Study (SiMES). The results show that the computed AVN values are highly correlated with the manual grading with a Spearman correlation coefficient of 0.70. This has demonstrated the accuracy of the proposed method and the feasibility to develop a computer method for automatic AV nicking detection. The quantitative measurements provided by the system may help to establish a more reliable link between AV nicking and known systemic and eye diseases, which deserves further examination and exploration.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 07/2013; 2013:5865-5868. DOI:10.1109/EMBC.2013.6610886
  • Mingzhong Wang, Liehuang Zhu, Kotagiri Ramamohanarao
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    ABSTRACT: Selecting appropriate services for task execution in workflows should not only consider budget and deadline constraints, but also ensure the best probability that workflow will succeed and minimize the potential loss in case of exceptions. This requirement is more critical for data-intensive applications in grids or clouds since any failure is costly. Therefore, we design a fine-grained risk evaluation model customized for workflows to precisely compute the cost of failure for selected services. In comparison with current course-grained model, ours takes the relation of task dependency into consideration and assigns higher impact factor to tasks at the end. Thereafter, we design the utility function with the model and apply a genetic algorithm to find the optimized service allocations, thereby maximizing the robustness of the workflow while minimizing the possible risk of failure. Experiments and analysis show that the application of customized risk evaluation model into service selection can generally improve the successful probability of a workflow while reducing its exposure to the risk.
    Computing 04/2013; 97(4). DOI:10.1007/s00607-013-0381-6 · 1.06 Impact Factor
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    ABSTRACT: Recent research suggests that retinal vessel caliber (or cross-sectional width) measured from retinal photographs is an important feature for predicting cardiovascular diseases (CVDs). One of the most utilized measures is to quantify retinal arteriolar and venular caliber as the Central Retinal Artery Equivalent (CRAE) and Central Retinal Vein Equivalent (CRVE). However, current computer tools utilize manual or semi-automatic grading methods to estimate CRAE and CRVE. These methods involve a significant amount of grader's time and can add a significant level of inaccuracy due to repetitive nature of grading and intragrader distances. An automatic and time efficient grading of the vessel caliber with highly repeatable measurement is essential, but is technically challenging due to a substantial variation of the retinal blood vessels' properties. In this paper, we propose a new technique to measure the retinal vessel caliber, which is an "edge-based" vessel tracking method. We measured CRAE and CRVE from each of the vessel types. We achieve very high accuracy (average 96.23%) for each of the cross-sectional width measurement compared to manually graded width. For overall vessel caliber measurement accuracy of CRAE and CRVE, we compared the results with an existing semi-automatic method which showed high correlation of 0.85 and 0.92, respectively. The intra-grader reproducibility of our method was high, with the correlation coefficient of 0.881 for CRAE and 0.875 for CRVE.
    Computer methods and programs in biomedicine 03/2013; 111(1). DOI:10.1016/j.cmpb.2013.02.004 · 1.09 Impact Factor
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    ABSTRACT: In this paper, we propose a new type of adaptive fuzzy inference system with a view to achieve improved performance for forecasting nonlinear time series data by dynamically adapting the fuzzy rules with arrival of new data. The structure of the fuzzy model utilized in the proposed system is developed based on the log-likelihood value of each data vector generated by a trained Hidden Markov Model. As part of its adaptation process, our system checks and computes the parameter values and generates new fuzzy rules as required, in response to new observations for obtaining better performance. In addition, it can also identify the most appropriate fuzzy rule in the system that covers the new data; and thus requires to adapt the parameters of the corresponding rule only, while keeping the rest of the model unchanged. This intelligent adaptive behavior enables our adaptive fuzzy inference system (FIS) to outperform standard FISs. We evaluate the performance of the proposed approach for forecasting stock price indices. The experimental results demonstrate that our approach can predict a number of stock indices, e.g., Dow Jones Industrial (DJI) index, NASDAQ index, Standard and Poor500 (S&P500) index and few other indices from UK (FTSE100), Germany (DAX) , Australia (AORD) and Japan (NIKKEI) stock markets, accurately compared with other existing computational and statistical methods.
    Neurocomputing 03/2013; 104:10–25. DOI:10.1016/j.neucom.2012.09.017 · 2.01 Impact Factor
  • Jianzhong Qi, Rui Zhang, Kotagiri Ramamohanarao, Hongzhi Wang, Zeyi Wen, Dan Wu
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    ABSTRACT: The volume of time series stream data grows rapidly in various applications. To reduce the storage, transmission and processing costs of time series data, segmentation and approximation is a common approach. In this paper, we propose a novel online segmentation algorithm that approximates time series by a set of different types of candidate functions (polynomials of different orders, exponential functions, etc.) and adaptively chooses the most compact one as the pattern of the time series changes. We call this algorithm the Adaptive Approximation (AA) algorithm. The AA algorithm incrementally narrows the feasible coefficient spaces (FCS) of candidate functions in coefficient coordinate systems to make each segment as long as possible given an error bound on each data point. We propose an algorithm called the FCS algorithm for the incremental computation of the feasible coefficient spaces. We further propose a mapping based index for similarity searches on the approximated time series. Experimental results show that our AA algorithm generates more compact approximations of the time series with lower average errors than the state-of-the-art algorithm, and our indexing method processes similarity searches on the approximated time series efficiently.
    World Wide Web 03/2013; 18(2). DOI:10.1007/s11280-013-0256-y · 1.62 Impact Factor
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    Kotagiri Ramamohanarao, Uyen T V Nguyen, Alauddin Bhuiyan
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    ABSTRACT: Retinal color imaging has enabled the identification and quantification of retinal vascular features such as vessel caliber, tortuosity and branching angle, etc. Studies have shown that these vascular features have association of pre-clinical or clinical cardiovascular diseases (CVD). In this paper, we discuss the automated measurement of retinal vascular features such as vessel width or caliber, vessel bifurcation and crossover point and vessel tortuosity. Vessel segmentation is the most important step for accurate and efficient vascular feature analysis. Therefore, we have proposed a method for retinal blood vessel segmentation to achieve high accuracy and efficiency. We introduce the existing techniques and their limitations. Following this, we discuss our proposed methods and analysis techniques to extract these vascular features from color retinal images. We also evaluated the results on our proposed feature extraction techniques by comparing other state of art methods.
    Biosignals and Biorobotics Conference (BRC); 01/2013
  • A. Hussain, A. Bhuiyan, A. Mian, K. Ramamohanarao
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    ABSTRACT: Retinal vascular branch and crossover points are unique features for each individual that can be used as a reliable biometric for personal authentication and can be used for information retrieval and security application. In this work, a novel biometric authentication scheme is proposed based on the retinal vascular network features. We apply an automatic technique to detect and identify retinal vascular branch and crossover points. These branch and crossover points are mapped from prominent blood vessels in the image. For this, a novel vessel width measurement method is applied and vessels more than certain widths are selected. Based on these vessel segments their corresponding branch and crossover points are identified. Invariant features are constructed through Geometric Hashing of the detected branch and crossover points. We consider the crossover points for modelling a basis pair and all other points together for locations in the hash table entries. Thus, the models are invariant to rotation, translation and scaling. For each person, the system is trained with the models to accept or reject a claimed identity. The initial results show that the proposed method has achieved 100% detection accuracy which is highly potential for reliable person identification.
    Digital Image Computing: Techniques and Applications (DICTA), 2013 International Conference on; 01/2013

Publication Stats

5k Citations
109.95 Total Impact Points

Institutions

  • 1982–2014
    • University of Melbourne
      • • Department of Computing and Information Systems
      • • Department of Electrical and Electronic Engineering
      • • Centre for Eye Research Australia
      Melbourne, Victoria, Australia
  • 1994–2012
    • Victoria University Melbourne
      Melbourne, Victoria, Australia
  • 1998
    • University of Vic
      Vic, Catalonia, Spain