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International Conference on data science and advanced analytics (DSAA)

作者： Saiyedul Islam Sundar Balasubramaniam Poonam Goyal Ankit Sultana Lakshit Bhutani Saurabh Raje Navneet Goyal Advanced Data Analytics & Parallel Technologies Laboratory Birla Institute of Technology and Science Pilani India

Big data has significantly increased the dependence of data analytics community on High Performance Computing (HPC) systems. However, efficiently programming an HPC system is still a tedious task requiring specialized skills in parallelization and the use of platform-specific languages as well as mechanisms. We present a framework for quickly prototyping new/existing density-based clustering algorithms while obtaining low running times and high speedups via automatic parallelization. The user is required only to specify the sequential algorithm in a Domain Specific Language (DSL) for clustering at a very high level of abstraction. The parallelizing compiler for the DSL does the rest to leverage distributed systems - in particular, typical scale-out clusters made of commodity hardware. Our approach is based on recurring, parallelizable programming patterns known as Kernels, which are identified and parallelized by the compiler. We demonstrate the ease of programming and scalable performance for DBSCAN, SNN, and RECOME algorithms. We also establish that the proposed approach can achieve performance comparable to state-of-the-art manually parallelized implementations while requiring minimal programming effort that is several orders of magnitude smaller than those required on other parallel platforms like MPI/Spark.

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Scalable Parallel Algorithms for Shared Nearest Neighbor Clustering

Scalable Parallel Algorithms for Shared Nearest Neighbor Clu...

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International Conference on High Performance Computing

作者： Sonal Kumari Saurabh Maurya Poonam Goyal Sundar S Balasubramaniam Navneet Goyal Department of Computer Science & Information Systems Advanced Data Analytics & Parallel Technologies Laboratory INDIA

ISBN: (纸本)9781509054121

Clustering is a popular data mining technique which discovers structure in unlabeled data by grouping objects together on the basis of a similarity criterion. Traditional similarity measures lose their meaning as the number of dimensions increases and as a consequence, distance or density based clustering algorithms become less meaningful. Shared Nearest Neighbor (SNN) is a solution to clustering high-dimensional data with the ability to find clusters of varying density. SNN assigns objects to a cluster, which share a large number of their nearest neighbors. However, SNN is compute and memory intensive for data of large size and/or dimensionality. Nearest neighbor queries are responsible for a major proportion of computations in SNN, resulting in lower efficiency for higher value of number of nearest neighbors (k). The main motivation of this work is to improve the efficiency of SNN and to parallelize it so that it can be used for clustering large high-dimensional datasets and for large values of k. Existing SNN algorithms become inefficient in these situations. In this paper, we present a new sequential SNN algorithm, R-SNN, which uses R-tree for executing neighborhood queries efficiently and exploiting spatial locality to minimize memory usage. R-SNN is benchmarked against the best available implementation of SNN and is found up to 77 times faster when tested on various real datasets. R-SNN is parallelized for distributed memory, shared memory, and hybrid systems. Significant speedup and scalability achieved can be attributed to parallelization and good load balancing strategies and also to exploitation of spatial locality. Experimental results demonstrate the same for datasets of varying dimensionality and size. The maximum speedup achieved for shared, distributed, and hybrid models are 427.19 using 48 threads, 394.24 using 32 processes, and 1380.69 on 32 nodes (with each node spawning 4 threads), respectively. Super-linear speedup for some datasets is attributed

关键词： Clustering algorithms Algorithm design and analysis Time complexity Spatial databases Parallel algorithms Memory management Density measurement

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An Improved Unbalanced data Classification Method Based on Hybrid Sampling Approach 4

An Improved Unbalanced Data Classification Method Based on H...

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2021 IEEE 4th International Conference on Big data and Artificial Intelligence, BDAI 2021

作者： Xu, Biru Wang, Wenjia Yang, Rui Han, Qi University of Liverpool Department of Computer Science Liverpool United Kingdom School of Advanced Technology Research Institute of Big Data Analytics Xi an Jiaotong-Liverpool University Suzhou China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9781665412704

The problem of data imbalance has received far- reaching concerns since they could affect the accuracy of classification problem in the area of machine learning. As the minority class instances can be ignored by traditional classifiers, it is necessary to improve the recognition rate of minority instances. Therefore, the paper proposes a new hybrid sampling method to solve the data imbalance problem by enlarging the proportion of minority instances. For the oversampling part, a variant of SMOTE is provided combining methods of LR-SMOTE and CCR (Combined Cleaning and Resampling Algorithm);for the under-sampling part, the Tomek-link method is utilized to complete the task. After the pre-processing stage, the data set is classified by Random Forest (RF). Experimental results show that the novel algorithm effectively enhances the performance of RF on the data set with a higher accuracy. © 2021 IEEE.

关键词： data mining

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Nitrogen-Doped Amorphous Carbon Homojunction from Palmyra Sugar as a Renewable Solar Cell

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Journal of Renewable Materials 2024年第1期12卷 57-69页

作者： Budhi Priyanto Imam Khambali Irma Septi Ardiani Khoirotun Nadhiyah Anna Zakiyatul Laila M.Chasrun Hasani Bima Romadhon Retno Asih Yoyok Cahyono Triwikantoro Darminto Department of Electronic Engineering Faculty of EngineeringUniversitas Muhammadiyah MalangKota MalangIndonesia Advanced Materials Research Group Department of PhysicsFaculty of Science and Data AnalyticsInstitut Teknologi Sepuluh NopemberSurabayaIndonesia Department of Electronic Engineering Faculty of Industrials EngineeringInstitut Teknologi National MalangKota MalangIndonesia

An a-C/a-C:N junction,which used palmyra sugar as the carbon source and ammonium hydroxide(NH4OH)as the dopant source,was successfully deposited on the ITO glass substrate using the nano-spraying *** current-voltage relationship of the junction was found to be a Schottky-like contact,and therefore the junction shows the characteristic *** means the a-C and a-C:N are semiconductors with different types of ***,the samples showed an increase in current and voltage value when exposed to visible light(bright state)compared to the dark condition,thereby,indicating the creation of electron-hole pairs during the *** was also discovered that the relationship between current and voltage for the a-C/a-C:N junction sample formed a curve that satisfies the rule of the photovoltaic effect when exposed to visible light from a light *** exposure of this sample to direct sunlight at AM 1.5 conditions produced a curve that meets the rules for the emergence of the photovoltaic effect with higher characteristics for the current-voltage ***,the a-C/a-C:N junction sample is a solar cell successfully fabricated using a sample method and has a maximum efficiency of 0.0013%.

关键词： Solar cell amorphous carbon palmyra sugar

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The Role of Lactate Metabolism in Heart Failure and Cardiogenic Shock: Clinical Insights and Therapeutic Implications

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Journal of Cardiac Failure 2025年

作者： YUZEFPOLSKAYA, MELANA SCHWARTZ, SARAH LADANYI, ANNAMARIA ABRAHAM, JACOB GALE, CHRIS P. GRINSTEIN, JONATHAN KLEIN, LIVIU MAJURE, DAVID T. PHANCAO, ANITA SHEIKH, FAROOQ H. COLOMBO, PAOLO C. JANUZZI, JAMES L. MOLINA, EZEQUIEL J. Division of Cardiovascular Medicine Department of Cardiology New York Presbyterian Hospital Columbia University New York NY United States Center for Cardiovascular Analytics Research & Data Science (CARDS) Providence St. Joseph Research Network Portland OR United States Leeds Institute of Cardiovascular and Metabolic Medicine University of Leeds Leeds UK Leeds Institute for Data Analytics University of Leeds Leeds UK Leeds Teaching Hospitals NHS Trust Leeds United Kingdom Division of Cardiovascular Medicine Department of Cardiology University of Chicago Chicago IL United States Division of Cardiovascular Medicine Department of Cardiology University of San Francisco San Francisco CA United States Division of Cardiovascular Medicine Department of Cardiology New York Presbyterian Hospital Weill Cornell Medical College New York NY United States Division of Cardiovascular Medicine Department of Cardiology University of Miami Miami FL United States Advanced Heart Failure Program MedStar Health Georgetown University School of Medicine Washington D.C. United States Cardiology Division Massachusetts General Hospital Harvard Medical School Baim Institute for Clinical Research Boston MA United States Division of Cardiothoracic Surgery Department of Surgery Piedmont Heart Institute Atlanta GA United States

Heart failure (HF) is associated with poor prognosis, especially when it progresses to cardiogenic shock (CS), where survival rates substantially decline. A key area of interest is the role of blood lactate as a biomarker in these conditions. Lactate is produced under normal physiological conditions but increases with impaired tissue perfusion, a hallmark of HF and CS. Elevated lactate levels result from increased production, reduced clearance or both and are often associated with worse outcomes. Traditionally considered a byproduct of anaerobic metabolism, lactate is now recognized as an important energy substrate, particularly in myocardial tissue during periods of metabolic stress. Recent studies suggest that dynamic lactate monitoring, including lactate clearance (LC), may provide critical insights into patients’ prognoses and responses to therapy. Serial measurements of lactate have been shown to predict survival in critically ill patients, including those with HF and CS. In CS, elevated lactate levels correlate with increased mortality risk, and LC is emerging as an important parameter in treatment protocols. Despite growing evidence of lactate's clinical relevance, research is needed to establish standardized thresholds and optimal monitoring timelines. Understanding the complexities of lactate metabolism and its role in HF and CS could lead to improved risk stratification and more personalized treatment approaches. © 2025

关键词： cardiogenic shock Heart failure lactate levels

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On Counterfactual Explanations of Cardiovascular Risk in Adolescent and Young Adult Breast Cancer Survivors 3

On Counterfactual Explanations of Cardiovascular Risk in Ado...

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3rd AIxIA Workshop on Artificial Intelligence For Healthcare, HC@AIxIA 2024

作者： Bernasconi, Alice Balordi, Antonio Zanga, Alessio Cabañas, Rafael University of Milano-Bicocca Milan Italy Evaluative Epidemiology Unit Department of Epidemiology and Data Science Fondazione IRCCS Istituto Nazionale dei tumori Milan Italy Data Science and Advanced Analytics F. Hoffmann - La Roche Ltd. Basel Switzerland Department of Mathematics and CDTIME University of Almería Almería Spain

In the last decades, the growing population of cancer survivors has shifted researchers' focus from primary toward tertiary prevention. Particularly, adolescents and young adults (AYAs) breast cancer (BC) survivors may face long-term outcomes as a result of their treatments, among which cardiovascular diseases (CVDs) are the most life-threatening ones. To plan effective follow-up guidelines for preventing and treating these events, it is essential to disentangle the causal role of cancer treatments in these patients. In this work, we aim to extend the current state of BC treatment guidelines by leveraging on the estimate of the risk of CVDs in AYAs who underwent BC treatments, as provided by a causal Bayesian network. In these regards, we provide counterfactual explanations of a causal query, using real-world data, algorithms and methods from the causal inference domain. We show that while ovarian suppression combined with tamoxifen may be a necessary cause for ischemic heart disease, it is not a sufficient one, i.e., this treatment alone is not enough to cause the disease, other factors must also be present. These findings can contribute to support clinicians in the treatment choice and help in refining treatment strategies and follow-up protocols for AYAs, advancing personalised healthcare in oncology. © 2024 CEUR-WS. All rights reserved.

关键词： Lung cancer

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Modeling the Impact of Transportation Availability and Travel Distance on Healthcare Outcomes: A Bagged Random Forest Approach

Modeling the Impact of Transportation Availability and Trave...

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IISE Annual Conference and Expo 2023

作者： Kasasbeh, Anemone Yildirim, Mehmet Booth, Amy Khan, Naseem Sayama, Hiroki Department of Systems Science and Industrial Engineering State University of New York at Binghamton BinghamtonNY13902 United States Advanced Data Analytics United Health Services Hospitals Inc. VestalNY13850 United States

ISBN: (纸本)9781713877851

The lack of access to healthcare can lead to poor health outcomes. Factors that prevent patients from getting to their medical care appointments can be associated with an increased risk of adverse health conditions. These factors, such as limited access to transportation and long travel distance, may lead to higher no-show rates and gaps in care. No-shows and gaps in care are claimed to be associated with more emergency department (ED) visits as well as more hospitalizations. In this study, we investigated the impact of travel distance and transportation availability on attending a medical care appointment. data used in this research were extracted from a community hospital in Broome County, New York. The acquired data spans over the time between January 2021 and July 2022. The response variable in this study is the patient-associated risk score. A bagged random forest meta-ensemble predictive model was developed using a k-fold cross-validation training approach. The model adequacy was assessed using performance measures, including accuracy, sensitivity, and ROC. Results showed that travel distance between a patient’s residential address and their healthcare provider negatively impacted their health outcomes. This finding can help design solutions and interventions such as transportation funding programs and/or expanding bus routes to provide broader transportation coverage. © IISE and Expo *** rights reserved.

关键词： Health risks

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Efficiency ordering of stochastic gradient descent 22

Efficiency ordering of stochastic gradient descent

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Jie Hu Vishwaraj Doshi Do Young Eun Department of Electrical and Computer Engineering North Carolina State University Data Science and Advanced Analytics IQVIA

ISBN: (纸本)9781713871088

We consider the stochastic gradient descent (SGD) algorithm driven by a general stochastic sequence, including i.i.d noise and random walk on an arbitrary graph, among others; and analyze it in the asymptotic sense. Specifically, we employ the notion of 'efficiency ordering', a well-analyzed tool for comparing the performance of Markov Chain Monte Carlo (MCMC) samplers, for SGD algorithms in the form of Loewner ordering of covariance matrices associated with the scaled iterate errors in the long term. Using this ordering, we show that input sequences that are more efficient for MCMC sampling also lead to smaller covariance of the errors for SGD algorithms in the limit. This also suggests that an arbitrarily weighted MSE of SGD iterates in the limit becomes smaller when driven by more efficient chains. Our finding is of particular interest in applications such as decentralized optimization and swarm learning, where SGD is implemented in a random walk fashion on the underlying communication graph for cost issues and/or data privacy. We demonstrate how certain non-Markovian processes, for which typical mixingtime based non-asymptotic bounds are intractable, can outperform their Markovian counterparts in the sense of efficiency ordering for SGD. We show the utility of our method by applying it to gradient descent with shuffling and mini-batch gradient descent, reaffirming key results from existing literature under a unified framework. Empirically, we also observe efficiency ordering for variants of SGD such as accelerated SGD and Adam, open up the possibility of extending our notion of efficiency ordering to a broader family of stochastic optimization algorithms.

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A Parallel Framework for Grid-Based Bottom-Up Subspace Clustering

A Parallel Framework for Grid-Based Bottom-Up Subspace Clust...

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International Conference on data science and advanced analytics (DSAA)

作者： Poonam Goyal Sonal Kumari Shubham Singh Vivek Kishore Sundar S. Balasubramaniam Navneet Goyal Department of Computer Science & Information Systems Advanced Data Analytics & Parallel Technologies Laboratory Pilani Campus INDIA

ISBN: (纸本)9781509052073

Clustering is a popular data mining and machine learning technique which discovers interesting patterns from unlabeled data by grouping similar objects together. Clustering high-dimensional data is a challenging task as points in high dimensional space are nearly equidistant from each other, rendering commonly used similarity measures ineffective. Subspace clustering has emerged as a possible solution to the problem of clustering high-dimensional data. In subspace clustering, we try to find clusters in different subspaces within a dataset. Many subspace clustering algorithms have been proposed in the last two decades to find clusters in multiple overlapping subspaces of high-dimensional data. Subspace clustering algorithms iteratively find the best subset of dimensions for a cluster from 2d-1 possible combinations in d-dimensional data. Subspace clustering is extremely compute intensive because of exhaustive search of subspaces, especially in the bottom-up subspace clustering algorithms. To address this issue, an efficient parallel framework for grid-based bottom-up subspace clustering algorithms is developed, considering popular algorithms belonging to this category. The framework is implemented for shared memory, distributed memory, and hybrid systems and is tested for three grid-based bottom-up subspace clustering algorithms: CLIQUE, MAFIA, and ENCLUS. All parallel implementations exhibit impressive speedup and scalability on real datasets.

关键词： Conferences

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Rapid Prototyping of Hierarchical Agglomerative Clustering Algorithms for Distributed Systems

Rapid Prototyping of Hierarchical Agglomerative Clustering A...

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IEEE International Conference on Big data

作者： Saiyedul Islam Navneet Goyal Sundar Balasubramaniam Poonam Goyal Achal Agarwal Kirti Singh Rathore Nischay Singh Advanced Data Analytics & Parallel Technologies Laboratory Birla Institute of Technology & Science Pilani Pilani Campus India

ISBN: (数字)9781728108582

ISBN: (纸本)9781728108599

Hierarchical Agglomerative Clustering (HAC) algorithms are used in many applications where clusters have a hierarchical relationship between them. Their parallelization is challenging due to the dependence of every agglomeration step on all previous agglomerations. Although a few parallel algorithms have been proposed for SLINK HAC algorithm, only limited work has been done to parallelize other HAC algorithms. In this paper, we present a high-level abstraction, which provides a uniform way to specify any HAC algorithm, and a framework for automatic parallelization of the same for distributed memory systems. The abstraction is supported by constructs in a high level, domain specific language, and a compiler translates algorithms expressed in this language to efficient parallel code targeting distributed systems. Our experiments on multiple HAC algorithms proves that the runtime performance achieved is comparable with state-of-the-art manual parallel implementations on Spark and MPI while requiring only a fraction of the programming effort. At runtime, master-slave execution is used, and load is balanced among the slaves in an algorithm-agnostic way, which is a significant contrast to custom load-balancing techniques seen in the literature on parallel HAC algorithms.

关键词： Clustering algorithms Programming Merging Heuristic algorithms DSL Measurement Manuals

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