TY - GEN
T1 - Parallel Implementation of kNN Algorithm for Breast Cancer Detection
AU - Athani, Suhas
AU - Joshi, Shreesha
AU - Rao, B. Ashwath
AU - Rai, Shwetha
AU - Kini, N. Gopalakrishna
N1 - Publisher Copyright:
© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2021
Y1 - 2021
N2 - Current advances in parallel processing technology aims at providing unmatched degree of computational power in upcoming days. Parallel computation is an efficient form of information processing which exploits the concurrency of execution. This paper investigates the use of parallel programming, when applied on k Nearest Neighbors (kNN) algorithm which is intended for classification and prediction of the large dataset. Breast cancer dataset is used for classification and prediction which consists of two labels namely, malignant and benign. kNN is a non-parametric algorithm which makes use of similarity measure to classify the dataset into different categories. The similarity between the data points is computed by using Euclidean distance formula. Multiple threads are created for parallel processing and an appropriate kNN graph is constructed, which helps in easier implementation. Finally, execution speeds for sequential and parallel programs is recorded. The results are verified by using frameworks namely, Message Passing Interface (MPI) and Compute Unified Device Architecture (CUDA) highlighting that parallel execution takes less time when compared to sequential execution.
AB - Current advances in parallel processing technology aims at providing unmatched degree of computational power in upcoming days. Parallel computation is an efficient form of information processing which exploits the concurrency of execution. This paper investigates the use of parallel programming, when applied on k Nearest Neighbors (kNN) algorithm which is intended for classification and prediction of the large dataset. Breast cancer dataset is used for classification and prediction which consists of two labels namely, malignant and benign. kNN is a non-parametric algorithm which makes use of similarity measure to classify the dataset into different categories. The similarity between the data points is computed by using Euclidean distance formula. Multiple threads are created for parallel processing and an appropriate kNN graph is constructed, which helps in easier implementation. Finally, execution speeds for sequential and parallel programs is recorded. The results are verified by using frameworks namely, Message Passing Interface (MPI) and Compute Unified Device Architecture (CUDA) highlighting that parallel execution takes less time when compared to sequential execution.
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U2 - 10.1007/978-981-15-5788-0_46
DO - 10.1007/978-981-15-5788-0_46
M3 - Conference contribution
AN - SCOPUS:85091331747
SN - 9789811557873
T3 - Advances in Intelligent Systems and Computing
SP - 475
EP - 483
BT - Evolution in Computational Intelligence - Frontiers in Intelligent Computing
A2 - Bhateja, Vikrant
A2 - Bhateja, Vikrant
A2 - Peng, Sheng-Lung
A2 - Zhang, Yu-Dong
A2 - Satapathy, Suresh Chandra
PB - Springer Science and Business Media Deutschland GmbH
T2 - 8th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2020
Y2 - 4 January 2020 through 5 January 2020
ER -
