Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item Model Footing Tests and Analytical Studies on Clayey Soil Bed Reinforced with Coconut Shell Mat(Springer Science and Business Media Deutschland GmbH, 2022) Kolathayar, S.; Gadekari, R.S.The cellular confinement systems are becoming popular in ground improvement because of their efficiency in improving the bearing capacity of soil due to the lateral confinement effect. The commercially available geocells are made from polymer materials and they are costly. This study presents the performance evaluation of coconut shell mat as a cellular confinement system in clayey soil. It is the first of its kind application of coconut shells for soil reinforcement through a lateral confinement mechanism. This soil reinforcement system using coconut shells is termed “Geococoshell” by the authors. A series of model plate load tests were conducted on unreinforced soil, soil reinforced with High-Density Polyethylene (HDPE) geocells, and soil reinforced with coconut shell mats to evaluate the performance of coconut shell mat reinforced soil bed. The results of the experiments showed that coconut shells reinforced clayey soil improved bearing capacity up to 1.5 times compared to HDPE geocell reinforced clayey bed. The effect of different patterns of placing coconut shell mat was also studied and discussed in the paper. The analytical studies have been conducted considering the reinforcement mechanisms of coconut shell mat embedded in the soil bed. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.Item An Efficient Parallel Branch Network for Multi-Class Classification of Prostate Cancer From Histopathological Images(John Wiley and Sons Inc, 2025) Srivastava, V.; Prabhu, A.; Sravya, S.; Vibha Damodara, K.; Lal, S.; Kini, J.Prostate cancer is one of the prevalent forms of cancer, posing a significant health concern for men. Accurate detection and classification of prostate cancer are crucial for effective diagnosis and treatment planning. Histopathological images play a pivotal role in identifying prostate cancer by enabling pathologists to identify cellular abnormalities and tumor characteristics. With the rapid advancements in deep learning, Convolutional Neural Networks (CNNs) have emerged as a powerful tool for tackling complex computer vision tasks, including object detection, classification, and segmentation. This paper proposes a Parallel Branch Network (PBN), a CNN architecture specifically designed for the automatic classification of prostate cancer into its subtypes from histopathological images. The paper introduces a novel Efficient Residual (ER) block that enhances feature representation using residual learning and multi-scale feature extraction. By utilizing multiple branches with different filter reduction ratios and dense attention mechanisms, the block captures diverse features while preserving essential information. The proposed PBN model achieved a classification accuracy of 93.16% on the Prostate Gleason dataset, outperforming all other comparison models. © 2025 Wiley Periodicals LLC.