6 results for “Fully-Connected Layer”

Set a search alert

Research ArticleOpen accessPhrase in the titleAnalyzing Effect on Residual Learning by Gradual Narrowing Fully-Connected Layer Width and Implementing Inception Block in Convolution LayerSaurabh SharmaResearch conducted on the advancement of CNN architecture for computer vision problems focuses on strategically choosing and modifying convolution hyperparameters (kernel, pooling, etc.). However, these research works don't exploit the advantage of employing multi fully-connected layers post the core schema to avail further performance improvements, which have been identified as the first research gap. Studies were also conducted to address … …Journal of Computer Science, Vol. 18, Issue 5 · 24 May 2022 · 6,044 accessesView PDF Cite

Research ArticleOpen accessIn the abstractLeveraging Modified Cascaded Fully Convolutional Neural Networks and Optimized Swin U-Net for Liver Lesion PredictionSugandha Chakraverti, Tejaswi Khanna, Vijay ShuklaLiver lesions are abnormal growths that can develop for various reasons, some of which are noncancerous (benign) while others are cancerous. Machine learning and, deep learning techniques are employed for liver health assessment and lesion detection, but the quality and, diversity of training data can hinder the performance, leading to overfitting or underfitting. These models often need extensive pre-processing and … …Journal of Computer Science, Vol. 22, Issue 10 · 8 Oct 2026 · 372 accessesView PDF Cite

Research ArticleOpen accessIn the abstractDual-Branch Shape Texture Learning With Pyramid Residual and Sobel Edge Layers for Rice Variety ClassificationM Pradeep, M SiddappaAutomated rice variety classification categorizes rice types based on features, such as grain texture, shape, color, and size. Traditional handcrafted feature-based models fail at rice classification due to lighting variations, background noise, and environmental conditions, leading to overlapping visual characteristics and frequent misclassification. To address these challenges, this research proposes a novel Dual-Branch Convolutional Neural Network with Pyramid Residual Units … …Journal of Computer Science, Vol. 22, Issue 9 · 24 Sep 2026 · 627 accessesView PDF Cite

Research ArticleOpen accessIn the abstractImproving Arabic Named Entity Recognition with a Modified Transformer EncoderHamid Sadeq Mahdi Alsultani, Ahmed H. AliwyThis article investigates the use of a transformer encoder for Arabic Named Entity Recognition (NER). The classic transformer that was originally proposed for machine translation adopts the absolute sinusoidal position embedding which is aware of distance but unfortunately is not aware of the directionality. However, in the NER task, both distance and orientation are crucial. Therefore, in this study, instead … …Journal of Computer Science, Vol. 19, Issue 5 · 10 Apr 2023 · 5,330 accessesView PDF Cite

Research ArticleOpen accessIn the abstractDeep Learning Approaches for Brain Tumor Diagnosis using Fused Layer AcceleratorMitrabinda Khuntia, Prabhat Kumar Sahu, Swagatika DeviDeep Convolutional Neural Networks (DCNNs) are an emerging field in biomedical processing. Tumor classification is a key stage in the pathology analysis process, and deep learning algorithms for brain tumor categorization have recently shown promising results. However, these approaches often require more storage and more expensive training procedures to input a large number of training variables. To address this issue, … …Journal of Computer Science, Vol. 19, Issue 2 · 19 Jan 2023 · 5,787 accessesView PDF Cite

Research ArticleOpen accessIn the abstractFine-Tuned MobileNet Classifier for Classification of Strawberry and Cherry Fruit TypesVenkatesh, Nagaraju Yallappa, Siddhanth Udayashankar Hegde, Sangeetha Raj StalinThis paper proposed an accurate, fast and reliable strawberry, cherry fruit detection and classification system for the automated strawberry cherry yield estimation. State-of-the-art deep learning-based fine-tuned MobileNet Convolutional Neural Network is developed to detect and classify strawberry and cherry fruit types in the outdoor field. The proposed CNN model is trained on 4250 strawberry fruit images, 3878 Cherry fruit images … …Journal of Computer Science, Vol. 17, Issue 1 · 20 Jan 2021 · 7,953 accessesView PDF Cite