Enhancing breast cancer prediction through stacking ensemble and deep learning integration by Fatih Gurcan

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Multiple sclerosis diagnosis with brain MRI retrieval: A deep learning approach by R.M. Haggag, Eman M. Ali, M.E. Khalifa, Mohamed Taha

“…Experiments on four public MS-MRI datasets demonstrated the end-to-end deep learning framework’s generalizability without extensive pre-processing, with mAP scores of 86.20%, 93.77%, 94.18%, and 90.46%, respectively demonstrating its effectiveness in retrieval. …”
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Pedestrian Motion Path Detection Method Based on Deep Learning and Foreground Detection by Meiman Li, Wenfu Xie

“…For the surveillance video images captured by monocular camera, this paper proposes a method combining foreground detection and deep learning to detect moving pedestrians, making full use of the invariable background of video image. …”
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Energy consumption forecasting for oil and coal in China based on hybrid deep learning by Jiao He, Yuhang Li, Xiaochuan Xu, Di Wu

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Deep Learning‐Assisted Label‐Free Parallel Cell Sorting with Digital Microfluidics by Zongliang Guo, Fenggang Li, Hang Li, Menglei Zhao, Haobing Liu, Haopu Wang, Hanqi Hu, Rongxin Fu, Yao Lu, Siyi Hu, Huikai Xie, Hanbin Ma, Shuailong Zhang

“…In this work, a novel label‐free cell sorting method is presented that integrates deep learning image recognition with microfluidic manipulation to differentiate cells based on morphology. …”
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Emotion Recognition from EEG Signals Using Advanced Transformations and Deep Learning by Jonathan Axel Cruz-Vazquez, Jesús Yaljá Montiel-Pérez, Rodolfo Romero-Herrera, Elsa Rubio-Espino

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Research on Gear Surface Damage Recognition Based on Small Sample Deep Learning by Wang Xiaopeng, Hua Hongpeng, Lu Changqing, Peng Kun, Zhong Yuan, Wu Biqiong

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Deep Learning Algorithm Analysis of Potato Disease Classification for System on Chip Implementation by John Adebisi, Sesham Srinu, Varqa Mitonga

“…The broad range of crops has witnessed setbacks in different capacities due to climate change among other factors, hence leading to diseases and infections; thereby leading to negatively impacted nutrition. This work uses a deep learning algorithm to investigate the classification of potato diseases as a case study which can be leveraged on by other agricultural products for reference.  …”
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A Survey on Review Spam Detection Methods using Deep Learning Approach by Mahmoud Aliarab, Kazim Fouladi-Ghaleh

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Modified Autoencoder Training and Scoring for Robust Unsupervised Anomaly Detection in Deep Learning by Nicholas Merrill, Azim Eskandarian

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Deep-Learning-Based Land Cover Mapping in Franciacorta Wine Growing Area by Girma Tariku, Isabella Ghiglieno, Andres Sanchez Morchio, Luca Facciano, Celine Birolleau, Anna Simonetto, Ivan Serina, Gianni Gilioli

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Comment on “Deep learning for the automatic detection and segmentation of parotid gland tumors on MRI” by Sesuraj Balasamy, Ashok K. Sundramoorthy

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TriangularSORT: A Deep Learning Approach for Ship Wake Detection and Tracking by Chengcheng Yu, Yanmei Zhang

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An Improvement of the Camshift Human Tracking Algorithm Based on Deep Learning and the Kalman Filter by Van-Truong Nguyen, Duc-Tuan Chu, Dinh-Hieu Phan, Ngoc-Tien Tran

“…In this paper, an improvement of the Camshift human tracking algorithm based on deep learning and the Kalman filter is proposed. To detail an approach by using YOLOv4-tiny to detect a human in real time, Camshift is used to track a particular person and the Kalman filter is applied to enhance the performance of this algorithm in case of occlusion, noise, and different light conditions. …”
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An Optimized Deep-Learning-Based Network with an Attention Module for Efficient Fire Detection by Muhammad Altaf, Muhammad Yasir, Naqqash Dilshad, Wooseong Kim

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Liver fibrosis stage classification in stacked microvascular images based on deep learning by Daisuke Miura, Hiromi Suenaga, Rino Hiwatashi, Shingo Mabu

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Robust Classification of Smartphone Captured Handwritten Document Images Using Deep Learning by K. S. Koushik, B. J. Bipin Nair, N. Shobha Rani, Mohammed Javed

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Emotion Estimation Using Noncontact Environmental Sensing with Machine and Deep Learning Models by Tsumugi Isogami, Nobuyoshi Komuro

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Forecasting Foreign Exchange Volatility Using Deep Learning Autoencoder-LSTM Techniques by Gunho Jung, Sun-Yong Choi

“…Additionally, the ability to learn the magnitude of data spread and singularities determines the accuracy of predictions made using deep learning models. In summary, this study established that FX volatility can be accurately predicted using a combination of deep learning models. …”
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Unlocking Potential Score Insights of Sentimental Analysis with a Deep Learning Revolutionizes by Ibrahim R. Alzahrani

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