Severity Classification of Conjunctival Hyperaemia by Deep Neural Network Ensembles by Hiroki Masumoto, Hitoshi Tabuchi, Tsuyoshi Yoneda, Shunsuke Nakakura, Hideharu Ohsugi, Tamaki Sumi, Atsuki Fukushima

“…Neural networks and deep learning have been utilised in ophthalmology, but not for the purpose of classifying the severity of conjunctival hyperaemia objectively. …”
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An Ante Hoc Enhancement Method for Image-Based Complex Financial Table Extraction by Weiyu Peng, Xuhui Li

“…In recent years, a large number of table extraction methods utilizing heuristic algorithms or deep learning models have been proposed to free people from manual processing tasks, which are time-consuming and troublesome. …”
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An Approach of Community Search with Minimum Spanning Tree Based on Node Embedding by Jinglian Liu, Daling Wang, Shi Feng, Yifei Zhang

“…Node embedding uses deep learning method to obtain feature representation of nodes directly from graph structure automatically and offers a new method to measure the distance between two nodes. …”
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Detection of Fungal Infections in Gloriosa Superba Plant Using the Convolution Neural Network Model by Guillermo Napoleón Pelaez-Diaz, Rosa Vílchez-Vásquez, Antonio Huaman-Osorio, R. Mahaveerakannan, S. Pushpa, Nilesh Shelke, Sumitha Jagadibabu, Jenifer Mahilraj

“…The money plant is also known as the Gloriosa superba. We used a deep learning-based convolution neural network (CNN) classifier model to optimize the CNN algorithm parameter for better prediction. …”
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STAIG: Spatial transcriptomics analysis via image-aided graph contrastive learning for domain exploration and alignment-free integration by Yitao Yang, Yang Cui, Xin Zeng, Yubo Zhang, Martin Loza, Sung-Joon Park, Kenta Nakai

“…Here, we propose STAIG, a deep-learning model that integrates gene expression, spatial coordinates, and histological images using graph-contrastive learning coupled with high-performance feature extraction. …”
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Transfer Learning for CNN-Based Damage Detection in Civil Structures with Insufficient Data by Mona Chamangard, Gholamreza Ghodrati Amiri, Ehsan Darvishan, Zahra Rastin

“…Among various methods proposed for health monitoring of structures, deep learning-based techniques with their powerful performance have attracted considerable attention in recent years. …”
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Drone Landing and Reinforcement Learning: State-of-Art, Challenges and Opportunities by Jose Amendola, Linga Reddy Cenkeramaddi, Ajit Jha

“…Finally, we present our critical analysis of how recent state-of-the-art deep learning concepts can be combined with reinforcement learning to leverage the latter in addressing the open gaps in future works.…”
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A Vortex Identification Method Based on Extreme Learning Machine by Jun Wang, Lei Guo, Yueqing Wang, Liang Deng, Fang Wang, Tong Li

“…Recently, proposed deep learning methods have long network training time and high computational complexity. …”
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Arrhythmia Classification Techniques Using Deep Neural Network by Ali Haider Khan, Muzammil Hussain, Muhammad Kamran Malik

“…The automated systems that can be adapted as a tool for screening arrhythmia classification play a vital role not only for the patients but can also assist the doctors. The deep learning-based automated arrhythmia classification techniques are developed with high accuracy results but still not adopted by healthcare professionals as the generalized approach. …”
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Potential Use and Limitation of Artificial Intelligence to Screen Diabetes Mellitus in Clinical Practice: A Literature Review by Aqsha Nur, Defin Yumnanisha, Sydney Tjandra, Adang Bachtiar, Dante Saksono Harbuwono

“…., machine learning and deep learning) have yielded prediction performances of up to 98% in various diseases. …”
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Applying MLP-Mixer and gMLP to Human Activity Recognition by Takeru Miyoshi, Makoto Koshino, Hidetaka Nambo

“…The development of deep learning has led to the proposal of various models for human activity recognition (HAR). …”
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A Bayesian Inference-Based Method for Uncertainty Analysis in Raman Spectroscopy by Hanxuan Zhou

“…The integration of Raman spectroscopy with deep learning methods has been shown to produce excellent chemical analysis results. …”
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Adversarial Attacks and Defenses in Fault Detection and Diagnosis: A Comprehensive Benchmark on the Tennessee Eastman Process by Vitaliy Pozdnyakov, Aleksandr Kovalenko, Ilya Makarov, Mikhail Drobyshevskiy, Kirill Lukyanov

“…This study explores the threats in deploying deep learning models for Fault Detection and Diagnosis (FDD) in ACS using the Tennessee Eastman Process dataset. …”
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The Application of Reinforcement Learning in Traffic Flow Prediction: Advantages, Problems, and Prospects by Li Minghui, Zhou Decheng, Zhang Shiqi

“…This article summarizes three traditional methods of TFP: parameter-based prediction, shallow machine learning-based prediction, and deep learning (DL)-based prediction. However, traditional TFP methods only focus on predicting time series in traffic data, and it is difficult for these methods to capture the interdependent relationship between the spatial distribution of traffic across a network and the temporal evolution of traffic conditions at each location. sequences. …”
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Fed-CL- an atrial fibrillation prediction system using ECG signals employing federated learning mechanism by Fayez Saud Alreshidi, Mohammad Alsaffar, Rajeswari Chengoden, Naif Khalaf Alshammari

“…Abstract Deep learning has shown great promise in predicting Atrial Fibrillation using ECG signals and other vital signs. …”
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Estimating the time-varying effective reproduction number via Cycle Threshold-based Transformer. by Xin-Yu Zhang, Lan-Lan Yu, Wei-Yi Wang, Gui-Quan Sun, Jian-Cheng Lv, Tao Zhou, Quan-Hui Liu

“…We demonstrate that the Ct-based deep learning method can improve the real-time estimates of Rt, enabling more easily adapted to the track of the newly emerged epidemic.…”
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Pixel‐wise supervision for presentation attack detection on identity document cards by Raghavendra Mudgalgundurao, Patrick Schuch, Kiran Raja, Raghavendra Ramachandra, Naser Damer

“…The baseline benchmark is presented using different handcrafted and deep learning models on a newly constructed in‐house database obtained from an operational system consisting of 886 users with 433 bona fide, 67 print and 366 display attacks. …”
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Advancements in the Application of Convolutional Neural Networks in Ultrasound Imaging for Breast Cancer Diagnosis and Treatment by An Zichen, Li Fan

“…Breast ultrasound (US) imaging technology plays a crucial role in the early diagnosis and intervention treatment of breast cancer patients. Deep learning (DL), as one of the most powerful machine learning techniques in the field of artificial intelligence (AI), has the ability to automatically select features from raw data, achieving remarkable advancements in breast US imaging. …”
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A Novel Chinese Entity Relationship Extraction Method Based on the Bidirectional Maximum Entropy Markov Model by Chengyao Lv, Deng Pan, Yaxiong Li, Jianxin Li, Zong Wang

“…Traditional methods of relationship extraction, either those proposed at the earlier times or those based on traditional machine learning and deep learning, have focused on keeping relationships and entities in their own silos: extracting relationships and entities are conducted in steps before obtaining the mappings. …”
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The first geospatial dataset of irrigated fields (2020–2024) in Vojvodina (Serbia) by Mirjana Radulović, Miljana Marković, Sanja Brdar, Ioannis Athanasiadis, Gordan Mimić

“…This study’s goal is to give accessibility to our dataset which further can be explored and used for building or fine-tuning machine learning and deep learning models for the automatic detection of irrigated fields using satellite imagery.…”
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