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Behavioral Motivation and Influencing Factors of Graduate Students Using AIGC Tool: An Empirical Analysis Based on Questionnaire Survey

Yijia WAN, Liping GU • Nongye tushu qingbao xuebao (2024)

[Purpose/Significance] To explore in depth the acceptance and usage habits of AIGC tools by graduate students in the process of academic research, and to promote the positive attention and application of emerging technologies by graduate st...

DOAJ Open Access (DOAJ) DOI: 10.13998/j.cnki.issn1002-1248.24-0716

The unreasonable effectiveness of deep learning in artificial intelligence

Terrence J. Sejnowski A B • Proceedings of the National Academy of Sciences (2020)

Deep learning networks have been trained to recognize speech, caption photographs, and translate text between languages at high levels of performance. Although applications of deep learning networks to real-world problems have become ubiqui...

OPENALEX Open Access DOI: https://doi.org/10.1073/pnas.1907373117 PMC: 7720171

Medical image segmentation using deep learning: A survey

Risheng Wang, Tao Lei, Ruixia Cui, Bingtao Zhang et al. • IET Image Processing (2022)

Abstract Deep learning has been widely used for medical image segmentation and a large number of papers has been presented recording the success of deep learning in the field. A comprehensive thematic survey on medical image segmentation us...

OPENALEX cc-by DOI: https://doi.org/10.1049/ipr2.12419

High-fidelity single-frame computational super-resolution using signal-preserving denoising-enabled deconvolution

Fudong Xue, Lin Yuan, Wenting He, Zuo’ang Xiang et al. • Nature Communications (2026)

Abstract Computational super-resolution (SR) methods enable nanoscale imaging from single-frame wide-field or spinning-disk confocal images without hardware modifications, yet face limitations: statistical restoration suffers from noise and...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41467-026-70791-8

A deep-reinforcement learning approach for optimizing homogeneous droplet routing in digital microfluidic biochips

Basudev Saha, Bidyut Das, Mukta Majumder • Nanotechnology and Precision Engineering (2023)

Over the past two decades, digital microfluidic biochips have been in much demand for safety-critical and biomedical applications and increasingly important in point-of-care analysis, drug discovery, and immunoassays, among other areas. How...

DOAJ Open Access (DOAJ) DOI: 10.1063/10.0017350

Deep learning approaches to biomedical image segmentation

Intisar Rizwan I Haque, Jeremiah J. Neubert • Informatics in Medicine Unlocked (2020)

The review covers automatic segmentation of images by means of deep learning approaches in the area of medical imaging. Current developments in machine learning, particularly related to deep learning, are proving instrumental in identificat...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1016/j.imu.2020.100297

Database Meets Deep Learning

Wei Wang, Meihui Zhang, Gang Chen, H. V. Jagadish et al. • ACM SIGMOD Record (2016)

Deep learning has recently become very popular on account of its incredible success in many complex datadriven applications, including image classification and speech recognition. The database community has worked on data-driven application...

OPENALEX Open Access DOI: https://doi.org/10.1145/3003665.3003669

An AI-Based Deep Learning with K-Mean Approach for Enhancing Altitude Estimation Accuracy in Unmanned Aerial Vehicles

Prot Piyakawanich, Pattarapong Phasukkit • Drones (2024)

In the rapidly evolving domain of Unmanned Aerial Vehicles (UAVs), precise altitude estimation remains a significant challenge, particularly for lightweight UAVs. This research presents an innovative approach to enhance altitude estimation ...

DOAJ Open Access (DOAJ) DOI: 10.3390/drones8120718

Learning to decode linear codes using deep learning

Eliya Nachmani, Yair Be'ery, David Burshtein • Allerton Conference on Communication, Control, and Computing (2016)

A novel deep learning method for improving the belief propagation algorithm is proposed. The method generalizes the standard belief propagation algorithm by assigning weights to the edges of the Tanner graph. These edges are then trained us...

OPENALEX Open Access DOI: https://doi.org/10.1109/allerton.2016.7852251

Deep Learning for Identifying Metastatic Breast Cancer

Wang, Dayong, Aditya Khosla, Rishab Gargeya, Humayun Irshad et al. • arXiv (Cornell University) (2016)

The International Symposium on Biomedical Imaging (ISBI) held a grand challenge to evaluate computational systems for the automated detection of metastatic breast cancer in whole slide images of sentinel lymph node biopsies. Our team won bo...

OPENALEX Open Access DOI: https://doi.org/10.48550/arxiv.1606.05718

A review on deep learning in medical image analysis

S. Suganyadevi, V. Seethalakshmi, K. Balasamy • International Journal of Multimedia Information Retrieval (2021)

Ongoing improvements in AI, particularly concerning deep learning techniques, are assisting to identify, classify, and quantify patterns in clinical images. Deep learning is the quickest developing field in artificial intelligence and is ef...

OPENALEX other-oa DOI: https://doi.org/10.1007/s13735-021-00218-1 PMC: 8417661

Gene expression inference with deep learning

Yifei Chen, Yi Li, Rajiv Narayan, Aravind Subramanian et al. • Bioinformatics (2016)

MOTIVATION: Large-scale gene expression profiling has been widely used to characterize cellular states in response to various disease conditions, genetic perturbations, etc. Although the cost of whole-genome expression profiles has been dro...

OPENALEX Open Access DOI: https://doi.org/10.1093/bioinformatics/btw074 PMC: 4908320

Research on a Decision Prediction Method Based on Causal Inference and a Multi-Expert FTOPJUDGE Mechanism

Qiang Zhao, Rundong Guo, Xiaowei Feng, Weifeng Hu et al. • Mathematics (2022)

Legal judgement prediction (LJP) is a crucial part of legal AI, and its goal is to predict the outcome of a case based on the information in the description of criminal facts. This paper proposes a decision prediction method based on causal...

DOAJ Open Access (DOAJ) DOI: 10.3390/math10132281

Opportunities and obstacles for deep learning in biology and medicine

Travers H. Ching, Daniel Scott Himmelstein, Brett K. Beaulieu‐Jones, Alexandr A. Kalinin et al. • Journal of The Royal Society Interface (2018)

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and m...

OPENALEX cc-by DOI: https://doi.org/10.1098/rsif.2017.0387 PMC: 5938574

A comprehensive construction of deep neural network‐based encoder–decoder framework for automatic image captioning systems

Md Mijanur Rahman, Ashik Uzzaman, Sadia Islam Sami, Fatema Khatun et al. • IET Image Processing (2024)

Abstract This study introduces a novel encoder–decoder framework based on deep neural networks and provides a thorough investigation into the field of automatic picture captioning systems. The suggested model uses a “long short‐term memory”...

DOAJ Open Access (DOAJ) DOI: 10.1049/ipr2.13287