Prediction of Causative Genes in Inherited Retinal Disorders from Spectral-Domain Optical Coherence Tomography Utilizing Deep Learning Techniques by Yu Fujinami-Yokokawa, Nikolas Pontikos, Lizhu Yang, Kazushige Tsunoda, Kazutoshi Yoshitake, Takeshi Iwata, Hiroaki Miyata, Kaoru Fujinami, on behalf of Japan Eye Genetics Consortium

“…It is anticipated that deep neural networks will be integrated into general screening to support clinical/genetic diagnosis, as well as enrich the clinical education.…”
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Classification of diabetic retinopathy stages based on neural networks by M. M. Lukashevich, Y. I. Golub

“…Classification problem of diabetic retinopathy stages is described.The architecture of deep neural networks based on VGG16 and VGG19 with the addition of custom layers is proposed. …”
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Uncertainty Estimation in Unsupervised MR-CT Synthesis of Scoliotic Spines by Enamundram Naga Karthik, Farida Cheriet, Catherine Laporte

“…Uncertainty estimations through approximate Bayesian inference provide interesting insights to deep neural networks' behavior. In unsupervised learning tasks, where expert labels are unavailable, it becomes ever more important to critique the model through uncertainties. …”
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Noise-agnostic quantum error mitigation with data augmented neural models by Manwen Liao, Yan Zhu, Giulio Chiribella, Yuxiang Yang

“…Most existing methods require prior knowledge of the noise model or the noise parameters. Deep neural networks have the potential to lift this requirement, but current models require training data produced by ideal processes in the absence of noise. …”
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Artificial intelligence in the service of entrepreneurial finance: knowledge structure and the foundational algorithmic paradigm by Robert Kudelić, Tamara Šmaguc, Sherry Robinson

“…The results demonstrate a high representation of artificial neural networks, deep neural networks, and support vector machines across almost all identified topic niches. …”
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A Mini-Review of Machine Learning in Big Data Analytics: Applications, Challenges, and Prospects by Isaac Kofi Nti, Juanita Ahia Quarcoo, Justice Aning, Godfred Kusi Fosu

“…The study outcome shows that deep neural networks (15%), support vector machines (15%), artificial neural networks (14%), decision trees (12%), and ensemble learning techniques (11%) are widely applied in BDA. …”
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Low-Shot Wall Defect Detection for Autonomous Decoration Robots Using Deep Reinforcement Learning by Fanyu Zeng, Xi Cai, Shuzhi Sam Ge

“…Object detection methods based on deep neural networks require a large number of images with the handcrafted bounding box for training. …”
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Preparing Schrödinger Cat States in a Microwave Cavity Using a Neural Network by Hector Hutin, Pavlo Bilous, Chengzhi Ye, Sepideh Abdollahi, Loris Cros, Tom Dvir, Tirth Shah, Yonatan Cohen, Audrey Bienfait, Florian Marquardt, Benjamin Huard

“…Our experimental results demonstrate more generally how deep neural networks and transfer learning can produce efficient simultaneous solutions to a range of quantum control tasks, which will benefit not only state preparation but also parametrized quantum gates.…”
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Random Frequency Division Multiplexing by Chanzi Liu, Jianjian Wu, Qingfeng Zhou

“…We take full account of the great power of deep neural networks (DNN) to detect the signal as it is an underdetermined equation. …”
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Transient State Estimation for Power System Based on Deep Transfer Learning by JIAO Hao, ZHAO Jiawei, WEI Lei, ZHU Weiping, MA Zhoujun, ZANG Haixiang

“…Simulations demonstrate that the proposed method exhibits a higher estimation accuracy and computational efficiency than that of deep neural networks without transfer learning，particularly during power system failures.…”
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On the Potential of Algorithm Fusion for Demographic Bias Mitigation in Face Recognition by Jascha Kolberg, Yannik Schäfer, Christian Rathgeb, Christoph Busch

“…With the rise of deep neural networks, the performance of biometric systems has increased tremendously. …”
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An Improved Image Processing Based on Deep Learning Backpropagation Technique by Yang Gao, Yue Tian

“…Also, in the process of image encryption, randomness is an important component, especially when used by smart learning methods. Deep neural networks are related to pixels used to manipulate position and value according to the predicted new value given from a variable neural system. …”
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MAGInet based on deep learning for magnetic multi-parameter inversion by Wudi Wen, Yi Li, Zhongle Liu

“…Comparative analyses reveal that MAGInet significantly outperforms traditional deep neural networks in terms of accuracy for predicting the magnetic multi-parameters of complex structures, showcasing superior performance.…”
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CODE-ACCORD: A Corpus of building regulatory data for rule generation towards automatic compliance checking by Hansi Hettiarachchi, Amna Dridi, Mohamed Medhat Gaber, Pouyan Parsafard, Nicoleta Bocaneala, Katja Breitenfelder, Gonçal Costa, Maria Hedblom, Mihaela Juganaru-Mathieu, Thamer Mecharnia, Sumee Park, He Tan, Abdel-Rahman H. Tawil, Edlira Vakaj

“…It enables applying recent trends, such as deep neural networks and large language models, to ACC.…”
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Machine learning for medical image classification by Gazi Husain, Jonathan Mayer, Molly Bekbolatova, Prince Vathappallil, Mihir Matalia, Milan Toma

“…It navigates through various ML methods utilized in healthcare, including Supervised Learning, Unsupervised Learning, Self-Supervised Learning, Deep Neural Networks, Reinforcement Learning, and Ensemble Methods. …”
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Explainable AI for DeepFake Detection by Nazneen Mansoor, Alexander I. Iliev

“…The findings highlight the importance of interpretability in deep neural networks, providing a better understanding of their hierarchical structures and decision processes.…”
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Locality preserving binary face representations using auto‐encoders by Mohamed Amine Hmani, Dijana Petrovska‐Delacrétaz, Bernadette Dorizzi

“…A novel approach to binarising biometric data using Deep Neural Networks applied to facial biometric data is introduced. …”
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Freeway Traffic Speed Prediction under the Intelligent Driving Environment: A Deep Learning Approach by Chengying Hua, Wei (David) Fan

“…To improve the prediction performance and investigate the temporal features, this study focuses on emerging deep neural networks (DNNs) using the Caltrans Performance Measurement System (PeMS) data. …”
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Deep Learning-Enabled Automatic Detection of Bridges for Promoting Transportation Surveillance under Different Imaging Conditions by Peng Han, Xiaoxia Yang

“…It is obvious that the generalization abilities of these deep neural networks are significantly improved using this data augmentation strategy. …”
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Copyright protection of deep image classification models by Y.D. Vybornova, D.I. Ulyanov

“…With the growing number of tasks solved using deep learning methods, the need for protection against unauthorized distribution of the intellectual property such as pre-trained models of deep neural networks is growing. To date, one of the most common ways to protect copyright in the digital space is through embedding digital watermarks. …”
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