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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Unified regularity measures for sample-wise learning and generalization by Chi Zhang, Meng Yuan, Xiaoning Ma, Yu Liu, Haoang Lu, Le Wang, Yuanqi Su, Yuehu Liu

“…Recent studies on deep neural networks (DNNs) suggest that such sample differences are rooted in the distribution of intrinsic pattern information, namely sample regularity. …”
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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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Text2Layout: Layout Generation From Text Representation Using Transformer by Haruka Takahashi, Shigeru Kuriyama

“…Our approach uses Transformer-based deep neural networks to synthesize scene representations of multiple objects. …”
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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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Graphic Perception System for Visually Impaired Groups by Jingzi Wen

“…In recent years, deep neural networks have promoted the development of image object recognition. …”
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Double adversarial attack against license plate recognition system by Xianyi CHEN, Jun GU, Kai YAN, Dong JIANG, Linfeng XU, Zhangjie FU

“…Recent studies have revealed that deep neural networks (DNN) used in artificial intelligence systems are highly vulnerable to adversarial sample-based attacks.To address this issue, a dual adversarial attack method was proposed for license plate recognition (LPR) systems in a DNN-based scenario.It was demonstrated that an adversarial patch added to the pattern location of the license plate can render the target detection subsystem of the LPR system unable to detect the license plate class.Additionally, the natural rust and stains were simulated by adding irregular single-connected area random points to the license plate image, which results in the misrecognition of the license plate number.or the license plate research, different shapes of adversarial patches and different colors of adversarial patches are designed as a way to generate adversarial license plates and migrate them to the physical world.Experimental results show that the designed adversarial samples are undetectable by the human eye and can deceive the license plate recognition system, such as EasyPR.The success rate of the attack in the physical world can reach 99%.The study sheds light on the vulnerability of deep learning and the adversarial attack of LPR, and offers a positive contribution toward improving the robustness of license plate recognition models.…”
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Adversarial Robust Modulation Recognition Guided by Attention Mechanisms by Quanhai Zhan, Xiongwei Zhang, Meng Sun, Lei Song, Zhenji Zhou

“…Deep neural networks have demonstrated considerable effectiveness in recognizing complex communications signals through their applications in the tasks of automatic modulation recognition. …”
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Hybrid machine learning-based 3-dimensional UAV node localization for UAV-assisted wireless networks by Workeneh Geleta Negassa, Demissie J. Gelmecha, Ram Sewak Singh, Davinder Singh Rathee

“…The hybrid framework combined the strengths of Graph Neural Networks (GNN) for feature aggregation, Deep Neural Networks (DNN) for efficient resource allocation, and Double Deep Q-Networks (DDQN) for distributed decision-making. …”
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G&G Attack: General and Geometry-Aware Adversarial Attack on the Point Cloud by Geng Chen, Zhiwen Zhang, Yuanxi Peng, Chunchao Li, Teng Li

“…Deep neural networks have been shown to produce incorrect predictions when imperceptible perturbations are introduced into the clean input. …”
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Efficient nonlinear function approximation in analog resistive crossbars for recurrent neural networks by Junyi Yang, Ruibin Mao, Mingrui Jiang, Yichuan Cheng, Pao-Sheng Vincent Sun, Shuai Dong, Giacomo Pedretti, Xia Sheng, Jim Ignowski, Haoliang Li, Can Li, Arindam Basu

“…Abstract Analog In-memory Computing (IMC) has demonstrated energy-efficient and low latency implementation of convolution and fully-connected layers in deep neural networks (DNN) by using physics for computing in parallel resistive memory arrays. …”
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A deep multiple instance learning framework improves microsatellite instability detection from tumor next generation sequencing by John Ziegler, Jaclyn F. Hechtman, Satshil Rana, Ryan N. Ptashkin, Gowtham Jayakumaran, Sumit Middha, Shweta S. Chavan, Chad Vanderbilt, Deborah DeLair, Jacklyn Casanova, Jinru Shia, Nicole DeGroat, Ryma Benayed, Marc Ladanyi, Michael F. Berger, Thomas J. Fuchs, A. Rose Brannon, Ahmet Zehir

“…To overcome this critical issue, we developed MiMSI, an MSI classifier based on deep neural networks and trained using a dataset that included low tumor purity MSI cases in a multiple instance learning framework. …”
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Probabilistic Automated Model Compression via Representation Mutual Information Optimization by Wenjie Nie, Shengchuan Zhang, Xiawu Zheng

“…Deep neural networks, despite their remarkable success in computer vision tasks, often face deployment challenges due to high computational demands and memory usage. …”
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