Models for the marrow: A comprehensive review of AI‐based cell classification methods and malignancy detection in bone marrow aspirate smears by Tabita Ghete, Farina Kock, Martina Pontones, David Pfrang, Max Westphal, Henning Höfener, Markus Metzler

“…Abstract Given the high prevalence of artificial intelligence (AI) research in medicine, the development of deep learning (DL) algorithms based on image recognition, such as the analysis of bone marrow aspirate (BMA) smears, is rapidly increasing in the field of hematology and oncology. …”
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Resource-Aware Multi-Hop Routing Protocol for Unicast Cellular V2V Communications by Faegheh Seifhashemi, Amirreza Hajrasouliha, Behrouz Shahgholi Ghahfarokhi

“…In the proposed resource-aware approach, we utilize deep learning to predict vehicle trajectories and traffic load in various areas. …”
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Multiscale characterization of medical devices and pharmaceutical formulations with 3D X-ray microscopy and computed tomography by Herminso Villarraga-Gómez, Ria L. Mitchell

“…This paper discusses advancements in 3D X-ray imaging workflows for characterizing medical devices and pharmaceutical formulations. It also covers deep-learning (DL) data reconstruction techniques that enhance 3D X-ray microscopy (XRM) and computed tomography (CT) applications in drug delivery and the pharmaceutical industry. …”
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Enhancing Physical Spatial Resolution of Synthetic Aperture Sonar Images Based on Convolutional Neural Network by Pan Xu, Dongbao Gao, Shui Yu, Guangming Li, Yun Zhao, Guojun Xu

“…The sonar image has limitations on the physical spatial resolution due to system configuration and underwater environment, which often leads to challenges for underwater targets detection. Here, the deep learning method is applied to enhance the physical spatial resolution of underwater sonar images. …”
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Computationally secure steganography based on speech synthesis by Menghan LI, Kejiang CHEN, Weiming ZHANG, Nenghai YU

“…The steganography theory of computing security has been proposed for a long time, but it has not been widely adopted for mainstream steganography using multimedia data as a carrier.The reason is that the prerequisite for calculating secure steganography is to obtain the accurate distribution of the carrier or to accurately sample according to the carrier distribution.However, naturally collected images and audio/video cannot meet this prerequisite.With the development of deep learning technology, various machine-generated media such as image generation and synthesized speech, have become more and more common on the Internet and then generated media has become a reasonable steganography carrier.Steganography can use normal generated media to cover up secret communications, and pursue in distinguishability from normal generated media.The distribution learned by some generative models is known or controllable, which provides an opportunity to push computational security steganography for practical use.Taking the widely used synthetic speech model as an example, a computationally secure symmetric key steganography algorithm was designed and implemented.The message was decompressed into the synthetic audio according to the decoding process of arithmetic coding based on the conditional probability of sample points, and the message receiver had the same generation model to complete the message extraction by reproducing the audio synthesis process.The public key steganography algorithm was additionally designed based on this algorithm, which provided algorithmic support for the realization of full-flow steganographic communication.Steganographic key exchange ensured the security of steganographic content and the security of steganographic behavior was also achieved.The theoretical analysis showed that the security of the proposed algorithm is determined by the randomness of the embedded message.And the steganography analysis experiment further verified that the attacker cannot distinguish the synthesized carrier audio from the encrypted audio.…”
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Surface defect detection on industrial drum rollers: Using enhanced YOLOv8n and structured light for accurate inspection. by Guofeng Qin, Qinkai Zou, Mengyan Li, Yi Deng, Peiwen Mi, Yongjian Zhu, Hao Liu

“…Aiming at solving the problems that the traditional light source visual imaging system, which does not clearly reflect defect features, the defect detection efficiency is low, and the accuracy is not enough, this paper designs an image acquisition system based on line fringe structured light and proposes an improved deep learning network model based on YOLOv8n to achieve efficient detection of defects on the rolling surface of a drum roller. …”
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Dynamic-budget superpixel active learning for semantic segmentation by Yuemin Wang, Ian Stavness

“…IntroductionActive learning can significantly decrease the labeling cost of deep learning workflows by prioritizing the limited labeling budget to high-impact data points that have the highest positive impact on model accuracy. …”
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ECG-based transfer learning for cardiovascular disease: A scoping review by Sharifah Noor Masidayu Sayed Ismail, Siti Fatimah Abdul Razak, Nor Azlina Ab Aziz

“…Advancements in medical diagnosis using artificial intelligence have popularised transfer learning among researchers in machine learning and deep learning. This scoping review focuses on the application of ECG-based transfer learning for CVD identification, examining the most common types of CVD studied, the input formats used, frequently referenced databases, and the extent of transfer learning's application in diagnosing CVD through ECG analysis. …”
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AI-based prediction of androgen receptor expression and its prognostic significance in prostate cancer by Jiawei Zhang, Feng Ding, Yitian Guo, Xiaoying Wei, Jibo Jing, Feng Xu, Huixing Chen, Zhongying Guo, Zonghao You, Baotai Liang, Ming Chen, Dongfang Jiang, Xiaobing Niu, Xiangxue Wang, Yifeng Xue

“…This study develops an AI-based prognostic model using deep learning that incorporates androgen receptor (AR) regional features from whole-slide images (WSIs). …”
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Multi-step multivariate forecasting of transmission power in NPPs using operational and meteorological data by Jaeseok Yoo, Young-jin Oh, Nam-hyun Kim, Soo-ill Lee, Jaepil Ko

“…In this study, we explored how to effectively handle the long-term dependence problem and various data characteristics to increase the forecasting accuracy of transmission power in NPPs by introducing a Seq2Seq model with an encoder-decoder structure and an attention mechanism, beyond traditional time series deep learning models, especially LSTM. This approach will improve the accuracy of transmission power forecasting and contribute to a stable power supply. …”
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A systematic review of progress test as longitudinal assessment in Saudi Arabia by Naseel Ahmed Moursy, Khaled Hamsho, Arwa Mohammad Gaber, Muhammad Faisal Ikram, Muhammad Raihan Sajid

“…Contrasting traditional assessments, PT promotes deep learning strategies and meaning-oriented approaches, fostering holistic understanding rather than rote memorization. …”
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Temperature and Humidity Monitoring in Hydroponic Cultivation Based on Internet of Things: Dataset Development for Smart Agriculture by Simon Prananta Barus, Jeriko Ichtus Seo

“…Further research will integrate more monitoring parameters, conduct direct hydroponic cultivation trials, and apply artificial intelligence such as machine learning and deep learning to improve efficiency and effectiveness in hydroponic cultivation.…”
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Health assessment of a brushless direct current motor stator using a physics-informed long short-term memory network by Yi Ren, Runfei Yi, Zhaoxin Lian, Quan Xia, Dezhen Yang, Bo Sun, Qiang Feng

“…These results indicate that integrating physical information into deep learning methods is a promising approach by which to assess BLDC motor stator health.…”
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A Preliminary Study on 2D Convolutional Neural Network-Based Discontinuous Rail Position Classification for Detection on Rail Breaks Using Distributed Acoustic Sensing Data by Hye-Yeun Chun, Jungtai Kim, Dongkue Kim, Ilmu Byun, Kyeongjun Ko

“…In this research, as a preliminary study on rail break detection system, a deep learning-based discontinuous rail position classification method, which is using vibration data obtained from distributed acoustic sensing (DAS) system during train operation, is proposed. …”
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A Framework for Evaluating Geomagnetic Indices Forecasting Models by Armando Collado‐Villaverde, Pablo Muñoz, Consuelo Cid

“…Abstract The use of Deep Learning models to forecast geomagnetic storms is achieving great results. …”
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New glacier thickness and bed topography maps for Svalbard by W. van Pelt, T. Frank

“…Specifically, we model large glaciers with the Parallel Ice Sheet Model (PISM) at 500 m resolution, while we resolve smaller mountain glaciers at 100 m resolution using the physics-informed deep-learning-based Instructed Glacier Model (IGM). Actively surging glaciers are modelled using a perfect-plasticity model. …”
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Steganography in NFT images by Zichi WANG, Guorui FENG, Xinpeng ZHANG

“…., NFT images, except of natural images and generated images.For further study, the deep learning-based steganographic method can be designed for NFT images using the strong fitting and learning ability of neural networks.…”
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Emboldening food security for global sustainability yoking artificial intelligence by Gull-e-laala Khan, Gulshan Irshad, Raina Ijaz, Sabah Javaid, Noor Tahir, Sajid Mehmood

“…From seed to fork a robust deep-learning systemic AI powered innovation models enhance global opportunities reshaping food industry and creating a resilient strategic transformation addressing food security following climate change challenges.…”
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Multi-modal framework for battery state of health evaluation using open-source electric vehicle data by Hongao Liu, Chang Li, Xiaosong Hu, Jinwen Li, Kai Zhang, Yang Xie, Ranglei Wu, Ziyou Song

“…Furthermore, we propose a deep learning-based multi-modal framework to effectively leverage historical vehicle data for efficient, accurate, and cost-effective state of health estimation. …”
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Performance Evaluation of a Visual Defects Detection System for Railways Monitoring by Radosavljevic Saša, Rivero Alain, Rodríguez Flórez Sergio, El Ouardi Abdelhafid, Michel Pauline, Bouamama Belkacem O., Vanheeghe Philippe

“…The paper details the system’s architecture, synchronisation methods, and decision fusion process to improve the precision of limited mono-modal systems. A deep-learning visual based railway defects inspection was explored. …”
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