An advanced 3D lymphatic system for assaying human cutaneous lymphangiogenesis in a microfluidic platform by Minseop Kim, Sieun Choi, Dong-Hee Choi, Jinchul Ahn, Dain Lee, Euijeong Song, Hyun Soo Kim, Mijin Kim, Sowoong Choi, Soojung Oh, Minsuh Kim, Seok Chung, Phil June Park

“…In addition, we rapidly analyzed prolymphangiogenic effects using methods that incorporate a high-speed image processing system and a deep learning-based vascular network analysis algorithm by 12 indices. …”
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A Survey on Event Tracking in Social Media Data Streams by Zixuan Han, Leilei Shi, Lu Liu, Liang Jiang, Jiawei Fang, Fanyuan Lin, Jinjuan Zhang, John Panneerselvam, Nick Antonopoulos

“…Event tracking in social networks finds various applications, such as network security and societal governance, which involves analyzing data generated by user groups on social networks in real time. Moreover, as deep learning techniques continue to advance and make important breakthroughs in various fields, researchers are using this technology to progressively optimize the effectiveness of Event Detection (ED) and tracking algorithms. …”
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Transfer Learning-Based Health Monitoring of Robotic Rotate Vector Reducer Under Variable Working Conditions by Muhammad Umar Elahi, Izaz Raouf, Salman Khalid, Faraz Ahmad, Heung Soo Kim

“…Traditional approaches for HM, including those using vibration and acoustic emission sensors, encounter such challenges as noise interference, data inconsistency, and high computational costs. Deep learning-based techniques, which use current electrical data embedded within industrial robots, address these issues, offering a more efficient solution. …”
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Personal Identification Using Embedded Raspberry Pi-Based Face Recognition Systems by Sebastian Pecolt, Andrzej Błażejewski, Tomasz Królikowski, Igor Maciejewski, Kacper Gierula, Sebastian Glowinski

“…It is shown that the system’s accuracy and scalability can be enhanced through testing with larger databases, hardware upgrades like higher-resolution cameras, and advanced deep learning algorithms to address challenges such as extreme facial angles. …”
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Application of Artificial Intelligence in Radiological Image Analysis for Pulmonary Disease Diagnosis: A Review of Current Methods and Challenges by Karolina Zalewa, Joanna Olszak, Wojciech Kapłan, Dominika Orłowska, Lidia Bartoszek, Marta Kaus, Natalia Klepacz

“… Introduction and purpose Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), is revolutionizing radiology by improving diagnostic accuracy and efficiency. …”
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Portable Handheld Slit-Lamp Based on a Smartphone Camera for Cataract Screening by Shenming Hu, Hong Wu, Xinze Luan, Zhuoshi Wang, Mary Adu, Xiaoting Wang, Chunhong Yan, Bo Li, Kewang Li, Ying Zou, Xiaoya Yu, Xiangdong He, Wei He

“…Furthermore, the images collected by the smartphone are uploaded to the deep learning cataract screening system, which can achieve real-time and effective screening of cataract. …”
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Моделі самоорганізації колективу однорідних безпілотних літальних апаратів при рішенні слабоформалізованих завдань... by А.В. Тристан, Д.І. Жуков

“…The practicality of this method lies in the fact that the artificial intelligence of the UAV will constantly self-learn and improve through the use of machine and deep learning. Thus, the results and time required to complete missions will improve significantly, and the number of control operators will decrease. …”
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Learning Air Traffic as Images: A Deep Convolutional Neural Network for Airspace Operation Complexity Evaluation by Hua Xie, Minghua Zhang, Jiaming Ge, Xinfang Dong, Haiyan Chen

“…Motivated by the applications of deep learning network, the specific CNN model is introduced to automatically extract high-level traffic features from MTSIs and learn the SOC pattern. …”
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Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR by Ahtisham Fazeel Abbasi, Muhammad Nabeel Asim, Andreas Dengel

“…Within the landscape of AI predictors in CRISPR-Cas9 multi-step process, it provides insights of representation learning methods, machine and deep learning methods trends, and performance values of existing 50 predictive pipelines. …”
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MDFGNN-SMMA: prediction of potential small molecule-miRNA associations based on multi-source data fusion and graph neural networks by Jianwei Li, Xukun Zhang, Bing Li, Ziyu Li, Zhenzhen Chen

“…Results In this study, we proposed a deep learning method called Multi-source Data Fusion and Graph Neural Networks for Small Molecule-MiRNA Association (MDFGNN-SMMA) to predict potential SM-miRNA associations. …”
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Addressing Label Noise in Colorectal Cancer Classification Using Cross-Entropy Loss and pLOF Methods With Stacking-Ensemble Technique by Ishrat Zahan Tani, Kah Ong Michael Goh, Md Nazmul Islam, Md Tarek Aziz, S. M. Hasan Mahmud, Dip Nandi

“…Many machine learning (ML) and deep learning (DL) methods have been proposed to facilitate automated early diagnosis of this cancer. …”
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MM-HiFuse: multi-modal multi-task hierarchical feature fusion for esophagus cancer staging and differentiation classification by Xiangzuo Huo, Shengwei Tian, Long Yu, Wendong Zhang, Aolun Li, Qimeng Yang, Jinmiao Song

“…However, some previous studies have employed deep learning-based methods for esophageal cancer analysis, which are limited to single-modal features, resulting in inadequate classification results. …”
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A Line of Sight/Non Line of Sight Recognition Method Based on the Dynamic Multi-Level Optimization of Comprehensive Features by Ziyao Ma, Zhongliang Deng, Zidu Tian, Yingjian Zhang, Jizhou Wang, Jilong Guo

“…Experimental results show that the NLOS/LOS recognition method proposed in this paper has higher accuracy than other deep learning methods.…”
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Enhanced multiscale human brain imaging by semi-supervised digital staining and serial sectioning optical coherence tomography by Shiyi Cheng, Shuaibin Chang, Yunzhe Li, Anna Novoseltseva, Sunni Lin, Yicun Wu, Jiahui Zhu, Ann C. McKee, Douglas L. Rosene, Hui Wang, Irving J. Bigio, David A. Boas, Lei Tian

“…Here, we present a novel 3D imaging framework that combines S-OCT with a deep-learning digital staining (DS) model. This enhanced imaging modality integrates high-throughput 3D imaging, low sample variability and high interpretability, making it suitable for 3D histology studies. …”
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Efektivitas mindful learning dalam konteks pendidikan di Indonesia (2000-2024) by Ryan Angga Pratama, Adhilla Salsabila Putri Artha, Nurfitri Zainal Abidin

“…Diharapkan hasil penelitian ini dapat menjadi dasar bagi pemangku kepentingan di dunia pendidikan, seperti guru, sekolah, dan dinas pendidikan, untuk mengoptimalkan kualitas pembelajaran di Indonesia, khususnya dalam mendukung implementasi pendekatan Deep Learning yang diusung oleh Mendikdasmen, dengan mindful learning sebagai salah satu elemen utamanya.…”
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AI predicting recurrence in non-muscle-invasive bladder cancer: systematic review with study strengths and weaknesses by Saram Abbas, Rishad Shafik, Naeem Soomro, Rakesh Heer, Rakesh Heer, Kabita Adhikari

“…., radiomics, clinical, histopathological, genomic) and types of ML models, such as neural networks, deep learning, and random forests. Each study was analysed for strengths, weaknesses, performance metrics, and limitations, with emphasis on generalisability, interpretability, and cost-effectiveness. …”
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Design and Experimental Evaluation of a Smart Intra-Row Weed Control System for Open-Field Cabbage by Shenyu Zheng, Xueguan Zhao, Hao Fu, Haoran Tan, Changyuan Zhai, Liping Chen

“…The system integrates deep learning technology for accurate identification and localization of cabbage, enabling precise control and dynamic obstacle avoidance for the weeding knives. …”
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MaDis-Stereo: Enhanced Stereo Matching via Distilled Masked Image Modeling by Jihye Ahn, Hyesong Choi, Soomin Kim, Dongbo Min

“…In stereo matching, Convolutional Neural Networks (CNNs), a class of deep learning models designed to process grid-like data such as images, have traditionally served as the predominant architectures. …”
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River Channel Microgeomorphic Feature Extraction and Potential Sandstorm Source Identification Method Based on a Convolutional Autoencoder Model by Kecong Wu, Lirong Chen, Yalige Bai, Xinhang Wang, Danzeng Pingcuo, Zhongpeng Han, Chengshan Wang

“…River channel's microgeomorphic features are crucial for identifying potential sandstorm sources and studying sediment source-sink processes. Current deep learning methods are predominantly applied to visible objects, rendering them unsuitable for latent objects with unstable spatiotemporal distributions, such as potential sandstorm sources. …”
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Automatic image generation and stage prediction of breast cancer immunobiological through a proposed IHC-GAN model by Afaf Saad, Noha Ghatwary, Safa M. Gasser, Mohamed S. ElMahallawy

“…To reduce these obstacles and expedite the procedure, we present an efficient deep-learning model that generates high-quality IHC-stained images directly from Hematoxylin and Eosin (H&E) stained images. …”
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