A Dual-Stage Processing Architecture for Unmanned Aerial Vehicle Object Detection and Tracking Using Lightweight Onboard and Ground Server Computations by Odysseas Ntousis, Evangelos Makris, Panayiotis Tsanakas, Christos Pavlatos

“…The UAV transmits selected frames to the ground server, which handles advanced tracking, trajectory prediction, and target repositioning using state-of-the-art deep learning models. Communication between the UAV and the server is maintained through a high-speed Wi-Fi link, with a fallback to a 4G connection when needed. …”
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Decoding Gestures in Electromyography: Spatiotemporal Graph Neural Networks for Generalizable and Interpretable Classification by Hunmin Lee, Ming Jiang, Jinhui Yang, Zhi Yang, Qi Zhao

“…In recent years, significant strides in deep learning have propelled the advancement of electromyography (EMG)-based upper-limb gesture recognition systems, yielding notable successes across a spectrum of domains, including rehabilitation, orthopedics, robotics, and human-computer interaction. …”
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Establishing a GRU-GCN coordination-based prediction model for miRNA-disease associations by Kai-Cheng Chuang, Ping-Sung Cheng, Yu-Hung Tsai, Meng-Hsiun Tsai

“…In recent years, machine learning (ML) and deep learning (DL) techniques are powerful tools to analyze large-scale biological data. …”
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Comparative analysis of the DCNN and HFCNN Based Computerized detection of liver cancer by Sandeep Dwarkanth Pande, Pala Kalyani, S Nagendram, Ala Saleh Alluhaidan, G Harish Babu, Sk Hasane Ahammad, Vivek Kumar Pandey, G Sridevi, Abhinav Kumar, Ebenezer Bonyah

“…Researchers have explored numerous machine learning (ML) techniques and deep learning (DL) approaches aimed at the automated recognition of liver disease by analysing computed tomography (CT) images. …”
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Transformer-based model for predicting length of stay in intensive care unit in sepsis patients by Jeesu Kim, Jeesu Kim, Geun-Hyeong Kim, Geun-Hyeong Kim, Jae-Woo Kim, Jae-Woo Kim, Ka Hyun Kim, Ka Hyun Kim, Jae-Young Maeng, Yong-Goo Shin, Seung Park, Seung Park

“…A transformer-based deep learning model was developed to predict ICU length of stay (LOS). …”
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Deep-TEMNet: A Hybrid U-Net–2D LSTM Network for Efficient and Accurate 2.5D Transient Electromagnetic Forward Modeling by Zhijie Qu, Yuan Gao, Kang Xing, Xiaojuan Zhang

“…To address these challenges, we present Deep-TEMNet, an advanced deep learning framework specifically designed for two-dimensional TEM forward modeling. …”
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Analysis of Feature Extraction and Anti-Interference of Face Image under Deep Reconstruction Network Algorithm by Jin Yang, Yuxuan Zhao, Shihao Yang, Xinxin Kang, Xinyan Cao, Xixin Cao

“…To explore the anti-interference performance of convolutional neural network (CNN) reconstructed by deep learning (DL) framework in face image feature extraction (FE) and recognition, in the paper, first, the inception structure in the GoogleNet network and the residual error in the ResNet network structure are combined to construct a new deep reconstruction network algorithm, with the random gradient descent (SGD) and triplet loss functions as the model optimizer and classifier, respectively, and it is applied to the face recognition in Labeled Faces in the Wild (LFW) face database. …”
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Loosening rocks detection at Draa Sfar deep underground mine in Morocco using infrared thermal imaging and image segmentation models by Kaoutar Clero, Said Ed-Diny, Mohammed Achalhi, Mouhamed Cherkaoui, Imad El Harraki, Sanaa El Fkihi, Intissar Benzakour, Tarik Soror, Said Rziki, Hamd Ait Abdelali, Hicham Tagemouati, François Bourzeix

“…However, further research is recommended to enhance these results, particularly through deep learning-based segmentation and object detection models.…”
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Electromagnetic metamaterial agent by Shengguo Hu, Mingyi Li, Jiawen Xu, Hongrui Zhang, Shanghang Zhang, Tie Jun Cui, Philipp del Hougne, Lianlin Li

“…Abstract Metamaterials have revolutionized wave control; in the last two decades, they evolved from passive devices via programmable devices to sensor-endowed self-adaptive devices realizing a user-specified functionality. Although deep-learning techniques play an increasingly important role in metamaterial inverse design, measurement post-processing and end-to-end optimization, their role is ultimately still limited to approximating specific mathematical relations; the metamaterial is still limited to serving as proxy of a human operator, realizing a predefined functionality. …”
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Benchmarking Vision-Based Object Tracking for USVs in Complex Maritime Environments by Muhayy Ud Din, Ahsan Baidar Bakht, Waseem Akram, Yihao Dong, Lakmal Seneviratne, Irfan Hussain

“…We benchmarked the performance of seven distinct trackers, developed using advanced deep learning techniques such as Siamese Networks and Transformers, by evaluating them on both simulated and real-world maritime datasets. …”
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Artificial intelligence for the detection of acute myeloid leukemia from microscopic blood images; a systematic review and meta-analysis by Feras Al-Obeidat, Wael Hafez, Wael Hafez, Asrar Rashid, Mahir Khalil Jallo, Munier Gador, Ivan Cherrez-Ojeda, Ivan Cherrez-Ojeda, Daniel Simancas-Racines

“…Accuracy and sensitivity were the primary outcome measures.ResultsTen studies were included in our review and meta-analysis, conducted between 2016 and 2023. Most deep-learning models have been utilized, including convolutional neural networks (CNNs). …”
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Explainable artificial intelligence with fusion-based transfer learning on adverse weather conditions detection using complex data for autonomous vehicles by Khaled Tarmissi, Hanan Abdullah Mengash, Noha Negm, Yahia Said, Ali M. Al-Sharafi

“…After developing driver assistance and AV methods, adversarial weather conditions have become an essential problem. Nowadays, deep learning (DL) and machine learning (ML) models are critical to enhancing object detection in AVs, particularly in adversarial weather conditions. …”
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DAShip: A Large-Scale Annotated Dataset for Ship Detection Using Distributed Acoustic Sensing Technique by Wenjin Huang, Shaoyi Chen, Yichang Wu, Ruihua Li, Tianrui Li, Yihua Huang, Xiaochun Cao, Zhaohui Li

“…In addition, the scarcity of datasets for ship passage events in the DAS field hampers the adoption of deep learning technologies for enhancing ship detection. …”
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ALL-Net: integrating CNN and explainable-AI for enhanced diagnosis and interpretation of acute lymphoblastic leukemia by Abhiram Thiriveedhi, Swetha Ghanta, Sujit Biswas, Ashok K. Pradhan

“…These findings highlight the effectiveness of CNNs in accurately detecting ALL from PBS images and emphasize the importance of addressing data imbalance issues through appropriate preprocessing techniques at the same time demonstrating the usage of XAI in solving the black box approach of the deep learning models. The proposed ALL-Net outperformed EfficientNet, MobileNetV3, VGG-19, Xception, InceptionV3, ResNet50V2, VGG-16, and NASNetLarge except for DenseNet201 with a slight variation of 0.5%. …”
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Clinical validation of explainable AI for fetal growth scans through multi-level, cross-institutional prospective end-user evaluation by Zahra Bashir, Manxi Lin, Aasa Feragen, Kamil Mikolaj, Caroline Taksøe-Vester, Anders Nymark Christensen, Morten B. S. Svendsen, Mette Hvilshøj Fabricius, Lisbeth Andreasen, Mads Nielsen, Martin Grønnebæk Tolsgaard

“…We developed, implemented, and tested a deep-learning model for fetal growth scans using both retrospective and prospective data. …”
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Evaluating the impact of ESICM 2023 guidelines and the new global definition of ARDS on clinical outcomes: insights from MIMIC-IV cohort data by Duanhong Song, Qingquan Chen, Shangbin Huang, Shengxun Qiu, Zeshun Chen, Yuanhang Cai, Yifu Zeng, Xiaoyang Chen, Yixiang Zhang

“…Data were analyzed using Python (version 3.9) and the deep learning framework Pytorch. Kaplan–Meier survival analysis was used to compare survival between the old and new definitions. …”
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The role of artificial intelligence in pandemic responses: from epidemiological modeling to vaccine development by Mayur Suresh Gawande, Nikita Zade, Praveen Kumar, Swapnil Gundewar, Induni Nayodhara Weerarathna, Prateek Verma

“…Conclusively, the review presents a comprehensive assessment of how AI impacts epidemiological modelling, builds AI-enabled dynamic models by collaborating ML and Deep Learning (DL) techniques, and develops and implements vaccines and clinical trials. …”
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Developing a semi-automated technique of surface water quality analysis using GEE and machine learning: A case study for Sundarbans by Sheikh Fahim Faysal Sowrav, Sujit Kumar Debsarma, Mohan Kumar Das, Khan Mohammad Ibtehal, Mahfujur Rahman, Noshin Tabassum Hridita, Atika Afia Broty, Muhammad Sajid Anam Hoque

“…The predictive framework leverages Google Earth Engine (GEE) and AutoML, utilizing deep learning libraries to create dynamic, adaptive models that enhance prediction accuracy. …”
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Multi-scale hydraulic graph neural networks for flood modelling by R. Bentivoglio, E. Isufi, S. N. Jonkman, R. Taormina

“…<p>Deep-learning-based surrogate models represent a powerful alternative to numerical models for speeding up flood mapping while preserving accuracy. …”
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MAEMC-NET: a hybrid self-supervised learning method for predicting the malignancy of solitary pulmonary nodules from CT images by Tianhu Zhao, Tianhu Zhao, Yong Yue, Hang Sun, Jingxu Li, Yanhua Wen, Yudong Yao, Wei Qian, Yubao Guan, Shouliang Qi, Shouliang Qi

“…This study aims to address this diagnostic challenge by developing a novel deep learning model.MethodsThis study proposes MAEMC-NET, a model integrating generative (Masked AutoEncoder) and contrastive (Momentum Contrast) self-supervised learning to learn CT image representations of intra- and inter-solitary nodules. …”
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