The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review by Ricardo Smits Serena, Florian Hinterwimmer, Rainer Burgkart, Rudiger von Eisenhart-Rothe, Daniel Rueckert

“…ConclusionsIn conclusion, the review calls for a collaborative effort to address the highlighted challenges, including improvements in data collection, increasing dataset sizes, a move that inherently pushes the field toward the adoption of more complex deep learning models, and the expansion of the application of AI models on IMU data methodologies across various medical fields. …”
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Validation of musculoskeletal segmentation model with uncertainty estimation for bone and muscle assessment in hip-to-knee clinical CT images by Mazen Soufi, Yoshito Otake, Makoto Iwasa, Keisuke Uemura, Tomoki Hakotani, Masahiro Hashimoto, Yoshitake Yamada, Minoru Yamada, Yoichi Yokoyama, Masahiro Jinzaki, Suzushi Kusano, Masaki Takao, Seiji Okada, Nobuhiko Sugano, Yoshinobu Sato

“…Abstract Deep learning-based image segmentation has allowed for the fully automated, accurate, and rapid analysis of musculoskeletal (MSK) structures from medical images. …”
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Rancang Bangun Alat Pengenal Finger Vein Menggunakan Raspberry Pi dengan Metode Convolutional Neural Network (CNN) by Barlian Henryranu Prasetio, Jevandika, Dahnial Syauqy

“…This research uses a Raspberry Pi 4-based system with the help of IR LEDs and webcams for the acquisition process of finger blood vessel image data, which is expected to be able to carry out the Finger Vein recognition process faster, and the use of the proven Convolutional Neural Network method to produce better accuracy with the Deep Learning process. Of the 30 data used as system testers alongside embedded software and hardware, the accuracy reached 96.66%. …”
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Artificial intelligence driven cyberattack detection system using integration of deep belief network with convolution neural network on industrial IoT by Mahmoud Ragab, Mohammed Basheri, Nasser N. Albogami, Alanoud Subahi, Omar A. Abdulkader, Hashem Alaidaros, Hassan Mousa, Abdullah AL-Malaise AL-Ghamdi

“…Therefore, this study designs a Next–Generation Cybersecurity Attack Detection using an ensemble deep learning model (NGCAD-EDLM) technique in the IIoT environment. …”
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Mapping Habitat Structures of Endangered Open Grassland Species (<i>E. aurinia</i>) Using a Biotope Classification Based on Very High-Resolution Imagery by Steffen Dietenberger, Marlin M. Mueller, Andreas Henkel, Clémence Dubois, Christian Thiel, Sören Hese

“…Through a deep learning classification technique, we conducted biotope mapping and generated fine-scale spatial variables to model habitat suitability. …”
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Annual change in the distribution and landscape health of mangrove ecosystems in China from 2016 to 2023 with Sentinel imagery by Yuchao Sun, Mingzhen Ye, Bin Ai, Zhenlin Lai, Jun Zhao, Zhuokai Jian, Xinyan Qi

“…The integration of remote sensing data and deep learning models enables precise identification of mangroves. …”
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AI-Driven Plant Health Assessment: A Comparative Analysis of Inception V3, ResNet-50 and ViT with SHAP for Accurate Disease Identification in Taro by Valeria Maeda-Gutiérrez, Juan José Oropeza-Valdez, Luis C. Reveles-Gómez, Cristian Padron-Manrique, Osbaldo Resendis-Antonio, Luis Octavio Solís-Sánchez, Hector A. Guerrero-Osuna, Carlos Alberto Olvera Olvera

“…The novelty of this work lies in the first-ever integration of SHapley Additive exPlanations (SHAP) with deep learning architectures to enhance model interpretability while providing a comprehensive comparison of state-of-the-art methods for this underexplored crop. …”
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The Applications and Prospects of Large Language Models in Traffic Flow Prediction by Liu Yuxuan

“…With advancements in deep learning (DL), DL models have made notable strides in prediction. …”
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Adversarial examples detection method based on boundary values invariants by Fei YAN, Minglun ZHANG, Liqiang ZHANG

“…Nowadays,deep learning has become one of the most widely studied and applied technologies in the computer field.Deep neural networks(DNNs) have achieved greatly noticeable success in many applications such as image recognition,speech,self-driving and text translation.However,deep neural networks are vulnerable to adversarial examples that are generated by perturbing correctly classified inputs to cause DNN modes to misbehave.A boundary check method based on traditional programs by fitting the distribution to find the invariants in the deep neural network was proposed and it use the invariants to detect adversarial examples.The selection of training sets was irrelevant to adversarial examples.The experiment results show that proposed method can effectively detect the current adversarial example attacks on LeNet,vgg19 model,Mnist,Cifar10 dataset,and has a low false positive rate.…”
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Gearbox Fault Diagnosis based on GAF-inceptionResNet by Changwen Li, Peng Li, Hua Ding

“…In order to improve the accuracy of gearbox fault diagnosis and accurately express the health status of the gearbox， combined with deep learning algorithms， a GAF-inceptionResNet model for gear fault diagnosis is proposed. …”
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Model split-based data privacy protection method for federated learning by CHEN Ka

“…Split learning (SL) enables data privacy preservation by allowing clients to collaboratively train a deep learning model with the server without sharing raw data. …”
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Parallel Bookkeeping Path of Accounting in Government Accounting System Based on Deep Neural Network by Qing Li

“…A deep neural network is the basis of deep learning. Up to now, the neural network has been applied in many fields, and its application in the financial field is more in-depth. …”
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RESEARCH ON BEARING FAULT DIAGNOSIS UNDER UNBALANCED DATA SET BASED ON IWAE (MT) by LI MengNan, LI Kun, WU Cong

“…It was trained by minority samples, and the generated samples were added into original data sets to obtain balanced data sets. Then, deep learning method was used as diagnose network, and the balanced data sets were fed into it as input, so as to adaptively learn fault characteristics and realize fault classification. …”
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Semantic communication system with efficient integration of global and local context features by Peng LUO, Yueling LIU, Yuyuan ZHANG, Kuo CAO, Haitao ZHAO, Jibo WEI

“…A communication system based on extended contextual semantic features was proposed by using an end-to-end integrated design method based on deep learning.Unlike existing research that focused only on local context while neglecting global context, the proposed system integrated both local and global contextual knowledge, semantic encoding and decoding was utilized by extended contextual knowledge, thereby enhancing the reliability of the semantic communication system.At the transmitter, efficient semantic representation was achieved through extended contextual semantic encoding.At the receiver, the accuracy of semantic inference was improved by combining mechanisms such as historical communication text mining, contextual semantic feature learning, and heuristic graph-based decoding strategy.When comparing with the traditional communication system and the existing semantic communication systems, simulation results demonstrate that the proposed system significantly improves the reliability of the communication system under low signal-to-noise ratio.…”
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Multilayer Deep Deterministic Policy Gradient for Static Safety and Stability Analysis of Novel Power Systems by Yun Long, Youfei Lu, Hongwei Zhao, Renbo Wu, Tao Bao, Jun Liu

“…The proposed multilayer deep deterministic policy gradient is compared with other deep learning algorithms. The feasibility, effectiveness, and superiority of the proposed method are verified by numerical simulations.…”
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Survey on video image reconstruction method based on generative model by Yanwen WANG, Weimin LEI, Wei ZHANG, Huan MENG, Xinyi CHEN, Wenhui YE, Qingyang JING

“…Traditional video compression technology based on pixel correlation has limited performance improvement space, semantic compression has become the new direction of video compression coding, and video image reconstruction is the key link of semantic compression coding.First, the video image reconstruction methods for traditional coding optimization were introduced, including how to use deep learning to improve prediction accuracy and enhance reconstruction quality with super-resolution techniques.Second, the video image reconstruction methods based on variational auto-encoders, generative adversarial networks, autoregressive models and transformer models were discussed emphatically.Then, the models were classified according to different semantic representations of images.The advantages, disadvantages, and applicable scenarios of various methods were compared.Finally, the existing problems of video image reconstruction were summarized, and the further research directions were prospected.…”
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Utilization of Artificial Intelligence for the automated recognition of fine arts. by Ruhua Chen, Mohammad Reza Ghavidel Aghdam, Mohammad Khishe

“…Fine art recognition, traditionally dependent on human expertise, is undergoing a significant transformation with the integration of Artificial Intelligence (AI) and deep learning. This article introduces a novel AI-based approach for fine art recognition, utilizing Convolutional Neural Networks (CNNs) and advanced feature extraction techniques. …”
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An Improved Prediction Model of IGBT Junction Temperature Based on Backpropagation Neural Network and Kalman Filter by Yu Dou

“…In the future, the application of neural networks or deep learning in power electronics will create more possibilities.…”
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A Discourse Coherence Analysis Method Combining Sentence Embedding and Dimension Grid by Lanlan Jiang, Shengjun Yuan, Jun Li

“…In this paper, we propose a discourse coherence analysis method combining sentence embedding and the dimension grid, we obtain sentence-level vector representation by deep learning, and we introduce a coherence model that captures the fine-grained semantic transitions in text. …”
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Severe deviation in protein fold prediction by advanced AI: a case study by Jacinto López-Sagaseta, Alejandro Urdiciain

“…Abstract Artificial intelligence (AI) and deep learning are making groundbreaking strides in protein structure prediction. …”
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