Potential Use and Limitation of Artificial Intelligence to Screen Diabetes Mellitus in Clinical Practice: A Literature Review by Aqsha Nur, Defin Yumnanisha, Sydney Tjandra, Adang Bachtiar, Dante Saksono Harbuwono

“…., machine learning and deep learning) have yielded prediction performances of up to 98% in various diseases. …”
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Applying MLP-Mixer and gMLP to Human Activity Recognition by Takeru Miyoshi, Makoto Koshino, Hidetaka Nambo

“…The development of deep learning has led to the proposal of various models for human activity recognition (HAR). …”
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The Application of Reinforcement Learning in Traffic Flow Prediction: Advantages, Problems, and Prospects by Li Minghui, Zhou Decheng, Zhang Shiqi

“…This article summarizes three traditional methods of TFP: parameter-based prediction, shallow machine learning-based prediction, and deep learning (DL)-based prediction. However, traditional TFP methods only focus on predicting time series in traffic data, and it is difficult for these methods to capture the interdependent relationship between the spatial distribution of traffic across a network and the temporal evolution of traffic conditions at each location. sequences. …”
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Pixel‐wise supervision for presentation attack detection on identity document cards by Raghavendra Mudgalgundurao, Patrick Schuch, Kiran Raja, Raghavendra Ramachandra, Naser Damer

“…The baseline benchmark is presented using different handcrafted and deep learning models on a newly constructed in‐house database obtained from an operational system consisting of 886 users with 433 bona fide, 67 print and 366 display attacks. …”
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Advancements in the Application of Convolutional Neural Networks in Ultrasound Imaging for Breast Cancer Diagnosis and Treatment by An Zichen, Li Fan

“…Breast ultrasound (US) imaging technology plays a crucial role in the early diagnosis and intervention treatment of breast cancer patients. Deep learning (DL), as one of the most powerful machine learning techniques in the field of artificial intelligence (AI), has the ability to automatically select features from raw data, achieving remarkable advancements in breast US imaging. …”
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A Novel Chinese Entity Relationship Extraction Method Based on the Bidirectional Maximum Entropy Markov Model by Chengyao Lv, Deng Pan, Yaxiong Li, Jianxin Li, Zong Wang

“…Traditional methods of relationship extraction, either those proposed at the earlier times or those based on traditional machine learning and deep learning, have focused on keeping relationships and entities in their own silos: extracting relationships and entities are conducted in steps before obtaining the mappings. …”
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The first geospatial dataset of irrigated fields (2020–2024) in Vojvodina (Serbia) by Mirjana Radulović, Miljana Marković, Sanja Brdar, Ioannis Athanasiadis, Gordan Mimić

“…This study’s goal is to give accessibility to our dataset which further can be explored and used for building or fine-tuning machine learning and deep learning models for the automatic detection of irrigated fields using satellite imagery.…”
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Residual trio feature network for efficient super-resolution by Junfeng Chen, Mao Mao, Azhu Guan, Altangerel Ayush

“…Abstract Deep learning-based approaches have demonstrated impressive performance in single-image super-resolution (SISR). …”
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Disorder-induced enhancement of lithium-ion transport in solid-state electrolytes by Zhimin Chen, Tao Du, N. M. Anoop Krishnan, Yuanzheng Yue, Morten M. Smedskjaer

“…Here, we address this challenge by establishing and employing a deep learning potential to simulate Li3PS4 electrolyte systems with varying levels of disorder. …”
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Physics-aware machine learning for glacier ice thickness estimation: a case study for Svalbard by V. Steidl, J. L. Bamber, J. L. Bamber, X. X. Zhu, X. X. Zhu

“…In this study, we use deep learning paired with physical knowledge to generate ice thickness estimates for all glaciers of Spitsbergen, Barentsøya, and Edgeøya in Svalbard. …”
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An Improved Deep Belief Network IDS on IoT-Based Network for Traffic Systems by Rayeesa Malik, Yashwant Singh, Zakir Ahmad Sheikh, Pooja Anand, Pradeep Kumar Singh, Tewabe Chekole Workneh

“…Hence, there is a need to use an intelligent mechanism based on machine learning (ML) and deep learning (DL), to detect attacks. In this study, the authors have proposed an intrusion detection engine with a deep belief network (DBN) being the core. …”
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Compensating Sparse-view Inline Computed Tomography Artifacts with Neural Representation and Incremental Forward-Backward Network Architecture by Manuel Buchfink, Faizan Ahmad, Guangpu Yang, Ammar Alsaffar, Charles Clark, Ahmed Baraka, Xingyu Liu, Sven Simon

“…Therefore, this paper discusses two deep-learning-based approaches for removing such artifacts. …”
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Advancements in training and deployment strategies for AI-based intrusion detection systems in IoT: a systematic literature review by S. Kumar Reddy Mallidi, Rajeswara Rao Ramisetty

“…It then examines various IDS architectures and delves into the integration of machine learning (ML) and deep learning (DL) technologies that improve detection capabilities and system responsiveness. …”
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Asynchronous Wireless Signal Modulation Recognition Based on In-Phase Quadrature Histogram by Xu Zhang, Xi Hui, Pengwu Wan, Tengfei Hui, Xiongfei Li

“…To address these challenges, deep learning-based modulation mode recognition technique is investigated in this paper for low-speed asynchronous sampled signals under channel conditions with varying SNR and delay. …”
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Multisignal VGG19 Network with Transposed Convolution for Rotating Machinery Fault Diagnosis Based on Deep Transfer Learning by Jianye Zhou, Xinyu Yang, Lin Zhang, Siyu Shao, Gangying Bian

“…To realize high-precision and high-efficiency machine fault diagnosis, a novel deep learning framework that combines transfer learning and transposed convolution is proposed. …”
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Smart Shift Decision Method Based on Stacked Autoencoders by Zengcai Wang, Yazhou Qi, Guoxin Zhang, Lei Zhao

“…Meanwhile, the network structure of SAE is determined through a comparative experiment on simple and deep-learning neural networks. Experimental results demonstrate that using the SAE intelligent shift control strategy to determine shift timing not only is feasible and accurate but also saves time and development costs.…”
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Using transformer-based models and social media posts for heat stroke detection by Sumiko Anno, Yoshitsugu Kimura, Satoru Sugita

“…This study demonstrates the potential of using Japanese tweets and deep learning algorithms based on transformer networks for event-based surveillance at high spatiotemporal levels to enable early detection of heat stroke risks.…”
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Towards automated recipe genre classification using semi-supervised learning. by Nazmus Sakib, G M Shahariar, Md Mohsinul Kabir, Md Kamrul Hasan, Hasan Mahmud

“…Furthermore, we have demonstrated traditional machine learning, deep learning and pre-trained language models to classify the recipes into their corresponding genre and achieved an overall accuracy of 98.6%. …”
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Research on Feature Fusion Method of Mine Microseismic Signal Based on Unsupervised Learning by Rui Liu

“…Aiming at the problem of unobvious feature extraction of multiclass mine microseismic signals, this paper is based on the unsupervised learning method in the deep learning method, combined with wavelet packet energy ratio and empirical modulus singular value decomposition, and proposes a method based on wavelet packet energy and empirical modulus singular value decomposition and proposes a method (M-W&E) based on wavelet packet energy and empirical modulus singular value decomposition. …”
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Enhancing Driving Safety and Environmental Consciousness through Automated Road Sign Recognition Using Convolutional Neural Networks by M. H. F. Md Fauadi, M. F. H. Mohd Zan, M. A. M Ali, L. Abdullah, S. N. Yaakop and A. Z. M. Noor

“…This study explores the application of Convolutional Neural Networks (CNNs) in automatically recognizing road signs. CNNs, as deep learning algorithms, possess the ability to process and classify visual data, making them well-suited for image-based tasks such as road sign recognition. …”
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