Exploring Artificial Intelligence for Enhanced Endodontic Practice: Applications, Challenges, and Future Directions by Santosh R. Patil, Mohmed Isaqali Karobari

“…The primary objective is to synthesize current knowledge on AI technologies such as machine learning, deep learning, and neural networks and their integration into endodontic practice. …”
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Ringworm Detection and Diagnosis System. by Asiimwe, Joab, Barigye, Deus Dedet

“…This report presents a novel ringworm detection system utilizing deep learning and image processing. We carried out our research within a period of seven months and our system performed the desired work. …”
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Research Progress and Prospects of Key Navigation Technologies for Facility Agricultural Robots by HE Yong, HUANG Zhenyu, YANG Ningyuan, LI Xiyao, WANG Yuwei, FENG Xuping

“…In regard to automatic obstacle avoidance technology for robots, the paper discusses sevelral commonly used obstacle avoidance control algorithms commonly used in facility agriculture, including artificial potential field, dynamic window approach and deep learning method. Among them, deep learning methods are often employed for perception and decision-making in obstacle avoidance scenarios.…”
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MFBTFF-Net: A Novel Multi-Frequency Brightness Temperature Feature Fusion Network for Global Lunar Surface Oxides Abundance Estimation With Chang'e-2 Lunar Microwave Sounder... by Yu Li, Zifeng Yuan, Sarah Mazhar, Zhiguo Meng, Yuanzhi Zhang, Jinsong Ping, Ferdinando Nunziata

“…Moreover, existing machine/deep learning models may not be suitable for processing the data acquired in lunar exploration. …”
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Image depth estimation assisted by multi-view projection by Liman Liu, Jinshan Tian, Guansheng Luo, Siyuan Xu, Chen Zhang, Huaifei Hu, Wenbing Tao

“…Abstract In recent years, deep learning has significantly advanced the development of image depth estimation algorithms. …”
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Random features and polynomial rules by Fabián Aguirre-López, Silvio Franz, Mauro Pastore

“…Random features models play a distinguished role in the theory of deep learning, describing the behavior of neural networks close to their infinite-width limit. …”
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Transformer Inrush Current and Internal Fault Discrimination Using Multitypes of Convolutional Neural Network Techniques by Abedalgany Abedallah Athamneh, Ali Mohammad Alqudah

“…This paper presents a new proposed method to discriminate the transformer’s internal fault from the inrush current; the discrimination process is based on a convolutional neural network (CNN) with a combination of the higher order spectral estimations that perform a deep learning classification with high accuracy. This research succeeded in proposing two robust and efficient CNN models; the first one is the 1D CNN, which takes the sole signal without any transformation, while the second model is the 2D CNN, which takes the short-time Fourier transform of the signal. …”
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A semi-supervised transfer learning recognition method for radar compound jamming under small samples by Jinqiang WANG, Minhong SUN, Xianghong TANG, Zhaoyang QIU, Deguo ZENG

“…Aiming at the problem that more and more kinds of radar compound jamming signals and too few training samples were difficult to make the deep learning model reach the optimal state, a semi-supervised transfer learning recognition method for radar compound jamming under small samples was proposed, which solved the problem of low network training accuracy caused by the difficulty in obtaining labeled samples through unlabeled samples.The feature extractor and classifier obtained after pre-training of single jamming data set were transferred to small-scale compound jamming data set, and the model was fine-tuning by using weight imprinting and semi-supervised learning.The model parameters were optimized by the proposed nearest neighbor correlation loss nearest neighbor correlation loss (NNCL).The experimental results show that the recognition accuracy of the model can reach 93.20% when the jamming-to-noise ratio is 10 dB and there are only 5 labeled samples of the new class of compound jamming signals.…”
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Real-Time Control of Intelligent Prosthetic Hand Based on the Improved TCN by Xiaoguang Liu, Jiawei Wang, Tingwen Han, Cunguang Lou, Tie Liang, Hongrui Wang, Xiuling Liu

“…In addition, the Temporal Convolutional Network (TCN) in deep learning has been improved to enhance the performance of the network. …”
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Intelligent Diagnosis of Rolling Bearing Fault Based on Improved Convolutional Neural Network and LightGBM by Yanwei Xu, Weiwei Cai, Liuyang Wang, Tancheng Xie

“…Aiming at the problems of weak generalization ability and long training time in most fault diagnosis models based on deep learning, such as support vector machines and random forest algorithms, one intelligent diagnosis method of rolling bearing fault based on the improved convolution neural network and light gradient boosting machine is proposed. …”
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Fast panoramic image stitching algorithm based on parameter regression by Fan GUO, Xiaohu LI, Wentao LIU, Jin TANG

“…In reality, the field of view of images acquired by cameras was usually limited, and the demand for panoramic images was increasing.Therefore, a fast panoramic image stitching algorithm based on parameter regression was proposed for panoramic image sequences.The traditional image registration task was transformed into deep learning combined with machine learning, a multi-scale deep convolutional neural network (MDCNN) based on Gaussian difference pyramid was designed to extract features of stitching images, and LightGBM regression model was used to predict stitching parameters.The transformation matrix and the focal length of the camera were obtained to align the images, and a hyperbolic image fusion algorithm was designed to eliminate the stitching seam between the images.The experimental results show that the proposed algorithm can quickly mosaic images and obtain clearer and more natural panoramic mosaic effects than the existing representative algorithms.It also has good adaptability for infrared images.…”
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Challenges and Prospects of Sensing Technology for the Promotion of Tele-Physiotherapy: A Narrative Review by Kei Kakegawa, Tadamitsu Matsuda

“…The development of these technologies will not only enhance the accuracy of deep learning by AI through the acquisition of big data, but also has the potential to elucidate movement characteristics associated with movement disorders or pathological conditions. …”
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Molecular optimization using a conditional transformer for reaction-aware compound exploration with reinforcement learning by Shogo Nakamura, Nobuaki Yasuo, Masakazu Sekijima

“…Because of recent advances in deep learning, molecular generative models have been developed. …”
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Fault Diagnosis of Small Sample Automobile Planetary Gearboxes Based on Continuous Wavelet Transform and Model Agnostic Meta Learning by Xinxin Lu, Jun Ma, Yingcong Zhang

“…Aiming at the problem that the vibration signal of planetary gearboxes has strong non-stationary characteristics， few fault samples and the dependence of traditional deep learning on data， an intelligent diagnosis method for planetary gearboxes based on continuous wavelet transform（CWT） and model agnostic meta learning（MAML） is proposed. …”
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Revolutionizing colorectal cancer detection: A breakthrough in microbiome data analysis. by Mwenge Mulenga, Arutchelvan Rajamanikam, Suresh Kumar, Saharuddin Bin Muhammad, Subha Bhassu, Chandramathi Samudid, Aznul Qalid Md Sabri, Manjeevan Seera, Christopher Ifeanyi Eke

“…Even the promising deep learning (DL) methods are not immune to these challenges. …”
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A Research: Investigation of Financial Applications with Blockchain Technology by Mohammed Ali Mohammed Mohammed, Fuat Türk

“…Future directions include integrating advanced deep learning models, additional data sources, and ensemble methods to enhance prediction accuracy and robustness.…”
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Target-Aware Deep Feature Compression for Power Intelligent Inspection Tracking by Wei Jiang, Zhimin Guo, Huanlong Zhang, Liyun Cheng, Yangyang Tian

“…Deep learning has brought revolutionary progress to computer vision, so intelligent inspection equipment based on computer vision has developed rapidly. …”
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Improved Convolutional Neural Image Recognition Algorithm based on LeNet-5 by Lijie Zhou, Weihai Yu

“…Convolutional neural network (CNN) is a very important method in deep learning, which solves many complex pattern recognition problems. …”
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PENERAPAN ARTIFICIAL INTELLIGENCE (AI) DALAM PERAMALAN AKUNTANSI TINJAUAN LITERATUR DAN AGENDA PENELITIAN MASA DEPAN by Gilang Surya Pratama, Agus Munandar

“…The analysis results show significant improvements in forecasting accuracy using AI technology, with Machine Learning achieving 78% accuracy in revenue forecasting, Deep Learning 85% in financial trend prediction, and Natural Language Processing 89% in sentiment analysis. …”
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Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations. by Yin-Jui Chang, Yuan-I Chen, Hannah M Stealey, Yi Zhao, Hung-Yun Lu, Enrique Contreras-Hernandez, Megan N Baker, Edward Castillo, Hsin-Chih Yeh, Samantha R Santacruz

“…Here we introduce a neurobiological-driven deep learning model, termed multiscale neural dynamics neural ordinary differential equation (msDyNODE), to describe multiscale brain communications governing cognition and behavior. …”
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