LMVT: A hybrid vision transformer with attention mechanisms for efficient and explainable lung cancer diagnosis by Jesika Debnath, Al Shahriar Uddin Khondakar Pranta, Amira Hossain, Anamul Sakib, Hamdadur Rahman, Rezaul Haque, Md.Redwan Ahmed, Ahmed Wasif Reza, S M Masfequier.Rahman Swapno, Abhishek Appaji

“…Furthermore, we integrate attention mechanisms based on the Convolutional Block Attention Module (CBAM) and feature selection techniques derived from the Simple Gray Level Difference Method (SGLDM) to improve discriminative focus and minimize redundancy. …”
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Video Visualization Technology and Its Application in Health Statistics Teaching for College Students by Chengfei Li, Yuan Xie, Shuanbao Li

“…The results show that the external model load difference between each explicit variable and latent variable is statistically significant. …”
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Research on leaf identification of table grape varieties based on deep learning by PAN Bowen, LIN Meiling, JU Yanlun, SU Baofeng, SUN Lei, FAN Xiucai, ZHANG Ying, ZHANG Yonghui, LIU Chonghuai, JIANG Jianfu, FANG Yulin

“…The front images of different leaves were taken, and a dataset of 29 713 fresh grape leaves was constructed. …”
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Short communication: Nanoscale heterogeneity of U and Pb in baddeleyite from atom probe tomography – ²³⁸U series alpha recoil effects and U atom clustering by S. Denyszyn, D. W. Davis, D. Fougerouse

“…Synthetic <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M10" display="inline" overflow="scroll" dspmath="mathml"><mrow class="chem"><msup><mi/><mn mathvariant="normal">206</mn></msup><mi mathvariant="normal">Pb</mi><msup><mo>/</mo><mn mathvariant="normal">238</mn></msup><mi mathvariant="normal">U</mi></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="57pt" height="15pt" class="svg-formula" dspmath="mathimg" md5hash="976d5de522f5330a2ea037e20b88a23d"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="gchron-6-607-2024-ie00002.svg" width="57pt" height="15pt" src="gchron-6-607-2024-ie00002.png"/></svg:svg></span></span> profiles were determined from the convolution of the observed U profile with the redistribution functions for different alpha recoil distances. …”
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Deep Learning-Aided Acoustic Source Localization in Thin-Walled Waveguides by Giacomo Donati, Federica Zonzini, Stefano Mariani, Denis Bogomolov, Luca De Marchi

“…In particular, the localization of acoustic emission sources is particularly important for the identification of damages caused by stress and can be achieved by estimating the Difference in Time of Arrival (DToA) between the waves captured by a sparse sensor array. …”
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Joint vibrotactile coding for machine recognition and human perception by Ying FANG, Yiwen XU, Tiesong ZHAO

“…In order to accurately transmit the content meaning of vibrotactile signals and achieve intelligent recognition and signal reconstruction, a joint vibrotactile coding scheme for machine recognition and human perception was proposed.At the encoding end, the original three-dimensional vibrotactile signals were converted into one-dimensional signals.Then the semantic information of the signals was extracted using a short-time Fourier transform before being effectively compressed and transmitted.At the decoding end, a fully convolutional neural network was used to intelligently recognize based on the semantic information.The difference between the original signals and the reconstructed signals based on semantic information was used as compensation for the semantic information, and the quality of the reconstructed signals was gradually improved to meet human perceptual needs.The experimental results show that the proposed scheme achieve tactile recognition with semantic information at a lower bit rate while improving the compression efficiency of tactile data, thus satisfying human perceptual needs.…”
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Lightweight Tea Shoot Picking Point Recognition Model Based on Improved DeepLabV3+ by HU Chengxi, TAN Lixin, WANG Wenyin, SONG Min

“…Initially, MobilenetV2 was employed as the feature extractor, substituting traditional convolution operations with depth wise separable convolutions. …”
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Rancang Bangun Aplikasi Berbasis Android untuk Perbaikan Kualitas Citra Tanaman Atas Perbedaan Spesifikasi Kamera Smartphone pada Prediksi Kandungan Pigmen Fotosintesis Secara Real... by Felix Adrian Tjokro Atmodjo, Kestrilia Rega Prilianti, Hendry Setiawan

“…However, Fuzzy Piction is still not invariant to differences in image quality that can occur due to differences in smartphone camera specifications. …”
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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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Recognition Algorithm of AE Signal of Rock Fracture Based on Multiscale 1DCNN-BLSTM by Weihua Wang

“…This article constructs a deep learning algorithm model to identify acoustic emission signals released from rock fractures with different brittle mineral contents. In response to the interference characteristics of acoustic emission signal data, a multiscale one-dimensional convolutional neural network embedded with efficient channel attention (ECA) module was incorporated into the model, and multiscale convolutional kernels were used to extract features of different levels of precision. …”
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The shallowest transparent and interpretable deep neural network for image recognition by Gurmail Singh, Stefano Frizzo Stefenon, Kin-Choong Yow

“…This model consists of a transparent prototype layer, followed by an indispensable fully connected layer that connects prototypes and logits, whereas usually, interpretable models are not fully transparent because they use some black-box part as their baseline. This is the difference between Shallow-ProtoPNet and prototypical part network (ProtoPNet), the proposed Shallow-ProtoPNet does not use any black box part as a baseline, whereas ProtoPNet uses convolutional layers of black-box models as the baseline. …”
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Application of a deep learning algorithm for the diagnosis of HCC by Philip Leung Ho Yu, Keith Wan-Hang Chiu, Jianliang Lu, Gilbert C.S. Lui, Jian Zhou, Ho-Ming Cheng, Xianhua Mao, Juan Wu, Xin-Ping Shen, King Ming Kwok, Wai Kuen Kan, Y.C. Ho, Hung Tat Chan, Peng Xiao, Lung-Yi Mak, Vivien W.M. Tsui, Cynthia Hui, Pui Mei Lam, Zijie Deng, Jiaqi Guo, Li Ni, Jinhua Huang, Sarah Yu, Chengzhi Peng, Wai Keung Li, Man-Fung Yuen, Wai-Kay Seto

“…We conducted sensitivity analyses of different subgroups, deep learning explainability evaluation, and misclassification analysis. …”
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Cumulative and offsetting effects of Streamflow Response to Climate change and Large Reservoir Group in the Jinsha River Basin, China by Ying Zhang, Zengxin Zhang, Qi Zhang, Xingnan Zhang, Yang Xu, Xiaoyang Liu, Jingqiao Mao, Chongyu Xu

“…Reservoir storage efficiency was more obvious in the midstream, especially after 2015 (with normalized difference (Sreconstruction – Sobservation)> 0 more frequent), while released more in the downstream after 2010 (with normalized difference<0 more frequent). …”
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Network intrusion detection method based on VAE-CWGAN and fusion of statistical importance of feature by Taotao LIU, Yu FU, Kun WANG, Xueyuan DUAN

“…Considering the problems of traditional intrusion detection methods limited by the class imbalance of datasets and the poor representation of selected features, a detection method based on VAE-CWGAN and fusion of statistical importance of features was proposed.Firstly, data preprocessing was conducted to enhance data quality.Secondly, a VAE-CWGAN model was constructed to generate new samples, addressing the problem of imbalanced datasets, ensuring that the classification model no longer biased towards the majority class.Next, standard deviation, difference of median and mean were used to rank the features and fusion their statistical importance for feature selection, aiming to obtain more representative features, which made the model can better learn data information.Finally, the mixed data set after feature selection was classified through a one-dimensional convolutional neural network.Experimental results show that the proposed method demonstrates good performance advantages on three datasets, namely NSL-KDD, UNSW-NB15, and CIC-IDS-2017.The accuracy rates are 98.95%, 96.24%, and 99.92%, respectively, effectively improving the performance of intrusion detection.…”
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Pulmonary Disease Classification on Electrocardiograms Using Machine Learning by Aboubacar Abdoulaye Soumana, Prajwol Lamichhane, Mehlam Shabbir, Xudong Liu, Mona Nasseri, Scott Helgeson

“…In the task of classifying whether a patient has obstructive lung disease, our results show that deep neural network models outperformed the non-neural models, though the difference is within 3% on accuracy and F1-score metrics.…”
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A cross-stage features fusion network for building extraction from remote sensing images by Xiaolong Zuo, Zhenfeng Shao, Jiaming Wang, Xiao Huang, Yu Wang

“…The deep learning-based building extraction methods produce different feature maps at different stages of the network, which contain different information features. …”
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Automation and Optimization of Food Process Using CNN and Six-Axis Robotic Arm by Youngjin Kim, Sangoh Kim

“…A comparative analysis between the Preliminary Coffee Sample (PCS) and Validation Coffee Sample (VCS) revealed that increasing roast intensity resulted in consistent trends for both samples, including an increase in weight loss and Gas sensor Initial Difference (GID) and a decrease in Sum of Pixel Grayscale Values (SPGVs). …”
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Multi-Step Peak Passenger Flow Prediction of Urban Rail Transit Based on Multi-Station Spatio-Temporal Feature Fusion Model by Jianan Sun, Xiaofei Ye, Xingchen Yan, Tao Wang, Jun Chen

“…A combination of a graph convolutional neural network and a Transformer is used. …”
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Simulation and Recognition of Concrete Lining Infiltration Degree via an Indoor Experiment by Dongsheng Wang, Jun Feng, Xinpeng Zhao, Yeping Bai, Yujie Wang, Xuezeng Liu

“…To solve this problem, we propose a recognition method by using a deep convolutional neural network. We carry out laboratory tests, prepare cement mortar specimens with different saturation levels, simulate different degrees of infiltration of tunnel concrete linings, and establish an infrared thermal image data set with different degrees of infiltration. …”
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A Study on Using Transfer Learning to Utilize Information From Similar Systems for Data-Driven Condition Diagnosis and Prognosis by Marcel Braig, Peter Zeiler

“…The former includes condition data of rolling bearings of different dimensioning, recorded under different operating conditions, and the latter includes degradation data of filters with different filtration areas. …”
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