Research and development of thick plate shape prediction system based on industrial big data by Yufei MA, Changxin LIU, Wei KONG, Jinliang DING

“…Thick plate shape is one of the important indicators to measure the quality of thick plate products.The timely prediction of the final plate shape in production is of great significance for adjusting the operation and control of thick plate production.In actual industrial production, thick plate data has many characteristics, such as multiple coupling information, large amount of redundant information, and multi-source heterogeneity of data.Combining the needs of thick plate shape prediction, a thick plate shape prediction system was designed and developed.The data dump function was used to filter and preprocess the industrial big data to remove the coupling information and redundant variables in the data.LSTM neural network, convolutional neural network and 3D convolutional neural network were used to extract data features from data of different dimensions, and the features were fused based on the maximum mutual information coefficient to establish an integrated learning prediction model, which effectively solved the modeling difficulties caused by multi-source heterogeneous data.The actual industrial data of a domestic thick plate production line was used for verification, and the results showed the effectiveness of the developed system.…”
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MAS-PD: Transferable Adversarial Attack Against Vision-Transformers-Based SAR Image Classification Task by Boshi Zheng, Jiabin Liu, Yunjie Li, Yan Li, Zhen Qin

“…We compare our method with four traditional adversarial attack techniques across different model architectures, including CNNs and ViTs, using the publicly available MSTAR SAR dataset. …”
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FUSCANet: Enhancing Skin Disease Classification Through Feature Fusion and Spatial-Channel Attention Mechanisms by Qinyang Liu, Xuan Wang, Hongjiu Liu, Xiangzhen Zang, Lei Li, Zhanlin Ji, Ivan Ganchev

“…Then, a novel Multi-Scale Feature Aggregation (MSFA) layer is added to enhance feature extraction across various scales, effectively capturing more comprehensive features and fusing feature maps from different scales to improve final feature reuse. Lastly, a newly designed Enhanced Convolutional Block Attention Module (ECBAM) increases the model’s focus on important information, allowing for the final extraction and summarization of features. …”
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Virtual Reality Video Image Classification Based on Texture Features by Guofang Qin, Guoliang Qin

“…As one of the most widely used methods in deep learning technology, convolutional neural networks have powerful feature extraction capabilities and nonlinear data fitting capabilities. …”
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Deep learning approach to bacterial colony classification. by Bartosz Zieliński, Anna Plichta, Krzysztof Misztal, Przemysław Spurek, Monika Brzychczy-Włoch, Dorota Ochońska

“…DIBaS dataset (Digital Image of Bacterial Species) contains 660 images with 33 different genera and species of bacteria.…”
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Potential for Evaluation of Interwell Connectivity under the Effect of Intraformational Bed in Reservoirs Utilizing Machine Learning Methods by Jinzi Liu

“…The dataset is trained with dynamic production data under different permeability, interlayer dip angle, and injection pressure. …”
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Fast QTMT partition decision based on deep learning by Shuang PENG, Xiaodong WANG, Zongju PENG, Fen CHEN

“…Compared with the predecessor standards, versatile video coding (VVC) significantly improves compression efficiency by a quadtree with nested multi-type tree (QTMT) structure but at the expense of extremely high coding complexity.To reduce the coding complexity of VVC, a fast QTMT partition method was proposed based on deep learning.Firstly, an attention-asymmetric convolutional neural network was proposed to predict the probability of partition modes.Then, the fast decision of partition modes based on the threshold was proposed.Finally, the cost of coding performance and time was proposed to obtain the optimal threshold, and the threshold decision method was proposed.Experimental results at different levels show that the proposed method achieves an average time saving of 48.62%/52.93%/62.01% with the negligible BDBR of 1.05%/1.33%/2.38%.Such results demonstrate that the proposed method significantly outperforms other state-of-the-art methods.…”
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Exploration and application of deep learning based wellbore deformation forecasting model by Hui LIU, Guoqiang LI, Xiaojun ZHU, Pengfei ZHANG, Hua CHENG, Jinzheng WANG, Peishuai LI

“…The study shows that: ① The wellbore tilt mainly occurs in the loose layer, the tilt value decreases linearly from shallow to deep, and is biased towards the side of the extraction zone, with a maximum of 352 mm, and the deformation of the bedrock layer is smaller, with a maximum of 88 mm; the increase in the range of deformation propagation in the thick loose layer caused by the mining, and the change of seepage hydrophobicity of the aquifer at the bottom along the wall of the well and the seepage field of the groundwater are the main causes of the tilted deformation of the wellbore. ② The Spearman correlation coefficient between the model and the measured value is 0.978 at the maximum and 0.867 at the minimum;the maximum difference between the four models and the field measured offsets is 0.043 m, the mean absolute error EMA is within 0.003–0.009 m, and the root mean square error ERMS is within 0.004–0.011 m. …”
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Deep learning-based accurate detection of insects and damage in cruciferous crops using YOLOv5 by Sourav Chakrabarty, Pathour Rajendra Shashank, Chandan Kumar Deb, Md. Ashraful Haque, Pradyuman Thakur, Deeba Kamil, Sudeep Marwaha, Mukesh Kumar Dhillon

“…A total of 2,730 images were captured from different fields and polyhouses using different smartphones and an SLR camera. …”
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Automatic detection of floating instream large wood in videos using deep learning by J. Aarnink, J. Aarnink, T. Beucler, T. Beucler, M. Vuaridel, V. Ruiz-Villanueva, V. Ruiz-Villanueva

“…We designed multiple scenarios using different data subsets with and without data augmentation. …”
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TCSRNet: a lightweight tobacco leaf curing stage recognition network model by Panzhen Zhao, Panzhen Zhao, Songfeng Wang, Shijiang Duan, Aihua Wang, Lingfeng Meng, Yichong Hu

“…Firstly, the model utilizes an Inception structure with parallel convolutional branches to capture features at different receptive fields, thereby better adapting to the appearance variations of tobacco leaves at different curing stages. …”
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Comprehensive Multi-indicator Prediction Model for Storage Quality of Multi-cultivar Kiwifruit Based on Visible-Near Infrared Spectroscopy by Zizhao LIANG, Xin LI, Pu LIU, Wenqiang GUAN, Ming LI

“…After the use of different preprocessing algorithms, such as first-order derivatives (FD), standard normal variate (SNV), second-order derivatives, convolutional smoothing, and FD+SNV, the data were combined with competitive adaptive reweighted sampling (CARS) for feature wavelength selection. …”
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Galaxy Morphological Classification with Zernike Moments and Machine Learning Approaches by Hamed Ghaderi, Nasibe Alipour, Hossein Safari

“…The uniqueness due to the orthogonality and completeness of Zernike polynomials, reconstruction of the original images with minimum errors, invariances (rotation, translation, and scaling), different block structures, and discriminant decision boundaries of ZMs’ probability density functions for different order numbers indicate the capability of ZMs in describing galaxy features. …”
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A Fog Computing-Based Cost-Effective Smart Health Monitoring Device for Infectious Disease Applications by Saranya Govindakumar, Vijayalakshmi Sankaran, Paramasivam Alagumariappan, Bhaskar Kosuru Bojji Raju, Daniel Ford

“…Further, the proposed device consists of three different biosensor modules, namely a MAX90614 infrared temperature sensor, a MAX30100 pulse oximeter, and a microphone sensor. …”
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Stealthy Adversarial Attacks on Machine Learning-Based Classifiers of Wireless Signals by Wenhan Zhang, Marwan Krunz, Gregory Ditzler

“…We then study their performance under three types of low-power AML perturbations (FGSM, PGD, and DeepFool), considering different amounts of information at the attacker. On one extreme (so-called “white-box” attack), the attacker has complete knowledge of the defender’s classifier and its training data. …”
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Toward AI-Driven Cough Sound Analysis for Respiratory Disease Diagnosis by Houda Benaliouche, Houda Hafi, Hakim Bendjenna, Zeyad Alshaikh

“…Coughs, as a diagnostic cue, provide distinctive information about glottis behavior related to different respiratory pathological cases. This work proposes a comparative investigation of respiratory disease detection techniques using cough sounds, with COVID-19 as a case study. …”
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The evaluation model of engineering practice teaching with complex network analytic hierarchy process based on deep learning by Xianlong Han, Xiaohui Chen

“…Through the hierarchical analysis of complex network, the relationship between different teaching elements is revealed and the hierarchical structure is constructed. …”
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Artificial intelligence-based CT-free quantitative thyroid SPECT for thyrotoxicosis: study protocol of a multicentre, prospective, non-inferiority study by Jae Hoon Moon, Ji Hye Kim, Soyeon Ahn, Dongkyu Oh, Hyun Gee Ryoo, Hyun Woo Chung, Sang-Geon Cho, Kyounghyoun Kwon, Young So, Won Woo Lee

“…The trial will continue until 152 completed datasets have been enrolled to assess whether the 95% (two-sided) lower confidence limit of the accuracy difference (CT-free SPECT accuracy—SPECT/CT accuracy) for Graves’ disease is greater than −0.1. …”
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Applying artificial neural networks to solve the inverse problem of evaluating concentrations in multianalyte mixtures from biosensor signals by Ignas Dapšys, Raimondas Čiegis, Vadimas Starikovičius

“…To solve the problem, we employ feed-forward and convolutional neural networks. Computational experiments were performed with different levels of additive and multiplicative noises for the batch and flow injection analysis modes of the biosensor. …”
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Neural network analysis of small samples using a large number of statistical criteria to test the sequence of hypotheses about the value of mathematical expectations of correlation... by A.I. Ivanov, A.I. Godunov, E.A. Malygina, N.A. Papusha, A.I. Ermakova

“…The networks increase the accuracy of estimates of correlation coefficients when testing the sequence of different statistical hypotheses with a second layer, which eliminates the code redundancy of a large number of neurons of the first layer. …”