Guiding a supersonic controlled object in a multi-position air basing radar by V. E. Markevich, V. V. Legkostup

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Autoencoder techniques for survival analysis on renal cell carcinoma. by Iñigo Sanz Ilundain, Laura Hernández-Lorenzo, Cristina Rodríguez-Antona, Jesús García-Donas, José L Ayala

“…This research underscores the utility of autoencoders in managing high-dimensional data problems.…”
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BLS-QLSTM: a novel hybrid quantum neural network for stock index forecasting by Liyun Su, Dan Li, Dongyang Qiu

“…Subsequently, BLS is introduced to enhance the feature representation of the data, while the gating structures within the long short-term memory (LSTM) network are replaced by variational quantum circuits (VQCs) to form QLSTM, thereby further improving prediction accuracy. …”
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Development of technologies and tools for drilling additional wells from artificial bottom-hole with whipstock of continuous action in hard and very hard rocks by V. V. Neskoromnykh, D. V. Lysacov

“…Various approaches are currently used to create borehole curvature depending on associated methodological and practical geological problems. Continuous action whipstockes are considered to be the most modern and accurate among these approaches. …”
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A lung nodule segmentation model based on the transformer with multiple thresholds and coordinate attention by Tianjiao Hu, Yihua Lan, Yingqi Zhang, Jiashu Xu, Shuai Li, Chih-Cheng Hung

“…In this model, we construct a multi-threshold feature separation module to capture edge and texture features from different levels and specified intensities of the input image. …”
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Enhanced Lightweight YOLO Model for Efficient Vehicle Detection in Satellite Imagery by Mohamad Haniff Junos, Anis Salwa Mohd Khairuddin, Elmi Abu Bakar, Ahmad Faizul Hawary

“…Additionally, an improved small efficient layer aggregation network is adopted in the modified path aggregation network to enhance feature extraction across various scales. Finally, the proposed model incorporates the Swish function and an extra YOLO head for detection. …”
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Deep representation learning using layer-wise VICReg losses by Joy Datta, Rawhatur Rabbi, Puja Saha, Aniqua Nusrat Zereen, M. Abdullah-Al-Wadud, Jia Uddin

“…Being able to update the parameters locally at each layer also handles problems such as vanishing gradient and initialization sensitivity in backpropagation. …”
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Infrared image monitoring algorithm for converter valve equipment by Jiashui Dai, Fali Tan, Yi Jiang, Yunlong Liang, Kangquan Lin, Kai Tong

“…Traditional converter valve monitoring methods have problems of inefficiency and failure to detect potential faults in time, which may lead to equipment damage or system failure. …”
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CNN–Transformer gated fusion network for medical image super-resolution by Juanjuan Qin, Jian Xiong, Zhantu Liang

“…Abstract To solve the problems of image detail blurring and insufficient utilization of global information in the existing medical image super-resolution reconstruction, this paper proposes a dual-branch fusion network based on residual Transformer network and dynamic convolutional neural network (CTGFSR). …”
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Prediction of Wind Velocity to Raise Vortex-Induced Vibration through a Road-Rail Bridge with Truss-Shaped Girder by Seungtaek Oh, Sung-il Seo, Hoyeop Lee, Hak-Eun Lee

“…Vortex-induced vibration (VIV) of bridges, related to fluid-structure interaction and maintenance of bridge monitoring system, causes fatigue and serviceability problems due to aerodynamic instability at low wind velocity. …”
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A Data Model and Method Framework for Cyberspace Map Visualization by Zheng Zhang, Chenghu Zhou, Minjie Chen, Yibing Cao, Shaojing Fan

“…To address these problems, this paper introduces an integrated data model that aligns spatial and cyberspace features based on a “proxy mode”. …”
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Enhancing geometric modeling in convolutional neural networks: limit deformable convolution by Wei Wang, Yuanze Meng, Han Li, Guiyong Chang, Shun Li, Chenghong Zhang

“…Abstract Convolutional neural networks (CNNs) are constrained in their capacity to model geometric transformations due to their fixed geometric structure. To overcome this problem, researchers introduce deformable convolution, which allows the convolution kernel to be deformable on the feature map. …”
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Reliability and Quality of Complex Systems by A.A. Barzov, A.S. Grigoriev, Ya.D. Seina, V.S. Puzakov, A.Zh. Kasenov, N.N. Safronova

“…It is shown that the mesodiagnostic apparatus allows solving problems not only of express assessment of the condition of materials and products, taking into account their current damage, but also to carry out resource and operational forecasting of changes in this condition. …”

Biodegraded polymers as materials for sowing of grain crops seeds by L. S. Shibryaeva, Yu. V. Tertyshnaya, D. D. Pal’Mina, N. S. Levina

“…Increase of efficiency of grain production, solution of problems of food security demand search and development of innovative technologies at all stages. …”
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ABL-SMOTE: A Novel Resampling Method by Handling Noisy and Borderline Challenge for Imbalanced Dataset for Software Defect Prediction by Kamal Bashir, Sara Abdelwahab Ghorashi, Ali Ahmed, Abdolraheem Khader

“…Machine learning algorithms face important implementation difficulties due to imbalanced learning since the Synthetic Minority Oversampling Technique (SMOTE) helps improve performance through the creation of new minority class examples in feature space before preprocessing. The underlying problem leading to performance deterioration emerges from noise and boundary instances found in the minority class rather than an excessive imbalance ratio. …”
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A Picture Guide to Cancer Progression and Evolutionary Accumulation Models: Systematic Critique, Plausible Interpretations, and Alternative Uses by Ramon Diaz-Uriarte, Iain G. Johnston

“…They have been used in computational oncology and virology but also in widely different problems such as malaria progression. These methods have been applied to predict future states of the system, identify routes of feature acquisition, and improve patient stratification, and they hold promise for evolutionary-based treatments. …”
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DETECTION OF NON-MELANOMA SKIN CANCER BY DEEP CONVOLUTIONAL NEURAL NETWORK AND STOCHASTIC GRADIENT DESCENT OPTIMIZATION ALGORITHM by Premananda Sahu, Srikanta Kumar Mohapatra, Prakash Kumar Sarang, Jayashree Mohanty, Pradeepta Kumar Sarangi

“…The expansion of skin problems for human beings has emerged as a significant problem, and the successful investigation has been observed as an arduous task for clinical experts or dermatologists. …”
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Hierarchical RUL Prediction for Turbofan Engines Based on Health Stage Classification and Change Point-Guided Data Augmentation by Kiymet Ensarioglu

“…Two deep learning models are used for RUL prediction in engines: an LSTM model for degraded engines, which captures long-term dependencies in deterioration data, and a feature-level fusion model for healthy engines with sparse sensor data, which enhances prediction accuracy by integrating CNN and LSTM, addressing unique problems in different engine health stages. …”
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A Self-Supervised Specific Emitter Identification Method Based on Contrastive Asymmetric Masked Learning by Dong Wang, Yonghui Huang, Tianshu Cui, Yan Zhu

“…To address these issues, this paper proposes a novel contrastive asymmetric masked learning-based SEI (CAML-SEI) method, effectively solving the problem of SEI under scarce labeled samples. The proposed method constructs an asymmetric auto-encoder architecture, comprising an encoder network based on channel squeeze-and-excitation residual blocks to capture radio frequency fingerprint (RFF) features embedded in signals, while employing a lightweight single-layer convolutional decoder for masked signal reconstruction. …”
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Human motion similarity evaluation based on deep metric learning by Yidan Zhang, Lei Nie

“…In addition, the dynamic time warping (DWT) algorithm is exploited to align the sequence of action information feature vectors temporally, which solves the problem of temporal axis discrepancies in realizing similarity metrics based on action information feature vectors. …”
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