Tasmanian devil whale optimization (TDWO) is introduced for secure video transmission in 5G networks. by Fengren Lin, Minrong Lu

“…However, this approach does not offer complete video security related to security against dynamic eavesdroppers or patent defilements. Thus, a resource allocation algorithm named Tasmanian devil whale optimization (TDWO) is introduced for secure video transmission in 5G networks. …”
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Advancements of Deep Learning Model-Based Rehabilitation Training System by Xu Chiyu

“…Traditional therapies for rehabilitation training in modern society are difficult to track patients dynamically, so this paper introduces a rehabilitation training evaluation system under deep learning modeling to help assess the effectiveness of rehabilitation training. …”
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Harnessing Deep Learning With AlexNet for Tomato Leaf Disease Detection in the Indian Himalayan Terrain by Ruchika Sharma, Sameena Naaz, Pankaj Vaidya

“…Our research leverages deep learning (DL), a dynamically growing technology proficient in handling large datasets. …”
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Adaptive fusion of multi-cultural visual elements using deep learning in cross-cultural visual communication design by HuPei Wang

“…The proposed algorithm dynamically adjusts color schemes, spatial arrangements, typography, and iconography based on target cultural preferences while maintaining visual communication design coherence and functional clarity. …”
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Prediction of Steering Angle in Autonomous Vehicles Using Deep Learning Approach by Karemore Yogesh, Dronkar Swati

“…Traditional rule-based systems often fall short in complex or dynamic environments, necessitating robust data-driven solutions. …”
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Forged Video Detection Using Deep Learning: A SLR by Maryam Munawar, Iram Noreen, Raed S. Alharthi, Nadeem Sarwar

“…The study’s findings shed light on several research methodologies integral to the detection of forged videos, including deep neural networks, convolutional neural networks, Deepfake analysis, watermarking networks, and clustering, amongst others. …”
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Method of Non-Destructive Control of Single-Phase and Three-Phase Transformers's Condition on the Basis of Frequency Characteristics by I. L. Hramyka, V. N. Galushko

“…This is due to the complexity of input signals, quite a large number of input factors, nonlinear multiple dynamic interrelationships with other parameters. …”
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DCFE-YOLO: A novel fabric defect detection method. by Lei Zhou, Bingya Ma, Yanyan Dong, Zhewen Yin, Fan Lu

“…To address the issues of inaccurate localization and false positives caused by complex textures and varying defect sizes, this paper proposes an improved YOLOv8-based fabric defect detection method. First, Dynamic Snake Convolution is introduced into the backbone network to enhance sensitivity to elongated and subtle defects, improving the extraction of edge and texture details. …”
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A predictive machine learning model for cannabinoid effect based on image detection of reactive oxygen species in microglia. by Patricia Sinclair, William Jeffries, Nadege Lebert, Maheen Saeed, Aman Ullah, Nadine Kabbani

“…Confocal microscopy images within microglia were coupled to a deep learning model using a convolutional neural network (CNN) to predict ROS responses. …”
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Harnessing Transfer Learning for Rapid Malware Family Classification in Large-Scale Cyber Datasets by Mohan Rishabh, Gupta Himanshu

“…By utilizing pre-trained architectures such as Convolutional Neural Networks (CNNs) and Transformers, we exploit their ability to learn transferable features, minimizing the need for domain-specific knowledge. …”
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Revisiting single inclusive jet production: timelike factorization and reciprocity by Kyle Lee, Ian Moult, Xiaoyuan Zhang

“…In the case of small radius jets, the dynamics of the jet clustering can be factorized from both the hard production dynamics, and the dynamics of the low scale jet substructure measurement, and is described by a matching coefficient that can be computed in perturbative Quantum Chromodynamics (QCD). …”
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CNN-LSTM Model Optimized by Bayesian Optimization for Predicting Single-Well Production in Water Flooding Reservoir by Lei Zhang, Hongen Dou, Kun Zhang, Ruijie Huang, Xia Lin, Shuhong Wu, Rui Zhang, Chenjun Zhang, Shaojing Zheng

“…Geared toward the problems of predicting the unsteadily changing single oil well production in water flooding reservoir, a machine learning model based on CNN (convolutional neural network) and LSTM (long short-term memory) is established which realizes precise predictions of monthly single-well production. …”
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Semi-supervised gearbox fault diagnosis under variable working conditions based on masked contrastive learning by ZHANG Huiyun, ZUO Fangjun, LI Hang, YU Xi

“…Firstly, a random mask was used to hide part of the information in the unlabeled dataset, generating two different masked instances for each unlabeled sample. Secondly, a dynamic convolutional neural network was employed to dynamically weight and aggregate the masked instances, enabling discriminative feature modeling of different masked instances. …”
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A Hybrid MLP and CNN Architecture for Sequential Recommendation by Li Yuan, Xue-Yi Zhao

“…Specifically, we combine Multi-Layer Perceptron (MLP) and Convolutional Neural Networks (CNN) to model users’ global long-term interests and recent dynamic behaviors, respectively. …”
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Multi-scale feature pyramid network with bidirectional attention for efficient mural image classification. by Shulan Wang, Siyu Liu, Mengting Jin, Pingmei Fan

“…Mural image recognition plays a critical role in the digital preservation of cultural heritage; however, it faces cross-cultural and multi-period style generalization challenges, compounded by limited sample sizes and intricate details, such as losses caused by natural weathering of mural surfaces and complex artistic patterns.This paper proposes a deep learning model based on DenseNet201-FPN, incorporating a Bidirectional Convolutional Block Attention Module (Bi-CBAM), dynamic focal distillation loss, and convex regularization. …”
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Improved simultaneous localization and mapping algorithm combined with semantic segmentation model by Xuerong Cui, Shengjie Xue, Juan Li, Shibao Li, Jianhang Liu, Haihua Chen

“…First, in the pre-processing stage, in order to reduce the influence of dynamic features, fully convolutional network model is used to find the dynamic object, and then the output image is masked and fused to obtain the final image without dynamic object features. …”
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Surface anomaly detection on island-based PV panels using edge neural networks by ZHANG Yinxian, ZHANG Zhanyao, ZHANG Xiya

“…Additionally， a dual dynamic model compression technique is employed to reduce redundant channels and feature blocks， significantly lowering the model’s computational complexity and enabling rapid and accurate anomaly detection. …”
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Altitude aware trajectory prediction methods for non towered terminal airspace by Haipeng Zhu, Qiang Tong, Jinqing Hu, Xiulei Liu, Shoulu Hou

“…The model independently extracts altitude features using temporal convolutional networks(TCN), it then incorporates a channel attention fusion mechanism to dynamically fuse altitude features into the trajectory representation across different channels. …”
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Electric Vehicle Charging Demand Prediction Model Based on Spatiotemporal Attention Mechanism by Yang Chen, Zeyang Tang, Yibo Cui, Wei Rao, Yiwen Li

“…To address the shortcomings of existing methods in utilizing the spatial interdependencies among urban regions, this paper proposes a forecasting approach that integrates dynamic time warping (DTW) with a spatial–temporal attention graph convolutional neural network (ASTGCN). …”
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Human-like Dexterous Grasping Through Reinforcement Learning and Multimodal Perception by Wen Qi, Haoyu Fan, Cankun Zheng, Hang Su, Samer Alfayad

“…The framework’s key innovation lies in its Tactile-Driven DCNN architecture—a lightweight convolutional network achieving 98.5% object recognition accuracy using spatiotemporal pressure patterns—coupled with an RL policy refinement mechanism that dynamically correlates finger kinematics with real-time tactile feedback. …”
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