Improving neural network training using dynamic learning rate schedule for PINNs and image classification by Veerababu Dharanalakota, Ashwin Arvind Raikar, Prasanta Kumar Ghosh

“…The learning rate is one of such crucial hyperparameters, which is usually kept static during the training process. Learning dynamics in complex systems often requires a more adaptive approach to the learning rate. …”
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VdaBSC: A Novel Vulnerability Detection Approach for Blockchain Smart Contract by Dynamic Analysis by Rexford Nii Ayitey Sosu, Jinfu Chen, Edward Kwadwo Boahen, Zikang Zhang

“…These flaws can be detected using dynamic analysis methods that extract various aspects from smart contract bytecode. …”
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Damage Detection and Identification on Elevator Systems Using Deep Learning Algorithms and Multibody Dynamics Models by Josef Koutsoupakis, Dimitrios Giagopoulos, Panagiotis Seventekidis, Georgios Karyofyllas, Amalia Giannakoula

“…High-quality training data are first generated through multibody dynamics simulations and are then combined with healthy state vibration measurements to train an ensemble of autoencoders and convolutional neural networks for damage detection and classification. …”
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Faster Dynamic Graph CNN: Faster Deep Learning on 3D Point Cloud Data by Jinseok Hong, Keeyoung Kim, Hongchul Lee

“…However, it has been difficult to apply such data as input to a convolutional neural network (CNN) or recurrent neural network (RNN) because of their unstructured and unordered features. …”
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A Dynamic Interference Detection Method of Underwater Scenes Based on Deep Learning and Attention Mechanism by Shuo Shang, Jianrong Cao, Yuanchang Wang, Ming Wang, Qianchuan Zhao, Yuanyuan Song, He Gao

“…Experimental results show that the mAP value of the improved YOLOv8 underwater dynamic target detection algorithm proposed in this article can reach 95.1%, and it can detect underwater dynamic targets more accurately, especially small dynamic targets in complex underwater scenes.…”
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MTGNet: Multi-Agent End-to-End Motion Trajectory Prediction with Multimodal Panoramic Dynamic Graph by Yinfei Dai, Yuantong Zhang, Xiuzhen Zhou, Qi Wang, Xiao Song, Shaoqiang Wang

“…Based on this, we propose the multimodal transformer graph convolution neural network (MTGNet) framework. The MTGNet framework can not only construct a panoramic, fully connected dynamic traffic map for agents but also dynamically adjust the size of traffic scenes. …”
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Multi-Scale DCNN with Dynamic Weight and Part Cross-Entropy Loss for Skin Lesion Diagnosis by Gaoshuai Wang, Linrunjia Liu, Fabrice Lauri, Amir HAJJAM El Hassani

“…Although present methods often use the multi-branch structure to get more clues, the rigescent methods of cropping zone and fusing branch results fail to handle the instability of the disease zone and the difference in branch results, which leads to improper cropping and degrades Deep Convolutional Neural Networks (DCNN)’s performance. To address these problems, we propose a Multi-scale DCNN with Dynamic weight and Part cross-entropy loss model (namely MDP-DCNN) to bootstrap skin lesion diagnosis. …”
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Temporal Dynamics in Short Text Classification: Enhancing Semantic Understanding Through Time-Aware Model by Khaled Abdalgader, Atheer A. Matroud, Ghaleb Al-Doboni

“…This work advances natural language processing by offering a comprehensive time-aware classification framework, addressing the challenges of temporal dynamics in language semantics.…”
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XCF-LSTMSATNet: A Classification Approach for EEG Signals Evoked by Dynamic Random Dot Stereograms by Tingting Zhang, Xu Yan, Xin Chen, Yi Mao

“…Following this, EEGNet employs deep convolutional layers to extract spatial features, while separable convolutions are subsequently used to derive high-dimensional spatial-temporal features. …”
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An Efficient Semantic Segmentation Framework with Attention-Driven Context Enhancement and Dynamic Fusion for Autonomous Driving by Jia Tian, Peizeng Xin, Xinlu Bai, Zhiguo Xiao, Nianfeng Li

“…The encoder leverages a high-efficiency backbone, while the decoder introduces a dynamic fusion mechanism designed to enhance information interaction between different feature branches. …”
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Development of Smart Models to Accurately Predict Dynamic Viscosity of CO2-Saturated Polyethylene Glycol by Ayat Hussein Adhab, Morug Salih Mahdi, Bhavesh Kanabar, Anupam Yadav, Ranganathaswamy M K, Rishabh Thakur, Parveen Kumar, Braj Krishna, Aseel Salah Mansoor, Usama Kadem Radi, Nasr Saadoun Abd, Samim Sherzod

“…This study, hence, introduces machine learning models utilizing K-nearest neighbors, decision tree, adaptive boosting, multilayer perceptron artificial neural network, convolutional neural network, support vector machine, random forest and ensemble learning algorithms to accurately forecast the dynamic viscosity of CO2-saturated PEG based on PEG molar mass, pressure, and temperature. …”
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A Lightweight GCT-EEGNet for EEG-Based Individual Recognition Under Diverse Brain Conditions by Laila Alshehri, Muhammad Hussain

“…Electroencephalography (EEG) brain signals present a promising alternative to other biometric traits due to their sensitivity, non-duplicability, resistance to theft, and individual-specific dynamics. However, existing EEG-based biometric systems employ deep neural networks, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which face challenges such as high parameter complexity, limiting their practical application. …”
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ResWLI: a new method to retrieve water levels in coastal zones by integrating optical remote sensing and deep learning by Nan Xu, Huichao Xin, Jiarui Wu, Jiaqi Yao, He Ren, Han-Su Zhang, Hao Xu, Hong Luan, Dong Xu, Yongze Song

“…This would further enhance our understanding of coastal dynamics and contribute to more effective coastal management strategies.…”
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Prediction of the Next Solar Rotation Synoptic Maps Using an Artificial Intelligence–based Surface Flux Transport Model by Hyun-Jin Jeong, Mingyu Jeon, Daeil Kim, Youngjae Kim, Ji-Hye Baek, Yong-Jae Moon, Seonghwan Choi

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Predicting Index Trend Using Hybrid Neural Networks with a Focus on Multi-Scale Temporal Feature Extraction in the Tehran Stock Exchange by Mohammad Osoolian, Ali Nikmaram, Mahdi Karimi

“…This approach seamlessly integrates multiscale temporal features, with the ultimate objective of bolstering prediction precision and offering profound insights into prevailing market trends and dynamics. MethodsThe hybrid neural network architecture that has been put forward integrates the unique capabilities of convolutional neural networks (CNNs) in the realm of feature extraction with the effectiveness of long short-term memory (LSTM) networks in capturing temporal dependencies. …”
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High-Precision Complex Orchard Passion Fruit Detection Using the PHD-YOLO Model Improved from YOLOv11n by Rongxiang Luo, Rongrui Zhao, Xue Ding, Shuangyun Peng, Fapeng Cai

“…The module under consideration has been demonstrated to enhance the efficacy of local feature extraction in dense fruit regions by integrating sub-group feature-independent convolution and channel concatenation mechanisms. Secondly, deep separable convolution (DWConv) is adopted to replace standard convolution. …”
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Capsule network based analysis of histopathological images of oral squamous cell carcinoma by Santisudha Panigrahi, Jayshankar Das, Tripti Swarnkar

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Analysis of Deep Learning Techniques for Vehicle Detection and Reidentification Using Data from Multiple Drones and Public Datasets by FELIPE P.A. EUPHRÁSIO, RAFAEL M. DE ANDRADE, ELCIO H. SHIGUEMORI, LIANGRID L. SILVA, MOISÉS JOSÉ S. FREITAS, NATHAN AUGUSTO Z. XAVIER, ARGEMIRO S.S. SOBRINHO

“…Abstract The detection and re-identification of vehicles in dynamic environments, such as highways monitored by a swarm of drones, presents significant challenges, particularly due to the variability of images captured from different angles and under various conditions. …”
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Research on UAV Jamming Signal Generation Based on Intelligent Jamming by Haonan Xue, Zhihai Zhuo, Weihao Yan, Yuexia Zhang

“…This paper introduces an enhanced jamming signal generation algorithm built on the traditional convolutional autoencoder. Without relying on prior signal knowledge, the algorithm introduces complex convolutional networks and residual modules. …”
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EEG-Based Seizure Detection Using Dual-Branch CNN-ViT Network Integrating Phase and Power Spectrograms by Zhuohan Wang, Yaoqi Hu, Qingyue Xin, Guanghao Jin, Yazhou Zhao, Weidong Zhou, Guoyang Liu

“…<b>Background/Objectives:</b> Epilepsy is a common neurological disorder with pathological mechanisms closely associated with the spatiotemporal dynamic characteristics of electroencephalogram (EEG) signals. …”
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