New Method of Impact Localization on Plate-like Structures Using Deep Learning and Wavelet Transform by Asaad Migot, Ahmed Saaudi, Victor Giurgiutiu

“…This paper presents a new methodology for localizing impact events on plate-like structures using a proposed two-dimensional convolutional neural network (CNN) and received impact signals. A network of four piezoelectric wafer active sensors (PWAS) was installed on the tested plate to acquire impact signals. …”
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PolyBuild: An End-to-End Method for Polygonal Building Contour Extraction From High-Resolution Remote Sensing Images by Yaoteng Zhang, Julin Zhang, Guangshuai Wang, Jiwei Deng, Hui Sheng, Yasir Muhammad, Shiqing Wei

“…The COM further refines the generated building contours by iteratively integrating convolutional neural network (CNN) features and contour positional information in a transformer-based decoder. …”
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A novel deep sequential learning architecture for drug drug interaction prediction using DDINet by Anindya Halder, Biswanath Saha, Moumita Roy, Sukanta Majumder

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Histopathological Image Analysis Using Deep Learning Framework by Sudha Rani Vupulluri, Jogendra Kumar Munagala

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Machine Learning-Based Mooring Failure Detection for FPSOs: A Two-Step ANN Approach by Omar Jebari, Do-Soo Kwon, Sung-Jae Kim, Chungkuk Jin, Moohyun Kim

“…This study presents a two-step artificial neural network (ANN) approach for detecting mooring failures in a spread-moored floating production storage and offloading (FPSO) vessel using platform motion data. …”
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Temporal dependency modeling for improved medical image segmentation: The R-UNet perspective by Ahmed Alweshah, Roohollah Barzamini, Farshid Hajati, Shoorangiz Shams Shamsabad Farahani, Mohammad Arabian, Behnaz Sohani

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Research on a PTSD Risk Assessment Model Using Multi-Modal Data Fusion by Youxi Luo, Yucui Shang, Dongfeng Zhu, Tian Zhang, Chaozhu Hu

“…For multi-modal data fusion, two sets of solutions are proposed: the first is to extract EEG features using B-spline basis functions, combined with questionnaire data, to construct a multi-modal Zero-Inflated Poisson regression model; the second is to build a multi-modal deep neural network fusion prediction model to automatically extract and fuse multi-modal data features. …”
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A multi-stage weakly supervised design for spheroid segmentation to explore mesenchymal stem cell differentiation dynamics by Arash Shahbazpoor Shahbazi, Farzin Irandoost, Reza Mahdavian, Seyedehsamaneh Shojaeilangari, Abdollah Allahvardi, Hossein Naderi-Manesh

“…Therefore, we developed an innovative, weakly supervised model, aided by convolutional neural networks, to perform label-free spheroid segmentation. …”
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Bayesian curriculum generation in sparse reward reinforcement learning environments by Onur Akgün, N. Kemal Üre

“…Diverging from traditional methodologies, this algorithm utilizes Bayesian networks to dynamically create tasks by altering problem parameters, thereby impacting task difficulty. …”
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Reconstruction of 12-lead ECG: a review of algorithms by Ekenedirichukwu N. Obianom, G. André Ng, G. André Ng, G. André Ng, G. André Ng, Xin Li, Xin Li, Xin Li

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Siamese-SAM: Remote Sensing Image Change Detection with Siamese Structure Segment Anything Model by Gang Wei, Yuqi Miao, Zhicheng Wang

“…To address this limitation, we propose Siamese-SAM, a novel Siamese network incorporating SAM as the encoder for each input image. …”
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Driving Behavior Classification Using a ConvLSTM by Alberto Pingo, João Castro, Paulo Loureiro, Sílvio Mendes, Anabela Bernardino, Rolando Miragaia, Iryna Husyeva

“…This work explores the classification of driving behaviors using a hybrid deep learning model that combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks (ConvLSTM). …”
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Cell-level deep learning as proxy model for reservoir simulation and production forecasting by Rafael M. Magalhães, Thiago J. Machado, Moisés D. Santos, Gustavo P. Oliveira

“…The methodology includes a robust feature selection process, model design, and training strategy, supplemented by comprehensive statistical evaluations and graphical tools. …”
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Integrated analysis of exosome-related genes and their role in psoriasis pathogenesis by Zhen Wang, Fang Luo

“…ObjectiveThis study aimed to analyze gene expression data from psoriasis and control samples, focusing on identifying exosome and cell senescence genes, integrating datasets, and validating batch effect removal using principal component analysis (PCA).MethodsWe analyzed gene expression profiles from Gene Expression Omnibus (GEO) to identify significant differences between healthy and diseased tissues. It evaluated immune cell proportion variations and used weighted gene co-expression network analysis (WGCNA) to find key modules. …”
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The Role of Artificial Intelligence in Predicting the Progression of Intraocular Hypertension to Glaucoma by Nicoleta Anton, Cătălin Lisa, Bogdan Doroftei, Ruxandra Angela Pîrvulescu, Ramona Ileana Barac, Ionuț Iulian Lungu, Camelia Margareta Bogdănici

“…Results: For all three patient groups, the best performance was achieved with neural networks featuring two hidden layers: MLP(9:18:9:3) for group 1, MLP(10:20:10:3) for group 2, and MLP(10:30:20:3) for group 3. …”
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Identification of Ion-kinetic Instabilities in Hybrid-PIC Simulations of Solar Wind Plasma with Machine Learning by Viacheslav M. Sadykov, Leon Ofman, Scott A. Boardsen, Yogesh, Parisa Mostafavi, Lan K. Jian, Kristopher Klein, Mihailo Martinović

“…Using 34 hybrid particle-in-cell simulations of kinetic protons and α -particles initialized using plasma parameters derived from solar wind (SW) observations, we prepare a data set of nearly 1600 VDFs representing stable/unstable cases and associated plasma and wave properties. We compare feature-based classifiers applied to VDF moments, such as support vector machine and random forest (RF), with DL convolutional neural networks (CNNs) applied directly to VDFs as images in the gyrotropic velocity plane. …”
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A Novel CNN-Based Framework for Detection and Classification of Power Quality Disturbances: Exploring Multi-Class Versus Multi-Label Classification by Aleksandra Zlatkova, Dimitar Taskovski

“…In this paper, we propose a novel model based on a deep convolutional neural network (DCNN) for the feature extraction and classification of PQ disturbances. …”
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Tourism Sentiment Chain Representation Model and Construction from Tourist Reviews by Bosen Li, Rui Li, Junhao Wang, Aihong Song

“…Leveraging multidimensional attribute perceptions derived from tourist reviews, this study proposes a Spatial–Semantic Integrated Model for Tourist Attraction Representation (SSIM-TAR), which holistically encodes the composite attributes and multifaceted evaluations of attractions. Integrating these multidimensional features with inter-attraction relationships, three relational metrics are defined and fused: spatial proximity, resonance correlation, and thematic-sentiment similarity, forming a Tourist Attraction Multidimensional Association Network (MAN-SRT). …”
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MalGEA: A malware analysis framework via matrix factorization based node embedding and graph external attention by Ruisheng Li, Qilong Zhang, Huimin Shen

“…However, these existing malware studies still have two major limitations. (1) The complex topological structures of malware graphs often result in high computational overhead during feature extraction and processing. (2) Most existing approaches rely on conventional graph neural networks that are not specifically designed for malware classification tasks, leading to suboptimal performance, especially when dealing with minority class samples. …”
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Enhancing driver emotion recognition through deep ensemble classification by Faizan Zaman, Zhigang Xu, Adil Hussain, Anees Ullah, Khalid Zaman

“…Moreover, the improved Faster R-CNN feature learning module is replaced with a new convolutional neural network module, VGG16, which maximizes the precision and effectiveness of facial detection in our system. …”
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