Concrete Dam Deformation Prediction Model Based on Attention Mechanism and Deep Learning by ZHANG Hongrui, CAO Xin, JIANG Chao, ZU Anjun, XU Mingxiang

“…Ablation experiments confirm the effectiveness of both feature and temporal attention mechanisms, with their synergistic effect significantly enhancing prediction accuracy. …”
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Quantitative characteristics of microstructure of shale reservoirs with different lithofacies in Dongying Sag， Jiyang Depression by NI Liangtian, DU Yushan, JIANG Long, YANG Feng, SUN Hongxia, FENG Mingshi, LIU Zupeng, LÜ Shichao, WANG Yunhe, LI Weizhong, LI Zhongxin

“…According to the differences in the connectivity efficiency and development of various micro-fractures， the matrix-type laminated shale exhibits a complex pore-fracture network connectivity pattern composed of bedding fractures， grain-edge fractures， intercrystalline fractures， intergranular pores， and intercrystalline pores （dissolution pores）， demonstrating high-efficiency storage spaces. …”
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Dynamic spatiotemporal graph network for traffic accident risk prediction by Pengcheng Zhang, Wen Yi, Yongze Song, Penggao Yan, Peng Wu, Ammar Shemery, Keith Hampson, Albert P. C. Chan

“…Our model uses channel-wise convolutional neural networks to detect spatial accident patterns across weekly, daily, and hourly time scales with automatic weight learning, simultaneously employing graph convolutional networks to process road network features, population feature while integrating external data like weather and dates. …”
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Spatiotemporal Forecasting of Solar and Wind Energy Production: A Robust Deep Learning Model with Attention Framework by Md. Shadman Abid, Razzaqul Ahshan, Mohammed Al-Abri, Rashid Al Abri

“…In this context, a novel robust deep learning model, termed the Convolutional Neural Network-Bidirectional Long Short-Term Memory model with spatiotemporal attention mechanism (CNN-BiLSTM-STA), is developed in this study. The suggested model integrates the feature extraction expertise of CNNs with the sequence modeling proficiency of BiLSTM networks to capture spatial linkages and temporal interdependence adeptly. …”
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Deep and hybrid learning of MRI diagnosis for early detection of the progression stages in Alzheimer’s disease by Ibrahim Abunadi

“…In this study, four proposed systems with different methodologies and materials for tracking the stages of AD development are presented. The first proposed system is to classify a data set using artificial neural networks (ANNs) and feed-forward neural networks (FFNN) based on the features extracted in a hybrid manner by using a combination of Local Binary Pattern (LBP), Discrete Wavelet Transform (DWT), and Gray Level Co-occurrence Matrix (GLCM) algorithms. …”
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Multimodal machine learning for analysing multifactorial causes of disease—The case of childhood overweight and obesity in Mexico by Rosario Silva Sepulveda, Magnus Boman, Magnus Boman

“…Unsupervised learning approaches varied in the optimal number of clusters but agreed on the importance of home environment features when analysing inter-cluster patterns. Main findings from this study differed from previous studies using only traditional statistical methods on the same database. …”
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A model for maxilloturbinate morphogenesis in seals. by Jonathan E Kings, Lars P Folkow, Øyvind Hammer, Signe Kjelstrup, Matthew J Mason, Fengzhu Xiong, Eirik G Flekkøy

“…The maxilloturbinates of Arctic seals develop into particularly elaborate labyrinthine patterns, which are well adapted to retain heat and moisture from exhaled gas. …”
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Integration of Machine Learning and Wavelet Algorithms for Processing Probing Signals: An Example of Oil Wells by Zukhra Abdiakhmetova, Zhanerke Temirbekova

“…By integrating wavelet-based feature extraction with machine learning-driven analysis, this approach enhances the ability to detect complex wave propagation patterns, leading to more precise subsurface modeling. …”
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GradCAM-PestDetNet: A deep learning-based hybrid model with explainable AI for pest detection and classification by Ramitha Vimala, Saharsh Mehrotra, Satish Kumar, Pooja Kamat, Arunkumar Bongale, Ketan Kotecha

“…The GradCAM-PestDetNet methodology utilizes object detection models like YOLOv8m, YOLOv8s and YOLOv8n, alongside transfer learning techniques such as VGG16, ResNet50, EfficientNetB0, MobileNetV2, InceptionV3 and DenseNet121 for feature extraction. Additionally, Vision Transformers (ViT) and Swim Transformers were explored for their ability to process complex data patterns. …”
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Navigating cognitive boundaries: the impact of CognifyNet AI-powered educational analytics on student improvement by Mrim M. Alnfiai, Faiz Abdullah Alotaibi, Mona Mohammed Alnahari, Nouf Abdullah Alsudairy, Asma Ibrahim Alharbi, Saad Alzahrani

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Identification of hub genes in myocardial infarction by bioinformatics and machine learning: insights into inflammation and immune regulation by Juan Yang, Xiang Li, Li Ma, Jun Zhang

“…Notably, S100A9, FCAR, and MMP9 emerged as druggable targets.ConclusionThe five hub genes identified in this study (ANPEP, S100A9, MMP9, DAPK2, and FCAR) significantly contribute to MI development by modulating inflammatory responses and immune regulation. …”
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Identify suitable artificial groundwater recharge zones using hybrid deep learning models by Navaz Khalillollahi, Mohsen Isari, Hamed Faroqi, Kaywan Othman Ahmed, Kamran Nobakht Vakili, Miklas Scholz, Saad Sh. Sammeng

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MobilitApp: A Deep Learning-Based Tool for Transport Mode Detection to Support Sustainable Urban Mobility by Gerard Caravaca Ibanez, Luis J. de la Cruz Llopis, Adrian Catalin Diaconeasa, Alberto Bazan Guillen, Monica Aguilar Igartua

“…By analyzing travel patterns, transport modes, and mode-switching behaviors, it delivers actionable insights to city planners, aiding in the enhancement of urban mobility, promotion of sustainable development, and transition to greener cities.…”
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Wind load impact on tall building facades: damage observations during severe wind events and wind tunnel testing by Omar Metwally, Haitham A. Ibrahim, Amal Elawady, Amal Elawady, Ioannis Zisis, Ioannis Zisis, Arindam Gan Chowdhury, Arindam Gan Chowdhury

“…As global urbanization accelerates, the construction of tall buildings has surged, becoming a defining feature of modern cityscapes. Tall buildings, while contributing to economic growth and urban development, face substantial risks from extreme wind events, such as hurricanes and downbursts. …”
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DTCMMA: Efficient Wind-Power Forecasting Based on Dimensional Transformation Combined with Multidimensional and Multiscale Convolutional Attention Mechanism by Wenhan Song, Enguang Zuo, Junyu Zhu, Chen Chen, Cheng Chen, Ziwei Yan, Xiaoyi Lv

“…Nevertheless, when dealing with long wind-power sequences, their quadratic computational complexity (O(L²)) leads to low efficiency, and their global attention mechanism often fails to capture local periodic features accurately, tending to overemphasize redundant information while overlooking key temporal patterns. …”
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Using machine learning to identify Parkinson’s disease severity subtypes with multimodal data by Hwayoung Park, Changhong Youm, Sang-Myung Cheon, Bohyun Kim, Hyejin Choi, Juseon Hwang, Minsoo Kim

“…Results We identified three PD severity subtypes, each exhibiting different patterns of clinical severity, with the severity increasing as it progressed from clusters 1 to 3. …”
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Assessing distortion in carbon fiber woven fabrics based on machine vision by Shiyue Li, Quanzhou Yao, Lin Ye

“…A defect recognition method combining isometric and random feature sampling enables segmentation of abnormal fiber distribution regions through standard sample comparisons. …”
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Intermittent Demand Forecasting for Spare Parts Using Artificial Neural Networks and Deep Learning: Literature Review by Omnia Nabil, Nahid Afia, T Ismail

“…Despite the advances, the availability and quality of datasets remain a significant limitation in developing robust models. Future research directions are identified, including the need for improved feature engineering, architecture optimization, and model interpretability. …”
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Efficient Classification of Pomegranate Diseases Using Deep Learning Models and Interactive Visualization by Mishra Gaurav, Nagaonkar Jagruti, Parmar Harsh, Joshi Gaurav

“…DenseNet 161 and DenseNet 121 outperformed the others due to improved feature propagation, gradient flow, and accuracy. …”
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Predicting Accident Severity on Taiwan Highways Using Machine Learning and Electronic Toll Collection (ETC) Data by Pei-Chun Lin, Kuan-Yen Chen, Jenhung Wang

“…This study aims to develop a machine learning-based framework for predicting the severity of highway traffic accidents by leveraging high-resolution data from Taiwan’s Electronic Toll Collection (ETC) system. …”
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