Longitudinal Trend Monitoring of Multiple Sclerosis Ambulation Using Smartphones by Andrew P. Creagh, Frank Dondelinger, Florian Lipsmeier, Michael Lindemann, Maarten De Vos

“…The ability to understand the impact of disease on daily-life between clinical visits, through objective digital outcomes, paves the way forward to better measure and identify signs of disease progression that may be occurring out-of-clinic, to monitor how different patients respond to various treatments, and to ultimately enable the development of better, and more personalised care.…”
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MS-YOLOv8: multi-scale adaptive recognition and counting model for peanut seedlings under salt-alkali stress from remote sensing by Fan Zhang, Longgang Zhao, Longgang Zhao, Dongwei Wang, Jiasheng Wang, Igor Smirnov, Juan Li

“…First, a lightweight adaptive feature fusion module (called MSModule) is constructed, which groups the channels of input feature maps and feeds them into different convolutional layers for multi-scale feature extraction. …”
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Coupling Deep Learning and Physically Based Hydrological Models for Monthly Streamflow Predictions by Wenxin Xu, Jie Chen, Gerald Corzo, Chong‐Yu Xu, Xunchang John Zhang, Lihua Xiong, Dedi Liu, Jun Xia

“…It also saves decision‐makers the time and effort of trying different combinations of predictors, which is indispensable when building DL models. …”
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HSD²Former: Hybrid-Scale Dual-Domain Transformer with Crisscrossed Interaction for Hyperspectral Image Classification by Binxin Luo, Meihui Li, Yuxing Wei, Haorui Zuo, Jianlin Zhang, Dongxu Liu

“…D²MSCE supersedes conventional patch embedding to generate spectral and spatial tokens at different scales, effectively enriching the diversity of spectral-spatial features. …”
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YOLOv8-SDC: An Improved YOLOv8n-Seg-Based Method for Grafting Feature Detection and Segmentation in Melon Rootstock Seedlings by Lixia Li, Kejian Gong, Zhihao Wang, Tingna Pan, Kai Jiang

“…Additionally, the DWR module enables the network to more flexibly adapt to the perception accuracy of different cotyledons, growth points, stem edges, and contours. …”
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DDoS Attack Detection in IoT: A Comparative Resource and Performance Analysis of Deep Learning and Machine Learning Models by Amer Abualhassan, Irfan Rashid, Farid Binbeshr, Muhammad Imam

“…The proposed IDS employs different lightweight deep learning models on image representations of network traffic data and machine learning models on tabular CSV representations of the same data. …”
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Assessing the effects of therapeutic combinations on SARS-CoV-2 infected patient outcomes: A big data approach. by Hamidreza Moradi, H Timothy Bunnell, Bradley S Price, Maryam Khodaverdi, Michael T Vest, James Z Porterfield, Alfred J Anzalone, Susan L Santangelo, Wesley Kimble, Jeremy Harper, William B Hillegass, Sally L Hodder, National COVID Cohort Collaborative (N3C) Consortium

“…Models leveraged the patients' characteristics, the severity of COVID-19 at diagnosis, and the calculated proportion of days on different treatment combinations after diagnosis as features to predict the outcome. …”
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Crop field extraction from high resolution remote sensing images based on semantic edges and spatial structure map by Liegang Xia, Ruiyan Liu, Yishao Su, Shulin Mi, Dezhi Yang, Jun Chen, Zhanfeng Shen

“…In recent years, deep convolutional neural networks (CNNs) have gained significant attention for edge detection tasks. …”
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A Novel Deep Hybrid Model for Automatic Femoral Stem Classification in Hip Arthroplasty From Radiographs: MSFT-Net With CBAM and Transformer Modules by Emre Gogus, Atinc Yilmaz, Meric Enercan

“…The proposed multi-scale feature transformer network was trained and validated on a dataset comprising 1266 anteroposterior (A.P.) hip radiographs of 10 different femoral stem implant types. The proposed hybrid deep learning architecture achieved a training accuracy of 96.7% and validation accuracy of 94.86%, significantly outperforming other baseline models. …”
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AI-Driven Ensemble Classifier for Jamming Attack Detection in VANETs to Enhance Security in Smart Cities by Walid El-Shafai, Ahmad Taher Azar, Saim Ahmed

“…Initially, we assessed the detection accuracy of 14 different ML classifiers and 4 DL classifiers. Subsequently, we proposed a voting-based ensemble AI classifier combining the most accurate ML and DL classifiers, namely Random Forest (RF), Extra Tree (ET), and fine-tuned Convolutional Neural Network (CNN). …”
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Vision Transformer-Based Unhealthy Tree Crown Detection in Mixed Northeastern US Forests and Evaluation of Annotation Uncertainty by Durga Joshi, Chandi Witharana

“…Additionally, we investigated the impact of different spectral band combinations on model performance to identify the most effective configuration without incurring additional data acquisition costs. …”
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Accelerating Deep Learning-Based Morphological Biometric Recognition with Field-Programmable Gate Arrays by Nourhan Zayed, Nahed Tawfik, Mervat M. A. Mahmoud, Ahmed Fawzy, Young-Im Cho, Mohamed S. Abdallah

“…The system was deployed on three hardware kits to appraise the performance of different programming approaches in terms of accuracy, latency, cost, and power consumption. …”
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A Data-Driven Approach for Automatic Aircraft Engine Borescope Inspection Defect Detection Using Computer Vision and Deep Learning by Thibaud Schaller, Jun Li, Karl W. Jenkins

“…Firstly, reactor blade images are collected from public resources and then annotated and preprocessed into different groups based on Computer Vision techniques. …”
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Land Cover Classification Model Using Multispectral Satellite Images Based on a Deep Learning Synergistic Semantic Segmentation Network by Abdorreza Alavi Gharahbagh, Vahid Hajihashemi, José J. M. Machado, João Manuel R. S. Tavares

“…In recent years, deep learning and Convolutional Neural Networks (CNNs) have significantly enhanced the segmentation of satellite images. …”
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DualTransAttNet: A Hybrid Model with a Dual Attention Mechanism for Corn Seed Classification by Fei Pan, Dawei He, Pengjun Xiang, Mengdie Hu, Daizhuang Yang, Fang Huang, Changmeng Peng

“…Varietal purity is a critical quality indicator for seeds, yet various production processes can lead to the mixing of seeds from different varieties. Consequently, seed variety classification is an essential step in seed production. …”
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MultiRepPI: a cross-modal feature fusion-based multiple characterization framework for plant peptide-protein interaction prediction by Yu Zhiguo, Li Zixuan, Li Peng

“…First, a cross-modal encoding module (CME) is designed by fusing convolutional neural networks, recurrent neural networks, and feature enhancement mechanisms, which is capable of extracting multi-scale deep features from peptide and protein sequences, and thus better capturing their interactions at different levels. …”
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Deep fusion approach: Combining hyperspectral imaging and ground penetrating radar for accurate cornfield soil moisture mapping by Milad Vahidi, Sanaz Shafian, William Hunter Frame

“…The research focuses on assessing soil moisture at different soil depths, with an emphasis on accuracy of 10 cm and 30 cm of the root zone. …”
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Attention-Module-Guided Time-Lapse Leakage Plume Imaging Driven by LeakInv-CUNet GPR Inversion Framework by Honghua Wang, Shan Wang, Fei Zhou, Yi Lei, Bin Zhang

“…Specifically, the training process utilizes the inversion result of GPR datasets corresponding to different leakage permittivity distributions, with feedbacks provided through functional mapping based on the Topp equation for water content distribution imaging. …”
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Lighting Spectrum Optimization With Deep Learning for Moss Species Classification by Kenichi Ito, Pauli Falt, Markku Hauta-Kasari, Shigeki Nakauchi

“…For the image classification tasks of five species of Sphagnum, an optimized light source that combines 10 different spectral distributions within 400–800 nm, excluding spectral information at 600–700 nm from the RGB image’s red channel and emphasizing that at 700–800 nm, improved discrimination accuracy by 10% compared with that of images obtained with the D65 sunlight source. …”
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Deep Learning Architectures for Single-Label and Multi-Label Surgical Tool Classification in Minimally Invasive Surgeries by Hisham ElMoaqet, Hamzeh Qaddoura, Mutaz Ryalat, Natheer Almtireen, Tamer Abdulbaki Alshirbaji, Nour Aldeen Jalal, Thomas Neumuth, Knut Moeller

“…We explored different modeling scenarios with respect to the location and number of SE blocks for multi-label surgical tool classification in the Cholec80 dataset. …”
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