Synchronous End-to-End Vehicle Pedestrian Detection Algorithm Based on Improved YOLOv8 in Complex Scenarios by Shi Lei, He Yi, Jeffrey S. Sarmiento

“…The motivation behind our design is twofold: first, to address the limitations of traditional methods in handling targets of different scales and severe occlusions, and second, to improve the efficiency and accuracy of real-time detection. …”
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Longitudinal Trend Monitoring of Multiple Sclerosis Ambulation Using Smartphones by Andrew P. Creagh, Frank Dondelinger, Florian Lipsmeier, Michael Lindemann, Maarten De Vos

“…<italic>Methods:</italic> Remotely collected smartphone inertial sensor data was transformed through state-of-the-art Deep Convolutional Neural Networks, to estimate a participant&#x0027;s daily ambulatory-related disease severity, longitudinally over a 24-week study. …”
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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

“…Specifically, the SAConv module dynamically adjusts the receptive field of convolutional kernels to enhance the model’s capability in extracting seedling shape features. …”
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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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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

“…To solve this problem, a novel hybrid deep learning architecture that includes a convolutional block attention module and a swin transformer with multi-scale feature fusion from pre-trained architectures DenseNet201, VGG19, and InceptionV3 under the transfer learning paradigm was proposed in this study. …”
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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

“…Convolutional neural networks (CNNs) are increasingly recognized as an important and potent artificial intelligence approach, widely employed in many computer vision applications, such as facial recognition. …”
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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

“…Hence, we propose a method for obtaining spectral information on moss in the forest using a deep learning model to train convolutional neural network models while optimizing a suitable light source for moss identification. …”
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Voice-activated home automation system for IoT edge devices using TinyML by Timothy Malche, Sandeep Budhani, Pramod Kumar Soni, Govind Murari Upadhyay

“…The keyword spotting model in the proposed system is built using Deep Convolutional Neural Network (DCNN). Different data pre-processing techniques are also applied to refine the dataset. …”
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Attention Enhanced InceptionNeXt-Based Hybrid Deep Learning Model for Lung Cancer Detection by Burhanettin Ozdemir, Emrah Aslan, Ishak Pacal

“…We present a hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) with Vision Transformers (ViTs). …”
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InBRwSANet: Self-attention based parallel inverted residual bottleneck architecture for human action recognition in smart cities. by Yasir Khan Jadoon, Muhammad Attique Khan, Yasir Noman Khalid, Jamel Baili, Nebojsa Bacanin, MinKyung Hong, Yunyoung Nam

“…These blocks aim to learn complex human actions in many convolutional layers. After that, the second module is designed based on the self-attention mechanism. …”
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Precision forecasting for hybrid energy systems using five deep learning algorithms for meteorological parameter prediction by Ceren Ceylan, Zehra Yumurtacı

“…This research contributes to the methodological advancement of renewable energy forecasting by systematically identifying optimal algorithmic approaches for different meteorological parameters in hybrid systems, thereby supporting the integration of intermittent renewable sources into sustainable energy infrastructures.…”
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