Enhancing security in 6G-enabled wireless sensor networks for smart cities: a multi-deep learning intrusion detection approach by Waqar Khan, Muhammad Usama, Muhammad Shahbaz Khan, Oumaima Saidani, Hussam Al Hamadi, Noha Alnazzawi, Mohammed S. Alshehri, Jawad Ahmad

“…As these networks evolve under 6G connectivity frameworks, their increasing reliance on heterogeneous communication protocols and decentralized architectures exposes them to sophisticated cyber threats. …”
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An Efficient Model for Real-Time Traffic Density Analysis and Management Using Visual Graph Networks by Nikhil Nigam, Dhirendra Pratap Singh, Jaytrilok Choudhary, Surendra Solanki

“…The proposed RDAMVGN framework incorporates both a LACF-YOLO detection model and a Faster Region-Based Convolutional Neural Network (Faster RCNN) detection model for high-speed and high-accuracy vehicle identification. …”
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A Combined Deep Learning Method with Attention-Based LSTM Model for Short-Term Traffic Speed Forecasting by Pan Wu, Zilin Huang, Yuzhuang Pian, Lunhui Xu, Jinlong Li, Kaixun Chen

“…Therefore, we propose a framework for short-term traffic speed prediction, including data preprocessing module and short-term traffic prediction module. …”
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High-precision segmentation and quantification of tunnel lining crack using an improved DeepLabV3+ by Zhutian Pan, Xuepeng Zhang, Yujing Jiang, Bo Li, Naser Golsanami, Hang Su, Yue Cai

“…EDeepLab improves upon the original DeepLabV3+ framework by replacing its backbone network with an optimized lightweight EfficientNetV2. …”
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MFA-net: Object detection for complex X-ray cargo and baggage security imagery. by Thanaporn Viriyasaranon, Seung-Hoon Chae, Jang-Hwan Choi

“…To our knowledge, the existing frameworks were developed to recognize threats using only baggage security X-ray scans. …”
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Deep-learning based multi-modal models for brain age, cognition and amyloid pathology prediction by Chenxi Wang, Weiwei Zhang, Ming Ni, Qiong Wang, Chang Liu, Linbin Dai, Mengguo Zhang, Yong Shen, Feng Gao

“…We designed a multi-modal deep-learning framework that employs 3D convolutional neural networks to analyze MRI and additional neural networks to evaluate demographic data. …”
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TLEABLCNN: Brain and Alzheimer’s Disease Detection Using Attention-Based Explainable Deep Learning and SMOTE Using Imbalanced Brain MRI by Erol Kina

“…This work presents a lightweight convolutional architecture based on EfficientNet with a Squeeze Attention Block using transfer learning. …”
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EmoBERTa–CNN: Hybrid Deep Learning Approach Capturing Global Semantics and Local Features for Enhanced Emotion Recognition in Conversational Settings by Mingfeng Zhang, Aihe Yu, Xuanyu Sheng, Jisun Park, Jongtae Rhee, Kyungeun Cho

“…To overcome these challenges, we developed EmoBERTa–CNN, a hybrid framework that combines EmoBERTa’s ability to capture global semantics with the capability of convolutional neural networks (CNNs) to extract local emotional features. …”
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LUNA: Loss-Construct Unsupervised Network Adjustment for Low-Dose CT Image Reconstruction by Ritu Gothwal, Shailendra Tiwari, Shivendra Shivani

“…We propose an unsupervised CT reconstruction technique that leverages the power of Deep convolutional neural networks (Deep CNNs), demonstrating that a randomly initialized neural network can serve as a prior. …”
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Deep learning-based time series prediction for precision field crop protection by Tao He, Meijin Li, Dong Jin

“…RAADA dynamically adapts decisions based on real-time field responses, ensuring efficiency and sustainability.ResultsThe experimental findings obtained from large-scale agricultural datasets show that our framework far exceeds the existing most advanced methods in terms of the accuracy of yield prediction, resource optimization, and environmental impact mitigation.DiscussionThis research offers a transformative solution for precision agriculture, aligning with the pressing need for advanced tools in sustainable crop management.…”
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Deep learning from three-dimensional lithium-ion battery multiphysics model part I: Data development by Yiheng Pang, Yun Wang, Zhiqiang Niu

“…The developed model proves to be capable of providing insightful and reliable data for the training of convolutional neural network and long short-term memory (CNN-LSTM) in part II.…”
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PD-Net: Parkinson’s Disease Detection Through Fusion of Two Spectral Features Using Attention-Based Hybrid Deep Neural Network by Munira Islam, Khadija Akter, Md. Azad Hossain, M. Ali Akber Dewan

“…This study adopts Italian raw audio data to establish an efficient detection framework specifically designed to classify the vocal data into two distinct categories: healthy individuals and patients diagnosed with Parkinson’s disease. …”
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Enhanced Heart Disease Classification Using Dual Attention Mechanisms and 3D-Echo Fusion Algorithm in Echocardiogram Videos by S Deepika, N. Jaisankar

“…In this paper, we present a novel hybrid deep learning framework that integrates convolutional neural networks (CNNs) with recurrent neural networks (RNNs) alongside a 3D-Echo Fusion approach and a Dual Attention Model for heart valve disease classification using echocardiogram videos. …”
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A Mobile Deep Learning Classification Model for Diabetic Retinopathy by Daniel Rimaru, Antonio Nehme, Musaed Alhussein, Khaled Mahbub, Khusheed Aurangzeb, Anas Khan

“…An Android application has then been developed, that makes calls to this model and then displays the results on screen with a simple-to-understand interface developed using the Kivy framework.…”
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Historical Blurry Video-Based Face Recognition by Lujun Zhai, Suxia Cui, Yonghui Wang, Song Wang, Jun Zhou, Greg Wilsbacher

“…Next, we build a deep neural network-integrated object-tracking algorithm to compensate for failed recognition over one or more video frames. The framework combines simple online and real-time tracking with deep data association (Deep SORT), and TB-MTCNN with the residual neural network (ResNet) model. …”
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Study of Cathode Materials for Na-Ion Batteries: Comparison Between Machine Learning Predictions and Density Functional Theory Calculations by Claudio Ronchetti, Sara Marchio, Francesco Buonocore, Simone Giusepponi, Sergio Ferlito, Massimo Celino

“…In the present work, we report cutting-edge research, where we explored a wide range of compositions of cathode materials for Na-ion batteries by first-principles calculations using workflow chains developed within the AiiDA framework. We trained crystal graph convolutional neural networks and geometric crystal graph neural networks, and we demonstrate the ability of the machine learning algorithms to predict the formation energy of the candidate materials as calculated by the density functional theory. …”
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MAK-Net: A Multi-Scale Attentive Kolmogorov–Arnold Network with BiGRU for Imbalanced ECG Arrhythmia Classification by Cong Zhao, Bingwei Lai, Yongzheng Xu, Yiping Wang, Haorong Dong

“…To address these limitations, we introduce MAK-Net, a hybrid deep learning framework that combines: (1) a four-branch multiscale convolutional module for comprehensive feature extraction across diverse waveform morphologies; (2) an efficient channel attention mechanism for adaptive weighting of clinically salient segments; (3) bidirectional gated recurrent units (BiGRU) to capture long-range temporal dependencies; and (4) Kolmogorov–Arnold Network (KAN) layers with learnable spline activations for enhanced nonlinear representation and interpretability. …”
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MFCANet: Multiscale Feature Context Aggregation Network for Oriented Object Detection in Remote-Sensing Images by Honghui Jiang, Tingting Luo, Hu Peng, Guozheng Zhang

“…To address this gap, we have extended the RTMDet framework by introducing three modules: the Focused Feature Context Aggregation Module, the Feature Context Information Enhancement Module, and the Multi-scale Feature Fusion Module. …”
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Machine Learning for Fire Safety in the Built Environment: A Bibliometric Insight into Research Trends and Key Methods by Mehmet Akif Yıldız

“…This study aims to identify research trends and provide a guiding framework for researchers by systematically reviewing the literature on integrating machine learning-based predictive methods into building fire safety design using bibliometric methods. …”
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Exhale-Focused Thermal Image Segmentation Using Optical Flow-Based Frame Filtering and Transformer-Aided Deep Networks by Do-Kyeong Lee, Jae-Sung Shin, Jae-Sung Choi, Min-Hyung Choi, Min Hong

“…To redress this, we propose a thermal imaging-based framework for respiratory segmentation aimed at estimating non-invasive pulmonary function. …”
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