Development of a CNN-based decision support system for lung disease diagnosis using chest radiographs by B. T. Magar, M. A. Rahman, P. K. Saha, M. Ahmad, M. A. Rashid, H. Higa

“…This study presents CXRNet, a novel, efficient convolutional neural network (CNN)-based framework designed for multi-class classification of common chest diseases, including cardiomegaly, COVID-19, pneumonia, tuberculosis, and normal. …”
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Comparative Analysis of Deep Learning Models for Intrusion Detection in IoT Networks by Abdullah Waqas, Sultan Daud Khan, Zaib Ullah, Mohib Ullah, Habib Ullah

“…This study addresses the problem of detecting intrusions in IoT environments by evaluating the performance of deep learning (DL) models under different data and algorithmic conditions. We conducted a comparative analysis of three widely used DL models—Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Bidirectional LSTM (biLSTM)—across four benchmark IoT intrusion detection datasets: BoTIoT, CiCIoT, ToNIoT, and WUSTL-IIoT-2021. …”
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A New Hybrid Wavelet Transform-Deep Learning for Smart Resilient Inverters in Microgrids Against Cyberattacks by Chou-Mo Yang, Pei-Min Huang, Chun-Lien Su, Mahmoud Elsisi

“…While the model shows high performance, further research is needed to validate its generalizability across different inverter hardware and against novel, zero-day attack variants.…”
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Automatic Detection of Tiny Drainage Outlets and Ventilations on Flat Rooftops from Aerial Imagery by L. Arzoumanidis, W. Li, W. Li, J. Knechtel, Y. Kosmayadi, Y. Dehbi

“…This paper presents an automated approach to detecting drainage outlets and ventilation systems on flat rooftops, using a custom-labeled dataset of highresolution aerial imagery. We evaluated two different object detection methods, with FCOS (Fully Convolutional One-Stage Object Detection) outperforming Faster R-CNN in identifying these small utilities. …”
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Integration of Nuclear, Clinical, and Genetic Features for Lung Cancer Subtype Classification and Survival Prediction Based on Machine- and Deep-Learning Models by Bin Xie, Mingda Mo, Haidong Cui, Yijie Dong, Hongping Yin, Zhe Lu

“…Four machine-learning models—light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost), random forest (RF), and adaptive boosting (AdaBoost)—and three deep-learning models—multilayer perceptron (MLP), TabNet, and convolutional neural network (CNN)—were employed for subtype classification and OS prediction. …”
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Investigation of a transformer-based hybrid artificial neural networks for climate data prediction and analysis by Shangke Liu, Ke Liu, Zheng Wang, Yuanyuan Liu, Bin Bai, Rui Zhao

“…While they have achieved some success, these models still face issues such as complexity, high computational cost, and insufficient handling of multivariable nonlinear relationships.MethodsIn light of this, this paper proposes a hybrid deep learning model based on Transformer-Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) to improve the accuracy of climate predictions. …”
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Enhancing FMCW Radar Gesture Classification With Physically Interpretable Data Augmentation by Alessandra Fusco, Zain Amir Zaman, Souvik Hazra, Lorenzo Servadei, Robert Wille

“…The augmentation techniques employed in this research include time scaling, range and angle transformation, and noise injection, effectively simulating different gesture speeds, orientations, distances, and interference levels. …”
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Improving subpixel impervious surface estimation based on point of interest (POI) data by Junzhe Wang, Wang Jin, Zheng Cao, Zhiyi Pan, Guang Yang, Yaolong Zhao

“…The proposed method was tested in two study areas with distinctly different urban land patterns: Shenzhen, China, and Chicago, USA. …”
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Real-time monitoring and optimization of machine learning intelligent control system in power data modeling technology by Qiong Wang, Zuohu Chen, Yongbo Zhou, Zhiyuan Liu, Zhenguo Peng

“…The average response times in three different testing methods were 139.8 ms, 151 ms, and 140.6 ms, respectively. …”
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A Review of Open Remote Sensing Data with GIS, AI, and UAV Support for Shoreline Detection and Coastal Erosion Monitoring by Demetris Christofi, Christodoulos Mettas, Evagoras Evagorou, Neophytos Stylianou, Marinos Eliades, Christos Theocharidis, Antonis Chatzipavlis, Thomas Hasiotis, Diofantos Hadjimitsis

“…Through integration with GIS programs such as the Digital Shoreline Analysis System (DSAS), AI-based processes such as sophisticated models including WaterNet, U-Net, and Convolutional Neural Networks (CNNs) are highly accurate in shoreline segmentation. …”
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Unlocking transcranial FUS-EEG feature fusion for non-invasive sleep staging in next-gen clinical applications by Suneet Gupta, Praveen Gupta, Bechoo Lal, Aniruddha Deka, Hirakjyoti Sarma, Sheifali Gupta

“…Addressing the variations in electroencephalogram (EEG) and electrooculogram (EOG) signals across different sleep stages, this study introduces a transcranial focused ultrasound (tFUS) based multimodal feature fusion deep learning model (MFDL) for automated sleep staging. …”
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A deep learning based multiple RNA methylation sites prediction across species by Sajid Shah, Saima Jabeen, Mohammed ElAffendi, Ishrat Khan, Muhammad Almas Anjum, Mohamed A. Bahloul

“…Firstly, this study introduces two novel deep learning models for predicting RNA methylation sites: Convolutional Neural Network (CNN)-based and transformer-based models. …”
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Deep learning enhanced light sheet fluorescence microscopy for in vivo 4D imaging of zebrafish heart beating by Meng Zhang, Renjian Li, Songnian Fu, Sunil Kumar, James Mcginty, Yuwen Qin, Lingling Chen

“…We demonstrate that the convolutional neural network (CNN)-transformer network developed here, namely U-net integrated transformer (UI-Trans), successfully achieves the mitigation of complex noise-scattering-coupled degradation and outperforms state-of-the-art deep learning networks, due to its capability of faithfully learning fine details while comprehending complex global features. …”
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Deep Learning and Recurrence Information Analysis for the Automatic Detection of Obstructive Sleep Apnea by Daniele Padovano, Arturo Martinez-Rodrigo, José M. Pastor, José J. Rieta, Raul Alcaraz

“…The present paper addresses this gap by integrating convolutional neural networks (CNNs) with HRV recurrence analysis. …”
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Evaluation of deep learning models for RGB image-based detection of potato virus y strain symptoms (O, NO, and NTN) in potato plants by Charanpreet Singh, Gurjit S. Randhawa, Aitazaz A. Farooque, Yuvraj S. Gill, Lokesh Kumar KM, Mathuresh Singh, Khalil Al-Mughrabi

“…In this study, the use of these models for the detection of infected plants with different strains of PVY has been explored and extended. …”
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Functional connectivity in EEG: a multiclass classification approach for disorders of consciousness by Sreelakshmi Raveendran, Kala S, Ramakrishnan A G, Ramakrishnan A G, Raghavendra Kenchaiah, Jayakrushna Sahoo, Santhos Kumar, Farsana M K, Ravindranadh Chowdary Mundlamuri, Sonia Bansal, Binu V S, Subasree R

“…The extracted SWC metrics, mean, reflecting the stability of connectivity, and standard deviation, indicating variability, are analyzed to discern FC differences at the group level. Multiclass classification is attempted using various models of artificial neural networks that include different multilayer perceptrons (MLP), recurrent neural networks, long-short-term memory networks, gated recurrent units, and a hybrid CNN-LSTM model that combines convolutional neural networks (CNN) and long-short-term memory network to validate the discriminative power of these FC features. …”
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Leveraging sentiment analysis of food delivery services reviews using deep learning and word embedding by Dheya Mustafa, Safaa M. Khabour, Mousa Al-kfairy, Ahmed Shatnawi

“…In addition, the article investigated different effective approaches for word embedding and stemming techniques. …”
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Enhanced Workload Prediction in Data Centers Using Two-Stage Decomposition and Hybrid Parallel Deep Learning by Dalal Alqahtani, Hamidreza Imani, Tarek El-Ghazawi

“…To improve this, we introduce CVCBM which blends signal processing techniques Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variational Mode Decomposition (VMD) with advanced deep learning models like Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. …”
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Long-term forecasting of shield tunnel position and attitude deviation using the 1DCNN-informer method by Jiajie Zhen, Ming Huang, Shuang Li, Kai Xu, Qianghu Zhao

“…This study introduces a novel deep learning model, termed 1DCNN-Informer, which integrates the one-dimensional convolutional neural network (1DCNN) and the Informer model. …”
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AI-Based Solar Panel Detection and Monitoring Using High-Resolution Drone Imagery by Raheleh Parsaeifar, Mojtaba Valinejadshoubi, Anthony Le Guen, Fernando Valdivieso

“…This is addressed by implementing a deep learning-based model using Mask Region-based Convolutional Neural Networks (Mask RCNN) to automate the detection of solar panels from time series high-resolution drone imagery. …”
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