Parkinson’s Disease Prediction: An Attention-Based Multimodal Fusion Framework Using Handwriting and Clinical Data by Sabrina Benredjem, Tahar Mekhaznia, Abdulghafor Rawad, Sherzod Turaev, Akram Bennour, Bourmatte Sofiane, Abdulaziz Aborujilah, Mohamed Al Sarem

“…Method: To assist medical professionals in timely diagnosis of PD, we introduce a cutting-edge Multimodal Diagnosis framework (PMMD). Based on deep learning techniques, the PMMD framework integrates imaging, handwriting, drawing, and clinical data to accurately detect PD. …”
Get full text

Expansion of supraglacial lake area, volume and extent on the Greenland ice sheet from 1985 to 2023 by Yubin Fan, Chang-Qing Ke, Lanhua Luo, Xiaoyi Shen, Stephen John Livingstone, James M. Lea

“…We leveraged spatiotemporally matched ICESat-2 and Landsat 8 reflectance data to develop a deep learning model for lake depth inversion for the period 2014–23. …”
Get full text

An improved ShuffleNetV2 method based on ensemble self-distillation for tomato leaf diseases recognition by Shuiping Ni, Yue Jia, Mingfu Zhu, Mingfu Zhu, Yizhe Zhang, Wendi Wang, Shangxin Liu, Yawei Chen

“…IntroductionTimely and accurate recognition of tomato diseases is crucial for improving tomato yield. While large deep learning models can achieve high-precision disease recognition, these models often have a large number of parameters, making them difficult to deploy on edge devices. …”
Get full text

Glaucoma detection and staging from visual field images using machine learning techniques. by Nahida Akter, Jack Gordon, Sherry Li, Mikki Poon, Stuart Perry, John Fletcher, Thomas Chan, Andrew White, Maitreyee Roy

“…<h4>Purpose</h4>In this study, we investigated the performance of deep learning (DL) models to differentiate between normal and glaucomatous visual fields (VFs) and classify glaucoma from early to the advanced stage to observe if the DL model can stage glaucoma as Mills criteria using only the pattern deviation (PD) plots. …”
Get full text

Use of machine learning in osteoarthritis research: a systematic literature review by Francis Berenbaum, Jérémie Sellam, David Klatzmann, Atul J Butte, Karine Louati, Encarnita Mariotti-Ferrandiz, Marie Binvignat, Valentina Pedoia

“…Overall, 35% of the articles described the use of deep learning And 74% imaging analyses. A total of 85% of the articles involved knee OA and 15% hip OA. …”
Get full text

A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions by Oumayma Jouini, Kaouthar Sethom, Abdallah Namoun, Nasser Aljohani, Meshari Huwaytim Alanazi, Mohammad N. Alanazi

“…., random forest, logistic regression) and deep learning methods (e.g., ResNet-50, YOLOv4, LSTM) are deployed on devices, distributed edge, and distributed cloud computing. …”
Get full text

Multi-scale feature fusion of deep convolutional neural networks on cancerous tumor detection and classification using biomedical images by U. M. Prakash, S. Iniyan, Ashit Kumar Dutta, Shtwai Alsubai, Janjhyam Venkata Naga Ramesh, Sachi Nandan Mohanty, Khasim Vali Dudekula

“…Current developments in medical technology, like smart recognition and analysis utilizing machine learning (ML) and deep learning (DL) techniques, have transformed the analysis and treatment of these conditions. …”
Get full text

Advanced artificial intelligence with federated learning framework for privacy-preserving cyberthreat detection in IoT-assisted sustainable smart cities by Mahmoud Ragab, Ehab Bahaudien Ashary, Bandar M. Alghamdi, Rania Aboalela, Naif Alsaadi, Louai A. Maghrabi, Khalid H. Allehaibi

“…Nevertheless, the possibility of FL regarding IoT forensics remains mostly unexplored. Deep learning (DL) focused cyberthreat detection has developed as a powerful and effective approach to identifying abnormal patterns or behaviours in the data field. …”
Get full text

A novel lightweight model for tea disease classification based on feature reuse and channel focus attention mechanism by Junjie Liang, Renjie Liang, Dongxia Wang

“…The complex backgrounds affect the recognition accuracy of traditional deep learning techniques. To improve the recognition accuracy, the traditional classic convolutional neural network (CNN) models require higher model complexity. …”
Get full text

The utility of artificial intelligence in identifying radiological evidence of lung cancer and pulmonary tuberculosis in a high-burden tuberculosis setting by Z Z Nxumalo, E M Irusen, B W Allwood, M Tadepalli, J Bassi, C F N Koegelenberg

“…Artificial intelligence (AI), using deep learning (DL) systems, can be utilised to detect radiological changes of various pulmonary diseases. …”
Get full text

Depression Detection and Diagnosis Based on Electroencephalogram (EEG) Analysis: A Systematic Review by Kholoud Elnaggar, Mostafa M. El-Gayar, Mohammed Elmogy

“…By focusing on studies that integrate EEG with machine learning (ML) and deep learning (DL) techniques, we systematically analyze methods utilizing EEG signals to identify depression biomarkers. …”
Get full text

AlphaFold 2, but not AlphaFold 3, predicts confident but unrealistic β-solenoid structures for repeat proteins by Olivia S. Pratt, Luc G. Elliott, Margaux Haon, Shahram Mesdaghi, Rebecca M. Price, Adam J. Simpkin, Daniel J. Rigden

“…Importantly, other deep learning-based structure prediction tools predict different structures or β-solenoids with much lower confidence suggesting that AF2 alone has an unreasonable tendency to predict confident but unrealistic β-solenoids for perfect repeat sequences. …”
Get full text

A Real-Time End-to-End Framework with a Stacked Model Using Ultrasound Video for Cardiac Septal Defect Decision-Making by Siti Nurmaini, Ria Nova, Ade Iriani Sapitri, Muhammad Naufal Rachmatullah, Bambang Tutuko, Firdaus Firdaus, Annisa Darmawahyuni, Anggun Islami, Satria Mandala, Radiyati Umi Partan, Akhiar Wista Arum, Rio Bastian

“…With digitization, deep learning (DL) can be used to improve the efficiency of the diagnosis. …”
Get full text

USSD: Unsupervised Sleep Spindle Detector by Edgardo Ramirez, Pablo A. Estevez, Martin D. Adams, Claudio A. Perez, Marcelo Garrido Gonzalez, Patricio Peirano

“…In addition, the SSs detected by USSD on the unlabeled CAP dataset are used to pre-train a supervised deep learning method, which after fine-tuning with 20% of the MODA dataset, reaches an F1-score of <inline-formula> <tex-math notation="LaTeX">$0.81 \pm 0.02$ </tex-math></inline-formula>.…”
Get full text

Toward Robust Lung Cancer Diagnosis: Integrating Multiple CT Datasets, Curriculum Learning, and Explainable AI by Amira Bouamrane, Makhlouf Derdour, Akram Bennour, Taiseer Abdalla Elfadil Eisa, Abdel-Hamid M. Emara, Mohammed Al-Sarem, Neesrin Ali Kurdi

“…This study proposes a novel model based on deep learning to enhance lung cancer diagnosis quality, understandability, and generalizability. …”
Get full text

An Empirical Analysis of Transformer-Based and Convolutional Neural Network Approaches for Early Detection and Diagnosis of Cancer Using Multimodal Imaging and Genomic Data by S. K. B. Sangeetha, Sandeep Kumar Mathivanan, V. Muthukumaran, Jaehyuk Cho, and Sathishkumar Veerappampalayam Easwaramoorthy

“…This study has shown how state-of-the-art deep learning methods can be effectively combined with multi-modal data for building more accurate and efficient systems in cancer diagnosis. …”
Get full text

WCMU-net: An Effective Method for Reducing the Impact of Speckle Noise in SAR Image Change Detection by Yu Duan, Kaimin Sun, Wangbin Li, Jinjiang Wei, Song Gao, Yingjiao Tan, Wanghui Zhou, Jun Liu, Junyi Liu

“…To address the issue of low SNR in SAR images, we construct a deep learning network focused on feature exploration and utilization to effectively distinguish between noise and change information. …”
Get full text

Milk Composition Is Predictive of Low Milk Supply Using Machine Learning Approaches by Xuehua Jin, Ching Tat Lai, Sharon L. Perrella, Xiaojie Zhou, Ghulam Mubashar Hassan, Jacki L. McEachran, Zoya Gridneva, Nicolas L. Taylor, Mary E. Wlodek, Donna T. Geddes

“…<b>Results:</b> Among the six machine learning algorithms tested, deep learning and gradient boosting machines methods had the best performance metrics. …”
Get full text

SSL-MBC: Self-Supervised Learning With Multibranch Consistency for Few-Shot PolSAR Image Classification by Wenmei Li, Hao Xia, Bin Xi, Yu Wang, Jing Lu, Yuhong He

“…Deep learning methods have recently made substantial advances in polarimetric synthetic aperture radar (PolSAR) image classification. …”
Get full text

A multi‐tagged SAR ocean image dataset identifying atmospheric boundary layer structure in winter tradewind conditions by Chen Wang, Justin E. Stopa, Doug Vandemark, Ralph Foster, Alex Ayet, Alexis Mouche, Bertrand Chapron, Peter Sadowski

“…The dataset complements existing hand‐labelled ocean SAR image datasets and offers the potential for new deep‐learning SAR image classification model developments. …”
Get full text