Machine learning approach to reconstruct density matrices from quantum marginals by Daniel Uzcategui-Contreras, Antonio Guerra, Sebastian Niklitschek, Aldo Delgado

“…Our method integrates a quantum marginal imposition technique with convolutional denoising autoencoders. The loss function is carefully designed to enforce essential physical constraints, including Hermiticity, positivity, and normalization. …”
Get full text

A systematic review of multimodal fake news detection on social media using deep learning models by Maged Nasser, Noreen Izza Arshad, Abdulalem Ali, Hitham Alhussian, Faisal Saeed, Aminu Da'u, Ibtehal Nafea

Get full text

Cloud based real-time multivariate multi-step prediction of systolic blood pressure and heart rate using temporal convolutional network and Apache Spark by Hager Saleh, Nora El-Rashidy, Sherif Mostafa, Abdulaziz AlMohimeed, Shaker El-Sappagh, Zainab H. Ali

“…Multi-task comprises forecasting HR and SBP in diverse multi-step heads as puerperal employing TCN, sequence-to-sequence (seq2seq), and Autoencoder models using LSTM and GRU. Extensive results are accomplished by the Medical Information Mart for Intensive Care III (MIMIC III) to assess the performance of the proposed multi-task DL model. …”
Get full text

CNN-based remote dental diagnosis model for caries detection with grad-CAM by Donghyeok Kim, Jangkyum Kim, Seong Gon Choi

Get full text

Deep learning for video-based assessment of endotracheal intubation skills by Jean-Paul Ainam, Erim Yanik, Rahul Rahul, Taylor Kunkes, Lora Cavuoto, Brian Clemency, Kaori Tanaka, Matthew Hackett, Jack Norfleet, Suvranu De

“…The system employs advanced video processing techniques, including a 2D convolutional autoencoder (AE) based on a self-supervision model, capable of recognizing complex patterns in videos. …”
Get full text

Artificial intelligence-driven ensemble deep learning models for smart monitoring of indoor activities in IoT environment for people with disabilities by Munya A. Arasi, Hussah Nasser AlEisa, Amani A. Alneil, Radwa Marzouk

“…For the detection of indoor activities, the proposed MOEM-SMIADP model utilizes an ensemble of three classifiers, namely the graph convolutional network model, long short-term memory sequence-to-sequence (LSTM-seq2seq) method, and convolutional autoencoder. …”
Get full text

Dual-coding Contrastive Learning Based on the ConvNeXt and ViT Models for Morphological Classification of Galaxies in COSMOS-Web by Shiwei Zhu, Guanwen Fang, Chichun Zhou, Jie Song, Zesen Lin, Yao Dai, Xu Kong

“…The upgraded UML method primarily consists of the following three aspects. (1) We employ a convolutional autoencoder to denoise galaxy images and adaptive polar coordinate transformation to enhance the model’s rotational invariance. (2) A pretrained dual-encoder convolutional neural network based on ConvNeXt and a vision transformer is used to encode the image data, while contrastive learning is then applied to reduce the dimension of the features. (3) We adopt a bagging-based clustering model to cluster galaxies with similar features into distinct groups. …”
Get full text

Advancements in Herpes Zoster Diagnosis, Treatment, and Management: Systematic Review of Artificial Intelligence Applications by Dasheng Wu, Na Liu, Rui Ma, Peilong Wu

“…AI applications were analyzed across three domains: (1) diagnosis, where mobile deep neural networks, convolutional neural network ensemble models, and mixed-scale attention-based models have improved diagnostic accuracy and efficiency; (2) treatment, where machine learning models, such as deep autoencoders combined with functional magnetic resonance imaging, electroencephalography, and clinical data, have enhanced treatment outcome predictions; and (3) management, where AI has facilitated case identification, epidemiological research, health care burden assessment, and risk factor exploration for postherpetic neuralgia and other complications. …”
Get full text

Multimodal Pain Recognition in Postoperative Patients: Machine Learning Approach by Ajan Subramanian, Rui Cao, Emad Kasaeyan Naeini, Seyed Amir Hossein Aqajari, Thomas D Hughes, Michael-David Calderon, Kai Zheng, Nikil Dutt, Pasi Liljeberg, Sanna Salanterä, Ariana M Nelson, Amir M Rahmani

“…Data preprocessing involved noise filtering, feature extraction, and combining handcrafted and automatic features through convolutional and long-short-term memory autoencoders. …”
Get full text

A systematic review of deep learning methods for community detection in social networks by Mohamed El-Moussaoui, Mohamed Hanine, Ali Kartit, Monica Garcia Villar, Monica Garcia Villar, Monica Garcia Villar, Helena Garay, Helena Garay, Helena Garay, Isabel de la Torre Díez

“…This review investigates the employed methodologies, evaluates their effectiveness, and discusses the challenges identified in these works.ResultsOur review shows that models like graph neural networks (GNNs), autoencoders, and convolutional neural networks (CNNs) are some of the most commonly used approaches for community detection. …”
Get full text