Evaluating the Performance of Large Language Models in Predicting Diagnostics for Spanish Clinical Cases in Cardiology by Julien Delaunay, Jordi Cusido

“…This study explores the potential of large language models (LLMs) in predicting medical diagnoses from Spanish-language clinical case descriptions, offering an alternative to traditional machine learning (ML) and deep learning (DL) techniques. Unlike ML and DL models, which typically rely on extensive domain-specific training and complex data preprocessing, LLMs can process unstructured text data directly without the need for specialized training on medical datasets. …”
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A Large-Scale Spatio-Temporal Multimodal Fusion Framework for Traffic Prediction by Bodong Zhou, Jiahui Liu, Songyi Cui, Yaping Zhao

“…Traffic prediction is crucial for urban planning and transportation management, and deep learning techniques have emerged as effective tools for this task. …”
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Refining the prediction of user satisfaction on chat-based AI applications with unsupervised filtering of rating text inconsistencies by Hae Sun Jung, Jang Hyun Kim, Haein Lee

“…Subsequently, the authors conduct supervised sentiment analysis using various machine learning and deep learning algorithms. The experimental results confirm the effectiveness of the proposed approach showing improvement in prediction accuracy with cost efficiency. …”
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Semantic segmentation of glaciological features across multiple remote sensing platforms with the Segment Anything Model (SAM) by Siddharth Shankar, Leigh A. Stearns, C. J. van der Veen

“…Widely-used conventional deep learning models such as UNet require tens of thousands of training labels to perform effectively. …”
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A Custom Backbone UNet Framework with DCGAN Augmentation for Efficient Segmentation of Leaf Spot Diseases in Jasmine Plant by Shwetha V., Arnav Bhagwat, Vijaya Laxmi, Sakshi Shrivastava

“…Furthermore, we explore different UNet-based segmentation approaches and evaluate the performance of various backbones to assess their effectiveness. By leveraging deep learning techniques, including DCGAN for dataset generation and the UNet framework for precise segmentation, we significantly contribute to the development of effective methods for detecting and segmenting leaf diseases in jasmine plants.…”
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A Convection Nowcasting Method Based on Machine Learning by Aifang Su, Han Li, Liman Cui, Yungang Chen

“…The test analysis demonstrated that the algorithm combined the image feature extraction ability of the convolutional neural network (CNN) and the sequential learning ability of the long short-term memory network (LSTM) model to establish an end-to-end deep learning network, which could deeply extract high-order features of radar echoes such as structural texture, spatial correlation, and temporal evolution compared with the traditional algorithm. …”
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AVP-GPT2: A Transformer-Powered Platform for De Novo Generation, Screening, and Explanation of Antiviral Peptides by Huajian Zhao, Gengshen Song

“…To address this urgent need, we present AVP-GPT2, a deep-learning model that significantly outperforms its predecessor, AVP-GPT, in designing and screening antiviral peptides. …”
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DARE: A decentralized association rules extraction scheme for embedded data sets in distributed IoT devices by Márcio Alencar, Raimundo Barreto, Horácio Fernandes, Eduardo Souto, Richard Pazzi

“…Performing knowledge extraction in a decentralized approach is a computational challenge considering the tight storage and processing constraints of IoT devices, unlike deep learning, which demands a massive amount of data, memory, and processing capability. …”
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Pseudo-siamese network image tampering localization model based on reinforced samples by Jinwei WANG, Zihe ZHANG, Xiangyang LUO, Bin MA

“…With the continuous development of the internet, an increasing number of images have been tampered with on the network, accompanied by a growing range of techniques to cover up tampering traces.However, most current detection models neglect the impact of image post-processing on tamper detection algorithms, limiting their real-life applications.To address these issues, a general image tampering location model based on enhanced samples and the pseudo-twin network was proposed.The pseudo-twin network enabled the model to learn tampering features in real images.On one hand, by applying convolution constraints, the image content was suppressed, allowing the model to focus more on residual trace information of tampering.The two-branch structure of the network facilitated the comprehensive utilization of image feature information.By utilizing enhanced samples, the model could dynamically generate the most crucial pictures for learning tamper types, enabling targeted training of the model.This approach ensured that the model converged in all directions, ultimately obtaining the global optimal model.The idea of data enhancement was employed to automatically generate abundant tampered images and corresponding masks, effectively resolving the limited tampering dataset issue.Extensive experiments were conducted on four datasets, demonstrating the feasibility and effectiveness of the proposed model in pixel-level tamper detection.Particularly on the Columbia dataset, the algorithm achieves a 33.5% increase in F1 score and a 23.3% increase in MCC score.These results indicate that the proposed model harnesses the advantages of deep learning models and significantly improves the effectiveness of tamper location detection.…”
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Leveraging the Turnpike Effect for Mean Field Games Numerics by Rene A. Carmona, Claire Zeng

“…Recently, a deep-learning algorithm referred to as Deep Galerkin Method (DGM), has gained a lot of attention among those trying to solve numerically Mean Field Games with finite horizon, even if the performance seems to be decreasing significantly with increasing horizon. …”
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Graph Convolutional Network with Neural Collaborative Filtering for Predicting miRNA-Disease Association by Jihwan Ha

“…By exploiting neural collaborative filtering, miRNAs and disease feature vectors were effectively learned through matrix factorization and deep learning, and disease-related miRNAs were identified. …”
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Opportunities of Artificial Intelligence and Machine Learning in the Food Industry by Indrajeet Kumar, Jyoti Rawat, Noor Mohd, Shahnawaz Husain

“…Automation is completely based on artificial intelligence (AI) or machine learning (ML) or deep learning (DL) algorithms. By using the AI-based system, food production and delivery processes can be efficiently handled and also enhance the operational competence. …”
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A guidance to intelligent metamaterials and metamaterials intelligence by Chao Qian, Ido Kaminer, Hongsheng Chen

“…For intelligent metamaterials, we discuss how artificial intelligence, exemplified by deep learning, streamline the photonic design, foster independent working manner, and unearth latent physics. …”
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YOLO-UNet Architecture for Detecting and Segmenting the Localized MRI Brain Tumor Image by Nur Iriawan, Anindya A. Pravitasari, Ulfa S. Nuraini, Nur I. Nirmalasari, Taufik Azmi, Muhammad Nasrudin, Adam F. Fandisyah, Kartika Fithriasari, Santi W. Purnami, null Irhamah, Widiana Ferriastuti

“…This paper employed deep learning to detect and segment brain tumor MRI images by combining the convolutional neural network (CNN) and fully convolutional network (FCN) methodology in serial. …”
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Histopathology and proteomics are synergistic for high-grade serous ovarian cancer platinum response prediction by Oz Kilim, Alex Olar, András Biricz, Lilla Madaras, Péter Pollner, Zoltán Szállási, Zsofia Sztupinszki, István Csabai

“…Our study demonstrates that combining H&E-stained whole slide images (WSIs) with proteomic signatures using a multimodal deep learning framework significantly improves the prediction of platinum response in both discovery and validation cohorts. …”
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Dynamics and triggers of misinformation on vaccines. by Emanuele Brugnoli, Marco Delmastro

“…We first use the symbolic transfer entropy analysis of news production time-series to dynamically determine which category of sources, questionable or reliable, causally drives the agenda on vaccines. Then, leveraging deep learning models capable to accurately classify vaccine-related content based on the conveyed stance and discussed topic, respectively, we evaluate the focus on various topics by news sources promoting opposing views and compare the resulting user engagement. …”
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A data Mining Approach using CNN and LSTM to Predict Divorce before Marriage by Touba Torabipour, Safieh Siadat, Hosein Taghavi

“…In this article, a method has been proposed that uses data mining and deep learning to help family counselors to predict the outcome of marriage as a practical tool. …”
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Investigating the effect of loss functions on single-image GAN performance by Eyyup YİLDİZ, Mehmet Erkan YUKSEL, Selcuk SEVGEN

“…GAN models, which typically handle large datasets, have been successful in the field of deep learning. However, exploring the factors that influence the success of GAN models developed for limited data problems is an important area of research. …”
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Temporally Deformable Convolution for Gait Recognition by Juyoung Kim, Beomseong Kim, Heesung Lee

“…With the advancement of deep learning based computer vision technology, gait recognition has significantly improved in performance and gained significant attention due to its non-invasive nature. …”
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Improving Imitation Skills in Children with Autism Spectrum Disorder Using the NAO Robot and a Human Action Recognition by Abeer Alnafjan, Maha Alghamdi, Noura Alhakbani, Yousef Al-Ohali

“…<b>Results:</b> We developed a deep learning approach based on the human action recognition algorithm for analyzing clapping imitation. …”
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