Phenotypic and molecular characterization of the largest worldwide cluster of hereditary angioedema type 1. by Juan Sebastian Arias-Flórez, Sandra Ximena Ramirez, Bibiana Bayona-Gomez, Lina Castro-Castillo, Valeria Correa-Martinez, Yasmín Sanchez-Gomez, William Usaquén-Martínez, Lilian Andrea Casas-Vargas, Carlos Eduardo Olmos Olmos, Nora Contreras Bravo, Camilo Andres Velandia-Piedrahita, Adrien Morel, Rodrigo Cabrera-Perez, Natalia Santiago-Tovar, Cristian Camilo Gaviria-Sabogal, Ingrid Tatyana Bernal, Dora Janeth Fonseca-Mendoza, Carlos M Restrepo

“…This variant had been previously reported to the patient prior to the beginning of this study. Using deep-learning methods, the structure of the C1-Inhibitor protein, p.Gln474* and p.Met413Arg was predicted, and we propose the molecular mechanism related to the etiology of the disease. …”
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Cybersecurity Solutions for Industrial Internet of Things–Edge Computing Integration: Challenges, Threats, and Future Directions by Tamara Zhukabayeva, Lazzat Zholshiyeva, Nurdaulet Karabayev, Shafiullah Khan, Noha Alnazzawi

“…The review emphasizes the integration of advanced security technologies, including machine learning (ML), federated learning (FL), blockchain, blockchain–ML, deep learning (DL), encryption, cryptography, IT/OT convergence, and digital twins, as essential for enhancing the security and real-time data protection of CPS in IIoT–edge computing. …”
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CNFA: ConvNeXt Fusion Attention Module for Age Recognition of the Tangerine Peel by Fuqin Deng, Junwei Li, Lanhui Fu, Chuanbo Qin, Yikui Zhai, Hongmin Wang, Ningbo Yi, Nannan Li, TinLun Lam

“…This work investigates the automatic age recognition of the tangerine peel based on deep learning and attention mechanisms. We proposed an effective ConvNeXt fusion attention module (CNFA), which consists of three parts, a ConvNeXt block for extracting low-level features’ information and aggregating hierarchical features, a channel squeeze-and-excitation (cSE) block and a spatial squeeze-and-excitation (sSE) block for generating sufficient high-level feature information from both channel and spatial dimensions. …”
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LSTM Recurrent Neural Network-Based Frequency Control Enhancement of the Power System with Electric Vehicles and Demand Management by G. Sundararajan, P. Sivakumar

“…This research work investigates a deep learning strategy based on a long short-term memory recurrent neural network to identify active power fluctuations in real-time. …”
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Artificial intelligence in CT diagnosis: Current status and future prospects for ear diseases by Ruowei Tang, Pengfei Zhao, Jia Li, Zhixiang Wang, Ning Xu, Zhenchang Wang

“…AI-driven measurement tools are enhancing the precision and personalization of surgical planning, while deep learning-based anomaly detection is utilized to address the challenges of detecting diverse ear lesions. …”
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Neural-field-based image reconstruction for bioluminescence tomography by Xuanxuan Zhang, Xu Cao, Jiulou Zhang, Lin Zhang, Guanglei Zhang

“…Deep learning (DL)-based image reconstruction methods have garnered increasing interest in the last few years. …”
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Vibration Images-Driven Fault Diagnosis Based on CNN and Transfer Learning of Rolling Bearing under Strong Noise by Hongwei Fan, Ceyi Xue, Xuhui Zhang, Xiangang Cao, Shuoqi Gao, Sijie Shao

“…Deep learning-based fault diagnosis of rolling bearings is a hot research topic, and a rapid and accurate diagnosis is important. …”
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MLDFNet: A Multilabel Dual-Flow Network for Change Detection in Bitemporal Remote Sensing Images by Daniyaer Sidekejiang, Panpan Zheng, Liejun Wang

“…With the development of deep learning (DL) in recent years, numerous remote sensing image change detection (CD) networks have emerged. …”
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Prediction of urinary tract infection using machine learning methods: a study for finding the most-informative variables by Sajjad Farashi, Hossein Emad Momtaz

“…Several types of machines including classical and deep learning models were used for this purpose. Results Eighteen selected features from urine test, blood test, and demographic data were found as the most informative features. …”
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Depth Semantic Segmentation of Tobacco Planting Areas from Unmanned Aerial Vehicle Remote Sensing Images in Plateau Mountains by Liang Huang, Xuequn Wu, Qiuzhi Peng, Xueqin Yu

“…To this end, the advantage of deep learning features self-learning is relied on in this paper. …”
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Artificial intelligence in the diagnosis of endocrine disorders: A focus on diabetes and thyroid diseases by Kimi Milić Marko, Sinanović Šćepan, Prodović Tanja, Ilanković Tanja

“…Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, has emerged as a pivotal technology in medicine, enabling early diagnosis and precise evaluation of complex medical conditions. …”
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Rethinking the Key Factors for the Generalization of Remote Sensing Stereo Matching Networks by Liting Jiang, Feng Wang, Wenyi Zhang, Peifeng Li, Hongjian You, Yuming Xiang

“…Stereo matching, a critical step of binocular 3-D reconstruction, has fully shifted to deep learning due to its strong feature representation of remote sensing images. …”
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Support Vector Machine – Recursive Feature Elimination for Feature Selection on Multi-omics Lung Cancer Data by Nuraina Syaza Azman, Azurah A Samah, Ji Tong Lin, Hairudin Abdul Majid, Zuraini Ali Shah, Nies Hui Wen, Chan Weng Howe

“…The study focuses on mitigating the curse of dimensionality by implementing Support Vector Machine – Recursive Feature Elimination (SVM-RFE) as the selected feature selection method in the lung cancer (LUSC) multi-omics dataset integrated from three single omics dataset comprising genomics, transcriptomics and epigenomics, and assess the quality of the selected feature subsets using SDAE and VAE deep learning classifiers. In this study, the LUSC datasets first undergo data pre-processing, including checking for missing values, normalization, and removing zero variance features. …”
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Estimating Aggregate Capacity of Connected DERs and Forecasting Feeder Power Flow With Limited Data Availability by Amir Reza Nikzad, Amr Adel Mohamed, Bala Venkatesh, John Penaranda

“…Our proposal comprises: 1) ovel deep learning-based architecture with a few convolutional neural network and long short-term memory (CNN-LSTM) modules to represent feeder connected aggregate models of DERs and loads and associated training algorithms; 2) method for estimating aggregate capacities of connected renewables and loads; and 3) method for short-term (hourly) high-resolution forecasting. …”
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An Effective Methodology for Diabetes Prediction in the Case of Class Imbalance by Borislava Toleva, Ivan Atanasov, Ivan Ivanov, Vincent Hooper

“…Our methodology can outperform existing machine learning algorithms and complex deep learning models. Applying our proposed methodology is a simple and fast way to predict labels with class imbalance. …”
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High-performance traffic volume prediction: An evaluation of RNN, GRU, and CNN for accuracy and computational trade-offs by Pranolo Andri, Saifullah Shoffan, Bella Utama Agung, Wibawa Aji Prasetya, Bastian Muhammad, Hardiyanti P Cicin

“…This study addresses these challenges by evaluating the effectiveness of three deep learning architectures— Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN)—in forecasting hourly traffic volume on Interstate 94. …”
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Improvement of reading platforms assisted by the spring framework: A recommendation technique integrating the KGMRA algorithm and BERT model by Yawen Su

“…Concurrently, the BERT model uses deep learning to generate robust semantic representations of article content, enhancing the system's capacity to understand and predict user interests with greater precision. …”
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Securing Urban Landscape: Cybersecurity Mechanisms for Resilient Smart Cities by Qiang Lyu, Sujuan Liu, Zhouyuan Shang

“…The implications of this approach are profound for securing urban infrastructures in smart cities. By combining deep learning with evolutionary algorithms, the CNN-GA framework offers a dynamic and adaptive solution to address the complex and evolving nature of cyber threats. …”
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Exploring the evolution of scientific publication on portfolio optimization in the light of artificial intelligence: A bibliometric study by Mostafa Shabani, Rouzbeh Ghousi, Emran Mohammadi

“…The rapid evolution of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has profoundly influenced various domains, including portfolio optimization. …”
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A novel deep synthesis-based insider intrusion detection (DS-IID) model for malicious insiders and AI-generated threats by Hazem M. Kotb, Tarek Gaber, Salem AlJanah, Hossam M. Zawbaa, Mohammed Alkhathami

“…The model employs deep feature synthesis to automatically generate detailed user profiles from event data and utilizes binary deep learning for accurate threat identification. The DS-IID model addresses three key issues: it (i) detects malicious insiders using supervised learning, (ii) evaluates the effectiveness of generative algorithms in replicating real user profiles, and (iii) distinguishes between real and synthetic abnormal user profiles. …”
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