Advancing arabic dialect detection with hybrid stacked transformer models by Hager Saleh, Hager Saleh, Hager Saleh, Abdulaziz AlMohimeed, Rasha Hassan, Mandour M. Ibrahim, Saeed Hamood Alsamhi, Moatamad Refaat Hassan, Sherif Mostafa

“…Recent advances in deep learning (DL) models have shown promise in overcoming potential challenges in identifying Arabic dialects. …”
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An FPGA-Based SiNW-FET Biosensing System for Real-Time Viral Detection: Hardware Amplification and 1D CNN for Adaptive Noise Reduction by Ahmed Hadded, Mossaad Ben Ayed, Shaya A. Alshaya

“…This attempt combines SiNW-FET biosensing technology with FPGA-implemented deep learning noise reduction, creating a compact system capable of real-time viral detection with minimal computational latency. …”
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A class imbalance aware hybrid model for accurate rice variety classification by Hanumesh Vaidya, K.V. Prasad, C. Rajashekhar, D Tripathi, Renuka S, Jyoti Shetty, Kumar Swamy, Shobha Y

“…As the complexity of this task has grown, advanced Machine Learning (ML) and Deep Learning (DL) models have emerged as powerful tools for accurate variety identification. …”
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A fast monocular 6D pose estimation method for textureless objects based on perceptual hashing and template matching by Jose Moises Araya-Martinez, Jose Moises Araya-Martinez, Vinicius Soares Matthiesen, Vinicius Soares Matthiesen, Simon Bøgh, Jens Lambrecht, Rui Pimentel de Figueiredo

“…Additionally, we compare our method to state-of-the-art deep learning models on a public dataset. Although our algorithm does not outperform them in absolute accuracy, it provides a more favorable trade-off between accuracy and processing time, which is especially relevant to applications using resource-constrained devices.…”
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Application of MRI image segmentation algorithm for brain tumors based on improved YOLO by Tao Yang, Xueqi Lu, Lanlan Yang, Miyang Yang, Jinghui Chen, Hongjia Zhao

“…ObjectiveTo assist in the rapid clinical identification of brain tumor types while achieving segmentation detection, this study investigates the feasibility of applying the deep learning YOLOv5s algorithm model to the segmentation of brain tumor magnetic resonance images and optimizes and upgrades it on this basis.MethodsThe research institute utilized two public datasets of meningioma and glioma magnetic resonance imaging from Kaggle. …”
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Autoencoder Artificial Neural Network Model for Air Pollution Index Prediction by Nor Irwin Basir, Kathlyn Kaiyun Tan, Danny Hartanto Djarum, Zainal Ahmad, Dai-Viet N. Vo, Zhang Jie

“…The findings confirm that both autoencoder models enhance API prediction accuracy, with the deep sparse autoencoder emerging as the optimal model, highlighting the potential of deep learning in improving air quality prediction. …”
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Widespread use of ChatGPT and other Artificial Intelligence tools among medical students in Uganda: A cross-sectional study. by Elizabeth Ajalo, David Mukunya, Ritah Nantale, Frank Kayemba, Kennedy Pangholi, Jonathan Babuya, Suzan Langoya Akuu, Amelia Margaret Namiiro, Yakobo Baddokwaya Nsubuga, Joseph Luwaga Mpagi, Milton W Musaba, Faith Oguttu, Job Kuteesa, Aloysius Gonzaga Mubuuke, Ian Guyton Munabi, Sarah Kiguli

“…<h4>Background</h4>Chat Generative Pre-trained Transformer (ChatGPT) is a 175-billion-parameter natural language processing model that uses deep learning algorithms trained on vast amounts of data to generate human-like texts such as essays. …”
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Determination of Sequential Well Placements Using a Multi-Modal Convolutional Neural Network Integrated with Evolutionary Optimization by Seoyoon Kwon, Minsoo Ji, Min Kim, Juliana Y. Leung, Baehyun Min

“…This complex multi-million-dollar problem involves optimizing multiple parameters using computationally intensive reservoir simulations, often employing advanced algorithms such as optimization algorithms and machine/deep learning techniques to find near-optimal solutions efficiently while accounting for uncertainties and risks. …”
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A multi-modal multi-branch framework for retinal vessel segmentation using ultra-widefield fundus photographs by Qihang Xie, Qihang Xie, Xuefei Li, Yuanyuan Li, Yuanyuan Li, Jiayi Lu, Jiayi Lu, Shaodong Ma, Yitian Zhao, Yitian Zhao, Jiong Zhang, Jiong Zhang

“…However, the high resolution and low contrast inherent to UWF fundus images present significant challenges for accurate segmentation using deep learning methods, thereby complicating disease analysis in this context.MethodsTo address these issues, this study introduces M3B-Net, a novel multi-modal, multi-branch framework that leverages fundus fluorescence angiography (FFA) images to improve retinal vessel segmentation in UWF fundus images. …”
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The Impact of Artificial Intelligence on Healthcare: A Comprehensive Review of Advancements in Diagnostics, Treatment, and Operational Efficiency by Md. Faiyazuddin, Syed Jalal Q. Rahman, Gaurav Anand, Reyaz Kausar Siddiqui, Rachana Mehta, Mahalaqua Nazli Khatib, Shilpa Gaidhane, Quazi Syed Zahiruddin, Arif Hussain, Ranjit Sah

“…The study aims to describe AI in healthcare, including important technologies like robotics, machine learning (ML), deep learning (DL), and natural language processing (NLP), and to investigate how these technologies are used in patient interaction, predictive analytics, and remote monitoring. …”
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Toward enhancement of antibody thermostability and affinity by computational design in the absence of antigen by Mark Hutchinson, Jeffrey A. Ruffolo, Nantaporn Haskins, Michael Iannotti, Giuliana Vozza, Tony Pham, Nurjahan Mehzabeen, Harini Shandilya, Keith Rickert, Rebecca Croasdale-Wood, Melissa Damschroder, Ying Fu, Andrew Dippel, Jeffrey J. Gray, Gilad Kaplan

“…In this study, DeepAb, a deep learning model for predicting antibody Fv structure directly from sequence, was used in conjunction with single-point experimental deep mutational scanning (DMS) enrichment data to design 200 potentially optimized variants of an anti-hen egg lysozyme (HEL) antibody. …”
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BeatProfiler: Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and Drugs by Youngbin Kim, Kunlun Wang, Roberta I. Lock, Trevor R. Nash, Sharon Fleischer, Bryan Z. Wang, Barry M. Fine, Gordana Vunjak-Novakovic

“…To accurately classify between these biological signals, we apply feature-based ML and deep learning models (temporal convolutional-bidirectional long short-term memory model or TCN-BiLSTM). …”
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Dynamic selectout and voting-based federated learning for enhanced medical image analysis by Saeed Iqbal, Adnan N Qureshi, Musaed Alhussein, Khursheed Aurangzeb, Atif Mahmood, Saaidal Razalli Bin Azzuhri

“…When these agents work together in the system, it is imperative to tackle the complexities of distributed deep learning. We suggest a novel approach that uses a voting mechanism and dynamic SelectOut inside the FL framework to address these problems. …”
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Remote-sensing-based forest canopy height mapping: some models are useful, but might they provide us with even more insights when combined? by N. Besic, N. Picard, C. Vega, J.-D. Bontemps, L. Hertzog, J.-P. Renaud, J.-P. Renaud, F. Fogel, M. Schwartz, A. Pellissier-Tanon, G. Destouet, F. Mortier, F. Mortier, M. Planells-Rodriguez, P. Ciais

“…<p>The development of high-resolution mapping models for forest attributes based on remote sensing data combined with machine or deep learning techniques has become a prominent topic in the field of forest observation and monitoring. …”
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Machine Learning-Based Alzheimer’s Disease Stage Diagnosis Utilizing Blood Gene Expression and Clinical Data: A Comparative Investigation by Manash Sarma, Subarna Chatterjee

“…For the clinical study data, eight of the most effective biomarkers were selected using SFBS. A deep learning (DL) classifier was used to identify the stages—cognitive normal (CN), mild cognitive impairment (MCI), and Alzheimer’s disease (AD)/dementia. …”
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Optimizing Automated Hematoma Expansion Classification from Baseline and Follow-Up Head Computed Tomography by Anh T. Tran, Dmitriy Desser, Tal Zeevi, Gaby Abou Karam, Julia Zietz, Andrea Dell’Orco, Min-Chiun Chen, Ajay Malhotra, Adnan I. Qureshi, Santosh B. Murthy, Shahram Majidi, Guido J. Falcone, Kevin N. Sheth, Jawed Nawabi, Seyedmehdi Payabvash

“…In this study, we combined a tandem deep-learning classification model with automated segmentation to generate probability measures for false HE classifications. …”
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Machine Learning Approaches in High Myopia: Systematic Review and Meta-Analysis by Huiyi Zuo, Baoyu Huang, Jian He, Liying Fang, Minli Huang

“…The meta-analysis results unveiled that for the diagnosis of pathologic myopia, the summary receiver operating characteristic (SROC), sensitivity, and specificity of ML were 0.97 (95% CI 0.95-0.98), 0.91 (95% CI 0.89-0.92), and 0.95 (95% CI 0.94-0.97), respectively. Specifically, deep learning (DL) showed an SROC of 0.97 (95% CI 0.95-0.98), sensitivity of 0.92 (95% CI 0.90-0.93), and specificity of 0.96 (95% CI 0.95-0.97), while conventional ML (non-DL) showed an SROC of 0.86 (95% CI 0.75-0.92), sensitivity of 0.77 (95% CI 0.69-0.84), and specificity of 0.85 (95% CI 0.75-0.92). …”
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Editorial by Teddy Surya Gunawan

“…Interdisciplinary efforts shine through research on 4D radar imaging and its fusion with deep learning for road-crossing classification, emphasizing the role of technology in creating safer urban environments. …”
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Application of a methodological framework for the development and multicenter validation of reliable artificial intelligence in embryo evaluation by D. Gilboa, Akhil Garg, M. Shapiro, M. Meseguer, Y. Amar, N. Lustgarten, N. Desai, T. Shavit, V. Silva, A. Papatheodorou, A. Chatziparasidou, S. Angras, J. H. Lee, L. Thiel, C. L. Curchoe, Y. Tauber, D. S. Seidman

“…Results The AI was designed as a deep learning classifier ranking embryos by score according to their likelihood of clinical pregnancy. …”
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Роль искусственного интеллекта в прогнозировании трудных дыхательных путей у взрослых: обзор литературы... by Андрей Юрьевич Зайцев, А. Б. Сорокин, Ю. А. Зайцев, К. В. Дубровин, Э. Г. Усикян

“…Поисковыми словами для англоязычных баз данных были artificial intelligence, deep learning, difficult airways; для русскоязычных — искусственный интеллект, глубокое машинное обучение, трудные дыхательные пути. …”
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