Clinical Decision Support Using Speech Signal Analysis: Systematic Scoping Review of Neurological Disorders by Upeka De Silva, Samaneh Madanian, Sharon Olsen, John Michael Templeton, Christian Poellabauer, Sandra L Schneider, Ajit Narayanan, Rahmina Rubaiat

“…Traditional machine learning and deep learning approaches were used to build predictive models, whereas statistical analysis assessed variable relationships and reliability of speech features. …”
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Informatics strategies for early detection and risk mitigation in pancreatic cancer patients by Di Jin, Najeeb Ullah Khan, Wei Gu, Huijun Lei, Ajay Goel, Tianhui Chen

“…AI-driven approaches, such as those employed in Project Felix and CancerSEEK, have been highlighted for their potential to enhance early detection through deep learning and biomarker discovery. This review underscores the importance of universal genetic testing and the integration of AI with traditional diagnostic methods to improve outcomes in high-risk individuals. …”
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Investigating Maps of Science Using Contextual Proximity of Citations Based on Deep Contextualized Word Representation by Muhammad Roman, Abdul Shahid, Shafiullah Khan, Lisu Yu, Muhammad Asif, Yazeed Yasin Ghadi

“…For automated classification, we need to train deep learning models, which take the citation context as input and provides the reason for citing a paper. …”
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Predictive value of dendritic cell-related genes for prognosis and immunotherapy response in lung adenocarcinoma by Zihao Sun, Mengfei Hu, Xiaoning Huang, Minghan Song, Xiujing Chen, Jiaxin Bei, Yiguang Lin, Size Chen

“…Conclusion We have innovatively established a deep learning-based prediction model, DCRGS, for the prediction of the prognosis of patients with LUAD. …”
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Inhibition of tumour necrosis factor alpha by Etanercept attenuates Shiga toxin-induced brain pathology by Robin Christ, Devon Siemes, Shuo Zhao, Lars Widera, Philippa Spangenberg, Julia Lill, Stephanie Thiebes, Jenny Bottek, Lars Borgards, Andreia G. Pinho, Nuno A. Silva, Susana Monteiro, Selina K. Jorch, Matthias Gunzer, Bente Siebels, Hannah Voss, Hartmut Schlüter, Olga Shevchuk, Jianxu Chen, Daniel R. Engel

“…Analysis of microglial populations using a novel human-in-the-loop deep learning algorithm for the segmentation of microscopic imaging data indicated specific morphological changes, which were reduced to healthy condition after inhibition of TNF-α. …”
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A Method for Quantifying Mung Bean Field Planting Layouts Using UAV Images and an Improved YOLOv8-obb Model by Kun Yang, Xiaohua Sun, Ruofan Li, Zhenxue He, Xinxin Wang, Chao Wang, Bin Wang, Fushun Wang, Hongquan Liu

“…Traditional information extraction methods are often hindered by engineering workloads, time consumption, and labor costs. Applying deep-learning technologies for information extraction reduces these burdens and yields precise and reliable results, enabling a visual analysis of seedling distribution. …”
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FoxA1 knockdown promotes BMSC osteogenesis in part by activating the ERK1/2 signaling pathway and preventing ovariectomy-induced bone loss by Lijun Li, Renjin Lin, Yang Xu, Lingdi Li, Zhijun Pan, Jian Huang

“…Abstract The influence of deep learning in the medical and molecular biology sectors is swiftly growing and holds the potential to improve numerous crucial domains. …”
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HEDDI-Net: heterogeneous network embedding for drug-disease association prediction and drug repurposing, with application to Alzheimer’s disease by Yin-Yuan Su, Hsuan-Cheng Huang, Yu-Ting Lin, Yi-Fang Chuang, Sheh-Yi Sheu, Chen-Ching Lin

“…Graph neural networks (GCNs) have emerged as a leading approach for predicting drug-disease associations by integrating drug and disease-related networks with advanced deep learning algorithms. However, GCNs generally infer association probabilities only for existing drugs and diseases, requiring network re-establishment and retraining for novel entities. …”
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XSE-TomatoNet: An explainable AI based tomato leaf disease classification method using EfficientNetB0 with squeeze-and-excitation blocks and multi-scale feature fusion by Md Assaduzzaman, Prayma Bishshash, Md. Asraful Sharker Nirob, Ahmed Al Marouf, Jon G. Rokne, Reda Alhajj

“…Accurate diagnosis of tomato leaf diseases is vital to avoid ineffective treatments that can harm plants and ecosystems. While deep learning models excel in classifying these diseases, distinguishing subtle variations remains challenging. …”
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A Novel Convolutional Neural Network-Based Approach for Fault Classification in Photovoltaic Arrays by Farkhanda Aziz, Azhar Ul Haq, Shahzor Ahmad, Yousef Mahmoud, Marium Jalal, Usman Ali

“…An in-depth quantitative evaluation of the proposed approach is presented and compared with previous classification methods for PV array faults – both classical machine learning based and deep learning based. Unlike contemporary work, five different faulty cases (including faults in PS – on which no work has been done before in the machine learning domain) have been considered in our study, along with the incorporation of MPPT. …”
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Application of machine learning algorithms in predicting new onset hypertension: a study based on the China Health and Nutrition Survey by Manhui Zhang, Xian Xia, Qiqi Wang, Yue Pan, Guanyi Zhang, Zhigang Wang

“…We tested and evaluated the performance of four traditional machine learning algorithms commonly used in epidemiological studies: Logistic Regression, Support Vector Machine, XGBoost, LightGBM, and two deep learning algorithms: TabNet and AMFormer model. …”
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Autoencoder Reconstruction of Cosmological Microlensing Magnification Maps by Somayeh Khakpash, Federica B. Bianco, Georgios Vernardos, Gregory Dobler, Charles Keeton

“…Rubin Legacy Survey of Space and Time, including thousands of lensed quasars and hundreds of multiply imaged supernovae, faster approaches become essential. We introduce a deep-learning model that is trained on pre-computed magnification maps covering the parameter space on a grid of κ , γ , and s . …”
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Non-Invasive Cancer Detection Using Blood Test and Predictive Modeling Approach by Tarawneh AS, Al Omari AK, Al-khlifeh EM, Tarawneh FS, Alghamdi M, Alrowaily MA, Alkhazi IS, Hassanat AB

“…The HGB model showed improved performance on the dataset.Conclusion: After investigating a number of machine learning methods, an efficient screening platform for non-invasive cancer detection is provided by the integration of haematological indicators with proper analytical data. Exploring deep learning methods in the future work, could provide insights into more complex patterns within the dataset, potentially improving the accuracy and robustness of the predictions.Keywords: cancer, machine learning, complete blood count, RF model, HGB model…”
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A multi-model feature fusion based transfer learning with heuristic search for copy-move video forgery detection by Hessa Alfraihi, Muhammad Swaileh A. Alzaidi, Hamed Alqahtani, Abdulbasit A. Darem, Ali M. Al-Sharafi, Ahmad A. Alzahrani, Menwa Alshammeri, Abdulwhab Alkharashi

“…In contrast, methods that depend on deep learning (DL) have exposed good performance and suggested outcomes. …”
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A land-cover-assisted super-resolution model for retrospective reconstruction of MODIS-like NDVI data across the continental United States by blending Landcover300m and GIMMS NDVI3... by Zhicheng Zhang, Zhenhua Xiong, Xuewen Zhou, Kun Xiao, Wei Wu, Qinchuan Xin

“…This study introduces a novel deep learning-based model, termed the Land-Cover-assisted Super-Resolution SpatioTemporal Fusion model (LCSRSTF), designed to produce biweekly 500-meter MODIS-like data spanning from 1992 to 2010 across the Continental United States (CONUS). …”
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ECG-LM: Understanding Electrocardiogram with a Large Language Model by Kai Yang, Massimo Hong, Jiahuan Zhang, Yizhen Luo, Suyuan Zhao, Ou Zhang, Xiaomao Yu, Jiawen Zhou, Liuqing Yang, Ping Zhang, Mu Qiao, Zaiqing Nie

“…However, the interpretation of ECG data alongside patient information demands substantial medical expertise and resources. While deep learning methods help streamline this process, they often fall short in integrating patient data with ECG readings and do not provide the nuanced clinical suggestions and insights necessary for accurate diagnosis. …”
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Novel Fusion Technique for High-Performance Automated Crop Edge Detection in Smart Agriculture by F. Martinez, James B. Romaine, P. Johnson, A. Cardona Ruiz, Pablo Millan Gata

“…To address this, a novel technique has been developed to automatically detect the vegetative area of lettuces, optimising time and eliminating subjectivity during crop inspections. The proposed deep learning model integrates the YOLOv10 object detector, the K-means classifier, and a segmentation method known as superpixel. …”
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Hierarchical Recognition for Urban Villages Fusing Multiview Feature Information by Zhenkang Wang, Nan Xia, Song Hua, Jiale Liang, Xiankai Ji, Ziyu Wang, Jiechen Wang

“…The spectral, textural, and structural features were extracted from Google RSI by machine-learning classifiers for each segmented block. The deep-learning method was applied to SVI to capture the architectural feature at each viewpoint. …”
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A preoperative predictive model based on multi-modal features to predict pathological complete response after neoadjuvant chemoimmunotherapy in esophageal cancer patients by Yana Qi, Yanran Hu, Chengting Lin, Ge Song, Liting Shi, Hui Zhu

“…Radiomics features were extracted from contrast-enhanced CT images using PyrRadiomics, while pathomics features were derived from whole-slide images (WSIs) of pathological specimens using a fine-tuned deep learning model (ResNet-50). After feature selection, three single-modality prediction models and a combined multi-modality model integrating two radiomics features, 11 pathomics features, and two clinicopathological features were constructed using the support vector machine (SVM) algorithm. …”
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A Novel and Automated Approach to Detect Sea- and Land-Based Aquaculture Facilities by Maxim Veroli, Marco Martinoli, Arianna Martini, Riccardo Napolitano, Domitilla Pulcini, Nicolò Tonachella, Fabrizio Capoccioni

“…The results demonstrate that the approach proposed can identify, characterize, and geolocate sea- and land-based aquaculture structures without performing any post-processing procedure, by directly applying customized deep learning and artificial intelligence algorithms.…”
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