Deep Learning for Glioblastoma Multiforme Detection from MRI: A Statistical Analysis for Demographic Bias by Kebin Contreras, Julio Gutierrez-Rengifo, Oscar Casanova-Carvajal, Angel Luis Alvarez, Patricia E. Vélez-Varela, Ana Lorena Urbano-Bojorge

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Reigniting work engagement through coping for burned-out academics: An open distance learning context by Bronwyn Wright, Linda Steyn, Annelize van Niekerk

“…Motivation for the study: It was necessary to hear the lived experiences of academics in a comprehensive open distance e-learning institution and the coping strategies they use to maintain work engagement despite experiencing burnout, as this is underexplored. …”
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Diagnosis of bipolar disorder based on extracted significant biomarkers using bioinformatics and machine learning algorithms by Hamid Mohseni, Massoud Sokouti, Akram Nezhadi, Ali Sayadi

“…One model was developed using artificial neural network and tanh functions and the other model was developed using decision tree algorithm. …”
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Utilizing deep learning for intelligent monitoring and early warning of slope disasters in public space design by Wang Ting, Ying Wang

“…The integration of neural network-based prediction with adaptive spatial planning enhances both the precision and timeliness of disaster warnings.DiscussionThis framework offers a transformative, safe, and functional approach to slope disaster management in dynamic public spaces. …”
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Recent Trends in Proxy Model Development for Well Placement Optimization Employing Machine Learning Techniques by Sameer Salasakar, Sabyasachi Prakash, Ganesh Thakur

“…The data-driven models include statistical- and machine learning (ML)-based approximations of nonlinear problems. …”
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FSProtoTransfer: Synergizing Few-Shot, Prototypical Networks, and Transfer Learning for Intrusion Detection in VANETs by Ayesha Dina, Colby Edell, Karim Elish, Arijet Sarker

“…Recently, Machine Learning (ML) and Deep Learning (DL)-based IDSs have gained significant attention for their ability to enhance the security of IoV and related vehicular networks. …”
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Integration of Deep Learning Neural Networks and Feature-Extracted Approach for Estimating Future Regional Precipitation by Shiu-Shin Lin, Kai-Yang Zhu, He-Yang Huang

“…KPCA utilizes kernel functions to extract nonlinear features from the source climatic data, reducing dimensionality and denoising. …”
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A comparative study of machine learning models for automated detection and classification of retinal diseases in Ghana. by Gifty Duah, Eric Nyarko, Anani Lotsi

“…<h4>Introduction</h4>Retinal diseases, a significant global health concern, often lead to severe vision impairment and blindness, resulting in substantial functional and social limitations. This study explored a novel goal of developing and comparing the performance of multiple state-of-the-art convolutional neural network (CNN) models for the automated detection and classification of retinal diseases using optical coherence tomography (OCT) images.…”
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Machine learning explores the prognostic and immuno-oncological impact of mitochondrial unfolded protein response in CESC by Keh-Sen Liu, Yen-Hsiang Chang, Hsing-Ju Wu, Hung-Yu Lin

“…Methods We employed machine learning to analyze UPRmt genes in CESC using TCGA multi-omics data. …”
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Understanding repertoires of pedagogical practices and multiliteracies in Chilean schools: how to connect content and experience for learning by Patricia Thibaut, Andrea Lizasoain, Ninoska Lira-Muñoz

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Machine learning-driven identification of exosome- related biomarkers in head and neck squamous cell carcinoma by Yaodong He, Yun Li, Jiaqi Tang, Yan Wang, Zhenyan Zhao, Rong Liu, Zihui Yang, Huan Li, Jianhua Wei

“…Furthermore, we thoroughly investigated the biological roles of these genes in HNSCC and their prospective treatment implications via functional enrichment analysis, immune microenvironment assessment, and molecular docking confirmation.ResultsThe study indicated that 10 pivotal exosome-related genes identified by the machine learning method had considerable differential expression in HNSCC. …”
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The learning curve for hood‐sparing robotic‐assisted radical prostatectomy: A single‐surgeon experience by Keith R. S. Simpson, Jamie Krishnan, Linda Taylor, Alan McNeill, Daniel W. Good

“…Abstract Objectives This study aimed to assess the impact of anterior hood‐sparing robot‐assisted radical prostatectomy (RARP) with posterior‐anterior reconstruction in a single‐surgeon series by analysing oncological and functional continence outcomes. Patients and Methods We carried out a cohort comparison study of a prospectively collected single‐surgeon series. …”
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Transcriptomic analysis and machine learning modeling identifies novel biomarkers and genetic characteristics of hypertrophic cardiomyopathy by Feng Zhang, Chunrui Li, Lulu Zhang

“…Computational analysis was performed to compare transcriptomic profiles between normal cardiac tissues from healthy donors and myocardial tissues from HCM patients. Functional annotation of differentially expressed genes (DEGs) was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. …”
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Deep reinforcement learning agents for dynamic spectrum access in television whitespace cognitive radio networks by Udeme C. Ukpong, Olabode Idowu-Bismark, Emmanuel Adetiba, Jules R. Kala, Emmanuel Owolabi, Oluwadamilola Oshin, Abdultaofeek Abayomi, Oluwatobi E. Dare

“…The implementation was done in the Radio Frequency Reinforcement Learning (RFRL) Gym, a training environment of the RF spectrum designed to provide comprehensive functionality. …”
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Identification of Ion-kinetic Instabilities in Hybrid-PIC Simulations of Solar Wind Plasma with Machine Learning by Viacheslav M. Sadykov, Leon Ofman, Scott A. Boardsen, Yogesh, Parisa Mostafavi, Lan K. Jian, Kristopher Klein, Mihailo Martinović

“…In this work, we explore machine learning (ML) and deep learning (DL) classification models to identify unstable cases of ion VDFs driving kinetic waves. …”
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Machine Learning-Enhanced 3D GIS Urban Noise Mapping with Multi-Modal Factors by Jianping Pan, Yuzhe He, Wei Ma, Shengwang An, Lu Li, Dan Huang, Dunxin Jia

“…The system integrates functions such as noise simulation, traffic simulation, and weather changes, enabling accurate noise visualization within a three-dimensional virtual environment. …”
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Machine learning developed an immune evasion signature for predicting prognosis and immunotherapy benefits in lung adenocarcinoma by Dongxiao Ding, Gang Huang, Liangbin Wang, Ke Shi, Junjie Ying, Wenjun Shang, Li Wang, Chong Zhang, Maofen Jiang, Yaxing Shen

“…Low IRS-based risk score indicated higher level of NK cells, CD8+ T cells, and immune activation-related functions. The C-index of IRS was higher than that of many developed signatures for LUAD and clinical stage. …”
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Optimizing VGG16 deep learning model with enhanced hunger games search for logo classification by Mohammed Hussain, Thaer Thaher, Mohamed Basel Almourad, Majdi Mafarja

“…First, the EHGS algorithm is evaluated through 30 real-valued benchmark functions from the IEEE CEC2014 suite. Second, a custom-developed VGG16 model is tested on the Flickr-27 logo classification dataset and compared against state-of-the-art deep learning models such as ResNet50V2, InceptionV3, DenseNet121, EfficientNetB0, and MobileNetV2. …”
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Dual smart sensor data-based deep learning network for premature infant hypoglycemia detection by Muhammad Shafiq, J. Kavitha, Dhruva R. Rinku, N. K. Senthil Kumar, Kamal Poon, Amar Y. Jaffar, V. Saravanan

“…This work is now introducing a system, HAPI-BELT, empowered by dual intelligent sensors and Deep Learning (DL) algorithms for tracking and continuously detecting hypoglycemia in preterm newborns. …”
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A Signal Processing-Guided Deep Learning Framework for Wind Shear Prediction on Airport Runways by Afaq Khattak, Pak-wai Chan, Feng Chen, Hashem Alyami, Masoud Alajmi

“…To predict wind shear more effectively, the proposed framework integrates a signal processing technique with a deep learning strategy. It begins with optimized variational mode decomposition (OVMD), which decomposes the wind shear time series into intrinsic mode functions (IMFs), each capturing distinct temporal characteristics. …”
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