New Strategies Based on Hierarchical Matrices for Matrix Polynomial Evaluation in Exascale Computing Era by Luisa Carracciuolo, Valeria Mele

“…Advancements in computing platform deployment have acted as both push and pull elements for the advancement of engineering design and scientific knowledge. Historically, improvements in computing platforms were mostly dependent on simultaneous developments in hardware, software, architecture, and algorithms (a process known as co-design), which raised the performance of computational models. …”
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Multimodal Deep Learning for Android Malware Classification by James Arrowsmith, Teo Susnjak, Julian Jang-Jaccard

“…DenseNet-GIN achieves 90.6% accuracy, with no further improvement obtained by expanding this ensemble to four models. …”
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Energy Optimisation of Industrial Limestone Grinding Using ANN by Dagmara Kołodziej, Patryk Bałazy, Paweł Knap, Krzysztof Lalik, Damian Krawczykowski

“…This paper presents methods for modelling and optimising the industrial limestone grinding process carried out using a real limestone plant. …”
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Image Synthesis in Nuclear Medicine Imaging with Deep Learning: A Review by Thanh Dat Le, Nchumpeni Chonpemo Shitiri, Sung-Hoon Jung, Seong-Young Kwon, Changho Lee

“…The implementation of deep learning models facilitates the combination of NMI with various imaging modalities, thereby broadening the clinical applications of nuclear medicine. …”
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Population-level predictive variation in machine learning diagnosis of symptomatic bacterial vaginosis by Diandra P. Ojo, Cameron Celeste, Dion Ming, Ruogu Fang, Ivana K. Parker

“…To determine the ability of ML models to perform equitably, this study evaluates the performance of ML algorithms in predicting symptomatic BV across different ethnic groups using 16S rRNA sequencing data. …”
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Advances and challenges in neoantigen prediction for cancer immunotherapy by Yi Zhang, Ting-Ting Chen, Xiong Li, Ai-Lin Lan, Peng-Fei Ji, Ya-Juan Zhu, Xue-Yao Ma

“…Future advancements will focus on dynamic tumor microenvironment monitoring, multi-omics integration, improved computational models and algorithms to refine neoantigen prediction, and developing optimized personalized vaccines.…”
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Understanding students’ sentiment from feedback with a new feature selection and semantics networks by Tran Anh Tuan, Dao Thi Thanh Loan, Nichnan Kittiphattanabawon

“… Sentiment analysis of students’ feedback using machine learning algorithms has emerged as a valuable tool for understanding students’ sentiments and improving educational outcomes. …”
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Deep Learning Frontiers in 3D Object Detection: A Comprehensive Review for Autonomous Driving by Ambati Pravallika, Mohammad Farukh Hashmi, Aditya Gupta

“…This work investigates the most recent 3D object detection methods for self-driving cars, emphasizing the importance of advanced deep learning models and multi-sensor fusion methods. In addition, we identify crucial topics for further investigation, such as enhancing sensor fusion algorithms, increasing computational efficiency, and addressing ethical, security, and privacy concerns. …”
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Advances in machine learning for the detection and characterization of microplastics in the environment by M. Maksuda Khanam, M. Khabir Uddin, Julhash U. Kazi

“…Extending these capabilities further, hyperspectral imaging combines spatial and spectral data to generate comprehensive chemical maps, enabling the simultaneous assessment of polymer composition and distribution. Integrating ML algorithms into these various approaches has improved sensitivity, speed, and scalability, thereby addressing critical challenges in high-throughput and real-time monitoring. …”
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Machine-Learning-Based Biomechanical Feature Analysis for Orthopedic Patient Classification with Disc Hernia and Spondylolisthesis by Daniel Nasef, Demarcus Nasef, Viola Sawiris, Peter Girgis, Milan Toma

“…The limitations of AI use in clinical settings are discussed, and areas for future improvement to create more accurate and informative models are suggested.…”
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SIMULATION TRAINING IN MEDICAL STUDENT PREPARATION by Oleg V. Zinchenko, Alla A. Muravyeva, Oksana V. Vladimirova

“…This article outlines new methods of three-stage preparation of students using simulation technics and involving the students in the role of a simulator for modeling different clinical situation cases. The developed method of preparation and improvement of professional skills of medical students allows to study algorithms of first aid in critical cases more deeply, to learn emergency medical manipulations, to be able to evaluate the results of performed manipulations. …”
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Crop yield prediction using machine learning: An extensive and systematic literature review by Sarowar Morshed Shawon, Falguny Barua Ema, Asura Khanom Mahi, Fahima Lokman Niha, H.T. Zubair

“…Also, the most applied machine learning algorithms are Linear Regression (LR), Random Forest (RF), and Gradient Boosting Trees (GBT) whereas the most applied deep learning algorithms are Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM). …”
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Physics-informed deep generative learning for quantitative assessment of the retina by Emmeline E. Brown, Andrew A. Guy, Natalie A. Holroyd, Paul W. Sweeney, Lucie Gourmet, Hannah Coleman, Claire Walsh, Athina E. Markaki, Rebecca Shipley, Ranjan Rajendram, Simon Walker-Samuel

“…We present here a novel algorithmic approach that generates highly realistic digital models of human retinal blood vessels, based on established biophysical principles, including fully-connected arterial and venous trees with a single inlet and outlet. …”
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The Heart of Transformation: Exploring Artificial Intelligence in Cardiovascular Disease by Mohammed A. Chowdhury, Rodrigue Rizk, Conroy Chiu, Jing J. Zhang, Jamie L. Scholl, Taylor J. Bosch, Arun Singh, Lee A. Baugh, Jeffrey S. McGough, KC Santosh, William C.W. Chen

“…We analyze and discuss commonly used AI and ML models, algorithms, and methodologies, highlighting their roles in improving clinical outcomes while addressing current limitations and future clinical applications. …”
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A Cautionary Perspective on Artificial Intelligence and Novel Imaging Technologies in Patient Selection for Retrograde Intrarenal Surgery by Samir Muter, Noorulhuda Al-Ani

“…AI-driven predictive models often rely on retrospective, uniform datasets taken from a single center, with evident interpretability concerns. …”
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A shape-controlled non-symmetric quaternary refinement scheme by Pakeeza Ashraf, Samra Younus, Amna Kalsoom, Jihad Younas

“…Abstract The interpolating and approximating refinement schemes are well-studied algorithms to generate smooth curves and surfaces. In this paper, we propose a novel non-symmetric refinement scheme that combines the strengths of interpolating and approximating refinement algorithms. …”
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QoS Routing in Telecommunications Networks by N. I. Listopad, O. A. Lavshuk

“…With the transition to new generation networks, the issues of improving routing algorithms and protocols seem to be especially relevant. …”
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Mapping of the susceptibility of China‒Russia crude oil pipelines to water damage in permafrost regions in Northeast China by Wen-Hui Wang, Hui-Jun Jin, Xiao-Ying Jin, Zi-Kang Ming, Xin-Yu Li, Yan Li, Xiao-Ying Li, Tao Zhan, Guang-Yin Xue, Fu-Qiang Che, Gang-Yi Zhou, Wei Wang, Valetin V. Spektor, Nikita Tananaev, Moisei Zakharov, David-R. Şerban, Hong-Wei Wang, Ze Zhang, Leonid Gagarin, Guo-Yu Li

“…Future research should focus on continuous monitoring of pipeline foundation soil safety, improving numerical models for pipeline river crossing evaluations and refining water damage risks assessment through deep learning-based models.…”
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Maintenance techniques to increase solar energy production: A review by Fernando Martinez-Gil, Christopher Sansom, Aránzazu Fernández-García, Alfredo Alcayde-García, Francisco Manzano-Agugliaro

“…Predictive models for solar energy generation and soiling detection, including artificial intelligence (AI) and machine learning (ML) algorithms and Internet of Things (IoT), are discussed as means for optimizing energy production and reducing maintenance costs. …”
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Generative and predictive neural networks for the design of functional RNA molecules by Aidan T. Riley, James M. Robson, Aiganysh Ulanova, Alexander A. Green

“…We pair these predictive models with generative adversarial RNA design networks (GARDN), allowing the generative modelling of a diverse range of functional RNA molecules with targeted experimental attributes. …”
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