Advancements in Hyperspectral Imaging and Computer-Aided Diagnostic Methods for the Enhanced Detection and Diagnosis of Head and Neck Cancer by I-Chen Wu, Yen-Chun Chen, Riya Karmakar, Arvind Mukundan, Gahiga Gabriel, Chih-Chiang Wang, Hsiang-Chen Wang

“…<b>Results:</b> The meta-analysis findings indicate that LDA surpasses other algorithms, achieving an accuracy of 92%, sensitivity of 91%, and specificity of 93%. …”
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Comparative evaluation of machine learning models versus TIMI score in ST-segment-elevation myocardial infarction patients by Mohit D. Gupta, Dixit Goyal, Shekhar Kunal, Manu Kumar Shetty, M.P. Girish, Vishal Batra, Ankit Bansal, Prashant Mishra, Mansavi Shukla, Vanshika Kohli, Akul Chadha, Arisha Fatima, Subrat Muduli, Anubha Gupta, Jamal Yusuf

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Combined impact of semantic segmentation and quantitative structure modelling of Southern pine trees using terrestrial laser scanning by Jinyi Xia, Timothy A. Martin, Gary F. Peter, Kody M. Brock, Jeff W. Atkins, Matthew A. Gitzendanner, Inacio Bueno, Kim Calders, Ana Paula Dalla Corte, Andrew T. Hudak, Monique Bohora Schlickmann, Michael G. Andreu, Caio Hamamura, Carine Klauberg, Carlos A. Silva

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A novel multi-agent dynamic portfolio optimization learning system based on hierarchical deep reinforcement learning by Ruoyu Sun, Yue Xi, Angelos Stefanidis, Zhengyong Jiang, Jionglong Su

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Masked Feature Modeling for Generative Self-Supervised Representation Learning of High-Resolution Remote Sensing Images by Shiyan Pang, Hanchun Hu, Zhiqi Zuo, Jia Chen, Xiangyun Hu

“…Extensive experiments involving main properties of the MFM for generative self-supervised learning, fine-tuning the MFM on the downstream semantic segmentation task, and comparisons with the other state-of-the-art generative self-supervised learning algorithms show that, through the combined advantages of the CNN and Transformer architectures, the proposed method has better feature extraction capability and higher accuracy on downstream tasks such as semantic segmentation.…”
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USE OF ARTIFICIAL INTELLIGENCE TO IDENTIFY AND CORRECT MISCONCEPTIONS ABOUT RADIATION by Oleksandr Tymoshchuk

“…AI tools, including natural language processing models for text analysis and machine learning algorithms for misconceptions classification, were used to provide personalised feedback and targeted corrective information. …”
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Optimal Coverage Path Planning for UAV-Assisted Multiple USVs: Map Modeling and Solutions by Shaohua Pan, Xiaosu Xu, Yi Cao, Liang Zhang

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Genomic Patterns are Associated with Different Sequelae of Patients with Long‐Term COVID‐19 by Nan Zhang, Xizi Luo, Xiangwen Ji, Tian Tian, Runze Wu, Shishun Zhao, Guoqing Wang

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DEVELOPMENT OF METHODOLOGY FOR DESIGNING TESTABLE COMPONENT STRUCTURE OF DISCIPLINARY COMPETENCE by Vladimir I. Freyman

“…The research findings can help promoting learning efficiency increase, a choice of adequate control devices, accuracy of assessment, and also efficient use of personnel, temporal and material resources of higher education institutions. Proposed algorithms, methods and approaches to procedure of control results organization and realization of developed competences and its components can be used as methodical base while designing the computerassisted system for educational process management and quality supervision of graduates’ competences. …”
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Integrative analysis of RNA-Seq data and machine learning approaches to identify Biomarkers for Rhizoctonia solani resistance in sugar beet by Bahman Panahi, Mahdi Hassani, Nahid Hosseinzaeh Gharajeh

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A Novel Method for Describing Texture of Scar Collagen Using Second Harmonic Generation Images by Guannan Chen, Gaoqiang Liu, Xiaoqin Zhu, Mingyu Liu, Encai Zhang, Jichun Li, Kun Zhang, Lihang Lin

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Investigating Internet of Things and Artificial Intelligence-Based Approaches to Smart Irrigation System Optimisation for Agricultural Water Resource Management by Balassem Zaid Ajzan, Alkhafaij Mahdi Abdulkhudur, Raju Nadimpalli Venkata Ganapathi

“…This system leverages IoT sensors to monitor environmental variables and uses AI algorithms to analyze this data and make real-time irrigation decisions. …”
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Systematic Security Analysis of Sensors and Controls in PV Inverters: Threat Validation and Countermeasures by Fengchen Yang, Kaikai Pan, Chen Yan, Xiaoyu Ji, Wenyuan Xu

“…These vulnerabilities can lead to incorrect sensor readings, disrupting control algorithms. We propose an IEMI attack that results in three potential outcomes: Denial of Service (DoS), physical damage to the inverter, and power output reduction. …”
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Toward reliable fluorescence imaging: Optical prior-guided probabilistic reconstruction for structured illumination microscopy by Kun Lin, Junkang Dai, Huaian Chen, Yi Jin

“…Furthermore, by leveraging the strong cognition ability of GRL to precisely learn the hierarchical representations of the SIM imaging process, PG-SIM itself also achieves a significant uncertainty reduction compared to current methods, overcoming the constraints of unreliability on the practical application of SIM. …”
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Hybrid neural network method for damage localization in structural health monitoring by Fatahlla Moreh, Yusuf Hasan, Zarghaam Haider Rizvi, Sven Tomforde, Frank Wuttke

“…Many existing methods for crack detection rely on deep learning algorithms or traditional approaches that typically use image data. …”
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Optimization Design Method of a 6-DOF Micromanipulation Mechanism for Extreme Ultraviolet Projection Lithography Objective Lens by Shiyu Li, Junbo Liu, Ji Zhou, Haifeng Sun, Chuan Jin, Xia Kang, Song Hu

“…The results demonstrate a 15.1% reduction in maximum driving force, significantly enhancing outrigger driving smoothness.…”
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Evaluation and optimization of carbon emission for federal edge intelligence network by Peng ZHANG, Yong XIAO, Jiwei HU, Liang LIAO, Jianxin LYU, Zegang BAI

“…In recent years, the continuous evolution of communication technology has led to a significant increase in energy consumption.With the widespread application and deep deployment of artificial intelligence (AI) technology and algorithms in telecommunication networks, the network architecture and technological evolution of network intelligent will pose even more severe challenges to the energy efficiency and emission reduction of future 6G.Federated edge intelligence (FEI), based on edge computing and distributed federated machine learning, has been widely acknowledged as one of the key pathway for implementing network native intelligence.However, evaluating and optimizing the comprehensive carbon emissions of federated edge intelligence networks remains a significant challenge.To address this issue, a framework and a method for assessing the carbon emissions of federated edge intelligence networks were proposed.Subsequently, three carbon emission optimization schemes for FEI networks were presented, including dynamic energy trading (DET), dynamic task allocation (DTA), and dynamic energy trading and task allocation (DETA).Finally, by utilizing a simulation network built on real hardware and employing real-world carbon intensity datasets, FEI networks lifecycle carbon emission experiments were conducted.The experimental results demonstrate that all three optimization schemes significantly reduce the carbon emissions of FEI networks under different scenarios and constraints.This provides a basis for the sustainable development of next-generation intelligent communication networks and the realization of low-carbon 6G networks.…”
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Hybrid Electric Propulsion Design and Analysis Based on Regional Aircraft Mission by Wenjuan Shan, Shengze Bao, Shixuan Lin, Le Kang

“…Hybrid propulsion systems have become a focal point of low-carbon aviation research due to their advantages in energy savings, emissions reduction, and noise abatement. This study develops an integrated design methodology for hybrid propulsion systems for aircraft, incorporating multidisciplinary algorithms to establish an overall performance model. …”
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Performance of 2D Compressive Sensing on Wide-Beam Through-the-Wall Imaging by Edison Cristofani, Mathias Becquaert, Marijke Vandewal

“…Hardware simplicity, data, and measurement time reduction and simplified imagery are some of its most attractive strengths. …”
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Solving differential‐algebraic equations in power system dynamic analysis with quantum computing by Huynh T. T. Tran, Hieu T. Nguyen, Long T. Vu, Samuel T. Ojetola

“…This paper demonstrates the use of quantum computing algorithms to solve DAEs for power system dynamic analysis. …”
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