A Subgradient Extragradient Framework Incorporating a Relaxation and Dual Inertial Technique for Variational Inequalities by Habib ur Rehman, Kanokwan Sitthithakerngkiet, Thidaporn Seangwattana

“…This paper presents an enhanced algorithm designed to solve variational inequality problems that involve a pseudomonotone and Lipschitz continuous operator in real Hilbert spaces. …”
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Artificial intelligence-driven mobile interpretation of a semi-quantitative cryptococcal antigen lateral flow assay by David Bermejo-Peláez, Ana Alastruey-Izquierdo, Narda Medina, Daniel Capellán-Martín, Oscar Bonilla, Miguel Luengo-Oroz, Juan Luis Rodríguez-Tudela

“…We developed an AI algorithm designed for the automated interpretation of CrAgSQ LFAs. …”
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Enhancing precision in multiple sclerosis lesion segmentation: A U-net based machine learning approach with data augmentation by Oezdemir Cetin, Berkay Canel, Gamze Dogali, Unal Sakoglu

“…This study proposes a robust machine learning algorithm designed to identify MS lesions using both single-modal and multi-modal MRI data. …”
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Modelling convective cell life cycles with a copula-based approach by C.-Y. Tseng, L.-P. Wang, L.-P. Wang, C. Onof

“…<p>This study proposes an algorithm designed to model convective cell life cycles, for the purpose of improving the representation of convective storms in rainfall modelling and forecasting. …”
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Development of Automated Image Processing for High-Throughput Screening of Potential Anti-Chikungunya Virus Compounds by Pathaphon Wiriwithya, Siwaporn Boonyasuppayakorn, Pattadon Sawetpiyakul, Duangpron Peypala, Gridsada Phanomchoeng

“…This study presents the development and validation of an automated image processing algorithm designed to improve the accuracy and speed of high-throughput screening for potential anti-chikungunya virus compounds. …”
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RETRACTED ARTICLE: Confidence level based complex polytopic fuzzy Einstein aggregation operators and their application to decision-making process by Khaista Rahman, Mohammad Khishe

“…With these operators, we create an algorithm designed to solve multiattribute decision-making problems in a Polytopic fuzzy environment. …”
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LoRa Resource Allocation Algorithm for Higher Data Rates by Hossein Keshmiri, Gazi M. E. Rahman, Khan A. Wahid

“…In this paper, we introduce the LoRa Resource Allocation (LRA) algorithm, designed to address these limitations by enabling parallel transmissions, thereby reducing the total transmission time (<i>T_tx</i>) and increasing the bit rate (BR). …”
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A novel method for developing post-quantum cryptoschemes and a practical signature algorithm by Nikolay Andreevich Moldovyan, Dmitriy Nikolaevich Moldovyan

“…The practical purpose is to propose a fundamentally new method for development of algebraic digital signature algorithms. Design/methodology/approach – The proposed method is distinguished by the use of two different finite commutative associative algebras as a single algebraic support of the digital signature scheme and setting two different verification equation for a single signature. …”
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Machine Learning Approach and Model for Predicting Proton Stopping Power Ratio and Other Parameters Using Computed Tomography Images by Charles Ekene Chika

“…Thirty-three ICRU human tissues were used as modeling data and 12 Gammex inserts as testing data for the machine learning algorithm designed. More ways of tissue classification were introduced to improve accuracy. …”
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Saber With Hybrid Striding Toom Cook-Based Multiplier: Implementation Using Open-Source Tool Flow and Industry Standard Chip Design Tools by Muhammad Naveed Abbasi, Abdul Rehman Aslam, Muhammad Awais Bin Altaf, Wala Saadeh

“…The Saber is a lattice-based post-quantum cryptographic or quantum-safe algorithm designed as a quantum computing attack-resistant protocol. …”
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Identifying Top-Performing Students via VKontakte Social Media Communities Using Advanced NLP Techniques by Sergei S. Gorshkov, Dmitry I. Ignatov, Anastasia Yu. Chernysheva, Vyacheslav L. Goiko, Vitaliy V. Kashpur

“…This paper introduces an algorithm designed to identify students with exceptional academic performance by analyzing their subscriptions to communities on the social network VKontakte. …”
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Cardiovascular Disease Detection through Innovative Imbalanced Learning and AUC Optimization by Karthikeyan Palanisamy, Krishnaveni Krishnasamy, Praba Venkadasamy

“…Furthermore, we have incorporated a tailored Differential Evolution (DE) algorithm designed to navigate the complex hyperparameter space with finesse. …”
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Cost-Effective Autonomous Drone Navigation Using Reinforcement Learning: Simulation and Real-World Validation by Tomasz Czarnecki, Marek Stawowy, Adam Kadłubowski

“…Reinforcement learning machine algorithms designed to solve problems requiring optimization of the solution for the highest possible reward were used. …”
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NOMA-Based Rate Optimization for Multi-UAV-Assisted D2D Communication Networks by Guowei Wu, Guifen Chen, Xinglong Gu

“…Simulation results show the following: (1) the proposed algorithm can achieve higher system throughput than several other benchmark algorithms with different numbers of D2D clusters, different D2D cluster communication spacing, and different UAV sizes; (2) the proposed algorithm designs UAV trajectory optimization with a 27% improvement in system throughput compared to the 2D trajectory; and (3) in the NOMA scenario, compared to the case of no decoding order constraints, the system throughput shows on average a 34% improvement.…”
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A Personalised Optimising Level Adaptation (OLA) Difficulty Algorithm for Scenario Simulations in Professional VR Simulators by Marcin Wolański Robert, Karol Jędrasiak

“…Aim: This study introduces the Optimising Level Adaptation (OLA) algorithm, designed to enhance scenario simulations for professional VR training by dynamically adjusting difficulty levels to match user performance, thereby supporting personalised learning and readiness for high-stakes situations such as firefighting and emergency response. …”
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MORKO: A Multi-objective Runge–Kutta Optimizer for Multi-domain Optimization Problems by Kanak Kalita, Pradeep Jangir, Sundaram B. Pandya, Ahmed Ibrahim Alzahrani, Fahad Alblehai, Laith Abualigah, Absalom E. Ezugwu

“…Abstract In the current landscape, there is a rapid increase in the creation of new algorithms designed for specialized problem scenarios. The performance of these algorithms in unfamiliar or practical settings often remains untested. …”
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How do electronic risk assessment tools affect the communication and understanding of diagnostic uncertainty in the primary care consultation? A systematic review and thematic synt... by Sarah Gerard Dean, Joshua Feyi-Waboso, Mark Tarrant, Raff Calitri, Elizabeth Shephard, Alex Burns, Brian Donnelly

“…Objectives To conduct a systematic review and synthesise qualitative research of electronic risk assessment tools (eRATs) in primary care, examining how they affect the communication and understanding of diagnostic risk and uncertainty. eRATs are computer-based algorithms designed to help clinicians avoid missing important diagnoses, pick up possible symptoms early and facilitate shared decision-making.Design Systematic search, using predefined criteria of the published literature and synthesis of the qualitative data, using Thematic Synthesis. …”
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LiDAR point cloud denoising for individual tree extraction based on the Noise4Denoise by Xiangfei Lu, Zongyu Ye, Liyong Fu, Huaiyi Wang, Kaiyu Wang, Yaquan Dou, Dongbo Xie, Xiaodi Zhao

“…The processing of LiDAR point cloud data is of critical importance in the context of forest resource surveys, as well as representing a pivotal element in the realm of forest physiological and ecological studies.Nonetheless, conventional denoising algorithms frequently exhibit deficiencies with regard to adaptability and denoising efficacy, particularly when employed in relation to disparate datasets.To address these issues, this study introduces DEN4, an unsupervised, deep learning-based point cloud denoising algorithm designed to improve the accuracy of single tree segmentation in LiDAR point clouds.DEN4 introduces a multilevel noise separation module that effectively distinguishes between signal and noise, thereby improving the signal-to-noise ratio (SNR) and reducing the error.The experimental results demonstrate that DEN4 significantly outperforms traditional denoising methods in several key metrics, including mean square error (MSE), SNR, Hausdorff distance, and structural similarity index (SSIM).In the 60 sample dataset, DEN4 achieved the best mean and standard deviation on all metrics: Specifically, the MSE mean was found to be 0.0094, with a standard deviation of 0.0008, the SNR mean was 149.1570, with a standard deviation of 0.5628, the Hausdorff mean was 0.8503, with a standard deviation of 0.0947, and the SSIM mean was 0.8399, with a standard deviation of 0.0054. …”
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Algorithm of Behaviour of Virtual Avatars During Public Speaking Training in a VR Environment by Karol Jędrasiak, Robert Robert Wolański, Damian Bereska

“…Project and methods: The project involved developing an innovative algorithm designed to control the behaviour of virtual avatars during public speaking training in a VR environment. …”
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A deep learning algorithm to generate synthetic computed tomography images for brain treatments from 0.35 T magnetic resonance imaging by Luca Vellini, Flaviovincenzo Quaranta, Sebastiano Menna, Elisa Pilloni, Francesco Catucci, Jacopo Lenkowicz, Claudio Votta, Michele Aquilano, Andrea D’Aviero, Martina Iezzi, Francesco Preziosi, Alessia Re, Althea Boschetti, Danila Piccari, Antonio Piras, Carmela Di Dio, Alessandro Bombini, Gian Carlo Mattiucci, Davide Cusumano

“…This study introduces a Deep Learning (DL) algorithm designed to quickly generate synthetic CT (sCT) images from low-field MR images in the brain, an area not yet explored. …”
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