Explainable Boosting Machines Identify Key Metabolomic Biomarkers in Rheumatoid Arthritis by Fatma Hilal Yagin, Cemil Colak, Abdulmohsen Algarni, Ali Algarni, Fahaid Al-Hashem, Luca Paolo Ardigò

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Image Super Resolution Reconstruction Based on Multi-Level Residual Skip Connection Network by WANG Xiaoyu, LU Jianyu, WEI Yuxin, YU Yue

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Electric Bus Charging Schedules Relying on Real Data-Driven Targets Based on Hierarchical Deep Reinforcement Learning by Jiaju Qi, Lei Lei, Thorsteinn Jonsson, Lajos Hanzo

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Emerging generalization advantage of quantum-inspired machine learning in the diagnosis of hepatocellular carcinoma by Domenico Pomarico, Alfonso Monaco, Nicola Amoroso, Loredana Bellantuono, Antonio Lacalamita, Marianna La Rocca, Tommaso Maggipinto, Ester Pantaleo, Sabina Tangaro, Sebastiano Stramaglia, Roberto Bellotti

“…By using previously characterized genetic communities, we minimize the computational complexity associated with the number of qubits, enabling the execution of quantum-inspired algorithms on classical machines. …”
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Transient Attracting Profiles in the Great Pacific Garbage Patch by L. Kunz, A. Griesel, C. Eden, R. Duran, R. Duran, B. Sainte-Rose

“…TRAPs are the attracting form of hyperbolic Objective Eulerian Coherent Structures and are computable from the instantaneous strain field on the ocean surface. …”
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Quick eVALUATION OF THE INTERVAL STABILITY OF THE LINEAR ASSIGNMENT problem SOLUTIONS by M. P. Revotjuk, M. K. Qaraleh, P. M. Batura

“…An efficient algorithm for evaluating the interval of solutions stability of opened and closed linear assignment problems based on a review of the results of optimization iterations is proposed. …”
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Among Artificial Intelligence/Machine Learning Methods, Automated Gradient-Boosting Models Accurately Score Intraoral Plaque in Non-Standardized Images by Eric Coy, William Santo, Bonnie Jue, Helen Betts, Francisco Ramos-Gomez, Stuart A. Gansky

“…Average and dominant hue, saturation, and brightness values were features for training plaque-scoring algorithms.Results Best performing models were: Support Vector Machine-Gaussian for image selection, 5-CV AUC-ROC of 0.99 and 0.76s of training time; Gradient-Boosting classification and regression models for individual teeth (5-CV AUC-ROC of 0.99 with 105s training); and mean plaque-scoring algorithms (5-CV R2 of 0.72 with 1415s training).Conclusions Accurate automated plaque-scoring is attainable without the high computational and financial costs of deep learning (DL) models. …”
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Area-Time-Efficient Secure Comb Scalar Multiplication Architecture Based on Recoding by Zhantao Zhang, Weijiang Wang, Jingqi Zhang, Xiang He, Mingzhi Ma, Shiwei Ren, Hua Dang

“…A low-area multi-functional architecture for comb is proposed, which takes into account different stages of the comb algorithm. Based on this, the data dependency is considered and the comb architecture is optimized to achieve a uniform and efficient execution pattern. …”
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Faster quantum subroutine for matrix chain multiplication via Chebyshev approximation by Xinying Li, Pei-Lin Zheng, Chengkang Pan, Fei Wang, Chunfeng Cui, Xian Lu

“…The algorithm can be applied to any complex matrix. Furthermore, we discuss integrating our QMM algorithm as a subroutine in other matrix operations and propose strategies to optimize QMM for matrices with large condition numbers with numerical simulation.…”
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Real time obstacle motion prediction using neural network based extended Kalman filter for robot path planning by Najva Hassan, Abdul Saleem P.K

“…In terms of computation time and robustness in closely spaced obstacles, simulation experiments demonstrated that the path planning using the proposed algorithm outperforms the hybrid A star, artificial potential field, and decision algorithms. …”
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A Novel Graph Reinforcement Learning-Based Approach for Dynamic Reconfiguration of Active Distribution Networks with Integrated Renewable Energy by Hua Zhan, Changxu Jiang, Zhen Lin

“…The dynamic reconfiguration of active distribution networks (ADNDR) essentially belongs to a complex high-dimensional mixed-integer nonlinear stochastic optimization problem. Traditional mathematical optimization algorithms tend to encounter issues like slow computational speed and difficulties in solving large-scale models, while heuristic algorithms are prone to fall into local optima. …”
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Online Discrete Anchor Graph Hashing for Mobile Person Re-Identification by Liang Xie, Xi Fang

“…In ODAGH, we propose an online discrete optimization algorithm to improve the efficiency of anchor graph learning in the online scenario. …”
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Detection of litchi fruit maturity states based on unmanned aerial vehicle remote sensing and improved YOLOv8 model by Changjiang Liang, Changjiang Liang, Dandan Liu, Dandan Liu, Weiyi Ge, Weiyi Ge, Wenzhong Huang, Wenzhong Huang, Yubin Lan, Yubin Lan, Yubin Lan, Yongbing Long, Yongbing Long, Yongbing Long, Yongbing Long

“…In addition, YOLOv8-FPDW was more competitive than mainstream object detection algorithms. The study predicted the optimal harvest period for litchis, providing scientific support for orchard batch harvesting and fine management.…”
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Premier Dynamic Bandwidth Management and Tensile Wavelength Selection Ensuring QoS for NG-EPONs by Purushottam Singh, Pushpendra Kumar, Harshita Patel, Kanojia Sindhuben Babulal, Gayathri Ananthakrishnan

“…Comparative analysis reveals that TW-DBA outperforms existing algorithms like Flexible Wavelength (FW), First-Fit, and Water Filled, both in computational efficiency and resource allocation. …”
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Research on a UAV-View Object-Detection Method Based on YOLOv7-Tiny by Yuyang Miao, Xihan Wang, Ning Zhang, Kai Wang, Lianhe Shao, Quanli Gao

“…Experiments conducted on the VisDrone2019 dataset show that the improved algorithm achieves a mAP@0.5 of 38.2%, with a model size of 29.01 MB and a computational complexity of 16.2 G. …”
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Prediction of Solar Radiation Data for Garlic Production in Magelang Regency Using Long Short-Term Memory by Muhammad Safrul Safrudin, Imas Sukaesih Sitanggang, Hari Agung Adrianto, Syarifah Aini

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Time-adaptive phase estimation by Brennan de Neeve, Andrey V. Lebedev, Vlad Negnevitsky, Jonathan P. Home

“…Phase estimation is known to be a robust method for single-qubit gate calibration in quantum computers, while Bayesian estimation is widely used in devising optimal methods for learning in quantum systems. …”
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A Detailed Review for Predicting the Quantity of Sugar From Sugarcane Using Various Models by Kathirvel Narayanasamy, Ilayaraja Venkatachalam

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Leveraging Multilingual Transformer for Multiclass Sentiment Analysis in Code-Mixed Data of Low-Resource Languages by Muhammad Kashif Nazir, Cm Nadeem Faisal, Muhammad Asif Habib, Haseeb Ahmad

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Learning from Octopuses: Cutting-Edge Developments and Future Directions by Jinjie Duan, Yuning Lei, Jie Fang, Qi Qi, Zhiming Zhan, Yuxiang Wu

“…These studies mainly explore how humans can learn from the physiological characteristics of octopuses for sensor design, actuator development, processor architecture optimization, and intelligent optimization algorithms. …”
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