UnidecNMR: automatic peak detection for NMR spectra in 1-4 dimensions by Charles Buchanan, Gogulan Karunanithy, Olga Tkachenko, Michael Barber, Michael T. Marty, Timothy J. Nott, Christina Redfield, Andrew J. Baldwin

“…In many applications, particularly those involving biomolecules, this procedure is typically a manual and laborious process. While many algorithms are available to tackle this problem, their performance tends to be inferior to that of an experienced user. …”
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An efficient method for identifying surface damage in hydraulic concrete buildings by Libo Yang, Dawei Zhu, Xuemei Liu

“…However, ensemble learning algorithms are subject to time consuming issues when applied to high-dimensional datasets. …”
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Robotic Motion Intelligence Using Vector Symbolic Architectures and Blockchain-Based Smart Contracts by Daswin De Silva, Sudheera Withanage, Vidura Sumanasena, Lakshitha Gunasekara, Harsha Moraliyage, Nishan Mills, Milos Manic

“…However, these algorithms are subject to computationally complex processing and operational needs such as high data dependency. …”
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An Interactive Human-in-the-Loop Framework for Skeleton-Based Posture Recognition in Model Education by Jing Shen, Ling Chen, Xiaotong He, Chuanlin Zuo, Xiangjun Li, Lin Dong

“…The proposed framework contributes both algorithmic insights and a novel application design for posture-driven educational support systems.…”
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Identification of neural outgrowth genes using genome-wide RNAi. by Katharine J Sepp, Pengyu Hong, Sofia B Lizarraga, Judy S Liu, Luis A Mejia, Christopher A Walsh, Norbert Perrimon

“…While genetic screens have identified many genes essential for neurite outgrowth, they have been limited in their ability to identify neural genes that also have earlier critical roles in the gastrula, or neural genes for which maternally contributed RNA compensates for gene mutations in the zygote. …”
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Discovering emergent connections in quantum physics research via dynamic word embeddings by Felix Frohnert, Xuemei Gu, Mario Krenn, Evert van Nieuwenburg

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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

“…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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THE IMPROVEMENT OF PROFESSIONAL TRAINING ORGANIZATION OF THE X-RAY SCREENING SYSTEMS OPERATORS BY USING THE EYE MOVEMENTS REGISTRATION SYSTEM AND METHODS OF CLUSTER AND DISCRIMINAN... by A. K. Volkov, V. V. Ionov

“…The X-ray screening systems operators’ professional training is based on the CBT (computer-based training) principle, which has algorithms of adaptive training. These algorithms in existing computer simulators include feedback mechanisms on the basis of trainability exponents – such as the frequency of detecting dangerous objects, the frequency of false alarms and detection time. …”
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Utilizing UAVs in Wireless Networks: Advantages, Challenges, Objectives, and Solution Methods by Mohammad Javad Sobouti, Amirhossein Mohajerzadeh, Haitham Y. Adarbah, Zahra Rahimi, Hamed Ahmadi

“…Additionally, we discuss the main methods and algorithms employed for UAV placement and trajectory optimization. …”
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On the role of diagnosis of prostate diseases at the outpatient appointment of a therapist by A. L. Kebina, A. S. Sycheva, A. L. Vertkin, G. Yu. Knorring

“…Malignant diseases remain an important problem in medicine, while its solution depends on the effectiveness of detecting the early stages of cancer at the outpatient stage, including the therapeutic link in the provision of medical care. …”
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Internet of Things in healthcare: An adaptive ethical framework for IoT in digital health by Abubakar Wakili, Sara Bakkali

“…These IoT applications, while promising significant enhancements in patient care and health outcomes, simultaneously present a myriad of ethical dilemmas. …”
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Neural representation of consciously seen and unseen information by Pablo Rodríguez-San Esteban, Jose A. Gonzalez-Lopez, Ana B. Chica

“…Participants’ EEG signal was recorded while performing the task and was then analysed by using ML algorithms to decode distinctive task-related parameters. …”
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Low Complexity CS/CB Techniques for Aerial Assisted Cellular Network With Imperfect CSI by Sheila N. Mugala, Jonathan Serugunda, Dorothy K. Okello

“…The beamforming algorithms are zero forcing beamforming (ZFBF), block diagonalization using singular value decomposition (BD SVD) and block diagonalization using Householder transformation (BD HH). …”
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Reproduction of reservoir pressure in oil field development: prospects and problems of using methods machine learning by I. N. Ponomarevа, D. A. Martyushev

“…Evaluation of the accuracy and reliability of the developed models is usually reduced to defining standard statistical criteria, while the developers do not always use a separate examination sample. …”
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Data-driven modeling of the Yld2000 yield criterion and its efficient application in numerical simulation by Xiaomin Zhang, Jianzhong Mao, Zhi Cheng

“…In contrast, other algorithmic models exhibited certain deficiencies in either convergence behavior or computational efficiency.…”
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A Novel Double Machine Learning Strategy for Producing High‐Precision Multi‐Source Merging Precipitation Estimates Over the Tibetan Plateau by Yi Lyu, Bin Yong

“…Traditional statistic methods are often insufficient to characterize the nonlinear relationship between different precipitation information, while machine learning techniques, particularly deep learning algorithms, offer a novel and powerful approach to improve the merging accuracy of multi‐source precipitation data by efficiently capturing their spatiotemporal dynamics features. …”
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IoT-edge anomaly detection for covariate shifted and point time series health data by Partha Pratim Ray, Dinesh Dash

“…We implement the ”IRKernel” to investigate the efficiency of exponentially weighted moving average (EWMA)-based light-weight statistical learning by using three variants e.g. probabilistic (P), shift-detect (SD), and two-stage shift-detect (TS-SD) over the EWMA algorithms. We perform evaluation of execution time of the algorithms in classical, incremental, and optimized modes while following the Kolmogorov–Smirnov hypothesis testing at the two-stage anomaly detection. …”
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A Review of Reinforcement Learning for Fixed-Wing Aircraft Control Tasks by David J. Richter, Ricardo A. Calix, Kyungbaek Kim

“…A lot of that can be attributed to the recent advancements in machine learning (ML) and deep learning (DL) as a whole, the power of deep neural networks and the incorporation of them into reinforcement learning algorithms and techniques. This marriage of deep neural networks and traditional reinforcement learning methods has enabled reinforcement learning algorithms to achieve incredible results in use-cases that were previously deemed to be too complex. …”
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Remaining Useful Life Estimation through Deep Learning Partial Differential Equation Models: A Framework for Degradation Dynamics Interpretation Using Latent Variables by Sergio Cofre-Martel, Enrique Lopez Droguett, Mohammad Modarres

“…For the past decade, researchers have explored the application of deep learning (DL) regression algorithms to predict the system’s health state behavior based on sensor readings from the monitoring system. …”
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Efficient Resource Allocation for Blockchain-Enabled Mobile Edge Computing: A Joint Optimization Approach by Moein Valitabar, Mohammad Fathi, Keivan Navaie

“…Performance evaluation results demonstrate the effectiveness of these algorithms, achieving significant reductions in total energy consumption while maximizing the efficiency of communication and computational resources. …”
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