An optimal weighting-based hybrid classifier for Children's congenital heart diseases signal processing by Morteza Ebrahimpour, Mehdi Khashei

“…The main objective of the proposed optimal weighting-based CNN-LSTM-SVM (OCLS) hybrid classifier is to simultaneously leverage the unique advantages of CNN in feature extraction from input signals, LSTM in modeling the sequential patterns of signals, SVM in classifying regular patterns, and especially the proposed weighting algorithm to optimally integrate the outputs of these components. …”
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Research on GNNs with stable learning by Wenbin Li, Wenxuan Wei, Peiyang Wang, Li Pan, Bo Yang, Yanling Xu

“…Abstract Traditional Graph Neural Network (GNN) learning patterns can only achieve optimal performance under the assumption of independent and identically distributed data. …”
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PICNIC accurately predicts condensate-forming proteins regardless of their structural disorder across organisms by Anna Hadarovich, Hari Raj Singh, Soumyadeep Ghosh, Maxim Scheremetjew, Nadia Rostam, Anthony A. Hyman, Agnes Toth-Petroczy

“…However, accurate identification of their components remains challenging and biased towards proteins with high structural disorder content with focus on self-phase separating (driver) proteins. Here, we present a machine learning algorithm, PICNIC (Proteins Involved in CoNdensates In Cells) to classify proteins that localize to biomolecular condensates regardless of their role in condensate formation. …”
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Development characteristics and intelligent identification method of natural fractures: A case study of the Upper Triassic Xujiahe Formation in the western Sichuan Depression, Sich... by LI Wei, WANG Min, XIAO Dianshi, JIN Hui, SHAO Haoming, CUI Junfeng, JIA Yidong, ZHANG Zeyuan, LI Ming

“…The conventional logging data with fracture and non-fracture labels were normalized, and machine learning algorithms were applied for fracture intelligent prediction. …”
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Competition for popularity and interventions on a Chinese microblogging site. by Hao Cui, János Kertész

“…We show how the circadian rhythm affects the popularity of hashtags, and observe categories of their rank trajectories by a machine learning clustering algorithm. By analyzing patterns of ranking dynamics using various measures, we identify anomalies that are likely to result from the platform provider's intervention into the ranking, including the anchoring of hashtags to certain ranks on the HSL. …”
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Early Warning for Stepwise Landslides Based on Traffic Light System: A Case Study in China by Shuangshuang Wu, Zhigang Tao, Li Zhang, Song Chen

“…Furthermore, leveraging the C5.0 machine learning algorithm, a comparison between the predictive capabilities of the TLS model and a pure rate threshold model reveals that the TLS model achieves a 93% accuracy rate, outperforming the latter by 7 percentage points. …”
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Integrated Time Series Analysis, Clustering, and Forecasting for Energy Efficiency Optimization and Tariff Management by Anderson Jhones Passos Nascimento, Menaouar Berrehil El Kattel, Jose Antonio Fernandes de Macedo, Fernando Luiz Marcelo Antunes

“…The research employs time series techniques and clustering algorithms to identify atypical consumption profiles and regular patterns based on average monthly consumption and contracted power demand. …”
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Developing a Sleep Algxorithm to Support a Digital Medicine System: Noninterventional, Observational Sleep Study by Jeffrey M Cochran

“… Abstract BackgroundSleep-wake patterns are important behavioral biomarkers for patients with serious mental illness (SMI), providing insight into their well-being. …”
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Toward Cyborg: Exploring Long-Term Clinical Outcomes of a Multi-Degree-of-Freedom Myoelectric Prosthetic Hand by Yuki Kuroda, Yusuke Yamanoi, Hai Jiang, Yoshiko Yabuki, Yuki Inoue, Dianchun Bai, Yinlai Jiang, Jinying Zhu, Hiroshi Yokoi

“…Progress in this field has been directed toward employing machine-learning algorithms to process electromyogram patterns, enabling a broad spectrum of hand movements. …”
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Effect of Hyperparameter Tuning on Performance on Classification model by Muhammad Sholeh, Uning Lestari, Dina Andayati

“…This research aims to analyze the effect of hyperparameter tuning on the performance of Logistic Regression, K-Nearest Neighbours, Support Vector Machine, Decision Tree, Random Forest, Random Forest Classifier, Naive Bayes algorithms.  …”
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Temporal Backtracking and Multistep Delay of Traffic Speed Series Prediction by Licheng Qu, Minghao Zhang, Zhaolu Li, Wei Li

“…The prediction results of 10 consecutive delay steps suggest that the accuracies of RNNs are far superior to those of other models because of the more powerful and accurate pattern representing ability in time series. It is also proved that RNNs can learn and mine longer time dependencies.…”
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Speculation resource provisioning in high-performance computing by Leena Sri, Balaji Narayanan

“…As an attempt to quantify resource allocation we use two level adaptive prediction mechanism to check the computational patterns of past resource allocation to the future requirement. …”
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EBOC: Ensemble-Based Ordinal Classification in Transportation by Pelin Yıldırım, Ulaş K. Birant, Derya Birant

“…Learning the latent patterns of historical data in an efficient way to model the behaviour of a system is a major need for making right decisions. …”
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The Relationship Between Surface Meteorological Variables and Air Pollutants in Simulated Temperature Increase Scenarios in a Medium-Sized Industrial City by Ronan Adler Tavella, Daniele Feijó das Neves, Gustavo de Oliveira Silveira, Gabriella Mello Gomes Vieira de Azevedo, Rodrigo de Lima Brum, Alicia da Silva Bonifácio, Ricardo Arend Machado, Letícia Willrich Brum, Romina Buffarini, Diana Francisca Adamatti, Flavio Manoel Rodrigues da Silva Júnior

“…This study utilized five years of daily meteorological data (from 1 January 2019 to 31 December 2023) to model atmospheric conditions and two years of daily air pollutant data (from 21 December 2021 to 20 December 2023) to simulate how pollutant levels would respond to annual temperature increases of 1 °C and 2 °C, employing a Support Vector Machine, a supervised machine learning algorithm. …”
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Apnea detection using wrist actigraphy in patients with heterogeneous sleep disorders by Xiaoman Xing, Sizhi Ai, Jihui Zhang, Rui Huang, Yaping Liu, Dongming Quan, Jiacheng Ma, Guoli Wu, Jiangen Xu, Yuan Zhang, Hongliang Feng, Wen-fei Dong

“…The main challenge in OSA pattern recognition is handling various disturbances in real-world applications. …”
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Enhanced Alzheimer’s Disease Prediction Through Integration of Protein-Protein Interaction Data and Meta-Learning by Hansa J. Thattil, M. N. Arunkumar, Francis Antony

“…We generated embeddings for Protein-Protein Interactions(PPI) using the node2vec algorithm, capturing interaction patterns in a low-dimensional space. …”
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A Fault Diagnosis Model for Rotating Machinery Using VWC and MSFLA-SVM Based on Vibration Signal Analysis by Lei You, Wenjie Fan, Zongwen Li, Ying Liang, Miao Fang, Jin Wang

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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

“…In each back-test experiment, the proposed learning system is compared to sixteen traditional strategies and ten strategies based on machine learning algorithms in the performance of profitability and risk control ability. …”
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Hierarchical Sensing Framework for Polymer Degradation Monitoring: A Physics-Constrained Reinforcement Learning Framework for Programmable Material Discovery by Xiaoyu Hu, Xiuyuan Zhao, Wenhe Liu

“…The design of materials with programmable degradation profiles presents a fundamental challenge in pattern recognition across molecular space, requiring the identification of complex structure–property relationships within an exponentially large chemical domain. …”
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A Vision-Based Method for Detecting the Position of Stacked Goods in Automated Storage and Retrieval Systems by Chuanjun Chen, Junjie Liu, Haonan Yin, Biqing Huang

“…This paper proposes a novel machine vision-based detection algorithm that integrates a pallet surface object detection network (STEGNet) with a box edge detection algorithm. …”
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