Mitochondria-related genes as prognostic signature of endometrial cancer and the effect of MACC1 on tumor cells. by Xuefen Lin, Jianfeng Zheng, Yanhong Li, Linying Liu, Qinying Liu, Jie Lin, Yang Sun

“…The predictive performance, immune features, and anti-tumor response of the mitochondrial signature were evaluated through multiple algorithms. …”
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Detection of child depression using machine learning methods. by Umme Marzia Haque, Enamul Kabir, Rasheda Khanam

“…Although model performance varied somewhat, RF outperformed all other algorithms in predicting depressed classes by 99% with 95% accuracy rate and 99% precision rate in 315 milliseconds (ms).…”
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Energy-Efficient Dual-Iteration Power Allocation for Two-Phase Relay System with Massive Antennas by Long Zhao, Wei Xiang, Jie Mei, Hui Zhao, Hang Long, Lin Li

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Unmasking social bots: how confident are we? by James Giroux, Gangani Ariyarathne, Alexander C. Nwala, Cristiano Fanelli

“…Specifically, our approach facilitates targeted interventions for bots when predictions are made with high confidence and suggests caution (e.g., gathering more data) when predictions are uncertain.…”
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Monitoring Pine Wilt Disease Using High-Resolution Satellite Remote Sensing at the Single-Tree Scale with Integrated Self-Attention by Wenhao Lv, Junhao Zhao, Jixia Huang

“…This study introduces several advanced self-attention algorithms into the task of satellite-based monitoring of pine wilt disease to enhance detection performance. …”
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A Monocyte-Driven Prognostic Model for Multiple Myeloma: Multi-Omics and Machine Learning Insights by Xie L, Gao M, Tan S, Zhou Y, Liu J, Wang L, Li X

“…Subsequently, based on 482 prognostic moDEGs, we developed and validated an optimal model, termed the Monocyte-related Gene Prognostic Signature (MGPS), by integrating 101 predictive models generated from 10 machine learning algorithms across multiple transcriptome sequencing datasets. …”
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Preliminary exploratory study on differential diagnosis between benign and malignant peripheral lung tumors: based on deep learning networks by Yuan Wang, Yuan Wang, Yutong Zhang, Yongxin Li, Tianyu She, Meiqing He, Hailing He, Dong Zhang, Dong Zhang, Jue Jiang

“…DeLong’s test was employed to compare the differences between the groups.ResultsAmong the five models, the one based on the ResNet18 algorithm demonstrated the highest performance. It exhibited statistically significant advantages in predictive accuracy (p < 0.05) compared to the models based on ResNet152, ResNet101, ResNet50, and ResNet34 algorithms. …”
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Best practices for developing microbiome-based disease diagnostic classifiers through machine learning by Peikun Li, Min Li, Wei-Hua Chen

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A comprehensive machine learning for high throughput Tuberculosis sequence analysis, functional annotation, and visualization by Md. Saddam Hossain, Md. Parvez Khandocar, Farzana Akter Riti, Md. Yeakub Ali, Prithbey Raj Dey, S M Jahurul Haque, Amira Metouekel, Atrsaw Asrat Mengistie, Mohammed Bourhia, Farid Khallouki, Khalid S. Almaary

“…Such algorithms provided prediction accuracies of 0.963, 0.739, 0.773, 0.866, and 0.866 sequentially. …”
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Energy Efficient Doppler Assisted Channel Estimation for Highly Mobile OFDM Systems by Mateen Ashraf, Adnan Shahid, Woon-Young Yeo, Kyung-Geun Lee

“…To reduce the complexity of the algorithm, the existing channel estimation algorithms take advantage of the correlations between the channel coefficients. …”
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Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network by Aythem Khairi Kareem, Mohammed M. AL-Ani, Ahmed Adil Nafea

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Dual Passive-Aggressive Stacking k-Nearest Neighbors for Class-Incremental Multi-Label Stream Classification by Hann Hsen Tan, Chu Kiong Loo, Chaw Seng Woo

“…MetakNN includes label-specific fitness for stored instances and a fitness rejuvenating mechanism based on relative performance to the base predictions for enhanced synergy. Our experiment compares DPAkNN against state-of-the-art kNN-based MLSC algorithms on 30 multi-label benchmark datasets. …”
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Experimental and numerical investigations on the bidirectional thermal contact performance by Chen Wang, Mingjun Qiu, Huijing Liu, Jun Hong, Feiyu Gu, Lifei Chen, Tao Wang, Hao Guan, Qiyin Lin

“…Subsequently, bidirectional thermal contact performance consisting of TCR ratio and thermal rectification coefficient was analyzed under varying temperatures and pressures. Additionally, a prediction model for TCR was developed using the Levenberg-Marquardt (L-M) algorithm. …”
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AI-Driven Particulate Matter Estimation Using Urban CCTV: A Comparative Analysis Under Various Experimental Conditions by Woochul Choi, Hongki Sung, Kyusoo Chong

“…All four experiments that excluded the nighttime, added the solar incidence angle variable, applied the distribution of PM per class, and removed the outlier removal algorithm showed high predictive power. In particular, the final experiment that satisfied all conditions, including the exclusion of nighttime, addition of solar incidence angle variable, and application of outlier removal algorithm, derived predictive values that are expected to be commercialized.…”
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Trends, outcomes and knowledge gaps in mobile apps for reproductive endocrinology and infertility: a scoping review protocol by Alba Regina de Abreu Lima, Emerson Roberto dos Santos, Aline Russomano de Gouvêa, Natália Almeida de Arnaldo Silva Rodriguez Castro, João Daniel de Souza Menezes, Matheus Querino da Silva, Helena Landin Gonçalves Cristóvão, Cíntia Canato Martins, Jéssica Gisleine de Oliveira, Patrícia da Silva Fucuta, Alexandre Lins Werneck, Gerardo Maria de Araújo Filho, Heloisa Cristina Caldas, Vânia Maria Sabadoto Brienze, Júlio César André, Antônio Hélio Oliani

“…Despite promising advancements such as the development of apps with sophisticated algorithms for ovulation prediction and comprehensive platforms offering integrated fertility education and emotional support, there remain gaps in the literature regarding the comprehensive evaluation of mobile apps for reproductive endocrinology and infertility. …”
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Machine Learning to Assess Relatedness: The Advantage of Using Firm-Level Data by Giambattista Albora, Andrea Zaccaria

“…We also discuss the effect of using parameter optimization and community detection algorithms to identify clusters of related companies and products, finding that a partition into a higher number of blocks decreases the computational time while maintaining a prediction performance well above the network-based benchmarks.…”
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Machine Learning-Assisted Hartree–Fock Approach for Energy Level Calculations in the Neutral Ytterbium Atom by Kaichen Ma, Chen Yang, Junyao Zhang, Yunfei Li, Gang Jiang, Junjie Chai

“…Data-driven machine learning approaches with precise predictive capabilities are proposed to address the long-standing challenges in the calculation of complex many-electron atomic systems, including high computational costs and limited accuracy. …”
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Investigating the contributory factors influencing speeding behavior among long-haul truck drivers traveling across India: Insights from binary logit and machine learning technique... by Balamurugan Shandhana Rashmi, Sankaran Marisamynathan

“…The analysis results showed that RF demonstrated superior performance in predicting speeding behavior over other competing algorithms with accuracy (0.80), F1 score (0.77), and AUROC (0.81). …”
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Determination of Colles fracture risk index by X-ray images with the computer vision application by Wisam A. Hussein, Hussain J. AlKhatteib, Jawad K. Abbas

“…Background. Modelling a predictive risk index for Colles fractures using X-ray image analysis is a crucial application in orthopaedics since these fractures have essential health and economic burdens, particularly among the elderly. …”
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Blasting Vibration Control Using an Improved Artificial Neural Network in the Ashele Copper Mine by Shida Xu, Tianxiao Chen, Jiaqi Liu, Chenrui Zhang, Zhiyang Chen

“…In this paper, a genetic algorithm (GA) and an artificial neural network (ANN) algorithm were jointly used to construct a neural network model with a 4-5-1 topology to predict the PPV. …”
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