A data driven predictive viscosity model for the microemulsion phase by Akash Talapatra, Bahareh Nojabaei, Pooya Khodaparast

“…Various machine learning (ML) based regression algorithms are employed on our dataset to train and fit the model. …”
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An intelligent algorithm to fast and accurately detect chaotic correlation dimension by Mengyan Shen, Miaomiao Ma, Zhicheng Su, Xuejun Zhang

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A Robust Conformal Framework for IoT-Based Predictive Maintenance by Alberto Moccardi, Claudia Conte, Rajib Chandra Ghosh, Francesco Moscato

“…This study, set within the vast and varied research field of industrial Internet of Things (IoT) systems, proposes a methodology to address uncertainty quantification (UQ) issues in predictive maintenance (PdM) practices. At its core, this paper leverages the commercial modular aero-propulsion system simulation (CMAPSS) dataset to evaluate different artificial intelligence (AI) prognostic algorithms for remaining useful life (RUL) forecasting while supporting the estimation of a robust confidence interval (CI). …”
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Design of a Prediction Model to Predict Students’ Performance Using Educational Data Mining and Machine Learning by Jayasree R, Sheela Selvakumari

“…Initially, there was inadequate study of the various prediction techniques to select the ones that would best predict students’ success in educational environments. …”
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Predictive reward-prediction errors of climbing fiber inputs integrate modular reinforcement learning with supervised learning. by Huu Hoang, Shinichiro Tsutsumi, Masanori Matsuzaki, Masanobu Kano, Keisuke Toyama, Kazuo Kitamura, Mitsuo Kawato

“…In this study, we investigated the cerebellum's role in executing reinforcement learning algorithms, with a particular emphasis on essential reward-prediction errors. …”
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Predictive estimations of health systems resilience using machine learning by Alessandro Jatobá, Paula de Castro-Nunes, Paloma Palmieri, Omara Machado Araujo de Oliveira, Patricia Passos Simões, Valéria da Silva Fonseca, Paulo Victor Rodrigues de Carvalho

“…Various ML algorithms, including regression models and decision trees, were applied to uncover insights into the resilience of health systems over time. …”
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Rapid Decision Algorithm for Patient Triage during Ebola Outbreaks by Denis-Luc Ardiet, Justus Nsio, Gaston Komanda, Rebecca M. Coulborn, Emmanuel Grellety, Francesco Grandesso, Richard Kitenge, Dolla L. Ngwanga, Bibiche Matady, Guyguy Manangama, Mathias Mossoko, John K. Ngwama, Placide Mbala, Francisco Luquero, Klaudia Porten, Steve Ahuka-Mundeke

“…Analyses of retrospective data from patients suspected of having Ebola virus infection identified 13 strong predictors and time from disease onset as constituents of a prediction score for Ebola virus disease. We also noted 4 highly predictive variables that could distinguish patients at high risk for infection, independent of their scores. …”
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LRST: searching tree anti-collision algorithm with low-redundancy by Qiong HUANG, Jiang-tao LING, Min ZHANG, Xiao-long YANG

“…To resolve this problem, a searching tree anti-collision algorithm with low-redundancy on the basis of regressive-style dynamic searching tree algorithm was proposed. …”
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Development and Validation of Predictive Models for Non-Adherence to Antihypertensive Medication by Cristian Daniel Marineci, Andrei Valeanu, Cornel Chiriță, Simona Negreș, Claudiu Stoicescu, Valentin Chioncel

“…Five machine learning models were developed to predict non-adherence, defined by ARMS quartile-based thresholds. …”
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A new algorithm for surgical treatment of infected pancreatic necrosis by E. A. Korymasov, S. A. Ivanov, N. I. Anor'ev, R. R. Yakovlev

“…The developed devices for necrectomy allowed for a reduction in operation time by 45.6% (p = 0.04). Conclusions: The application of the developed treatment algorithm and instrumentation using minimally invasive technologies allowed for a reduction in the frequency of postoperative complications from 94.3% to 38.1% and mortality from 54.7% to 33,3% in patients with infected pancreatic necrosis.…”
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Improved Algorithm to Detect Clandestine Airstrips in Amazon RainForest by Gabriel R. Pardini, Paulo M. Tasinaffo, Elcio H. Shiguemori, Tahisa N. Kuck, Marcos R. O. A. Maximo, William R. Gyotoku

“…These results suggest that, despite a slight reduction in recall, the modifications significantly improved the original algorithm by minimizing its limitations. …”
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Smart diabetes management: remote monitoring and predictive health insights by K.S. Smelyakov, I.A. Lurin, K.V. Misiura, A.S. Chupryna, T.V. Tyzhnenko, O.D. Dolhanenko, V.M. Repikhov

“…The use of deep learning and neural network algorithms enhances the accuracy of these predictions by capturing complex data trends over time. …”
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Karatsuba Algorithm Revisited for 2D Convolution Computation Optimization by Qi Wang, Jianghan Zhu, Can He, Shihang Wang, Xingbo Wang, Yuan Ren, Terry Tao Ye

“…Our analysis and benchmarks have shown that for convolution operations of the same dimensions, the Karatsuba algorithm requires the same number of multiplications but fewer additions as compared with the Winograd algorithm. …”
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Novel Multimodal Fusion Algorithm for Non-Intrusive Anxiety Detection by Mahir Shadid, Mushfiqus Salehin Afnan, Rashed Mustafa, M. Jamshed Alam Patwary

“…The study utilizes six advanced machine learning algorithms (Gaussian Naive Bayes, XGB Classifier, K-Neighbors, SVM, Decision-Tree, and RandomForest) for data classification, pattern recognition, and predictive accuracy. …”
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Utility channel estimation algorithm with interference suppression in OFDM system by Xiaoyang LAI, Lin DUO

“…In OFDM system,the least square（LS）algorithm has the shortage of noise cancelling and interference suppressing.An utility channel estimation algorithm which took advantage of the time frequency characteristic in OFDM system was proposed.The new channel estimation algorithm realized noise cancelling and interference suppressing through time-frequency transform by three times.In view of time-frequency transform could be achieved by FFT/IFFT,the new channel estimation algorithm complexity was low.In the TU channel simulation,the new channel estimation algorithm has the high performance,which was 3 dB better than the LS algorithm.In the TU channel simulation with interference suppression,the new channel estimation algorithm has only 1.5 dB reduction in performance.…”
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Dynamic beam shut-off algorithm for LEO satellite constellation by Shuaijun LIU, Yuemei HU, Chunshi FAN, Teng LING, Lixiang LIU

“…Aiming at the problems of the relatively dense coverage of the satellites in the space segment of the low earth orbit (LEO) multibeam satellite constellation network,which resulted in relatively dense areas in some regions,causing severe inter-beam interference and unnecessary beam resource overhead,a beam shut-off algorithm was proposed with global coverage requirements.The dynamic beam shut-off (DBSO) optimization problem was formulated in LEO multibeam satellite constellation network.Then,the problem was proved to be NP-complete and a heuristic DBSO algorithm was proposed.Simulation scenario considers the Iridium constellation network scenario with total 3 168 beams,and it shows that the number of required activated beams is only 1 913 with 39.61% beam resource reduction.…”
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YOLO-MECD: Citrus Detection Algorithm Based on YOLOv11 by Yue Liao, Lerong Li, Huiqiang Xiao, Feijian Xu, Bochen Shan, Hua Yin

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Clinical Prediction Models Based on Traditional Methods and Machine Learning for Predicting First Stroke: Status and Prospects by ZHANG Zijiao, DING Shunjing, ZHAO Di, LIANG Jun, LEI Jianbo

“…In recent years, advancements in big data and artificial intelligence technologies have opened new avenues for stroke risk prediction. This article reviews the current research status of traditional methods and machine learning models in predicting first-ever stroke risk and outlines future development trends from three perspectives: First, emphasis should be placed on technological innovation by incorporating advanced algorithms such as deep learning and large models to further enhance the accuracy of predictive models. …”
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Initialization improvement and clustering quality evaluation of K-means algorithm by HE Xuansen, HE Fan, YU Hailan

“…In order to solve the problem of random initialization of K-means algorithm, an improved scheme was proposed. By standardizing the features of data and using principal component analysis (PCA), data dimensionality reduction was achieved. …”
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Improved Image Compression Scheme Using Hybrid Encoding Algorithm by Yusra Ahmed Salih, Aree Ali Mohammed, Loay Edwar George

“…In this work, an effective compression method for hybrid images is proposed based on the discrete wavelet transformation and hybrid encoding algorithm (Huffman and SPIHT). This paper's primary participation is to take advantage of the hybrid encoding technique to maintain the quality of the reconstructed image and the reduction of time complexity. …”
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