Self-Supervised Social Recommendation Algorithm Fusing Residual Networks by WANG Yujie, YANG Zhe

“…Firstly, the algorithm employs a variational hypergraph auto-encoder for link prediction in social networks to obtain a reconstructed social graph, which is used to mine the positive link relationships hidden among users. …”
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Explainable AI based LightGBM prediction model to predict default borrower in social lending platform by Li-Hua Li, Alok Kumar Sharma, Sheng-Tzong Cheng

“…This paper proposes an explainable AI (XAI)-based prediction model utilizing the LightGBM algorithm to predict the likelihood of borrower default on a social lending platform. …”
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Reversible Data Hiding in Encrypted Images Based on Edge-Directed Prediction and Multi-MSB Self-Prediction by Yingqiang Qiu

“…A binary location map marks the prediction errors for each bit-plane. After being losslessly compressed using the Joint Bi-level Image Experts Group (JBIG) algorithm, the location maps are embedded into the encrypted image as auxiliary data, thereby creating ample room for embedding additional data. …”
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Optimizing Apache Spark MLlib: Predictive Performance of Large-Scale Models for Big Data Analytics by Leonidas Theodorakopoulos, Aristeidis Karras, George A. Krimpas

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CLASSYFYING STUDENTS DECISION MAKING ABILITY USING K-NEAREST NEIGHBOR FOR DETERMINING STUDENTS SUPPLEMENTARY LEARNING by Giyanti Giyanti, Rina Oktaviyanthi, Usep Sholahudin

Subjects: “…classification algorithm…”
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AI-based algorithms for estimating hydrochar properties in terms of biomass ultimate analysis by Raouf Hassan, Ali sharifzadegan, Alireza Baghban

“…The CatBoost model outperformed other algorithms for HHV prediction, yielding an R² of 0.9788, an MSE of 1.63, and an MRD of 3.71 %. …”
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Optimization model for enterprise financial management utilizing genetic algorithms and fuzzy logic by Sujuan Wang, Musadaq Mansoor

“…The genetic algorithm’s search and optimization process identifies parameter combinations that maximize neural network performance, further improving predictive capabilities. …”
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Assessment of Information predictability of stochastic processes by A. V. Ausiannikau

“…The necessary theoretical information for parameter estimation algorithms informational predictability of stochastic processes. …”
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An Online Tool for Correcting Performance Measures of Electronic Phenotyping Algorithms for Verification Bias by Ajay Bhasin, Sue Bielinski, Abel N. Kho, Nicholas Larson, Laura J. Rasmussen-Torvik

“… Objectives Computable or electronic phenotypes of patient conditions are becoming more commonplace in quality improvement and clinical research. During phenotyping algorithm validation, standard classification performance measures (i.e., sensitivity, specificity, positive predictive value, negative predictive value, and accuracy) are often employed. …”
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Decoding survival in MASLD: the dominant role of metabolic factors by Zhiqiang Jin, Cheng Zeng, Yang Yang, Shan Zhong, Zhi Zhou

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Predicting dementia in people with Parkinson’s disease by Mohamed Aborageh, Tom Hähnel, Patricia Martins Conde, Jochen Klucken, Holger Fröhlich

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Patient with joint pain at an outpatient appointment by A. L. Vertkin, M. M. Shamuilova, Yu. V. Sedyakina, G. Yu. Knorring, A. V. Nosova

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Farmer Ants Optimization Algorithm: A Novel Metaheuristic for Solving Discrete Optimization Problems by Ali Asghari, Mahdi Zeinalabedinmalekmian, Hossein Azgomi, Mahmoud Alimoradi, Shirin Ghaziantafrishi

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Review of pedestrian trajectory prediction methods by Linhui LI, Bin ZHOU, Weiwei REN, Jing LIAN

“…With the breakthrough of deep learning technology and the proposal of large data sets, the accuracy of pedestrian trajectory prediction has become one of the research hotspots in the field of artificial intelligence.The technical classification and research status of pedestrian trajectory prediction were mainly reviewed.According to the different modeling methods, the existing methods were divided into shallow learning and deep learning based trajectory prediction algorithms, the advantages and disadvantages of representative algorithms in each type of method were analyzed and introduced.Then, the current mainstream public data sets were summarized, and the performance of mainstream trajectory prediction methods based on the data sets was compared.Finally, the challenges faced by the trajectory prediction technology and the development direction of future work were prospected.…”
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Acoustic impedance inversion via voting stacked regression (VStaR) algorithms by Lutfi Mulyadi Surachman, Sanlin I. Kaka, Abdullatif Al-Shuhail

“…To refine the AI estimation, we used stacking and voting regression algorithms, with depth, two-way travel time (TWTT), and nine seismic attributes as inputs. …”
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Dimensionality cutback and deep learning algorithms efficacy as to the breast cancer diagnostic dataset by Gennady Chuiko, Denys Honcharov

“…The results indicate that the dimensionality of the Wisconsin Breast Cancer dataset, which is increasingly becoming the "gold standard" for diagnosing Malignant-Benign tumors, can be significantly reduced without losing predictive power. The Deep Learning algorithms in WEKA deliver excellent performance for both supervised and unsupervised learning, regardless of whether dealing with full or reduced datasets.…”
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Adaptive Management of Multi-Scenario Projects in Cybersecurity: Models and Algorithms for Decision-Making by Vadim Tynchenko, Alexander Lomazov, Vadim Lomazov, Dmitry Evsyukov, Vladimir Nelyub, Aleksei Borodulin, Andrei Gantimurov, Ivan Malashin

“…Zadeh’s theory of linguistic variables, the methodology integrates expert analysis, linguistic variables, and a continuous genetic algorithm to predict membership function parameters. …”
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Modern Tanscriptome Data Rrocessing Algorithms: a Review of Methods and Results of Approbation by M. V. Sprindzuk, L. V. Mozharovskaya, A. P. Konchits, L. P. Titov

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Modern Tanscriptome Data Rrocessing Algorithms: a Review of Methods and Results of Approbation by M. V. Sprindzuk, L. V. Mozharovskaya, A. P. Konchits, L. P. Titov

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Research on Fractional-Order Control of Anchor Drilling Machine Optimized by Intelligent Algorithms by Jingkai Li, Jun Zhang, Jiaquan Xie, Wei Shi, Jianzhong Zhao

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