Algorithm for Non-Contact Monitoring of the Performance of Electronic Equipment Elements by A. V. Grinkevich, А. А. Denis, Т. М. Marchuk

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Information Encryption Algorithm Using a Discrete Quantum Map by A. V. Sidorenko, E. A. Vysotskaya

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Variational Autoencoders-Based Algorithm for Multi-Criteria Recommendation Systems by Salam Fraihat, Qusai Shambour, Mohammed Azmi Al-Betar, Sharif Naser Makhadmeh

“…The VAE-MCRS model utilizes the latent features generated by the VAE in conjunction with user–item interactions to enhance recommendation accuracy and predict ratings for unrated items. Experiments carried out using the Yahoo! …”
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Algorithm of Classroom Teaching Quality Evaluation Based on Markov Chain by Tongqing Yuan

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Synthesis of algorithm of unmanned aerial vehicle detection by acoustic noise by A. D. Puzanau, D. S. Nefedov

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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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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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Exploring unsupervised feature extraction algorithms: tackling high dimensionality in small datasets by Hongqi Niu, Gabrielle B. McCallum, Anne B. Chang, Khalid Khan, Sami Azam

“…These datasets often contain high-dimensional features, yet present significant challenges of dimensionality, wherein the sparsity of data in high-dimensional spaces makes it difficult to extract meaningful information and less accurate predictive models are produced. In this regard, feature extraction algorithms are important in addressing these challenges by reducing dimensionality while retaining essential information. …”
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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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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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A “Salt and Pepper” Noise Reduction Scheme for Digital Images Based on Support Vector Machines Classification and Regression by Hilario Gómez-Moreno, Pedro Gil-Jiménez, Sergio Lafuente-Arroyo, Roberto López-Sastre, Saturnino Maldonado-Bascón

“…A modified median filter based on a previous noise detection stage and a regression-based filter are presented and compared to other well-known state-of-the-art noise reduction algorithms. The results show that the filters proposed achieved good results, outperforming other state-of-the-art algorithms for low and medium noise ratios, and were comparable for very highly corrupted images.…”
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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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