IMPLEMENTASI METODE RANDOM FOREST DALAM MEMPREDIKSI SINYAL PERGERAKAN SAHAM by MOCH. ANJAS APRIHARTHA, M. HUSNIYADI, TAUFIK NUR ALAM

“…Random forest is a combination algorithm of several decision trees used to solve prediction or classification problems. …”
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Characterization of the Collagen Extraction Manufacturing Process Using Markov Chains and Artificial Neural Networks by Rosa Trasvina-Osorio, Sergio Alonso-Romero, Juan de Anda-Suarez, Valentin Calzada-Ledesma, Javier Yanez-Mendiola, Luis Fernando Villanueva-Jimenez, Erick Rojas-Mancera

“…In the second stage, the predictive model MCMC-ANN presented a mean square error (MSE) of 0.00171, and a standard deviation (SD) of 0.00133. …”
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Optimal design of frame structures equipped with viscous dampers using machine learning techniques by Yi Wen, Jianze Wang, Jun Xu, Kaoshan Dai, Yuanfeng Shi, Reza Sharbati

“…The built models achieved an average R2 of 0.955 for the testing dataset, indicating a high level of predictive accuracy. Then, with the target of minimizing seismic responses of the structural system, the developed DP-based optimization algorithm effectively identifies the optimal damper arrangement for the considered three structure cases. …”
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Delta-radiomics Entropy Based on Tumor Heterogeneity Concept – Response Predictor to Irradiation for Unresectable/recurrent Glioblastoma by Camil Ciprian MIRESTEAN, Roxana Irina IANCU, Dragos Teodor IANCU

“…Radiomics, a new subdomain of artificial intelligence (AI), relies on advanced analysis in high-resolution medical imaging to establish diagnostic, prognostic and predictive models for clinical medicine. The variation of the delta-radiomics parameters analyzed within a tumor volume may be via tumor heterogeneity indirectly correlated with the response to treatment. …”
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Embedding With Preservation of Semantics of the Original Data by M. E. Vatkin, D. A. Vorobey, M. V. Yakovlev, M. G. Krivova

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Interperiod coherent integration of the received signal with a variable repetition period of the probing signal by S. R. Heister, P. G. Semashko

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Assessing Similarity Between Datasets Using Vector Representations by А. А. Usatoff, A. M. Nedzved, Guo Jiran

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Using Machine Learning for Recognition of Alzheimer’s Disease Based on Transcription Information by U. A. Vishniakou, Chu Yue Yu

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Gas adsorption meets geometric deep learning: points, set and match by Antonios P. Sarikas, Konstantinos Gkagkas, George E. Froudakis

“…Recently, machine learning (ML) pipelines have been established as the go-to method for large scale screening by means of predictive models. These are typically built in a descriptor-based manner, meaning that the structure must be first coarse-grained into a 1D fingerprint before it is fed to the ML algorithm. …”
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Magnetic resonance imaging of cruciate ligament disorders: current updates by Tanda Yang, Yexin Li, Longtao Yang, Qian Liu

“…AI plays a crucial role in automating radiological estimations and enabling data-driven predictions of future events. Despite the transformative impact of advanced MRI techniques on the analytical and diagnostic algorithms related to cruciate ligament disorders, future efforts are warranted to address challenges such as economic burdens and ethical considerations.…”
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Multimodal Deep Learning for Android Malware Classification by James Arrowsmith, Teo Susnjak, Julian Jang-Jaccard

“…We synthesise these modalities by combining predictions from convolutional and graph neural networks with a multilayer perceptron. …”
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Performances Comparison for a Rotating Shaft Suspended by 4-Axis Radial Active Magnetic Bearings via 𝜇-Synthesis, Loop-Shaping Design, and Sub(𝐻)∞with Uncertainties by G. Barbaraci, G. Virzi' Mariotti

“…On contrary PID controller does not depend on angular speed and it can be easily implemented according some simple rules. Predictive control is another interesting controller characterized by a multiple controller operating in different condition in order to get the minimum of cost function, but also in this case the angular speed is introduce for the same reason discussed before.…”
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Realtime Precise Orbit Determination Technology for LEO Mega-Constellation by Zhen LI, Chuang SHI

“…Supported by GNSS, it analyzed the advantages and disadvantages of solving LEO satellite orbit on the ground or onboard the satellites.The high-fidelity non-conservative force models were applied to the real-time orbit determination algorithm.To separate the kinematic and dynamic orbit determination process had been a great advantage for LEO satellites with much frequent orbital maneuvers.The test results showed that the accuracy of accelerations computed from the radiation pressure and atmospheric drag non-conservation force models were around 5 nm/s².Use the broadcast ephemeris, the real-time orbit determination algorithm could get an accuracy of 0.8 m in the satellites′ positions and 1.1 mm/s accuracy in the satellites′ velocity.When the real-time precise ephemeris of GNSS satellites were provided, the algorithm could get an accuracy of 8 cm in the LEO satellites′ position and 0.1 mm/s in the velocity.The method for real-time orbit determination and orbit prediction in this paper could be used to support the construction of navigation system with the mega-constellations.…”
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A recurrent sigma pi sigma neural network by Fei Deng, Shibin Liang, Kaiguo Qian, Jing Yu, Xuanxuan Li

“…The RSPSNN is successfully applied to the function approximation problem, prediction problem, parity problem, classification problem, and image simulation, which verifies its effectiveness and practicability.…”
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Dynamic VaR Measurement of Gold Market with SV-T-MN Model by Fenglan Li, Jie Wang, Liyun Su, Bao Yang

“…Secondly, future sample volatility prediction was realized by using approximate filtering algorithm. …”
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Artificial Neural Network Model for Monitoring Oil Film Regime in Spur Gear Based on Acoustic Emission Data by Yasir Hassan Ali, Roslan Abd Rahman, Raja Ishak Raja Hamzah

“…The methods used in this paper shows accurate predictions from ANN and the feed-forward network performance is superior to the Elman neural network.…”
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Cotton T-Shirt Size Estimation Using Convolutional Neural Network by John King D. Alfonso, Ckyle Joshua G. Casumpang, Jocelyn F. Villaverde

“…The estimation accuracy was 70%. Inaccurate predictions were attributed to the precision of body measurements from computer vision and image quality, which needs to be solved in further studies.…”
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Review of the Research on Underwater Explosion Ice-Breaking Technology by Xiao Huang, Zi-Xian Zhong, Xiao Luo, Yuan-Dong Wang

“…Future advancements require multiscale fluid–structure interaction models integrating brine migration effects, enhanced experimental diagnostics for transient processes, and optimized numerical algorithms to enable reliable predictions for engineering applications.…”
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MicroRNA-dependent mechanisms of taxane resistance in breast cancer by V. S. Apollonova, E. I. Sidina, E. V. Tkachenko, A. V. Malek

“…However, currently used algorithms of taxan-based regimens application do not always provide with desire effect. …”
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A comprehensive review on smart manufacturing using machine learning applicable to fused deposition modeling by Swapnil Deokar, Narendra Kumar, Ravi Pratap Singh

“…This study focuses on the use of ML algorithms for predicting part quality of AM parts, such as tensile strength, wear strength, detection of defect, geometric accuracy and minimizing the material waste.…”
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