Investigation and application of data balancing and combined discriminant model in rock burst severity prediction by Shaohong Yan, Runze Liu, Yanbo Zhang, Xulong Yao, Yueqi Yang, Qi Wang, Bin Guo, Shuai Wang

“…To accurately predict rock burst disasters and mitigate or eliminate related threats, this paper proposes a composite prediction model that integrates Density-Based Nonlinear Resampling (DBNR)-Tomek Link data balancing algorithms with Bayesian Optimization (BO)-Multilayer Perceptron (MLP)-Random Forest (RF). …”
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Link Prediction Model for Weighted Networks Based on Evidence Theory and the Influence of Common Neighbours by Miaomiao Liu, Yang Wang, Jing Chen, Yongsheng Zhang

“…Experiments are performed on 9 real and 40 simulation-weighted datasets, and these findings are compared with several classic algorithms. Results show that the proposed method has higher precision than other methods, which can achieve good performance in link prediction in weighted networks.…”
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Development of data-driven machine learning models and their potential role in predicting dengue outbreak by Bushra Mazhar, Nazish Mazhar Ali, Farkhanda Manzoor, Muhammad Kamran Khan, Muhammad Nasir, Muhammad Ramzan

“…This artificial intelligence model uses real world data such as dengue surveillance, climatic variables, and epidemiological data and combines big data with machine learning algorithms to forecast dengue. Monitoring and predicting dengue incidences has been significantly enhanced through innovative approaches. …”
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Wheat yield prediction of Rajasthan using climatic and satellite data and machine learning techniques by KAVITA JHAJHARIA

“…The solar induced chlorophyll fluorescence is more sensitive to photosynthesis than any other vegetation indices, so it is crucial to uncover its potential for accurately predicting wheat yields. In the present study, we implemented three machine learning algorithms, support vector regression, Random Forest and XGBoost, one linear regression method, Least Absolute Shrinkage and Selection Operator regression, and one deep learning method, long short-term memory, to predict the wheat yield prediction from 2008 to 2019 using satellite data (SIF) and vegetation indices. …”
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TradeWise: Towards Context-Aware Stock Market Predictions with Sentiment and Political Insights by Andreas Marpaung, David Masterson

“…Our novel approach suggests that sentiment and political insights, when processed and integrated effectively, offer substantial predictive value that could refine the accuracy of financial prediction models. …”
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Research on multimodal social media information popularity prediction based on large language model by WANG Jie, WANG Zitong, PENG Yan, HAO Bowen

“…To address the limitations of strong feature dependency, insufficient generalization, and inadequate performance in few-shot/cold-start settings in existing multimodal social media popularity prediction algorithms, a MultiSmpLLM model based on large language model with instruction fine-tuning and human alignment was proposed. …”
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Machine learning applications for chloride ingress prediction in concrete: insights from recent literature by Quynh-Chau Truong, Anh-Thu Nguyen Vu

“…Various algorithms, such as Artificial Neural Networks (ANNs), Gene Expression Programming (GEP), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM) and Ensemble Learning, have shown potential in estimating corrosion processes, predicting material properties, and evaluating structural durability. …”
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Establishment of interpretable cytotoxicity prediction models using machine learning analysis of transcriptome features by You Wu, Ke Tang, Chunzheng Wang, Hao Song, Fanfan Zhou, Ying Guo

“…In this study, by integrating cellular transcriptome and cell viability data using four machine learning algorithms (support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM)) and two ensemble algorithms (voting and stacking), highly accurate prediction models of 50% and 80% cell viability were developed with area under the receiver operating characteristic curve (AUROC) of 0.90 and 0.84, respectively; these models also showed good performance when utilized for diverse cell lines. …”
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Carbon Quota Allocation Prediction for Power Grids Using PSO-Optimized Neural Networks by Yixin Xu, Yanli Sun, Yina Teng, Shanglai Liu, Shiyu Ji, Zhen Zou, Yang Yu

“…Results indicate that the PSO algorithm mitigates local optimization constraints of the standard BP algorithm; the prediction error of carbon emissions by the combined model is significantly smaller than that of the single model, while its identification accuracy reaches 99.46%. …”
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Position Weight Matrix, Gibbs Sampler, and the Associated Significance Tests in Motif Characterization and Prediction by Xuhua Xia

“…Here I review PWM-based methods used in motif characterization and prediction (including a detailed illustration of the Gibbs sampler for de novo motif discovery), present statistical and probabilistic rationales behind statistical significance tests relevant to PWM, and illustrate their application with real data. …”
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Mounting Angle Prediction for Automotive Radar Using Complex-Valued Convolutional Neural Network by Sunghoon Moon, Younglok Kim

“…The predicted offsets can then be used for physical radar alignment or integrated into compensation algorithms to enhance data interpretation accuracy in ADAS applications. …”
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Reversible data hiding in encrypted image based on bit-plane compression of prediction error by Youqing WU, Wenjing MA, Zhaoxia YIN, Yinyin PENG, Xinpeng ZHANG

“…To further improve the performance of reversible data hiding in encrypted image, an algorithm for lossless compression of the prediction error bit-plane using joint encoding was proposed, which could make full use of image redundancy and reserve more embedding room.Firstly, the image owner calculated the prediction error of the image and divided the prediction error bit-plane into non-overlapping blocks of the same size.Then, the prediction error bit-plane was rearranged according to blocks and the rearranged bitstream was compressed by run-length encoding and Huffman encoding to reserve room.The data hider embedded information in the reserved room of the encrypted image.At the receiving end, the legitimate receiver extracted information and recovered images losslessly and separately.Experimental results show that the proposed algorithm makes full use of the bit-plane distribution characteristics and achieves higher embedding performance.The average embedding rates in BOSSbase and BOWS-2 datasets reach 3.763 bpp and 3.642 bpp, which are at least 0.081 bpp and 0.058 bpp higher than the state-of-the-art algorithms.…”
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Bearing remaining useful life prediction based on optimized VMD and BiLSTM-CBAM. by Wei Liu, Sen Liu

“…To address the issue of low accuracy in existing remaining useful life (RUL) prediction algorithms for rolling bearings, this paper proposes a novel RUL prediction method based on the Beluga Whale Optimization (BWO) algorithm, Variational Mode Decomposition (VMD), an improved Convolutional Block Attention Module (CBAM*), and a Bidirectional Long Short-Term Memory (BiLSTM) network. …”
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EvoWeaver: large-scale prediction of gene functional associations from coevolutionary signals by Aidan H. Lakshman, Erik S. Wright

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A supervised machine learning approach with feature selection for sex-specific biomarker prediction by Luke Meyer, Danielle Mulder, Joshua Wallace

“…Machine learning (ML) has emerged as an effective tool for identifying novel biomarkers and enhancing predictive modelling. However, sex-based bias in ML algorithms remains a concern. …”
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Development and Validation of an AI-Based Risk Prediction Model for Osteoporosis in Post-Menopausal Women by Juhi Deshpande, Chanchal Kumar Singh

“…The aim is to develop and validate an AI-based predictive model for osteoporosis in postmenopausal women. …”
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A Review on Fall Prediction and Prevention System for Personal Devices: Evaluation and Experimental Results by Masoud Hemmatpour, Renato Ferrero, Bartolomeo Montrucchio, Maurizio Rebaudengo

“…Kinematic features obtained from the data collected from accelerometer and gyroscope have been evaluated in combination with different machine learning algorithms. An experimental analysis compares the evaluated approaches by evaluating their accuracy and ability to predict and prevent a fall. …”
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Prediction of Spontaneous Protein Deamidation from Sequence-Derived Secondary Structure and Intrinsic Disorder. by J Ramiro Lorenzo, Leonardo G Alonso, Ignacio E Sánchez

“…Compared to previous algorithms, NGOME does not require three-dimensional structures yet yields better predictions than available sequence-only methods. …”
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Visual Classification of Music Style Transfer Based on PSO-BP Rating Prediction Model by Tianjiao Li

“…At the same time, we take advantage of the BP neural network’s ability to handle complex nonlinear problems and construct a rating prediction model between the user and item attribute features, referred to as the PSO-BP rating prediction model, by combining the features of global optimization of particle swarm optimization algorithm, and make further improvements based on the traditional collaborative filtering algorithm.…”
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A probabilistic gap based condition prediction approach for desulfurization slurry circulating pump by Jiaxing Zhu, Buyun Sheng, Junlan Hu, Yanfei Li, Ruiping Luo, Yue Shi

“…The Desulfurization Slurry Circulation (DSC) Pump is crucial for controlling gas emissions in limestone-gypsum wet flue gas desulfurization (WFGD) power plants. However, predicting the condition of DSC pumps is hindered by a lack of samples, obstructing field development. …”
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