Application of collaborative innovation between the logical brain and the associative brain in oil and gas gathering and transportation systems by Jing GONG, Siheng SHEN, Daqian LIU, Qi KANG, Shangfei SONG, Haihao WU, Bohui SHI

“…ConclusionThe dual-brain synergy mechanism is based on the integration of mechanisms and data, which ensures model accuracy while enhancing prediction speed, computational efficiency, and fault diagnosis accuracy. …”
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Estimating Trends in Cardiovascular Disease Risk for the EXPOSE (Explaining Population Trends in Cardiovascular Risk: A Comparative Analysis of Health Transitions in South Africa a... by Shaun Scholes, Jennifer S Mindell, Mari Toomse-Smith, Annibale Cois, Kafui Adjaye-Gbewonyo

“…Laboratory- and non–laboratory-based World Health Organization (WHO) and Globorisk algorithms were used to calculate the predicted 10-year total (fatal and nonfatal) CVD risk. …”
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Ga-based MPC for satellite’s attitude and orbit control by Prasitthichai Naronglerdrit, Manukid Parnichkun

“…We propose a novel method that utilizes a nonlinear control strategy optimized by a Genetic Algorithm (GA) in conjunction with Model Predictive Control (MPC), focusing on managing both attitude and orbit simultaneously. …”
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Clinical utility of artificial intelligence–augmented endobronchial ultrasound elastography in lymph node staging for lung cancerCentral MessagePerspective by Yogita S. Patel, BSc, Anthony A. Gatti, PhD, Forough Farrokhyar, MPhil, PhD, Feng Xie, PhD, Waël C. Hanna, MDCM, MBA

“…NeuralSeg was able to predict 98 of 143 true negatives and 34 of 44 true positives, resulting in an overall accuracy of 70.59% (95% CI, 63.50-77.01), sensitivity of 43.04% (95% CI, 31.94-54.67), specificity of 90.74% (95% CI, 83.63-95.47), positive predictive value of 77.27% (95% CI, 64.13-86.60), negative predictive value of 68.53% (95% CI, 64.05-72.70), and area under the curve of 0.820 (95% CI, 0.758-0.883). …”
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Differential Evolution for Optimizing Model Parameters in Simulation of Direct Dimethyl Carbonate Synthesis by Outi Ruusunen, Riitta Keiski, Mika Ruusunen

“…When validated with data of a real-world batch process, the model accurately predicted evolution of reaction composition ratios in time, offering a tool for predictive optimization. …”
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An artificial intelligence model integrating culprit lesion diagnosis and risk assessment for acute coronary syndromeResearch in context by Peng Peng Xu, Bin Hu, Fan Zhou, Zhi Han Xu, Qian Chen, Tong Yuan Liu, Bang Jun Guo, Chang Sheng Zhou, Xin Wei Tao, Hong Yan Qiao, Jia Ni Zou, Xiang Ming Fang, Wen Cai Huang, Long Jiang Zhang

“…Summary: Background: An integrated machine learning (ML) approach capable of both diagnosing acute coronary syndrome (ACS, encompassing myocardial infarction and unstable angina) and predicting future ACS risk within defined optimal timeframes remains an unmet need in cardiovascular risk stratification. …”
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Adaptive WSN Scheduling for Lifetime Extension in Environmental Monitoring Applications by Jong Chern Lim, Chris Bleakley

“…For two-tier networks, the proposed algorithm outperforms a highly cited previously published algorithm by up to 512% in terms of lifetime and by up to 30% in terms of prediction accuracy. …”
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Automatic speech signal segmentation with chosen parametrization method by Cz. BASZTURA, T. SAWCZYN

“…The usability of three different parametrization methods - based on the analysis of zero-crossings, spectral analysis and linear prediction coding - was analyzed. No significant differences were observed in the efficiency of investigated parameters.…”
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Statistical Evaluation of Smartphone-Based Automated Grading System for Ocular Redness Associated with Dry Eye Disease and Implications for Clinical Trials by Rodriguez JD, Hamm A, Bensinger E, Kerti SJ, Gomes PJ, Ousler III GW, Gupta P, De Moraes CG, Abelson MB

“…The model utilizes the DeepLabV3 architecture for image segmentation, extracting two key features—horizontality and redness. The algorithm then uses these features to predict eye redness, validated by comparison with expert grader scores.Results: The bivariate model using both redness and horizontality performed best, with a Mean Absolute Error (MAE) of 0.450 points (SD=0.334) on the redness scale relative to expert scores. …”
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Forecasting Financial Crashes: Revisit to Log-Periodic Power Law by Bingcun Dai, Fan Zhang, Domenico Tarzia, Kwangwon Ahn

“…We aim to provide an algorithm to predict the distribution of the critical times of financial bubbles employing a log-periodic power law. …”
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Fast and resource efficient method for indoor localization based on fingerprint with varied scales by Yanfen LE, Zhuo TANG, Cunbao SHENG, Weibin SHI

“…To improve the prediction speed in indoor localization,a novel algorithm based on fingerprint with varied scales was proposed.It divided the region of interest into distinct zones with distinctive coverage indicators,and reference positions with different distribution density were set in the region.According the time relevance and strength vary of the RSS from the anchors,the grids-matching process was greatly sped up for the usage of coverage indictors and the features of the location fingerprint extracted with the PCA,which made the proposed method fit the demand of application with limited power and memory.Experimental results indicate that accuracy of the positioning is ensured with the reduced energy-consuming,and more flexible about the number of anchors and the grid distribution.…”
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Balancing Innovation and Tradition: Real-Time, Interactive Learning with LightRAG and Dual-Factor PageRank by Sirisha Velampalli

“…This paper presents a groundbreaking approach to enhancing educational experiences by integrating dynamic QA systems and recommendation algorithms. …”
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NeuralConstraints: integrating a neural generative model with constraint-based composition by Juan S. Vassallo, Örjan Sandred, Julien Vincenot

“…‘NeuralConstraints’ combines the predictive generative abilities of neural networks trained on symbolic musical data with an advanced backtracking constraint algorithm. …”
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PRINCIPLES OF PHASE-LOCKED LOOPS MATHEMATICAL MODELS CONSTRUCTION by M. P. Batura, A. P. Kuznetsov, L. Yu. Shilin, D. P. Kukin

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OPTIMAL ROUTING INFORMATION FLOWS WITHIN THE PARAMETERS OF QOS by N. I. Listopad, I. O. Velichkevich

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Signal processing of little elements monopulse direction finder in the conditions of strong interference using neural networks by S. V. Kozlov

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RATIONALE FOR PREVENTION OF CARDIO-CEREBRAL COMPLICATIONS IN THE METABOLIC SYNDROME BASED ON MATHEMATICAL FORECASTING TECHNIQUES by S. V. Chernavskii, A. N. Fursov, N. P. Potekhin, V. N. Yakovlev

“…A discriminant analysis of clinical and instrumental data of patients with MS.Results of this study allow the early (prenosological) stages of the metabolic syndrome using the linear discriminant equations to predict thedevelopment of myocardial infarction up to 89.3 %, stroke — up to 87.8 %.Conclusion. …”
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Evaluation of Nonlinear Autoregressive Network with Exogenous Inputs Architectures for Wind Speed forecasting by Kacimi Houda, Fennane Sara, Mabchour Hamza, ALtalqi Fatehi, Echchelh Adil

“…Predictive performance was assessed using RMSE to measure the gap between predicted and actual values. …”
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Cognitive Apprenticeship Strategies for the Media Literacy Classroom by Nate Floyd, Jaclyn Spraetz

“…., misinformation, climate change, algorithms, right-to-repair, blockchain, artificial intelligence, etc.), and create engagement-driven course content. …”
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Artificial intelligence for smart irrigation: Reducing water consumption and improving agricultural output by Arlanova A. A., Hojamkuliyeva B. A., Babanazarov N. Sh., Arlanov M. S.

“…A mathematical model based on machine learning algorithms is developed to predict the optimal water volume required for irrigation of agricultural crops. …”
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