Empirical Analysis of Honeybees Acoustics as Biosensors Signals for Swarm Prediction in Beehives by Kainat Iqbal, Bayan Alabdullah, Naif Al Mudawi, Asaad Algarni, Ahmad Jalal, Jeongmin Park

“…Monitoring of these beehives is therefore of paramount importance to keep an eye on their irregular behavior. Swarm prediction can be done by visually inspecting hives, monitoring temperature, or analyzing acoustic features with machine learning. …”
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Research on Pedestrian Avoidance Behavior for School Section Based on Improved BP Neural Network and XGboost Algorithm by Guiliang Lu, Mingwei Liu

“…While traditional studies on pedestrian evasive behavior have mainly focused on general traffic environments and used image-based features to predict trajectories, few have specifically addressed the behavior of pedestrians in school zones. …”
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Vehicle trajectory prediction based on spatio-temporal Transformer feature fusion by ZHAO Wenhong, WANG Wei, WAN Zilu

“…Subsequently, a temporal self-attention mechanism was utilized to extract the temporal dependencies between consecutive frames, thereby generating a set of spatiotemporal features that reflect the dynamic behavior of vehicles. These features were then fed into a decoder to predict the motion trajectories of vehicles over the next 5 s. …”
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Age of machine learning: new trends in autism spectrum disorder prediction by Weihong Xu, Haibei Li, Junwen Li, Min Jin

“…Through the integration of artificial intelligence and the medical field, researchers can establish a machine learning (ML) risk prediction model to estimate the risk of ASD. Currently, a variety of risk models have been developed using multiple factors, such as genetic background, gaze behavior, adverse conditions during pregnancy and childbirth, magnetic resonance imaging of the brain, and intestinal microbial composition, to predict ASD. …”
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Predicting the Time-dependent Mechanics of Concrete Based on a Multiscale Model by Shijun Wang, Changqing Du, Mingqing Gu, Chunlin Pan, Teng Tong

“…To accurately predict the time-dependent deformation of concrete, a multiscale model with its focus pinned on mesoscale is proposed here to break down the constitutive law of concrete to the mechanics of its different constituent phases. …”
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Predicting Antecedents of Employee Smart Work Adoption Using SEM-Multilayer Perceptron Approach by Wen-Bao Wang, Chich-Jen Shieh, Hamza Mohammed Ridha Al-Khafaji, Andrei Sevbitov, Aras Masood Ismael, Paitoon Chetthamrongchai, Wanich Suksatan, Parvaneh Bahrami

“…Finally, the output of the SEM model was considered as the input of the multilayer perceptron (MLP) model, which is an artificial neural network model, to determine the importance of each antecedent in the prediction of employee behavior. The present study provides quantitative empirical evidence that perceived value, institutional and technological support, perceived limited communication, and perceived cost are antecedents of employee SW adoption that are, respectively, important in predicting the behavioral intentions of employees in acceptance of SW. …”
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Peripheral blood transcriptomic differences predict depression status in individuals undergoing bariatric surgery by Rebecca Milton, Anna P. McLaughlin, Nicole Mariani, Melisa Kose, Zuzanna Zajkowska, Giulia Lombardo, Naghmeh Nikkheslat, Esperanza Perucha, Valeria Mondelli

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YouTube advertising appeals on generation Z’s purchase intention of beauty products: a predictive approach by Tak Jie Chan, Kirttine Somasundram, Yang Tian, Nor Azura Adzharuddin, Nor Hazlina Hashim

“…The study contributes to both theoretical and practical implications for marketers aiming to enhance digital advertising strategies tailored to Generation Z’s unique consumer behaviors.…”
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The Possibility or Impossibility of Stock Price Prediction: Evidence from the Petrochemical Industry by Masoud Alizadeh Chamazkoti, Mehdi Fathabadi, Mahmod Mahmodzadeh, Saleh Ghavidel Doostkouei

“…ConclusionAccording to the findings of the two stages, Nouri, Pars, Tapico, and Shapna companies have weak efficiency which means that the stock price behavior of these companies cannot be predicted. On the other hand, Parsan, Shapedis, Shatran, Fars and Shabandar companies show weak efficiency which means that their stock price behavior is predictable. …”
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Decoding the Factors behind Rising Fatal Roadway Crashes in Iran: an In-Depth Analysis and Predictive Modelling Study by Seyedeh Zeinab Almasi, Seyed Ahmadreza Almasi, Ahmad Goli Khorasgani, Chamroeun Se, Krzysztof Goniewicz, Saeid Bitaraf, Vatanavongs Ratanavaraha, Hossein Bahmani, Maxim A. Dulebenets

“…We recommend enhanced public education to foster adherence to road safety measures, curtailment of irresponsible and dangerous behaviors, and elevation in vehicle safety standards as key steps toward enforcing driving regulations.…”
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Investigating the Machining Behavior of the Additively Manufactured Polymer-Based Composite Using Adaptive Neuro-Fuzzy Learning by Anastasios Tzotzis, Dumitru Nedelcu, Simona-Nicoleta Mazurchevici, Panagiotis Kyratsis

“…An Adaptive Neuro-Fuzzy Inference System (ANFIS) was developed to model the process behavior, utilizing a 3–3–3 architecture with triangular membership functions. …”
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Temporal Synchrony in Bodily Interaction Enhances the Aha! Experience: Evidence for an Implicit Metacognitive Predictive Processing Mechanism by Jiajia Su, Haosheng Ye

“…Grounded in the theory of metacognitive prediction error minimization, this study is the first to propose and empirically validate the mechanism of implicit metacognitive predictive processing by which bodily interaction influences the Aha! …”
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Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli by Robert Schuetz, Lars Kuepfer, Uwe Sauer

“…Under nutrient scarcity in continuous cultures, in contrast, linear maximization of the overall ATP or biomass yields achieved the highest predictive accuracy. Since these particular objectives predict the system behavior without preconditioning of the network structure, the identified optimality principles reflect, to some extent, the evolutionary selection of metabolic network regulation that realizes the various flux states.…”
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Preoperative prediction of gastric cancer T-staging based on ordinal regression models by O. V. Krasko, M. Yu. Reutovich, A. L. Patseika

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Analysis and Prediction of Energy Consumption Data, Using Data Mining Software by KAZOLIS Dimitrios, FANTIDIS Jacob, FOTAKIS Christos Dionyshs, TRAMANTZAS Kostantinos, TSIANTOS Vassilios

“…This paper explores the ability of prediction in electricity consumption using data mining techniques and emphasizes the integration of weather conditions and previous consumption trends. …”
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Understanding and predicting trends in adsorption energetics on monolayer transition metal dichalcogenides by Brian H. Lee, Jameela Fatheema, Deji Akinwande, Wennie Wang

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On the estimation of inverse-probability-of-censoring weights for the evaluation of survival prediction error. by Thomas Prince, Andrea Bommert, Jörg Rahnenführer, Matthias Schmid

“…This paper considers IPW applied to right-censored time-to-event data. We investigate the behavior of the inverse-probability-of-censoring weighted (IPCW) Brier score, which is frequently used to assess the predictive performance of time-to-event models. …”
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Prediction of Multivariate Chaotic Time Series using GRU, LSTM and RNN by Osman Eldoğan, Gülyeter Öztürk

“…Through successful prediction studies conducted on the generated chaotic time series, forecasts can be made about events displaying unpredictable behavior in nature, which have not yet been modeled. …”
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Expression‐based machine learning models for predicting plant tissue identity by Sourabh Palande, Jeremy Arsenault, Patricia Basurto‐Lozada, Andrew Bleich, Brianna N. I. Brown, Sophia F. Buysse, Noelle A. Connors, Sikta Das Adhikari, Kara C. Dobson, Francisco Xavier Guerra‐Castillo, Maria F. Guerrero‐Carrillo, Sophia Harlow, Héctor Herrera‐Orozco, Asia T. Hightower, Paulo Izquierdo, MacKenzie Jacobs, Nicholas A. Johnson, Wendy Leuenberger, Alessandro Lopez‐Hernandez, Alicia Luckie‐Duque, Camila Martínez‐Avila, Eddy J. Mendoza‐Galindo, David Cruz Plancarte, Jenny M. Schuster, Harry Shomer, Sidney C. Sitar, Anne K. Steensma, Joanne Elise Thomson, Damián Villaseñor‐Amador, Robin Waterman, Brandon M. Webster, Madison Whyte, Sofía Zorilla‐Azcué, Beronda L. Montgomery, Aman Y. Husbands, Arjun Krishnan, Sarah Percival, Elizabeth Munch, Robert VanBuren, Daniel H. Chitwood, Alejandra Rougon‐Cardoso

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Vehicle trajectory prediction in weaving area of expressway based on transfer learning by YIN Zijian, XU Liangjie, LIU Wei, MA Yukang, LIN Hai

“…The experimental results show that the accuracy of lateral and longitudinal behavior prediction can reach 98.35% and 93.01%, respectively, and the root mean square error of trajectory prediction is 2.04 cm. …”
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