Quantum neural networks with data re-uploading for urban traffic time series forecasting by Nikolaos Schetakis, Paolo Bonfini, Negin Alisoltani, Konstantinos Blazakis, Symeon I. Tsintzos, Alexis Askitopoulos, Davit Aghamalyan, Panagiotis Fafoutellis, Eleni I. Vlahogianni

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Prediction of performance and emission features of diesel engine using alumina nanoparticles with neem oil biodiesel based on advanced ML algorithms by M. S. Aswathanrayan, N. Santhosh, Srikanth Holalu Venkataramana, Kurugundla Sunil Kumar, Sarfaraz Kamangar, Amir Ibrahim Ali Arabi, Sameer Algburi, Osamah J. Al-sareji, A. Bhowmik

“…The random forest model also outperformed other approaches in predicting emissions, achieving the highest accuracy with a test R2 of 0.9826 and the lowest test MAPE of 9.3067%.This integrated experimental and predictive approach provided a robust framework for optimizing biodiesel formulations, identifying the ideal combination of biodiesel blend ratio and nanoparticle concentration. …”
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Research review on intelligent object detection technology for coal mines based on deep learning by Fan ZHANG, Jiarong ZHANG, Haixing CHENG

“…Then, key technologies such as data augmentation, super-resolution, and feature extraction for intelligent target detection in mines were studied, and the research progress of deep learning based-target detection in underground personnel safety monitoring, intelligent detection of mining equipment, and perception of working environment was elaborated in detail around the application requirements of “human machine environment” in coal mines. Finally, it was pointed out that there are still challenges in the construction of datasets, model optimization, and multi-source heterogeneous data fusion of intelligent target detection technology in coal mine application scenarios. …”
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Enhanced ensemble learning-based uncertainty and sensitivity analysis of ventilation rate in a novel radiative cooling building by Majid Mohsenpour, Mohsen Salimi, Atieh Kermani, Majid Amidpour

“…This study proposes a novel machine learning-based ensemble stacking model to predict ventilation rates in passive cooling buildings, addressing the challenges of black-box modeling. …”
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Synergistic effect of artificial intelligence and new real-time disassembly sensors: Overcoming limitations and expanding application scope by Bozhou Li, Dajiang Ju, Xingwang Li, Yan Liu, Hongru He, Hao Wang

“…Then, based on the gated recurrent unit (GRU) model, the article applied the particle swarm optimization (PSO) algorithm to optimize the parameters of the GRU network and used the support vector machine (SVM) model to optimize the classification function of the network output. …”
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Human-based metaheuristics and non-parametric learning for groundwater-prone area mapping by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Seyedeh Zeinab Shogrkhodaei, Biswajeet Pradhan, Soo-Mi Choi

“…Traditional methods often struggle with optimizing model performance and handling complex, nonlinear relationships in environmental factors. …”
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Application of Fully Connected Neural Network-Based PyTorch in Concrete Compressive Strength Prediction by Xuwei Dong, Yang Liu, Jinpeng Dai

“…In this study, a fully connected neural network model is developed using the PyTorch framework to predict the compressive strength of concrete and compared with six other machine learning models. …”
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Research on the Spatial Network Connection Characteristics and Influencing Factors of Chengdu–Chongqing Urban Agglomeration from the Perspective of Flow Space by Yangguang Hao, Zhongwei Shen, Jiexi Ma, Jiawei Li, Mengqian Yang

“…Based on the results of the SOM neural network machine learning model, the connectivity capabilities of cities within the CCUA are divided into five levels. …”
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Sound-Based Unsupervised Fault Diagnosis of Industrial Equipment Considering Environmental Noise by Jeong-Geun Lee, Kwang Sik Kim, Jang Hyun Lee

“…In particular, by adapting the model learned in the source domain to the target domain and considering the domain differences based on signal-to-noise ratio, high diagnostic accuracy was maintained regardless of the noise levels. …”
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Defining a Novel Meaning of the New Organic Architecture by Jia-Rey Chang

“…At present, people are confronting the unprecedented unification of machine and biology which has been revealed by the means of advancing industrial processes towards the organic model. …”
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Solar Agro Savior: Smart Agricultural Monitoring Using Drones and Deep Learning Techniques by Manu Mundappat Ramachandran, Bisni Fahad Mon, Mohammad Hayajneh, Najah Abu Ali, Elarbi Badidi

“…It also utilizes an attention-based mechanism that enables the model to assign meaningful weights to the most important parts of the data sequence. …”
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Determination of geometrical parameters of the diamond gear hones for the finishing of gears by Michael Storchak, Yuriy Danylchenko

“…Based on this analysis, a model of the tool geometry machine engagement with the machining gear was developed. …”
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Research Progress of Computational Fluid Dynamics in Mixed Ionic–Electronic Conducting Oxygen-Permeable Membranes by Jun Liu, Jing Zhao, Yulu Liu, Yongfan Zhu, Wanglin Zhou, Zhenbin Gu, Guangru Zhang, Zhengkun Liu

“…Additionally, we delve into how CFD, through multiscale modeling and parameter optimization, improves separation efficiency and facilitates practical engineering applications. …”
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Multi Objective Prioritized Workflow Scheduling Using Deep Reinforcement Based Learning in Cloud Computing by Sudheer Mangalampalli, Syed Shakeel Hashmi, Amit Gupta, Ganesh Reddy Karri, K. Varada Rajkumar, Tulika Chakrabarti, Prasun Chakrabarti, Martin Margala

“…These priorities are fed to scheduler which uses Deep Q-Network model to dynamically schedule tasks by considering both priorities of tasks and VMs. …”
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Data base of the steel materials characteristics by S. P. Kundas, A. V. Lemzikov, B. A. Tonkonogov, P. S. Gurchenko, I. S. Gauhshtein

“…Data base contains information about thermalphysic of steel and also thermo-kinetic drawings and is part of the program complex ThermoSim, intended for modeling, diagnostics and optimization of the thermotreatment processes of the details of instrument- making and machine-building.…”
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From Baseline to Best Practice: An Advanced Feature Selection, Feature Resampling and Grid Search Techniques to Improve Injury Severity Prediction by Soukaina EL Ferouali, Zouhair Elamrani Abou Elassad, Sara Qassimi, Abdelmounaîm Abdali

“…Our findings provide insight into the requirement for accurate forecasting models in optimization and balanced approaches to enhancing traffic safety. …”
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Wind speed forecasting approach using conformal prediction and feature importance selection by Cesar Vinicius Zuege, Stefano Frizzo Stefenon, Cristina Keiko Yamaguchi, Viviana Cocco Mariani, Gabriel Villarrubia Gonzalez, Leandro dos Santos Coelho

“…Furthermore, a Bayesian Optimization with Tree-structured Parzen Estimators (BO-TPE) will be used to tune the hyperparameters of the models. …”
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Improving SLICES crystal representation through CHGNet integration and parameter tuning by Bizhu Zhang, Kedeng Wu, Chang Zhang, Hang Xiao, Liangliang Zhu

“…To bridge this gap, we present an enhanced approach to the simplified line-input crystal-encoding system (SLICES) representation by incorporating the Crystal Hamiltonian Graph Neural Network (CHGNet) machine learning force field model. Comprehensive evaluations across multiple datasets (MP-20, MP21-40, and MOF) using different neighbor recognition algorithms (EconNN and CrystalNN) show that our approach outperforms the original in reconstructing structures with fewer than 20 atoms per unit cell, achieving up to a 1.34% improvement in reconstruction rate (from 92.55% to 93.89%). …”
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A Consensus Reaching Process for Product Design Decision-Making by Integrating Intuitionistic Fuzzy Sets and Trust Network by Yanpu Yang, Kai Zhang, Zijing Lei

“…By establishing a consensus measurement model, DMs contributing less to consensus are identified, and consensus optimization is achieved through the modification of DMs’ preferences or the calibration of their trust relationships. …”
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A Novel Low-Rank Embedded Latent Multi-View Subspace Clustering Approach by Sen Wang, Lian Chen, Zhijian Liang, Qingyang Liu

“…To alleviate these issues, we present a latent multi-view representation learning model based on low-rank embedding by implicitly uncovering the latent consistency structure of data, which allows us to achieve robust and efficient multi-view feature fusion. …”
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