Detection of Crack Sealant in the Pretreatment Process of Hot In-Place Recycling of Asphalt Pavement via Deep Learning Method by Kai Zhao, Tianzhen Liu, Xu Xia, Yongli Zhao

“…However, during asphalt pavement hot in-place recycling (HIR), irregular shapes and random distribution of crack sealants can cause issues like agglomeration and ignition. …”
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Detection of breast cancer using machine learning and explainable artificial intelligence by Tharunya Arravalli, Krishnaraj Chadaga, H Muralikrishna, Niranjana Sampathila, D. Cenitta, Rajagopala Chadaga, K. S. Swathi

“…Incorporating explainable artificial intelligence techniques revealed the underlying factors for the model predictions, adding a layer of transparency and interpretability. Out of the algorithms, random forest showed the best result, an F1-score of 84%. …”
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Investigating contact resistance between WOx and metal electrodes in ECRAMs via interface analysis by Juhee Kim, Woochan Song, Jeonghyeon Park, Seungkun Kim, Hyejin Kim, Jong Kyu Kim, Seyoung Kim

“…Moreover, we demonstrate Mo only forms a metal oxidation layer of 9 nm at the interface between WOx and Mo. …”
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A text mining-based approach for comprehensive understanding of Chinese railway operational equipment failure reports by Xiaorui Yang, Honghui Li, Yi Xu, Nahao Shen, Ruiyi He

“…This model combines bidirectional encoder representations from transformers (BERT), bidirectional long short-term memory (BiLSTM) networks, and conditional random fields (CRF), with an additional entity attention layer to deeply extract entity features. …”
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Meta-analysis of laying hen performance and egg quality characteristics in response to Spirulina supplementation by Muhammad Maulana Sadid, Moh Sofi'ul Anam

“… Because of its essential and beneficial nutritional content, the utilization of Spirulina in the layer chicken diet has received more attention nowadays. …”
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Accurate Time-to-Target Forecasting for Autonomous Mobile Robots by Stefan-Alexandru Precup, Arpad Gellert, Alexandru Matei, Bogdan-Constantin Pirvu, Constantin-Bala Zamfirescu

“…This paper addresses this challenge by evaluating the effectiveness of four time forecasting methods: Linear Regression, Random Forest Regression, Transformer and Bidirectional Long Short-Term Memory (BiLSTM) networks. …”
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A Deep Learning Algorithm for Multi-Source Data Fusion to Predict Effluent Quality of Wastewater Treatment Plant by Shitao Zhang, Jiafei Cao, Yang Gao, Fangfang Sun, Yong Yang

“…Deep learning algorithms including long short-term memory (LSTM) and gated recurrent unit (GRU) were found to have a favourable prediction performance by comparing with traditional machine learning algorithms (random forest, RF) and multi-layer perceptron (MLP). …”
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Predicting the Probability of Failure in Truss Structures Using Artificial Neural Networks by Mohammadreza Gholami, Seyed Ahmad Mobinipour, Mohammad Javad Haji Mazdarani, Seyed Rohollah Hoseini Vaez

“…The optimal number of neurons in each layer is determined through a trial-and-error process, based on the lowest error of the predicted data and the highest regression coefficient of responses. …”
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Spatial Zoning of Housing Quality by Combining CRITIC Weighting Method and Fuzzy Logic; Case Study: Qaen City by Saeed Hossein Abadi

“…After fuzzy zoning and layer combination, the final map of Qaen city housing quality was produced. …”
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Stacking classical-quantum hybrid learning approach for corrosion inhibition efficiency of N-heterocyclic compounds by Muhamad Akrom, Supriadi Rustad, Totok Sutojo, Wahyu Aji Eko Prabowo, Hermawan Kresno Dipojono, Ryo Maezono, Hideaki Kasai

“…SCQM integrates classical models such as Multi-Layer Perceptron Neural Network (MLPNN) and Random Forest (RF) as base learners, with Quantum Neural Network (QNN) as the meta-learner. …”
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Analysis of Electric Field and Temperature Distributions of Non-Uniformly Contaminated Silicone Composite Insulators Using Deep Learning by Irem Gorgoz, Mehmet Cebeci

“…Considering that the conductivity of the contamination layer on the insulator surface affects leakage currents, surface temperature, and electric field distribution, five critical regions of the insulator surface, significant in terms of contamination, were identified, and distinct conductivity levels were assigned to each. …”
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Spatial Evaluation of <i>Salurnis marginella</i> Occurrence According to Climate Change Using Multiple Species Distribution Models by Jae-Woo Song, Jaho Seo, Wang-Hee Lee

“…Specifically, we developed the CLIMEX model and three machine learning-based models (MaxEnt, random forest, and multi-layer perceptron), integrated them to conservatively assess pest occurrence under current and future climates, and overlaid the host distribution with climatically suitable areas of <i>S. marginella</i> to identify high-risk areas vulnerable to the spread and invasion of the pest. …”
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Adaptive Energy Management for MATTER-Enabled Smart Homes by Navodit Bhardwaj, Pallavi Joshi

“…The energy demand prediction module employs a stacked ensemble learning model combining Random Forest Regression, Support Vector Regression, Gradient Boosting Machines, and AdaBoost with a meta-learner, achieving a prediction accuracy of 92%. …”
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Fault Classification of 3D-Printing Operations Using Different Types of Machine and Deep Learning Techniques by Satish Kumar, Sameer Sayyad, Arunkumar Bongale

“…Fused deposition modeling (FDM), a method of additive manufacturing (AM), comprises the extrusion of materials via a nozzle and the subsequent combining of the layers to create 3D-printed objects. FDM is a widely used method for 3D-printing objects since it is affordable, effective, and easy to use. …”
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Impacts of Photooxidation on Commercially Available Homo and Copolymers of Polypropylene on Their Microstructure and Mechanics: A Cross‐Sectional Analysis by Marta Chiapasco, Michele Valsecchi, Gavin Hill, Christopher Wallis, Alexandra E. Porter, Finn Giuliani

“…The work emphasizes the importance of incorporating ethylene comonomers into polyolefins to limit variation of the microstructure across the core to the skin layer, for improved future design of packaging that degrades fully.…”
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Presenting an Innovative Method Based on Ensemble Learning for a Credit Approval System by Eshonkulov Uchkun, Elmurodov Tulkin, Ravshanov Zavqiddin, Каramanov Asqar

“…The findings of the suggested methodology are compared with those of several methodologies, including Random Forest (RF), Gaussian Kernel (GK), Support Vector Machines (SVM), Adaptive Boosting (AdaBoost), and Multi-Layer Perceptron (MLP). …”
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Research Progress on Coarse and Fine Sediment Exchange Near Riverbed by JIN Zhong-wu, LI Shan, LU Jin-you, GUO Xiao-hu

“…Moreover, formulas for exchange layer thickness dominated by sand wave migration require modifications to account for the influence of resistance transitions in sandy rivers. …”
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Prevalence of endoepicardial asynchrony and breakthrough patterns in a bilayer computational model of heterogeneous endoepicardial dissociation in the left atrium. by Elham Zakeri Zafarghandi, Vincent Jacquemet

“…Activation time series were extracted in both layers to compute endoepicardial delays and detect breakthrough patterns.…”
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Relative Roles of Deterministic and Stochastic Processes in Driving the Vertical Distribution of Bacterial Communities in a Permafrost Core from the Qinghai-Tibet Plateau, China. by Weigang Hu, Qi Zhang, Tian Tian, Dingyao Li, Gang Cheng, Jing Mu, Qingbai Wu, Fujun Niu, James C Stegen, Lizhe An, Huyuan Feng

“…Bacterial communities in five soil horizons (two originated from the active layer and three from permafrost) of the permafrost core were phylogenetically random, indicator of stochastic processes. …”
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URM: A Unified RAM Management Scheme for NAND Flash Storage Devices by A. Xiaochang Li, B. Jichen Chen, C. Zhengjun Zhai, D. Mingchen Feng, E. Xin Ye

“…In NAND flash storage devices, the random access memory (RAM) is composed of a data buffer and mapping cache that play critical roles in storage performance. …”
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