Simulating urban growth through case-based reasoning by X. Ye, W. W. Yu, W. H. Yu, L. N. Lv

“…However, CBR analyses of land-use changes are difficult to apply in the study of urban growth due to shortcomings in the case structure and model algorithms. In response, this study proposes a three-step urban-growth simulation model based on CBR (UGSCBR). …”
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High-Frequency Cryptocurrency Price Forecasting Using Machine Learning Models: A Comparative Study by Fátima Rodrigues, Miguel Machado

“…We compare various machine learning models, including recurrent neural networks (RNNs), time series analysis (ARIMA), and conventional regression algorithms, using minute-step Bitcoin price data over a 30-day period to predict prices 60 min ahead. …”
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FORECASTING STOCK MARKET LIQUIDITY WITH MACHINE LEARNING: AN EMPIRICAL EVALUATION IN THE GERMAN MARKET by Bogdan Ionut ANGHEL

“…The study benchmarks four machine-learning algorithms— Random Forest, XGBoost, CatBoost and Long Short-Term Memory (LSTM) networks—for forecasting stock market liquidity in Germany’s DAX equity market. …”
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Radiomics in glioma: emerging trends and challenges by Zihan Wang, Lei Wang, Yinyan Wang

“…Integrating deep learning algorithms enhances various radiomics components, including image normalization, region of interest segmentation, feature extraction, feature selection, and model construction and can potentially improve model accuracy and performance. …”
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Effects of neighborhood streetscape on the single-family housing price: Focusing on nonlinear and interaction effects using interpretable machine learning. by Jaewon Han, Ayoung Woo, Sugie Lee

“…Previous studies using the conventional Hedonic Price Model to predict existing housing prices may have limitations in addressing the relationship between house prices and streetscapes as visually perceived at the human eye level, due to the constraints of streetscape estimations. …”
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Travel Time Variability and Spatio-Temporal Analysis of Urban Streets Using Global Positioning System: A Review by Zainab Ahmed Alkaissi, Ruba Yousif Hussain

“…Travel time estimation algorithms based on speed data loop detectors induced insignificant differences when the study route was a relatively short and slow transition from free state to congestion state. …”
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The Discriminants of Long and Short Duration Failures in Fulfillment Sortation Equipment: A Machine Learning Approach by Abed Mutemi, Fernando Bacao

“…Under a variety of hyperparameter configurations, we test and compare the outcomes of eight different machine learning classification algorithms, seven individual classifiers, and a stacked ensemble. …”
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A review of machine learning approaches for the discovery of thermoelectric materials by Övgü C. Yelgel, Celal Yelgel

“…Recent progress in computational infrastructure, along with the advent of data-centric approaches such as machine learning (ML), has transformed the landscape of thermoelectric research. ML algorithms, trained on comprehensive datasets including experimental measurements, crystallographic data, and density functional theory (DFT) results can predict key TE metrics, such as the figure of merit (ZT), with remarkable speed and accuracy. …”
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MiR-124 Radiosensitizes human colorectal cancer cells by targeting PRRX1. by Yuqin Zhang, Lin Zheng, Jing Huang, Fei Gao, Xiaoshan Lin, Lian He, Dan Li, Zhijun Li, Yi Ding, Longhua Chen

“…We identified PRRX1, a new EMT inducer and stemness regulator as a novel direct target of miR-124 by using target prediction algorithms and luciferase assay. PRRX1 knockdown could sensitize CRC cells to IR similar to the effects caused by miR-124. …”
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Clustering and Vectorizing Acoustic Emission Events of Large Infrastructures’ Normal Operation by Theocharis Tsenis, Vassilios Kappatos

“…Latest machine learning outlier detection algorithms, like One-Class Support Vector Machine, Variational Auto-Encoder, and others, were incorporated, producing a vector of confidence on upcoming out-of-the-normal gate operation and failure prediction, achieving detection contrast enhancement on out-of-the-normal operation points up to 400%.…”
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MEAT TRADE DYNAMICS: MONTHLY PATTERNS IN ROMANIA’S IMPORTS (CMA AND HOLT-WINTERS METHODS) by Aurelia Vasilica BĂLAN, Elena TOMA

“…The analysis aims to project the values of meat imports until May 2025 by using various forecasting algorithms to understand import trends and seasonality. …”
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Leveraging Feature Fusion of Image Features and Laser Reflectance for Automated Fish Freshness Classification by Caner Balım, Nevzat Olgun, Mücahit Çalışan

“…The combined features were classified using SVM, MLP, and RF algorithms. The experimental results demonstrated that the proposed multimodal approach significantly outperformed single-modality methods, achieving average classification accuracy of 88.44%. …”
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Applications, Challenges, and Future Perspectives of Artificial Intelligence in Psychopharmacology, Psychological Disorders and Physiological Psychology: A Comprehensive Review by Mohammad Hossein Salemi, Elham Foroozandeh, Molouk Khademi Ashkzari

“…Personalized medicine, powered by AI, predicts individual medication responses, minimizing side effects and optimizing outcomes. …”
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The siRNA Non-seed Region and Its Target Sequences Are Auxiliary Determinants of Off-Target Effects. by Piotr J Kamola, Yuko Nakano, Tomoko Takahashi, Paul A Wilson, Kumiko Ui-Tei

“…These observations may prove useful as parameters for off-target prediction algorithms and improve siRNA 'specificity' design rules.…”
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Expanding kinetoplastid genome annotation through protein structure comparison. by Juan Manuel Trinidad-Barnech, José Sotelo-Silveira, Darío Fernández Do Porto, Pablo Smircich

“…Recently, significant advancements have been made in in-silico protein structure prediction and algorithms for rapid and precise large-scale protein structure comparisons. …”
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Blind HDR image quality assessment based on aggregating perception and inference features by Donghui Wan, Xiuhua Jiang, Qiangguo Yu

“…However, the significant increase in peak luminance and contrast inherent in HDR images often accentuates artifacts, thus limiting the effectiveness of traditional LDR-based image quality assessment (IQA) algorithms when applied to HDR content. To address this, we propose a novel blind IQA method tailored specifically for HDR images, which incorporates both the perception and inference processes of the human visual system (HVS). …”
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Modelling trial-by-trial changes in the mismatch negativity. by Falk Lieder, Jean Daunizeau, Marta I Garrido, Karl J Friston, Klaas E Stephan

“…It has been used to demonstrate that the brain learns the statistical structure of its environment and predicts future sensory inputs. However, the algorithmic nature of these computations and the underlying neurobiological implementation remain controversial. …”
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Research on Air Temperature Inversion Method Based on Land Surface Temperature of Different Land Surface Cover by Rui Fang, Xiaofang Shan, Qinli Deng

“…Due to the characteristics of the algorithms, it is recommended to prioritize random forest for prediction when the sample range covers the observed data range and to use BP neural networks when it does not. …”
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Delay-Driven Routing for Low-Duty-Cycle Sensor Networks by Zuzhi Fan

“…We compare our design against state-of-the-art routing algorithms derived in wireless networks through large-scale simulations and testbed experiments, which show that our algorithms can achieve a significant reduction in delivery delay.…”
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Empowering Sustainability: The Crucial Role of IoT-Enabled Distributed Learning Systems in Reducing Carbon Footprints by Anjana M S, Aryadevi Remanidevi Devidas, Maneesha Vinodini Ramesh

“…Key observations include, EnMS with IoT devices and automation offers smartness, they do not lead to a significant reduction in energy consumption. Moreover, these IoT devices and centralized learning consume more energy. …”
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