Data Mining–Based Model for Computer-Aided Diagnosis of Autism and Gelotophobia: Mixed Methods Deep Learning Approach by Mohamed Eldawansy, Hazem El Bakry, Samaa M Shohieb

“…MethodsThe system was trained to identify ASD status using a balanced dataset of 2932 facial images (n=1466; 50% from individuals with ASD and n=1466; 50% from neurotypical individuals). …”
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Application of the Machine Learning Methods to Assess the Impact of physico-chemical characteristics of water on Feed Consumption in Fish Farms by Hava Şimşek, Mükerrem Oral, Mesut Yılmaz, Mustafa Çakır, Nedim Özdemir, Okan Oral

“…Machine learning (ML) methods, which are one of the subfields of artificial intelligence (AI) and have gained popularity in applications in recent years, play an important role in solving many challenges in aquaculture. …”
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Ecological Suitability Assessment Methods of Waste Pile-Up along Railway Routes Based on Machine Learning Algorithms by Cuicui Ji, Zaoyang Huang, Xiangjun Pei, Bin Sun, Lichuan Chen, Dan Liang, Yanfei Kang

“…We tested 3 machine learning methods—random forest (RF), deep neural network (DNN), and extreme gradient boosting (XGBoost)—using 7 key indicators as input parameters. …”
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Integration of UAV-sensed features using machine learning methods to assess species richness in wet grassland ecosystems by Clara Oliva Gonçalves Bazzo, Bahareh Kamali, Murilo dos Santos Vianna, Dominik Behrend, Hubert Hueging, Inga Schleip, Paul Mosebach, Almut Haub, Axel Behrendt, Thomas Gaiser

“…The analysis revealed that the performance of the machine learning methods varied with the feature combinations. …”
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Investigating the integration of technology-aided assessment methods in teaching and learning 3D geometry in Tanzanian secondary schools by Marcellina Mjenda, Florence Kyaruzi

Subjects: “…technology-aided assessment methods…”
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Assessing the effectiveness of using electronic distance learning methods and the relationship with the results of the Bennett and Unified State Exam tests by Maksim S. Ivanov, Lyudmila V. Vinogradova, Vera A. Patrina, Oksana V. Makarova

Subjects: “…electronic distance learning methods…”
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From experience to engagement: a mixed methods exploration of learning environments using artificial intelligence and extended reality by Mohammad Hmoud, Wajeeh Daher, Abedalkarim Ayyoub

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Enhancing the students’ perception of machine learning methods-based drug formulation using R_programming educational protocols by Rania M. Hathout, Shaimaa S. Ibrahim

“…Abstract Background Recently, the need for artificial intelligence (AI) and machine learning (ML) methods in drug development and research is gaining high concern and more grounds. …”
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Structural Damage Detection in the Wooden Bridge Using the Fourier Decomposition, Time Series Modeling and Machine Learning Methods by Younes Nouri, Farzad Shahabian, Hashem Shariatmadar, Alireza Entezami

Subjects: “…the fourier decomposition method…”
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Optimizing machine learning methods for groundwater quality prediction: Case study in District Bagh, Azad Kashmir, Pakistan by Usman Basharat, Wenjing Zhang, Cuihong Han, Shoukat Husain Khan, Arshad Abbasi, Sehrish Mahroof, Shuxin Li

“…The aim was to establish a reliable method for predicting groundwater quality classification. …”
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Multi‐Model Assessment of PCA‐Informer Hybrid Model Against Empirical and Deep Learning Methods in TEC Forecasting by Yang Lin, Hanxian Fang, Die Duan, Ding Yang, Hongtao Huang, Chao Xiao, Ganming Ren

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High precision water quality retrieval in Dianchi Lake using Gaofen 5 data and machine learning methods by Yuewen Feng, Jun Zhang, Sanjie Guo, Yunbai Zhang, Zhongwei Zhang

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A Comparative Study of Statistical and Machine Learning Methods for Solar Irradiance Forecasting Using the Folsom PLC Dataset by Oscar Trull, Juan Carlos García-Díaz, Angel Peiró-Signes

“…This study presents a critical comparison of traditional statistical methods and machine learning approaches for forecasting solar irradiance using the benchmark Folsom PLC dataset. …”
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Machine learning methods for predicting essential metabolic genes from Plasmodium falciparum genome-scale metabolic network by Itunuoluwa Isewon, Stephen Binaansim, Faith Adegoke, Jerry Emmanuel, Jelili Oyelade

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Predicting temporomandibular disorders in adults using interpretable machine learning methods: a model development and validation study by Yuchen Cui, Fujia Kang, Xinpeng Li, Xinning Shi, Han Zhang, Xianchun Zhu

“…The purpose of this study was to apply a machine learning (ML) approach to identify risk factors for the occurrence of TMD in adults and to develop and validate an interpretable predictive model for the risk of TMD in adults.MethodsA total of 949 adults who underwent oral examinations were enrolled in our study. 5 different ML algorithms were used for model development and comparison, and feature selection was performed by feature importance ranking and feature decreasing methods. …”
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Effects of SBAS-InSAR Deformation Integration Methods and Machine Learning Model Selection on Landslide Susceptibility Mapping by Zhen Yang, Xiangchao Jiang, Meinan Zheng, Qingbiao Guo

“…However, current LSM methods primarily rely on static indicators, that do not consider the significant surface deformation during landslide events, resulting in inadequate reliability of the assessment results. …”
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Non-Destructive Methods Based on Machine Learning for the Prediction of Sweet Potato Leaf Area: A Comparative Approach by Joao Everthon Da Silva Ribeiro, Ester Dos Santos Coelho, Antonio Gideilson Correia Da Silva, Pablo Henrique De Almeida Oliveira, Elania Freire Da Silva, Gisele Lopes Dos Santos, Anna Kezia Soares De Oliveira, John Victor Lucas Lima, Walter Esfrain Pereira, Lindomar Maria Da Silveira, Aurelio Paes Barros

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An improved model accuracy for forecasting risk measures: application of ensemble methods by Katleho Makatjane, Kesaobaka Mmelesi

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A Review of Point Cloud Fusion Technology: Methods, Applications, and Challenges by Shaojing SONG, Xinjian LI, Feiyi FANG

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Data-Driven Computational Methods in Fuel Combustion: A Review of Applications by Jacek Lukasz Wilk-Jakubowski, Lukasz Pawlik, Damian Frej, Grzegorz Wilk-Jakubowski

“…The study examines the integration of computational methods, including computational fluid dynamics, neural networks, and genetic algorithms, with various fuel types such as biodiesel, biomass, coal, gasoline, hydrogen, and natural gas. …”
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