Research on fine-grained classification of rare metal granite lithology based on deep learning by ZHAO Hengqian, WANG Pan, LIU Zhiguo, MIAO Qunfeng, LI Zhibin, TANG Guanglong, QI Yunfei, XIE Yu, WANG Mengmeng

“…In the future, the accuracy of fine-grained classification of rare metal granite lithology can be further improved by improving the quantity and quality of rock samples and optimizing the model algorithm.…”
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A Novel Supervoxel-Based NE-PC Model for Separating Wood and Leaf Components from Terrestrial Laser Scanning Data by Shengqin Gong, Xin Shen, Lin Cao

“…However, previous studies on wood–leaf separation exhibited limitations in unsupervised adaptability and robustness to complex tree architectures, while demonstrating inadequate performance in fine branch detection. …”
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Optimal Sizing, Techno-Economic Feasibility and Reliability Analysis of Hybrid Renewable Energy System: A Systematic Review of Energy Storage Systems’ Integration by Akhlaque Ahmad Khan, Ahmad Faiz Minai, Rakesh Kumar Godi, Vijay Shankar Sharma, Hasmat Malik, Asyraf Afthanorhan

“…In order to minimize expenses and emissions while satisfying energy demands, the sizing process consists of evaluating the appropriate capacity of each component. …”
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Validation of the Turkish version of the Developmental Behavior Checklist: a comprehensive tool for assessing emotional, behavioral, and autism spectrum disorders in children and a... by Sabide Duygu Uygun, Merve Çıkılı Uytun, Hande Konşuk Ünlü, Ayşegül Akgül Doğru, Serpil Aktaş Altunay, Didem Behice Öztop, Birim Günay Kılıç, Kerim Munir

“…This study aims to validate the Turkish versions of the DBC autism screening algorithm (ASA), parent (P) and teacher (T) forms.MethodsParents and teachers of 312 children and adolescents aged 4–17 with ID completed the Turkish versions of the DBC forms and the Strengths and Difficulties Questionnaire (SDQ). …”
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Optimizing DD-SIMCA modeling for accurate classification of rice varieties via raman spectroscopy by Somaye Vali Zade, Hamed Sahebi, Adel Mirza Alizadeh, Behrooz Jannat, Hossein Rastegar, Solmaz Abedinzadeh, Fataneh Hashempour-Baltork, Amin Mousavi Khaneghah

“…The rice samples were then separated into training and validation sets using the Kennard-Stone (70–30) algorithm. The smoothing and differential pretreatment algorithms were applied to prepare the spectra for analysis. …”
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Inflammation-Driven Prognosis in Advanced Heart Failure: A Machine Learning-Based Risk Prediction Model for One-Year Mortality by Zhou M, Du X

“…Data were split into training and validation sets. Seven ML algorithms were applied to build and evaluate models. …”
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Local power flow control by L. M. Chetochnikova, N. I. Smolentsev, N. V. Tomskich, D. A. Gimgina, D. A. Lavrentieva

“…The  considered power management algorithm is a simple implementation of classical load balancing, and at the same time allows for optimal energy distribution within a given error. …”
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Deep and Shallow Learning Model-Based Sleep Apnea Diagnosis Systems: A Comprehensive Study by Roksana Akter Raisa, Ayesha Siddika Rodela, Mohammad Abu Yousuf, Akm Azad, Salem A. Alyami, Pietro Lio, Md Zahidul Islam, Ganna Pogrebna, Mohammad Ali Moni

“…A detailed analysis of data preprocessing, feature extraction and classification algorithm is also addressed. It is found that most of the studies are based on signal analysis for identifying sleep apnea, which yields results with substantial reliability, while contemporary research emphases have been on heart rate variability and pulse oximetry outcomes.…”
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Dynamic Optimization of Recurrent Networks for Wind Speed Prediction on Edge Devices by Laeeq Aslam, Runmin Zou, Ebrahim Shahzad Awan, Sayyed Shahid Hussain, Muhammad Asim, Samia Allaoua Chelloug, Mohammed A. ELAffendi

“…An adaptive Simulated Annealing algorithm with memory-based rejection (aSAR) navigates the discrete design space, employing nine objective functions that balance Mean Absolute Percentage Error (MAPE) against model compactness. …”
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Urban Land Use Classification Model Fusing Multimodal Deep Features by Yougui Ren, Zhiwei Xie, Shuaizhi Zhai

“…The spectral features and subgraph features are then constructed, and a graph convolutional network (GCN) is utilized to extract the node relational features from both the global and local graphs, forming the topological structure deep features while aggregating local features into global ones. …”
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Research on Fracture Energy Prediction and Size Effect of Concrete Based on Deep Learning with SHAP Interpretability Method by Huiming Wang, Weiqi Zhang, Jie Lin, Shengpin Guo

“…Therefore, in order to swiftly and accurately predict the fracture energy of concrete and investigate the impact of various factors on it, this study employs a deep learning algorithm to establish the correlation between parameters and fracture energy. …”
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Experimental and computational evaluation of knee implant wear and creep under in vivo and ISO boundary conditions by Michael J. Dreyer, Seyyed Hamed Hosseini Nasab, Philippe Favre, Fabian Amstad, Rowena Crockett, William R. Taylor, Bernhard Weisse

“…In an effort to overcome these limitations, we hereby develop and experimentally validate a finite-element (FE)-based algorithm, including a novel cross-shear and contact pressure dependent wear and creep model, and apply it towards understanding the sensitivity of wear outcomes to the applied boundary conditions. …”
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Prediction of Human Papillomavirus-Host Oncoprotein Interactions Using Deep Learning by Sheila Santa, Samuel Kojo Kwofie, Kwasi Agyenkwa-Mawuli, Osbourne Quaye, Charles A Brown, Emmanuel A Tagoe

“…The model predicted an interaction between E6 and E7 of both HPV types with AKT, while only HPV31 E7 was shown to interact with IQGAP1 and MMP16 with confidence scores of 0.9638 and 0.5793, respectively. …”
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The relationship between clinical-anamnestic data and cell-free fetal DNA level assessed by semiconductor sequencing within non-invasive prenatal testing by E. S. Vashukova, O. A. Tarasenko, A. R. Maltseva, A. K. Popova, O. V. Pachuliia, O. N. Bespalova, A. S. Glotov

“…The identified patterns are important to take into consideration while using and interpreting NIPТ.…”
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Body mass index as the main predictor for length of stay in COVID-19 patients with mild and moderate symptoms: a cross-sectional study in COVID-19 emergency hospital in Indonesia by Siti Rahayu Nadhiroh, Anisa Lailatul Fitria, Armedy Ronny Hasugian, Erwin Astha Triyono, Nono Tri Nugroho, Alfadhila Khairil Sinatrya, Hazreen B Abdul Majid

“…Introduction: COVID-19 emerged as a novel global health crisis. While it has since been downgraded from its status as a public health emergency of international concern, the virus persists as a global health challenge. …”
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MHOE-DETR: A Ship Detection Method for Small and Fuzzy Targets Based on Satellite Remote Sensing Image Data by Zhuhua Hu, Xiyu Fan, Yaochi Zhao, Wei Wu, Jie Liu

“…The average accuracy, recall, and AP50 reached 96.3%, 91.4%, and 95.4%, respectively, while maintaining a low GFLOPS value (54.4 G) and parametric count (77.3 M). …”
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Durability prediction of sustainable marine concrete under freeze-thaw cycles using multi-objective machine learning models by Aïssa Rezzoug, Ali H. AlAteah, Sadiq Alinsaif, Sahar A. Mostafa

“…This study introduces machine learning (ML)-based algorithms for forecasting FTC-induced durability of concrete while simultaneously predicting sustainability metrics such as CO₂ emissions and production cost. …”
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An artificial intelligence and machine learning-driven CFD simulation for optimizing thermal performance of blood-integrated ternary nano-fluid by Mohib Hussain, Du Lin, Hassan Waqas, Qasem M. Al-Mdallal

“…The heat transfer ability of ternary nano-fluid is enhanced with an increase in the couple stress parameter while, a rising Hartmann number results in more thermal diffusion. …”
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Building an Associative Classification Data Model Based on the Apriori Method by K. V. Mulyukova, V. M. Kureichik

“…With manual integration, the database management system is not important. Algorithm methods work with selected data. This factor greatly simplifies the further development of program code.…”
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Local Sub-Block Contrast and Spatial–Spectral Gradient Feature Fusion for Hyperspectral Anomaly Detection by Dong Zhao, Xingchen Xu, Mingtao You, Pattathal V. Arun, Zhe Zhao, Jiahong Ren, Li Wu, Huixin Zhou

“…Most existing hyperspectral anomaly detection algorithms primarily rely on spatial information to identify anomalous targets. …”
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