A smartphone application integrating deep learning and OpenCV for rapid, non-destructive grade assessment of Paeoniae Radix Alba slices by Miaohua Qian, Tao Wang, Zhikun Zhang, Weitao Chen, Yong Liu, Jianhao He, Jinsheng Wang, Qing Xia, Liangquan Jia, Chong Yao

“…Paeoniae Radix Alba slices, widely used in traditional Chinese medicine and as a functional food, play a vital role in health maintenance. …”
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Deep Transfer Learning-Based Performance Prediction Considering 3-D Flux in Outer Rotor Interior Permanent Magnet Synchronous Motors by Moo-Hyun Sung, Soo-Hwan Park, Kyoung-Soo Cha, Jae-Han Sim, Myung-Seop Lim

“…This method uses deep transfer learning (DTL) to transfer knowledge from a large 2-D FEA dataset to a smaller, computationally costly 3-D FEA dataset. …”
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Application of Machine Learning and Multi-Dimensional Perception in Urban Spatial Quality Evaluation: A Case Study of Shanghai Underground Pedestrian Street by Tianning Yao, Yao Xu, Liang Sun, Pan Liao, Jin Wang

“…The research results indicate variability in pedestrians’ evaluations of spatial quality across different functionally oriented spaces. Key factors influencing these evaluations include Gorgeous, Warm, Good Ventilation, and Flavour indicators. …”
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ACD-ML: Advanced CKD detection using machine learning: A tri-phase ensemble and multi-layered stacking and blending approach by Mir Faiyaz Hossain, Shajreen Tabassum Diya, Riasat Khan

“…Chronic Kidney Disease (CKD), the gradual loss and irreversible damage of the kidney’s functionality, is one of the leading contributors to death and causes about 1.3 million people to die annually. …”
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TSF-MDD: A Deep Learning Approach for Electroencephalography-Based Diagnosis of Major Depressive Disorder with Temporal–Spatial–Frequency Feature Fusion by Wei Gan, Ruochen Zhao, Yujie Ma, Xiaolin Ning

“…Major depressive disorder (MDD) is a prevalent mental illness characterized by persistent sadness, loss of interest in activities, and significant functional impairment. It poses severe risks to individuals’ physical and psychological well-being. …”
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Applying effective feedback principles to promote the ethics of care and justice during emergency remote teaching and learning in three chemical engineering modules by Rishen Roopchund, Vizelle Naidoo

“…Lecturer 1 adopted a blended learning approach in modules 1 and 2 before the lockdown, while lecturer 2 functioned as a full contact module. …”
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Regional Short‐Term Wind Power Prediction Based on CEEMDAN‐FTC Feature Mapping and EC‐TCN‐BiLSTM Deep Learning by Guoyuan Qin, Xiaosheng Peng, Zimin Yang

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Deep learning algorithm on H&E whole slide images to characterize TP53 alterations frequency and spatial distribution in breast cancer by Chiara Frascarelli, Konstantinos Venetis, Antonio Marra, Eltjona Mane, Mariia Ivanova, Giulia Cursano, Francesca Maria Porta, Alberto Concardi, Arnaud Gerard Michel Ceol, Annarosa Farina, Carmen Criscitiello, Giuseppe Curigliano, Elena Guerini-Rocco, Nicola Fusco

“…Traditional ancillary methods like immunohistochemistry (IHC) to assess TP53 functionality face pre- and post-analytical challenges. …”
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Artificial-Intelligence Bio-Inspired Peptide for Salivary Detection of SARS-CoV-2 in Electrochemical Biosensor Integrated with Machine Learning Algorithms by Marcelo Augusto Garcia-Junior, Bruno Silva Andrade, Ana Paula Lima, Iara Pereira Soares, Ana Flávia Oliveira Notário, Sttephany Silva Bernardino, Marco Fidel Guevara-Vega, Ghabriel Honório-Silva, Rodrigo Alejandro Abarza Munoz, Ana Carolina Gomes Jardim, Mário Machado Martins, Luiz Ricardo Goulart, Thulio Marquez Cunha, Murillo Guimarães Carneiro, Robinson Sabino-Silva

“…Support vector machine (SVM)-based machine learning analyzed electrochemical data, enhancing sensitivity and specificity. …”
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Machine learning approach effectively discriminates between Parkinson’s disease and progressive supranuclear palsy: Multi-level indices of rs-fMRI by Weiling Cheng, Xiao Liang, Wei Zeng, Jiali Guo, Zhibiao Yin, Jiankun Dai, Daojun Hong, Fuqing Zhou, Fangjun Li, Xin Fang

“…Therefore, we aimed to discriminate between PD and PSP based on multi-level indices of resting-state functional magnetic resonance imaging (rs-fMRI) via the machine learning approach. …”
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LSN-GTDA: Learning Symmetrical Network via Global Thermal Diffusion Analysis for Pedestrian Trajectory Prediction in Unmanned Aerial Vehicle Scenarios by Ling Mei, Mingyu Fu, Bingjie Wang, Lvxiang Jia, Mingyu Yu, Yu Zhang, Lijun Zhang

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A Deep Learning Method for Improving Community Multiscale Air Quality Forecast: Bias Correction, Event Detection, and Temporal Pattern Alignment by Ioannis Stergiou, Nektaria Traka, Dimitrios Melas, Efthimios Tagaris, Rafaella-Eleni P. Sotiropoulou

“…Addressing these limitations, this study introduces a hybrid deep learning model that integrates convolutional neural networks (CNNs) and Long Short-Term Memory (LSTM) for ozone forecast bias correction. …”
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A clustering-aided multi-agent deep reinforcement learning for multi-objective parallel batch processing machines scheduling in semiconductor manufacturing by Peng Zhang, Mengyu Jin, Ming Wang, Jie Zhang, Junjie He, Peng Zheng

“…Specifically, to achieve diverse nondominated solutions, an offline multi-objective scheduling algorithm named Multi-Subpopulation fast elitist Non-Dominated Sorting Genetic Algorithm (MS-NSGA-II) is firstly developed to obtain the Pareto Fronts, and a clustering algorithm based on cosine distance is employed to analyze the distribution of Pareto frontier solution, which would be used to guide reward functions design in multi-agent deep reinforcement learning. …”
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Identification of M2 macrophage-related genes associated with diffuse large B-cell lymphoma via bioinformatics and machine learning approaches by Jiayi Zhang, Zhixiang Jia, Jiahui Zhang, Xiaohui Mu, Limei Ai

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Comparison of FORCE trained spiking and rate neural networks shows spiking networks learn slowly with noisy, cross-trial firing rates. by Thomas Robert Newton, Wilten Nicola

“…Despite this, rate networks still functioned well when their weight matrices were replaced with those learned from spiking networks. …”
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Method for Evaluating Urban Building Renewal Potential Based on Multimachine Learning Integration: A Case Study of Longgang and Longhua Districts in Shenzhen by Dengkuo Sun, Yuefeng Lu, Yong Qin, Miao Lu, Zhenqi Song, Ziqi Ding

“…With the continuous advancement of urbanization, urban renewal has become a vital means of enhancing urban functionality and improving living environments. Traditional urban renewal research primarily focuses on the macro level, analyzing regions or units, with limited studies targeting individual buildings. …”
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Long-term comparative analysis of machine learning models: A deep dive into applications of artificial intelligence for enhancing photovoltaic performance prediction by Ali Akbar Yaghoubi, Mahdi Gandomzadeh, Aslan Gholami, Roghayeh Gavagsaz-Ghoachani, Majid Zandi

“…Six supervised machine learning models were trained: K-Nearest Neighbors (KNN), Cross-Decomposition Regression, Decision Trees, Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), and Ensemble Learning. …”
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Enhanced Cuckoo Search Optimization with Opposition-Based Learning for the Optimal Placement of Sensor Nodes and Enhanced Network Coverage in Wireless Sensor Networks by Mandli Rami Reddy, M. L. Ravi Chandra, Ravilla Dilli

“…The performance of ECSO-OBL is evaluated using eight benchmark functions, and the results of three cases are compared with the existing methods. …”
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Exon level machine learning analyses elucidate novel candidate miRNA targets in an avian model of fetal alcohol spectrum disorder. by Abrar E Al-Shaer, George R Flentke, Mark E Berres, Ana Garic, Susan M Smith

“…We address these limitations with a novel approach and implemented an unsupervised machine learning model, which we applied to an exon-level analysis to reduce data complexity to the most likely functionally relevant exons, without loss of novel information. …”
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Integrating environmental and LULC drivers of groundwater droughts in groundwater-dependent ecosystems: a machine learning (XGBoost)-SEM analysis with ecosystem implications by Kawawa Banda, Christopher Shilengwe, Imasiku Nyambe

“…This understanding is not only critical for sustaining groundwater availability but also for preserving the integrity and functioning of groundwater-dependent ecosystems.…”
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