Remote sensing image interpretation of geological lithology via a sensitive feature self-aggregation deep fusion network by Kang He, Jie Dong, Haozheng Ma, Yujie Cai, Ruyi Feng, Yusen Dong, Lizhe Wang

“…Although deep learning (DL) methods has significantly improved the performance of lithological remote sensing interpretation, its accuracy remains far below the level achieved by visual interpretation performed by domain experts. …”
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Research on Multi-Scale Spatio-Temporal Graph Convolutional Human Behavior Recognition Method Incorporating Multi-Granularity Features by Yulin Wang, Tao Song, Yichen Yang, Zheng Hong

“…An adaptive cross-scale feature fusion layer is designed using a normalized Gaussian function to perform feature fusion among different granularities, guiding the model to focus on discriminative feature representations among similar behaviors through fine-grained features. …”
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Simple Yet Powerful: Machine Learning-Based IoT Intrusion System With Smart Preprocessing and Feature Generation Rivals Deep Learning by Kazim Kivanc Eren, Kerem Kucuk, Fatih Ozyurt, Omar H. Alhazmi

“…Experimental results show that our model achieves an area under the curve (AUC) score of 0.99 on both training and test sets with high performance in a variety of attack categories. …”
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Method for EEG signal recognition based on multi-domain feature fusion and optimization of multi-kernel extreme learning machine by Shan Guan, Tingrui Dong, Long-kun Cong

“…Secondly, multivariate autoregressive (MVAR) model, wavelet packet decomposition, and Riemannian geometry methods are used to extract features from the time domain, frequency domain, and spatial domain, respectively, to construct a joint time-frequency-space feature vector. …”
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Advancing Glaucoma Diagnosis Through Multi‐Scale Feature Extraction and Cross‐Attention Mechanisms in Optical Coherence Tomography Images by Hamid Reza Khajeha, Mansoor Fateh, Vahid Abolghasemi, Amir Reza Fateh, Mohammad Hassan Emamian, Hassan Hashemi, Akbar Fotouhi

“…Glaucoma samples were subsequently merged into each group, and independent training was performed. In addition to data balancing, the proposed method incorporates key architectural innovations, including multi‐scale feature extraction, a cross‐attention mechanism, and a Channel and Spatial Attention Module (CSAM), to improve feature extraction and focus on critical image regions. …”
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Transcription of a centromere-enriched retroelement and local retention of its RNA are significant features of the CENP-A chromatin landscape by B. J. Chabot, R. Sun, A. Amjad, S. J. Hoyt, L. Ouyang, C. Courret, R. Drennan, L. Leo, A. M. Larracuente, L. J. Core, R. J. O’Neill, B. G. Mellone

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BRAFV600E mutation and its association with clinicopathological features of colorectal cancer: a systematic review and meta-analysis. by Dong Chen, Jun-Fu Huang, Kai Liu, Li-Qun Zhang, Zhao Yang, Zheng-Ran Chuai, Yun-Xia Wang, Da-Chuan Shi, Qing Huang, Wei-Ling Fu

“…However, the association between the BRAFV600E mutation and the clinicopathological features of CRC remains controversial. We performed a systematic review and meta-analysis to estimate the effect of BRAFV600E mutation on the clinicopathological characteristics of CRC.…”
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High-Performance YOLOv5s: Traffic Sign Detection Algorithm for Small Target by Mengxia Hu, Shengwen Pi, Jinying Zhou, Xinming Wan, Qiaoshou Liu

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Analysis and selection of the structure of a multiprocessor computing system according to the performance criterion by G. V. Petushkov, A. S. Sigov

“…The main results of the work were obtained using methods of mathematical analysis and modeling.Results. The study considers the structure of modern multicore microprocessors as the basis for building CMs of cluster CSs. …”
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A method based on clustering fast search for bearing performance degradation assessment by ShengWen Zhou, Li Zhang, Xiaoming Yang, Fan Xu, BaiGang Du, RuiPing Luo, Wenhui Zeng

“…Few studies have explored the application of CFS for bearing performance degradation assessment (PDA). Unlike traditional cluster models, such as Fuzzy C-Means, Gustafson–Kessel, Gath–Geva, K-means, and K-medoids, CFS automatically selects the cluster centers according to local density and distance. …”
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Characterization and feature selection of volatile metabolites in Yangxian pigmented rice varieties through GC-MS and machine learning algorithms by Kaiqi Cheng, Ruonan Dong, Fei Pan, Wen Su, Lingjie Xi, Meng Zhang, Jingzhang Geng, Ruichang Gao, Ruichang Gao, Wengang Jin, A. M. Abd El-Aty, A. M. Abd El-Aty

“…Four machine learning models were further used for the classification of various colored rice varieties, and random forest model was the optimum for predicting classification, with an accuracy of 0.97. …”
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Bridging the Gap: Missing Data Imputation Methods and Their Effect on Dementia Classification Performance by Federica Aracri, Maria Giovanna Bianco, Andrea Quattrone, Alessia Sarica

“…Inadequate handling of missing values can compromise the performance and interpretability of machine learning (ML) models. …”
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AED-Net: A High-Resolution Remote Sensing Image Road Extraction Method Integrating Atrous Spatial Pyramid Pooling and Efficient Channel Attention Mechanism by Jintong Ren, Lizhi Liu, Zixuan Xia, Yang Liu

“…We propose a road extraction model named AED-Net, which employs a lightweight MobileNet v2 as the feature extractor, combines the multi-scale feature extraction capability of ASPP with an encoder-decoder structure, and integrates an ECA mechanism to enhance feature learning ability. …”
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Performance comparison of machine learning algorithms for condition monitoring of tapered roller bearings by Harshal Aher, Nilesh Ghuge

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Machine Learning and Deep Learning for Crop Disease Diagnosis: Performance Analysis and Review by Habiba Njeri Ngugi, Andronicus A. Akinyelu, Absalom E. Ezugwu

“…This imbalance complicates model generalization, indicating a need for preprocessing steps to enhance performance. …”
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Prediction of the sound absorption performance for micro-perforated panel based on machine learning by Binxia Yuan, Tianqi You, Huanhuan Jiang, Hong Qian, Lan Cao, Rui Zhu

“…The prediction performance, stability, and generalization ability of the four predictive models are evaluated using R 2, MAE, and RMSE. …”
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Open-source analysis of the potential configuration and kinetic performance of the Oreshnik ballistic missile by Mykola Bondarenko, Volodymyr Habrinets, Mykhailo Vorobei

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Exploring the performance of LBP-capsule networks with K-Means routing on complex images by Patrick Mensah Kwabena, Benjamin Asubam Weyori, Ayidzoe Abra Mighty

“…Experimental results show that the proposed model generates fewer parameters and performs comparably well with the state-of-the-art multi-lane capsule networks on complex images.…”
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Energy performance estimation for large building portfolios with machine learning-based techniques by Frédéric Montet, Alessandro Pongelli, Jonathan Rial, Stefanie Schwab, Jean Hennebert, Thomas Jusselme

“…It also opens the door for further improvements through the inclusion of supplementary building features at the input of the predictive system. This work includes (a) the integration of a knowledge database thanks to the Swiss CECB energy performance certificates, referencing more than 70 000 buildings, (b) the preparation of a training data set through the selection of relevant physical characteristics of buildings (input) and the corresponding energy consumption labels (output), (c) the development of predictive models used in a supervised way, (d) their evaluation on an independent test set.…”
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Assessing the Influence of Occupancy Factors on Energy Performance in US Small Office Buildings by Seddigheh Norouziasl, Sorena Vosoughkhosravi, Amirhosein Jafari, Zhihong Pang

“…This creates a dataset of occupancy parameters and building energy performance across various climate zones. Finally, various feature selection and statistical analysis methods are applied to the generated dataset. …”
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