Human Activity Recognition: A Comparative Study of Validation Methods and Impact of Feature Extraction in Wearable Sensors by Saeed Ur Rehman, Anwar Ali, Adil Mehmood Khan, Cynthia Okpala

“…The feature models demonstrate a remarkable 30% higher accuracy, underscoring the importance of feature engineering in enhancing the robustness and precision of HAR systems.…”
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Meta-Features Extracted from Use of kNN Regressor to Improve Sugarcane Crop Yield Prediction by Luiz Antonio Falaguasta Barbosa, Ivan Rizzo Guilherme, Daniel Carlos Guimarães Pedronette, Bruno Tisseyre

“…This study is based on an experiment conducted by researchers from the Commonwealth Scientific and Industrial Research Organisation (CSIRO), who employed a UAV-mounted LiDAR and multispectral imaging sensors to monitor two sugarcane field trials subjected to varying nitrogen (N) fertilization regimes in the Wet Tropics region of Australia. The predictive performance of models utilizing multispectral features, LiDAR-derived features, and a fusion of both modalities was evaluated against a benchmark model based on the Normalized Difference Vegetation Index (NDVI). …”
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Analyzing infant cry to detect birth asphyxia using a hybrid CNN and feature extraction approach by Samrat Kumar Dey, Khandaker Mohammad Mohi Uddin, Arpita Howlader, Md. Mahbubur Rahman, Hafiz Md. Hasan Babu, Nitish Biswas, Umme Raihan Siddiqi, Badhan Mazumder

“…Hyperparameter optimization is performed using GridSearchCV for various machine-learning models. …”
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Prediction of Parkinson Disease Using Long-Term, Short-Term Acoustic Features Based on Machine Learning by Mehdi Rashidi, Serena Arima, Andrea Claudio Stetco, Chiara Coppola, Debora Musarò, Marco Greco, Marina Damato, Filomena My, Angela Lupo, Marta Lorenzo, Antonio Danieli, Giuseppe Maruccio, Alberto Argentiero, Andrea Buccoliero, Marcello Dorian Donzella, Michele Maffia

“…The K-nearest neighbor (KNN) and decision tree (DT) performed the worst. Notably, by combining a comprehensive set of long-term, short-term, and non-standard acoustic features, unlike previous studies that typically focused on only a subset, our study achieved higher predictive performance, offering a more robust model for early PD detection. …”
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Customized Spectro-Temporal CNN Feature Extraction and ELM-Based Classifier for Accurate Respiratory Obstruction Detection by M. Muthulakshmi, K. Venkatesan, Syarifah Bahiyah Rahayu, K. L. Nayana Sree

“…The fusion of deep features from different spatiotemporal structures outperforms individual features when fed into the ELM model, resulting in clear discrimination of obstructive and restrictive respiratory diseases. …”
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Global Optical and SAR Image Registration Method Based on Local Distortion Division by Bangjie Li, Dongdong Guan, Yuzhen Xie, Xiaolong Zheng, Zhengsheng Chen, Lefei Pan, Weiheng Zhao, Deliang Xiang

“…Additionally, a hard negative mining loss is incorporated to further enhance feature discriminability. Feature descriptors are extracted separately from regions with different distortion levels, and corresponding transformation models are built for local registration. …”
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A method for identifying gully-type debris flows based on adaptive multi-scale feature extraction by Qiuyu Liu, Ting Wang, Zhijie Zheng, Baoyun Wang

“…When integrated with traditional CNNs, the module significantly improves the recognition performance of gully-type debris flows. For instance, with ResNet18, the model achieves an accuracy of 89.7% and recall of 85.7%, representing improvements of 17.3% and 21.4%, respectively, over the baseline.…”
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Hierarchical Multi-Scale Patch Attention and Global Feature-Adaptive Fusion for Robust Occluded Face Recognition by Elhamsadat Hejazi, Majid Ahmadi, Arash Ahmadi

“…Occluded face recognition remains a challenging problem in biometric identification, where real-world obstructions such as masks, sunglasses, scarves, and hands obscure key facial features. To address this, we introduce a dual-branch architecture that combines a Local Multi-Patch Attention Module (LMPAM) for extracting localized features with a Global Self-Attention Channel Module (GSACM) to enhance overall feature representation. …”
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Precision Measurement and Feature Selection in Medical Diagnostics using Hybrid Genetic Algorithm and Support Vector Machine by Gowri Subadra K, Sathish Babu P

“…This study introduces a hybrid feature selection method based on genetic algorithm (GA) and Bucket of Models (BoM) approach to improve breast cancer detection and classification. …”
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A Transcriptomic Biomarker for Predicting the Response to TACE Correlates with the Tumor Microenvironment and Radiomics Features in Hepatocellular Carcinoma by Wang C, Leng B, You R, Yu Z, Lu Y, Diao L, Jiang H, Cheng Y, Yin G, Xu Q

“…After obtaining images from The Cancer Imaging Archive (TCIA), tumor labeling and radiomics feature extraction, the Rad-score model was generated. …”
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Prediction and validation of nanowire proteins in Oleidesulfovibrio alaskensis G20 using machine learning and feature engineering by Dheeraj Raya, Vincent Peta, Alain Bomgni, Shiva Aryal, Tuyen Duc Do, Kalimuthu Jawaharraj, David R. Salem, Venkataramana Gadhamshetty, Saurabh Sudha Dhiman, Etienne Z. Gnimpieba

“…Protein feature, such as dipeptide amino acid composition, transition, and distribution enhance the model’s performance. …”
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Analysis of Cardiac Arrhythmias Based on ResNet-ICBAM-2DCNN Dual-Channel Feature Fusion by Chuanjiang Wang, Junhao Ma, Guohui Wei, Xiujuan Sun

“…Subsequently, in the primary channel, region of interest features are emphasized using a ResNet-ICBAM network model for feature extraction. …”
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Multi-Scale Geometric Feature Extraction and Global Transformer for Real-World Indoor Point Cloud Analysis by Yisheng Chen, Yu Xiao, Hui Wu, Chongcheng Chen, Ding Lin

“…The local geometric structure helps the model learn the shape features of objects at the detail level, while the global context provides overall scene semantics and spatial relationship information between objects. …”
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Road Roughness Recognition: Feature Extraction and Speed-Adaptive Classification Based on Simulation and Real-Vehicle Tests by Jie Xing, Zhun Cheng, Shuai Ye, Songwei Liu, Jiawei Lin

“…Road roughness exerts a direct influence on the vertical dynamic performance of vehicles, and the accurate characterization of road roughness is essential for optimizing vehicle suspension systems. …”
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Few-shot biomedical NER empowered by LLMs-assisted data augmentation and multi-scale feature extraction by Di Zhao, Wenxuan Mu, Xiangxing Jia, Shuang Liu, Yonghe Chu, Jiana Meng, Hongfei Lin

“…Simultaneously, we employ dynamic convolution to capture multi-scale semantic information in sentences and enhance feature representation based on PubMedBERT. We evaluated the experiments on four biomedical NER datasets (BC5CDR-Disease, NCBI, BioNLP11EPI, BioNLP13GE), and the results exceeded the current state-of-the-art models in most few-shot scenarios, including mainstream large language models like ChatGPT. …”
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Consistent Vertical Federated Deep Learning Using Task-Driven Features to Construct Integrated IoT Services by Soyeon Oh, Minsoo Lee

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Building Recognition on Subregion’s Multiscale Gist Feature Extraction and Corresponding Columns Information Based Dimensionality Reduction by Bin Li, Wei Pang, Yuhao Liu, Xiangchun Yu, Anan Du, Yecheng Zhang, Zhezhou Yu

“…In this paper, we proposed a new building recognition method named subregion’s multiscale gist feature (SM-gist) extraction and corresponding columns information based dimensionality reduction (CCI-DR). …”
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Complex Texture Contour Feature Extraction of Cracks in Timber Structures of Ancient Architecture Based on YOLO Algorithm by Jian Ma, Weidong Yan, Guoqi Liu, Shiyu Xing, Siqi Niu, Tong Wei

“…In the comparing process, we mainly have discussed the index performance of the three models in terms of training time, loss function, recall rate, and mAP value. …”
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Automatic grading of barley grain for brewery industries using convolutional neural network based on texture features by Debalke Embeyale, Yao-Tien Chen, Yaregal Assabie

“…Unlike the conventional approach of using raw images of barley grains, our method uses texture features as inputs to the CNN model. To train and test the system, we collected images of barley grains in Ethiopia and categorized them into four quality classes (Grade 1, Grade 2, Grade 3, and Under-grade) according to the Ethiopian Standards Authority specifications. …”
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An optimized feature selection using triangle mutation rule and restart strategy in enhanced slime mould algorithm by Ibrahim Musa Conteh, Gibril Njai, Abass Conteh, Qingguo Du

“…Then we combine the TRSMA with Support Vector Machines (SVM) and propose the TRSMA-SVM model to select the joint feature and classifier parameters. …”
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