Supervised contrastive loss helps uncover more robust features for photoacoustic prostate cancer identification by Yingna Chen, Yingna Chen, Feifan Li, Zhuoheng Dai, Ying Liu, Shengsong Huang, Qian Cheng, Qian Cheng

“…Three distinct models, namely the CNN-based model, the supervised contrastive (SC) model, and the supervised contrastive loss adjust (SCL-adjust) model, have been compared, along with traditional feature extraction and machine learning-based methods.ResultsThe outcomes have indicated that the SCL-adjust model exhibits the optimal performance, its accuracy rate has increased by more than 10% compared with the traditional method. …”
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Federated Learning for Privacy-Preserving Employee Performance Analytics by Jay Barach

“…The framework integrates feature-level and instance-level attention to model complex workforce patterns, applies differential privacy through gradient masking to ensure compliance with data protection regulations, and enhances interpretability using local SHAP and LIME explanations. …”
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Expeditious Dynamic Clustering in Preprocessing for High-Performance Classification by Anamika Pimpa, Narissara Eiamkanitchat

“…The results showcased strong classification performance and high accuracy. Furthermore, in addition to EDC data preprocessing, appending it to the classification model alongside the aforementioned two models can facilitate feature selection without compromising classification accuracy.…”
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A stacked generalization approach for day ahead hourly photovoltaic power forecasting by Fatema Islam Tania, Pinki Rani, Tofael Ahmed, Shameem Ahmad

“…Short-term PV power generation forecasting relies on some key meteorological feature behaviors. This study aims to design a robust model for day-ahead PV power generation forecasting that performs well under various sub-conditions. …”
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Swift prediction of personalized head and chest organ doses from CT examinations via neural networks with optimized quantity of hidden layers and radiomics features by Wencheng Shao, Xin Lin, Ying Huang, Liangyong Qu, Weihai Zhuo, Haikuan Liu

“…Objective: To utilize radiomics features to enhance the prediction of personalized organ doses from CT scans, in order to explore methods for improving neural network-based models. …”
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From digitized whole‐slide histology images to biomarker discovery: A protocol for handcrafted feature analysis in brain cancer pathology by Xuanjun Lu, Yawen Ying, Jing Chen, Zhiyang Chen, Yuxin Wu, Prateek Prasanna, Xin Chen, Mingli Jing, Zaiyi Liu, Cheng Lu

“…The PHBCP comprises seven main steps: (1) problem definition, (2) data quality control, (3) image preprocessing, (4) feature extraction, (5) feature filtering, (6) modeling, and (7) performance analysis. …”
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Fusion feature-based hybrid methods for diagnosing oral squamous cell carcinoma in histopathological images by Jiaxing Li

“…Model performance was evaluated based on classification accuracy on two independent OSCC datasets.ResultsThe proposed hybrid feature model demonstrated excellent performance in pathological diagnosis, achieving accuracies of 99.36% and 99.59%, respectively. …”
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DEVELOPMENT AND EFFICIENCY OF BUDGET OF PUBLIC INSTITUTIONS BY THE METHOD BASED ON PERFORMANCE by Irina-Ștefana, CIBOTARIU

“…In Romania, public sector performance is a concept on which it has been written very little and rarely applied in practice. …”
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Improving distributed systems failure prediction via multi-objective feature selection and deep forest by Zhidan Yuan, Yikai Zhang, Yingqi Yu, Taizhi Lv, Tao Huang

“…Distributed systems failure prediction uses Key Performance Indicator (KPI) metrics to train machine learning models to identify potential system failures. …”
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EfficientNet B0 Feature Extraction with L2-SVM Classification for Robust Facial Expression Recognition by Ahmad Taufiq Akbar, Shoffan Saifullah, Hari Prapcoyo, Heru Rustamadji, Nur Heri Cahyana

“…Future work will extend the framework to in-the-wild datasets and incorporate model explainability techniques to improve interpretability in practical deployment Keywords: Facial Expression Recognition, EfficientNet, SVM, Deep Features, Emotion Classification…”
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Acoustic-based machine learning approaches for depression detection in Chinese university students by Yange Wei, Yange Wei, Shisen Qin, Fengyi Liu, Rongxun Liu, Yunze Zhou, Yuanle Chen, Xingliang Xiong, Wei Zheng, Guangjun Ji, Yong Meng, Fei Wang, Fei Wang, Ruiling Zhang

“…Acoustic analysis and feature extraction were performed using the OpenSMILE toolkit, yielding 523 features encompassing spectral, glottal, and prosodic characteristics. …”
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Machine Learning-Based Approach for HIV/AIDS Prediction: Feature Selection and Data Balancing Strategy by Abdul Mizwar A Rahim, Ahmad Ridwan, Bambang Pilu Hartato, Firman Asharudin

“…This study aims to enhance the performance of AIDS infection prediction models by integrating feature selection, data balancing, and machine learning classification techniques. …”
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Machine-learning-driven feature importance analysis for guiding the protonic ceramic fuel cell manufacturing by Jinwoo Kim, Jaewan Baek, Mingi Choi

“…Based on the selected model of XGBoost, we also conduct the feature analysis using Shapley additive explanations, which successfully determine the factors contributing most to the PCFC performance in terms of the materials and manufacturing processes for the anode, cathode, and electrolyte in each case. …”
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FSDENet: A Frequency and Spatial Domains-Based Detail Enhancement Network for Remote Sensing Semantic Segmentation by Jiahao Fu, Yinfeng Yu, Liejun Wang

“…Our framework employs spatial processing methods to extract rich multiscale spatial features and fine-grained semantic details. By effectively integrating global and frequency-domain information through the fast Fourier transform (FFT) in global mappings, the model’s capability to discern global representations under grayscale variations is significantly strengthened. …”
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Dual Branch Encoding Feature Aggregation for Cloud and Cloud Shadow Detection of Remote Sensing Image by Naikang Shi, Haifeng Lin, Huiwen Ji, Min Xia

“…Specifically, two novel modules are introduced: the Dual-branch Lightweight Aggregation Module (DLAM), which fuses CNN and Transformer features in the early encoding stage and emphasizes key information through a feature weight allocation mechanism; and the Dual-branch Attention Aggregation Module (DAAM), which further integrates local and global features in the late encoding stage, improving the model’s differentiation performance between cloud and cloud shadow areas. …”
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Development of Robot Feature for Stunting Analysis Using Long-Short Term Memory (LSTM) Algorithm by Muhammad Rahadian Abdurrahman, Halim Al Aziz, Farras Adhani Zayn, Muhammad Agus Purnomo, Heru Agus Santoso

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PFRNet: A Small Object Detection Method Based on Parallel Feature Extraction and Attention Mechanism by Hai Lin, Ji Wang, Jingguo Li

“…In addition, PFRNet introduces the spatial pyramid pooling fusion with spatial attention (SPPFSPA) module, which integrates multi-scale features with an attention mechanism, enabling the model to better focus on areas of interest, thereby improving detection performance. …”
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Dual-Branch Spatial–Spectral Transformer with Similarity Propagation for Hyperspectral Image Classification by Teng Wen, Heng Wang, Liguo Wang

“…Specifically, this model first employs a Hybrid Pooling Spatial Channel Attention (HPSCA) module to integrate global information by pooling across different dimensional directions, thereby enhancing its ability to extract salient features. …”
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Deep Learning-Enhanced Ultrasound Analysis: Classifying Breast Tumors Using Segmentation and Feature Extraction by Ali Hamza, Martin Mezl

“…Feature extraction is then performed using MobileNetV2, and these features are used to train and validate classification models to differentiate between malignant and benign breast masses. …”
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Fine-Grained Feature Extraction in Key Sentence Selection for Explainable Sentiment Classification Using BERT and CNN by Thennakoon Mudiyanselage Anupama Udayangani Gunathilaka, Jinglan Zhang, Yuefeng Li

“…First, the BERT pre-trained language model captures global features through a feature vector representation. …”
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