NMS-KSD: Efficient Knowledge Distillation for Dense Object Detection via Non-Maximum Suppression and Feature Storage by Suho Son, Byung Cheol Song

“…The core of NMS-KSD is the integration of Non-Maximum Suppression (NMS) and channel max pooling to effectively select and store key features from the teacher model’s intermediate feature maps. …”
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A comprehensive analysis of perturbation methods in explainable AI feature attribution validation for neural time series classifiers by Ilija Šimić, Eduardo Veas, Vedran Sabol

“…However, ensuring that explanation methods provide faithful and trustworthy explanations requires rigorous validation. Feature attribution methods (AMs) are among the most prevalent explanation methods, as they identify decisive aspects that influence model predictions through feature importance estimates. …”
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Improving Performance Of Hmm-based Asr For Gsm-efr Speech Coding by Lallouani Bouchakour, Mohamed Debyeche

“…In this paper, we investigate different features extractions techniques for GSM EFR (Enhanced Full Rate) coding with the aim to improve the performance of ASR in the GSM domain. …”
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Human and environmental feature-driven neural network for path-constrained robot navigation using deep reinforcement learning by Nabih Pico, Estrella Montero, Alisher Amirbek, Eugene Auh, Jeongmin Jeon, Manuel S. Alvarez-Alvarado, Babar Jamil, Redhwan Algabri, Hyungpil Moon

“…The paper provides a GitHub repository that includes comparative performance videos with state-of-the-art models in path-constrained scenarios, along with strategies for reward functions. …”
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Integrating peritumor and tumor CT radiomics features in predicting local control after SBRT in patients with pulmonary oligometastases by Yao Lu, Yiqi Wang, Yuxi Ding, Danni Chen, Wenguang He, Weixiang Zhong, Jing Yang, Senxiang Yan, Ge Ren, Feng Zhao

“…Meanwhile, Model-GP (with GTV and pGTV features) demonstrated an improved performance with a validation AUC of 0.851, reflecting the added value of peritumoral features. …”
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Prediction Method of Tangerine Peel Drying Moisture Ratio Based on KAN-BiLSTM and Multimodal Feature Fusion by Qi Ren, Jiandong Fang, Yudong Zhao

“…These multimodal features are fused and input into a KAN-BiLSTM model, where the KAN layer enhances nonlinear feature representation and a multi-head attention (MHA) mechanism highlights critical temporal and spatial features to improve prediction accuracy. …”
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NetMHCpan-4.2: improved prediction of CD8+ epitopes by use of transfer learning and structural features by Jonas Birkelund Nilsson, Jason Greenbaum, Bjoern Peters, Bjoern Peters, Morten Nielsen

“…We next apply transfer learning using experimentally validated pathogen- and cancer-derived epitopes from public databases to refine our prediction method, ensuring comprehensive data partitioning to prevent performance overestimation. ResultsIntegration of structural features results in improved predictive power and enhanced identification of peptide residues likely to interact with the MHC. …”
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Temporal Relational Graph Convolutional Network Approach to Financial Performance Prediction by Brindha Priyadarshini Jeyaraman, Bing Tian Dai, Yuan Fang

“…A logistic regression model uses these combined features and social media embeddings for performance prediction. …”
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ECG features improve multimodal deep learning prediction of incident T2DM in a Middle Eastern cohort by Farida Mohsen, Ali Safa, Zubair Shah

“…This study introduces ECG-DiaNet, a multimodal deep learning model that integrates electrocardiogram (ECG) features with established clinical risk factors (CRFs) to improve the prediction of T2DM onset. …”
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FA-Unet: A Deep Learning Method with Fusion of Frequency Domain Features for Fruit Leaf Disease Identification by Xiaowei Li, Wenlin Wu, Fenghua Zhu, Shenhao Guan, Wenliang Zhang, Zheng Li

“…However, deep learning models often perform poorly in complex backgrounds, and in some cases, they even outperform traditional algorithms. …”
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Enhancing Laser-Induced Breakdown Spectroscopy Quantification Through Minimum Redundancy and Maximum Relevance-Based Feature Selection by Manping Wang, Yang Lu, Man Liu, Fuhui Cui, Rongke Gao, Feifei Wang, Xiaozhe Chen, Liandong Yu

“…Our results demonstrate that the mRMR method significantly reduces the number of selected features while improving model performance. This study validates the effectiveness of the mRMR algorithm for LIBS feature extraction and highlights the potential of feature selection techniques to enhance predictive accuracy. …”
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Intelligent brain tumor detection using hybrid finetuned deep transfer features and ensemble machine learning algorithms by Rakesh Salakapuri, Panduranga Vital Terlapu, Kishore Raju Kalidindi, Ramesh Naidu Balaka, D. Jayaram, T. Ravikumar

“…The system uses advanced models like Inception-V3, ResNet-50, and VGG-16 for feature extraction, and for dimensional reduction, it uses the PCA model. …”
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Novel Sentiment Majority Voting Classifier and Transfer Learning-Based Feature Engineering for Sentiment Analysis of Deepfake Tweets by Madiha Khalid, Ali Raza, Faizan Younas, Furqan Rustam, Monica Gracia Villar, Imran Ashraf, Adnan Akhtar

“…For sentiment classification, we propose a novel transfer feature where embedding features are fed to a long short-term memory (LSTM), and decision tree (DT) models and outputs are combined into a single feature set. …”
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A Unified Approach to Video Anomaly Detection: Advancements in Feature Extraction, Weak Supervision, and Strategies for Class Imbalance by Rui Z. Barbosa, Hugo S. Oliveira

“…This paper explores advancements in Video Anomaly Detection (VAD), combining theoretical insights with practical solutions to address model limitations. Through comprehensive experimental analysis, the study examines the role of feature representations, sampling strategies, and curriculum learning in enhancing VAD performance. …”
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Radiomic features and tumor immune microenvironment associated with anaplastic lymphoma kinase-rearranged lung adenocarcinoma and their prognostic value by Ying Han, Wenya Feng, Wenya Feng, Huaxin Li, Hua Wang, Hua Wang, Zhaoxiang Ye

“…Combined radiomic, clinicopathological, and clinicopathological-radiomic models were built to predict ALK rearrangements. The models’ prediction performance was analyzed using receiver operating characteristic (ROC) curves with five-fold cross-validation. …”
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Automatic Recognition of Tunnel Water Leakage Based on Adaptive Information Extraction Network and Multiscale Feature Enhancement Module by Dandan Wang, Gongyu Hou, Qinhuang Chen, Weiyi Li, Haoxiang Li, Yaohua Shao, Xunan Yu

“…The decoder employs a sequential up-sampling strategy, complemented by skip connections, to integrate multiscale features effectively and enhance gradient flow. The experimental results show that the proposed model has better segmentation performance than other compared models, which is very suitable for detecting metro tunnel leakage in complex environments.…”
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Heart Sound Classification Using Harmonic and Percussive Spectral Features from Phonocardiograms with a Deep ANN Approach by Anupinder Singh, Vinay Arora, Mandeep Singh

“…These are refined using the ROC-AUC feature selection method to ensure optimal performance. …”
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Proteomics and machine learning: Leveraging domain knowledge for feature selection in a skeletal muscle tissue meta-analysis by Alireza Shahin-Shamsabadi, John Cappuccitti

“…This was used to handle high-dimensional and sparsely labeled dataset by breaking it down into five smaller categories using cellular composition information and then training a Random Forest model for each category separately. Using biological context for interpreting the data resulted in improved model performance and made tailored analysis possible by reducing the dimensionality and increasing signal-to-noise ratio as well as only preserving biologically relevant features in each category. …”
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Feature Extraction from Regenerated EEG – A Better Approach for ICA Based Eye Blink Artifact Detection by Maliha Rashida, Mohammad Ashfak Habib

“…This paper presents a new model of eye blink detection for ICA based approach, where the decomposed ICs are projected to their corresponding EEG segments (ReEEG), and feature extraction is performed on the ReEEG instead of the IC. …”
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Diagnosis of Schizophrenia Using Feature Extraction from EEG Signals Based on Markov Transition Fields and Deep Learning by Alka Jalan, Deepti Mishra, Marisha, Manjari Gupta

“…After the transformation, a pre-trained VGG-16 model is employed to extract meaningful features from the images. …”
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