Interpreting CNN models for musical instrument recognition using multi-spectrogram heatmap analysis: a preliminary study by Rujia Chen, Akbar Ghobakhlou, Ajit Narayanan

“…The NSynth database was used for training and evaluation. Visual heatmap analysis and statistical metrics, including Difference Mean, KL Divergence, JS Divergence, and Earth Mover’s Distance, were utilized to assess feature importance and model interpretability.ResultsOur findings highlight the strengths and limitations of each spectrogram type in capturing distinctive features of different instruments. …”
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Deep Learning with Dual-Channel Feature Fusion for Epileptic EEG Signal Classification by Bingbing Yu, Mingliang Zuo, Li Sui

“…Channel 2 employs a dual-branch convolutional neural network (CNN) to extract deeper and distinct features. …”
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OPTICALS: A Novel Framework for Optimizing Predictive Trading Indicators in Cryptocurrency Using Advanced Learning Simulations by Hasib Shamshad, Fasee Ullah, Syed Adeel Ali Shah, Muhammad Faheem, Beena Shamshad

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Multiview Multimodal Feature Fusion for Breast Cancer Classification Using Deep Learning by Sadam Hussain, Mansoor Ali Teevno, Usman Naseem, Daly Betzabeth Avendano Avalos, Servando Cardona-Huerta, Jose Gerardo Tamez-Pena

“…Various augmentation techniques are applied to both imaging and textual data to expand the training dataset size. Imaging features were extracted using a Squeeze-and-Excitation (SE) network-based ResNet50 model, while textual features were extracted using an artificial neural network (ANN). …”
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Aeroengine Remaining Life Prediction Using Feature Selection and Improved SE Blocks by Hairui Wang, Shijie Xu, Guifu Zhu, Ya Li

“…To reduce computational costs and improve prediction performance, we use random forest to evaluate the feature importance of sensor data. Based on the size of the feature corresponding to the Gini index, we select the appropriate sensor. …”
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Multi-source image feature extraction and segmentation techniques for karst collapse monitoring by Wenbo Lin, Xiao Li, Tingting Li

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Skin Lesion Diagnosis Through Deep Learning and Hybrid Texture Feature Augmentation by Irpan Adiputra Pardosi, Roni Yunis, Arwin Halim

“…The framework integrates handcrafted features with Convolutional Neural Networks (CNNs) to enhance classification accuracy. …”
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Application of big data technology in visitor behavioral characteristics identification and energy efficiency assessment of intelligent scenic spots by Wei Zhou, Jin Cui

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StomachNet: Optimal Deep Learning Features Fusion for Stomach Abnormalities Classification by Muhammad Attique Khan, Muhammad Shahzad Sarfraz, Majed Alhaisoni, Abdulaziz A. Albesher, Shuihua Wang, Imran Ashraf

“…The proposed method is evaluated on a combined database. It accomplished an accuracy of 99.46%, which shows significant improvement over preceding techniques and other neural network architectures.…”
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Multi-granularity feature intersection learning for visible-infrared person re-identification by Sixian Chan, Jie Wang, Jiaao Cui, Jie Hu, Zhuorong Li, Jiafa Mao

“…Abstract This paper proposes a multi-granularity feature intersection network (MGFINet) for visible-infrared person re-identification (VI-ReID). …”
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Exploring unsupervised feature extraction algorithms: tackling high dimensionality in small datasets by Hongqi Niu, Gabrielle B. McCallum, Anne B. Chang, Khalid Khan, Sami Azam

“…In this regard, feature extraction algorithms are important in addressing these challenges by reducing dimensionality while retaining essential information. …”
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Automated Root Cause Analysis of Network Failures in IP/MPLS Network Using Machine Learning and Case-Based Reasoning by Tikumporn Wankvar, Apichon Witayangkurn

“…Managing IP/MPLS networks requires advanced tools due to their inherent complexity. …”
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Comprehensive Analysis of the Role of Metabolic Features in Osteoporosis: A Multi-Omics Analysis by Chang S, Tao W, Shi P, Wu H, Liu H, Xu J, Chen J, Zhu J

“…The constructed multi-omics regulatory network aids in understanding the molecular mechanisms of metabolic features in OP progression.Keywords: osteoporosis, metabolic features, multi-omics analysis, machine learning, biomarkers…”
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Unsupervised Visual-to-Geometric Feature Reconstruction for Vision-Based Industrial Anomaly Detection by Dinh-Cuong Hoang, Phan Xuan Tan, Anh-Nhat Nguyen, Duc-Thanh Tran, Van-Hiep Duong, Anh-Truong Mai, Duc-Long Pham, Khanh-Toan Phan, Minh-Quang Do, Ta Huu Anh Duong, Tuan-Minh Huynh, Son-Anh Bui, Duc-Manh Nguyen, Viet-Anh Trinh, Khanh-Duong Tran, Thu-Uyen Nguyen

“…Instead of directly fusing these features, we propose a geometric feature reconstruction network that predicts 3D geometric features from the 2D visual features. …”
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Reinforcing long lead time drought forecasting with a novel hybrid deep learning model: a case study in Iran by Mahnoosh Moghaddasi, Mansour Moradi, Mahdi Mohammadi Ghaleni, Zaher Mundher Yaseen

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Towards a minimum universal features set for IoT DDoS attack detection by Osama Ebrahem, Salah Dowaji, Suhel Alhammoud

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Lightweight ECG signal classification via linear law-based feature extraction by Péter Pósfay, Marcell T Kurbucz, Péter Kovács, Antal Jakovác

“…The method identifies linear laws that capture shared patterns within a reference class, enabling compact and verifiable representations of time series data. We evaluate the method on two PhysioNet datasets, TwoLeadECG and variable projection networks (VPNet). …”
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Beyond Handcrafted Features: A Deep Learning Framework for Optical Flow and SLAM by Kamran Kazi, Arbab Nighat Kalhoro, Farida Memon, Azam Rafique Memon, Muddesar Iqbal

“…This paper presents a novel approach for visual Simultaneous Localization and Mapping (SLAM) using Convolution Neural Networks (CNNs) for robust map creation. Traditional SLAM methods rely on handcrafted features, which are susceptible to viewpoint changes, occlusions, and illumination variations. …”
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Machine Learning-Based Diabetes Risk Prediction Using Associated Behavioral Features by Ayodeji O. J. Ibitoye, Joseph D. Akinyemi, Olufade F. W. Onifade

“…The models’ performances were evaluated using accuracy, precision, recall and F1-score and NN presented the best performance overall achieving an F1-score of 85% for the correlated feature pairs (CF) and 75% for the direct feature pairs. …”
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A Deep Learning Method for Automatic Coronal Mass Ejection Feature Identification by P. Yang, H. S. Fu, J. B. Cao, F. Shen

“…Here, we present a deep learning–based algorithm for automated CME feature extraction, comprising four key stages: image preprocessing, segmentation, tracking, and feature extraction. …”
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