A novel approach for music genre identification using ZFNet, ELM, and modified electric eel foraging optimizer by Shuang Zhang, Zhiyong Sun, Hasan Jafari

“…The proposed model uses a pre-trained Zeiler and Fergus Network (ZFNet) to extract high-level features from audio signals, while an Extreme Learning Machines (ELM) is utilized for efficient classification. …”
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Reliable Execution Based on CPN and Skyline Optimization for Web Service Composition by Liping Chen, Weitao Ha, Guojun Zhang

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Vector visibility graph for rare event classification in complex system multivariate time series data by Bo Peng, Shan Gao

“…The study further highlights the potential of incorporating VVG-derived network statistics as additional features for machine learning and deep learning models. …”
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Detection of Covid-19 from Chest CT Images using Xception Architecture: A Deep Transfer Learning based Approach by Özlem Polat

“…This study proposes a solution for detecting Covid-19 using chest computed tomography (CT) scan images. Firstly, image features are extracted using Xception network, convolutional neural network (CNN) based transfer learning architecture, then classification process is performed with a fully connected neural network (FCNN) added at the end of this architecture. …”
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A Mutual Information Constrained Multitask Learning Method for Very High-Resolution Building Segmentation by Yongchuang Wu, Yingchun Wang, Hui Yang, Peng Zhang, Yanlan Wu, Biao Wang

“…Multitask learning (MTL) has shown its potential on improving segmentation accuracy with shared network weights to simultaneously capture various building-related features. …”
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Machine Learning Prediction Model of Waitlist Outcomes in Patients with Primary Sclerosing Cholangitis by Xun Zhao, MD, Maryam Naghibzadeh, MD, Yingji Sun, MSc, Arya Rahmani, BSc, Leslie Lilly, MD, Nazia Selzner, MD, PhD, Cynthia Tsien, MD, MPH, Elmar Jaeckel, MD, Mary Pressley Vyas, Rahul Krishnan, PhD, Gideon Hirschfield, MD, PhD, MB, Mamatha Bhat, MD, PhD

“…We developed 3 machine learning architectures using data from 4666 patients with PSC in the Scientific Registry of Transplant Recipients (SRTR) and tested our models on our institutional data set of 144 patients at the University Health Network (UHN). We evaluated their time-dependent concordance index (C-index) for mortality prediction and compared it against MELD-sodium and MELD 3.0. …”
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AI-Powered Vocalization Analysis in Poultry: Systematic Review of Health, Behavior, and Welfare Monitoring by Venkatraman Manikandan, Suresh Neethirajan

“…This comprehensive systematic review critically examines the transformative evolution from traditional acoustic feature extraction—including Mel-Frequency Cepstral Coefficients (MFCCs), spectral entropy, and spectrograms—to cutting-edge deep learning architectures encompassing Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, attention mechanisms, and groundbreaking self-supervised models such as wav2vec2 and Whisper. …”
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River floating object detection with transformer model in real time by Chong Zhang, Jie Yue, Jianglong Fu, Shouluan Wu

“…This model incorporates the High-level Screening-feature Path Aggregation Network (HS-PAN), which refines feature fusion through a novel bottom-up fusion path, significantly enhancing its expressive power. …”
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KEXNet: A Knowledge-Enhanced Model for Improved Chest X-Ray Lesion Detection by Quan Yan, Junwen Duan, Jianxin Wang

“…For global lesion detection, KEXNet synergizes knowledge-enhanced local features with global image features, enhancing diagnostic accuracy. …”
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Novel Deep Learning Model for Glaucoma Detection Using Fusion of Fundus and Optical Coherence Tomography Images by Saad Islam, Ravinesh C. Deo, Prabal Datta Barua, Jeffrey Soar, U. Rajendra Acharya

“…We develop separate convolutional neural network models for fundus and optical coherence tomography images and a fusion model that integrates features from both modalities for each eye. …”
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Sistema Distribuido de Detección de Sismos Usando una Red de Sensores Inalámbrica para Alerta Temprana. by Ana Zambrano Vizuete, Israel Pérez Llopis, Carlos Palau Salvador, Manuel Esteve Domingo

“…We propose an innovative real-time solution which considers time and spatial analyses, not present in another works, making it more precise and customizable, coupling it to the features of the geographical zone, network and resources, so as providing evidence of the feasibility of earthquake early warning using a distributed network of cell phones. …”
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Multi-level fusion with fine-grained alignment for multimodal sentiment analysis by Xiaoge Li, Yanan Ma, Xiaochun An, Jinshuo Xing, Ren Liu, Yunsheng Ren

“…Therefore, we propose a novel method, Fine-grained Multimodal Fusion Network (MMTA). Firstly, a Fine-grained Alignment (FGA) module is introduced to align and extract word-level features to bridge heterogeneous modal gaps. …”
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Research on Wellbore Trajectory Prediction Based on a Pi-GRU Model by Hanlin Liu, Yule Hu, Zhenkun Wu

“…The GRU network captures the temporal dependencies of sequence data (such as dip angle and azimuth angle), while the BP neural network extracts deep correlations from non-sequence features (such as stratum lithology), thereby achieving multi-source data fusion modeling. …”
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Interpretable multi-label classification model for predicting post-anesthesia care unit complications: a prospective cohort study by Guoting Ma, Wenjun Yan, Zunqiang Zhao, Yanjia Li, Lingkai Wang

“…A multi-label classification model was developed on the basis of 16 key features, and a Markov network was embedded to quantify and analyze the association network among these complications. …”
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A novel intelligent fault diagnosis method for gearbox based on multi-dimensional attention denoising convolution by Wei Liu, Zeqiao Zhang, Zhiwei Ye, Qiyi He

“…To address these challenges, this study proposes a novel deep neural network framework, termed the Multidimensional Fusion Residual Attention Network (MFRANet), for gearbox fault diagnosis. …”
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Similarity based city data transfer framework in urban digitization by Haoxiang Wang, Xiaoping Che, Enyao Chang, Chenxin Qu, Ganghua Zhang, Zihan Zhou, Zhenlin Wei, Gengyu Lyu, Pengfei Li

“…Specifically, we first constructed an urban similarity model, which utilizes the urban POI (Point Of Interest) data to group the cities with similar characteristics into the same cluster. Then, we build a feature extractor network, that uses convolution neural network (CNN) and Gated Recurrent Unit (GRU) to extract more representative features of time series data. …”
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SiNC: Saliency-injected neural codes for representation and efficient retrieval of medical radiographs. by Jamil Ahmad, Muhammad Sajjad, Irfan Mehmood, Sung Wook Baik

“…In this paper, we present an efficient method for representing medical images by incorporating visual saliency and deep features obtained from a fine-tuned convolutional neural network (CNN) pre-trained on natural images. …”
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Predictive Modeling of Dairy Sales Using Multi-Perspective Fusion Bi-LSTM Integrated with Universal Scale CNN: Insights from the Dairy Supply Chain by Naveen D. Chandavarkar, Dr. Soumya S

“…The proposed research uses dairy supply chain dataset to assess the proposed model CNN (Convolutional Neural Network). The present research uses Universal Scale CNN, specifically 1D-CNN, that is able to acquiring the features in ideal and in effective rates. …”
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Immunological and clinical overlap between autoimmune gastritis and autoimmune liver diseases: a prospective cohort study by Sara Massironi, Giulia Dispinzieri, Giulia Dispinzieri, Alberto Rossi, Laura Cristoferi, Marco Vincenzo Lenti, Marco Vincenzo Lenti, Alessio Gerussi, Alessio Gerussi, Alessandra Elvevi, Marco Carbone, Marco Carbone, Alessandra Bonfichi, Alessandra Bonfichi, Antonio Di Sabatino, Antonio Di Sabatino, Silvio Danese, Silvio Danese, Pietro Invernizzi, Pietro Invernizzi

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Empirical Analysis of Honeybees Acoustics as Biosensors Signals for Swarm Prediction in Beehives by Kainat Iqbal, Bayan Alabdullah, Naif Al Mudawi, Asaad Algarni, Ahmad Jalal, Jeongmin Park

“…In this paper, we aim to evaluate various state-of-the-art machine learning and deep learning models for swarm prediction by studying wave plot features, Mel Spectrogram, and Melfrequency Cepstral coefficients (MFCC). …”
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