Kidney Ensemble-Net: Enhancing Renal Carcinoma Detection Through Probabilistic Feature Selection and Ensemble Learning by Zaib Akram, Kashif Munir, Muhammad Usama Tanveer, Atiq Ur Rehman, Amine Bermak

“…Our approach begins by acquiring spatial features from contrast-enhanced images using a Convolutional Neural Network (CNN) effectively capturing intricate patterns and structures characteristic of different carcinoma subtypes. …”
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A Hybrid AI Approach for Fault Detection in Induction Motors Under Dynamic Speed and Load Operations by Muhammad Irfan Ishaq, Muhammad Adnan, Muhammad Ali Akbar, Amine Bermak, Nimra Saeed, Maaz Ansar

“…From existing literature, conventional fault diagnosis approaches in an IM struggle to reliably identify fault patterns at different speeds, particularly under variable speed and changing load conditions. …”
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Prediction of Temperature Distribution with Deep Learning Approaches for SM1 Flame Configuration by Gökhan Deveci, Özgün Yücel, Ali Bahadır Olcay

“…In addition, a comparison was made with different deep learning networks, namely Res-Net, EfficientNetB0, and Inception Net V3, to better understand the performance of the model. …”
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Harnessing Multi-Source Data and Deep Learning for High-Resolution Land Surface Temperature Gap-Filling Supporting Climate Change Adaptation Activities by Katja Kustura, David Conti, Matthias Sammer, Michael Riffler

“…Land surface temperature (LST) is a widely used proxy for investigating climate-change-induced phenomena, providing insights into the surface radiative properties of different land cover types and the impact of urbanization on local climate characteristics. …”
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1D CNN-Based Intracranial Aneurysms Detection in 3D TOF-MRA by Wenguang Hou, Shaojie Mei, Qiuling Gui, Yingcheng Zou, Yifan Wang, Xianbo Deng, Qimin Cheng

“…It transfers 3D classification into 2D case by projecting the 3D patch into 2D planes along different directions on the basis of voxel’s intensity. …”
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Multi-modal denoised data-driven milling chatter detection using an optimized hybrid neural network architecture by Haining Gao, Haoyu Wang, Hongdan Shen, Shule Xing, Yong Yang, Yinlin Wang, Wenfu Liu, Lei Yu, Mazhar Ali, Imran Ali Khan

“…Multi-modal data features of different machining states are then obtained using time–frequency domain methods and Markov transition field methods. …”
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Precision in practice: exploring the impact of ai and machine learning on ultrasound guided regional anaesthesia by Noor Ul Huda Bhatti, Syed Ghazi Ali Kirmani, Maryam Butt

“…In one experiment, Alkhatib et al. used Convolutional neural network (CNN) based deep trackers to track the median and sciatic nerve with a surprising accuracy of 0.87.2 Another study employed the same CNN model to locate and discriminate accurate images of sacrum, vertebral levels and intervertebral gaps during percutaneous spinal needle insertion.3 Another study used a different AI model called SVM (support vector machine) classification, image processing, and template matching to locate lumbar level L3-L4 and the ideal puncture site for epidural anaesthesia in real-time. …”
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Review of Recent Advances in Remote Sensing and Machine Learning Methods for Lake Water Quality Management by Ying Deng, Yue Zhang, Daiwei Pan, Simon X. Yang, Bahram Gharabaghi

“…This review highlights the specific advantages of each satellite platform, considering factors like spatial and temporal resolution, spectral coverage, and the suitability of these platforms for different lake sizes and characteristics. In addition to remote sensing platforms, this paper explores the application of a wide range of machine learning models, from traditional linear and tree-based methods to more advanced deep learning techniques like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). …”
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Accurate bladder cancer diagnosis using ensemble deep leaning by Rana A. El-Atier, M. S. Saraya, Ahmed I. Saleh, Asmaa H. Rabie

“…In fact, the used voting method depends on using majority voting based on two different scenarios according to the results of CNN, GAN, and XDL. …”
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EEG-based neurodegenerative disease diagnosis: comparative analysis of conventional methods and deep learning models by B. R. Nayana, M. N. Pavithra, S. Chaitra, T. N. Bhuvana Mohini, Thompson Stephan, Vijay Mohan, Neha Agarwal

“…The implementation is carried out under three different verticals. Firstly, a conventional machine learning model was developed post-pre-processing, and feature extraction from the power spectral density was done using a Random Forest classifier. …”
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Comparison of Deep Learning Sentiment Analysis Methods, Including LSTM and Machine Learning by Jean Max T. Habib, A. A. Poguda

“…We present a comparison of several deep learning models, including convolutional neural networks, recurrent neural networks, and long-term and shortterm bidirectional memory, evaluated using different approaches to word integration, including Bidirectional Encoder Representations from Transformers (BERT) and its variants, FastText and Word2Vec. …”
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An Improved Backbone Fusion Neural Network for Orchard Extraction by Baiyu Dong, Ziqi Wang, Chongzhi Chen, Ke Wang, Jing Zhang

“…However, different backbone networks exhibit varying capabilities and characteristics in feature extraction, limiting the performance of a single backbone model. …”
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A Multifrequency Brain Network-Based Deep Learning Framework for Motor Imagery Decoding by Juntao Xue, Feiyue Ren, Xinlin Sun, Miaomiao Yin, Jialing Wu, Chao Ma, Zhongke Gao

“…Further, a multilayer convolutional network model is designed to distinguish different MI tasks accurately, which allows extracting and exploiting the topology in the multifrequency brain network. …”
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Enhancing Learning-Based Cross-Modality Prediction for Lossless Medical Imaging Compression by Daniel S. Nicolau, Lucas A. Thomaz, Luis M. N. Tavora, Sergio M. M. Faria

“…Multimodal medical imaging, which involves the simultaneous acquisition of different modalities, enhances diagnostic accuracy and provides comprehensive visualization of anatomy and physiology. …”
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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

“…Variations in terrain elevation cause images acquired under different imaging modalities to deviate from a linear mapping relationship. …”
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Twofold dynamic attention guided deep network and noise-aware mechanism for image denoising by Zihao Chen, Alex Noel Joseph Raj, Vijayarajan Rajangam, Wei Li, Vijayalakshmi G.V. Mahesh, Zhemin Zhuang

“…Convolutional neural networks are given extensive attention towards noise removal due to their good performance over traditional denoising algorithms. …”
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Contrasted Trends in Chlorophyll‐a Satellite Products by Etienne Pauthenet, Elodie Martinez, Thomas Gorgues, Joana Roussillon, Lucas Drumetz, Ronan Fablet, Maïlys Roux

“…Significant regional variations are observed, with contrasting trends observed among different products. To assess if these trends can be related to changes in the environment or to bias in radiometric products, a convolutional neural network is used to examine the relationship between physical ocean variables versus Schl. …”
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A Deep Learning Strategy for the Retrieval of Sea Wave Spectra from Marine Radar Data by Giovanni Ludeno, Giuseppe Esposito, Claudio Lugni, Francesco Soldovieri, Gianluca Gennarelli

“…The results demonstrate that the proposed approach is effective in reconstructing the directional wave spectrum across different sea states.…”
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An Improved Deep Learning Model for Online Tool Condition Monitoring Using Output Power Signals by Lang Dai, Tianyu Liu, Zhongyong Liu, Lisa Jackson, Paul Goodall, Changqing Shen, Lei Mao

“…Furthermore, with test data collected at cutting tools with different sizes, the robustness of the proposed method can be further clarified.…”
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Multi-anchor adaptive fusion and bi-focus attention for enhanced gait-based emotion recognition by Jincheng Li, Xuejing Dai, Ruiao Yan, Chengqing Tang, Yunpeng Li

“…The MAAF module captures multi-scale temporal features to understand emotional expressions across different time ranges, while the BFA module focuses on both local and global features, enhancing the model’s ability to capture complex emotional information. …”
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