What Influences Low-cost Sensor Data Calibration? - A Systematic Assessment of Algorithms, Duration, and Predictor Selection by Lu Liang, Jacob Daniels

“…While confirming that the latest research tendency is deep learning, regression is still a viable option for studies with limited effort in parameter tuning and method selection, especially considering its computational efficiency and simplicity. …”
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Plant Leaf Identification Using Feature Fusion of Wavelet Scattering Network and CNN With PCA Classifier by S. Gowthaman, Abhishek Das

“…Deep learning models, particularly Convolutional Neural Networks (CNNs), are pivotal in enabling botanists to efficiently identify plant species, which is essential for applications in medicine, agriculture, and the food industry. …”
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Efficient guided inpainting of larger hole missing images based on hierarchical decoding network by Xiucheng Dong, Yaling Ju, Dangcheng Zhang, Bing Hou, Jinqing He

“…Abstract When dealing with images containing large hole-missing regions, deep learning-based image inpainting algorithms often face challenges such as local structural distortions and blurriness. …”
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Identification of Civil Infrastructure Damage Using Ensemble Transfer Learning Model by A. Shamila Ebenezer, S. Deepa Kanmani, V. Sheela, K. Ramalakshmi, V. Chandran, M. G. Sumithra, B. Elakkiya, Bharani Murugesan

“…This article uses cutting-edge deep learning technology to identify structural damage from images for a civil engineering application. …”
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Anomaly Detection in Spatiotemporal Data from Fiber Optic Distributed Temperature Sensing for Outdoor Fire Monitoring by Haitao Bian, Xiaohan Luo, Zhichao Zhu, Xiaowei Zang, Yu Tian

“…To address this issue, this study developed a fixed-power fire source simulation device to establish a reliable small-scale experimental platform incorporating various environmental influences for generating anomalous temperature data. We employed deep learning autoencoders (AEs) to integrate spatiotemporal data, aiming to minimize the impact of outdoor conditions on detection performance. …”
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The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review. by Quan Duy Vo, Yukihiro Saito, Toshihiro Ida, Kazufumi Nakamura, Shinsuke Yuasa

“…The integration of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has played a pivotal role in refining iPSC classification, monitoring cell functionality, and conducting genetic analysis. …”
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Improving Industrial Quality Control: A Transfer Learning Approach to Surface Defect Detection by Ângela Semitela, Miguel Pereira, António Completo, Nuno Lau, José P. Santos

“…To automate the quality control of painted surfaces of heating devices, an automatic defect detection and classification system was developed by combining deflectometry and bright light-based illumination on the image acquisition, deep learning models for the classification of non-defective (OK) and defective (NOK) surfaces that fused dual-modal information at the decision level, and an online network for information dispatching and visualization. …”
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Unlocking precision medicine: clinical applications of integrating health records, genetics, and immunology through artificial intelligence by Yi-Ming Chen, Tzu-Hung Hsiao, Ching-Heng Lin, Yang C. Fann

“…Machine learning models excel at identifying high-risk patients, predicting disease activity, and optimizing therapeutic strategies based on clinical, genomic, and immunological profiles. Deep learning techniques have significantly advanced variant calling, pathogenicity prediction, splicing analysis, and MHC-peptide binding predictions in genetics. …”
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Polarity-JaM: an image analysis toolbox for cell polarity, junction and morphology quantification by Wolfgang Giese, Jan Philipp Albrecht, Olya Oppenheim, Emir Bora Akmeriç, Julia Kraxner, Deborah Schmidt, Kyle Harrington, Holger Gerhardt

“…Advances in fluorescence microscopy and deep learning algorithms open up a wealth of unprecedented opportunities to characterise various aspects of cell polarity, but also create new challenges for comprehensible and interpretable image data analysis workflows to fully exploit these new opportunities. …”
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Adaptive Learning Algorithms for Low Dose Optimization in Coronary Arteries Angiography: A Comprehensive Review by Komal Tariq, Muhammad Adnan Munir, Hafiza Tooba Aftab, Amir Naveed, Ayesha Yousaf, Sajjad Ul Hassan

“…Innovative methods such as Model-Based Deep Learning (MBDL) and Self-Attention Generative Adversarial Networks (SAGAN) demonstrate efficient reconstruction capabilities. …”
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A Review of CNN Applications in Smart Agriculture Using Multimodal Data by Mohammad El Sakka, Mihai Ivanovici, Lotfi Chaari, Josiane Mothe

“…A comparative analysis shows how CNNs perform with respect to other techniques that involve traditional machine learning and recent deep learning models in image processing, particularly when applied to high-dimensional or temporal data. …”
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From Data to Decisions: The Power of Machine Learning in Business Recommendations by Kapilya Gangadharan, Anoop Purandaran, K. Malathi, Barathi Subramanian, Rathinaraja Jeyaraj, Soon Ki Jung

“…Future research includes exploring advances in deep learning models, ethical considerations in the deployment of RS, and addressing scalability challenges. …”
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CNFA: ConvNeXt Fusion Attention Module for Age Recognition of the Tangerine Peel by Fuqin Deng, Junwei Li, Lanhui Fu, Chuanbo Qin, Yikui Zhai, Hongmin Wang, Ningbo Yi, Nannan Li, TinLun Lam

“…This work investigates the automatic age recognition of the tangerine peel based on deep learning and attention mechanisms. We proposed an effective ConvNeXt fusion attention module (CNFA), which consists of three parts, a ConvNeXt block for extracting low-level features’ information and aggregating hierarchical features, a channel squeeze-and-excitation (cSE) block and a spatial squeeze-and-excitation (sSE) block for generating sufficient high-level feature information from both channel and spatial dimensions. …”
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LSTM Recurrent Neural Network-Based Frequency Control Enhancement of the Power System with Electric Vehicles and Demand Management by G. Sundararajan, P. Sivakumar

“…This research work investigates a deep learning strategy based on a long short-term memory recurrent neural network to identify active power fluctuations in real-time. …”
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Neural-field-based image reconstruction for bioluminescence tomography by Xuanxuan Zhang, Xu Cao, Jiulou Zhang, Lin Zhang, Guanglei Zhang

“…Deep learning (DL)-based image reconstruction methods have garnered increasing interest in the last few years. …”
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Vibration Images-Driven Fault Diagnosis Based on CNN and Transfer Learning of Rolling Bearing under Strong Noise by Hongwei Fan, Ceyi Xue, Xuhui Zhang, Xiangang Cao, Shuoqi Gao, Sijie Shao

“…Deep learning-based fault diagnosis of rolling bearings is a hot research topic, and a rapid and accurate diagnosis is important. …”
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MLDFNet: A Multilabel Dual-Flow Network for Change Detection in Bitemporal Remote Sensing Images by Daniyaer Sidekejiang, Panpan Zheng, Liejun Wang

“…With the development of deep learning (DL) in recent years, numerous remote sensing image change detection (CD) networks have emerged. …”
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Depth Semantic Segmentation of Tobacco Planting Areas from Unmanned Aerial Vehicle Remote Sensing Images in Plateau Mountains by Liang Huang, Xuequn Wu, Qiuzhi Peng, Xueqin Yu

“…To this end, the advantage of deep learning features self-learning is relied on in this paper. …”
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Rethinking the Key Factors for the Generalization of Remote Sensing Stereo Matching Networks by Liting Jiang, Feng Wang, Wenyi Zhang, Peifeng Li, Hongjian You, Yuming Xiang

“…Stereo matching, a critical step of binocular 3-D reconstruction, has fully shifted to deep learning due to its strong feature representation of remote sensing images. …”
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Support Vector Machine – Recursive Feature Elimination for Feature Selection on Multi-omics Lung Cancer Data by Nuraina Syaza Azman, Azurah A Samah, Ji Tong Lin, Hairudin Abdul Majid, Zuraini Ali Shah, Nies Hui Wen, Chan Weng Howe

“…The study focuses on mitigating the curse of dimensionality by implementing Support Vector Machine – Recursive Feature Elimination (SVM-RFE) as the selected feature selection method in the lung cancer (LUSC) multi-omics dataset integrated from three single omics dataset comprising genomics, transcriptomics and epigenomics, and assess the quality of the selected feature subsets using SDAE and VAE deep learning classifiers. In this study, the LUSC datasets first undergo data pre-processing, including checking for missing values, normalization, and removing zero variance features. …”
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