Shelf Life Identification and Quality Analysis of Golden Delicious Apples Based on Hyperspectral Imaging and Near Infrared Spectroscopy by Xin ZHAO, Shuliang ZHENG, Xiaoying NIU, Jiankang CAO, Han CHEN, Zhilei ZHAO

“…In order to predict SSC and pH of apple, gray level cooccurrence matrix (GLCM) was used to extract 8 texture features from the hyperspectral images of apple. Feature variables were extracted from the spectral data of pre-processed hyperspectral images, spectral and texture fusion data of hyperspectral images, and near-infrared spectral data by CARS, and predictive models were established by partial least squares regression (PLSR) and LS-SVM. …”
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Improving Wind Turbine Gearbox Reliability: A Hybrid Deep Learning Approach Using Bispectrum Image Analysis by Muhammad Zacky Asy'Ari, Jun Kondoh, Benyamin Kusumoputro

“…This study explores the potential of bispectrum image analysis combined with Convolutional Neural Network (CNN), CNN-Long Short-Term Memory (LSTM), and CNN-Bidirectional LSTM networks for enhanced fault detection and predictive maintenance for wind turbine systems. …”
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Multimodal data fusion for Alzheimer's disease based on dynamic heterogeneous graph convolutional neural network and generative adversarial network by Xiaoyu Chen, Shuaiqun Wang, Wei Kong

“…Current research has progressed from single-modal data analysis to multi-modal data fusion, leveraging deep learning's efficient data analysis capabilities to handle complex datasets. …”
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An Efficient Weed Detection Method Using Latent Diffusion Transformer for Enhanced Agricultural Image Analysis and Mobile Deployment by Yuzhuo Cui, Yingqiu Yang, Yuqing Xia, Yan Li, Zhaoxi Feng, Shiya Liu, Guangqi Yuan, Chunli Lv

“…This paper presents an efficient weed detection method based on the latent diffusion transformer, aimed at enhancing the accuracy and applicability of agricultural image analysis. The experimental results demonstrate that the proposed model achieves a precision of 0.92, a recall of 0.89, an accuracy of 0.91, a mean average precision (mAP) of 0.91, and an F1 score of 0.90, indicating its outstanding performance in complex scenarios. …”
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DCDGAN-STF: A Multiscale Deformable Convolution Distillation GAN for Remote Sensing Image Spatiotemporal Fusion by Yan Zhang, Rongbo Fan, PeiPei Duan, Jinfang Dong, Zhiyong Lei

“…However, compared to traditional image super-resolution tasks, remote sensing image STF involves merging a larger amount of multitemporal data with greater resolution difference. …”
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Prediction of Vanadium Contamination Distribution Pattern Through Remote Sensing Image Fusion and Machine Learning by Zipeng Zhao, Yuman Sun, Weiwei Jia, Jinyan Yang, Fan Wang

“…A dual-branch convolutional neural network (DB-CNN) fused hyperspectral and multispectral images and confirmed the fusion’s effectiveness. …”
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Co-Community Network Analysis Reveals Alterations in Brain Networks in Alzheimer’s Disease by Xiaodong Wang, Zhaokai Zhang, Lingli Deng, Jiyang Dong

“…We used CCPMs and enrichment analysis to compare differences in brain network topological characteristics among three typical brain states. …”
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Multispectral imaging as a predictive tool for freshness of whole Atlantic cod: Compared with sensory, chemical and microbiological analysis by Andrea Rakel Sigurðardóttir, Hildur Inga Sveinsdóttir, Nette Schultz, Hafsteinn Einarsson, María Gudjónsdóttir

“…Spectral data were acquired from key anatomical regions - the gills, skin, and eyes - and analyzed using chemometrics methods, including partial least squares regression (PLSR) and artificial neural networks (ANNs). …”
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Comprehensive Review on the Exploitation of Advanced Memory Optimization Strategies to Improve Performance for Convolutional and Spiking Neural Networks in Medical Imaging Using Ha... by N. Srikanth Prasad, S. Sundar

“…Advanced memory optimization techniques are reviewed to enhance the performance of Convolutional Neural Networks (CNNs) and Spiking Neural Networks (SNNs) on hardware accelerators, addressing the real-world challenges in medical imaging. …”
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Achieving high-accuracy skin cancer classification with deep learning optimized by ant colony algorithm by Amany M. Sarhan, Hesham A. Ali, Shady Yasser, Mohamed Gobara, Ahmed A. Kandil, Ghada Sherif, Esraa Moustafa

“…Recent advancements in deep learning have shown significant promise in medical image analysis, including skin cancer classification. …”
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Review of applications of deep learning in veterinary diagnostics and animal health by Sam Xiao, Navneet K. Dhand, Zhiyong Wang, Kun Hu, Kun Hu, Peter C. Thomson, John K. House, Mehar S. Khatkar, Mehar S. Khatkar

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The Creation of Artificial Data for Training a Neural Network Using the Example of a Conveyor Production Line for Flooring by Alexey Zaripov, Roman Kulshin, Anatoly Sidorov

“…A comparative analysis showed that the YOLOv8 model trained on synthetic data significantly outperformed the model trained on real images: the mAP50 metric reached 0.95 versus 0.36, respectively. …”
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Advanced AI-driven detection of cinnamon powder adulteration using near-infrared spectroscopy and deep learning image recognition technique by Hao-Hsiang Ku, Ya-Chuan Liao, Ching-Ho Chi

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The online destination image as portrayed by the user-generated content on social media and its impact on tourists' engagement by Manuela Guerreiro, Patrícia Pinto, Célia M.Q. Ramos, Nelson Matos, Homayoun Golestaneh, Bernardete Sequeira, Luís Nobre Pereira, Dora Agapito, Rafaela Martins, Magda Wijkesjö

“…Tourism Destination Image (TDI) is complex, dynamic and multidimensional by nature and shaped by a multitude of events, interactions and sensory impressions, especially once the tourist is at the destination, generating tourists' engagement on social media networks. …”
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A Comprehensive Survey on Deep Learning in Abdominal Imaging: Datasets, Techniques, and Performance Metrics by Mariem Bellal, Sanaa El Fkihi, Korhan Cengiz, Nikola Ivkovic

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Addressing data scarcity in nanomaterial segmentation networks with differentiable rendering and generative modeling by Dennis Possart, Leonid Mill, Florian Vollnhals, Tor Hildebrand, Peter Suter, Mathis Hoffmann, Jonas Utz, Daniel Augsburger, Mareike Thies, Mingxuan Gu, Fabian Wagner, George Sarau, Silke Christiansen, Katharina Breininger

“…Deep learning segmentation networks offer precise, automated analysis, but their effectiveness depends on representative annotated datasets, which are difficult to obtain due to the high cost and manual effort required for imaging and annotation. …”
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Machine learning for medical image classification by Gazi Husain, Jonathan Mayer, Molly Bekbolatova, Prince Vathappallil, Mihir Matalia, Milan Toma

“… This review article focuses on the application of machine learning (ML) algorithms in medical image classification. It highlights the intricate process involved in selecting the most suitable ML algorithm for predicting specific medical conditions, emphasizing the critical role of real-world data in testing and validation. …”
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Integration of histopathological images and immunological analysis to predict M2 macrophage infiltration and prognosis in patients with serous ovarian cancer by Ling Zhao, Jiajia Tan, Qiuyuan Su, Yan Kuang, Yan Kuang

“…HIF were recognized by deep multiple instance learning (MIL) to predict M2 macrophage infiltration via theResNet18 network in the training set. The final model was evaluated using the internal and external validation set.ResultsUsing data acquired from the TCGA database, we applied univariate Cox analysis and determined that higher levels of M2 macrophage infiltration were associated with a poor prognosis (hazard ratio [HR]=6.8; 95% CI [confidence interval]: 1.6–28, P=0.0083). …”
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Aircraft Position Estimation Using Deep Convolutional Neural Networks for Low SNR (Signal-to-Noise Ratio) Values by Przemyslaw Mazurek, Wojciech Chlewicki

“…For example, if the standard deviation equals 0.1, it was twice as large. Deep dream analysis for network layers is presented, which shows a preference for images with horizontal contrast lines. …”
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Enhanced skin cancer diagnosis: a deep feature extraction-based framework for the multi-classification of skin cancer utilizing dermoscopy images by Hadeel Alharbi, Gabriel Avelino Sampedro, Roben A. Juanatas, Se-jung Lim

“…This article presented four variants of the Convolutional Neural Network (CNN) model (i.e., original CNN, no batch normalization CNN, few filters CNN, and strided CNN) for the classification and prediction of skin cancer in lesion images with the aim of helping physicians in their diagnosis. …”
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