Automated karyogram analysis for early detection of genetic and neurodegenerative disorders: a hybrid machine learning approach by Sumaira Tabassum, M. Jawad Khan, Javaid Iqbal, Asim Waris, M. Adeel Ijaz

“…We also used a structural similarity index measure and template matching to identify the part of the abnormal chromosome that differed from the normal one. This automated model has the potential to significantly contribute to the early detection and diagnosis of chromosome-related disorders that affect both genetic health and neurological behavior.…”
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Attention-enhanced and integrated deep learning approach for fishing vessel classification based on multiple features by Xin Cheng, Jintao Wang, Xinjun Chen, Fan Zhang

“…Then, a multidimensional feature vector was constructed by combining the geometric, static and dynamic characteristics of fishing vessels to explain the behavioral differences of various types of fishing vessels more effectively. …”
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Co-occurrence feature learning for visual recognition of immature leukocytes by Yi-Ting Hsiao, Si-Wa Chan, Yen-Chieh Ouyang, Ju-Huei Chien

“…However, the subtle visual differences among the five types of immature neutrophils pose a significant challenge, even for experienced professionals. …”
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Geographic origin discrimination and quantification of phenolic compounds and moisture in Artemisia argyi folium using NIRS and chemometrics by Lifei Hu, Yifan Wang, Xin Wu, Yuanyuan Shan, Fengxiao Zhu, Fan Zhang, Qiang Yang, Mingxing Liu

“…The results showed that partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) outperformed unsupervised methods, with key wavenumbers in high and low-frequency regions showing similarities, but exhibiting differences mainly in the 7783–6773 cm−1 range. Spectral preprocessing methods (Savitzky-Golay smoothing, normalization, standard normal variate, and multiplicative scatter correction) enhanced machine learning performance, with support vector machine (SVM), radial basis function (RBF), and convolutional neural network (CNN) models achieving scores of 1.0000 across performance metrics, indicating strong generalization and robustness. …”
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Plot-scale peanut yield estimation using a phenotyping robot and transformer-based image analysis by Zhengkun Li, Rui Xu, Nino Brown, Barry L. Tillman, Changying Li

“…A workflow was developed to estimate yield accurately across different genotypes by counting the pods from stitched plot-scale images. …”
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Simplified Physical Stability Assessment of Chilean Mine Waste Storage Facilities Using GIS and AI: Application in the Antofagasta Region by Gabriel Hermosilla, Gabriel Villavicencio, Giovanni Cocca-Guardia, Vicente Aprigliano, Manuel Silva, Juan Carlos Quezada, Pierre Breul, Vinicius Minatogawa, Jaime Morales

“…The successful application suggests scalability to other mining regions and adaptability to different facility types, including tailings storage facilities. …”
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Multimodal diagnosis of Alzheimer’s disease based on resting-state electroencephalography and structural magnetic resonance imaging by Junxiu Liu, Junxiu Liu, Shangxiao Wu, Shangxiao Wu, Shangxiao Wu, Qiang Fu, Qiang Fu, Xiwen Luo, Xiwen Luo, Yuling Luo, Yuling Luo, Sheng Qin, Sheng Qin, Yiting Huang, Yiting Huang, Zhaohui Chen, Zhaohui Chen

“…Moreover, most multimodal studies on AD use convolutional neural networks (CNNs) to extract features from different modalities and perform fusion classification. …”
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kMetha-Mamba: K-means clustering mamba for methane plumes segmentation by Yuquan Liu, Hailiang Shi, Ke Cao, Shichao Wu, Hanhan Ye, Xianhua Wang, Erchang Sun, Yunfei Han, Wei Xiong

“…Extensive experiments on hyperspectral and multispectral datasets from different sensors have shown that kMetha-Mamba has the best performance compared to the state-of-the-art methods. …”
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EILnet: An intelligent model for the segmentation of multiple fracture types in karst carbonate reservoirs using electrical image logs by Zhuolin Li, Guoyin Zhang, Xiangbo Zhang, Xin Zhang, Yuchen Long, Yanan Sun, Chengyan Lin

“…In this paper, the electrical image logs network (EILnet)—a deep-learning-based intelligent semantic segmentation model with a selective attention mechanism and selective feature fusion module—was created to enable the intelligent identification and segmentation of different types of fractures through electrical logging images. …”
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Contactless Detection of Abnormal Breathing Using Orthogonal Frequency Division Multiplexing Signals and Deep Learning in Multi-Person Scenarios by Muneeb Ullah, Xiaodong Yang, Zhiya Zhang, Tong Wu, Nan Zhao, Lei Guan, Malik Muhammad Arslan, Akram Alomainy, Hafiza Maryum Ishfaq, Qammer H. Abbasi

“…Using SDR technology, the system leverages OFDM signals to detect subtle respiratory movements, allowing real-time classification in different environments. A hybrid deep learning model, VGG16-GRU, combining convolutional neural networks (CNNs) and gated recurrent units (GRUs), was developed to capture both spatial and temporal features of continuous respiratory data. …”
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Development and validation of a 3-D deep learning system for diabetic macular oedema classification on optical coherence tomography images by Mingzhi Zhang, Tsz Kin Ng, Yi Zheng, Guihua Zhang, Jian-Wei Lin, Ji Wang, Jie Ji, Peiwen Xie, Yongqun Xiong, Hanfu Wu, Cui Liu, Huishan Zhu, Jinqu Huang, Leixian Lin

“…Objectives To develop and validate an automated diabetic macular oedema (DME) classification system based on the images from different three-dimensional optical coherence tomography (3-D OCT) devices.Design A multicentre, platform-based development study using retrospective and cross-sectional data. …”
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Early Detection and Classification of Diabetic Retinopathy: A Deep Learning Approach by Mustafa Youldash, Atta Rahman, Manar Alsayed, Abrar Sebiany, Joury Alzayat, Noor Aljishi, Ghaida Alshammari, Mona Alqahtani

“…In the first experiment, we trained and evaluated different models using fundus images from the publicly available APTOS dataset. …”
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Testing the reliability of geometric morphometric and computer vision methods to identify carnivore agency using Bi-Dimensional information by Manuel Domínguez-Rodrigo, Marina Vegara-Riquelme, Juan Palomeque-González, Blanca Jiménez-García, Gabriel Cifuentes-Alcobendas, Marcos Pizarro-Monzo, Elia Organista, Enrique Baquedano

“…Here, we establish a methodological comparison on a controlled experimentally-derived set of BSM generated by four different types of carnivores, using geometric morphometric (GMM) and computer vision (CV) methods. …”
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Enabling Automated Device Size Selection for Transcatheter Aortic Valve Implantation by Patricio Astudillo, Peter Mortier, Johan Bosmans, Ole De Backer, Peter de Jaegere, Matthieu De Beule, Joni Dambre

“…The method was validated against an interoperator variability study of the same 118 patients. The differences between the manually obtained aortic annulus measurements and the automatic predictions were similar to the differences between two independent observers (paired diff. of 3.3 ± 16.8 mm2 vs. 1.3 ± 21.1 mm2 for the area and a paired diff. of 0.6 ± 1.7 mm vs. 0.2 ± 2.5 mm for the perimeter). …”
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Orchard-Wide Visual Perception and Autonomous Operation of Fruit Picking Robots: A Review by CHEN Mingyou, LUO Lufeng, LIU Wei, WEI Huiling, WANG Jinhai, LU Qinghua, LUO Shaoming

“…Improved adaptation techniques, possibly through machine learning models that can learn and adjust to different environmental conditions, are suggested as a way forward. …”
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Application of artificial intelligence in the diagnosis of malignant digestive tract tumors: focusing on opportunities and challenges in endoscopy and pathology by Yinhu Gao, Peizhen Wen, Yuan Liu, Yahuang Sun, Hui Qian, Xin Zhang, Huan Peng, Yanli Gao, Cuiyu Li, Zhangyuan Gu, Huajin Zeng, Zhijun Hong, Weijun Wang, Ronglin Yan, Zunqi Hu, Hongbing Fu

“…However, the scale and quality of data across different studies vary widely, and the generalizability of models to multi-center, multi-device environments remains to be verified. …”
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Forecasting Major Flares Using Magnetograms and Knowledge-informed Features: A Comparative Study of Deep Learning Models with Generalization to Multiple Data Products by Xuebao Li, Shunhuang Zhang, Yanfang Zheng, Ting Li, Rui Wang, Yingbo Liu, Hongwei Ye, Noraisyah Mohamed Shah, Pengchao Yan, Xuefeng Li, Xiaotian Wang, Yongshang Lv, Jinfang Wei, Honglei Jin, Changtian Xiang

“…Then, we investigate the generalization ability of the models across three different data products. Finally, we fairly compare the forecasting performance of iTransformer with that of the currently advanced NASA/CCMC models. …”
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Learning Power Systems Waveform Incipient Patterns Through Few-Shot Meta-Learning by Lixian Shi, Qiushi Cui, Yang Weng, Yigong Zhang, Shilong Chen, Jian Li, Wenyuan Li

“…Next, at the meta-training stage, an adaptability-enhancing weighting initialization strategy is constructed to address the data differences between the meta-training stage and IF detection stage. …”
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Architecture-Aware Augmentation: A Hybrid Deep Learning and Machine Learning Approach for Enhanced Parkinson’s Disease Detection by Madjda Khedimi, Tao Zhang, Hanine Merzougui, Xin Zhao, Yanzhang Geng, Khamsa Djaroudib, Pascal Lorenz

“…These results highlight that hybrid models respond differently to augmentation, and careful selection of augmentation strategies is necessary for optimizing model performance. …”
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TCN-MAML: A TCN-Based Model with Model-Agnostic Meta-Learning for Cross-Subject Human Activity Recognition by Chih-Yang Lin, Chia-Yu Lin, Yu-Tso Liu, Yi-Wei Chen, Hui-Fuang Ng, Timothy K. Shih

“…However, real-world deployment faces two major challenges: (1) significant cross-subject signal variability due to physical and behavioral differences among individuals, and (2) limited labeled data, which restricts model generalization. …”
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