The Short-Term Wind Power Forecasting by Utilizing Machine Learning and Hybrid Deep Learning Frameworks by Sunku V.S., Namboodiri V., Mukkamala R.

“…Statistical validation was also performed using the Diebold-Mariano test to establish significant differences in performance. The most important results reveal that the CNN GRU model outperformed the other models, achieving a MAE of 0.2104 MW, an MSE of 0.1028 MW, an RMSE of 0.3206 MW, and an R² of 0.9768. …”
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Deep Learning-Based Anomaly Detection in Occupational Accident Data Using Fractional Dimensions by Ömer Akgüller, Larissa M. Batrancea, Mehmet Ali Balcı, Gökhan Tuna, Anca Nichita

“…Among the fractional dimension methods, Genton and Hall–Wood reveal the most significant differences in anomaly detection performance between the models, while Box Counting and Wavelet yield more consistent outcomes across sectors. …”
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Developing an Algorithm for Robotic Precision Application of Crop Protection Products by M. A. Mirzaev

“…The image parameters tend to differ significantly in applied solutions. (Research purpose) To develop an algorithm for crop plant recognition by a robotic device using a state-of-the-art convolutional neural network (R-CNN) and deep learning technology. …”
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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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Mild autonomous cortisol secretion leads to reduced volumetric BMD at lumbar spine in patients with primary aldosteronism by Nabeel Mansour, Denise Bruedgam, Daniel Heinrich, Ulrich Dischinger, Nicole Reisch, Friederike Völter, Friederike Völter, Isabel Stüfchen, Elisabeth Nowak, Stephanie Zopp, Victoriya Vasileva, Osman Öcal, Moritz Wildgruber, Max Seidensticker, Jens Ricke, Martin Bidlingmaier, Martin Reincke, Juínia Ribeiro de Oliveira Longo Schweizer

“…Cortisol after DST negatively correlated with vBMD (Spearman’s r=−0.33, p=0.00042). No significant differences in bone turnover markers were found, and classifications based on visible lesions on CT or PA-lateralization via adrenal venous sampling did not reveal any significant differences in these markers (p > 0.05 for all comparisons).ConclusionDespite non-significant differences in bone turnover markers between patients with PA with or without MACS, CT scans revealed significantly reduced vBMD in PA and MACS patients, indicating compromised bone health and vBMD significantly negatively correlated with cortisol post DST. …”
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Building a machine learning-assisted echocardiography prediction tool for children at risk for cancer therapy-related cardiomyopathy by Lindsay A. Edwards, Christina Yang, Surbhi Sharma, Zih-Hua Chen, Lahari Gorantla, Sanika A. Joshi, Nicolas J. Longhi, Nahom Worku, Jamie S. Yang, Brandy Martinez Di Pietro, Saro Armenian, Aarti Bhat, William Border, Sujatha Buddhe, Nancy Blythe, Kayla Stratton, Kasey J. Leger, Wendy M. Leisenring, Lillian R. Meacham, Paul C. Nathan, Shanti Narasimhan, Ritu Sachdeva, Karim Sadak, Eric J. Chow, Patrick M. Boyle

“…We built DCNNs with four subsets of echocardiographic data differing in timing relative to case (survivor who developed cardiomyopathy) index diagnosis and two input formats (montages) with differing image selections. …”
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Urban Public Space Safety Perception and the Influence of the Built Environment from a Female Perspective: Combining Street View Data and Deep Learning by Shudi Chen, Sainan Lin, Yao Yao, Xingang Zhou

“…The results reveal the following key findings: (1) The safety perception rankings in Wuhan align with its multi-center urban pattern, with significant differences observed in the central area. (2) Built environment features significantly influence women’s safety perceptions, with the Sky View Factor, Green View Index, and Roadway Visibility identified as the most impactful factors. …”
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SDNet: Sandwich Decoder Network for Waterbody Segmentation in Remote Sensing Imagery by Hao Ni, Jianfeng Li, Chenxu Wang, Zhiquan Zhou, Xinsheng Wang

“…Waterbody extraction is essential for monitoring surface changes and supporting disaster response. However, differences in morphology, dimensions, and spectral reflectance make it problematic to segregate waterbodies accurately in remote sensing (RS) photographs. …”
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Induction of proximal tubular proliferation and lengthening in response to sodium glucose linked cotransporter‐2 inhibition in experimental rats by Ellen Wu, Sarah Macklin, Yanling Zhang, Kerri Thai, Linda Nghiem, Caterina Di Ciano‐Oliveira, Nuno Coelho, Hai Wang, Suzanne L. Advani, Jean‐François Desjardins, Darren A Yuen, Paraish Misra, Kim A. Connelly, Jens R. Nyengaard, Richard E. Gilbert

“…Taking advantage of their differing anatomical locations, stereological techniques were used to differentiate the SGLT1 expressing straight proximal tubule that lies within the outer stripe of the outer medulla (S3 segment) and that of the predominantly SGLT2 expressing early proximal convoluted tubule located within the kidney cortex (S1, S2 segments). …”
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AI-Driven Neuro-Monitoring: Advancing Schizophrenia Detection and Management Through Deep Learning and EEG Analysis by Elena-Anca Paraschiv, Lidia Băjenaru, Cristian Petrache, Ovidiu Bica, Dragoș-Nicolae Nicolau

“…The generated TE matrices revealed significant differences in connectivity between the two groups, particularly in frontal and central brain regions, which are critical for cognitive processing. …”
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YOLO-DroneMS: Multi-Scale Object Detection Network for Unmanned Aerial Vehicle (UAV) Images by Xueqiang Zhao, Yangbo Chen

“…Compared to traditional remote-sensing images, UAV images exhibit complex backgrounds, high resolution, and large differences in object scales. Therefore, UAV object detection is an essential yet challenging task. …”
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Thermal canopy segmentation in tomato plants: A novel approach with integration of YOLOv8-C and FastSAM by Hemamalini P, Chandraprakash MK, Laxman RH, Rathinakumari C, Senthil Kumaran G, Suneetha K

“…The compact YOLOv8-C model differs from the original YOLOv8l (large) model by simplifying the Neck architecture and reducing the number of convolutional and upsampling layers. …”
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Transfer learning based hybrid feature learning framework for enhanced skin cancer diagnosis using deep feature integration by Maridu Bhargavi, Sivadi Balakrishna

“…Among the primary challenges in automated skin cancer classification are addressing differences in lesion appearance, occlusions, and data class imbalance that impact model performance and reliability. …”
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DiffMamba: semantic diffusion guided feature modeling network for semantic segmentation of remote sensing images by Zhen Wang, Nan Xu, Zhuhong You, Shanwen Zhang

“…SDAM utilizes the diffusion propagation process to fuse local and global information, alleviating the feature redundancy caused by semantic information differences. CAMamba employs state space transformation to construct the correlation of enhanced local features, and guides the model to achieve feature decoding to obtain refined semantic segmentation results. …”
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P-68 LIVGUARD, A DEEP NEURAL NETWORK FOR CIRRHOSIS DETECTION IN LIVER ULTRASOUND (USD) IMAGES by DIEGO ARUFE, Pablo Gomez del Campo, Ezequiel Demirdjian, Carlos Galmarini

“…Conflict of interest: No Introduction and Objectives: Differents ultrasound (USD) signs have been described for the diagnosis of cirrhosis. …”
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TERAHERTZ TRAVELING-WAVE TUBE ON A RECTANGULAR WAVEGUIDE FOLDED IN A CIRCULAR SPIRAL by A. A. Kurayev, V. V. Matveyenka

“…To replace the zigzag convoluted waveguide with the spiraled for the TWT and BWT on a curved rectangular waveguide is the best way to remove the ribbon beam width restriction. …”
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Modeling Temperature in the Ecuadorian Paramo Through Deep Learning by Marco Javier Castelo Cabay, Jose Antonio Piedra-Fernandez, Rosa Maria Ayala

“…The neural network analysis underscores significant climatic differences between the paramo and the city. Mula Corral exhibits lower and more stable temperatures, consistent with the cold, uniform conditions of high-altitude grasslands, whereas the Ambato airport station reflects higher temperatures with greater variability, likely due to urbanization and human activity. …”
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Transfer Learning-Based Accurate Detection of Shrub Crown Boundaries Using UAS Imagery by Jiawei Li, Huihui Zhang, David Barnard

“…Results showed that transfer learning alone did not achieve satisfactory performance due to differences in object characteristics and environmental conditions. …”
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