Suchergebnisse - "Naznine M"

Understanding Ureteropelvic Junction Obstruction From a Scientometric Perspective: How Is the Global Research Activity and Collaboration Going?

Autoren: Shu, Boshen Zhang, Shufeng Gao, Jian et al.

Quelle: BioMed Research International; 11/3/2025, Vol. 2025, p1-12, 12p

Schlagwörter: INTERPROFESSIONAL relations, RESEARCH funding, LAPAROSCOPY, URETERIC obstruction, DESCRIPTIVE statistics, SYSTEMATIC reviews, KIDNEY pelvis, MEDICAL research, ROBOTICS, HYDRONEPHROSIS, DATA analysis software

Real-time jute leaf disease classification using an explainable lightweight CNN via a supervised and semi-supervised self-training approach.

Autoren: Jannat, Meftahul Uddin, Md Shahab Hasan, Mohammad Asif et al.

Quelle: Frontiers in Plant Science; 2025, p1-24, 24p

Schlagwörter: DEEP learning, CONVOLUTIONAL neural networks, REAL-time computing, SUPERVISED learning, AGRICULTURAL technology, PLANT diseases, ARTIFICIAL intelligence

Diagnosis of colorectal cancer using residual transformer with mixed attention and explainable AI.

Autoren: Sharma, Poonam Sharma, Bhisham Noonia, Ajit et al.

Quelle: PLoS ONE; 11/3/2025, Vol. 20 Issue 10, p1-23, 23p

Schlagwörter: COLORECTAL cancer, DEEP learning, IMAGE recognition (Computer vision), ARTIFICIAL intelligence, TRANSFORMER models, RECEIVER operating characteristic curves

Evaluation of vision transformers for the detection of fullness of garbage bins for efficient waste management.

Autoren: Tanwer, Parakram Singh Maheshwari, Shishir Behera, Sushree et al.

Quelle: Frontiers in Artificial Intelligence; 2025, p1-16, 16p

Fine tuned CatBoost machine learning approach for early detection of cardiovascular disease through predictive modeling.

Autoren: Hamid, Muhammad Hajjej, Fahima Alluhaidan, Ala Saleh et al.

Quelle: Scientific Reports; 8/25/2025, Vol. 15 Issue 1, p1-15, 15p

Schlagwörter: CARDIOVASCULAR diseases, MACHINE learning, FEATURE selection, ENSEMBLE learning, PREDICTION models, DATA augmentation, CLINICAL decision making, EARLY diagnosis

YOLOv9t-DM: a lightweight multi-target detection method for walnut shell kernel materials.

Autoren: Yang, Guanming Wang, Yingbiao Li, Xinying et al.

Quelle: Signal, Image & Video Processing; Jul2025, Vol. 19 Issue 7, p1-9, 9p

Interpretable deep learning for gastric cancer detection: a fusion of AI architectures and explainability analysis.

Autoren: Ma, Junjie Yang, Fang Yang, Rong et al.

Quelle: Frontiers in Immunology; 2025, p1-16, 16p

Schlagwörter: ARTIFICIAL intelligence, CLINICAL decision support systems, CONVOLUTIONAL neural networks, EARLY detection of cancer, STOMACH cancer

AI-Driven Predictive Modeling for Lung Cancer Detection and Management Using Synthetic Data Augmentation and Random Forest Classifier.

Autoren: Innab, Nisreen Aldrees, Asma AlHammadi, Dina Abdulaziz et al.

Quelle: International Journal of Computational Intelligence Systems; 6/10/2025, Vol. 18 Issue 1, p1-20, 20p

Mulberry leaf disease detection by CNN-ViT with XAI integration.

Autoren: Hasan, Mohammad Asif Haque, Fariha Sarker, Hasan et al.

Quelle: PLoS ONE; 6/4/2025, Vol. 20 Issue 6, p1-29, 29p

Schlagwörter: CONVOLUTIONAL neural networks, TRANSFORMER models, FEATURE extraction, LEAF rust, ARTIFICIAL intelligence

Integration of novel artificial intelligence tools in pediatric urologic practice.

Autoren: McGee, Lauren M. Soo, Elizabeth Seideman, Casey A.

Quelle: Current Opinion in Urology; May2025, Vol. 35 Issue 3, p230-235, 6p

A multi-head YOLOv12 with self-supervised pretraining for urinary sediment particle detection.

Autoren: Alizadeh, Mehdi Karimi, Ali Barikbin, Mohammad Javad et al.

Quelle: Scientific Reports; 11/21/2025, Vol. 15 Issue 1, p1-18, 18p

Schlagwörter: DEEP learning, OBJECT recognition (Computer vision), SUPERVISED learning, URINALYSIS, KIDNEY diseases

A multi-scale CNN-GRU fusion model with stationary wavelet transform for 14-day ahead dam water level prediction.

Autoren: Ng, Kai Wen Huang, Yuk Feng Koo, Chai Hoon et al.

Quelle: Scientific Reports; 11/21/2025, Vol. 15 Issue 1, p1-15, 15p

Schlagwörter: HYDROLOGICAL forecasting, FORECASTING, DEEP learning, ARTIFICIAL neural networks, DAMS

Deep learning with refined single candidate optimizer for early polyp detection.

Autoren: Wen, Guoyi Yan, Jiayu Chen, Xin et al.

Quelle: Scientific Reports; 11/18/2025, Vol. 15 Issue 1, p1-23, 23p

Schlagwörter: COLORECTAL cancer, COLONOSCOPY, DEEP learning, COLON polyps, MATHEMATICAL optimization, SUPPORT vector machines, FEATURE extraction

Real-Time and Fully Automated Robotic Stacking System with Deep Learning-Based Visual Perception.

Autoren: Ozer, Ali Sait Cinar, Ilkay

Quelle: Sensors (14248220); Nov2025, Vol. 25 Issue 22, p6960, 38p

An ensemble KANs method with XAI for mitosis segmentation in histopathological images.

Autoren: Samet, Refik Nemati, Nooshin Hancer, Emrah et al.

Quelle: Signal, Image & Video Processing; Nov2025, Vol. 19 Issue 11, p1-10, 10p

Navigating the challenges: ultrasound innovations in brain glioma surgery.

Autoren: Sadrinasab, Shayan Aminiyan, Armin Saket, Sadaf et al.

Quelle: Frontiers in Neurology; 2025, p1-20, 20p

Schlagwörter: CONTRAST-enhanced ultrasound, GLIOMAS, ARTIFICIAL intelligence, DIAGNOSTIC imaging, COMPUTER-assisted surgery, COMPUTER-assisted image analysis (Medicine), TUMOR diagnosis

Transparent brain tumor detection using DenseNet169 and LIME.

Autoren: Abraham, Lincy Annet Palanisamy, Gopinath Veerapu, Goutham

Quelle: Scientific Reports; 8/4/2025, Vol. 15 Issue 1, p1-21, 21p

Schlagwörter: BRAIN tumors, DEEP learning, CLASSIFICATION, COMPUTER-aided diagnosis, COMPUTER-assisted image analysis (Medicine), TUMOR classification

Hybrid deep learning framework based on EfficientViT for classification of gastrointestinal diseases.

Autoren: Tanwar, Vishesh Sharma, Bhisham Yadav, Dhirendra Prasad et al.

Quelle: Scientific Reports; 7/24/2025, Vol. 15 Issue 1, p1-24, 24p

Mamba-YOLO-ML: A State-Space Model-Based Approach for Mulberry Leaf Disease Detection.

Autoren: Yuan, Chang Li, Shicheng Wang, Ke et al.

Quelle: Plants (2223-7747); Jul2025, Vol. 14 Issue 13, p2084, 27p

Schlagwörter: MULBERRY, COMPUTER vision, PEST control, DEEP learning, REAL-time computing, STATE-space methods, EARLY diagnosis, PRECISION farming

Em-t2i: ensemble AI model for text-to-image synthesis using meta AI and microsoft copilot.

Autoren: Faryad, Shagufta Shoukat, Ijaz Ali Ullah, Ubaid et al.

Quelle: Signal, Image & Video Processing; Jul2025, Vol. 19 Issue 7, p1-14, 14p