Clinical Characteristics of Neurofibromatosis Type 1 in the Republic of Bashkortostan by R. N. Mustafin

“…Discussion. A comparative analysis of the characteristics of NF1 in patients from the Republic of Bashkortostan with global data showed a significantly rarer detection of neurofibromas, Lisch nodules, optic nerve gliomas, intellectual disabilities and psychological disorders. …”
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Artificial intelligence in pancreatic intraductal papillary mucinous neoplasm imaging: A systematic review. by Muhammad Ibtsaam Qadir, Jackson A Baril, Michele T Yip-Schneider, Duane Schonlau, Thi Thanh Thoa Tran, C Max Schmidt, Fiona R Kolbinger

“…Based on the Fukuoka and Kyoto international consensus guidelines, the current clinical management of intraductal papillary mucinous neoplasm (IPMN) largely depends on imaging features. While these criteria are highly sensitive in detecting high-risk IPMN, they lack specificity, resulting in surgical overtreatment. …”
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Association of chromosomal aberrations in chromosomes 3 and 7, and P16 mutations with malignancy in salivary gland tumors by Yanxia Wu, Zhiyue Xie, Nan Peng, Rui Zhou, Liang Zhao

“…Background Salivary gland tumors, a prevalent type of head and neck neoplasm, exhibit significant morphological diversity and overlapping features, complicating pathological diagnosis. Although fluorescence in situ hybridization (FISH) is widely used for tumor detection, its diagnostic utility in salivary gland tumors remains unclear. …”
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A Noise-Robust, Baseline-Free, and Adaptive Damage Indicator of Plate-like Structures Based on the Multicomponent Information Separation of High-Resolution Mode Shapes Using Wavele... by Yuexin Wang, Tongfa Deng, Jinwen Huang, Maosen Cao, Dragoslav Sumarac

“…The method decomposes mode shape information into noise, damage-related features, and waveform trends. The methodology begins with optical dynamic measurement to capture high-resolution mode shapes, followed by two-dimensional continuous wavelet transform (2D-CWT) for multiscale analysis. …”
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Using Partial Differential Equation Face Recognition Model to Evaluate Students’ Attention in a College Chinese Classroom by Xia Miao, Ziyao Yu, Ming Liu

“…The extracted features are invariant to rotation and translation and more robust to illumination changes. …”
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Evaluating Red Patches and Burning Mouth Symptoms as Oral Biomarkers in Cobalamin Deficiency: A Comparative Study by Sanjay Chikkarasinakere Jogigowda MDS, Karthikeya Patil MDS, Varusha Sharon Christopher MDS, Namrata Suresh MDS, Shilpa Padar Shastry MDS, Vidya Gowdappa Doddawad MDS

“…Logistic regression analysis showed high accuracy (98.3%) in distinguishing between groups based on these markers. …”
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Revisiting the Chemical Composition of WD 1145+017: Impact of Circumstellar Disk Contamination on Photospheric Abundances by Érika Le Bourdais, Patrick Dufour, Siyi Xu

“…We present an updated gas disk model with improved opacity calculations and temperature profiles to properly account for all circumstellar absorption features. Incorporating these changes into our models, we identified at least 11 elements in the disk, including a detection of circumstellar Na. …”
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Spatiotemporal molecular tracing of ultralow-volume biofluids via a soft skin-adaptive optical monolithic patch sensor by Yeon Soo Lee, Seyoung Shin, Gyun Ro Kang, Siyeon Lee, Da Wan Kim, Seongcheol Park, Youngwook Cho, Dohyun Lim, Seung Hwan Jeon, Soo-Yeon Cho, Changhyun Pang

“…Inspired by tree frog toe pads, the 3D MIN features soft, hexagonally aligned pillars and microchannels for conformal adhesion and targeted fluid management. …”
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Advancing ECG Signal Classification With a Fuzzy Classifier Approach by Jan Rabcan, Vitaly Levashenko, Elena Zaitseva, Miroslav Kvassay

“…The analysis of ECG signals plays an important role in healthcare, particularly for the detection of heart conditions such as arrhythmia, coronary artery disease, or heart attack. …”
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Spatiotemporal Flood Hazard Classification in Bangkok Using Graph Convolutional Network and Temporal Fusion Transformer by Pakpoom Chaimook, Nirattaya Khamsemanan, Cholwich Nattee, Alice Sharp

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GENOTYPE-PHENOTYPE CORRELATION IN CASES WITH LISSENCEPHALY SPECTRUM by Ayça Dilruba Aslanger, Zehra Oya Uyguner, Birsen Karaman, Seher Başaran, Hülya Kayserili Karabey

“…In Case 6 with lissencephaly accompanied by cerebellar hypoplasia, a homozygous c.204C>G (p.Tyr68Ter) novel variant was detected in the RELN gene. Conclusion: When the genotype-phenotype correlation approach was chosen for lissencephaly, clinical and radiological features helped in pinpointing the genetic mutations in cases of lissencephaly. …”
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Diet Engine: A real-time food nutrition assistant system for personalized dietary guidance by Asim Moin Saad, Md. Raihanul Haque Rahi, Md. Manirul Islam, Gulam Rabbani

“…This study introduces Diet Engine, an innovative smartphone application powered by machine learning that enhances health outcomes by providing immediate food classification and personalized dietary suggestions. The system features modules using deep learning (DL) and Convolutional Neural Networks (CNNs) to detect food, as well as textual analysis and natural language processing (NLP) to estimate components such as nutritional content. …”
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ScaleViM-PDD: Multi-Scale EfficientViM with Physical Decoupling and Dual-Domain Fusion for Remote Sensing Image Dehazing by Hao Zhou, Yalun Wang, Wanting Peng, Xin Guan, Tao Tao

“…Remote sensing images are often degraded by atmospheric haze, which not only reduces image quality but also complicates information extraction, particularly in high-level visual analysis tasks such as object detection and scene classification. …”
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JASBO: Jaya Average Subtraction Based Optimization with Deep Learning Model for Multi-Classification of Infectious Disease from Unstructured Data by Vian Sabeeh, Ahmed Bahaaulddin A. Alwahhab, Ali Abdulmunim Ibrahim Al-kharaz

“…Medical documents are considered an essential source for modern, invented, and robust analysis methods for accurate infection diagnoses. …”
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BlendNet: a blending-based convolutional neural network for effective deep learning of electrocardiogram signals by S. Premanand, Sathiya Narayanan

“…However, this traditional approach captures only a limited set of features of ECG and thereby limits the effectiveness of DL architectures in disease detection.MethodsThis work proposes “BlendNet,” a DL architecture that effectively extracts the features of an ECG signal using a blending approach termed “alpha blending.” …”
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Automatic Movement Recognition for Evaluating the Gross Motor Development of Infants by Yin-Zhang Yang, Jia-An Tsai, Ya-Lan Yu, Mary Hsin-Ju Ko, Hung-Yi Chiou, Tun-Wen Pai, Hui-Ju Chen

“…A one-way ANOVA analysis revealed 106 significant features that were retained for constructing prediction models. …”
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SE-DeepLabV3+: Cervical Cell Segmentation and Classification Using a Novel SE-Based DeepLabV3+ and Ensemble Method by Betelhem Zewdu Wubineh, Andrzej Rusiecki, Krzysztof Halawa

“…The classification model utilizes an ensemble approach by combining features from multiple pre-trained convolutional neural networks (CNNs) and introducing learnable weights (dense layers applied to the global average pooling layer) to dynamically adjust the importance of each model’s feature and improve prediction accuracy. …”
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Extracting Vehicle Trajectories Using Unmanned Aerial Vehicles in Congested Traffic Conditions by Eui-Jin Kim, Ho-Chul Park, Seung-Woo Ham, Seung-Young Kho, Dong-Kyu Kim

“…In this study, we propose and compare two learning-based frameworks for detecting vehicles: the aggregated channel feature (ACF), which is based on human-made features, and the faster region-based convolutional neural network (Faster R-CNN), which is based on data-driven features. …”
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FARVNet: A Fast and Accurate Range-View-Based Method for Semantic Segmentation of Point Clouds by Chuang Chen, Lulu Zhao, Wenwu Guo, Xia Yuan, Shihan Tan, Jing Hu, Zhenyuan Yang, Shengjie Wang, Wenyi Ge

“…This paper presents FARVNet, a novel real-time Range-View (RV)-based semantic segmentation framework that explicitly models the intrinsic correlation between intensity features and spatial coordinates to enhance feature representation in point cloud analysis. …”
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Hybrid Machine Learning for CNC Process Monitoring by Robin Strobel, Samuel Deucker, Hanlin Zhou, Hafez Kader, Alexander Puchta, Benjamin Noack, Jurgen Fleischer

“…By accurately predicting reference signals and comparing them with real-time measurements, deviations can be used for effective model-based process monitoring and anomaly detection. This study explores the efficacy of hybrid machine learning (ML) models in predicting reference signals for CNC axes using features derived from a physical model. …”
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