Segmentation-based lightweight multi-class classification model for crop disease detection, classification, and severity assessment using DCNN. by Chatla Subbarayudu, Mohan Kubendiran

“…Experimental analysis has been carried out to demonstrate the effectiveness of the proposed approach in detecting maize crop leaf diseases and assessing their severity. …”
Get full text

The TERT promoter mutation status and MGMT promoter methylation status, combined with dichotomized MRI‐derived and clinical features, predict adult primary glioblastoma survival by Chang Shu, Qiong Wang, Xiaoling Yan, Jinhuan Wang

“…Abstract Purpose This study aimed to integrate the TERT promoter mutation status, MGMT promoter methylation status, MRI‐derived features, and clinical features into a survival analysis model to better understand adult primary glioblastoma prognosis‐related markers. …”
Get full text

Multi-frequency EEG and multi-functional connectivity graph convolutional network based detection method of patients with Alzheimer’s disease by Yujian Liu, Libing An, Haiqiang Yang, Shuzhi Sam Ge

“…By leveraging a multi-dimensional feature extraction and fusion strategy, the model effectively identifies EEG pattern changes associated with AD, enhancing detection accuracy. …”
Get full text

Fusion-Based Deep Learning Approach for Renal Cell Carcinoma Subtype Detection Using Multi-Phasic MRI Data by Gulhan Kilicarslan, Dilber Cetintas, Taner Tuncer, Muhammed Yildirim

Get full text

Optimizing ML models for cybercrime detection: balancing performance, energy consumption, and carbon footprint through multi-objective optimization by Romil Rawat

Get full text

Large Scale Mowing Event Detection on Dense Time Series Data Using Deep Learning Methods and Knowledge Distillation by T. Moumouris, V. Tsironis, A. Psalta, K. Karantzalos

“…Leveraging Sentinel-2 and Landsat data, we developed a benchmark dataset of over 1,600 annotated parcels in Greece, capturing mowing events through photo-interpretation and Enhanced Vegetation Index (EVI) analysis. Four DL architectures were evaluated, including MLP, ResNet18, MLP+Transformer, and Conv+Transformer, with additional handcrafted features incorporated to assess their impact on performance. …”
Get full text

A study on improved random forest-based anomaly detection of regional tariff data under distributed photovoltaic access by Shujun Ji, Kai Liu, Bo Ling, Jiadong Li, Jinteng Wang, Xun Ma

“…Test results demonstrate the method's strong application performance, with waveform entropy results remaining below 0.11, centroid results exceeding 0.907, and effective feature extraction outcomes. With an oversampling factor of 0.6 and G-mean values consistently above 0.927, the method reliably completes anomaly detection for electricity price data across both low and high latitude regions.…”
Get full text

Zero-Shot Eggshell Crack Detection Using Grounding DINO and FFT-Based Outer-to-Inner Ring Energy Ratio by Tomorn Soontornnapar, Tuchsanai Ploysuwan

“…This study presents a novel approach to eggshell crack detection by integrating zero-shot learning with advanced image analysis techniques. …”
Get full text

Detection of Photovoltaic Arrays in High-Spatial-Resolution Remote Sensing Images Using a Weight-Adaptive YOLO Model by Zhumao Lu, Xiaokai Meng, Jinsong Li, Hua Yu, Shuai Wang, Zeng Qu, Jiayun Wang

“…Comparative analysis reveals that the optimized model reduces the error rate for small object detection in black-and-white imagery and complex scenarios by 19.8% compared to the baseline model. …”
Get full text

Decision-Aid Framework for Face Authentication Detection Using ResNext50 and BiLSTM to Enhance Media Integrity by Ayat Abd-Muti Alrawahneh, Siti Norul Huda Sheikh Abdullah, Tarik Abuain, Sharifah Nurul Asyikin Syed Abdullah, Sarah Khadijah Taylor, Nur Hanis Sabrina Suhaimi

“…Unlike conventional approaches focusing solely on spatial features, the proposed hybrid model incorporates temporal analysis, enabling the detection of subtle manipulations distributed across sequential video frames. …”
Get full text

A Novel Spectrum Sensing Method for Multiple Unknown Signal Sources Using Frequency Domain Energy Detection and DBSCAN by Rui Gao, Guanghui Yan, Ruiting Niu, Wenwen Chang, Tianfeng Yan, Chunyang Tang

“…To address these challenges, we present a novel spectrum sensing approach called FDED-DBSCAN, which combines frequency domain energy detection (FDED), frequency domain noise estimation (FDNE), and the density-based spatial clustering of applications with noise (DBSCAN) algorithm; The DBSCAN algorithm clusters signals based on their time-frequency two-dimensional feature space. …”
Get full text

A Deep Learning Approach for Fault Detection and Localization in MT-VSC-HVDC System Utilizing Wavelet Scattering Transform by Manohar Mishra, Debadatta Amaresh Gadanayak, Abha Pragati, Jai Govind Singh

“…The approach integrates the wavelet scattering transform (WST) to extract low-variance feature vectors and a newly developed variable batch size long-short-term-memory (VB-LSTM) network for accurate fault detection. …”
Get full text

Multisensor Data Fusion for Coastal Boundary Detection by Res-U-Net Implementation Using High-Resolution UAV Imagery by Qin Wang, Nyasha J. Kavhiza, Fakhrul Islam, Ilyas Ahmad Huqqani, Mohsin Abbas, Sanjoy Barman

“…Large-scale geospatial analytics and real-time coastal change detection can both benefit from the framework’s extension to multitemporal and multisensor data.…”
Get full text

Prediction of Synchronous Serum CEA Expression Status Based on Baseline MRI Features of Primary Rectal Cancer Lesions Pre-treatment: A Retrospective Study by Baohua Lv, Donghai Li, Jizheng Li, Kai Shang, Ke Wu, Erhu Jin, Xiujuan Li

“…The nomogram’s discriminative ability and clinical utility were tested using the receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). The baseline CEA high-expression group had significantly higher MRI-detected metastatic lymph node (mLN), MRI-detected extramural vascular invasion (mEMVI), infiltrating tumor border configuration (iTBC), peritoneal invasion, annular infiltration, maximum extramural depth (MED), and tumor length than the normal CEA group (P < 0.05). …”
Get full text

Automatic Detection of Occluded Main Coronary Arteries of NSTEMI Patients with MI-MS ConvMixer + WSSE Without CAG by Mehmet Cagri Goktekin, Evrim Gul, Tolga Çakmak, Fatih Demir, Mehmet Ali Kobat, Yaman Akbulut, Ömer Işık, Zehra Kadiroğlu, Kürşat Demir, Abdulkadir Şengür

“…The features extracted from this model were classified with the WSSE algorithm, and an accuracy of 88.72% was achieved. …”
Get full text

Early detection of emerging SARS-CoV-2 Variants from wastewater through genome sequencing and machine learning by Xiaowei Zhuang, Van Vo, Michael A. Moshi, Ketan Dhede, Nabih Ghani, Shahraiz Akbar, Ching-Lan Chang, Angelia K. Young, Erin Buttery, William Bendik, Hong Zhang, Salman Afzal, Duane Moser, Dietmar Cordes, Cassius Lockett, Daniel Gerrity, Horng-Yuan Kan, Edwin C. Oh

“…The multivariate nature of our pipeline boosts statistical power and supports accurate early detection of SARS-CoV-2 variants. This feature offers a unique opportunity to detect emerging variants and pathogens, even in the absence of clinical testing.…”
Get full text

Concurrent papillary thyroid carcinoma and incidental cervical lymph node indolent B cell non-Hodgkin lymphoma: clinicopathological features, outcomes, and potential relationships by Yanhui Zhang, Yanyan Song, Runfen Cheng, Qiongli Zhai

“…Methods A retrospective analysis was conducted on patients who underwent thyroid lobectomy and were diagnosed with PTC and incidental CLN-B-NHL based on final pathological assessments at a single cancer center from 2015 to 2018. …”
Get full text

Fault Prediction of Hydropower Station Based on CNN-LSTM-GAN with Biased Data by Bei Liu, Xiao Wang, Zhaoxin Zhang, Zhenjie Zhao, Xiaoming Wang, Ting Liu

“…Meanwhile, a multi-scale feature extraction network with time–frequency information is designed to improve the accuracy of fault detection. …”
Get full text

An Evaluation of Skin Lesion Segmentation Using Deep Learning Architectures by Rabia Öztaş Kara, Devrim Akgün, Bekir Murat Aydın, Gökçen Çetinel, Sevda Gül

Get full text

Interpretable Machine Learning for Serum-Based Metabolomics in Breast Cancer Diagnostics: Insights from Multi-Objective Feature Selection-Driven LightGBM-SHAP Models by Emek Guldogan, Fatma Hilal Yagin, Hasan Ucuzal, Sarah A. Alzakari, Amel Ali Alhussan, Luca Paolo Ardigò

“…<i>Background and Objectives:</i> Breast cancer accounts for 12.5% of all new cancer cases in women worldwide. Early detection significantly improves survival rates, but traditional biomarkers like CA 15-3 and HER2 lack sensitivity and specificity, particularly for early-stage disease. …”
Get full text