Eeg-based detection of epileptic seizures in patients with disabilities using a novel attention-driven deep learning framework with SHAP interpretability by Tawfeeq Shawly, Ahmed A. Alsheikhy

“…Additionally, the computational complexity of the proposed framework was evaluated in terms of floating-point operations per second (FLOPs) and the total number of parameters, ensuring its efficiency for real-time biomedical applications. …”
Get full text

Fiber Nonlinearity and Tight Filtering Impairments Mitigation of DP-16QAM Nyquist-WDM Systems Using Multiplier-Free MAP Detection by Tao Yang, Chen Shi, Liqian Wang, Xue Chen, Haoyuan Pan, Huan Chen, Min Zhang, Zhiguo Zhang

“…Besides, the computational complexity (CC) is drastically reduced to about 34%, as the multiplier of the proposed MAP detection is completely removed. …”
Get full text

BRA-YOLOv7: improvements on large leaf disease object detection using FasterNet and dual-level routing attention in YOLOv7 by Rong Ye, Rong Ye, Quan Gao, Quan Gao, Tong Li, Tong Li

“…In the Yunnan region, where the climate is suitable for tea cultivation, tea leaf diseases are small, scattered, and vary in scale, making their detection challenging due to complex backgrounds and issues such as occlusion, overlap, and lighting variations. …”
Get full text

RETRACTED ARTICLE: An intelligent dynamic cyber physical system threat detection system for ensuring secured communication in 6G autonomous vehicle networks by Shanthalakshmi M, Ponmagal R S

“…Autonomous vehicles rely on a complex network of sensors, cameras, and software to operate. …”
Get full text

Automated Coronary Artery Identification in CT Angiography: A Deep Learning Approach Using Bounding Boxes by Marin Sakamoto, Takaaki Yoshimura, Hiroyuki Sugimori

“…The evaluation metrics included Average Precision (AP), Intersection over Union (IoU), Dice Similarity Coefficient (DSC), and Mean Absolute Error (MAE) to achieve both detection accuracy and spatial localization precision. …”
Get full text

Deep Learning-Based Detection of Separated Root Canal Instruments in Panoramic Radiographs Using a U²-Net Architecture by Nildem İnönü, Umut Aksoy, Dilan Kırmızı, Seçil Aksoy, Nurullah Akkaya, Kaan Orhan

“…<b>Background:</b> Separated endodontic instruments are a significant complication in root canal treatment, affecting disinfection and long-term prognosis. Their detection on panoramic radiographs is challenging, particularly in complex anatomy or for less experienced clinicians. …”
Get full text

Deep learning-based approach to third molar impaction analysis with clinical classifications by Yunus Balel, Kaan Sağtaş

“…This model automates the complex classification of impacted third molars, offering a reliable, efficient decision support system for clinical applications, streamlining workflows, reducing variability, and improving diagnostic precision.…”
Get full text

Third Ventricle Width Measurements Based on YOLO and Localized Intensity Features by Xiao Zhou, Ao Wan, Xingang Mou, Hongling Gao, Zheng Xue

“…Firstly, to solve the problem of third ventricle detection in the complex background of TCS images, the YOLO-TV improvement model is proposed to improve the detection accuracy. …”
Get full text

Performance-Oriented and Sustainability-Oriented Design of an Effective Android Malware Detector by Sana Qadir, Amna Naeem, Mehdi Hussain, Huma Ghafoor, Aisha Hassan Abdalla Hashim

“…Effective Android malware detection is a complex problem because of the rapidly-evolving, complicated, and diverse nature of malware. …”
Get full text

BREAST-RANKNet: a fuzzy rank-based ensemble of CNNs with residual learning for enhanced breast cancer detection from ultrasound and mammogram images by Sohaib Asif, Lingying Zhu, Dane Yan, Luman Xu, Zhengqiu Huang, Haimin Xu, Ruxuan Yan, Linghong Cai, Changfu Zheng, Jiamei Lin, Enyu Wang

“…This block improves computational efficiency and enables the model to capture both local and global features, addressing the challenges posed by complex medical imaging data. …”
Get full text

CoDAC: Autonomous Obstacle Avoidance Optimization for Unmanned Surface Vehicle Clusters via Multi-Modal Dynamic Perception and Collaborative Community Detection by Youhong Li, Jiawei Ye, Le Gao, Min Cai

“…Second, an incremental dynamic community detection mechanism achieves adaptive task group partitioning for clusters, substantially reducing communication loads and computational complexity while ensuring high-efficiency collaboration at scale. …”
Get full text

Enhancing Intelligent Road Target Monitoring: A Novel BGS-YOLO Approach Based on the YOLOv8 Algorithm by Xingyu Liu, Yuanfeng Chu, Yiheng Hu, Nan Zhao

“…This integration overcomes the limitations in feature extraction and recognition found in existing target detection algorithms. Following this, this paper introduces the Global Attention Module (GAM), which markedly improves the efficiency and accuracy of extracting key target information in complex data environments. …”
Get full text

Hyperspectral Anomaly Detection by Spatial&#x2013;Spectral Fusion Based on Extreme Value-Entropy Band Selection and Cauchy Graph Distance Optimization by Song Zhao, Yali Lv, Wen Zhang, Lijun Wang, Zhiru Yang, Gaofeng Ren, Bin Wang, Xiaobin Zhao, Tongwei Lu, Jiayao Wang, Wei Li

“…Hyperspectral technology for detecting camouflaged targets in complex backgrounds represents a current research hotspot. …”
Get full text

Multi-Dimensional Anomaly Detection and Fault Localization in Microservice Architectures: A Dual-Channel Deep Learning Approach with Causal Inference for Intelligent Sensing by Suchuan Xing, Yihan Wang, Wenhe Liu

“…Modern data centers face increasing complexity with distributed microservice architectures, making anomaly detection and fault localization challenging yet critical. …”
Get full text

Advanced Cotton Boll Segmentation, Detection, and Counting Using Multi-Level Thresholding Optimized with an Anchor-Free Compact Central Attention Network Model by Arathi Bairi, Uma N. Dulhare

“…There are limited techniques developed to segment, detect, and count cotton bolls precisely. This analysis identified several limitations and issues with these techniques, including their complex structure, low performance, time complexity, poor quality data, and so on. …”
Get full text

Simultaneous Determination of Quercetin and Trans-Resveratrol in Winemaking Waste by Solid Phase Microextraction Coupled to High-Performance Liquid Chromatography with Fluorescence... by Antonella Maria Aresta, Giovanna Mancini, Nicoletta De Vietro, Carlo Zambonin

“…This study establishes a reliable, sensitive, and efficient approach for the determination of these bioactive compounds in complex matrices, with potential applications in the food and pharmaceutical industries.…”
Get full text

Individual Tree Segmentation Based on Region-Growing and Density-Guided Canopy 3-D Morphology Detection Using UAV LiDAR Data by Shihua Li, Shunda Zhao, Zhilin Tian, Hao Tang, Zhonghua Su

“…Airborne laser scanning is an efficient and rapid method for acquiring 3-D point cloud data of forests, making up for the shortcomings of optical remote sensing. …”
Get full text

Structural Damage Detection Using an Unmanned Aerial Vehicle-Based 3D Model and Deep Learning on a Reinforced Concrete Arch Bridge by Mary C. Alfaro, Rodrigo S. Vidal, Rick M. Delgadillo, Luis Moya, Joan R. Casas

“…These networks are effective in detecting complex patterns, improving the accuracy and efficiency of damage identification based on simple visual inspection. …”
Get full text

Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within Deep Learning Frameworks by Deependra Rastogi, Prashant Johri, Massimo Donelli, Lalit Kumar, Shantanu Bindewari, Abhinav Raghav, Sunil Kumar Khatri

“…This result underscores its capability in high-precision brain tumor detection. The study concludes that fine-tuned deep transfer learning architectures, particularly Xception, significantly improve the accuracy and efficiency of brain tumor diagnosis. …”
Get full text

A two-stage deep learning architecture for detection global coastal and offshore submesoscale ocean eddy using SDGSAT-1 multispectral imagery by Linghui Xia, Baoxiang Huang, Ruijiao Li, Ge Chen

“…Introducing high-resolution multispectral data can alleviate these problems, yet it has been largely overlooked. A generalized and efficient deep learning architecture that combines developments in deep learning with Sustainable Development Goals Science Satellite 1 (SDGSAT-1) multispectral data from earth observations offers a potential pathway for more fine detection of ocean eddies. …”
Get full text