Deep Learning–Based Enhanced Optimization for Automated Rice Plant Disease Detection and Classification by P. Preethi, R. Swathika, S. Kaliraj, R. Premkumar, J. Yogapriya

“…ABSTRACT Ensuring global food security necessitates innovative solutions for early detection and precise classification of diseases in staple crops like rice. …”
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Sentiment analysis for depression detection: A stacking ensemble-based deep learning approach by Kinza Noor, Mariam Rehman, Maria Anjum, Afzaal Hussain, Rabia Saleem

“…Due to the widespread prevalence of this disorder in society, novel and efficient methods must be developed for effective detection and treatment. …”
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Dynamically Tunable Multidimensional Feature Focusing and Diffusion Networks for Water Surface Debris Detection by Chong Zhang, Jie Yue, Jianglong Fu

“…However, challenges such as complex backgrounds, overlapping targets, and reflections on water surfaces often hinder effective waste detection, resulting in low model accuracy and unreliable recognition. …”
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Federated Learning for Fall Detection With Multimodal Residual Fusion and Pareto-Optimized Client Selection by Bao-Quan Wang, Fan Yang, Yi Wang, Fan Zhao, Yun-Fei Han, Yu-Peng Ma

“…However, challenges such as multimodal data integration and joint analysis in Internet of Medical Things (IoMT) environments and data heterogeneity across sources hinder efficient and accurate fall detection. This paper proposes a Federated Learning-based framework with Multimodal Residual Fusion and Pareto-optimized Client Selection (FLPCS-MRF). …”
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Hybrid Wavelet-Attention Model for Detecting Changes in High-Resolution Remote Sensing Images by Lhuqita Fazry, MGS M. Luthfi Ramadhan, Alif Wicaksana Ramadhan, Muhammad Febrian Rachmadi, Aprinaldi Jasa Mantau, Lukito Edi Nugroho, Chi-Hung Chi, Wisnu Jatmiko

“…However, this causes information loss, resulting in a trade-off between the effectiveness and efficiency of the method. To solve the problem, we developed a new change detection method called WaveCD. …”
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Cotton Leaf Disease Detection Using LLM-Synthetic Data and DEMM-YOLO Model by Lijun Gao, Tiantian Ran, Hua Zou, Huanhuan Wu

“…It plays a crucial role in enhancing cotton yield and quality while promoting the advancement of intelligent agriculture and efficient crop harvesting. This study proposes a novel method for detecting cotton leaf diseases based on large language model (LLM)-generated image synthesis and an improved DEMM-YOLO model, which is enhanced from the YOLOv11 model. …”
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Hybrid Optimization Method for Social Internet of Things Service Provision Based on Community Detection by Bahar Allakaram Tawfeeq, Amir Masoud Rahmani, Abbas Koochari, Nima Jafari Navimipour

“…The average response time of IGBSA‐CD presents that it is efficient in all three tasks is 0.04 s. It also has a consistently lower response time, even when the task is complex. …”
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Fibromyalgia Detection and Diagnosis: A Systematic Review of Data-Driven Approaches and Clinical Implications by Anirudh Atmakuru, Subrata Chakraborty, Massimo Salvi, Oliver Faust, Prabal Datta Barua, Makiko Kobayashi, Ru San Tan, Filippo Molinari, Abdul Hafeez-Baig, U. Rajendra Acharya

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Comparative Analysis of Improved YOLO v5 Models for Corrosion Detection in Coastal Environments by Qifeng Yu, Yudong Han, Xinjia Gao, Wuguang Lin, Yi Han

“…YOLO v5, known for its speed, accuracy, and ease of deployment, has been employed for the rapid detection and identification of marine corrosion. However, corrosion images often feature complex characteristics and high variability in detection targets, presenting significant challenges for YOLO v5 in recognizing and extracting corrosion features. …”
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Multi-Scale Residual Convolutional Neural Network with Hybrid Attention for Bearing Fault Detection by Yanping Zhu, Wenlong Chen, Sen Yan, Jianqiang Zhang, Chenyang Zhu, Fang Wang, Qi Chen

“…This paper proposes an advanced deep convolutional neural network model for motor bearing fault detection that was designed to overcome the limitations of traditional models in feature extraction, accuracy, and generalization under complex operating conditions. …”
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A Noninvasive System for the Automatic Detection of Gliomas Based on Hybrid Features and PSO-KSVM by Guoli Song, Zheng Huang, Yiwen Zhao, Xingang Zhao, Yunhui Liu, Min Bao, Jianda Han, Peng Li

“…However, it is a challenging task to detect the gliomas within the complex structure of the brain. …”
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Toward Model-Independent Separative Training for Deep Hyperspectral Anomaly Detection With Mask Guidance by Xi Su, Xiangfei Shen, Haijun Liu, Lihui Chen, Gemine Vivone, Xichuan Zhou

“…Hyperspectral anomaly detection (HAD) aims to recognize a minority of anomalies that are spectrally different from their surrounding background without prior knowledge. …”
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Oriented SAR Ship Detection Based on Edge Deformable Convolution and Point Set Representation by Tianyue Guan, Sheng Chang, Yunkai Deng, Fengli Xue, Chunle Wang, Xiaoxue Jia

“…Although extensive research has been conducted in this field, the interference of speckle noise in SAR images and the potential discontinuity of target contours continue to pose challenges for the accurate detection of multi-directional ships in complex scenes. …”
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Optimized Flexible Microheater With Integrated Electrodes and Microchannel for On-Chip Bacterial Incubation and Electrochemical Detection by Sonal Fande, Khairunnisa Amreen, D. Sriram, Sanket Goel

“…While these systems aim to incorporate more complex diagnostic methods into simple portable chips, they remain tethered to laboratory incubators requiring high-power inputs. …”
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HOIL-1L interacting protein (HOIP) as an NF-kappaB regulating component of the CD40 signaling complex. by Bruce S Hostager, Daniel K Fox, Douglas Whitten, Curtis G Wilkerson, Betty A Eipper, Victor P Francone, Paul B Rothman, John D Colgan

“…One family member, CD40, is important for the efficient activation of antibody-producing B cells and other antigen-presenting cells. …”
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Enhanced neurological anomaly detection in MRI images using deep convolutional neural networks by Ahmed Mateen Buttar, Zubair Shaheen, Abdu H. Gumaei, Mogeeb A. A. Mosleh, Mogeeb A. A. Mosleh, Indrajeet Gupta, Samah M. Alzanin, Muhammad Azeem Akbar

“…IntroductionNeurodegenerative diseases, including Parkinson’s, Alzheimer’s, and epilepsy, pose significant diagnostic and treatment challenges due to their complexity and the gradual degeneration of central nervous system structures. …”
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Research and application of deep learning object detection methods for forest fire smoke recognition by Luhao He, Yongzhang Zhou, Lei Liu, Yuqing Zhang, Jianhua Ma

“…This study investigates the application effectiveness of deep learning-based object detection technology in forest fire smoke recognition by using the YOLOv11x algorithm to develop an efficient fire detection model. …”
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Few-shot object detection for pest insects via features aggregation and contrastive learning by Shuqian He, Shuqian He, Biao Jin, Biao Jin, Xuechao Sun, Wenjuan Jiang, Wenjuan Jiang, Jiaxing Gu, Fenglin Gu

“…This research provides a practical and efficient solution for pest detection under challenging conditions, reducing dependency on large annotated datasets and improving detection accuracy for minority pest classes. …”
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Integrated neural network framework for multi-object detection and recognition using UAV imagery by Mohammed Alshehri, Tingting Xue, Tingting Xue, Ghulam Mujtaba, Yahya AlQahtani, Nouf Abdullah Almujally, Ahmad Jalal, Ahmad Jalal, Hui Liu, Hui Liu, Hui Liu

“…The proposed framwork integrates multiple advanced modules: RetinexNet for preprocessing, segmentation using HRNet to preserve high-resolution semantic information, and vehicle detection using the YOLOv11 framework. Deep SORT is employed for efficient vehicle tracking, while CSRNet facilitates high-density vehicle counting. …”
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Dynahead-YOLO-Otsu: an efficient DCNN-based landslide semantic segmentation method using remote sensing images by Zheng Han, Bangjie Fu, Zhenxiong Fang, Yange Li, Jiaying Li, Nan Jiang, Guangqi Chen

“…This is achieved by locating potential landslide regions in advance using the ODD-based Dynahead-YOLO model, which enhances the capacity for detecting landslides with variable proportions and complex background in the images. …”
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