Real-time diagnosis of multi-category skin diseases based on IR-VGG by Ling TAN, Shanshan RONG, Jingming XIA, Sarker SAJIB, Wenjie MA

“…Malignant skin lesions have a very high cure rate in the early stage.In recent years, dermatological diagnosis research based on deep learning has been continuously promoted, with high diagnostic accuracy.However, computational resource consumption is huge and it relies on large computing equipment in hospitals.In order to realize rapid and accurate diagnosis of skin diseases on Internet of things (IoT) mobile devices, a real-time diagnosis system of multiple categories of skin diseases based on inverted residual visual geometry group (IR-VGG) was proposed.The contour detection algorithm was used to segment the lesion area of skin image.The convolutional block of the first layer of VGG16 was replaced with reverse residual block to reduce the network parameter weight and memory overhead.The original image and the segmented lesion image was inputed into IR-VGG network, and the dermatological diagnosis results after global and local feature extraction were outputed.The experimental results show that the IR-VGG network structure can achieve 94.71% and 85.28% accuracy in Skindata-1 and Skindata-2 skin diseases data sets respectively, and can effectively reduce complexity, making it easier for the diagnostic system to make real-time skin diseases diagnosis on IoT mobile devices.…”
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SCL-YOLOv11: A Lightweight Object Detection Network for Low-Illumination Environments by Shulong Zhuo, Hao Bai, Lifeng Jiang, Xiaojian Zhou, Xu Duan, Yiqun Ma, Zihan Zhou

“…First, the StarNet architecture is introduced into the Backbone to enhance the extraction of shallow image features and significantly reduce computational complexity. …”
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Utilizing GCN-Based Deep Learning for Road Extraction from Remote Sensing Images by Yu Jiang, Jiasen Zhao, Wei Luo, Bincheng Guo, Zhulin An, Yongjun Xu

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An interpretable machine learning model for predicting bone marrow invasion in patients with lymphoma via 18F-FDG PET/CT: a multicenter study by Xinyu Zhu, Denglu Lu, Yang Wu, Yanqi Lu, Liang He, Yanyun Deng, Xingyu Mu, Wei Fu

“…We aimed to develop and validate an interpretable machine learning model that integrates clinical data, 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) parameters, radiomic features, and deep learning features to predict BMI in lymphoma patients. …”
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First-principles calculations on Ti2AlB2, Ti3AlB4 and Ti4AlB6, three potential new members of the orthorhombic MAB phase by Chen Tang, Lei Dong, Shihui Feng, Bowen Han, Mengying Liu, Zhaowei Liu, Yufei Tang, Kang Zhao

“…In this study, three potential Ti-based MABs (Ti2AlB2, Ti3AlB4 and Ti4AlB6) are predicted via first-principles calculations and density functional theory (DFT). The computational analyses confirm the thermodynamic and mechanical stability of these phases, with their lattice parameters determined, providing valuable references for future experimental endeavors. …”
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Detection algorithm for wearing safety helmet under mine based on improved YOLOv5s by Yuanbin WANG, Sixiong WEI, Huaying WU, Yu DUAN, Meng LIU

“…Aiming at the problems of low accuracy and high missed detection rate of personnel safety helmet detection algorithm caused by complex environment under mine, an improved mine safety helmet detection algorithm based on YOLOv5s is proposed. Due to the computer system, the global context information of the image is easily lost when the convolutional neural network extracts the features, resulting in poor detection effect of the downhole small target safety helmet. …”
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A cross dataset meta-model for hepatitis C detection using multi-dimensional pre-clustering by Aryan Sharma, Tanmay Khade, Shashank Mouli Satapathy

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Lightweight grading segmentation network for powdery mildew based on model pruning and knowledge distillation by Yuheng Li, Lisha Ye, Yu Zhang, Lijuan Zhou, Qian Chen

“…Our contributions are fourfold: First, we establish a high-performance baseline, PMSeg, which integrates a Mixed Aggregation Network (MANet) and a Channel-wise Cross Fusion Transformer (CCFT) to significantly enhance sensitivity to small targets and multi-scale feature extraction. Second, to achieve on-device feasibility, we systematically compress PMSeg into light-PMSeg through a novel combination of slimming pruning and knowledge distillation (KD), which restores performance after parameter reduction. …”
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Enhanced deep learning model for apple detection, localization, and counting in complex orchards for robotic arm-based harvesting by Tantan Jin, Xiongzhe Han, Pingan Wang, Zhao Zhang, Jie Guo, Fan Ding

“…These results indicate improvements of 1.06 %, 1.42 %, 1.28 %, and 1.61 % over the original YOLOv8n, while preserving comparable model parameters, computational efficiency, and detection speed. …”
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An Evolutionary Deep Reinforcement Learning-Based Framework for Efficient Anomaly Detection in Smart Power Distribution Grids by Mohammad Mehdi Sharifi Nevisi, Mehrdad Shoeibi, Francisco Hernando-Gallego, Diego Martín, Sarvenaz Sadat Khatami

“…To address these challenges, this study proposes a novel deep reinforcement learning (DRL)-based framework, integrating a convolutional neural network (CNN) for hierarchical feature extraction and a recurrent neural network (RNN) for sequential pattern recognition and time-series modeling. …”
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Lightweight vision transformer model for pine wilt disease detection using aerial RGB image and adversarial data augmentation by Qing Li, Wenhui Chen

“…The framework achieves 7.4 % mAP improvement over baseline models on our established PWD dataset, with a 51.1 % frames per second (FPS) increase and 50 % parameter reduction. Firstly, a FasterNet-based backbone reduces computational complexity while preserving high-level semantic features. …”
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Noninvasive image texture analysis differentiates K-ras mutation from pan-wildtype NSCLC and is prognostic. by Glen J Weiss, Balaji Ganeshan, Kenneth A Miles, David H Campbell, Philip Y Cheung, Samuel Frank, Ronald L Korn

“…<h4>Background</h4>Non-invasive characterization of a tumor's molecular features could enhance treatment management. Quantitative computed tomography (CT) based texture analysis (QTA) has been used to derive tumor heterogeneity information, and the appearance of the tumors has been shown to relate to patient outcome in non-small cell lung cancer (NSCLC) and other cancers. …”
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Reliability of radiomic analysis on multiparametric MRI for patients affected by autosomal dominant polycystic kidney disease by Francesca Lussana, Ettore Lanzarone, Giulia Villa, Alfonso Mastropietro, Anna Caroli, Elisa Scalco

“…Additionally, lower-order features, including those computed from histograms and co-occurrence matrices, demonstrate higher reproducibility than other texture features.…”
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Failure Analysis of Static Analysis Software Module Based on Big Data Tendency Prediction by Jian Zhu, Qian Li, Shi Ying

“…This method can learn features from original defect data, directly and efficiently extract required features of all levels from software defect data by setting different number of hidden layers, sparse regularization parameters, and noise ratio, and then classify and predict the extracted features by combining with big data. …”
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A Novel Quadrilateral Contour Disentangled Algorithm for Industrial Instrument Reading Detection by Xiang Li, Changchang Zeng, Yong Yao, Jide Qian, Haiding Zhang, Sen Zhang, Suixian Yang

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Optimizing deep learning models to combat amyotrophic lateral sclerosis (ALS) disease progression by Haoshen Qin, Lal Hussain, Ziang Liu, Xu Yan, Fuad A. Awwad, Faisal Mehmood Butt, Umair Ahmad Salaria, Emad A.A. Ismail

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SageNet: Fast Neural Network Emulation of the Stiff-amplified Gravitational Waves from Inflation by Fan Zhang, Yifang Luo, Bohua Li, Ruihan Cao, Wenjin Peng, Joel Meyers, Paul R. Shapiro

“…The dual capability of learning both physical and artificial features of the numerical GW spectra establishes SageNet as a robust alternative to exact numerical methods. …”
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A lightweight real-time unified detection model for rice and wheat ears in complex agricultural environments by Xiaojun Shen, Shuai Li, Fen Qiu, Lili Yao

“…The model utilizes the lightweight MobileNetV3 network combined with the dynamic detection head DyHead to reconstruct the YOLOv5s network. Through multi-scale feature aggregation and attention mechanisms, the model effectively enhances its ability to capture dense targets in complex scenarios while reducing computational redundancy. …”
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L-ENet: An Ultralightweight SAR Image Detection Network by Yutong Wang, Min Miao, Shiliang Zhu

“…Experimental results show that L-ENet has a computational cost of 0.6 M and a parameter count of 2.1 giga floating point operations per second. …”
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Bridging Sensor Gaps via Attention-Gated Tuning for Hyperspectral Image Classification by Xizhe Xue, Haokui Zhang, Haizhao Jing, Lijie Tao, Zongwen Bai, Ying Li

“…Instead of inserting additional parameters inside the basic model, we train a lightweight auxiliary branch that takes intermediate features as input from the basic model and makes predictions. …”
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