MugenNet: A Novel Combined Convolution Neural Network and Transformer Network with Application in Colonic Polyp Image Segmentation by Chen Peng, Zhiqin Qian, Kunyu Wang, Lanzhu Zhang, Qi Luo, Zhuming Bi, Wenjun Zhang

“…The overall outcome of this study is a method to optimally combine two methods of machine learning which are complementary to each other.…”
Get full text

Enhancing smart city sustainability with explainable federated learning for vehicular energy control by Khalid Ishaq Abdullah Almaazmi, Saif Jasim Almheiri, Muhammad Adnan Khan, Asghar Ali Shah, Sagheer Abbas, Munir Ahmad

“…Traditional centralized machine learning models and cloud-based Energy Management Systems (EMSs) struggle with real-time adaptability, high-dimensional data processing, and data privacy risks. …”
Get full text

A Holistic Strategy of Modified Superpixel Segmentation and Randomized Adam Hyperparameter Tuning with Deep Learning Approaches for the Classification of Breast Cancer from BreakHi... by Gowri Shankar Manivannan, Karthikeyan Shanmugam, Harikumar Rajaguru, Satish V. Talawar, Rajanna Siddaiah

“…The extracted features are analyzed with machine learning models such as Gaussian Mixture Model (GMM), Decision Tree (DT), Softmax Discriminant Classifier (SDC), SVM using RBF kernel and Naive Bayes Classifier (NBC) as well as deep learning models ResNet-50, VGG16, VGG19 and EfficientNet-B0. …”
Get full text

Enhancing Yield Estimation and Field Zoning Accuracy in Precision Agriculture Using Solar-Powered Drone-Based Remote Sensing by Abbas Haider Mohammed, Obaid Mohammed Kadhim, Vittalaiah A.

“…The system processes this data using advanced machine learning algorithms to forecast crop yields and generate detailed field zoning maps, enabling optimized resource allocation and improved farm management. …”
Get full text

Automated Detection of Central Retinal Artery Occlusion Using OCT Imaging via Explainable Deep Learning by Ansgar Beuse, Daniel Alexander Wenzel, MD, Martin Stephan Spitzer, MD, Karl Ulrich Bartz-Schmidt, MD, Maximilian Schultheiss, MD, Sven Poli, MD, Carsten Grohmann, PhD, MD

“…Objective: To demonstrate the capability of a deep learning model to detect central retinal artery occlusion (CRAO), a retinal pathology with significant clinical urgency, using OCT data. …”
Get full text

From Coarse to Crisp: Enhancing Tree Species Maps with Deep Learning and Satellite Imagery by Taebin Choe, Seungpyo Jeon, Byeongcheol Kim, Seonyoung Park

“…The MLP model demonstrated optimal performance, achieving over 85% overall accuracy (OA) and more than 81% accuracy in classifying spectrally similar and difficult-to-distinguish species, specifically <i>Quercus mongolica</i> (QM) and <i>Quercus variabilis</i> (QV). …”
Get full text

Real-Time Intelligent Recognition and Precise Drilling in Strongly Heterogeneous Formations Based on Multi-Parameter Logging While Drilling and Drilling Engineering by Aosai Zhao, Yang Yu, Bin Wang, Yewen Liu, Jingyue Liu, Xubiao Fu, Wenhao Zheng, Fei Tian

“…The K-means clustering algorithm is employed to extract the deep geo-engineering characteristics from multi-source LWD data, thereby constructing a lithology label library and categorizing the training and testing datasets. The optimized CatBoost machine learning model is subsequently utilized for lithology classification, enabling real-time and high-precision geological evaluation during directional drilling. …”
Get full text

Shelf Life Identification and Quality Analysis of Golden Delicious Apples Based on Hyperspectral Imaging and Near Infrared Spectroscopy by Xin ZHAO, Shuliang ZHENG, Xiaoying NIU, Jiankang CAO, Han CHEN, Zhilei ZHAO

“…The results showed that both NIR and hyperspectral imaging techniques could determine the shelf life of Golden Delicious apples. The optimal model was established by 1D+UVE+BP based on hyperspectral images, and the accuracy rate was 100%. …”
Get full text

Perilesional dominance: radiomics of multiparametric MRI enhances differentiation of IgG4-Related ophthalmic disease and orbital MALT lymphoma by Jie Li, Chenran Zhou, Xiaoxia Qu, Lianze Du, Qinghai Yuan, Qinghe Han, Junfang Xian

“…A LASSO-SVM classifier was optimized through comparative evaluation of seven machine learning models, incorporating fused radiomic features (1,197 features) from ILN/PLN regions. …”
Get full text

Distributed Collaborative Learning with Representative Knowledge Sharing by Joseph Casey, Qianjiao Chen, Mengchen Fan, Baocheng Geng, Roman Shterenberg, Zhong Chen, Keren Li

“…By leveraging Energy Coefficients to quantify node similarity, CTL dynamically selects optimal collaborators and refines local models through knowledge distillation on shared representative datasets. …”
Get full text

GA-SVM method for single-phase grounding fault line selection in distribution network based on feature fusion by ZHANG Xiaopeng, BAI Jie, SUN Naijun, LI Jie, ZHENG Shuai, WAN Qingzhu

“…Aiming at the low accuracy of line selection method when the data amount of single-phase grounding fault in distribution network is small, a genetic algorithm optimized support vector machine (GA-SVM) method for single-phase grounding fault line selection in distribution network based on feature fusion is proposed, which adopts Fourier transform, the active power method and wavelet packet transform decompose the transient zero-sequence current of each line under different fault conditions, extracts four features, including fundamental wave amplitude, fifth harmonic amplitude, average active power component and wavelet energy value. …”
Get full text

Design of a novel noise resilient algorithm for fault detection in wind turbines on supervisory control and data acquisition system by Muhammad Irfan, Nabeel Ahmed Khan, Muhammad Abubakar, Zohaib Mushtaq, Tomasz Jakubowski, Paweł Sokołowski, Grzegorz Nawalany, Saifur Rahman

Get full text

Transfer Learning-Empowered Physical Layer Security in Aerial Reconfigurable Intelligent Surfaces-Based Mobile Networks by Yosefine Triwidyastuti, Tri Nhu Do, Ridho Hendra Yoga Perdana, Kyusung Shim, Beongku An

“…To address these challenges, we develop robust algorithms for optimizing the phase-shift configurations of RIS. Additionally, we employ Artificial Intelligence (AI) and Machine Learning (ML) techniques, specifically Deep Neural Networks (DNN), for performance prediction of PHY security metrics. …”
Get full text

Hybrid cryogenic/MQL helical milling for hole-making of Inconel 718 by Robson Bruno Dutra Pereira, Gaizka Gómez-Escudero, Amaia Calleja-Ochoa, Octavio Pereira, Haizea González-Barrio, Lincoln Cardoso Brandão, Luis Norberto López de Lacalle

Get full text

Discerning Misclassified Flat-spectrum Radio Quasars from Low-frequency-peaked BL Lacertae Objects by S. Liang, W. G. Yang, Y. G. Zheng, S. J. Kang

“…A support vector machine in the $\mathrm{log}{L}_{\gamma }$ –Γ _γ frame is utilized to delineate the optimal boundary between FSRQs and LBL sources. …”
Get full text

Study on the Geographic Traceability and Growth Age of <i>Panax ginseng</i> C. A. Meyer Base on an Electronic Nose and Fourier Infrared Spectroscopy by Jinying Li, Jianlei Qiao, Chang Liu, Zhigang Zhou, Cheng Kong, Zhiyong Chang, Xiaohui Weng, Shujun Zhang

“…In the proposed method, five types of ginseng samples have been successfully tested. The optimal Mean-SVM model combined with an E-nose system classified ginseng samples with different geographic traceability and different growth years with accuracies of 100% and 82% in the training and test sets, respectively. …”
Get full text

IMPROVE OF UNCERTAIN MICROSATELLITE MAGNETIC CLEANLINESS BASED ON MAGNETIC FIELD SPATIAL HARMONICS COMPENSATION by Б.І. Кузнецов, Т.Б. Нікітіна, І.В. Бовдуй, К.В. Чуніхін, В.В. Коломієць, Б.Б. Кобилянський

Get full text

CDFA: Calibrated deep feature aggregation for screening synergistic drug combinations by Xiaorui Kang, Xiaoyan Liu, Quan Zou, Quan Zou, Tiantian Li, Ximei Luo, Ximei Luo

“…However, conventional wet-lab experimentation for identifying optimal drug combinations is resource-intensive due to the vast combinatorial search space. …”
Get full text

Application of spectral characteristics of electrocardiogram signals in sleep apnea by Jiayue Hu, Liu Yang, Xintong Zhao, Haicheng Wei, Jing Zhao, Miaomiao Li

“…These features are classified via a random forest machine learning model.ResultsThe femax and IMF7 components of the reconstructed signal exhibited statistically significant differences (p &lt; 0.001) between normal and sleep apnea subjects. …”
Get full text

How to Learn More? Exploring Kolmogorov–Arnold Networks for Hyperspectral Image Classification by Ali Jamali, Swalpa Kumar Roy, Danfeng Hong, Bing Lu, Pedram Ghamisi

Get full text