DEGNN: A Deep Learning-Based Method for Unmanned Aerial Vehicle Software Security Analysis by Jiang Du, Qiang Wei, Yisen Wang, Xingyu Bai

Get full text

Construction of a Web Game for the Teaching-Learning Process of Electronics during the COVID-19 Pandemic by Ricardo-Adán Salas-Rueda, Clara Alvarado-Zamorano, Jesús Ramírez-Ortega

“…This web game consists of a simulator that presents the contents of the output function for two variables and their representation through logic gates. …”
Get full text

Malware Detection and Classification in Android Application Using Simhash-Based Feature Extraction and Machine Learning by Wafaa Al-Kahla, Eyad Taqieddin, Ahmed S. Shatnawi, Rami Al-Ouran

“…These vectors are then used to train different Machine Learning algorithms for detecting and classifying malware. …”
Get full text

Research on Dynamic Path Planning of Wheeled Robot Based on Deep Reinforcement Learning on the Slope Ground by Peng Wang, Xiaoqiang Li, Chunxiao Song, Shipeng Zhai

Get full text

Transfer learning of neural operators for partial differential equations based on sparse network λ-FNO. by Jinghong Xu, Yuqian Zhou, Qian Liu, Kebing Li, Haolin Yang

“…By introducing the λ parameter matrix and using a new pruning method to make the network sparse, the operator learning ability of λ-FNO is greatly improved. Using λ-FNO can efficiently learn the operator from the discrete initial function space on the uniform grid to the discrete equation's solution space on the unstructured grid, which is not available in FNO. …”
Get full text

Deep learning-driven automated mitochondrial segmentation for analysis of complex transmission electron microscopy images by Chan Jang, Hojun Lee, Jaejun Yoo, Haejin Yoon

“…Abstract Mitochondria are central to cellular energy production and regulation, with their morphology tightly linked to functional performance. Precise analysis of mitochondrial ultrastructure is crucial for understanding cellular bioenergetics and pathology. …”
Get full text

Assessing Locomotive Syndrome Through Instrumented Five-Time Sit-to-Stand Test and Machine Learning by Iman Hosseini, Maryam Ghahramani

“…The Geriatric Locomotive Function Scale (GLFS-25), a 25-question assessment, has been proposed for categorizing individuals into different stages of LS. …”
Get full text

Improving electric vehicle charging forecasting: A hybrid deep learning approach for probabilistic predictions by Ali Jamali Jahromi, Mohammad Reza Masoudi, Mohammad Mohammadi, Shahabodin Afrasiabi

“…Therefore, probabilistic forecasting, represented as a probability distribution function (PDF) containing comprehensive statistical information, is preferred. …”
Get full text

Model-Aware End-to-End Learning for SISO Optical Wireless Communication Over Poisson Channel by Ling-Han Si-Ma, Zhao-Rui Zhu, Hong-Yi Yu

“…Extensive computer simulations reveal that 1) the transceiver learned by the DNN scheme always performs better than or comparably to the currently available artificially designed transceivers, and 2) compared with the transceiver learned by DNN, the transceiver learned by SR-AE suffers performance loss in some cases, but the SR-AE scheme has a lower complexity to compute the loss function and fewer network parameters. …”
Get full text

A Ship Underwater Radiated Noise Prediction Method Based on Semi-Supervised Ensemble Learning by Xin Huang, Rongwu Xu, Ruibiao Li

“…First, an anti-perturbation regularization is constructed using unlabeled data to optimize the objective function of EL, which is then used in the Genetic algorithm to adaptively optimize base learner weights, to enhance pseudo-label quality. …”
Get full text

Hybrid intelligent optimization strategy of battery swapping station for electric vehicles based on reinforcement learning by Hongwei Yi, Huan Zhang, Jianghong Li, Yanling Zhao

“…The GSO-PPO algorithm is constructed, where PPO algorithm learns the optimal scheduling strategy for the battery swapping station in a dynamic environment, and the GSO algorithm optimizes the hyperparameters of PPO and adjusts the weight of the reward function to achieve the multi-objective optimization of minimizing battery life, shortening vehicle waiting time, and efficiently managing grid load. …”
Get full text

miR‐33 in cardiometabolic diseases: lessons learned from novel animal models and approaches by Nathan L Price, Leigh Goedeke, Yajaira Suárez, Carlos Fernández‐Hernando

Get full text

Photoplethysmography Feature Extraction for Non-Invasive Glucose Estimation by Means of MFCC and Machine Learning Techniques by Christian Salamea-Palacios, Melissa Montalvo-López, Raquel Orellana-Peralta, Javier Viñanzaca-Figueroa

“…Two variants of the MFCC feature extraction methods are evaluated along with three machine learning techniques for the development of an effective regression function for the estimation of glucose concentration. …”
Get full text

Adapting Machine Learning Models for Indoor Temperature Prediction from Kuwait to the French Riviera Climate by Sedaghat Ahmad, Nazififard Mohammad, Franquet Erwin, Kalbasi Rasool, Mostafaeipour Ali

“…This study investigates how machine learning models that were first created to forecast indoor temperatures in Kuwaiti portable cabins may be modified to replicate indoor temperature conditions in Nice, France. …”
Get full text

Analysis of Data and Feature Processing on Stroke Prediction using Wide Range Machine Learning Model by Untari Novia Wisesty, Tjokorda Agung Budi Wirayuda, Febryanti Sthevanie, Rita Rismala

“…Stroke is a disease which cause the death of brain cells, so that the part of the body controlled by the brain loses its function. If not treated immediately, this disease can cause long-term disability, brain damage, and death. …”
Get full text

Intellectual disability-associated disruption of O-GlcNAc cycling impairs habituation learning in Drosophila. by Michaela Fenckova, Villo Muha, Daniel Mariappa, Marica Catinozzi, Ignacy Czajewski, Laura E R Blok, Andrew T Ferenbach, Erik Storkebaum, Annette Schenck, Daan M F van Aalten

“…Introduction of patient-specific missense mutations into Drosophila O-GlcNAc transferase using CRISPR/Cas9 gene editing leads to deficits in locomotor function and habituation learning. The habituation deficit can be corrected by blocking O-GlcNAc hydrolysis, indicating that OGT-CDG mutations affect cognition-relevant habituation via reduced protein O-GlcNAcylation. …”
Get full text

LMGD: Log-Metric Combined Microservice Anomaly Detection Through Graph-Based Deep Learning by Xu Liu, Yuewen Liu, Miaomiao Wei, Peng Xu

“…Its core idea is to split the application into a set of microservices with a single function and independent deployment. Due to their complexity and large scale, microservice systems are typically fragile and failures are inevitable. …”
Get full text

A joint learning approach for automated diagnosis of keratinocyte carcinoma using optical attenuation coefficients by Lei Zhang, Xiaoran Li, Wen Chen, Yuanjie Gu, Hao Wu, Zhong Lu, Biqin Dong

“…By incorporating probability distribution function (PDF) information alongside OAC images, the model achieved an accuracy of over 80% and approaching 100% by utilizing 3D OAC data to enhance robustness. …”
Get full text

Synergizing BRDF correction and deep learning for enhanced crop classification in GF-1 WFV imagery by Yuanwei Chen, Yang Li, Runze Li, Chongzheng Guo, Jilin Li

“…GF-1 Wide Field View (WFV) imagery suffers from Bidirectional Reflectance Distribution Function (BRDF) effects due to large viewing angles (0°–48°), reducing crop classification accuracy. …”
Get full text

Hybrid modeling of adsorption process using mass transfer and machine learning techniques for concentration prediction by Jing Lv, Lei Wang

“…Three supervised regression models of Kernel Ridge Regression (KRR), Decision Tree Regression (DT), and Radial Basis Function Support Vector Machine (RBF-SVM) were developed to map spatial coordinates to solute concentrations. …”
Get full text