A Multistage Detection Framework Based on TFA and Multiframe Correlation for HFSWR by Zongtai Li, Gangsheng Li, Ling Zhang, Lanjun Liu, Q. M. Jonathan Wu

“…In this article, TFA, multiframe correlation, and deep neural networks are integrated to develop a three-stage detection framework. …”
Get full text

D2CBDAMAttUnet: Dual-Decoder Convolution Block Dual Attention Unet for Accurate Retinal Vessel Segmentation From Fundus Images by Vo Trong Quang Huy, Chih-Min Lin

“…In light of this, Deep Convolutional Neural Networks (DCNNs), particularly those based on the U-Net architecture, have been acknowledged for their effectiveness in capturing and utilizing contextual features within this context. …”
Get full text

An Ensemble Based Machine Learning Classification for Automated Glaucoma Detection by Digvijay J. Pawar, Yuvraj K. Kanse, Suhas S. Patil

“…This paper has been designed to evaluate the performance of Probabilistic Neural Networks (PNN), K-Nearest Neighbour (KNN), Support Vector Machines (SVM), Naïve Bayes (NB) and Logistic Regression (LR) as individual and ensemble classifiers. …”
Get full text

Simulation and explanatory analysis of dissolved oxygen dynamics in Lake Ulansuhai, China by Fan Zhang, Xiaohong Shi, Shengnan Zhao, Ruonan Hao, Biao Sun, Guohua Li, Shihuan Wang, Hao Zhang

“…Study focus: After implementing the optimal noise reduction strategies based on wavelet transform for the high-frequency monitoring data, hybrid models coupling random forests, support vector machines, and artificial neural networks were employed to simulate the dissolved oxygen in the lake during both the open-water and ice-covered periods. …”
Get full text

Robust Malware identification via deep temporal convolutional network with symmetric cross entropy learning by Jiankun Sun, Xiong Luo, Weiping Wang, Yang Gao, Wenbing Zhao

“…In the experiments, the proposed method is compared with several traditional statistical methods and more recent neural networks on a synthetic Malware dataset and a real‐world dataset. …”
Get full text

Novel transfer learning approach for hand drawn mathematical geometric shapes classification by Aneeza Alam, Ali Raza, Nisrean Thalji, Laith Abualigah, Helena Garay, Josep Alemany Iturriaga, Imran Ashraf

“…We introduced a novel machine-learning algorithm CnN-RFc that uses convolution neural networks (CNN) for spatial feature extraction and the random forest classifier for probabilistic feature extraction from image data. …”
Get full text

A robust, deep learning-based analysis of time-domain signals for NMR spectroscopy by Kyungdoe Han, Eunhee Kim, Kyoung-Seok Ryu, Donghan Lee

“…In this study, we demonstrate that neural networks can replace FT in NMR spectroscopy, enabling robust and rapid prediction of spectra and peak lists from FID signals. …”
Get full text

Empowering Security Operation Center With Artificial Intelligence and Machine Learning—A Systematic Literature Review by Mohamad Khayat, Ezedin Barka, Mohamed Adel Serhani, Farag Sallabi, Khaled Shuaib, Heba M. Khater

“…Various methods, ranging from automated incident response and behavioral analytics to neural networks and deep learning, have been classified and compared. …”
Get full text

A Comprehensive Review of Vision-Based Sensor Systems for Human Gait Analysis by Xiaofeng Han, Diego Guffanti, Alberto Brunete

“…Furthermore, depth learning algorithms, such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, are being employed with increasing frequency. …”
Get full text

Lithium Battery Life Prediction for Electric Vehicles Using Enhanced TCN and SVN Quantile Regression by Xinyue Li, Jiangwei Chu

“…Furthermore, it proceeds to integrate self-coding neural networks with temporal convolutional networks for the purpose of processing and extracting battery life data, and finally proposes a novel prediction model. …”
Get full text

Ultra-broadband all-optical nonlinear activation function enabled by MoTe2/optical waveguide integrated devices by Chenduan Chen, Zhan Yang, Tao Wang, Yalun Wang, Kai Gao, Jiajia Wu, Jun Wang, Jianrong Qiu, Dezhi Tan

“…Abstract All-optical nonlinear activation functions (NAFs) are crucial for enabling rapid optical neural networks (ONNs). As linear matrix computation advances in integrated ONNs, on-chip all-optical NAFs face challenges such as limited integration, high latency, substantial power consumption, and a high activation threshold. …”
Get full text

Co-optimization of Demand Response Aggregators and distribution system operator for resilient operation using machine learning based wind generation forecasting: A bilevel approach by Farid Hamzeh Aghdam, Aleksandr Zavodovski, Adeleye Adetunji, Mehdi Rasti, Eva Pongracz, Mohammad Sadegh Javadi, João P.S. Catalão

“…The model also incorporates Long Short-Term Memory (LSTM) neural networks for wind generation forecasting, enhancing decision-making precision. …”
Get full text

A Hybrid Approach Integrating Multiple ICEEMDANs, WOA, and RVFL Networks for Economic and Financial Time Series Forecasting by Jiang Wu, Tengfei Zhou, Taiyong Li

“…In this study, a novel multidecomposition and self-optimizing hybrid approach integrating multiple improved complete ensemble empirical mode decompositions with adaptive noise (ICEEMDANs), whale optimization algorithm (WOA), and random vector functional link (RVFL) neural networks, namely, MICEEMDAN-WOA-RVFL, is developed to predict economic and financial time series. …”
Get full text

Deep Reinforcement Learning Assisted UAV Path Planning Relying on Cumulative Reward Mode and Region Segmentation by Zhipeng Wang, Soon Xin Ng, Mohammed EI-Hajjar

“…The proposed region segmentation algorithm and cumulative reward model have been tested in different DRL techniques, where we show that the cumulative reward model can improve the training efficiency of deep neural networks by 30.8% and the region segmentation algorithm enables deep Q-network agent to avoid 99% of local optimal traps and assists deep deterministic policy gradient agent to avoid 92% of local optimal traps.…”
Get full text

Comparative Study of Statistical Features to Detect the Target Event During Disaster by Madichetty Sreenivasulu, M. Sridevi

“…Additionally, different classifiers such as Artificial Neural Networks (ANN), decision tree, and K-Nearest Neighbor (KNN) are compared by using these two features. …”
Get full text

Exploring the Effectiveness of Machine Learning and Deep Learning Techniques for EEG Signal Classification in Neurological Disorders by Souhaila Khalfallah, William Puech, Mehdi Tlija, Kais Bouallegue

“…Moreover, deep learning models, including Convolutional Neural Networks (CNN) and ChronoNet, demonstrated accuracy rates ranging from 92.5% to 100%. …”
Get full text

Multi-Scale Long- and Short-Range Structure Aggregation Learning for Low-Illumination Remote Sensing Imagery Enhancement by Yu Cao, Yuyuan Tian, Xiuqin Su, Meilin Xie, Wei Hao, Haitao Wang, Fan Wang

“…Profiting from the surprising non-linear expressive capacity, deep convolutional neural networks have inspired lots of progress in low illumination (LI) remote sensing image enhancement. …”
Get full text

Forecasting High‐Speed Solar Wind Streams From Solar Images by Daniel Collin, Yuri Shprits, Stefan J. Hofmeister, Stefano Bianco, Guillermo Gallego

“…The study shows that a small number of physical features explains most of the solar wind variation, and that focusing on these features with simple machine learning algorithms even outperforms current approaches based on deep neural networks and MHD simulations. In addition, we explain why the typically used loss function, the mean squared error, systematically underestimates the HSS peak velocities, aggravates operational space weather forecasts, and how a distribution transformation can resolve this issue.…”
Get full text

Mapping 3D Overthrust Structures by a Hybrid Modeling Method by Weisheng Hou, Yanhua Li, Shuwan Ye, Songhua Yang, Fan Xiao

“…Combined with the multi‐point statistics (MPS) method and fully connected neural networks (FCNs), this study presented a hybrid framework for 3D geological modeling. …”
Get full text

E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review by Abed Mutemi, Fernando Bacao

“…Employing the PRISMA approach, we conducted a content analysis of 101 publications, identifying research gaps, recent techniques, and highlighting the increasing utilization of artificial neural networks in fraud detection within the industry.…”
Get full text