Effective DDoS attack detection in software-defined vehicular networks using statistical flow analysis and machine learning. by Himanshi Babbar, Shalli Rani, Maha Driss

“…Vehicular Networks (VN) utilizing Software Defined Networking (SDN) have garnered significant attention recently, paralleling the advancements in wireless networks. …”
Get full text

Parallel convolutional neural network and empirical mode decomposition for high accuracy in motor imagery EEG signal classification. by Jaipriya D, Sriharipriya K C

“…In this work, we propose a novel method that addresses these challenges by employing empirical mode decomposition (EMD) for feature extraction and a parallel convolutional neural network (PCNN) for feature classification. …”
Get full text

The prediction method for ground surface settlement of pipe jacking tunnels based on a spatiotemporal transfer learning network by Hairong Huang, Lian Yuan, Jian Chen, Shixia Zhang

“…The Long Short-Term Memory-Convolutional Neural Network model with Transfer Learning (LSTM-CNN-TL) is proposed to achieve settlement prediction under data-scarce conditions. …”
Get full text

Boosting the Development and Management of Wind Energy: Self-Organizing Map Neural Networks for Clustering Wind Power Outputs by Yanqian Li, Yanlai Zhou, Yuxuan Luo, Zhihao Ning, Chong-Yu Xu

“…Aimed at the information loss problem of using discrete indicators in wind power output characteristics analysis, a self-organizing map neural network-based clustering method is proposed in this study. …”
Get full text

Predicting Wastewater Characteristics Using Artificial Neural Network and Machine Learning Methods for Enhanced Operation of Oxidation Ditch by Igor Gulshin, Nikolay Makisha

“…The SMAPE score of 1.052% on test data demonstrates the model’s accuracy and highlights the potential of integrating artificial neural networks (ANN) and machine learning (ML) with mechanistic models for optimizing wastewater treatment processes. …”
Get full text

Comprehensive evaluation of machine learning models for predicting the cognitive status of Alzheimer's disease subjects and susceptible by Lucien Gnegne Meteumba, Vaghawan Prasad Ojha, Shantia Yarahmadian

Get full text

CAML-PSPNet: A Medical Image Segmentation Network Based on Coordinate Attention and a Mixed Loss Function by Yuxia Li, Peng Li, Hailing Wang, Xiaomei Gong, Zhijun Fang

“…Firstly, the coordinate attention module splits the input feature map into horizontal and vertical directions to locate the edge position of the segmentation target. …”
Get full text

A review of deep-learning-based models for afaan oromo fake news detection on social media networks by Kedir Lemma Arega, Kula Kekeba Tune, Asrat Mulatu Beyene, Wegderes Tariku, Nurhussen Menza Bune

Get full text

Research on automatic assessment of the severity of unilateral vocal cord paralysis based on Mel-spectrogram and convolutional neural networks by Shuaichi Ma, Wenwen Liao, Yi Zhang, Fan Zhang, Yimiao Wang, Zhiyan Lu, Chen Zhao, Jianbo Yu, Peijie He

“…Using Mel-spectrograms and their first- and second-order differential features as inputs, the TripleConvNet model classified patients by severity and was systematically evaluated for its performance in UVCP severity grading tasks. …”
Get full text

Enhanced hierarchical attention networks for predictive interactome analysis of LncRNA and CircRNA in oral herpes virus by Pradeep Kumar Yadalam, Carlos M. Ardila

Get full text

Array inspired wideband and high gain antenna with enhanced pattern diversity for 5G mm wave networks by Nawaf A. Alqwaifly, Wahaj Abbas Awan, Nabeel Alsaab, Fahad Nasser Alsunaydih, Khaled Alhassoon

Get full text

SegPhase: development of arrival time picking models for Japan’s seismic network using the hierarchical vision transformer by Shinya Katoh, Yoshihisa Iio, Hiromichi Nagao, Hiroshi Katao, Masayo Sawada, Kazuhide Tomisaka

“…Abstract Seismic phase picking is a fundamental task in seismology that is crucial for event detection and earthquake cataloging; however, manual analysis is impractical given the scale of modern seismic networks. We present SegPhase, a novel seismic arrival time picking model designed to efficiently process large-scale seismic data recorded by dense seismic networks in Japan. …”
Get full text

AE-BPNN: autoencoder and backpropagation neural network-based model for lithium-ion battery state of health estimation by Abdullah Ahmed Al-Dulaimi, Muhammet Tahir Guneser, Raghad Al-Shabandar, Yeonghyeon Gu, Muhammad Syafrudin, Norma Latif Fitriyani

“…An autoencoder backpropagation neural network (AE-BPNN) was developed for unsupervised processing, dimensionality reduction, feature extraction, and SOH estimation. …”
Get full text

Deep Learning Network Selection and Optimized Information Fusion for Enhanced COVID-19 Detection: A Literature Review by Olga Adriana Caliman Sturdza, Florin Filip, Monica Terteliu Baitan, Mihai Dimian

“…Multimodal diagnostic systems now incorporate alternative methods, in addition to imaging, which use lung ultrasounds, clinical data, and cough sound evaluation. Information fusion techniques, which operate at the data, feature, and decision levels, enhance diagnostic performance. …”
Get full text

A hybrid deep learning framework for early detection of diabetic retinopathy using retinal fundus images by Mishmala Sushith, A. Sathiya, V. Kalaipoonguzhali, V. Sathya

“…The proposed model utilizes publicly available DRIVE and Kaggle diabetic retinopathy datasets to evaluate the performance. The benchmark datasets provide a diverse set of annotated retinal images and the proposed hybrid model employs a CNN to extract spatial features from retinal images. …”
Get full text

DCCPNet: A Dual-Branch Channel Cross-Concatenation Pan-Sharpening Network for Satellite Remote Sensing Imagery by Zechun Li, Xunqiang Gong, Ailong Ma, Haiqing He, Pengyuan Lv, Xiansan Liu, Yanfei Zhong

“…To surmount these limitations, a dual-branch channel cross-concatenation (DCC) pan-sharpening network, i.e., DCCPNet, is proposed, which introduces DCC blocks to facilitate bidirectional feature exchange between panchromatic and multispectral branches. …”
Get full text

Multi-Level Spectral Attention Network for Hyperspectral BRDF Reconstruction from Multi-Angle Multi-Spectral Images by Liyao Song, Haiwei Li

Get full text

FISNET: A Learnable Fusion-Based Iris Segmentation Network Improving Robustness Across NIR and VIS Modalities by Geetanjali Sharma, Gaurav Jaswal, Aditya Nigam, Raghavendra Ramachandra

“…To tackle this problem, we present Fused Iris Segmentation Network (FISNET) that combines segmentation maps from two models to achieve enhanced precision and accuracy. …”
Get full text

Petrographic image classification of complex carbonate rocks from the Brazilian pre-salt using convolutional neural networks by Mateus Basso, João Paulo da Ponte Souza, Guilherme Furlan Chinelatto, Luis Augusto Antoniossi Mansini, Alexandre Campane Vidal

“…Among these techniques, Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification in various geoscientific applications. …”
Get full text

Lower Limb Torque Prediction for Sit-To-Walk Strategies Using Long Short-Term Memory Neural Networks by Chamalka Kenneth Perera, Alpha. A. Gopalai, Darwin Gouwanda, Siti. A. Ahmad, Pei-Lee Teh

“…Joint torque prediction is crucial when investigating biomechanics, evaluating treatments, and designing powered assistive devices. …”
Get full text