Advanced intrusion detection technique (AIDT) for secure communication among devices in internet of medical things (IoMT) by M. Ramya, Pradeep Sudhakaran, Yuvaraj Sivagnanam, C. Santhana Krishnan

“…The model uses a probabilistic neural network (PNN) to classify whether intrusions are present or not, and particle swarm optimization (PSO) for feature extraction. By employing the integrated patient sensing and network traffic datasets, our research achieves a superior accuracy rate of 96.4% in network intrusion detection compared to the competing algorithms. …”
Get full text

Federated Deep Learning for Scalable and Privacy-Preserving Distributed Denial-of-Service Attack Detection in Internet of Things Networks by Abdulrahman A. Alshdadi, Abdulwahab Ali Almazroi, Nasir Ayub, Miltiadis D. Lytras, Eesa Alsolami, Faisal S. Alsubaei, Riad Alharbey

“…We need scalable, privacy-preserving, and resource-efficient IoT intrusion detection algorithms to solve this essential problem. …”
Get full text

A Complementary Dataset of Scalp EEG Recordings Featuring Participants with Alzheimer’s Disease, Frontotemporal Dementia, and Healthy Controls, Obtained from Photostimulation EEG by Aimilia Ntetska, Andreas Miltiadous, Markos G. Tsipouras, Katerina D. Tzimourta, Theodora Afrantou, Panagiotis Ioannidis, Dimitrios G. Tsalikakis, Konstantinos Sakkas, Emmanouil D. Oikonomou, Nikolaos Grigoriadis, Pantelis Angelidis, Nikolaos Giannakeas, Alexandros T. Tzallas

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

“…The paper analyzes various deep-learning models for detecting fake news in the Afaan Oromo language. It discusses model performance, data challenges, feature extraction techniques, implications for social media, and future directions, including the need for robust datasets, improved model architectures, and cross-linguistic studies. …”
Get full text

YOLO-SAIL: Attention-Enhanced YOLOv5 With Optimized Bi-FPN for Ship Target Detection in SAR Images by Prabu Selvam, P. Shanmuga Sundari, M. Tamilselvi, T. Suresh, M. Murugappan, Muhammad E. H. Chowdhury

“…Finally, a new layer of feature fusion is introduced to locate smaller ships. …”
Get full text

Pavement condition detection using acceleration data collected by smartphones based on 1D convolutional neural network by Yudong Han, Zhaobo Li, Jiaqi Li

“…Vibration-based pavement condition detection methods have advanced in recent years, and it has been proven to be feasible to identify pavement conditions by analysing acceleration data. …”
Get full text

Wheel Tread Dynamic Detection Benchmark Positioning Method Based on Iterative Reweighted Least-squares Line Fitting by LI Miaocheng, WANG Junping, SHEN Yunbo, YOU Yong, DAI Bowang, LAN Qiangqiang

“…A reference positioning method for wheel tread dynamic detection is proposed in this paper. After extracting the point cloud data through structured light calibration, center-line extraction and other algorithms, reference feature points of the inner side are segmented combined with tread features, and an iterative re-weighted least squares line fitting (IRLS-LF) method is used to realize robust positioning of the inner side benchmark. …”
Get full text

DCMC-UNet: A Novel Segmentation Model for Carbon Traces in Oil-Immersed Transformers Improved with Dynamic Feature Fusion and Adaptive Illumination Enhancement by Hongxin Ji, Jiaqi Li, Zhennan Shi, Zijian Tang, Xinghua Liu, Peilin Han

“…To address these challenges, we propose the DCMC-UNet, a semantic segmentation model for carbon traces containing adaptive illumination enhancement and dynamic feature fusion. For blurred carbon trace images caused by unstable light reflection and illumination in transformer oil, an improved CLAHE algorithm is developed, incorporating learnable parameters to balance luminance and contrast while enhancing edge features of carbon traces. …”
Get full text

YOLOv8-OCHD: A Lightweight Wood Surface Defect Detection Method Based on Improved YOLOv8 by Zuxing Chen, Junjie Feng, Xueyan Zhu, Bin Wang

“…Finally, at the output stage, a DyHead (Dynamic Head) attention mechanism detection head is introduced to enhance the detection capability of the algorithm by integrating scale perception, spatial perception, and task perception, significantly improving the feature expression ability at the output. …”
Get full text

FASTSeg3D: A Fast, Efficient, and Adaptive Ground Filtering Algorithm for 3D Point Clouds in Mobile Sensing Applications by Daniel Ayo Oladele, Elisha Didam Markus, Adnan M. Abu-Mahfouz

“…Methods: This study proposes FASTSeg3D, a novel two-stage algorithm for real-time ground filtering. First, Range Elevation Estimation (REE) organizes point clouds efficiently while filtering outliers. …”
Get full text

GPC-YOLO: An Improved Lightweight YOLOv8n Network for the Detection of Tomato Maturity in Unstructured Natural Environments by Yaolin Dong, Jinwei Qiao, Na Liu, Yunze He, Shuzan Li, Xucai Hu, Chengyan Yu, Chengyu Zhang

“…Current deep learning detection algorithms typically demand significant computational resources and memory. …”
Get full text

DCA-YOLOv8: A Novel Framework Combined with AICI Loss Function for Coronary Artery Stenosis Detection by Hualin Duan, Sanli Yi, Yanyou Ren

“…The experimental results show that the DCA-YOLOv8 framework performs better than existing object detection algorithms in coronary artery stenosis detection, with precision, recall, F1-score and mean average precision (mAP) at 96.62%, 95.06%, 95.83% and 97.6%, respectively. …”
Get full text

A multi-factor integration-based semi-supervised learning for address resolution protocol attack detection in SDIIoT by Zhong Li, Huimin Zhuang

“…Recently, the idea of applying the software-defined networking paradigm to industrial Internet of things is proposed by many scholars since this paradigm has the advantages of flexible deployment of intelligent algorithms and global coordination capabilities. These advantages prompt us to propose a multi-factor integration-based semi-supervised learning address resolution protocol detection method deployed in software-defined networking, called MIS, to specially solve the problems of limited labeled training data and incomplete features extraction in the traditional address resolution protocol detection methods. …”
Get full text

A novel approach for breast cancer detection using a Nesterov accelerated adam optimizer with an attention mechanism by Abeer Saber, Tamer Emara, Samar Elbedwehy, Esraa Hassan

“…Abstract Image-based automatic breast tumor detection has become a significant research focus, driven by recent advancements in machine learning (ML) algorithms. …”
Get full text

R-SABMNet: A YOLOv8-Based Model for Oriented SAR Ship Detection with Spatial Adaptive Aggregation by Xiaoting Li, Wei Duan, Xikai Fu, Xiaolei Lv

“…First, we introduce the Spatial-Guided Adaptive Feature Aggregation (SG-AFA) module, which enhances sensitivity to ship features while suppressing land scattering interference. …”
Get full text

Improved YOLOv5s With Coordinate Attention for Small and Dense Object Detection From Optical Remote Sensing Images by Qinggang Wu, Yonglei Wu, Yang Li, Wei Huang

“…Experimental results on the NWPU VHR-10 dataset demonstrate that the proposed YOLOv5s method performs well compared with state-of-the-art algorithms.…”
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

“…Information fusion techniques, which operate at the data, feature, and decision levels, enhance diagnostic performance. …”
Get full text

Registered report protocol: A scoping review to identify potential predictors as features for developing automated estimation of the probability of being frail in secondary care. by Dirk H van Dalen, Angèle P M Kerckhoffs, Esther de Vries

“…We aim to identify potential predictors that could be used as features for modeling algorithms on the basis of routine hospital EHR data to incorporate in an automated tool for estimating the probability of being frail.…”
Get full text

Classification of Brain Tumors by Using a Hybrid CNN-SVM Model by Talitha Safa Nabila, Abu Salam

“…The research was conducted to classify whether or not there was a tumor in the brain by using a combination of algorithms, namely CNN, to extract features from image data and then use SVM as a classification. …”
Get full text

A Machine Learning Based Framework for a Stage-Wise Classification of Date Palm White Scale Disease by Abdelaaziz Hessane, Ahmed El Youssefi, Yousef Farhaoui, Badraddine Aghoutane, Fatima Amounas

“…The results indicate that SVM classifier outperformed on combined GLCM and HSV features with an accuracy of 98.29%. The proposed framework could be beneficial to the oasis agricultural community in terms of early detection of date palm white scale disease (DPWSD) and assisting in the adoption of preventive measures to protect both date palm trees and crop yield.…”
Get full text