Detection of subclinical rheumatic heart disease in children using a deep learning algorithm on digital stethoscope: a study protocol by Huzaifa Ahmad, Zainab Bhuriwala, Muhammad Hanif, Shahab U Ansari

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Integrating hybrid bald eagle crow search algorithm and deep learning for enhanced malicious node detection in secure distributed systems by Feras Mohammed Al-Matarneh

“…This study designs a Hybrid Bald Eagle-Crow Search Algorithm and Deep Learning for Enhanced Malicious Node Detection (HBECSA-DLMND) technique in Secure Distributed Systems. …”
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A hybrid deep learning-based intrusion detection system for EV and UAV charging stations by Rosebell Paul, Mercy Paul Selvan

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Optimizing Defect Detection on Glossy and Curved Surfaces Using Deep Learning and Advanced Imaging Systems by Joung-Hwan Yoon, Chibuzo Nwabufo Okwuosa, Nnamdi Chukwunweike Aronwora, Jang-Wook Hur

“…Our approach employed image data generated from normal and two defect conditions to train eight deep learning algorithms: four custom convolutional neural networks (CNNs), two variations of VGG-16, and two variations of ResNet-50. …”
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Harnessing feature pruning with optimal deep learning based DDoS cyberattack detection on IoT environment by Eunmok Yang, Sooyong Jeong, Changho Seo

“…This manuscript proposes an effective Feature Pruning with Optimal Deep Learning-based DDoS Attack Detection (FPODL-DDoSAD) technique in the IoT framework. …”
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Artificial intelligence-driven cybersecurity: enhancing malicious domain detection using attention-based deep learning model with optimization algorithms by Fatimah Alhayan, Asma Alshuhail, Ahmed Omer Ahmed Ismail, Othman Alrusaini, Sultan Alahmari, Abdulsamad Ebrahim Yahya, Monir Abdullah, Samah Al Zanin

“…This manuscript presents an Enhance Malicious Domain Detection Using an Attention-Based Deep Learning Model with Optimization Algorithms (EMDD-ADLMOA) technique. …”
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Hybridization of deep learning models with crested porcupine optimizer algorithm-based cybersecurity detection on industrial IoT for smart city environments by Sarah A. Alzakari, Mohammed Aljebreen, Mashael M. Asiri, Wahida MANSOURI, Sultan Alahmari, Mohammed Alqahtani, Shaymaa Sorour, Wafi Bedewi

“…This study presents Cybersecurity using a Crested Porcupine Optimizer Algorithm with Hybrid Deep Learning Models (CCPOA-HDLM). …”
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An AI deep learning algorithm for detecting pulmonary nodules on ultra-low-dose CT in an emergency setting: a reader study by Inge A. H. van den Berk, Colin Jacobs, Maadrika M. N. P. Kanglie, Onno M. Mets, Miranda Snoeren, Alexander D. Montauban van Swijndregt, Elisabeth M. Taal, Tjitske S. R. van Engelen, Jan M. Prins, Shandra Bipat, Patrick M. M. Bossuyt, Jaap Stoker, The OPTIMACT study group

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Research on Anomaly Detection Model for Power Consumption Data Based on Time-Series Reconstruction by Zhenghui Mao, Bijun Zhou, Jiaxuan Huang, Dandan Liu, Qiangqiang Yang

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Lightweight CNN model for automatic detection and depth estimation of subsurface voids using GPR B-scan data by Abdelaziz Mojahid, Driss EL Ouai, Khalid EL Amraoui, Khalil EL-Hami, Hamou Aitbenamer, Jochem Verrelst, Pier Matteo Barone

“…Therefore, automated approaches using machine learning algorithms for identifying subsurface anomalies have recently emerged, providing promising pathways for real-time cavity detection. …”
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Distributed Denial of Service Attack Detection in Software-Defined Networks Using Decision Tree Algorithms by Ali Zaman, Salman A. Khan, Nazeeruddin Mohammad, Abdelhamied A. Ateya, Sadique Ahmad, Mohammed A. ElAffendi

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Lightweight malicious domain name detection model based on separable convolution by Luhui YANG, Huiwen BAI, Guangjie LIU, Yuewei DAI

“…The application of artificial intelligence in the detection of malicious domain names needs to consider both accuracy and calculation speed,which can make it closer to the actual application.Based on the above considerations,a lightweight malicious domain name detection model based on separable convolution was proposed.The model uses a separable convolution structure.It first applies depthwise convolution on every input channel,and then performs pointwise convolution on all output channels.This can effectively reduce the parameters of convolution process without impacting the effectiveness of convolution feature extraction,and realize faster convolution process while keeping high accuracy.To improve the detection accuracy considering the imbalance of the number and difficulty of positive and negative samples,a focal loss function was introduced in the training process of the model.The proposed algorithm was compared with three typical deep-learning-based detection models on a public data set.Experimental results denote that the proposed algorithm achieves detection accuracy close to the state-of-the-art model,and can significantly improve model inference speed on CPU.…”
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Two-Stage Deep Learning Framework for Individual Tree Crown Detection and Delineation in Mixed-Wood Forests Using High-Resolution Light Detection and Ranging Data by Qian Li, Baoxin Hu, Jiali Shang, Tarmo K. Remmel

“…This study presents a two-stage deep learning framework that integrates Canopy Height Model (CHM)-based treetop detection with three-dimensional (3D) ITC delineation using high-resolution airborne LiDAR point cloud data. …”
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Efficient tree species classification using machine and deep learning algorithms based on UAV-LiDAR data in North China by Hanyu Zhang, Bingjie Liu, Bin Yang, Jiachang Guo, Zhenhua Hu, Mengtao Zhang, Zhaohui Yang, Jianshuang Zhang

“…IntroductionThe unmanned aerial vehicle -based light detection and ranging (UAV-LiDAR) can quickly acquire the three-dimensional information of large areas of vegetation, and has been widely used in tree species classification.MethodsUAV-LiDAR point clouds of Populus alba, Populus simonii, Pinus sylvestris, and Pinus tabuliformis from 12 sample plots, 2,622 tree in total, were obtained in North China, training and testing sets were constructed through data pre-processing, individual tree segmentation, feature extraction, Non-uniform Grid and Farther Point Sampling (NGFPS), and then four tree species were classified efficiently by two machine learning algorithms and two deep learning algorithms.ResultsResults showed that PointMLP achieved the best accuracy for identification of the tree species (overall accuracy = 96.94%), followed by RF (overall accuracy = 95.62%), SVM (overall accuracy = 94.89%) and PointNet++(overall accuracy = 85.65%). …”
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Multi-Task Perception Algorithm for Rail Transit Scenarios Based on Triplet Attention by GAO Rui, XIONG Yanping, WEI Chenfeng, XIE Guotao, GAO Ming

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Machine Learning Algorithms of Remote Sensing Data Processing for Mapping Changes in Land Cover Types over Central Apennines, Italy by Polina Lemenkova

“…The latter included four ML algorithms embedded from the Python’s Scikit-Learn library. …”
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Examining Deep Learning Pixel-Based Classification Algorithms for Mapping Weed Canopy Cover in Wheat Production Using Drone Data by Judith N. Oppong, Clement E. Akumu, Samuel Dennis, Stephanie Anyanwu

“…Despite their potential, limited research has focused on the performance of pixel-based deep learning algorithms for detecting and mapping weed canopy cover. …”
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COMPARISON OF DATASET OVERSAMPLING ALGORITHMS AND THEIR APPLICABILITY TO THE CATEGORIZATION PROBLEM by Denys Teslenko, Anna Sorokina, Artem Khovrat, Nural Huliiev, Valentyna Kyriy

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A Comprehensive Review of EEG-Based Seizure Detection Techniques by Goldwyn Sudhakar Jebaraj, Konguvel Elango

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Simulation of Ground Visibility Based on Atmospheric Boundary Layer Data Using K-Nearest Neighbors and Ensemble Model Algorithms by Ruolan Liu, Shujie Yuan, Duanyang Liu, Lin Han, Fan Zu, Hong Wu, Hongbin Wang

“…This study introduces a machine learning approach for simulating visibility, utilizing the K-Nearest Neighbors algorithm and an ensemble model, which incorporate data from atmospheric boundary layer detection and conventional ground meteorological observations as simulation inputs. …”
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