A Study on the Differences in Optimized Inputs of Various Data-Driven Methods for Battery Capacity Prediction by Kuo Xin, Fu Jia, Byoungik Choi, Geesoo Lee

“…This paper extracts 11 types of lithium battery-related health features, and experiments are conducted on two traditional machine learning networks and three advanced deep learning networks in three aspects of input differences. …”
Get full text

FFL-IDS: A Fog-Enabled Federated Learning-Based Intrusion Detection System to Counter Jamming and Spoofing Attacks for the Industrial Internet of Things by Tayyab Rehman, Noshina Tariq, Farrukh Aslam Khan, Shafqat Ur Rehman

“…This framework allows multiple parties in IIoT networks to train deep learning models with data privacy preserved and low-latency detection ensured using fog computing. …”
Get full text

Classification Algorithms in Automatic Diagnosis of ECG Arrhythmias: A Review by Haibo Sun, Dan Luo, Xin Niu, Xiaoxi Zeng, Bin Zheng, Hao Liu, Jingye Pan

“…With the application and development of neural network-based deep learning technology in automatic ECG diagnosis, the accuracy and reliability of automatic ECG arrhythmia classification have been significantly improved. …”
Get full text

Innovative approaches for skin disease identification in machine learning: A comprehensive study by Kuldeep Vayadande, Amol A. Bhosle, Rajendra G. Pawar, Deepali J. Joshi, Preeti A. Bailke, Om Lohade

“…Investigate the effectiveness and performance of several algorithms, such as the flexible k-nearest neighbor, the sturdy support vector machine (SVM), and the complex convolutional neural networks (CNNs), advanced techniques for automated skin disease detection encompass deep learning methods such as recurrent neural networks (RNNs) for sequential data processing, generative adversarial networks (GANs) for generating synthetic data, and attention mechanisms for focusing on relevant image regions by means of a thorough examination of the most recent studies. …”
Get full text

An educational advancement in the form of Andragogy based education: A comparison of two different peer assisted learning strategies by Nirmala Anand, Vijaya Dandannavar, Rajesh Shenoy, Jaysheela Bagi

“…SLOT enhances critical thinking, communication, and presentation, while Socratic Seminars promote higher order thinking. Both encourage deep learning through discussion and debate.…”
Get full text

ZooCNN: A Zero-Order Optimized Convolutional Neural Network for Pneumonia Classification Using Chest Radiographs by Saravana Kumar Ganesan, Parthasarathy Velusamy, Santhosh Rajendran, Ranjithkumar Sakthivel, Manikandan Bose, Baskaran Stephen Inbaraj

“…This article presents a Deep Learning model, Zero-Order Optimized Convolutional Neural Network (ZooCNN), a Zero-Order Optimization (Zoo)-based CNN model for classifying CXR images into three classes, Normal Lungs (NL), Bacterial Pneumonia (BP), and Viral Pneumonia (VP); this model utilizes the Adaptive Synthetic Sampling (ADASYN) approach to ensure class balance in the Kaggle CXR Images (Pneumonia) dataset. …”
Get full text

Spatio-Temporal Feature Aware Vision Transformers for Real-Time Unmanned Aerial Vehicle Tracking by Hao Zhang, Hengzhou Ye, Xiaoyu Guo, Xu Zhang, Yao Rong, Shuiwang Li

“…Our experimental results on several challenging benchmark datasets demonstrate that NT-Track surpasses existing lightweight and deep learning trackers in terms of precision and success rate. …”
Get full text

YOLOv8s-Longan: a lightweight detection method for the longan fruit-picking UAV by Jun Li, Jun Li, Jun Li, Kaixuan Wu, Meiqi Zhang, Hengxu Chen, Hengyi Lin, Yuju Mai, Linlin Shi

“…IntroductionDue to the limited computing power and fast flight speed of the picking of unmanned aerial vehicles (UAVs), it is important to design a quick and accurate detecting algorithm to obtain the fruit position.MethodsThis paper proposes a lightweight deep learning algorithm, named YOLOv8s-Longan, to improve the detection accuracy and reduce the number of model parameters for fruitpicking UAVs. …”
Get full text

MambaShadowDet: A High-Speed and High-Accuracy Moving Target Shadow Detection Network for Video SAR by Xiaowo Xu, Tianwen Zhang, Xiaoling Zhang, Wensi Zhang, Xiao Ke, Tianjiao Zeng

“…To handle these issues, this paper proposes MambaShadowDet, a novel lightweight deep learning (DL) detector based on a state space model (SSM), dedicated to high-speed and high-accuracy moving target shadow detection in video SAR images. …”
Get full text

Astronomaly Protege: Discovery through Human-machine Collaboration by Michelle Lochner, Lawrence Rudnick

“…We train PROTEGE on images from the MeerKAT Galaxy Cluster Legacy Survey, leveraging the self-supervised deep learning algorithm Bootstrap Your Own Latent to find a low-dimensional representation of the radio galaxy cutouts. …”
Get full text

Investigating the intrinsic top-down dynamics of deep generative models by Lorenzo Tausani, Alberto Testolin, Marco Zorzi

“…A popular class of hierarchical generative models is that of Deep Belief Networks (DBNs), which are energy-based deep learning architectures that can learn multiple levels of representations in a completely unsupervised way exploiting Hebbian-like learning mechanisms. …”
Get full text

A Novel Ensemble Classifier Selection Method for Software Defect Prediction by Xin Dong, Jie Wang, Yan Liang

“…The experimental results demonstrate that the DFD ensemble learning-based software defect prediction model outperforms the ten other models, including five common machine learning (ML) classification algorithms (logistic regression (LR), naïve Bayes (NB), K-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM)), two deep learning (DL) algorithms (multi-layer perceptron (MLP) and convolutional neural network (CNN)), and three ensemble learning algorithms (random forest (RF), extreme gradient boosting (XGB), and stacking). …”
Get full text

IAE-CDNet: A Remote Sensing Change Detection Network for Buildings With Interactive Attention-Enhanced by Zhaoyang Han, Linlin Zhang, Qingyan Meng, Chongchang Wang, Wenxu Shi, Maofan Zhao

“…Currently, the development of deep learning has had a positive impact on remote sensing image change detection tasks, but many current methods still face challenges in effectively processing global and local features, especially in the task of building change detection in high-resolution images containing complex scenes. …”
Get full text

Artificial intelligence in precision medicine for lung cancer: A bibliometric analysis by Yuchai Wang, Weilong Zhang, Xiang Liu, Li Tian, Wenjiao Li, Peng He, Sheng Huang, Fuyuan He, Xue Pan

“…Conclusion This bibliometric study reveals deep learning frameworks and AI-based radiomics are receiving attention. …”
Get full text

Prediction of mechanical characteristics of shearer intelligent cables under bending conditions. by Lijuan Zhao, Dongyang Wang, Guocong Lin, Shuo Tian, Hongqiang Zhang, Yadong Wang

“…The research shows that the TCN-BiLSTM-SEAttention model demonstrates outstanding predictive capability under complex operating conditions, providing a novel approach for improving cable management and equipment safety through optical fiber monitoring technology in the intelligent development of coal mines, highlighting the potential of deep learning in complex mechanical predictions.…”
Get full text

Steel surface defect detection and segmentation using deep neural networks by Sara Ashrafi, Sobhan Teymouri, Sepideh Etaati, Javad Khoramdel, Yasamin Borhani, Esmaeil Najafi

“…This paper proposes several deep-learning-based computer vision techniques, including semantic segmentation and object detection models, to detect surface defects on steel sheets. …”
Get full text

Soft-Label Supervised Meta-Model with Adversarial Samples for Uncertainty Quantification by Kyle Lucke, Aleksandar Vakanski, Min Xian

“…Despite the recent success of deep-learning models, traditional models are overconfident and poorly calibrated. …”
Get full text

MMFW-UAV dataset: multi-sensor and multi-view fixed-wing UAV dataset for air-to-air vision tasks by Yang Liu, Zhihao Sun, Lele Xi, Lele Zhang, Wei Dong, Chen Chen, Maobin Lu, Hailing Fu, Fang Deng

“…Several mainstream deep learning-based object detection architectures are evaluated on MMFW-UAV and the experimental results demonstrate that MMFW-UAV can be utilized for fixed-wing UAV identification, detection, and monitoring. …”
Get full text

Technology applications in bovine gait analysis: A scoping review. by Amir Nejati, Anna Bradtmueller, Elise Shepley, Elsa Vasseur

“…Among emergent technologies, deep learning and wearable sensors (e.g., accelerometers) appear to be the most promising options. …”
Get full text

A transferable approach for quantifying benthic fish sizes and densities in annotated underwater images by Peter C. Esselman, Shadi Moradi, Joseph Geisz, Christopher Roussi

“…Advances in image‐acquisition technologies, machine vision and deep learning have made capturing and quantifying fishes with cameras increasingly feasible. …”
Get full text