Three-Dimensional Diffusion Model in Sports Dance Video Human Skeleton Detection and Extraction by Zhi Li

“…The research in this paper mainly includes as follows: for the principle of action recognition based on the 3D diffusion model convolutional neural network, the whole detection process is carried out from fine to coarse using a bottom-up approach; for the human skeleton detection accuracy, a multibranch multistage cascaded CNN structure is proposed, and this network structure enables the model to learn the relationship between the joints of the human body from the original image and effectively predict the occluded parts, allowing simultaneous prediction of skeleton point positions and skeleton point association information on the one hand, and refinement of the detection results in an iterative manner on the other. …”
Get full text

A Free-Space-Based Model for Predicting Peanut Moisture Content during Natural Drying by Xin Xu, Ying Sun, Yuanyuan Yin, Yiwei Xue, Fangyan Ma, Chao Song, Hang Yin, Liqing Zhao

“…According to the findings, the ELM neural network model, which is based on the optimization of the SSA, has an improved prediction accuracy. …”
Get full text

Advancing hospital healthcare: achieving IoT-based secure health monitoring through multilayer machine learning by Ke Qi

“…Results This cloud-based smart C-IoT system shows the results approximately with 91% accuracy while using Artificial Neural Network (ANN) algorithms. This smart C-IoT-based health issue diagnostic model is one step ahead toward the modernization of society 5.0. …”
Get full text

An Automatic Recognition Method of Microseismic Signals Based on S Transformation and Improved Gaussian Mixture Model by Kaikai Wang, Chun’an Tang, Ke Ma, Xintang Wang, Qiang Li

“…The identification accuracy is as high as 94%, and its recognition effect is superior to other recognition models (such as traditional Gaussian Mixture Model based on Expectation-Maximum (EM-GMM), Backpropagation (BP) neural network, Random Forests (RF), Bayes (Bayes) methods, and Logistic Regression (LR) method). …”
Get full text

Application of Artificial Intelligence in Landslide Susceptibility Assessment: Review of Recent Progress by Muratbek Kudaibergenov, Serik Nurakynov, Berik Iskakov, Gulnara Iskaliyeva, Yelaman Maksum, Elmira Orynbassarova, Bakytzhan Akhmetov, Nurmakhambet Sydyk

“…Among models, random forest (RF), support vector machine (SVM), convolutional neural network (CNN), and multilayer perception (MLP) are used as the baseline to compare any new model introduced to develop LSM. …”
Get full text

Personalized lightweight distributed network intrusion detection system in fog computing by Tianpeng YE, Xiang LIN, Jianhua LI, Xuankai ZHANG, Liwen XU

“…With the continuous development of Internet of Things (IoT) technology, there is a constant emergency of new IoT applications with low latency, high dynamics, and large bandwidth requirements.This has led to the widespread aggregation of massive devices and information at the network edge, promoting the emergence and deep development of fog computing architecture.However, with the widespread and in-depth application of fog computing architecture, the distributed network security architecture deployed to ensure its security is facing critical challenges brought by fog computing itself, such as the limitations of fog computing node computing and network communication resources, and the high dynamics of fog computing applications, which limit the edge deployment of complex network intrusion detection algorithms.To effectively solve the above problems, a personalized lightweight distributed network intrusion detection system (PLD-NIDS) was proposed based on the fog computing architecture.A large-scale complex network flow intrusion detection model was trained based on the convolutional neural network architecture, and furthermore the network traffic type distribution of each fog computing node was collected.The personalized model distillation algorithm and the weighted first-order Taylor approximation pruning algorithm were proposed to quickly compress the complex model, breaking through the limitation of traditional model compression algorithms that can only provide single compressed models for edge node deployment due to the high compression calculation overhead when facing a large number of personalized nodes.According to experimental results, the proposed PLD-NIDS architecture can achieve fast personalized compression of edge intrusion detection models.Compared with traditional model pruning algorithms, the proposed architecture achieves a good balance between computational loss and model accuracy.In terms of model accuracy, the proposed weighted first-order Taylor approximation pruning algorithm can achieve about 4% model compression ratio improvement under the same 0.2% model accuracy loss condition compared with the traditional first-order Taylor approximation pruning algorithm.…”
Get full text

Reliability Testing of Machine Learning Model Prediction Capability towards Unidentifiable Microplastic Spectral Data: Triple Battery and Colorant Investigation by Wesley A. Williams, Shyam Aravamudhan

“…Firstly, the SD test determined subspace k-nearest neighbors (SKNN) and wide neural network (WNN) as champions (µ-FTIR and µ-Raman, respectively) with an accuracy of, 99%/100% and 98%/100% (χ2 = 31.99/69, p = .0024/ < .0001). …”
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), na&#x00EF;ve 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

Research Progress of Common Rehabilitation Training Methods in Mild Cognitive Impairment by WANG Yiyuan, FAN Chenyu, WANG Nianhong, WU Yi

“…Cognitive training is a kind of continuous stimulation of patients through multi-task combined pattern to rebuild the neural network in the brain so as to improve cognitive function. …”
Get full text

Multi-level lag scheme significantly improves training efficiency in deep learning: a case study in air quality alert service over sub-tropical area by Benedito Chi Man Tam, Su-Kit Tang, Alberto Cardoso

“…In multivariate time series (MTS) models, the predictive accuracy of artificial neural network ANN-type models can be improved by including more features. …”
Get full text

Caption Generation Based on Emotions Using CSPDenseNet and BiLSTM with Self-Attention by Kavi Priya S, Pon Karthika K, Jayakumar Kaliappan, Senthil Kumaran Selvaraj, Nagalakshmi R, Baye Molla

“…The encoding unit captures the facial expressions and dense image features using a Facial Expression Recognition (FER) model and CSPDense neural network, respectively. Further, the word embedding vectors of the ground truth image captions are created and learned using the Word2Vec embedding technique. …”
Get full text

Estimation of Bearing Capacity of Strip Footing Rested on Bilayered Soil Profile Using FEM-AI-Coupled Techniques by Ahmed M. Ebid, Kennedy C. Onyelowe, M. Salah

“…Multiple numerical data were generated for the case under study and artificial intelligence (AI)-based techniques; generalized reduced gradient (GRG), genetic programming (GP), artificial neural network (ANN), and evolutionary polynomial regression (EPR) were used to predict the UBC. …”
Get full text

Automation of image processing through ML algorithms of GRASS GIS using embedded Scikit-Learn library of Python by Polina Lemenkova

“…Image processing using Machine Learning (ML) and Artificial Neural Network (ANN) methods was investigated by employing the algorithms of Geographic Resources Analysis Support System (GRASS) Geographic Information System GIS with embedded Scikit-Learn library of Python language. …”
Get full text

Research on Feature Extracted Method for Flutter Test Based on EMD and CNN by Hua Zheng, Zhenglong Wu, Shiqiang Duan, Jiangtao Zhou

“…Inspired by deep learning concepts, a novel feature extraction method for flutter signal analysis was established in this study by combining the convolutional neural network (CNN) with empirical mode decomposition (EMD). …”
Get full text

A Metaheuristic Approach to Detecting and Mitigating DDoS Attacks in Blockchain-Integrated Deep Learning Models for IoT Applications by Manal Alkhammash

“…Besides, the MHADMA-BCIDL technique employs an attention-based convolutional neural network with bi-directional long short-term memory (CNN-BiLSTM-Attention) method for the detection and classification of attacks. …”
Get full text

Improving Deep Learning Forecasting Model Based on LSTM for Türkiye’s Hydro-Electricity Generation by Mehmet Bulut

“…LSTM (Long Short-Term Memory) plays an important role in hydropower forecasting, as it is a special artificial neural network designed to model complex relationships on time series data, which is affected by various meteorological factors such as precipitation, temperature, and hydrological data such as water level, such as hydroelectric power production. …”
Get full text

Human Posture Recognition and Estimation Method Based on 3D Multiview Basketball Sports Dataset by Xuhui Song, Linyuan Fan

“…The convolutional neural network framework used in this research is VGG11, and the basketball dataset Image Net is used for pretraining. …”
Get full text

A hybrid CNN-LSTM model with adaptive instance normalization for one shot singing voice conversion by Assila Yousuf, David Solomon George

“…In the proposed singing voice conversion technique, an encoder decoder framework was implemented using a hybrid model of convolutional neural network (CNN) accompanied by long short term memory (LSTM). …”
Get full text

Evaluating the predictive potential of RSM and ANN models in treatment of greywater-syrup mixture using Ekowe clay-PEM microbial fuel cell by Livinus A. Obasi, Cornelius O. Nevo

“… This study provides a comparative evaluation of the ability of response surface methodology (RSM) and artificial neural network (ANN) to predict the performance of microbial fuel cell (MFC) driven by greywater-syrup substrate system as anolyte with respect to power generation and wastewater treatment. …”
Get full text

Prediction of BlastInduced Ground Vibration (BIGV) of Metro Construction Using Difference Evolution AlgorithmOptimized Gaussian Process (DE-GP) by Tengfei Jiang, Annan Jiang, Shuai Zheng, Mengfei Xu

“…The proposed model is compared with the empirical formulas, least square support vector machine (LSSVM), artificial neural network (ANN), and GP model, and its prediction performance is evaluated by statistical indicators such as root mean square error (RMSE). …”
Get full text