Automatically Learning HTN Methods from Landmarks by Ruoxi Li, Dana Nau, Mark Roberts, Morgan Fine-Morris

“…Even HTN-MAKER, a well-known method-learning algorithm, requires a domain engineer to annotate the tasks with information about what to learn. …”
Get full text

Transforming Forms and Methods of Learning: Challenges and Opportunities by L. S. Babynina, L. V. Kartashova, Yu. G. Odegov

“…They are associated with the forms and methods of teaching, technical and information support of the educational process, the ratio of distance learning, the transformation of digital and personal competencies of professors. …”
Get full text

Principles of Teaching and Learning by R. Kirby Barrick, Andrew C. Thoron

Get full text

Principles of Teaching and Learning by R. Kirby Barrick, Andrew C. Thoron

Get full text

The Potential of Machine Learning Methods for Separated Turbulent Flow Simulations: Classical Versus Dynamic Methods by Stefan Heinz

Subjects: Get full text

Improving the Quality of Learning through Storytelling Method by Heru Widoyo

“…This paper analyzes the learning methods used by monotonous teachers, a teacher's low understanding of storytelling teaching methods greatly affects his effectiveness in carrying out his duties and obligations, an effective learning process in Buddhist education is determined by the use of variations in a teacher's learning methods and understanding of the use of storytelling methods. will affect the quality of Buddhist learning.…”
Get full text

Intrusion detection method based on machine learning by TIAN Xin-guang1, GAO Li-zhi2, ZHANG Er-yang1

“…A new intrusion detection method was presented based on machine learning for intrusion detection systems using shell commands as audit data.In the method,multiple dictionaries of shell command sequences of different lengths were constructed to represent the normal behavior profile of a network user.During the detection stage,the similarities between the command sequences generated by the monitored user and the sequence dictionaries were calculated.These similarities were then smoothed with sliding windows,and the smoothed similarities were used to determine whether the monitored user’s behaviors were normal or anomalous.The results of the experience show the method can achieve higher detection accuracy and shorter detection time than the instance-based method presented by Lane T.…”
Get full text

An Unsupervised Learning Method for Radio Interferometry Deconvolution by Lei Yu, Bin Liu, Cheng-Jin Jin, Ru-Rong Chen, Hong-Wei Xi, Bo Peng

“…Building on this insight, we develop a deep dictionary (realized through a convolutional neural network), which is designed to be multiresolution and overcomplete, to achieve sparse representation and integrate it within the CS framework. The resulting method is a novel, fully interpretable unsupervised learning approach that combines the mathematical rigor of CS with the expressive power of deep neural networks, effectively bridging the gap between deep learning and classical dictionary methods. …”
Get full text

Application of Machine Learning Method in Genomics and Proteomics by Hao Lin, Wei Chen, Ramu Anandakrishnan, Dariusz Plewczynski

Get full text

Deep Learning Method for Bearing Fault Diagnosis by LIU Xiu, MA Shan-tao, XIE Yi-ning, HE Yong-jun

“…In recent years, deep learning technology has shown great potential in bearing fault diagnosis based on vibration signals.However, in the fault diagnosis method based on deep learning, the traditional single network topology feature extraction has weak discrimination and low noise robustness, and the accuracy of fault diagnosis is not high.In addition, most of the current research methods have a low fault recognition rate in a variable load environment.In response to the above problems, this paper proposes an improved neural network end-to-end fault diagnosis model.The model combines convolutional neural networks (CNN) and the attention long short-term memory (ALSTM) based on the attention mechanism, and uses ALSTM to capture long-distance correlations in time series data , Effectively suppress the high frequency noise in the input signal.At the same time, a multi-scale and attention mechanism is introduced to broaden the range of the convolution kernel to capture high and low frequency features, and highlight the key features of the fault. …”
Get full text

A Tilt Receiver Correction Method for Visible Light Positioning Using Machine Learning Method by Tao Yuan, Yiqin Xu, Yong Wang, Peng Han, Junfang Chen

Get full text

Editorial: Machine-learning/deep-learning methods in neuromarketing and consumer neuroscience by Marco Bilucaglia, Marco Bilucaglia, Luca Mainardi, Thomas Zoëga Ramsøy, Thomas Zoëga Ramsøy, Paul J. Zak, Paul J. Zak, Margherita Zito, Margherita Zito, Vincenzo Russo, Vincenzo Russo

Get full text

ONLINE MATHEMATICS LEARNING STRATEGY APPROACH: TEACHING METHODS AND LEARNING ASSESSMENT by Maria Zefanya Sampe, Syafrudi Syafrudi

“… The primary focus of this research is to develop strategies aimed at identifying effective methods applicable to online mathematics instruction and assessment. …”
Get full text

Increasing learning outcomes for volleyball passing using cooperative learning methods by Endang Pratiwi, Andi Kasanrawali, Norma Anggara

“…This study aims to determine the learning outcome passing volleyball at SMK Bhakti Nations Banjarbaru South Kalimantan using cooperative learning methods. …”
Get full text

Node selection method in federated learning based on deep reinforcement learning by Wenchen HE, Shaoyong GUO, Xuesong QIU, Liandong CHEN, Suxiang ZHANG

“…To cope with the impact of different device computing capabilities and non-independent uniformly distributed data on federated learning performance, and to efficiently schedule terminal devices to complete model aggregation, a method of node selection based on deep reinforcement learning was proposed.It considered training quality and efficiency of heterogeneous terminal devices, and filtrate malicious nodes to guarantee higher model accuracy and shorter training delay of federated learning.Firstly, according to characteristics of model distributed training in federated learning, a node selection system model based on deep reinforcement learning was constructed.Secondly, considering such factors as device training delay, model transmission delay and accuracy, an optimization model of accuracy for node selection was proposed.Finally, the problem model was constructed as a Markov decision process and a node selection algorithm based on distributed proximal strategy optimization was designed to obtain a reasonable set of devices before each training iteration to complete model aggregation.Simulation results demonstrate that the proposed method significantly improves the accuracy and training speed of federated learning, and its convergence and robustness are also well.…”
Get full text

Node selection method in federated learning based on deep reinforcement learning by Wenchen HE, Shaoyong GUO, Xuesong QIU, Liandong CHEN, Suxiang ZHANG

“…To cope with the impact of different device computing capabilities and non-independent uniformly distributed data on federated learning performance, and to efficiently schedule terminal devices to complete model aggregation, a method of node selection based on deep reinforcement learning was proposed.It considered training quality and efficiency of heterogeneous terminal devices, and filtrate malicious nodes to guarantee higher model accuracy and shorter training delay of federated learning.Firstly, according to characteristics of model distributed training in federated learning, a node selection system model based on deep reinforcement learning was constructed.Secondly, considering such factors as device training delay, model transmission delay and accuracy, an optimization model of accuracy for node selection was proposed.Finally, the problem model was constructed as a Markov decision process and a node selection algorithm based on distributed proximal strategy optimization was designed to obtain a reasonable set of devices before each training iteration to complete model aggregation.Simulation results demonstrate that the proposed method significantly improves the accuracy and training speed of federated learning, and its convergence and robustness are also well.…”
Get full text

Identifying G-Protein Coupled Receptors Using Mixed-Feature Extraction Methods and Machine Learning Methods by Chunyan Ao, Lin Gao, Liang Yu

“…In this paper, we propose a method for identifying GPCRs based on mixed-feature vectors. …”
Get full text

Application of Supervised Learning Methods and Information Gain Methods in the Determination of Asbestos–Cement Roofs’ Deterioration State by Manuel Saba, David Valdelamar Martínez, Leydy K. Torres Gil, Gabriel E. Chanchí Golondrino, Manuel A. Ospina Alarcón

Get full text

A Comparative Study between Traditional Learning and Virtual Learning (Design a Virtual Learning System) by Maisireem Al-Sayeg

Subjects: Get full text

Traffic congestion forecasting using machine learning methods by Ramil R. Zagidullin, Almaz N. Khaybullin

“…Additionally, to compare and validate the results, the ensemble method Random Forest was used, configured with the following hyperparameters: 200 trees, maximum depth of 12, minimum samples per leaf of 2. …”
Get full text