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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Subramani, Neelakandan Sardar, Suman Kalyan Lee, Seul Chan Department of Industrial and Systems Engineering Gyeongsang National University Jinju52828 Korea Republic of

ISBN: (纸本)9798350305708

Electroencephalogram (EEG) data-based emotion recognition has acquired popularity in recent years. Because EEG signals are noisy, non-linear, and non-stationary, it is challenging to develop an intelligent framework capable of delivering high accuracy for emotion recognition. Human emotion recognition is a critical component of human-computer interaction and assessing emotional well-being. In this paper, we describe a new method for increasing the accuracy of human emotion recognition using EEG data. Adaptive adam optimization is used to enhance the performance of Long Short-Term Memory (LSTM) neural networks by utilizing their power. Readings of the EEG, which record brain activity, provide crucial insight into emotional states. EEG data are utilized to extract intricate patterns and temporal connections using LSTM networks, which are well-known for their ability to efficiently characterize sequential data. To further optimize training and convergence, we provide the adaptive adam optimization algorithm (AOA), which dynamically modifies learning rates during training to improve the model's ability to capture minute variations in EEG patterns. Experiment results from comprehensive evaluations on benchmark datasets indicate that our method is effective at improving the accuracy of identifying human emotions from EEG signals. The proposed model, LSTM-AOA, outperforms conventional methods and demonstrates exceptional proficiency in dealing with the complex temporal dependencies inherent to emotional states. © 2023 IEEE.

关键词： adam optimization algorithm Electroencephalogram human emotion recognition Long Short-Term Memory Neural Networks

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Predicting the total Unified Parkinson's Disease Rating Scale (UPDRS) based on ML techniques and cloud-based update

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JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2023年第1期12卷 1-16页

作者： Hamzehei, Sahand Akbarzadeh, Omid Attar, Hani Rezaee, Khosro Fasihihour, Nazanin Khosravi, Mohammad R. Polytech Univ Turin Dept Elect & Telecommun Turin Italy Zarqa Univ Dept Energy Engn Zarqa Jordan Meybod Univ Dept Biomed Engn Meybod Iran Weifang Univ Sci & Technol Weifang Shandong Peoples R China

Nowadays, smart health technologies are used in different life and environmental areas, such as smart life, healthcare, cognitive smart cities, and social systems. Intelligent, reliable, and ubiquitous healthcare systems are a part of the modern developing technology that should be more seriously considered. Data collection through different ways, such as the Internet of things (IoT)-assisted sensors, enables physicians to predict, prevent and treat diseases. Machine Learning (ML) algorithms may lead to higher accuracy in medical diagnosis/prognosis based on health data provided by the sensors to help physicians in tracking symptom significance and treatment steps. In this study, we applied four ML methods to the data on Parkinson's disease to assess the methods' performance and identify the essential features that may be used to predict the total Unified Parkinson's disease Rating Scale (UPDRS). Since accessibility and high-performance decision-making are so vital for updating physicians and supporting IoT nodes (e.g., wearable sensors), all the data is stored, updated as rule-based, and protected in the cloud. Moreover, by assigning more computational equipment and memory in use, cloud computing makes it possible to reduce the time complexity of the training phase of ML algorithms in the cases we want to create a complete structure of cloud/edge architecture. In this situation, it is possible to investigate the approaches with varying iterations without concern for system configuration, temporal complexity, and real-time performance. Analyzing the coefficient of determination and Mean Square Error (MSE) reveals that the outcomes of the applied methods are mostly at an acceptable performance level. Moreover, the algorithm's estimated weight indicates that Motor UPDRS is the most significant predictor of Total UPDRS.

关键词： Machine learning Lineal regression Linear least square Conjugate gradient adam optimization algorithm Ridge regression Machine learning for healthcare Parkinson's disease

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Cooperative Control of Recurrent Neural Network for PID-Based Single Phase Hotplate Temperature Control Systems

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IEEE ACCESS 2023年 11卷 105557-105569页

作者： Xu, Song Shi, Siyuan Jiang, Wei Hashimoto, Seiji Jiangsu Univ Sci & Technol Sch Automat Zhenjiang 212100 Peoples R China Yangzhou Univ Dept Elect Engn Yangzhou 225000 Peoples R China Gunma Univ Div Elect & Informat Kiryu 3768515 Japan

High-precision temperature control technology is currently more and more important in industrial thermal processing systems. In this paper, an RNN controller with integral-proportional-derivative (IPD) compensation driven by a reference model is proposed for single phase hotplate temperature control systems. A reference model is introduced based on the real controlled plant for the RNN controller to obtain better self-learning and adjusting efficiency by providing a more valuable teaching signal. Further, an adam optimization algorithm is applied to improve the control performance of the RNN controller. The simulations were developed under a MATLAB environment and the experiments were performed on a temperature experimental platform that used a digital-signal-processor (DSP) as digital controller. The results of simulations and experiments were quantitatively compared with those for a conventional temperature control system which only had an IPD controller. The control efficiency of the proposed RNN method was successfully evaluated.

关键词： Cooperative temperature control recurrent neural network controller adam optimization algorithm single phase hotplate temperature control

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optimization Driven adam-Cuckoo Search-Based Deep Belief Network Classifier for Data Classification

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IEEE ACCESS 2020年 8卷 105542-105560页

作者： Mohsin, Mohammed Li, Hong Abdalla, Hemn Barzan Huazhong Univ Sci & Technol Dept Math & Stat Wuhan 430074 Peoples R China Univ Basra Dept Stat Basra 61014 Iraq Wenzhou Kean Univ Dept Comp Sci Wenzhou 325060 Peoples R China

Data classification effectively classifies the data based on the labeled class distribution. To classify the data using the imbalanced distribution poses a significant challenge in the class inequity problem. Various data classification methods are developed in the learning framework, but proving better classification accuracy is a significant challenge in the application domain. Therefore, an effective classification method named adam-Cuckoo search based Deep Belief Network (adam-CS based DBN) is proposed to perform the classification process. At first, the input data is forwarded to the pre-processing stage, and then the feature selection stage. The wrapper-based feature selection model conducts the search in space with the possible parameters. The operators specify the connectivity between the states and select the features based on their state. The classification is performed using the Deep Belief Network (DBN) classifier such that the multilayer perceptron (MLP) layer of Deep Belief Network (DBN) is trained using the proposed adam based Cuckoo search (adam-CS) algorithm. The breeding behavior of cuckoos is integrated with the step size parameter to enhance the accuracy of the classification process. The adaptive learning rate parameter effectively estimates the moments using a sparse gradient. The proposed adam based Cuckoo search (adam-CS) algorithm attained better performance using the metrics, such as accuracy, specificity, and sensitivity, with 90% training data.

关键词： Cuckoo search algorithm adam optimization algorithm deep belief network (DBN) deep learning approach data classification

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Voronoi subdivision reconstruction of point clouds under optimal transport

Voronoi subdivision reconstruction of point clouds under opt...

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2024 International Conference on Computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2024

作者： Zhang, Yuanwei Xizang Museum of Natural Science Xizang Lhasa 850000 China

ISBN: (纸本)9781510689275

Due to the limitations of scanning equipment, the distribution of point clouds varies significantly across different curvatures. 3D point cloud reconstruction that preserves original features presents a considerable challenge. To address this issue, an optimal transport-based method for surface mesh reconstruction is proposed to aim at achieving high-quality point cloud resampling. Initially, the spatial structure of the original point cloud is dynamically partitioned using centroidal Voronoi tessellation and its triangular dualization outcomes to establish the desired resampling distribution. Subsequently, optimal transport reformulates the resampling task as an optimization problem to determine the transport strategy. Finally, the adam optimization algorithm is employed to iteratively adjust the positions of the point cloud. Experimental results demonstrate that this algorithm performs comparably to, or even better than, many traditional algorithms across various datasets. © 2025 SPIE.

关键词： adam optimization algorithm centroidal Voronoi tessellation optimal transport point cloud resampling surface reconstruction

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Nesterov adam Iterative Fast Gradient Method for Adversarial Attacks 31st

Nesterov Adam Iterative Fast Gradient Method for Adversarial...

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31st International Conference on Artificial Neural Networks (ICANN)

作者： Chen, Cheng Wang, Zhiguang Fan, Yongnian Zhang, Xue Li, Dawei Lu, Qiang China Univ Petr Beijing Key Lab Petr Data Min Beijing Peoples R China China Univ Petr Dept Comp Sci & Technol Beijing Peoples R China PetroChina Res Inst Petr Explorat & Dev Beijing Peoples R China

ISBN: (纸本)9783031159190;9783031159183

Deep Neural Networks (DNNs) are vulnerable to adversarial examples that mislead DNNs with imperceptible perturbations. Existing adversarial attacks often exhibit weak transferability under the black-box setting, especially when attacking the models with defense mechanisms. In this work, we regard the adversarial example generation problem as the problem of optimizing DNNs, and propose Nesterov adam Iterative Fast Gradient Method (NAI-FGM) which applies Nesterov accelerated gradient and adam to iterative attacks to improve the transferability of the gradient-based attack method so as to adjust the attack step size by itself and avoid local optimum more effectively. Empirical results on ImageNet dataset demonstrate that NAI-FGM could improve transferability of adversarial examples. Under the setting of ensemble model, the integrated method of NAI-FGM with various input transformations could achieve an average attack success rate of 91.88% against six advanced defense models, 1.78%-3.3% higher than the benchmarks. Code is available at https://***/NinelM/NAI-FGM.

关键词： Adversarial examples Nesterov accelerated gradient adam optimization algorithm

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Self-correcting wavelet neural network control of continuous rotary electro-hydraulic servo motor

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High Technology Letters 2021年第1期27卷 26-37页

作者： Wang Xiaojing Li Chunhui Peng Yiwen Key Laboratory of Advanced Manufacturing and Intelligent Technology Ministry of EducationHarbin University of Science and TechnologyHarbin 150080P.R.China

In allusion to the problem of friction,leakage,vibration and noise existing in continuous rotary motor electro-hydraulic servo system,highly nonlinearity and uncertainties affecting the system performance,based on the transfer function of electro-hydraulic servo system,a kind of Pol-Ind friction model is *** parameters of Pol-Ind friction model are identified and the accurate mathematical model of friction torque is obtained by *** self-correcting wavelet neural network(WNN)controller is proposed,and adam optimization algorithm is used to perform gradient optimization on scale factor and displacement factor in wavelet basis function,so as to improve the speed and precision of parameter *** comparative simulation analysis,it is clearly that the self-correcting WNN controller can effectively improve the frequency response and tracking accuracy of continuous rotary motor electro-hydraulic servo system.

关键词： continuous rotary electro-hydraulic servo motor Pol-Ind friction model self correcting wavelet neural network(WNN) adam optimization algorithm

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Time-Delay Temperature Control System Design based on Recurrent Neural Network 4

Time-Delay Temperature Control System Design based on Recurr...

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4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS)

作者： Liu, Yuan Xu, Song Kobori, Shinya Hashimoto, Seiji Kawaguchi, Takahiro Gunma Univ Div Elect & Informat Kiryu Gumma Japan Jiangsu Univ Sci & Technol Dept Elect & Informat Zhenjiang Jiangsu Peoples R China

ISBN: (纸本)9781728162072

A Recurrent Neural Network (RNN) is a special neural network sequence model that is very suitable for dealing with time series tasks. In various industrial processing systems, it has achieved good performances. In this paper, a RNN model which is driven by an ideal reference model is proposed for the single-input single-output(SISO) temperature control system with time-delay. An ideal reference model is introduced to provides a more valuable teaching signal for helping RNN controller to obtain higher learning efficiency and providing suitable control input to the temperature control system. Meanwhile, adam optimization algorithm which can get adaptive learning rates is used to update parameters and improve the control performance of the RNN. Further, a classical integral proportional derivative (I-PD) controller is designed to reduce the effects caused by the temperature setting value kick during the RNN learning period. Simulations were developed under the MATLAB environment to evaluate the proposed control system performance. In order to demonstrate the efficiency and application of the proposed RNN control method, the simulation results based on the actual temperature model are compared quantitatively.

关键词： Recurrent Neural Network adam optimization algorithm I-PD controller temperature control

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Predicting the customer's opinion on amazon products using selective memory architecture-based convolutional neural network

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JOURNAL OF SUPERCOMPUTING 2020年第8期76卷 5923-5947页

作者： Mandhula, Trupthi Pabboju, Suresh Gugulotu, Narsimha Chaitanya Bharathi Inst Technol Informat Technol Hyderabad 75 Telangana India CBIT Dept Informat Technol Hyderabad 75 Telangana India JNTUH Comp Sci Engn Coll Engn Hyderabad 85 Telangana India

Opinion mining and sentiment analysis are useful to extract subjective information out of bulk text documents. Predicting the customer's opinion on amazon products has several benefits like reducing customer churn, agent monitoring, handling multiple customers, tracking overall customer satisfaction, quick escalations, and upselling opportunities. Though performing sentiment analysis is a challenging task for the researchers to identify the user's sentiments from the large datasets, it is unstructured in nature, and also includes slangs, misspells, and abbreviations. To address this problem, a new proposed system is developed in this research study. Here, the proposed system comprises of four major phases;they are data collection, pre-processing, keyword extraction, and classification. Initially, the input data were collected from the dataset: amazon customer review. After collecting the data, pre-processing was carried out for enhancing the quality of collected data. The pre-processing phase comprises of three systems: lemmatization, review spam detection, and removal of stop words and URLs. Then, an effective topic modelling approach latent Dirichlet allocation along with modified possibilistic fuzzy C-Means was applied to extract the keywords and also helps in identifying the concerned topics. The extracted keywords were classified into three forms (positive, negative, and neutral) by applying an effective machine learning classifier: Selective memory architecture-based convolutional neural network. The experimental outcome showed that the proposed system enhanced the accuracy in sentiment analysis up to 6-20% related to the existing systems.

关键词： Convolutional neural network Latent Dirichlet allocation Lemmatization Modified possibilistic fuzzy c-means adam optimization algorithm Sentiment analysis

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A Super-resolution Reconstruction Method for Remote Sensing Images Based on adam Optimized Depth Convolution Network

A Super-resolution Reconstruction Method for Remote Sensing ...

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International Conference on Optical Instruments and Technology - Optoelectronic Imaging/Spectroscopy and Signal Processing Technology

作者： Jia, Yanqin Beijing Inst Technol Key Lab Photo Elect Imaging Technol & Syst Minist Educ China Beijing 100081 Peoples R China

ISBN: (纸本)9781510636552

Super-resolution reconstructed convolution neural network (SRCNN) is widely used in image quality improvement of single image. Traditional SRCNN training uses the loss function of minimum mean square error (MSE) and the method based on stochastic gradient descent (SGD) to optimize. Its learning rate adjustment strategy is limited by pre-specified adjustment rules, and it is difficult to select the initial value. Considering the complex texture and low resolution of remote sensing images, a deconvolution layer is proposed to replace the bi-cubic interpolation enlarged image in the traditional SRCNN network to overcome the mosaic effect. At the same time, adam optimizer is used to control the network training. After considering the first and second moment estimation of gradient comprehensively, the update step is calculated. Thus, the adaptive update of learning rate is realized and the speed of network training is greatly accelerated. The simulation results show that this method has advantages in edge reconstruction and texture details compared with the conventional superresolution reconstruction algorithm.

关键词： Remote sensing super-resolution reconstruction Convolution neural network adam optimization algorithm Deconvolution

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