Optimal distributed parallel algorithms for deep learning framework Tensorflow

Xie, Yuanlun; He, Majun; Ma, Tingsong; Tian, Wenhong

doi:10.1007/s10489-021-02588-9

Cited by 12 publications

(7 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The second disadvantage is that, unlike batch learning, SGD algorithms have little room for serious optimization because one sample with a noisy nature per iteration renders the output unreliable for further optimization. The third disadvantage is that SGD algorithms are inherently sequential and are exceptionally challenging to parallelize using GPUs or to be distributed with a computer cluster [52].…”

Section: Methodsmentioning

confidence: 99%

Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid

Çavdar

Feryad

2021

Energies

View full text Add to dashboard Cite

One of the basic conditions for the successful implementation of energy demand-side management (EDM) in smart grids is the monitoring of different loads with an electrical load monitoring system. Energy and sustainability concerns present a multitude of issues that can be addressed using approaches of data mining and machine learning. However, resolving such problems due to the lack of publicly available datasets is cumbersome. In this study, we first designed an efficient energy disaggregation (ED) model and evaluated it on the basis of publicly available benchmark data from the Residential Energy Disaggregation Dataset (REDD), and then we aimed to advance ED research in smart grids using the Turkey Electrical Appliances Dataset (TEAD) containing household electricity usage data. In addition, the TEAD was evaluated using the proposed ED model tested with benchmark REDD data. The Internet of things (IoT) architecture with sensors and Node-Red software installations were established to collect data in the research. In the context of smart metering, a nonintrusive load monitoring (NILM) model was designed to classify household appliances according to TEAD data. A highly accurate supervised ED is introduced, which was designed to raise awareness to customers and generate feedback by demand without the need for smart sensors. It is also cost-effective, maintainable, and easy to install, it does not require much space, and it can be trained to monitor multiple devices. We propose an efficient BERT-NILM tuned by new adaptive gradient descent with exponential long-term memory (Adax), using a deep learning (DL) architecture based on bidirectional encoder representations from transformers (BERT). In this paper, an improved training function was designed specifically for tuning of NILM neural networks. We adapted the Adax optimization technique to the ED field and learned the sequence-to-sequence patterns. With the updated training function, BERT-NILM outperformed state-of-the-art adaptive moment estimation (Adam) optimization across various metrics on REDD datasets; lastly, we evaluated the TEAD dataset using BERT-NILM training.

show abstract

Section: Methodsmentioning

confidence: 99%

Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid

Çavdar

Feryad

2021

Energies

View full text Add to dashboard Cite

show abstract

“…So this article uses the TensorFlow framework to build models. TensorFlow is characterized by its high flexibility, strong portability, multi-language support, and maximum performance [7]. The TensorFlow computing framework with high system stability can support a variety of deep learning algorithms and computing platforms.…”

Section: Tensorflow Frameworkmentioning

confidence: 99%

Research on course teaching evaluation and academic early warning based on data mining

Liao,

Cai,

2024

Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023)

View full text Add to dashboard Cite

Based on the development requirements of modern vocational education and the development trend of information-based teaching, the purpose of the paper is to build a three-dimensional and diversified teaching evaluation method by introducing data mining technology, use appropriate data mining algorithms to summarize the knowledge as well as master rules, predict the learning status of students, timely carry out early warning and teaching intervention, and explore a teaching evaluation and academic early warning method suitable for vocational education courses through practical classroom practice so as to improve curriculum mastery level of the students and optimize the quality of personnel training technical skills.

show abstract

“…TensorFlow is an open-source interface developed by Google researchers to perform deep learning and other statistical and predictive analysis workloads. It is designed for running advanced analytics applications for users such as predictive modelers and data scientists [20,21]. 3) Simulation results: The evaluation of our proposal is based on different metrics, where the confusion matrix, the false positive (FP), the true positive (VP), and the true negative (TN) rates are calculated.…”

Section: A Mask Detection Camera 1) Mobilenetv2implementationmentioning

confidence: 99%

AI-based System for the Detection and Prevention of COVID-19

Chokri¹,

Daoud²,

Hanini³

et al. 2022

IJACSA

View full text Add to dashboard Cite

The COVID-19 pandemic has had catastrophic consequences all over the world since the detection of the first case in December 2019. Currently, exponential growth is expected. In order to stop the spread of this pandemic, it is necessary to respect sanitary protocols such as the mandatory wearing of masks. In this research paper, we present an affordable artificial intelligence-based solution to increase the protection against COVID-19, covering several relevant aspects to facilitate the detection and prevention of this pandemic: noncontact temperature measurement, mask detection, automatic gel-dispensing, and automatic sterilization. Our main contribution is to provide high-quality, real-time learning and analysis. To achieve this goal, we used a deep convolutional neural network (CNN) based on MobileNetV2 architecture as the learning algorithm and Advanced Encryption Standard (AES) as an encryption protocol for sending secure data to notify hospital staff. The experimental results show the effectiveness of our model by providing 99.7% accuracy in detecting masks with a runtime of 1.54 s.

show abstract

Optimal distributed parallel algorithms for deep learning framework Tensorflow

Cited by 12 publications

References 33 publications

Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid

Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid

Research on course teaching evaluation and academic early warning based on data mining

AI-based System for the Detection and Prevention of COVID-19

Contact Info

Product

Resources

About