An Innovative synthesis of deep learning techniques (DCapsNet &amp; DCOM) for generation electrical renewable energy from wind energy

Al-Janabi, Samaher; Alkaim, Ayad F.; Adel, Zuhal

doi:10.1007/s00500-020-04905-9

Cited by 127 publications

(25 citation statements)

References 18 publications

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“…Considering the Nursing diagnoses and true linkages between diagnoses, evidence based interventions, and patient outcomes [ 46 ]. In other studies, the use of creative learning models in other areas and the use of different and interconnected phases of the nursing process was mentioned [ 47 ]. Therefore, in this study the nursing process software design was based on all interrelated stages of the nursing process as recommended in nursing theory text books.…”

Section: Discussionmentioning

confidence: 99%

Attitude of nursing students following the implementation of comprehensive computer-based nursing process in medical surgical internship: a quasi-experimental study

Parvan

Hosseini

Jasemi

et al. 2021

BMC Med Inform Decis Mak

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Background The nursing process is the core and the standard of practice in nursing profession. Nowadays, the use of information technology in the field of nursing processes, education and practice has been emphasized. Since nurse’s attitudes towards clinical information systems are considered as an indicator of the success rate of information systems, and nurse’s attitudes about the nursing process can affect their execution of the process. So the purpose of this study was to evaluate nursing students’ attitudes towards the nursing process software. Methods In this quasi-experimental study, 160 undergraduate nursing students (terms 4–8) in Tabriz University of Medical Sciences were selected by convenience sampling. To evaluate the effectiveness of nursing process software in this study, Mazlom and Rajabpoor (IJME 14(4):312–322, 2014) a questionnaire consisting of 21 components based on a five-point Likert scale was completed by students after using the software. Data were then analyzed by SPSS 19 software. Results The mean score of students’ attitude toward nursing process software was high (80.70 ± 5.58). The nursing students’ highest scoring attitudes were respectively related to “Effectiveness of software in prioritizing patient care and problems”, “Completeness of patient’s electronic information compared to handwritten mode” and “Software’s effectiveness in saving your time”. The lowest scoring attitudes towards the software was respectively related to the “feeling of fairness in labor division”, “the effectiveness of the software in determining your workload” and “the feeling of satisfaction in labor division”. There was a statistically significant relationship between gender and age, and student’s attitude toward nursing process software. Conclusions According to the results and analysis of nursing student’s attitudes toward nursing process software, the use of such software would be welcomed by students. It seems that changing policies in the educational and clinical substructure of nursing in order to develop, adapt and use the nursing process software is an important responsibility for nursing authorities to consider. Providing educational and clinical technology equipment, periodic evaluation of software by stakeholders and promoting the use of this software, can be fundamental steps in operationalizing the findings of this research.

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Section: Discussionmentioning

confidence: 99%

Attitude of nursing students following the implementation of comprehensive computer-based nursing process in medical surgical internship: a quasi-experimental study

Parvan

Hosseini

Jasemi

et al. 2021

BMC Med Inform Decis Mak

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“…DRFLLS aimed to estimate missing values of various datasets. While the multi-objective method presented in the article [33], focused on improving the production of electrical energy from wind. Similarly, a predicted tool based on developing MARS data mining technique is introduced in [34] to solve a multi-objectives optimization problem.…”

Section: Related Workmentioning

confidence: 99%

Multi-objective workflow scheduling in cloud computing: trade-off between makespan and cost

Belgacem

Beghdad-Bey

2021

Cluster Comput

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Recently, modern businesses have started to transform into cloud computing platforms to deploy their workflow applications. However, scheduling workflow under resource allocation is significantly challenging due to the computational intensity of the workflow, the dependency between tasks, and the heterogeneity of cloud resources. During resource allocation, the cloud computing environment may encounter considerable problems in terms of execution time and execution cost, which may lead to disruptions in service quality given to users. Therefore, there is a necessity to reduce the makespan and the cost at the same time. Often, this is modeled as a multi-objective optimization problem. In this respect, the fundamental research issue we address in this paper is the potential trade-off between the makespan and the cost of virtual machine usage. We propose a HEFT-ACO approach, which is based on the heterogeneous earliest end time (HEFT), and the ant colony algorithm (ACO) to minimize them. Experimental simulations are performed on three types of realworld science workflows and take into account the properties of the Amazon EC2 cloud platform. The experimental results show that the proposed algorithm performs better than basic ACO, PEFT-ACO, and FR-MOS.

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“…Ohsugi et al (2017) applied DL in material medicine, using ultra-wide-field fundus images to detect the Rhegmatogenous Retinal Detachment (RRD). They found that ophthalmology clinics’ medical services in remote areas were significantly improved [ 14 ]. Sremac et al (2018) established an online shopping management system applicable to various supply chain goods with high accuracy [ 15 ].…”

Section: Research On Dl’s Application Trendmentioning

confidence: 99%

Applying TS-DBN model into sports behavior recognition with deep learning approach

Guo

Wang

2021

J Supercomput

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The purposes are to automatically collect information about human sports behavior from massive video data and provide an explicit recognition and analysis of body movements. The analysis of multi-scale input data, the improvement of spatiotemporal Deep Belief Network (DBN), and the different pooling strategies are regarded as the focuses to improve the belief networks in deep learning (DL). Moreover, a human sports behavior recognition model is proposed based on particular spatio-temporal features. Also, video frame data are collected from the Royal Institute of Technology (KTH) and University of Central Florida (UCF) datasets for training. The TensorFlow platform is employed to simulate the built algorithm. Finally, the constructed algorithm model is compared with the DBN proposed by Yang et al. the Convolutional Neural Network (CNN) proposed by Ullah et al. and the DBN-Hidden Markov Model (HMM) algorithm proposed by Xu et al. to analyse its performance. The recognition effects of each algorithm in the two datasets are analyzed. Results demonstrate that CNN developed by Ullah et al. has the worst accuracy on the KTH and UCF datasets, followed by DBN developed by Yang et al. and DBN-HMM developed by Xu et al. The constructed algorithm model can provide the highest accuracy, reaching about 90%, and the recognition accuracy of human sports behaviors of each algorithm on the KTH dataset is lower than that on the UCF dataset. On the KTH dataset, the recognition accuracy for boxing is the highest and running the lowest. Analyzing the model’s accuracy in the four scenes (S1, S2, S3, and S4) on the KTH dataset suggests that the recognition accuracy for the indoor scene (S4) is higher than that of the outdoor scenes (S1, S2, and S3). On the UCF dataset, the recognition accuracy for lifting is the highest, reaching 99%, and that for walking is the lowest, reaching 51%. Therefore, the proposed human sports recognition model can provide a higher accuracy than other classic DL algorithms, providing an experimental basis for subsequent human sports recognition research.

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An Innovative synthesis of deep learning techniques (DCapsNet & DCOM) for generation electrical renewable energy from wind energy

Cited by 127 publications

References 18 publications

Attitude of nursing students following the implementation of comprehensive computer-based nursing process in medical surgical internship: a quasi-experimental study

Attitude of nursing students following the implementation of comprehensive computer-based nursing process in medical surgical internship: a quasi-experimental study

Multi-objective workflow scheduling in cloud computing: trade-off between makespan and cost

Applying TS-DBN model into sports behavior recognition with deep learning approach

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