A novel trust model for fog computing using fuzzy neural networks and weighted weakest link

2020 ◽  
Vol 28 (5) ◽  
pp. 763-800
Author(s):  
Mhamed Zineddine
Keyword(s):  
Fog Computing ◽  
Trust Model ◽  
Fuzzy Neural Networks ◽  
Content Type ◽  
Weakest Link ◽  
Proposed Model ◽  
Fuzzy Neural ◽  
Fog Network ◽  
Model Algorithm

Purpose Trust is one of the main pillars of many communication and interaction domains. Computing is no exception. Fog computing (FC) has emerged as mitigation of several cloud computing limitations. However, selecting a trustworthy node from the fog network still presents serious challenges. This paper aims to propose an algorithm intended to mitigate the trust and the security issues related to selecting a node of a fog network. Design/methodology/approach The proposed model/algorithm is based on two main concepts, namely, machine learning using fuzzy neural networks (FNNs) and the weighted weakest link (WWL) algorithm. The crux of the proposed model is to be trained, validated and used to classify the fog nodes according to their trust scores. A total of 2,482 certified computing products, in addition to a set of nodes composed of multiple items, are used to train, validate and test the proposed model. A scenario including nodes composed of multiple computing items is designed for applying and evaluating the performance of the proposed model/algorithm. Findings The results show a well-performing trust model with an accuracy of 0.9996. Thus, the end-users of FC services adopting the proposed approach could be more confident when selecting elected fog nodes. The trained, validated and tested model was able to classify the nodes according to their trust level. The proposed model is a novel approach to fog nodes selection in a fog network. Research limitations/implications Certainly, all data could be collected, however, some features are very difficult to have their scores. Available techniques such as regression analysis and the use of the experts have their own limitations. Experts might be subjective, even though the author used the fuzzy group decision-making model to mitigate the subjectivity effect. A methodical evaluation by specialized bodies such as the security certification process is paramount to mitigate these issues. The author recommends the repetition of the same study when data form such bodies is available. Originality/value The novel combination of FNN and WWL in a trust model mitigates uncertainty, subjectivity and enables the trust classification of complex FC nodes. Furthermore, the combination also allowed the classification of fog nodes composed of diverse computing items, which is not possible without the WWL. The proposed algorithm will provide the required intelligence for end-users (devices) to make sound decisions when requesting fog services.

Predicting young customers’ intention to repurchase green plastic products: incorporating trust model into purchase intention model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Dwi Suhartanto ◽  
Ani Kartikasari ◽  
Raditha Hapsari ◽  
Bambang Setio Budianto ◽  
Mukhamad Najib ◽  
...  
Keyword(s):  
Purchase Intention ◽  
Developed Countries ◽  
Trust Model ◽  
Repurchase Intention ◽  
Content Type ◽  
Repurchase Intentions ◽  
Proposed Model ◽  
Young Consumers ◽  
Green Trust ◽  
Plastic Products

Purpose This study aims to assess young customers’ repurchasing intentions toward green plastic products by incorporating green trust model into green purchase intention model. It also evaluates the role of gender moderation in the green repurchase intention formation model. Design/methodology/approach A total of 314 young consumers of green plastic products in Bandung, Indonesia were determined for this study. This study used variance-based partial least squares (PLS) to evaluate the proposed model and examine the hypothesized relationship, by means of SmartPLS 3. The construct validity and reliability were evaluated by testing the measurement model, while the proposed hypotheses were examined by testing the structural model. Findings The assessment of the proposed model using PLS reveals that the incorporation of green trust model increases the prediction strength of green repurchase intentions model on green plastic products. Further, this study shows that, in general, gender did not moderate the formation of green repurchase intentions. Research limitations/implications Besides broadening the green repurchase intention theory, this finding offers a direction for green plastic businesses to improve their capability and their marketing strategies. This study offers an important contribution in understanding young consumers’ intentions to buy green plastic products, although it has several drawbacks. In the future, to increase its generalization, this study can be replicated on young consumers in other developing and developed countries, and this model can also be tested in other segments. Originality/value To the best of the authors’ knowledge, there are no published studies that have tested the repurchase intention model for green plastic products, and none of the past studies have incorporated these models to explain repurchase intention toward green plastic products. Furthermore, the inclusion of gender roles in green repurchase intentions for green plastic products is important to be explored.

Pulsar Detection for Wavelets SODA and Regularized Fuzzy Neural Networks Based on Andneuron and Robust Activation Function

2019 ◽  
Vol 28 (01) ◽  
pp. 1950003 ◽  
Author(s):  
Paulo Vitor de Campos Souza ◽  
Luiz Carlos Bambirra Torres ◽  
Augusto Junio Guimarães ◽  
Vanessa Souza Araujo
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Activation Function ◽  
Fuzzy Neural Networks ◽  
Activation Functions ◽  
Large Scale Problems ◽  
Proposed Model ◽  
Fuzzy Neural ◽  
Learning Machine ◽  
Intelligent Models

The use of intelligent models may be slow because of the number of samples involved in the problem. The identification of pulsars (stars that emit Earth-catchable signals) involves collecting thousands of signals by professionals of astronomy and their identification may be hampered by the nature of the problem, which requires many dimensions and samples to be analyzed. This paper proposes the use of hybrid models based on concepts of regularized fuzzy neural networks that use the representativeness of input data to define the groupings that make up the neurons of the initial layers of the model. The andneurons are used to aggregate the neurons of the first layer and can create fuzzy rules. The training uses fast extreme learning machine concepts to generate the weights of neurons that use robust activation functions to perform pattern classification. To solve large-scale problems involving the nature of pulsar detection problems, the model proposes a fast and highly accurate approach to address complex issues. In the execution of the tests with the proposed model, experiments were conducted explanation in two databases of pulsars, and the results prove the viability of the fast and interpretable approach in identifying such involved stars.

An efficient deep learning model for classification of thermal face images

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Basma Abd El-Rahiem ◽  
Ahmed Sedik ◽  
Ghada M. El Banby ◽  
Hani M. Ibrahem ◽  
Mohamed Amin ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Low Contrast ◽  
Content Type ◽  
Automatic Feature Extraction ◽  
Face Images ◽  
Proposed Model ◽  
Thermal Face ◽  
Deep Learning Model

PurposeThe objective of this paper is to perform infrared (IR) face recognition efficiently with convolutional neural networks (CNNs). The proposed model in this paper has several advantages such as the automatic feature extraction using convolutional and pooling layers and the ability to distinguish between faces without visual details.Design/methodology/approachA model which comprises five convolutional layers in addition to five max-pooling layers is introduced for the recognition of IR faces.FindingsThe experimental results and analysis reveal high recognition rates of IR faces with the proposed model.Originality/valueA designed CNN model is presented for IR face recognition. Both the feature extraction and classification tasks are incorporated into this model. The problems of low contrast and absence of details in IR images are overcome with the proposed model. The recognition accuracy reaches 100% in experiments on the Terravic Facial IR Database (TFIRDB).

scholarly journals Brain-Computer Interface for Control of Wheelchair Using Fuzzy Neural Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Rahib H. Abiyev ◽  
Nurullah Akkaya ◽  
Ersin Aytac ◽  
Irfan Günsel ◽  
Ahmet Çağman
Keyword(s):  
Neural Networks ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Fuzzy Neural Networks ◽  
Training Data ◽  
Computer Interface ◽  
Human Control ◽  
Fuzzy Neural ◽  
Physically Disabled People

The design of brain-computer interface for the wheelchair for physically disabled people is presented. The design of the proposed system is based on receiving, processing, and classification of the electroencephalographic (EEG) signals and then performing the control of the wheelchair. The number of experimental measurements of brain activity has been done using human control commands of the wheelchair. Based on the mental activity of the user and the control commands of the wheelchair, the design of classification system based on fuzzy neural networks (FNN) is considered. The design of FNN based algorithm is used for brain-actuated control. The training data is used to design the system and then test data is applied to measure the performance of the control system. The control of the wheelchair is performed under real conditions using direction and speed control commands of the wheelchair. The approach used in the paper allows reducing the probability of misclassification and improving the control accuracy of the wheelchair.

Prediction of user interest fluctuation using fuzzy neural networks in web search

2020 ◽  
Vol 8 (4) ◽  
pp. 307-319
Author(s):  
Senthilkumar N C ◽  
Pradeep Reddy Ch
Keyword(s):  
Design Methodology ◽  
Web Search ◽  
Fuzzy Neural Networks ◽  
User Interest ◽  
Short Term ◽  
Content Type ◽  
Temporal Aspects ◽  
Fuzzy Neural ◽  
Practical Implications

PurposeThe user interest in content searching in the web will be changed over by time.Design/methodology/approachThe system is in need to find the content of user over the temporal aspects.FindingsSo, predicting the user interest over the time by analyzing the fluctuations of their search keyword is important.Research limitations/implicationsSo, predicting the user interest over the time by analyzing the fluctuations of their search keyword is important.Practical implicationsIn this work, fuzzy neural network techniques are used to predict the user interest fluctuation in different times in different scenarios.Social implicationsIn this proposed work, both the long-term and short-term interest are evaluated using the specialized user interface designed to retrieve the user interest based on the user searching activities.Originality/valueThis work also categorizes the future needs of users using this proposed system.

Fog computing architectures for healthcare

2016 ◽  
Vol 14 (4) ◽  
pp. 334-349 ◽  
Author(s):  
Lisardo Prieto González ◽  
Corvin Jaedicke ◽  
Johannes Schubert ◽  
Vladimir Stantchev
Keyword(s):  
Cloud Computing ◽  
Fog Computing ◽  
Content Type ◽  
Useful Knowledge ◽  
Smart Healthcare ◽  
Data Application ◽  
Depth Analysis ◽  
Three Layer Architecture ◽  
Self Powered

Purpose The purpose of this study is to analyze how embedding of self-powered wireless sensors into cloud computing further enables such a system to become a sustainable part of work environment. Design/methodology/approach This is exemplified by an application scenario in healthcare that was developed in the context of the OpSIT project in Germany. A clearly outlined three-layer architecture, in the sense of Internet of Things, is presented. It provides the basis for integrating a broad range of sensors into smart healthcare infrastructure. More specifically, by making use of short-range communication sensors (sensing layer), gateways which implement data transmission and low-level computation (fog layer) and cloud computing for processing the data (application layer). Findings A technical in-depth analysis of the first two layers of the infrastructure is given to prove reliability and to determine the communication quality and availability in real-world scenarios. Furthermore, two example use-cases that directly apply to a healthcare environment are examined, concluding with the feasibility of the presented approach. Practical implications Finally, the next research steps, oriented towards the semantic tagging and classification of data received from sensors, and the usage of advanced artificial intelligence-based algorithms on this information to produce useful knowledge, are described together with the derived social benefits. Originality/value The work presents an innovative, extensible and scalable system, proven to be useful in healthcare environments.

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