The Experts below are selected from a list of 36159 Experts worldwide ranked by ideXlab platform

Wael A. Altabey - One of the best experts on this subject based on the ideXlab platform.

  • A Non-parametric Approach Toward Structural Health Monitoring for Processing Big Data Collected from the Sensor Network
    Structural Health Monitoring 2019, 2019
    Co-Authors: Ramin Ghiasi, Mohammad Reza Ghasemi, Mohmmad Noori, Wael A. Altabey
    Abstract:

    Advanced sensor network techniques recently allow collecting large amounts of Data for structural health monitoring and damage detection, while how to effectively interpret these complex sensor Data to technical information poses many challenges. This paper presents a machine learning algorithm for Processing of Big Data collected from the sensor networks of civil structure. The proposed approach consists of training and monitoring phases. The training phase was focused on the extracting statistical features and conducting Moving Kernel Principal Component Analysis (MKPCA) in order to derive the damage sensitive indices. The monitoring phase included tracking of errors associated with the derived models. The main goal was to analyze the efficiency of the developed system for health monitoring of the benchmark experimental Data with the 17 different damage scenarios. In this paper KPCA has been implemented in a new form as Moving KPCA (MKPCA) for effectively segmenting large Data and for determining the changes, as Data are continuously collected. Numerical results revealed that, the proposed health monitoring system has a satisfactory performance for the detection of the damage scenarios in three-story frame aluminum structure. Furthermore, enhanced version of KPCA methods exhibited significantly improvement in sensitivity, accuracy and effectiveness over conventional methods.

Olga Kosheleva - One of the best experts on this subject based on the ideXlab platform.

  • how to gauge accuracy of Processing Big Data teaching machine learning techniques to gauge their own accuracy
    International Conference of the Thailand Econometrics Society, 2018
    Co-Authors: Vladik Kreinovich, Thongchai Dumrongpokaphan, Hung T Nguyen, Olga Kosheleva
    Abstract:

    When the amount of Data is reasonably small, we can usually fit this Data to a simple model and use the traditional statistical methods both to estimate the parameters of this model and to gauge this model’s accuracy. For Big Data, it is often no longer possible to fit them by a simple model. Thus, we need to use generic machine learning techniques to find the corresponding model. The current machine learning techniques estimate the values of the corresponding parameters, but they usually do not gauge the accuracy of the corresponding general non-linear model. In this paper, we show how to modify the existing machine learning methodology so that it will not only estimate the parameters, but also estimate the accuracy of the resulting model.

  • Predictive Econometrics and Big Data - How to Gauge Accuracy of Processing Big Data: Teaching Machine Learning Techniques to Gauge Their Own Accuracy
    Predictive Econometrics and Big Data, 2017
    Co-Authors: Vladik Kreinovich, Thongchai Dumrongpokaphan, Hung T Nguyen, Olga Kosheleva
    Abstract:

    When the amount of Data is reasonably small, we can usually fit this Data to a simple model and use the traditional statistical methods both to estimate the parameters of this model and to gauge this model’s accuracy. For Big Data, it is often no longer possible to fit them by a simple model. Thus, we need to use generic machine learning techniques to find the corresponding model. The current machine learning techniques estimate the values of the corresponding parameters, but they usually do not gauge the accuracy of the corresponding general non-linear model. In this paper, we show how to modify the existing machine learning methodology so that it will not only estimate the parameters, but also estimate the accuracy of the resulting model.

Ramin Ghiasi - One of the best experts on this subject based on the ideXlab platform.

  • A Non-parametric Approach Toward Structural Health Monitoring for Processing Big Data Collected from the Sensor Network
    Structural Health Monitoring 2019, 2019
    Co-Authors: Ramin Ghiasi, Mohammad Reza Ghasemi, Mohmmad Noori, Wael A. Altabey
    Abstract:

    Advanced sensor network techniques recently allow collecting large amounts of Data for structural health monitoring and damage detection, while how to effectively interpret these complex sensor Data to technical information poses many challenges. This paper presents a machine learning algorithm for Processing of Big Data collected from the sensor networks of civil structure. The proposed approach consists of training and monitoring phases. The training phase was focused on the extracting statistical features and conducting Moving Kernel Principal Component Analysis (MKPCA) in order to derive the damage sensitive indices. The monitoring phase included tracking of errors associated with the derived models. The main goal was to analyze the efficiency of the developed system for health monitoring of the benchmark experimental Data with the 17 different damage scenarios. In this paper KPCA has been implemented in a new form as Moving KPCA (MKPCA) for effectively segmenting large Data and for determining the changes, as Data are continuously collected. Numerical results revealed that, the proposed health monitoring system has a satisfactory performance for the detection of the damage scenarios in three-story frame aluminum structure. Furthermore, enhanced version of KPCA methods exhibited significantly improvement in sensitivity, accuracy and effectiveness over conventional methods.

Mohamed Adel Serhani - One of the best experts on this subject based on the ideXlab platform.

  • A Multi-Dimensional Trust Model for Processing Big Data Over Competing Clouds
    IEEE Access, 2018
    Co-Authors: Hadeel T. El Kassabi, Mohamed Adel Serhani, Rachida Dssouli, Boualem Benatallah
    Abstract:

    4Cloud computing has emerged as a powerful paradigm for delivering Data-intensive services over the Internet. Cloud computing has enabled the implementation and success of Big Data, a recent phenomenon handling huge Data being generated from different sources. Competing clouds have made it challenging to select a cloud provider that guarantees quality of cloud service (QoCS). Also, cloud providers’ claims of guaranteeing QoCS are exaggerated for marketing purposes; hence, they cannot often be trusted. Therefore, a comprehensive trust model is necessary to evaluate the QoCS prior to making any selection decision. In this paper, we propose a multi-dimensional trust model for Big Data workflow Processing over different clouds. It evaluates the trustworthiness of cloud providers based on: the most up-to-date cloud resource capabilities, the reputation evidence measured by neighboring users, and a recorded personal history of experiences with the cloud provider. The ultimate goal is to ensure an efficient selection of trustworthiness cloud provider who eventually will guarantee high QoCS and fulfills key Big Data workflow requirements. Various experiments were conducted to validate our proposed model. The results show that our model captures the different components of trust, ensures high QoCS, and effectively adapts to the dynamic nature of the cloud.

  • AFRICATEK - Trust Assessment-Based Multiple Linear Regression for Processing Big Data Over Diverse Clouds
    Lecture Notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering, 2017
    Co-Authors: Hadeel T. El-kassabi, Mohamed Adel Serhani, Chafik Bouhaddioui, Rachida Dssouli
    Abstract:

    Assessing trust of cloud providers is considered to be a key factor to discriminate between them, especially once dealing with Big Data. In this paper, we apply Multiple Linear Regression (MLR) to develop a trust model for Processing Big Data over diverse Clouds. The model relies on MLR to predict trust score of different cloud service providers. Therefore, support selection of the trustworthiness provider. Trust is evaluated not only on evidenced information collected about cloud resources availability, but also on past experiences with the cloud provider, and the reputation collected from other users experienced with the same cloud services. We use cross validation to test the consistency of the estimated regression equation, and we found that the model can perfectly be used to predict the response variable trust. We also, use bootstrap scheme to evaluate the confidence intervals for each pair of variables used in building our trust model.

  • BigData Congress - De-Centralized Reputation-Based Trust Model to Discriminate between Cloud Providers Capable of Processing Big Data
    2017 IEEE International Congress on Big Data (BigData Congress), 2017
    Co-Authors: Hadeel T. El Kassabi, Mohamed Adel Serhani
    Abstract:

    Trust and reputation systems represent a significant trend in decision support including selection of best match cloud providers to process Big Data. Reputation is often considered as a collective measure of trustworthiness based on the referrals or ratings from members in a community. Reputation systems have been applied in various applications such as online service provision. However, reputation models do not reflect user's quality of service (QoS) preferences and thus they might not be satisfied with the recommendations from others. In this paper, we propose a de-centralized reputation-based trust model that incorporates the user QoS preferences to select the best match Cloud Service Provider to process Big Data. Our trust model relies on three multi-attribute decision-making (MADM) methods including Simple Additive Weighting (SAW), Weighted Product Method (WPM), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). We conducted several experiments using simulated cloud environment to validate our trust model and assess the three MADM methods. The results show that the proposed model is pliable to users' requirements and efficiently evaluate trust of cloud providers.

Hadeel T. El Kassabi - One of the best experts on this subject based on the ideXlab platform.

  • A Multi-Dimensional Trust Model for Processing Big Data Over Competing Clouds
    IEEE Access, 2018
    Co-Authors: Hadeel T. El Kassabi, Mohamed Adel Serhani, Rachida Dssouli, Boualem Benatallah
    Abstract:

    4Cloud computing has emerged as a powerful paradigm for delivering Data-intensive services over the Internet. Cloud computing has enabled the implementation and success of Big Data, a recent phenomenon handling huge Data being generated from different sources. Competing clouds have made it challenging to select a cloud provider that guarantees quality of cloud service (QoCS). Also, cloud providers’ claims of guaranteeing QoCS are exaggerated for marketing purposes; hence, they cannot often be trusted. Therefore, a comprehensive trust model is necessary to evaluate the QoCS prior to making any selection decision. In this paper, we propose a multi-dimensional trust model for Big Data workflow Processing over different clouds. It evaluates the trustworthiness of cloud providers based on: the most up-to-date cloud resource capabilities, the reputation evidence measured by neighboring users, and a recorded personal history of experiences with the cloud provider. The ultimate goal is to ensure an efficient selection of trustworthiness cloud provider who eventually will guarantee high QoCS and fulfills key Big Data workflow requirements. Various experiments were conducted to validate our proposed model. The results show that our model captures the different components of trust, ensures high QoCS, and effectively adapts to the dynamic nature of the cloud.

  • BigData Congress - De-Centralized Reputation-Based Trust Model to Discriminate between Cloud Providers Capable of Processing Big Data
    2017 IEEE International Congress on Big Data (BigData Congress), 2017
    Co-Authors: Hadeel T. El Kassabi, Mohamed Adel Serhani
    Abstract:

    Trust and reputation systems represent a significant trend in decision support including selection of best match cloud providers to process Big Data. Reputation is often considered as a collective measure of trustworthiness based on the referrals or ratings from members in a community. Reputation systems have been applied in various applications such as online service provision. However, reputation models do not reflect user's quality of service (QoS) preferences and thus they might not be satisfied with the recommendations from others. In this paper, we propose a de-centralized reputation-based trust model that incorporates the user QoS preferences to select the best match Cloud Service Provider to process Big Data. Our trust model relies on three multi-attribute decision-making (MADM) methods including Simple Additive Weighting (SAW), Weighted Product Method (WPM), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). We conducted several experiments using simulated cloud environment to validate our trust model and assess the three MADM methods. The results show that the proposed model is pliable to users' requirements and efficiently evaluate trust of cloud providers.