An analytics model for TelecoVAS customers’ basket clustering using ensemble learning approach

Etebarian, Akbar; Azmi, Reza; Dastjerdi, Reza Ebrahimzadeh

doi:10.1186/s40537-021-00421-1

Cited by 19 publications

(13 citation statements)

References 24 publications

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“…In [27], a combination of the X-Means algorithm, the group learning system, and the N-List structure is used for analyzing and classifying the customers of a mobile company. The X-Means algorithm is applied to determine the optimal number of clusters and customers clustering in a mobile company.…”

Section: Related Workmentioning

confidence: 99%

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Analyzing IoT big data in healthcare using Deep learning and Distributed Fog Computing

Iranpak

Shahbahrami

Shakeri

2022

Preprint

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High amounts of patient and healthcare data are generated daily on the Internet of Things (IoT). The processing time and analyzing the big data received from IoT devices, as well as providing the necessary accuracy for data classification, are considered as the most important challenges in IoT. Therefore, this study aims to improve the accuracy of data classification by implementing hybrid approaches of feature selection (FS) and deep learning (DL) within the fog computing infrastructure while reducing the response time (RT) and bandwidth usage. Also, the major goal is to analyze and classify the states of patients remotely through IoT, cloud computing and fog computing technologies. The information processing time is reduced by implementing Particle Swarm Optimization (PSO) and Imperialist Competitive Algorithm (ICA) in fog computing to select the prominent features, and the classification accuracy is improved through classifying new data by a deep neural network (DNN) model. The simulation results show that the accuracy of the classification and remote monitoring of patients is 98.54%, which is about 4.5% better than the case without using PSO-ICA algorithms, and it is also improved by about 10% on average compared to other methods such as Linear Regression (LR), K Nearest Neighbors (KNN), Neural Network (NN), and Bayesian Belief Net (BBN).

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Section: Related Workmentioning

confidence: 99%

“…In this paper, the criteria of accuracy, precision, recall, F-Score, error, and run (response) time are used to evaluate the performance of the proposed method, which are as follows [27].…”

Section: Evaluation Criteriamentioning

confidence: 99%

Analyzing IoT big data in healthcare using Deep learning and Distributed Fog Computing

Iranpak

Shahbahrami

Shakeri

2022

Preprint

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“…In [27] the researcher is able to provide a method for analyzing the customer portfolio using clustering techniques, deep neural network, and online hybrid similarity criteria. However, each of the proposed methods has challenges such as inaccuracy and high error of recommendations.…”

Section: Related Workmentioning

confidence: 99%

“…The following equations measure accuracy, precision and recall [27,29]. TP (True Positive) indicates patients who are in a normal health condition and identified as normal by the proposed method.…”

Section: Assessment Criteriamentioning

confidence: 99%

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Remote patient monitoring and classifying using the internet of things platform combined with cloud computing

2021

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Many researchers have recently considered patients’ health and provided an optimal and appropriate solution. With the advent of technologies such as cloud computing, Internet of Things and 5G, information can be exchanged faster and more securely. The Internet of things (IoT) offers many opportunities in the field of e-health. This technology can improve health services and lead to various innovations in this regard. Using cloud computing and IoT in this process can significantly improve the monitoring of patients. Therefore, it is important to provide a useful method in the medical industry and computer science to monitor the status of patients using connected sensors. Thus, due to its optimal efficiency, speed, and accuracy of data processing and classification, the use of cloud computing to process the data collected from remote patient sensors and IoT platform has been suggested. In this paper, a prioritization system is used to prioritize sensitive information in IoT, and in cloud computing, LSTM deep neural network is applied to classify and monitor patients’ condition remotely, which can be considered as an important innovative aspect of this paper. Sensor data in the IoT platform is sent to the cloud with the help of the 5th generation Internet. The core of cloud computing uses the LSTM (long short-term memory) deep neural network algorithm. By simulating the proposed method and comparing the obtained results with other methods, it is observed that the accuracy of the proposed method is 97.13%, which has been improved by 10.41% in average over the other methods.

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Developing ensemble clustering through similarity measures: A semi‐supervised hierarchical clustering learning

Wang,

2024

Concurrency and Computation

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SummaryA generic variation of hierarchical clustering (HC) that builds clusters from the bottom up is called agglomerative hierarchical clustering (AHC). The extension of AHC techniques using similarity criteria is the main topic of this research. Based on this, we create an AHC method that accomplishes clustering through ensemble approaches and combines the clustering of clusters with an original similarity measure. Three steps make up the proposed method's primary section. The first phase combines several individual AHC techniques to identify links between samples and create preliminary clusters. A heuristic similarity measure based on the developed clusters is used to determine how similar the samples are. The initial clusters produced using various techniques are all re‐clustered to create superclusters in the second step. The third phase involves creating the final clusters by assigning each sample to a supercluster with the greatest similarity after the clusters have been formed. Based on several benchmark datasets from the UCI machine learning repository, extensive experimental research has been done to assess the performance of the suggested approach. The outcomes unequivocally demonstrate that the suggested AHC‐based paradigm outperforms cutting‐edge techniques.

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An analytics model for TelecoVAS customers’ basket clustering using ensemble learning approach

Cited by 19 publications

References 24 publications

Analyzing IoT big data in healthcare using Deep learning and Distributed Fog Computing

Analyzing IoT big data in healthcare using Deep learning and Distributed Fog Computing

Remote patient monitoring and classifying using the internet of things platform combined with cloud computing

Developing ensemble clustering through similarity measures: A semi‐supervised hierarchical clustering learning

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