Impact of Social Media Marketing on Business Performance: A Hybrid Performance Measurement Approach Using Data Analytics and Machine Learning

Kongar, Elif; Adebayo, Olumide

doi:10.1109/emr.2021.3055036

Cited by 14 publications

(9 citation statements)

References 66 publications

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“…At present, the development of IoT technology has accelerated, and enterprise management performance has gradually become an internal factor of enterprise economic growth, which directly affects its competitiveness. In order to enhance competitiveness, it is necessary to improve enterprise management performance [5]. Enterprise performance management is produced under this background.…”

Section: Introductionmentioning

confidence: 99%

Enterprise Management Performance Evaluation Model Using Improved Fuzzy Clustering Algorithm in IoT Networks

Li¹,

2022

Security and Communication Networks

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Enterprise core competence is closely related to enterprise management performance, and it is important to evaluate enterprise management performance. However, the current enterprise management performance evaluation model has the problems of high eigenvalues of sample data, low cumulative contribution and correlation, high error rate in the calculation of business management performance evaluation index weights, low evaluation accuracy, and long evaluation time. Therefore, the enterprise management performance evaluation model using improved fuzzy clustering algorithm in Internet of things (IoT) networks is proposed. First, in the IoT architecture, the enterprise management performance evaluation index system is established by using the balanced scorecard theory. Second, the evaluation index system is reduced in dimensionality by combining principal component analysis and kernel-independent component analysis, the fuzzy C-mean clustering algorithm based on the objective function is designed, and finally, the improved fuzzy clustering algorithm is obtained to establish the enterprise management performance evaluation model, the reduced evaluation index system is input, and the evaluation results are output. The results show that the sample data eigenvalue of this model is low. The maximum error rate of weight calculation is 2.3%, the accuracy is always more than 95%, and the average value of evaluation time is 0.57 s, which effectively realize enterprise management performance evaluation in IoT networks.

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Section: Introductionmentioning

confidence: 99%

Enterprise Management Performance Evaluation Model Using Improved Fuzzy Clustering Algorithm in IoT Networks

Li¹,

2022

Security and Communication Networks

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“…The proposed method investigates the clusters with the performance metrics given in the equations 5 to 7, Comparing with the existing methods which are discussed in the review literature. (2) 3 75.34 84.24 83.56 DBSCAN (5) 5,7 83.15 89.00 90.31 GSVM (6) 5 79.76 90.45 92.22 SOM (8) 5 75.00 78.73 93.51 PCA (11) 3, 5 85.69 94.88 93.11 ocSVM (15) 3, 5 80.23 92.57 90.55 ICA (20) 3 From Table 3, K-Means process the three different cluster classes to find the accuracy level to 83.56%, the energy consumption rate is low to compute the cluster set. But the Algorithm improves the classification to 84.24% comparing the other network functionality to fabricate the attacking nodes.…”

Section: Resultsmentioning

confidence: 99%

“…Dash L et al (5) developed a novel machine learning-based approach to data redundancy reduction with an aggregation tree is built using a specific number of nodes. The Genetic Support Vector Machine (SVM) method is used to remove the unnecessary data from the tree and additionally, Locality Sensitive Hashing (LSH) removes incorrect data on likeness and masses nonredundant facts to the higher node (6,7) . Inayat Ali et al (8) mentioned a Correlation Degree based on Data Density (DDCD) clustering approach for the network's sink nodes in order to compute the locally available data for those nodes.…”

Section: Introductionmentioning

confidence: 99%

Intellectual Data Aggregation Using Independent Cluster based Medicaid Method for Network Functionality Fabrication

Moorthy¹,

Shanthakumar²,

Janarthanam³

2022

IJST

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Objectives: Network functionality-based tracking is difficult for the fabrication processing under the various environment condition. In order to achieve the efficient result Intellectual data cluster-based Medicaid aggregation method applied for the network functionality analysis of the attack on network entities aggregate the fabrication process for balancing the network to normal flow. Methods: The proposed Independent Cluster based Medicaid Intellectual Data Aggregation (ICAMA) is compared with the K-means and GSVM, DBSCAN algorithm for classification of the attack entities to understand the response and reinforcement of the network functionality against the interconnection nodes by using energy and weighted aggregation, accuracy metrics on the CIC-IDS 2019 dataset. Findings: The Experiment studies provide the 15.23% lower energy consumption for interconnection of nodes comparing with the DBSCAN and 17.18% against the K-means algorithms. The network functional fabrication progress of proposed method has the weighted aggregation15.07%,8.32%, 6.41%, 5.12%, 5.52%, 8.08% and 2.21% against the given K-means, DBSCAN, GSVM, SOM, PCA, ocSVM and ICA methods and also improves accuracy to 11.07% compared with the GSVM method. ICAMA applies the intellectual functionality based on the distribution of entities in the network as a cluster or a single fabrication process. Novelty: In order to restore the regular network progress, ICAMA continuously runs the dynamic collection of network analysis by employing intellectual functional support to identify various types of network anomalies.

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“…Incredible content disturbs the balance of society [9]. Accordingly, a great deal of exploration has been done on the unwavering quality of mechanised microblogging; however, the greater part of them offer various ideas of dependability [10]. It appears to be that the issue isn't tackled.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Enabled Social Media Recommendation Based on User Comments

Saraswathi¹,

Mohanraj²,

Suresh³

et al. 2023

Computer Systems Science and Engineering

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Nowadays, review systems have been developed with social media Recommendation systems (RS). Although research on RS social media is increasing year by year, the comprehensive literature review and classification of this RS research is limited and needs to be improved. The previous method did not find any user reviews within a time, so it gets poor accuracy and doesn't filter the irrelevant comments efficiently. The Recursive Neural Network-based Trust Recommender System (RNN-TRS) is proposed to overcome this method's problem. So it is efficient to analyse the trust comment and remove the irrelevant sentence appropriately. The first step is to collect the data based on the transactional reviews of social media. The second step is pre-processing using Imbalanced Collaborative Filtering (ICF) to remove the null values from the dataset. Extract the features from the pre-processing step using the Maximum Support Grade Scale (MSGS) to extract the maximum number of scaling features in the dataset and grade the weights (length, count, etc.). In the Extracting features for Training and testing method before that in the feature weights evaluating the softmax activation function for calculating the average weights of the features. Finally, In the classification method, the Recursive Neural Network-based Trust Recommender System (RNN-TRS) for User reviews based on the Positive and negative scores is analysed by the system. The simulation results improve the predicting accuracy and reduce time complexity better than previous methods.

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Impact of Social Media Marketing on Business Performance: A Hybrid Performance Measurement Approach Using Data Analytics and Machine Learning

Cited by 14 publications

References 66 publications

Enterprise Management Performance Evaluation Model Using Improved Fuzzy Clustering Algorithm in IoT Networks

Enterprise Management Performance Evaluation Model Using Improved Fuzzy Clustering Algorithm in IoT Networks

Intellectual Data Aggregation Using Independent Cluster based Medicaid Method for Network Functionality Fabrication

Deep Learning Enabled Social Media Recommendation Based on User Comments

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