An investigation of various machine learning techniques for mobile call data analysis for reducing call drop

Ashok, G.V.; Kumari, V. S. R.

doi:10.1016/j.matpr.2021.11.622

Cited by 3 publications

(1 citation statement)

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“…The input data values are first obtained from the CDR Dataset [29,30], and then they are preprocessed, which may include noise removal or the replacement of missing data. The noiseless data aids in the detection of patterns linked with call drops.…”

Section: B Data Preprocessingmentioning

confidence: 99%

A Novel Chimp Optimized Linear Kernel Regression (COLKR) Model for Call Drop Prediction in Mobile Networks

G V,

Kumari P.

2023

IJRITCC

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Call failure can be caused by a variety of factors, including inadequate cellular infrastructure, undesirable system structuring, busy mobile phone towers, changing between towers, and many more. Outdated equipment and networks worsen call failure, and installing more towers to improve coverage might harm the regional ecosystems. In the existing studies, a variety of machine learning algorithms are implemented for call drop prediction in the mobile networks. But it facing problems in terms of high error rate, low prediction accuracy, system complexity, and more training time. Therefore, the proposed work intends to develop a new and sophisticated framework, named as, Chimp Optimized Linear Kernel Regression (COLKR) for predicting call drops in the mobile networks. For the analysis, the Call Detail Record (CDR) has been collected and used in this framework. By preprocessing the attributes, the normalized dataset is constructed using the median regression-based filtering technique. To extract the most significant features for training the classifier with minimum processing complexity, a sophisticated Chimp Optimization Algorithm (COA) is applied. Then, a new machine learning model known as the Linear Kernel Regression Model (LKRM) has been deployed to predict call drops with greater accuracy and less error. For the performance assessment of COLKR, several machine learning classifiers are compared with the proposed model using a variety of measures. By using the proposed COLKR mechanism, the call drop detection accuracy is improved to 99.4%, and the error rate is reduced to 0.098%, which determines the efficiency and superiority of the proposed system.

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Section: B Data Preprocessingmentioning

confidence: 99%