Imran Ullah Khan's scientific contributions

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Publications (2)


Research on the analytics of traffic pumping in telecommunications via data science using rehabilitated frog leaping algorithm
  • Article
  • Full-text available

March 2023

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42 Reads

Imran Ullah Khan

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One kind of telecom crime known as "traffic pumping" occurs when local phone companies artificially overstate the volume of calls flowing into their systems so that they may charge the calling party a greater access fee than their own. Lacking labels for training set makes it difficult to determine whether congestion pumps has occurred. In this study, we suggested a decision-support system based on cluster analysis and decision trees for identifying fraudulent cases. In this study, we use the IBM Telco and Cell2cell datasets. The gathered information can be preprocessed using normalization. When we have collected enough data, we use the rehabilitation frog leaping algorithm (RFLA) to divide up the possible incidents of fraud into distinct groups. Next, we used the cluster participation labels to build a decision tree that led us to the criteria that must be met in order to pursue legal action against the circumstances that raised red flags. Professionals in the field of telecommunications (TC) verify these guidelines in an effort to find a legal remedy against accused offenders. The results are demonstrated and proved the efficiency of the proposed system by comparing it with the conventional techniques.

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Research on the analytics of traffic pumping in telecommunications via data science using rehabilitated frog leaping algorithm

March 2023

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24 Reads

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2 Citations

International Journal of Data Informatics and Intelligent Computing

One kind of telecom crime known as "traffic pumping" occurs when local phone companies artificially overstate the volume of calls flowing into their systems so that they may charge the calling party a greater access fee than their own. Lacking labels for training set makes it difficult to determine whether congestion pumps has occurred. In this study, we suggested a decision-support system based on cluster analysis and decision trees for identifying fraudulent cases. In this study, we use the IBM Telco and Cell2cell datasets. The gathered information can be preprocessed using normalization. When we have collected enough data, we use the rehabilitation frog leaping algorithm (RFLA) to divide up the possible incidents of fraud into distinct groups. Next, we used the cluster participation labels to build a decision tree that led us to the criteria that must be met in order to pursue legal action against the circumstances that raised red flags. Professionals in the field of telecommunications (TC) verify these guidelines in an effort to find a legal remedy against accused offenders. The results are demonstrated and proved the efficiency of the proposed system by comparing it with the conventional techniques.

Citations (1)


... optimization strategy based on a two-dimensional mechanism that describes the fuel efficiency of hybrid vehicles. [16] used wavelet transform and fuzzy logic methodologies to improve the energy management of hybrid trams. The main goal of the project is the development of an ideal EMS to reduce hydrogen consumption and loss of FC functionality. ...

Reference:

Enhancing Performance of Hybrid Electric Vehicle using Optimized Energy Management Methodology
Research on the analytics of traffic pumping in telecommunications via data science using rehabilitated frog leaping algorithm

International Journal of Data Informatics and Intelligent Computing