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Improving energy-efficient management for identifying software requirement prioritization based on optimized fuzzy logic social spider optimization

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Abstract

Energy management is important for choosing the right software recommendation to reduce the cost of service utilization. Time factor and CPU performance will be the challenges for feature analysis in smart devices like smartphones, tabs, and computers. The energy makes highly desirable needs for Priority Software which contains the recommendation for choosing right features to make the right choice of execution software. Different techniques are used to reduce the energy for considering software, but they are insufficient to recordation in which they are not used to prioritize the software utilization to make better device-driven hardware. The recommendations are considered to choose good quality software based on feature analysis from risk, cost, and requirement of the stakeholders’ which needs the priority of the requirements. To overcome this problem, an analytical hierarchy process of identifying software requirement prioritization based on optimized fuzzy logic social spider optimization (FLSP) is proposed to reduce energy consumption. By utilizing the software feature evaluation, the decision rule is attained to make priority using finite decision ranking (FDR). Initially, social spider optimization is intended to evaluate the spectral weight which is selected from the customer behavioral features and strategies to provide priority processing through fitness evaluation. The efficiency of these features depend on software factors which are evaluated to communal weight and rule with fuzzy logic mapping function (FLMF). Then the logical regression creates a stack to assign weights based on software features to make ranking to prioritize the utilization of software. The contrast of these approaches proves that measurement performance leads to higher efficiency and accuracy in software recommendation to use with best energy consumption.

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Authors and Affiliations

  1. Department of Artificial Intelligence and Data Science, M. Kumarasamy College of Engineering, Karur, India

    K. Jeyaganesh Kumar

  2. Department of Computer Science and Engineering, KGisl Institute of Technology, Coimbatore, India

    N. Rajkumar

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  1. K. Jeyaganesh Kumar
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  2. N. Rajkumar
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Correspondence to K. Jeyaganesh Kumar.

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Kumar, K.J., Rajkumar, N. Improving energy-efficient management for identifying software requirement prioritization based on optimized fuzzy logic social spider optimization. Pers Ubiquit Comput 27, 1419–1428 (2023). https://doi.org/10.1007/s00779-021-01617-1

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  • DOI: https://doi.org/10.1007/s00779-021-01617-1

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