Jeongcheol Lee, Sunil Ahn^*, Hyunseob Kim, Jongsuk Ruth Lee

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 255-277, 2022, DOI:10.32604/iasc.2022.018558

Abstract Automated hyperparameter optimization (HPO) is a crucial and time-consuming part in the automatic generation of efficient machine learning models. Previous studies could be classified into two major categories in terms of reducing training overhead: (1) sampling a promising hyperparameter configuration and (2) pruning non-promising configurations. These adaptive sampling and resource scheduling are combined to reduce cost, increasing the number of evaluations done on more promising configurations to find the best model in a given time. That is, these strategies are preferred to identify the best-performing models at an early stage within a certain deadline. Although these time and resource constraints… More >

Tauqeer ul Amin^1,*, Basit Shahzad¹, Fazal-e-Amin², Muhammad Shoaib²

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2665-2680, 2021, DOI:10.32604/cmc.2021.013263

Abstract Requirements elicitation is a fundamental phase of software development in which an analyst discovers the needs of different stakeholders and transforms them into requirements. This phase is cost- and time-intensive, and a project may fail if there are excessive costs and schedule overruns. COVID-19 has affected the software industry by reducing interactions between developers and customers. Such a lack of interaction is a key reason for the failure of software projects. Projects can also fail when customers do not know precisely what they want. Furthermore, selecting the unsuitable elicitation technique can also cause project failure. The present study, therefore, aimed… More >