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Journal from gdlhub / 2022-02-14 15:18:39
Oleh : Deepti Mehrotra, Rashi Srivastava, Renuka Nagpal, Deepshikha Nagpal, King Saud University
Dibuat : 2022-02-14, dengan 0 file

Keyword : Mobile applications, Power tutor, Multiclass classification, R tool, Energy consumption
Url : http://www.sciencedirect.com/science/article/pii/S1319157818300600
Sumber pengambilan dokumen : web

Today even the commonly used smartphones have high computational power and storage capacity, which make even execution of resource intensive operations possible with them. The increase in computational capability and varied communication features, have resulted in enhancement of energy requirement of these devices. The increasing popularity of mobile computing is always bottlenecked with its battery life. To study the power consumption pattern experiment was conducted on some popular mobile applications under different environment. The energy used by applications was recorded and used as training set to develop a machine learning model that classify these mobiles applications into three categories namely low, medium and high depending on its energy usage, using classification approach. Various Multiclass classification algorithms are explored to find the most accurate model that can be used for classifying the mobile applications. Every day a new mobile application is created for catering a wide range of applications. These applications have new and more interactive features, and consume relatively high energy for execution, leading to drainage of battery. The detailed analysis of logical reasoning behind machine learning models developed in this study will provide designer deeper insight of type of mobile application and its power usage pattern.

Deskripsi Alternatif :

Today even the commonly used smartphones have high computational power and storage capacity, which make even execution of resource intensive operations possible with them. The increase in computational capability and varied communication features, have resulted in enhancement of energy requirement of these devices. The increasing popularity of mobile computing is always bottlenecked with its battery life. To study the power consumption pattern experiment was conducted on some popular mobile applications under different environment. The energy used by applications was recorded and used as training set to develop a machine learning model that classify these mobiles applications into three categories namely low, medium and high depending on its energy usage, using classification approach. Various Multiclass classification algorithms are explored to find the most accurate model that can be used for classifying the mobile applications. Every day a new mobile application is created for catering a wide range of applications. These applications have new and more interactive features, and consume relatively high energy for execution, leading to drainage of battery. The detailed analysis of logical reasoning behind machine learning models developed in this study will provide designer deeper insight of type of mobile application and its power usage pattern.

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