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Analyzing App-Based Methods for Internet De-Addiction in Young Population

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Applications of Artificial Intelligence and Machine Learning

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 778))

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Abstract

With recent advancements in technology and the excessive use of smartphones, all internet-based applications like WhatsApp, Facebook, Netflix, etc. are one tap away, thereby resulting in increased internet usage on an average, especially among the young population. This has affected the cognitive and affective processes of the users and has caused various problems like loss of focus, fatigue, and burning sensations in the eyes, severe harm to mental health, reduction in response to events happening around, and many more. An unconventional method of recovering from internet addiction could be the use of mobile applications that help users monitor their usage and motivate them to have better self-control. There are a number of such applications, henceforth called apps, available that claim to help recover from internet addiction. However, their efficacy in curbing internet use has not been studied previously. This study is primarily based on assessing the efficiency of these app-based recovery methods from internet addiction. Using statistical analysis and polynomial regression, it was found that these apps do help in lowering internet use. This effect is largely seen in the first week of app use, after which significant reduction is not observed.

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Sharma, L., Hooda, P., Bansal, R., Garg, S., Aggarwal, S. (2021). Analyzing App-Based Methods for Internet De-Addiction in Young Population. In: Choudhary, A., Agrawal, A.P., Logeswaran, R., Unhelkar, B. (eds) Applications of Artificial Intelligence and Machine Learning. Lecture Notes in Electrical Engineering, vol 778. Springer, Singapore. https://doi.org/10.1007/978-981-16-3067-5_17

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  • DOI: https://doi.org/10.1007/978-981-16-3067-5_17

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-3066-8

  • Online ISBN: 978-981-16-3067-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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