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Online gaming is associated with an elevated risk
of mental disorders, including depression. Both
Novel Recurrent Neural Networks and Artificial
Neural Networks are recent subjects of research as
promising tools for predicting and addressing the
adverse effects of online gaming on mental health
(Paulus et al. 2018). One potential application of
Artificial Neural Networks and Novel Recurrent
Neural Networks in this realm is to predict a gamer's
probability of developing depression based on their
gaming patterns and other factors (Biolcati, Pupi, and
Mancini 2021). The Novel Recurrent Neural
Networks, a subtype of Artificial Neural Networks, is
adept at analysing a player's in-game behaviour over
time since it excels in handling sequential data
(Hussain and Griffiths 2009).
Another possible application of Artificial Neural
Networks and Novel Recurrent Neural Networks in
the domain of online gaming and mental health is to
identify early indicators of potential problems
(Hussain and Griffiths 2009; Mancini, Imperato, and
Sibilla 2019; Jung, Yi, and JeongDongJin 2018). In
summary, Artificial Neural Networks and Novel
Recurrent Neural Networks possess the unique
capability to serve as potent tools for predicting and
mitigating the negative effects of online gaming on
mental health. However, further research is vital to
fully understand their potential and limitations in this
context (Jung, Yi, and JeongDongJin 2018).
Moreover, due to lockdowns and the subsequent
disruptions to work and education, individuals might
have excess leisure time, or they might find
themselves more easily distracted by online gaming
while working from home. Additionally, diverse
connections were observed between gamers'
motivations for playing and their choice of game
genres in relation to their psychological well-being.
Notably, those motivated by distraction and action
game enthusiasts displayed the most pronounced
effects. Further studies are essential to ascertain
whether these threats to mental health are caused by
or a consequence of video gaming.
5 CONCLUSION
The study at hand sought to utilise cutting-edge
machine learning methods to forecast mental
depression among online video game players. The
findings revealed that the Novel Recurrent Neural
Network algorithm notched an accuracy of 94%,
while its counterpart, the Artificial Neural Network
algorithm, achieved a slightly lower rate of 91%. In
juxtaposing the two, the Novel Recurrent Neural
Network algorithm exhibited superior performance in
the accuracy domain over the Artificial Neural
Network algorithm, with a mean accuracy difference
of 3.83500 between them. Delving deeper into the
research paper, it was accentuated that the Novel
Recurrent Neural Network algorithm, in the context
of predicting mental depression amongst online
gamers, surpasses the Artificial Neural Network
algorithm. Such results underscore the significance of
progressive machine learning methods, with special
emphasis on the Novel Recurrent Neural Network
algorithm, as promising tools for pinpointing and
addressing mental health challenges tied to online
gaming.
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Comparing Novel Recurrent Neural Networks with Artificial Neural Networks for Predicting Mental Depression from Online Video Gaming
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