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An Ensemble-Based Machine Learning Model for Emotion and Mental Health Detection
(World Scientific, 2022) Jonnalagadda, Annapurna; Rajvir, Manan; Singh, Shovan; Chandramouliswaran S.; George, Joshua; Kamalov, Firuz
Recent studies have highlighted several mental health problems in India, caused by factors such as lack of trained counsellors and a stigma associated with discussing mental health. These challenges have raised an increasing need for alternate methods that can be used to detect a person's emotion and monitor their mental health. Existing research in this field explores several approaches ranging from studying body language to analysing micro-expressions to detect a person's emotions. However, these solutions often rely on techniques that invade people's privacy and thus face challenges with mass adoption. The goal is to build a solution that can detect people's emotions, in a non-invasive manner. This research proposes a journaling web application wherein the users enter their daily reflections. The application extracts the user's typing patterns (keystroke data) and primary phone usage data. It uses this data to train an ensemble machine learning model, which can then detect the user's emotions. The proposed solution has various applications in today's world. People can use it to keep track of their emotions and study their emotional health. Also, any individual family can use this application to detect early signs of anxiety or depression amongst the members. © 2023 World Scientific Publishing Co.
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The moderating effects of power distance on corporate social responsibility and multinational enterprises performance
(Springer Science and Business Media Deutschland GmbH, 2022) Le, Minh-Hieu; Lu, Wen-Min; Kweh, Qian Long
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Computational study of MHD mixed convective flow of Cu/Al2O3-water nanofluid in a porous rectangular cavity with slits, viscous heating, Joule dissipation and heat source/sink effects
(Taylor and Francis Ltd., 2023) Santhosh N.; Sivaraj R.; Ramachandra Prasad V.; Anwar Bég O.; Leung, Ho-Hon; Kamalov, Firuz; Kuharat S.
A mathematical model is presented to analyze the mixed convective magnetohydrodynamic (MHD) flow of two different nanofluids within a cavity saturated with porous media. The Tiwari–Das model, along with Maxwell and Brinkman formulations, is adopted to feature the characteristics of the considered nanofluids. The two different working fluids of this investigation are considered aluminum oxide (Formula presented.) -water and copper (Formula presented.) -water nanofluids. The impacts of viscous dissipation, internal heat generation/absorption, magnetic field, and Joule heating are examined in this model. The robust, well-tested Marker And Cell (MAC) algorithm is utilized to numerically solve the transformed, dimensionless, nonlinear coupled two-dimensional momentum and energy conservation equations with the prescribed wall boundary conditions. The comparative study finds an upright accordance with the literature. The effect of various pertinent parameters on the rate of heat transfer, isotherms and streamlines contour distributions in the enclosure is graphically displayed. With an increment in nanoparticles volume fraction, the velocity and heat transfer inside the rectangular enclosure are increased. The (Formula presented.) -water nanofluid and (Formula presented.) -water nanofluid in order have (Formula presented.) and (Formula presented.) higher average heat transfer rate when (Formula presented.) (Formula presented.) nanoparticles are suspended into water. This kind of simulation may be useful in electromagnetic nanomaterials processing and hybrid fuel cells. © 2023 Informa UK Limited, trading as Taylor & Francis Group.
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E-Supply Chain Issues in Internet of Medical Things
(Institute of Electrical and Electronics Engineers Inc., 2022) Ghazal, Taher M.; Al-Dmour, Nidal A.; Mohamed, Tamer; Chabani, Zakariya; Harguem, Saida; Noamas, Samar; Almaazmi, Noura
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Towards an ML-based semantic IoT for pandemic management: A survey of enabling technologies for COVID-19
(Elsevier B.V., 2023-04-01) Zgheib, Rita; Chahbandarian, Ghazar; Kamalov, Firuz; Messiry, Haythem El; Al-Gindy, Ahmed