Machine Learning Models based Mental Health Detection

Karunakaran, Manivannan; Balusamy, Jeevanantham; Selvaraj, Krishnakumar

doi:10.1109/icicict54557.2022.9917622

Cited by 8 publications

(2 citation statements)

References 24 publications

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“…Neural Network Based Model for Predicting Psychological Conditions: Guo, Y., Friston, K., Aldo, F., Hill, S., Peng, H. [3] Preventive care attempts to inform individuals and clinicians of potential complications or conditions a patient might encounter. With the recent interest on leveraging big data in the healthcare domain to better design data-driven models for preventive medicine and the increased awareness of the long-lasting effects of concussions, being able to predict psychological conditions postconcussion can have a paramount effect on mild traumatic brain injury patients.…”

Section: Mental Health Chatbot Delivering Cognitive Behaviourmentioning

confidence: 99%

Mental Health Companion

Barke

2024

IJSREM

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This research endeavors to address the critical challenge of timely identification and intervention in mental health disorders, particularly depression. Utilizing self- reported symptoms obtained through survey responses, alongside essential demographic and personal data, we aim to develop a predictive model for the early detection of mental health issues. The dataset, comprising diverse survey responses, provides a comprehensive understanding of participants' mental well-being. Employing advanced machine learning techniques, we seek to uncover patterns that can contribute to an effective prediction model. The ultimate aspiration is to empower healthcare practitioners with a sophisticated tool that enhances the overall efficacy of mental health interventions. By navigating the complexities of mental health identification, our research underscores the importance of proactive measures, striving for a paradigm shift towards a more comprehensive and timely approach to mental healthcare. Through systematic testing and model selection, we aim to pave the way for a transformative tool that can positively impact public health by enabling early detection and intervention. Key Words: Mental Health Prediction, Depression, Random Forest Classifier, Decision Tree Classifier, Gaussian Naive Bayes, K-Nearest Neighbors, Machine Learning, Classification, Anxiety, Stress, Mental Health.

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Section: Mental Health Chatbot Delivering Cognitive Behaviourmentioning

confidence: 99%

Mental Health Companion

Barke

2024

IJSREM

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“…With rise of the concept beyond 5G, the Internet of Things (IoT) has developed rapidly [1]. It is widely used in scenarios that require low-power computing and remote access control, such as health detection [2], logistics [3], and automated home [4]. As a result, many IoT devices have been spawned, which are designed to achieve automatic data transmission and provide ubiquitous wireless connections to billions of devices with sensing [5], communication [6], computing and control capabilities, such as smartphones and sensors.…”

Section: Introductionmentioning

confidence: 99%

Software-Defined GPU-CPU Empowered Efficient Wireless Federated Learning With Embedding Communication Coding for Beyond 5G

Hong

Al‐Otaibi

et al. 2023

IEEE Open J. Commun. Soc.

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Currently, with the widespread of the intelligent internet of things (IoT) in beyond 5G, wireless federated learning (WFL) has attracted a lot of attention to enable knowledge construction and sharing among a huge amount of distributed edge devices. However, under unstable wireless channel conditions, existing WFL schemes exist the following challenges: First, learning model parameters will be disturbed by bit errors because of interference and noise during wireless transmission, which will affect the training accuracy and the loss of the learning model. Second, traditional edge devices with CPU acceleration are inefficient due to the low throughout computation, especially in accelerating the encoding and decoding process during wireless transmission. Third, current hardware-level GPU acceleration methods cannot optimize complex operations, for instance, complex wireless coding in the WFL environment. To address the above challenges, we propose a software-defined GPU-CPU empowered efficient WFL architecture with embedding LDPC communication coding. Specifically, we embed wireless channel coding into the server weight aggregation and the client local training process respectively to resist interruptions in the learning process and design a GPU-CPU acceleration scheme for this architecture. The experimental results show its anti-interference ability and GPU-CPU acceleration ability during wireless transmission, which is 10 times the error control capability and 100 times faster than existing WFL schemes.

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