Hybrid Machine Learning Approaches: A Method to Improve Expected Output of Semi-structured Sequential Data

Abdelrahim, Mohammed; Merlosy, Carlos; Wang, Taehyung

doi:10.1109/icsc.2016.72

Cited by 14 publications

(8 citation statements)

References 7 publications

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“…In this view, hybrid machine learning is a recent development with the promise of solving high dimensional problems with adequate intricacies. Hybrid machine learning works on the idea of combining multiple ML algorithms to increase the overall prediction capability by tuning mutually and generalizing or adapting to unseen data [411]. Ensemble based methods are an example of hybrid machine learning, which has been adopted by many in the areas of anomaly detection, speech recognition, uncertainty quantification and prediction of mechanical response concerning different types of composites [412][413][414][415].…”

Section: Latest Trends and Future Road Mapsmentioning

confidence: 99%

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Sharma

Mukhopadhyay

Rangappa

et al. 2022

Arch Computat Methods Eng

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The superior multi-functional properties of polymer composites have made them an ideal choice for aerospace, automobile, marine, civil, and many other technologically demanding industries. The increasing demand of these composites calls for an extensive investigation of their physical, chemical and mechanical behavior under different exposure conditions. Machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modeling, leading to unprecedented insights and exploration of the system properties beyond the capability of traditional computational and experimental analyses. Here we aim to abridge the findings of the large volume of relevant literature and highlight the broad spectrum potential of ML in applications like prediction, optimization, feature identification, uncertainty quantification, reliability and sensitivity analysis along with the framework of different ML algorithms concerning polymer composites. Challenges like the curse of dimensionality, overfitting, noise and mixed variable problems are discussed, including the latest advancements in ML that have the potential to be integrated in the field of polymer composites.Based on the extensive literature survey, a few recommendations on the exploitation of various ML algorithms for addressing different critical problems concerning polymer composites are provided along with insightful perspectives on the potential directions of future research.

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Section: Latest Trends and Future Road Mapsmentioning

confidence: 99%

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Sharma

Mukhopadhyay

Rangappa

et al. 2022

Arch Computat Methods Eng

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show abstract

“…While designing a system to detect changes in the behaviour of the users, we used ensemble model and then combined the results. Abdelrahim et al (2016) and Murni et al (2019) show that the authors used a hybrid method as a substitute solution for addressing mismatching reviews by star rating given by the users of the websites by combining two techniques: (1) the lexical-based technique and (2) the machine learning-based technique. By combining the sentiment calculation method with machine learning algorithms, they achieved high accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Abdelrahim et al. (2016) and Murni et al. (2019) show that the authors used a hybrid method as a substitute solution for addressing mismatching reviews by star rating given by the users of the websites by combining two techniques: (1) the lexical‐based technique and (2) the machine learning‐based technique.…”

Section: Literature Reviewmentioning

confidence: 99%

Tracing prodromal behaviour by analysing data patterns from social media with ensemble machine learning approach

Joshi

Patwardhan

2022

Int Social Sci J

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This paper presents a novel solution for tracing prodromal behaviour of Twitter users for early detection and prevention of mental illness. A very large number of people are using Twitter to share their daily happenings. This is creating a rich source of data portraying individual behaviour. Machine learning can be used to screen individual users who show prodromal behavioural change over a period and need to consult a psychiatrist. The system comprises of an ensemble model with multiple modules including classification and clustering in addition to lexicon‐based approach, and, for the final verdict, a voting system which emphasises a better precision model is proposed. The model has been validated and tested on standard Self‐Reported Mental Health Diagnoses dataset and promises to give a better result than the baseline.

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“…Thus, the cerebellum is perhaps best understood as a part of a larger hybrid machine learning system (or many hybrid learning machines), with reinforcement and unsupervised components. Hybrid machines benefit from advantages of each style of machine learning, and as such have high performance, generalizability and may show rates of learning over shorter time scales, with emergent abilities not available to each individual component [92]. Emergent abilities that hybrid machines have include anomaly and novelty detection, and making predictions [93]; which are functions classically associated with cerebellum [94].…”

Section: Theoretical Framework For Understanding the Role Of Cerebellum In Cognitive Behaviormentioning

confidence: 99%

Resistance, vulnerability and resilience: A review of the cognitive cerebellum in aging and neurodegenerative diseases

Liang

Carlson

2020

Neurobiology of Learning and Memory

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In the context of neurodegeneration and aging, the cerebellum is an enigma. Genetic markers of cellular aging in cerebellum accumulate more slowly than in the rest of the brain, and it generates unknown factors that may slow or even reverse neurodegenerative pathology in animal models of Alzheimer's Disease (AD). Cerebellum shows increased activity in early AD and Parkinson's disease (PD), suggesting a compensatory function that may mitigate early symptoms of neurodegenerative pathophysiology. Perhaps most notably, different parts of the brain accumulate neuropathological markers of AD in a recognized progression and generally, cerebellum is the last brain region to do so. Taken together, these data suggest that cerebellum may be resistant to certain neurodegenerative mechanisms.On the other hand, in some contexts of accelerated neurodegeneration, such as that seen in chronic traumatic encephalopathy (CTE) following repeated traumatic brain injury (TBI), the cerebellum appears to be one of the most susceptible brain regions to injury and one of the first to exhibit signs of pathology. Cerebellar pathology in neurodegenerative disorders is strongly associated with cognitive dysfunction. In neurodegenerative or neurological disorders associated with cerebellar pathology, such as spinocerebellar ataxia, cerebellar cortical atrophy, and essential tremor, rates of cognitive dysfunction, dementia and neuropsychiatric symptoms increase. When the cerebellum shows AD pathology, such as in familial AD, it is associated with earlier onset and greater severity of disease. These data suggest that when neurodegenerative processes are active in the cerebellum, it may contribute to pathological behavioral outcomes.The cerebellum is well known for comparing internal representations of information with observed outcomes and providing real-time feedback to cortical regions, a critical function that is disturbed in neuropsychiatric disorders such as intellectual disability, schizophrenia, dementia, and autism,

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Hybrid Machine Learning Approaches: A Method to Improve Expected Output of Semi-structured Sequential Data

Cited by 14 publications

References 7 publications

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Tracing prodromal behaviour by analysing data patterns from social media with ensemble machine learning approach

Resistance, vulnerability and resilience: A review of the cognitive cerebellum in aging and neurodegenerative diseases

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