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Many aspects of the self are lost as a consequence of having multiple sclerosis (MS). A person's identity can be altered by negative self-concepts, which are associated…
Many aspects of the self are lost as a consequence of having multiple sclerosis (MS). A person's identity can be altered by negative self-concepts, which are associated with poor psychological wellbeing and can lead individuals to reconstruct their sense of self. The Social Identity Model of Identity Change argues that previously established identities form a basis of continued social support, by providing grounding and connectedness to others to facilitate the establishment of new identities. Family support is a salient factor in adjustment to MS and may enable the establishment of new identities. The purpose of this paper is to investigate identity reconstruction following a diagnosis of MS.
A meta-synthesis of the qualitative literature was conducted to examine the relationship between identity change and family identity of people with MS and other family members.
In all, 16 studies were identified that examined identity change and the family following a diagnosis of MS. Coping strategies used by people with MS and their wider family groups, affect the reconstruction of people's identity and the adjustment to MS. Receiving support from the family whilst a new identity is constructed can buffer against the negative effects of identity loss.
The family base is strengthened if MS-related problems in daily life are adapted into the individual and family identity using positive coping styles.
This review provides an interpretation and explanation for results of previous qualitative studies in this area.
This work aims to develop a novel fuzzy associator rule-based fuzzified deep convolutional neural network (FDCNN) architecture for the classification of smartphone…
This work aims to develop a novel fuzzy associator rule-based fuzzified deep convolutional neural network (FDCNN) architecture for the classification of smartphone sensor-based human activity recognition. This work mainly focuses on fusing the λmax method for weight initialization, as a data normalization technique, to achieve high accuracy of classification.
The major contributions of this work are modeled as FDCNN architecture, which is initially fused with a fuzzy logic based data aggregator. This work significantly focuses on normalizing the University of California, Irvine data set’s statistical parameters before feeding that to convolutional neural network layers. This FDCNN model with λmax method is instrumental in ensuring the faster convergence with improved performance accuracy in sensor based human activity recognition. Impact analysis is carried out to validate the appropriateness of the results with hyper-parameter tuning on the proposed FDCNN model with λmax method.
The effectiveness of the proposed FDCNN model with λmax method was outperformed than state-of-the-art models and attained with overall accuracy of 97.89% with overall F1 score as 0.9795.
The proposed fuzzy associate rule layer (FAL) layer is responsible for feature association based on fuzzy rules and regulates the uncertainty in the sensor data because of signal inferences and noises. Also, the normalized data is subjectively grouped based on the FAL kernel structure weights assigned with the λmax method.
Contributed a novel FDCNN architecture that can support those who are keen in advancing human activity recognition (HAR) recognition.
A novel FDCNN architecture is implemented with appropriate FAL kernel structures.