More vertical service areas than only data processing, storing, and communication are promised by fog-cloud computing. Due to its great efficiency and scalability, distributed deep learning (DDL) across fog-cloud comp...
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More vertical service areas than only data processing, storing, and communication are promised by fog-cloud computing. Due to its great efficiency and scalability, distributed deep learning (DDL) across fog-cloud computing environments is a widely used application among them. With training limited to sharing parameters, DDL can offer more privacy protection than centralized deep learning. Nevertheless, DDL still faces two significant security obstacles when it comes to fog-cloud computing are How to ensure that users' identities are not stolen by outside enemies, and How to prevent users' privacy from being disclosed to other internal participants in the process of training. In this manuscript, Interference Tolerant Fast Convergence Zeroing Neural Network for Security and Privacy Preservation with reptile search optimization algorithm in Fog-Cloud Computing environment (SPP-ITFCZNN-RSOA-FCC) is proposed. ITFCZNN is proposed for security and privacy preservation, Then reptile search optimization algorithm (RSOA) is proposed to optimize the ITFCZNN, and Effective Lightweight Homomorphic Cryptographic algorithm (ELHCA) is used to encrypt and decrypt the local gradients. The proposed SPP-ITFCZNN-RSOA-FCC system attains a better security balance, efficiency, and functionality than existing efforts. The proposed SPP-ITFCZNN-RSOA-FCC is implemented using Python. The performance metrics like accuracy, resource overhead, computation overhead, and communication overhead are considered. The performance of the SPP-ITFCZNN-RSOA-FCC approach attains 29.16%, 20.14%, and 18.93% high accuracy, and 11.03%, 26.04%, and 23.51% lower Resource overhead compared with existing methods including FedSDM: Federated learning dependent smart decision making component for ECG data at internet of things incorporated Edge-Fog-Cloud computing (SPP-FSDM-FCC), A collaborative computation with offloading in dew-enabled vehicular fog computing to compute-intensive with latency-sensitive dependence-awar
An Automatic Diagnosis of Mental Illness Using Optimized Dynamically Stabilized Recurrent Neural Network is proposed in this paper. The aim of the proposed method is " to automatically detect mental illness disor...
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An Automatic Diagnosis of Mental Illness Using Optimized Dynamically Stabilized Recurrent Neural Network is proposed in this paper. The aim of the proposed method is " to automatically detect mental illness disorder from OSMI data records " . The proposed approach automatically extracts the features needed for the network ' s training from the OSMI records in contrast to comparable literature research that employs conventional machine learning algorithms. Using the dual domain feature extraction method the time domain features (minimum, maximum, mean and medium) and statistic features (standard deviation, mean, kurtosis, and skew ness) of OSMI dataset is extracted. This research uses these extracted features for automatic diagnosis of mental illness disorder disease, and it has better accuracy performance. This is trained with Dynamically Stabilized Recurrent Neural Network (DSRNN) to extract key features in the dataset and train the network. By using OSMI dataset from different age groups, this research demonstrates a high success rate in categorizing mental illness and healthy individuals with 98 % and 99.5 % accuracy. DSRNN clearly shows the relation of frequency components among mental illness patients and the healthy individual. These results make it simple to discriminate between people with mental illnesses and healthy people.
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