SASDL AND RBATQ: SPARSE AUTOENCODER WITH SWARM BASED DEEP LEARNING AND REINFORCEMENT BASED Q-LEARNING FOR EEG CLASSIFICATION
ABSTARCT :
The most vital information about the electrical activities of the brain can be obtained with the help of Electroencephalography (EEG) signals. It is quite a powerful tool to analyze the neural activities of the brain and various neurological disorders like epilepsy, schizophrenia, sleep related disorders, parkinson disease etc. can be investigated well with the help of EEG signals. Goal: In this paper, two versatile deep learning methods are proposed for the efficient classification of epilepsy and schizophrenia from EEG datasets.The main advantage of using deep learning when compared to other machine learning algorithms is that it has the capability to accomplish feature engineering on its own. It leverages only implicit pairwise labels (weak supervision) when learning the hidden modules. When training the output module, on the other hand, it requires full supervision but achieves high label efficiency, needing as few as ten randomly selected labeled examples (one from each class) to achieve 94.88% accuracy on CIFAR-10 using a ResNet-18 backbone oreover, modular training enables fully modularized DL workflows, which then simplify the design and implementation of pipelines and improve the maintainability and reusability of models Swarm intelligence is also a highly useful technique to solve a wide range of real-world, complex, and non-linear problems. Therefore, taking advantage of these factors, the first method proposed is a Sparse Autoencoder (SAE) with swarm based deep learning method and it is named as (SASDL) using Particle Swarm Optimization (PSO) technique, Cuckoo Search Optimization (CSO) technique and Bat Algorithm (BA) technique; and the second technique proposed is the Reinforcement Learning based on Bidirectional Long-Short Term Memory (BiLSTM), Attention Mechanism, Tree LSTM and Q learning, and it is named as (RBATQ) technique.Both these two novel deep learning techniques are tested on epilepsy and schizophrenia EEG datasets and the results are analyzed comprehensively, and a good classification accuracy of more than 93% is obtained for all the datasets..
EXISTING SYSTEM :
? In this paper, two versatile deep learning methods are proposed for the efficient classification of epilepsy and schizophrenia from EEG datasets. Methods: The main advantage of using deep learning when compared to other machine learning algorithms is that it has the capability to accomplish feature engineering on its own. Swarm intelligence is also a highly useful technique to solve a wide range of real-world, complex, and non-linear problems
? A transfer learning along with semi-supervised learning for seizure classification from EEG signals was proposed by Jiang et al., where the average accuracy was shown to be higher than 95% in most cases
DISADVANTAGE :
? Swarm intelligence is also a highly useful technique to solve a wide range of real-world, complex, and non-linear problems
? These three approaches produced a high classification accuracy of more than 95% as per the consideration of their problem requirement..
? Schizophrenia is a serious mental disorder where people interpret reality in an abnormal manner Schizophrenia results in a combination of delusion, hallucination, and disordered thinking thereby the daily functions are severely impaired [31]. Therefore, schizophrenia involves a range of problems with cognition, emotion, and behaviour.
PROPOSED SYSTEM :
• This work will review existing computational and digital pathology methods for breast cancer diagnosis with a special focus on deep learning. The paper starts by reviewing public datasets related to breast cancer diagnosis.
• Additionally, existing deep learning methods for breast cancer diagnosis are reviewed. The publicly available code repositories are introduced as well. The paper is closed by highlighting challenges and future works for deep learning-based diagnosis
ADVANTAGE :
? When dealing with CSO, the following tree main rules are used in expressing the cuckoo search process Firstly, one egg is laid by a cuckoo at a particular time and its egg is dropped in a randomly chosen nest.
? In order to sense the distance, echo location is used by the bats and the difference between the prey and the different background barriers are known by the bats
? For an easy implementation, any wavelength can be used depending on the specific problem. By means of adjusting the frequencies the range of the wavelength can be adjusted. While fixing the wavelength ?, the frequency too can be varied as ? and f are closely related.
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