Parkinson Disease (PD) is a kind of neural disorder that affects a range of people. This disease has continuously growing stages to halt entire neural activities of any people. There are many techniques proposed to detect and predict PD using medical symptoms and measurements. The medical measurements provided by different experiments must be effectively handled to produce concrete results on the detection of PD. This saves many people from PD at earlier stage itself. Recent technologies focus on Machine Learning (ML) and Deep Learning (DL) techniques for effective PD data analysis for making efficient prediction system. They are concentrating to build complex artificial neural systems using effective learning functions. However, the existing systems are lacking to attain multi-attribute and multivariant data analysis to predict PD. To attain multi-variant Parkinson symptom analysis, the artificial neural systems must be equipped with more characteristics. In this regard, the Proposed system is developed using Multi-Variant Stacked Auto Encoder (MVSAE). The MVSAE based PD Prediction System (MSAEPD) helps to analyze more PD symptoms than existing systems. This article provides four different variants of SAE construction procedures to predict PD symptoms. The MSAEPD is implemented and compared with existing works such as MANN, GAE and UMLBD. This comparison shows the MSAEPD system gives 5% to 10% better results than existing works.
Cybersecurity is the domain that ensures safeness in both individual system and overall network systems. The classification and learning approaches used in different machine learning (ML) techniques improve the protection of the cyber systems against various attacks. Techniques such as support vector machine (SVM), neural networks (NN), principle component analysis (PCA), and reinforcement learning (RL) are used against various cyber threats. Applying these techniques at the front-end services (either online or offline) makes less effect than back end process-level services of any computer system. The proposed work analyzes the benefits of implementing customized ML and deep learning (DL) techniques on the core of the operating system than application level services, which in effect increases the speed and correctness of attack detection. The core (kernel) of the operating system has the capability to extract all internal attributes of process and file systems. The kernel space security activities can be improved by proposed work where the process level attributes classified using ML and DL techniques. The cloud service helps in sharing of the kernel abilities of the system ensuring core level security. The following work uses recurrent NN (RNN), SVM, PCA, and RL for analyzing the system data collected using Process Explorer. This technique finds application in manufacturing domain where the systems are protected from the various attacks to secure the data of the manufacturing company.
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