Social, psychological, and emotional well-being are all aspects of mental health. Mental illness can cause problems in daily life, physical health, and interpersonal connections. Severe changes in education, attitude, or emotional management of students cause suffering are defined as children's mental disorders. Artificial intelligence (AI) technology has lately been advanced to help intellectual fitness professionals, especially psychiatrists and clinicians, in making choices primarily based totally on affected person records along with medical history, behavioural records, social media use, and so on. There is a pressing need to address core mental health concerns in children, which can progress to more serious problems if not addressed early. As a result, a shallow learning technique-assisted integrated prediction model (SLIPM) has been presented in this research to predict and diagnose mental illness in children early. Convolutional neural networks (CNN) are built first in the proposed model to learn deep-learned patient behavioural data characteristics.
Cloud computing provides elastic computational resources that have been widely deployed in data centres to provide Infrastructure as a Service (IaaS). Virtualization is a key technology in cloud computing for resource sharing. In the virtualization technology the most challenging issue is Virtual Machine Placement Problem (VMPP). Virtual Machine Placement is a significant process executed as a branch of VM migration, which involves placing a Virtual Machine (VM) on a suitable physical machine (PM) in order to improve the resource utilization efficiently. Many existing VMP algorithms considered the overall CPU capacity of a PM, without considering the number of cores available in a PM and core-CPU capacity of a PM. i.e. VMs are mapped onto PMs, if the CPU capacity of the VM is less than or equal to the total CPU capacity of a PM. Such an allocation results in core overload which leads to performance degradation and violation of Service Level Agreement. However, in the real scenario, PM and VM consists of multiple cores. So, to place VM on PM, the core-CPU capacity of a VM must be mapped to a core-CPU capacity of a PM. i.e., the core of a VM should be mapped to a core of a PM. In this paper, we have proposed a Multi-Core Aware Virtual Machine Placement Algorithm (MCA-VMP). In MCA-VMP, the number of cores available in a PM and core-CPU capacity of a PM is considered instead of total CPU capacity of a PM. We used, Google Cluster Traces to generate the virtual machine configurations. Based on Google Cluster Traces the dataset is generated. Monte Carlo Simulation method is used to produce Google Cloud Jobs (GoCJ). Our simulation results shows that MCA-VMP is efficient than traditional non-Core Aware VMP algorithms. Our proposed algorithm MCA-VMP improves the performance of a DataCenter in terms of resource utilization, PM overload and resource wastage.
A great many dynamic clients all around the globe are utilizing on the web informal community, for example, Facebook, Twitter, Tumbler and LinkedIn. These shortcomings make it easy to abuse client's data and do personality cloning assault to frame counterfeit profile. In this proposed framework, information concealing systems to shroud some data in profile pictures with a specific end goal to identify botnets and counterfeit profiles. This venture introduces an order and examination of discovery instruments of clone attacks on online interpersonal organization, in light of trait similitude, companion arrange closeness, and profile investigation for a period interim and record of Internet Protocol groupings. In this task we have proposed discrete wavelet change calculation for information covering up. In this manner this would keep the clone assaults and giving complete client information protection saving. Likewise when clients transfer the profile pictures they can be watermarked and refreshed. For the watermarking method Java static watermark can be used. Any phony clients refreshing a similar profile picture can be distinguished and their separate IP would be followed and blocked. Likewise for secure picture transmission, we utilized Discrete Wavelet Transform (DWT) for information concealing/steganography and Discrete Cosine Transform (DCT) for picture pressure.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.