The passage of electric signals throughout the human body produces an electromagnetic field, known as the human biofield, which carries information about a person’s psychological health. The human biofield can be rehabilitated by using healing techniques such as sound therapy and many others in a smart grid. However, psychiatrists and psychologists often face difficulties in clarifying the mental state of a patient in a quantifiable form. Therefore, the objective of this research work was to transform human emotions using sound healing therapy and produce visible results, confirming the transformation. The present research was based on the amalgamation of image processing and machine learning techniques, including a real-time aura-visualization interpretation and an emotion-detection classifier. The experimental results highlight the effectiveness of healing emotions through the aforementioned techniques. The accuracy of the proposed method, specifically, the module combining both emotion and aura, was determined to be ~88%. Additionally, the participants’ feedbacks were recorded and analyzed based on the prediction capability of the proposed module and their overall satisfaction. The participants were strongly satisfied with the prediction capability (~81%) of the proposed module and future recommendations (~84%). The results indicate the positive impact of sound therapy on emotions and the biofield. In the future, experimentation using different therapies and integrating more advanced techniques are anticipated to open new gateways in healthcare.

Cloud computing is revolutionizing on how information technology is used by organizations and individuals. Cloud computing provides dynamic services with virtualized resources over the Internet. It ensures facilities to develop, deploy, and manage applications `on the cloud' for end users entailing resources virtualization that maintains and manages itself. Scheduling is a task performed to get maximum profit to increase cloud computing work load efficiency. Its objective is using resources properly and managing load between resources with minimum execution time. High communication cost incurs in clouds prevent task schedulers from being applied in a large scale distributed environment. This study proposes a hybrid Particle Swarm Optimization (PSO) which performs better in execution ratio and average schedule length.

Several deep models were proposed in image processing, data interpretation, speech recognition, and video analysis. Most of these architectures need a massive proportion of training samples and use arbitrary configuration. This paper constructs a deep learning architecture with feature learning. Graph convolution networks (GCNs), semi-supervised learning and graph data representation, have become increasingly popular as cost-effective and efficient methods. Most existing merging node descriptions for node distribution on the graph use stabilised neighbourhood knowledge, typically requiring a significant amount of variables and a high degree of computational complexity. To address these concerns, this research presents DLM-SSC, a unique method semi-supervised node classification tasks that can combine knowledge from multiple neighbourhoods at the same time by integrating high-order convolution and feature learning. This paper employs two function learning techniques for reducing the number of parameters and hidden layers: modified marginal fisher analysis (MMFA) and kernel principal component analysis (KPCA). The MMFA and KPCA weight matrices are modified layer by layer when implementing the DLM, a supervised pretraining technique that doesn't require a lot of information. Free measuring on citation datasets (Citeseer, Pubmed, and Cora) and other data sets demonstrate that the suggested approaches outperform similar algorithms.

The cloud computing is the one that deals with the trading of the resources efficiently in accordance to the user’s need. A Job scheduling is the choice of an ideal resource for any job to be executed with regard to waiting time, cost or turnaround time. A cloud job scheduling will be an NP-hard problem that contains n jobs and m machines and every job is processed with each of these m machines to minimize the make span. The security here is one of the top most concerns in the cloud. In order to calculate the value of fitness the fuzzy inference system makes use of the membership function for determining the degree up to which the input parameters that belong to every fuzzy set is relevant. Here the fuzzy is used for the purpose of scheduling energy as well as security in the cloud computing.

Security has gained a lot of importance as information technology is widely used. Cryptography refers to the study of mathematical techniques and related aspects of Information Security like data confidentiality, data Integrity, and of data authentication. Visual cryptography (VC) is a process where a secret image is encrypted into shares which refuse to divulge information about the original secret image. Its strength is a fact that the decryption of the secret image is through human visual system without computation. A basic model for visual cryptography for natural images was proposed by Naor and Shamir. In this paper, a novel method of VC is presented for halftone images which represent the resultant image in the same size as the original secret image. Hiding of the visual information based on pseudo randomization and pixel reversal is also proposed.