Bimodal Emotion Classification Using Deep Learning

Multimodal Emotion Recognition is an emerging associative field in the area of Human Computer Interaction and Sentiment Analysis. It extracts information from each modality to predict the emotions accurately. In this research, Bimodal Emotion Recognition framework is developed with the decision-level fusion of Audio and Video modality using RAVDES dataset. Designing such frameworks are computationally expensive and require more time to train the network. Thus, a relatively small dataset has been used for the scope of this research. The conducted research is inspired by the use of neural networks for emotion classification from multimodal data. The developed framework further confirmed the fact that merging modality can enhance the accuracy in classifying emotions. Later, decision-level fusion is further explored with changes in the architecture of the Unimodal networks. The research showed that the Bimodal framework formed with the fusion of unimodal networks having wide layer with more nodes outperformed the framework designed with the fusion of narrow unimodal networks having lesser nodes

Web Structure Mining: Exploring Hyperlinks and Algorithms for Information Retrieval

This paper focus on the Hyperlink analysis, the algorithms used for link analysis, compare those algorithms and the role of hyperlink analysis in Web searching. In the hyperlink analysis, the number of incoming links to a page and the number of outgoing links from that page will be analyzed and the reliability of the linking will be analyzed. Authorities and Hubs concept of Web pages will be explored. The different algorithms used for Link analysis like PageRank, HITS (Hyperlink-Induced Topic Search) and other algorithms will be discussed and compared. The formula used by those algorithms will be explored

OSC-MC: Online Secure Communication Model for Cloud Environment

A malicious cloud user may exploit outsourced data involved in online communication, co-residency, and hypervisor vulnerabilities to breach and hamper sensitive information, and inject malicious traffic-based congestion, rendering services to other benign users. To address this critical and challenging the problem, this letter proposes an Online Secure Communication Model for Cloud (OSC-MC) by identifying and terminating malicious VMs and inter-VM links prior to the occurrence of security threats. The anomalous network traffic, bandwidth usage, and unauthorized inter-VM links are security breach indicators which guides secure cloud communication and resource allocation. The simulation and comparison of the proposed model with existing approaches reveal that it significantly improves authorised inter-communication links up to 34.5% with a reduction of network hogs, and power consumption by 66.46% and 39.31%, respectively

A smart resource management mechanism with trust access control for cloud computing environment

The core of the computer business now offers subscription-based on-demand services with the help of cloud computing. We may now share resources among multiple users by using virtualization, which creates a virtual instance of a computer system running in an abstracted hardware layer. It provides infinite computing capabilities through its massive cloud datacenters, in contrast to early distributed computing models, and has been incredibly popular in recent years because to its continually growing infrastructure, user base, and hosted data volume. This article suggests a conceptual framework for a workload management paradigm in cloud settings that is both safe and performance-efficient. A resource management unit is used in this paradigm for energy and performing virtual machine allocation with efficiency, assuring the safe execution of users' applications, and protecting against data breaches brought on by unauthorised virtual machine access real-time. A secure virtual machine management unit controls the resource management unit and is created to produce data on unlawful access or intercommunication. Additionally, a workload analyzer unit works simultaneously to estimate resource consumption data to help the resource management unit be more effective during virtual machine allocation. The suggested model functions differently to effectively serve the same objective, including data encryption and decryption prior to transfer, usage of trust access mechanism to prevent unauthorised access to virtual machines, which creates extra computational cost overhead

THREE PHASE GRID-CONNECTED PHOTOVOLTAIC UNIVERSAL BRIDGE INVERTER APPLYING A BOOST CONVERTER

In this paper, a three phase grid connected universal bridge inverter using a boost converter is suggested for photovoltaic (PV) systems and grid connected systems to improve performance of three phase inverter connected to the grid. The PV grid system has a disadvantage that the output voltage of PV array is low and we need to boost then in order to efficiently convert them into alternating voltage. The boost inductor boosts the output of the PV array to a suitable level so that inverter could convert it into alternating form. The DC/AC inverter is most important part of the PV system

Dynamic Resource Allocation Method for Load Balance Scheduling over Cloud Data Center Networks

The cloud datacenter has numerous hosts as well as application requests where resources are dynamic. The demands placed on the resource allocation are diverse. These factors could lead to load imbalances, which affect scheduling efficiency and resource utilization. A scheduling method called Dynamic Resource Allocation for Load Balancing (DRALB) is proposed. The proposed solution constitutes two steps: First, the load manager analyzes the resource requirements such as CPU, Memory, Energy and Bandwidth usage and allocates an appropriate number of VMs for each application. Second, the resource information is collected and updated where resources are sorted into four queues according to the loads of resources i.e. CPU intensive, Memory intensive, Energy intensive and Bandwidth intensive. We demonstarate that SLA-aware scheduling not only facilitates the cloud consumers by resources availability and improves throughput, response time etc. but also maximizes the cloud profits with less resource utilization and SLA (Service Level Agreement) violation penalties. This method is based on diversity of clients applications and searching the optimal resources for the particular deployment. Experiments were carried out based on following parameters i.e. average response time; resource utilization, SLA violation rate and load balancing. The experimental results demonstrate that this method can reduce the wastage of resources and reduces the traffic upto 44.89 and 58.49 in the network

A Privacy-Preserving Outsourced Data Model in Cloud Environment

Nowadays, more and more machine learning applications, such as medical diagnosis, online fraud detection, email spam filtering, etc., services are provided by cloud computing. The cloud service provider collects the data from the various owners to train or classify the machine learning system in the cloud environment. However, multiple data owners may not entirely rely on the cloud platform that a third party engages. Therefore, data security and privacy problems are among the critical hindrances to using machine learning tools, particularly with multiple data owners. In addition, unauthorized entities can detect the statistical input data and infer the machine learning model parameters. Therefore, a privacy-preserving model is proposed, which protects the privacy of the data without compromising machine learning efficiency. In order to protect the data of data owners, the epsilon-differential privacy is used, and fog nodes are used to address the problem of the lower bandwidth and latency in this proposed scheme. The noise is produced by the epsilon-differential mechanism, which is then added to the data. Moreover, the noise is injected at the data owner site to protect the owners data. Fog nodes collect the noise-added data from the data owners, then shift it to the cloud platform for storage, computation, and performing the classification tasks purposes

Computation of Probability Coefficients using Binary Decision Diagram and their Application in Test Vector Generation

This paper deals with efficient computation of probability coefficients which offers computational simplicity as compared to spectral coefficients. It eliminates the need of inner product evaluations in determination of signature of a combinational circuit realizing given Boolean function. The method for computation of probability coefficients using transform matrix, fast transform method and using BDD is given. Theoretical relations for achievable computational advantage in terms of required additions in computing all 2n probability coefficients of n variable function have been developed. It is shown that for n>5, only 50% additions are needed to compute all probability coefficients as compared to spectral coefficients. The fault detection techniques based on spectral signature can be used with probability signature also to offer computational advantage