Enhancing the Security and Performance of Cloud for E-Governance Infrastructure

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Large Scale ◽  
Governance System ◽  
Readiness Assessment ◽  
Cloud Data ◽  
Governance Model ◽  
Proposed Model ◽  
And Performance ◽  
Governance Infrastructure ◽  
Hashing Function ◽  
Distributed Infrastructure

E-Governance is getting momentous in India. Over the years, e-Governance has played a major part in every sphere of the economy. In this paper, we have proposed E-MODI (E-governance model for open distributed infrastructure) a centralized e-Governance system for government of India, the implementation of this system is technically based on open distributed infrastructure which comprises of various government bodies in one single centralized unit. Our proposed model identifies three different patterns of cloud computing which are DGC, SGC and CGC. In addition, readiness assessment of the services needs to migrate into cloud. In this paper, we propose energy efficient VM allocation algorithm to achieve higher energy efficiency in large scale cloud data centers when system on optimum mode. Our objectives have been explained in details and experiments were designed to demonstrate the robustness of the multi-layered security which is an integration of High secure lightweight block cipher CSL along with Ultra powerful BLAKE3 hashing function in order to maintain information security triad.

scholarly journals Antonym-Synonym Classification Based on New Sub-Space Embeddings

2019 ◽  
Vol 33 ◽  
pp. 6204-6211
Author(s):  
Muhammad Asif Ali ◽  
Yifang Sun ◽  
Xiaoling Zhou ◽  
Wei Wang ◽  
Xiang Zhao
Keyword(s):  
Large Scale ◽  
Semantic Information ◽  
Relation Extraction ◽  
Specific Information ◽  
Lexical Semantic ◽  
Word Level ◽  
Novel Approach ◽  
Proposed Model ◽  
Low Performance ◽  
And Performance

Distinguishing antonyms from synonyms is a key challenge for many NLP applications focused on the lexical-semantic relation extraction. Existing solutions relying on large-scale corpora yield low performance because of huge contextual overlap of antonym and synonym pairs. We propose a novel approach entirely based on pre-trained embeddings. We hypothesize that the pre-trained embeddings comprehend a blend of lexical-semantic information and we may distill the task-specific information using Distiller, a model proposed in this paper. Later, a classifier is trained based on features constructed from the distilled sub-spaces along with some word level features to distinguish antonyms from synonyms. Experimental results show that the proposed model outperforms existing research on antonym synonym distinction in both speed and performance.

scholarly journals Features of metropolitan area governance models

Upravlenie ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 57-75
Author(s):  
Yu. V. Pavlov ◽  
G. A. Khmeleva
Keyword(s):  
Large Scale ◽  
Governance System ◽  
Scientific Methods ◽  
The Public ◽  
Administrative Efficiency ◽  
Governance Model ◽  
Accelerate Growth ◽  
Intermunicipal Cooperation ◽  
General Scientific ◽  
Governance Models

The article develops a methodological basis for choosing the optimal model for managing the agglomeration of settlements. The existing agglomeration problems and the need to accelerate growth due to the agglomeration effect form a challenge to the agglomeration governance system. For an adequate response it is important to understand the capabilities of governance models. These capabilities can be expressed through the description of the features of the models.For a large-scale description of the features of models, it is necessary to form groups of features, characterize each feature, compare the models according to the degree of its manifestation, and, if possible, explain the reason for the differences. The article investigates research papers within the framework of the theory of municipal reformers, the theory of public choice, in particular on the issues of centralization / decentralization of municipalities, intermunicipal cooperation, the efficiency of the economy of the public sector. The authors used general scientific methods of analysis, synthesis, grouping, comparison.The revealed features make it possible to assess the governance model in terms of what the agglomeration will receive from its implementation. All features are combined into five groups: economic, social, administrative efficiency, democratization, stability. The study revealed 29 features, presented their explanation and degree of development, depending on the type of governance model.As a result of the study, the possibilities for justifying the feasibility of introducing models have increased. The authors represented scientific novelty by a more complete list of the features of models, a more detailed presentation of them, the approach of presenting information “from features”, a convenient grouping that allows you to use the characteristic to assess the effectiveness of the activities of government authorities. The study can be useful for civil servants in charge of agglomeration processes.

scholarly journals Distributed Cooperative Jamming with Neighborhood Selection Strategy for Unmanned Aerial Vehicle Swarms

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 184
Author(s):  
Yongkun Zhou ◽  
Dan Song ◽  
Bowen Ding ◽  
Bin Rao ◽  
Man Su ◽  
...  
Keyword(s):  
Large Scale ◽  
State Transition ◽  
Selection Strategy ◽  
Cooperative Jamming ◽  
Proposed Model ◽  
Neighborhood Selection ◽  
Aerial Vehicle ◽  
Ca Model ◽  
And Performance ◽  
Better Than

In system science, a swarm possesses certain characteristics which the isolated parts and the sum do not have. In order to explore emergence mechanism of a large–scale electromagnetic agents (EAs), a neighborhood selection (NS) strategy–based electromagnetic agent cellular automata (EA–CA) model is proposed in this paper. The model describes the process of agent state transition, in which a neighbor with the smallest state difference in each sector area is selected for state transition. Meanwhile, the evolution rules of the traditional CA are improved, and performance of different evolution strategies are compared. An application scenario in which the emergence of multi–jammers suppresses the radar radiation source is designed to demonstrate the effect of the EA–CA model. Experimental results show that the convergence speed of NS strategy is better than those of the traditional CA evolution rules, and the system achieves effective jamming with the target after emergence. It verifies the effectiveness and prospects of the proposed model in the application of electronic countermeasures (ECM).

scholarly journals Protected and Flexible Fulti-Keyword Search Model Over Encoded Cloud Data

2019 ◽  
Vol 8 (6S3) ◽  
pp. 2075-2079
Keyword(s):  
Large Scale ◽  
Keyword Search ◽  
Search Algorithm ◽  
Modern Technology ◽  
Index Structure ◽  
Inner Product ◽  
Cloud Data ◽  
Dynamic Update ◽  
And Performance ◽  
Cryptographic Techniques

Cloud computing is modern technology as a new computing model in number of business domains. Large numbers of large scale departments are starting to shift the data on to the cloud environment. With the benefit of storage as a service many enterprises are moving their valuable data to the cloud, since it costs less, easily scalable and can be accessed from anywhere any time. Improved dynamic multi-keyword ranking search scheme with top key via encrypted cloud data that simultaneously supports dynamic update operations as deleting and inserting documents. Greedy depth first search algorithm is provided for efficiency multi keywords on place and index structure. Cryptography is one of the establishing trust models. Searchable security is a cryptographic method to provide security. In number of researchers have been working on developing privacy and efficient searchable encryptiontypes. We take new effective cryptographic techniques based on data structures like CRSA and B-Tree to enhance the level of privacy. We propose new multi-keyword search query over encrypted cloud information in retrieving top k scored documents. The vector space model and TFIDF model are used to build index and query generation. This paper focuses on multi keyword search based on ranking over an encrypted cloud data. The search uses the feature of similarity and inner product similarity matching. We propose to support the top-k Multi-full-text search for security and performance analysis show that the proposed model guarantees a high safety and practicality and dynamic update operations, such as deleting and adding documents. The experimental results show that the overhead in computation and communication is low.

The Influence of Cognitive and Affective Reactions to Feedback on Subsequent Goals

2010 ◽  
Vol 15 (2) ◽  
pp. 121-131 ◽  
Author(s):  
Remus Ilies ◽  
Timothy A. Judge ◽  
David T. Wagner
Keyword(s):  
Performance Feedback ◽  
Business School ◽  
Structural Equation ◽  
Emotional Reactions ◽  
Equation Model ◽  
Proposed Model ◽  
And Performance ◽  
Dispositional Measure ◽  
The Relationship ◽  
Reactions To Feedback

This paper focuses on explaining how individuals set goals on multiple performance episodes, in the context of performance feedback comparing their performance on each episode with their respective goal. The proposed model was tested through a longitudinal study of 493 university students’ actual goals and performance on business school exams. Results of a structural equation model supported the proposed conceptual model in which self-efficacy and emotional reactions to feedback mediate the relationship between feedback and subsequent goals. In addition, as expected, participants’ standing on a dispositional measure of behavioral inhibition influenced the strength of their emotional reactions to negative feedback.

An Analytical Transient Tire Model

2001 ◽  
Vol 29 (2) ◽  
pp. 108-132 ◽  
Author(s):  
A. Ghazi Zadeh ◽  
A. Fahim
Keyword(s):  
Transient Behavior ◽  
Stability Control ◽  
Vehicle Stability ◽  
Tire Model ◽  
Steering Angle ◽  
First Order ◽  
Vehicle Stability Control ◽  
Proposed Model ◽  
Depth Study ◽  
And Performance

Abstract The dynamics of a vehicle's tires is a major contributor to the vehicle stability, control, and performance. A better understanding of the handling performance and lateral stability of the vehicle can be achieved by an in-depth study of the transient behavior of the tire. In this article, the transient response of the tire to a steering angle input is examined and an analytical second order tire model is proposed. This model provides a means for a better understanding of the transient behavior of the tire. The proposed model is also applied to a vehicle model and its performance is compared with a first order tire model.

Model and Method for Contributor’s Quality Assessment in Community Image Tagging Systems

2018 ◽  
pp. 45-51
Author(s):  
A. V. Ponomarev
Keyword(s):  
Large Scale ◽  
Wide Spectrum ◽  
Preference Relation ◽  
Pairwise Comparison ◽  
Ground Truth ◽  
Comparison Method ◽  
Characteristic Matrix ◽  
Image Tagging ◽  
Proposed Model

Introduction: Large-scale human-computer systems involving people of various skills and motivation into the information processing process are currently used in a wide spectrum of applications. An acute problem in such systems is assessing the expected quality of each contributor; for example, in order to penalize incompetent or inaccurate ones and to promote diligent ones.Purpose: To develop a method of assessing the expected contributor’s quality in community tagging systems. This method should only use generally unreliable and incomplete information provided by contributors (with ground truth tags unknown).Results:A mathematical model is proposed for community image tagging (including the model of a contributor), along with a method of assessing the expected contributor’s quality. The method is based on comparing tag sets provided by different contributors for the same images, being a modification of pairwise comparison method with preference relation replaced by a special domination characteristic. Expected contributors’ quality is evaluated as a positive eigenvector of a pairwise domination characteristic matrix. Community tagging simulation has confirmed that the proposed method allows you to adequately estimate the expected quality of community tagging system contributors (provided that the contributors' behavior fits the proposed model).Practical relevance: The obtained results can be used in the development of systems based on coordinated efforts of community (primarily, community tagging systems). 

Multi Disease-Prediction Framework Using Hybrid Deep Learning: An Optimal Prediction Model (Preprint)

2020 ◽  
Author(s):  
Anusha Ampavathi ◽  
Vijaya Saradhi T
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Deep Learning ◽  
Weight Function ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Disease Prediction ◽  
Health Care Decisions ◽  
Proposed Model

UNSTRUCTURED Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient’s symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to “Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson’s disease, and Alzheimer’s disease”, from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like “Deep Belief Network (DBN) and Recurrent Neural Network (RNN)”. As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

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