scholarly journals SURVEY PAPER ON THE VARIOUS SECURITY ALGORITHMS USED FOR E-COMMERCE SECURITY

2021 ◽  
pp. 39-46
Author(s):  
Polsani Jahnavi ◽  
Balla Manoj Kumar
Keyword(s):  
Machine Learning ◽  
Trust Model ◽  
Distributed Networks ◽  
Online Platform ◽  
Survey Paper ◽  
High Security ◽  
Cryptographic Algorithms ◽  
Authentication Security ◽  
The Way

Due to the growth of e-commerce, most of the banking transactions are made on the online platform and all these transactions are made on the websites provided by the merchant or the payable apps and because of this the vulnerability of attacks has increased and there are also chances of using fraudulent websites and apps by the attackers though there are many high-security algorithms are been used for safeguarding against vulnerabilities. The way of judging the relation of trust on the online platform has become a major issue, so a proper trust model also needs to be maintained. In this paper, we reviewed various security algorithms which involve cryptographic algorithms, machine learning, anonymization, and masking techniques, blockchain, distributed networks, and many more to provide integrity, privacy, reliability, authentication, security, and risk-less e-commerce platform.

Machine Learning based Trust Model for Secure Internet of Vehicle Data Exchange

2020 ◽  
Author(s):  
Gunasekaran Manogaran ◽  
Bharat S. Rawal
Keyword(s):  
Machine Learning ◽  
Data Exchange ◽  
Trust Model ◽  
Vehicle Data

scholarly journals Explainable AI: A Review of Machine Learning Interpretability Methods

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 18
Author(s):  
Pantelis Linardatos ◽  
Vasilis Papastefanopoulos ◽  
Sotiris Kotsiantis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Black Box ◽  
Learning Systems ◽  
Model Complexity ◽  
Learning Models ◽  
New Methods ◽  
Industrial Adoption ◽  
Machine Learning Models ◽  
The Way

Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating superhuman performance in a significant number of tasks. However, this surge in performance, has often been achieved through increased model complexity, turning such systems into “black box” approaches and causing uncertainty regarding the way they operate and, ultimately, the way that they come to decisions. This ambiguity has made it problematic for machine learning systems to be adopted in sensitive yet critical domains, where their value could be immense, such as healthcare. As a result, scientific interest in the field of Explainable Artificial Intelligence (XAI), a field that is concerned with the development of new methods that explain and interpret machine learning models, has been tremendously reignited over recent years. This study focuses on machine learning interpretability methods; more specifically, a literature review and taxonomy of these methods are presented, as well as links to their programming implementations, in the hope that this survey would serve as a reference point for both theorists and practitioners.

scholarly journals Software Testing: Issues and Challenges of Artificial Intelligence & Machine Learning

2021 ◽  
Vol 12 (1) ◽  
pp. 101-112
Author(s):  
Kishore Sugali ◽  
Chris Sprunger ◽  
Venkata N Inukollu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Software Testing ◽  
Future Research ◽  
Critical Component ◽  
Development Methodology ◽  
Testing Strategies ◽  
History Of ◽  
Shed Light ◽  
The Way

The history of Artificial Intelligence and Machine Learning dates back to 1950’s. In recent years, there has been an increase in popularity for applications that implement AI and ML technology. As with traditional development, software testing is a critical component of an efficient AI/ML application. However, the approach to development methodology used in AI/ML varies significantly from traditional development. Owing to these variations, numerous software testing challenges occur. This paper aims to recognize and to explain some of the biggest challenges that software testers face in dealing with AI/ML applications. For future research, this study has key implications. Each of the challenges outlined in this paper is ideal for further investigation and has great potential to shed light on the way to more productive software testing strategies and methodologies that can be applied to AI/ML applications.

scholarly journals Pawnder: An Online Platform for Canine Adoption

2021 ◽  
Vol 9 (VI) ◽  
pp. 5439-5445
Author(s):  
Dipankar Bhatia
Keyword(s):  
Machine Learning ◽  
High Mortality ◽  
Significant Proportion ◽  
Mortality Rates ◽  
Web Development ◽  
Online Tool ◽  
Online Platform ◽  
Need Of Care ◽  
Care And Support ◽  
Human Animal

This paper presents a new online tool, Pawnder, a dog adoption website which allows users to access and navigate through the database of dogs, in need of care and support, which constitutes a significant proportion of the canine's population in India with the subsequent aim of adoption, thus helping to reduce cases of human-animal interference along with their high mortality rates. Using the concepts of Machine learning and Web development using React.js, Pawnder is designed to run on any browser on any device creating easy accessibility for its users thus allowing a greater reach which consequently would help in providing all the resources needed for these innocent animals. The objective behind its development is to utilise the network base so created to eventually facilitate in their adoption and helping them find their forever homes.

scholarly journals Machine learning for nuclear cardiology: The way forward

2018 ◽  
Vol 26 (5) ◽  
pp. 1755-1758 ◽  
Author(s):  
Sirish Shrestha ◽  
Partho P. Sengupta
Keyword(s):  
Machine Learning ◽  
Nuclear Cardiology ◽  
The Way

The Role of Data Analytics and Machine Learning in Resurrecting Inductive-Based Accounting Research

2021 ◽  
Vol 9 (2) ◽  
pp. 1-19
Author(s):  
Lawrence A. Gordon
Keyword(s):  
Machine Learning ◽  
Empirical Evidence ◽  
Data Analytics ◽  
Accounting Research ◽  
Recent Developments ◽  
Important Trend ◽  
The Impact ◽  
The Way

The objective of this paper is to assess the impact of data analytics (DA) and machine learning (ML) on accounting research.[1] As discussed in the paper, the inherent inductive nature of DA and ML is creating an important trend in the way accounting research is being conducted. That trend is the increasing utilization of inductive-based research among accounting researchers. Indeed, as a result of the recent developments with DA and ML, a rebalancing is taking place between inductive-based and deductive-based research in accounting.[2] In essence, we are witnessing the resurrection of inductive-based accounting research. A brief review of some empirical evidence to support the above argument is also provided in the paper.   

A Novel Trust Model for Secure Group Communication in Distributed Computing

2021 ◽  
pp. 2231-2245
Author(s):  
Naresh Ramu ◽  
Vijayakumar Pandi ◽  
Jegatha Deborah Lazarus ◽  
Sivakumar Radhakrishnan
Keyword(s):  
Distributed Computing ◽  
Group Communication ◽  
Trust Model ◽  
Distributed Networks ◽  
Packet Delivery Ratio ◽  
Delivery Ratio ◽  
Secure Group Communication ◽  
Packet Delivery ◽  
The Past ◽  
Dynamic Group

Distributed networks are networks in which each node can act as a server or client and hence any node can provide service to any other node. In such a scenario, establishing a trust model between the service providing user and the service utilizing user is a challenging task. At present, only a few approaches are available in the past literature to provide this facility. Moreover, the existing approaches do not provide high trust accuracy. Therefore,a novel efficient trust model has been proposed in this article to support the secure dynamic group communication in distributed networks. The main advantage of the proposed work is that it provides higher trust accuracy. Moreover, the proposed work takes less memory for maintaining the trust values and increases the packet delivery ratio in comparison with other existing works which are in the literature.

scholarly journals Performance Evaluation in Machine Learning: The Good, the Bad, the Ugly, and the Way Forward

2019 ◽  
Vol 33 ◽  
pp. 9808-9814 ◽  
Author(s):  
Peter Flach
Keyword(s):  
Machine Learning ◽  
Performance Evaluation ◽  
Causal Inference ◽  
Latent Variable ◽  
Measurement Theory ◽  
Latent Variable Models ◽  
Data Set ◽  
Evaluation Measures ◽  
Classifier Evaluation ◽  
The Way

This paper gives an overview of some ways in which our understanding of performance evaluation measures for machine-learned classifiers has improved over the last twenty years. I also highlight a range of areas where this understanding is still lacking, leading to ill-advised practices in classifier evaluation. This suggests that in order to make further progress we need to develop a proper measurement theory of machine learning. I then demonstrate by example what such a measurement theory might look like and what kinds of new results it would entail. Finally, I argue that key properties such as classification ability and data set difficulty are unlikely to be directly observable, suggesting the need for latent-variable models and causal inference.

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