Abstract
During the recent years World Wide Web very fast increased a fundamental part in our everyday life. In commerce, personal relationship, the effect of the universal network has wholly changed the way people interact with each other and with machines. The problem is after rising the Artificial Intelligence to presenting human feelings, everything changed including web applications. In this paper, we describe the intelligent web applications as present and future of web applications, moreover we highlight the special features and their roles in increasing intelligence of web applications as well as impact this application in the process development web systems. The goals of this paper led to developers to create smart and modern web applications.
Highlights
Evolution of Web 1.0 into the Web 4.0 [1] and sometime new web is Web 5.0 [2] of the World Wide Web (WWW), has resulted in the introduction of several web applications [3]
We describe the intelligent web applications as present and future web applications, we highlight the special features and their roles in increasing intelligence of web applications
Rimmer V. et al (2018) discovered that an adversary could mechanize the process of feature engineering, and automatically deanonymize tor traffic by applying our novel method based on deep learning. They assemble a dataset consisting of traces of network that exceed three million, which is the most prevalent dataset of web traffic ever applied for fingerprinting of website, and discover that the performance accomplished by their deep learning methods is similar to famous techniques, including a variety of research struggles that span over many years
Summary
Evolution of Web 1.0 into the Web 4.0 [1] and sometime new web is Web 5.0 [2] of the World Wide Web (WWW), has resulted in the introduction of several web applications [3]. Rimmer V. et al (2018) discovered that an adversary could mechanize the process of feature engineering, and automatically deanonymize tor traffic by applying our novel method based on deep learning They assemble a dataset consisting of traces of network that exceed three million, which is the most prevalent dataset of web traffic ever applied for fingerprinting of website, and discover that the performance accomplished by their deep learning methods is similar to famous techniques, including a variety of research struggles that span over many years. The authors in another work applied intelligent web They introduced their contribution for performing adaptive and intelligent WBES (Web-based Education Systems) that consider the individual student learning necessities, through a complete architecture and structure for developing WBES. A stated SOA (Service Oriented Architecture) focused on deploying services that are durable, reusable, interoperable and accessible [19]
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