Author name: Nasim Gazerani

Nasim Gazerani was born in 1983 in Arak. She holds a Master's degree in Software Engineering from UM University of Malaysia.

A Conceptual System Architecture for Cloud-Based E-learning Systems for Higher Education in India

A Conceptual System Architecture for Cloud-Based E-learning Systems for Higher Education in India

Abstract E-learning is the new means which has the potential to support the traditional education system. E-learning is gaining popularity and is considered as a viable and efficient means of […]

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computer-aided software engineering

The Definition of Intelligent Computer Aided Software Engineering (I-CASE) Tools

Abstract The growing complexity of the software systems being developed and the use of different methodologies indicate the need for more computer support for automating software development process and evolution

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CONSTRUCTION OF SPECIALIZED COMPUTER AIDED SOFTWARE ENGINEERING (CASE) TOOLS FOR THE DEVELOPMENT OF EXPERT SYSTEMS

CONSTRUCTION OF SPECIALIZED COMPUTER AIDED SOFTWARE ENGINEERING (CASE) TOOLS FOR THE DEVELOPMENT OF EXPERT SYSTEMS

ABSTRACT This report presents an approach to building specialized computer-aided software engineering (CASE) tools for the development of expert systems. These tools form an integrated development environment allowing the computer-aided

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Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach

Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach

Abstract Internet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each

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Mitigating the Risk of Software Vulnerabilities by Adopting a Secure Software Development Framework (SSDF)

Mitigating the Risk of Software Vulnerabilities by Adopting a Secure Software Development Framework (SSDF)

Abstract Few software development life cycle (SDLC) models explicitly address software security in detail, so secure software development practices usually need to be added to each SDLC model to ensure

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Traffic Prediction for Intelligent Transportation System using Machine Learning

Traffic Prediction for Intelligent Transportation System using Machine Learning

Abstract This paper aims to develop a tool for predicting accurate and timely traffic flow Information. Traffic Environment involves everything that can affect the traffic flowing on the road, whether

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Supervised Machine Learning Algorithms for Credit Card Fraud Detection: A Comparison

Supervised Machine Learning Algorithms for Credit Card Fraud Detection: A Comparison

Abstract In today’s economic scenario, credit card use has become extremely commonplace. These cards allow the user to make payments of large sums of money without the need to carry

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INTEGRATING EDUCATION, TECHNOLOGY, AND SDG’S: A THREE-PRONGED COLLABORATION

INTEGRATING EDUCATION, TECHNOLOGY, AND SDG’S: A THREE-PRONGED COLLABORATION

ABSTRACT Social and technological evolutions are forcing changes in education worldwide. An important guide for such changes is the sustainable development goals (SDGs) adopted by the  United Nations. SDG 17

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An Energy Efficient and Secure IoT-Based WSN Framework: An Application to Smart Agriculture

An Energy Efficient and Secure IoT-Based WSN Framework: An Application to Smart Agriculture

Abstract Wireless sensor networks (WSNs) have demonstrated research and developmental interests in numerous fields, like communication, agriculture, industry, smart health, monitoring, and surveillance. In the area of agriculture production, IoT-based

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Temporarily-Aware Context Modeling Using Generative Adversarial Networks for Speech Activity Detection

Temporarily-Aware Context Modeling Using Generative Adversarial Networks for Speech Activity Detection

Abstract This paper presents a novel framework for Speech Activity Detection (SAD). Inspired by the recent success of multi-task learning approaches in the speech processing domain, we propose a novel

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