The Future of Internet: IPv6 Fulfilling the Routing Needs in Internet of Things

Samad, Fahad; Abbasi, Asad; Memon, Zulfiqar Ali; Aziz, Abdul; Rahman, Abdul

doi:10.14257/ijfgcn.2018.11.1.02

Cited by 15 publications

(4 citation statements)

References 12 publications

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“…Two approaches were tried; NMF and LDA topic modelling which were implemented by means of a code. As topic modelling is an unsupervised technique, determination of the number of topics is the trickiest and the hardest part of topic modelling (Samad, Asad, Memon, Aziz, & Rahman, 2018). Therefore, the evaluation of topic modelling is done by using two most popular techniques: 1) Perplexity 2) Topic coherence Perplexity is the weak indicator of the quality of the topics as it cannot capture the semantic closeness between the words even the results are sometimes slightly anti-correlated.…”

Section: Resultsmentioning

confidence: 99%

App store mining for feature extraction: analyzing user reviews

Memon,

Munawar,

Kamal

2023

Acta Sci Technol

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A recent study shows that the most commonly used app stores, iOS app store and Android's play store had up to 2 million apps from where users can explore, purchase, share, download and install applications on a single click. More and more apps are being added daily which makes the app stores a large software repository. An enormous amount of data is provided by the end users in the form of user reviews. This data can provide valuable insights for acquiring requirements. User reviews include plenty of information as they contain information about faulty features (Bug reports), ideas for new features and improvements (feature requests), or user experience that can help app developers and vendors to achieve software enhancement and evolution tasks. As feature requests are the ones that are most helpful for the purpose of eliciting new requirements, the work is done on feature requests out of the 3 categories mentioned above. This study is conducted to provide a general approach that extracts feature request from user reviews. The proposed approach has five main building blocks, namely, (i) Extraction of Feature Requests, (ii) Feature Extraction from Feature Requests, (iii) topic modelling, (iv) sentiment analysis and (v) Classification into Functional Requirements (FR) and Non-Functional Requirements (NFR). Firstly, it finds the feature requests out of the reviews, then perform extraction of features from feature requests, then further work on grouping the features into topics, next apply sentiment analysis to mine the user opinions on the extracted topic and finally group them into Functional and Non-Functional requirements. This article provides the app developers a more user-centred definition of requirements and improvements. At the topic modelling phase, the results received the highest coherence score, 0.70, with k=22 topics. Sentiment analysis is used to classify feature request, with an accuracy of 80.20%, precision of 84.25%, and recall of 80.42%. With an accuracy of 84.4%, requirements are classified quite successfully as well.

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Section: Resultsmentioning

confidence: 99%

App store mining for feature extraction: analyzing user reviews

Memon,

Munawar,

Kamal

2023

Acta Sci Technol

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“…The example IoT node naming for identification such as electronic product codes (EPC) [119], Radio Frequency Identification (RFID) [120]- [123], Finger Print [124], ubiquitous code (uCode) [125], [126]. The example IoT node addressing for identification such as IPv4 [127], [128], and IPv6 [129]- [132].…”

Section: A Identificationmentioning

confidence: 99%

Survey Paper Artificial and Computational Intelligence in the Internet of Things and Wireless Sensor Network

Hakim

Septiyana

Suwarno

2022

Journal of Robotics and Control

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In this modern age, Internet of Things (IoT) and Wireless Sensor Network (WSN) as its derivatives have become one of the most popular and important technological advancements. In IoT, all things and services in the real world are digitalized and it continues to grow exponentially every year. This growth in number of IoT device in the end has created a tremendous amount of data and new data services such as big data systems. These new technologies can be managed to produce additional value to the existing business model. It also can provide a forecasting service and is capable to produce decision-making support using computational intelligence methods. In this survey paper, we provide detailed research activities concerning Computational Intelligence methods application in IoT WSN. To build a good understanding, in this paper we also present various challenges and issues for Computational Intelligence in IoT WSN. In the last presentation, we discuss the future direction of Computational Intelligence applications in IoT WSN such as Self-Organizing Network (dynamic network) concept.

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“…Figure 3 is taken from source [32] which is highlighted the monthly active users of major social sites and online tools. The study in [33] investigated the role of SNSs in e-learning. The findings are concluded as the SNSs should be utilized with e-learning for improving their information and collaboration activities.…”

Section: Effective Utilization Of Snss In Vlesmentioning

confidence: 99%

An Integrated Model to Enhance Virtual Learning Environments with Current Social Networking Perspective

Laeeq

Memon

2018

Int. J. Emerg. Technol. Learn.

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A Virtual Learning Environment (VLE) is an accumulation of incorporated soft-ware components, empowering the administration of web-based learning (online learning, e-learning). The quality of VLE is indispensable for the accomplishment of e-learning goals and responsible to engage students during web based learning. The attributes of existing VLES does not completely meet with the requirements of today’s e-learning. Consequently, decreases students’ interest, participation and engagement with web-based e-learning. In contrast, the learners’ engagement and participation are higher in social networking sites (SNS). For this reason, many researchers believe that the inclusion of features, similar to SNSs, in VLE may in-crease its effectivity. The goal of this research article is to investigate existing e-learning platform and make a room for SNSs and online tools for e-learning. The paper proposes an integrated model to enhance existing e-learning environment by incorporating the strength of SNSs and other potential tools with VLEs. This re-search article will provide a good direction and new thoughts for the researchers of technology supported learning-domain.

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The Future of Internet: IPv6 Fulfilling the Routing Needs in Internet of Things

Abstract: Abstract

Cited by 15 publications

References 12 publications

App store mining for feature extraction: analyzing user reviews

App store mining for feature extraction: analyzing user reviews

Survey Paper Artificial and Computational Intelligence in the Internet of Things and Wireless Sensor Network

An Integrated Model to Enhance Virtual Learning Environments with Current Social Networking Perspective

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