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Jawdat Alshaer

Researcher at Al-Balqa` Applied University

Publications -  18
Citations -  175

Jawdat Alshaer is an academic researcher from Al-Balqa` Applied University. The author has contributed to research in topics: The Internet & Computer security model. The author has an hindex of 7, co-authored 18 publications receiving 128 citations. Previous affiliations of Jawdat Alshaer include International University, Cambodia & Novosibirsk State Technical University.

Papers
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Book ChapterDOI

Challenges of Internet of Things and Big Data Integration

TL;DR: It is envisioned that the Internet of Things is the technology of the future; while to-day’s real-world devices can provide real and valuable analytics, and people in the real world use many IoT devices.
Journal ArticleDOI

Development of Temperature-based Weather Forecasting Models Using Neural Networks and Fuzzy Logic

TL;DR: This study proposes computer-based models for weather forecasting based on temperature to predict the daily temperature using two techniques, artificial neural networks and fuzzy logic, and develops different weather forecasting models based on the two techniques over different regions.
Posted Content

Internet of Things: Infrastructure, Architecture, Security and Privacy

TL;DR: A security model to be utilised by the researchers, manufacturers and developers of IoT devices, applications and services is put forward resulting in an enhanced overall security of the connected devices as well as the data generated.
Proceedings ArticleDOI

Internet of Things: Infrastructure, Architecture, Security and Privacy

TL;DR: In this article, the authors used IoT five layered architecture of the Internet of Things to address the security and private issues of IoT enabled services and applications, and presented a detailed survey on IoT infrastructure, architecture, security, and privacy of the heterogeneous objects.
Journal ArticleDOI

Monarch butterfly optimization algorithm for computed tomography image segmentation

TL;DR: In this article, the authors applied the monarch butterfly optimization (MBO) algorithm for image segmentation at multiple threshold values and evaluated the performance of the implemented MBO algorithm, a comparison is made with the brute-force (i.e., Otsu) algorithm and two meta-heuristic algorithms (e.g., Darwinian particle swarm optimization (DPSO) and fractional-order DPSO).