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Noor Zaman

Bio: Noor Zaman is an academic researcher from Taylors University. The author has contributed to research in topics: Routing protocol & Wireless sensor network. The author has an hindex of 22, co-authored 175 publications receiving 1601 citations. Previous affiliations of Noor Zaman include Pakistan Institute of Nuclear Science and Technology & King Faisal University.


Papers
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TL;DR: This article is comparing the performance of ML (Support Vector Machine, Random Forest), Random Forest, Stochastic Gradient Descent (SGD), & DL (Inception-v3, V GG-16, VGG-19) in terms of citrus plant disease detection as DL methods perform better than that of ML methods in case of disease detection.

223 citations

Journal ArticleDOI
TL;DR: This research review will analyze smart home approaches, challenges and will suggest possible solutions for them and illustrate open issues that still need to be addressed.
Abstract: The smart home is considered as an essential domain in Internet of Things (IoT) applications, it is an interconnected home where all types of things interact with each other via the Internet. This helps to automate the home by making it smart and interconnected. However, at the same time, it raises a great concern of the privacy and security for the users due to its capability to be controlled remotely. Hence, the rapid technologically growth of IoT raises abundant challenges such as how to provide the home users with safe and secure services keeping privacy in the account and how to manage the smart home successfully under the controlled condition to avoid any further secrecy or theft of personal data. A number of the research papers are available to address these critical issues, researchers presented different approaches to overcome these stated issues. This research review will analyze smart home approaches, challenges and will suggest possible solutions for them and illustrate open issues that still need to be addressed.

165 citations

Journal ArticleDOI
25 Jun 2020
TL;DR: A layered framework, namely BCTLF, for smart logistics and transportation that integrates IoT and Blockchain to provide an intelligent logistics and Transportation system is proposed.
Abstract: Transportation and logistics management play a vital role in the development of a country With the advancement of the Internet of Things (IoT) devices, smart transportation is becoming a reality However, these abundant connected IoT devices are vulnerable to security attacks Recently, Blockchain has emerged as one of the most widely accepted technologies for trusted, secure and decentralized intelligent transportation systems This research study aims to contribute to the field of logistics and transportation by exploring the potential of IoT and Blockchain technology in smart logistics and transportation We propose a layered framework, namely BCTLF, for smart logistics and transportation that integrates IoT and Blockchain to provide an intelligent logistics and transportation system Finally, we present two real-life IoT and Blockchain-based case studies to highlight the contribution of IoT and Blockchain in logistics and transportation

130 citations

Journal ArticleDOI
TL;DR: This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep Convolution Neural Networks (CNN).
Abstract: The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of multimedia healthcare data is being explored to find a solution. This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep Convolution Neural Networks (CNN). In this study, three pre-trained CNN models (EfficientNetB0, VGG16, and InceptionV3) are evaluated through transfer learning. The rationale for selecting these specific models is their balance of accuracy and efficiency with fewer parameters suitable for mobile applications. The dataset used for the study is publicly available and compiled from different sources. This study uses deep learning techniques and performance metrics (accuracy, recall, specificity, precision, and F1 scores). The results show that the proposed approach produced a high-quality model, with an overall accuracy of 92.93%, COVID-19, a sensitivity of 94.79%. The work indicates a definite possibility to implement computer vision design to enable effective detection and screening measures.

125 citations

Journal ArticleDOI
TL;DR: A systematic mapping study was conducted, and in total, 78 primary studies were identified and analyzed, showing that most of the selected studies in this review targeted only a few common security vulnerabilities such as phishing, denial-of-service and malware.
Abstract: There has been a tremendous increase in research in the area of cyber security to support cyber applications and to avoid key security threats faced by these applications. The goal of this study is to identify and analyze the common cyber security vulnerabilities. To achieve this goal, a systematic mapping study was conducted, and in total, 78 primary studies were identified and analyzed. After a detailed analysis of the selected studies, we identified the important security vulnerabilities and their frequency of occurrence. Data were also synthesized and analyzed to present the venue of publication, country of publication, key targeted infrastructures and applications. The results show that the security approaches mentioned so far only target security in general, and the solutions provided in these studies need more empirical validation and real implementation. In addition, our results show that most of the selected studies in this review targeted only a few common security vulnerabilities such as phishing, denial-of-service and malware. However, there is a need, in future research, to identify the key cyber security vulnerabilities, targeted/victimized applications, mitigation techniques and infrastructures, so that researchers and practitioners could get a better insight into it.

123 citations


Cited by
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08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

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01 Jan 2016
TL;DR: This application applied longitudinal data analysis modeling change and event occurrence will help people to enjoy a good book with a cup of coffee in the afternoon instead of facing with some infectious virus inside their computer.
Abstract: Thank you very much for downloading applied longitudinal data analysis modeling change and event occurrence. As you may know, people have look hundreds times for their favorite novels like this applied longitudinal data analysis modeling change and event occurrence, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they are facing with some infectious virus inside their computer.

2,102 citations