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Mustafa Musa Jaber

Bio: Mustafa Musa Jaber is an academic researcher from Dijlah University College. The author has contributed to research in topics: Computer science & Artificial intelligence. The author has an hindex of 12, co-authored 25 publications receiving 573 citations. Previous affiliations of Mustafa Musa Jaber include Universiti Tun Hussein Onn Malaysia & Universiti Teknikal Malaysia Melaka.

Papers published on a yearly basis

Papers
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Journal ArticleDOI
TL;DR: Learning based Deep-Q-Networks has been introduced for reducing the malware attacks while managing the health information and helps to minimize the intermediate attacks with less complexity.
Abstract: In the recent past, Internet of Things (IoT) plays a significant role in different applications such as health care, industrial sector, defense and research etc… It provides effective framework in maintaining the security, privacy and reliability of the information in internet environment Among various applications as mentioned health care place a major role, because security, privacy and reliability of the medical information is maintained in an effective way Even though, IoT provides the effective protocols for maintaining the information, several intermediate attacks and intruders trying to access the health information which in turn reduce the privacy, security and reliability of the entire health care system in internet environment As a result and to solve the issues, in this research Learning based Deep-Q-Networks has been introduced for reducing the malware attacks while managing the health information This method examines the medical information in different layers according to the Q-learning concept which helps to minimize the intermediate attacks with less complexity The efficiency of the system has been evaluated with the help of experimental results and discussions

166 citations

Journal ArticleDOI
TL;DR: The effective and optimized neural computing and soft computing techniques to minimize the difficulties and issues in the feature set of lung cancer features are introduced.
Abstract: Today, most of the people are affected by lung cancer, mainly because of the genetic changes of the tissues in the lungs. Other factors such as smoking, alcohol, and exposure to dangerous gases can also be considered the contributory causes of lung cancer. Due to the serious consequences of lung cancer, the medical associations have been striving to diagnose cancer in its early stage of growth by applying the computer-aided diagnosis process. Although the CAD system at healthcare centers is able to diagnose lung cancer during its early stage of growth, the accuracy of cancer detection is difficult to achieve, mainly because of the overfitting of lung cancer features and the dimensionality of the feature set. Thus, this paper introduces the effective and optimized neural computing and soft computing techniques to minimize the difficulties and issues in the feature set. Initially, lung biomedical data were collected from the ELVIRA Biomedical Data Set Repository. The noise present in the data was eliminated by applying the bin smoothing normalization process. The minimum repetition and Wolf heuristic features were subsequently selected to minimize the dimensionality and complexity of the features. The selected lung features were analyzed using discrete AdaBoost optimized ensemble learning generalized neural networks, which successfully analyzed the biomedical lung data and classified the normal and abnormal features with great effectiveness. The efficiency of the system was then evaluated using MATLAB experimental setup in terms of error rate, precision, recall, G-mean, F-measure, and prediction rate.

96 citations

Journal ArticleDOI
24 Sep 2018
TL;DR: The predicted result is used to diagnose which age group and gender are mostly affected by diabetes, and the efficiency of two different clustering techniques suitable for the environment are compared.
Abstract: Diabetes mellitus is a serious health problem affecting the entire population all over the world for many decades. It is a group of metabolic disorder characterized by chronic disease which occurs due to high blood sugar, unhealthy foods, lack of physical activity and also hereditary. The sorts of diabetes mellitus are type1, type2 and gestational diabetes. The type1 appears during childhood and type2 diabetes develop at any age, mostly affects older than 40. The gestational diabetes occurs for pregnant women. According to the statistical report of WHO 79% of deaths occurred in people under the age of 60, due to diabetes. With a specific end goal to deal with the vast volume, speed, assortment, veracity and estimation of information a scalable environment is needed. Cloud computing is an interesting computing model suitable for accommodating huge volume of dynamic data. To overcome the data handling problems this work focused on Hadoop framework along with clustering technique. This work also predicts the occurrence of diabetes under various circumstances which is more useful for the human. This paper also compares the efficiency of two different clustering techniques suitable for the environment. The predicted result is used to diagnose which age group and gender are mostly affected by diabetes. Further some of the attributes such as hyper tension and work nature are also taken into consideration for analysis.

91 citations

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TL;DR: The basis of the proposed analysis method is the connection between heart rate variability and oxygen saturation with d apnea events, which was transferred to a cloud-based system architecture to diagnose and warn the remote patients.

65 citations


Cited by
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01 Jan 2002

9,314 citations

Journal ArticleDOI
TL;DR: This paper systematically review the security requirements, attack vectors, and the current security solutions for the IoT networks, and sheds light on the gaps in these security solutions that call for ML and DL approaches.
Abstract: The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. The participating nodes in IoT networks are usually resource-constrained, which makes them luring targets for cyber attacks. In this regard, extensive efforts have been made to address the security and privacy issues in IoT networks primarily through traditional cryptographic approaches. However, the unique characteristics of IoT nodes render the existing solutions insufficient to encompass the entire security spectrum of the IoT networks. Machine Learning (ML) and Deep Learning (DL) techniques, which are able to provide embedded intelligence in the IoT devices and networks, can be leveraged to cope with different security problems. In this paper, we systematically review the security requirements, attack vectors, and the current security solutions for the IoT networks. We then shed light on the gaps in these security solutions that call for ML and DL approaches. Finally, we discuss in detail the existing ML and DL solutions for addressing different security problems in IoT networks. We also discuss several future research directions for ML- and DL-based IoT security.

407 citations

Journal ArticleDOI
TL;DR: A novel ensemble convolutional neural networks (CNNs) based architecture for effective detection of both packed and unpacked malware, named Image-based Malware Classification using Ensemble of CNNs (IMCEC).

221 citations

Journal ArticleDOI
TL;DR: An overview of feature selection techniques and instability of the feature selection algorithm is provided and some of the solutions which can handle the different source of instability are presented.

184 citations