Papers by Alan Fuad Jahwar
Journal of Applied Science and Technology Trends, 2021
Abstract: Microarray data plays a major role in diagnosing and treating cancer. In several microa... more Abstract: Microarray data plays a major role in diagnosing and treating cancer. In several microarray data sets, many gene fragments are not associated with the target diseases. A solution to the gene selection problem might become important when analyzing large gene datasets. The key task is to better represent genes through optimum accuracy in classifying the samples. Different gene classification algorithms have been provided in past studies; after all, they suffered due to the selection of several genes mostly in high-dimensional microarray data. This paper aims to review classification and feature selection with different microarray datasets focused on swarm intelligence algorithms. We explain microarray data and its types in this paper briefly. Moreover, our paper presents an introduction to most common swarm intelligence algorithms. A review on swarm intelligence algorithms in gene selection profile based on classification of Microarray Data is presented in this paper.
Journal of Soft Computing and Data Mining, 2021
Abstract: Machin learning (ML) and Deep Learning (DL) technique have been widely applied to areas... more Abstract: Machin learning (ML) and Deep Learning (DL) technique have been widely applied to areas like image
processing and speech recognition so far. Likewise, ML and DL play a critical role in detecting and preventing in
the field of cybersecurity. In this review, we focus on recent ML and DL algorithms that have been proposed in
cybersecurity, network intrusion detection, malware detection. We also discuss key elements of cybersecurity, the
main principle of information security, and the most common methods used to threaten cybersecurity. Finally,
concluding remarks are discussed, including the possible research topics that can be taken into consideration to
enhance various cyber security applications using DL and ML algorithms.
PalArch's Journal of Archaeology of Egypt / Egyptology, 2021
Abstract: The increase in the data available attracted the concern of clustering approaches to in... more Abstract: The increase in the data available attracted the concern of clustering approaches to integrate them coherently and to identify patterns for big data. Hence, Meta-Heuristic algorithms can be better than standard optimization algorithms in some instances. Previously, optimization issues have been considered as significant weaknesses in the K-means algorithm is one of the simplest methods for clustering. and with less additional information it can easily solve the optimization problem. In this paper, a review of clustering k-means algorithm and meta-heuristics algorithms are reviewed.
2022 IEEE 18th International Colloquium on Signal Processing & Applications (CSPA), 2022
Abstract: Deep Learning (DL) has rapidly become a methodology of choice for analyzing medical ima... more Abstract: Deep Learning (DL) has rapidly become a methodology of choice for analyzing medical images and increasingly attracts researchers’ attention in the medical research community. Breast cancer is a common disease among women throughout the world. The medical images and especially Breast Ultrasound (BUS) images are of poor quality, low contrast, and ambiguous. To avoid misdiagnosis, a Computer-Aided Diagnosis (CAD) system has been created for the diagnosis of breast cancer. This study discusses a variety of ultrasonic image segmentation approaches, with an emphasis on several methods developed in the recent four years. As a result, breast ultrasound image segmentation remains a difficult and demanding problem because of several ultrasound aberrations, including strong speckle noise, preprocessing, classification, feature extraction, and segmentation technique to find the accuracy. Lastly, this study outlines the current trends and issues in breast ultrasound images diagnosis, segmentation, and classifications. This review may be useful for both clinicians and researchers who utilize CAD systems for early breast cancer detection.
inproceedings by Alan Fuad Jahwar
Asian Journal of Research in Computer Science, 2021
Abstract: The Internet of Things (IoT) is a paradigm shift that enables billions of devices to co... more Abstract: The Internet of Things (IoT) is a paradigm shift that enables billions of devices to connect to the
Internet. The IoT's diverse application domains, including smart cities, smart homes, and e-health,
have created new challenges, chief among them security threats. To accommodate the current
networking model, traditional security measures such as firewalls and Intrusion Detection Systems
(IDS) must be modified. Additionally, the Internet of Things and Cloud Computing complement one
another, frequently used interchangeably when discussing technical services and collaborating to
provide a more comprehensive IoT service. In this review, we focus on recent Machine Learning
(ML) and Deep Learning (DL) algorithms proposed in IoT security, which can be used to address
various security issues. This paper systematically reviews the architecture of IoT applications, the
security aspect of IoT, service models of cloud computing, and cloud deployment models. Finally,
we discuss the latest ML and DL strategies for solving various security issues in IoT networks.
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Papers by Alan Fuad Jahwar
processing and speech recognition so far. Likewise, ML and DL play a critical role in detecting and preventing in
the field of cybersecurity. In this review, we focus on recent ML and DL algorithms that have been proposed in
cybersecurity, network intrusion detection, malware detection. We also discuss key elements of cybersecurity, the
main principle of information security, and the most common methods used to threaten cybersecurity. Finally,
concluding remarks are discussed, including the possible research topics that can be taken into consideration to
enhance various cyber security applications using DL and ML algorithms.
inproceedings by Alan Fuad Jahwar
Internet. The IoT's diverse application domains, including smart cities, smart homes, and e-health,
have created new challenges, chief among them security threats. To accommodate the current
networking model, traditional security measures such as firewalls and Intrusion Detection Systems
(IDS) must be modified. Additionally, the Internet of Things and Cloud Computing complement one
another, frequently used interchangeably when discussing technical services and collaborating to
provide a more comprehensive IoT service. In this review, we focus on recent Machine Learning
(ML) and Deep Learning (DL) algorithms proposed in IoT security, which can be used to address
various security issues. This paper systematically reviews the architecture of IoT applications, the
security aspect of IoT, service models of cloud computing, and cloud deployment models. Finally,
we discuss the latest ML and DL strategies for solving various security issues in IoT networks.
processing and speech recognition so far. Likewise, ML and DL play a critical role in detecting and preventing in
the field of cybersecurity. In this review, we focus on recent ML and DL algorithms that have been proposed in
cybersecurity, network intrusion detection, malware detection. We also discuss key elements of cybersecurity, the
main principle of information security, and the most common methods used to threaten cybersecurity. Finally,
concluding remarks are discussed, including the possible research topics that can be taken into consideration to
enhance various cyber security applications using DL and ML algorithms.
Internet. The IoT's diverse application domains, including smart cities, smart homes, and e-health,
have created new challenges, chief among them security threats. To accommodate the current
networking model, traditional security measures such as firewalls and Intrusion Detection Systems
(IDS) must be modified. Additionally, the Internet of Things and Cloud Computing complement one
another, frequently used interchangeably when discussing technical services and collaborating to
provide a more comprehensive IoT service. In this review, we focus on recent Machine Learning
(ML) and Deep Learning (DL) algorithms proposed in IoT security, which can be used to address
various security issues. This paper systematically reviews the architecture of IoT applications, the
security aspect of IoT, service models of cloud computing, and cloud deployment models. Finally,
we discuss the latest ML and DL strategies for solving various security issues in IoT networks.