Fusion: Practice and Applications

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2692-4048ISSN (Online) 2770-0070ISSN (Print)

Mobile Cloud Database Security: Problems and Solutions

Mahmoud Ismail , Naif El-Rashidy , Nabil M. Abdel-aziz

The rise in mobile Internet usage and increased reliance on cloud computing have led to increased fear of cloud database security. Mobile cloud computing has emerged as the only promising way of providing solutions for the mobile computing environment, including computation offloading and data binding. This paper discusses the overview of mobile cloud computing features and its prone computing security issues and how to walk over them with the most promising solutions. More specifically, it explores in detail a wide range of threats that may attack the mobile cloud-computing platform and the various devices and applications that work extremely well in supporting and mitigating the wide range of problems related to security issues in mobile applications. Moreover, this paper studies some of the ways to make mobile cloud computing more secure and productive no matter the intensity of the required computation. This study takes into consideration, the most common threats that affect the security issues of the mobile cloud database and its solutions. It is deemed necessary to note that, the duty of various cloud service providers is to keep all mobile cloud data safe. Consequently, they must come up with solutions to the problems affecting the day-to-day mobile-cloud database security.

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Doi: https://doi.org/10.54216/FPA.070102

Vol. 7 Issue. 1 PP. 15-29, (2022)

A Review of the Common DDoS Attack: Types and Protection Approaches Based on Artificial Intelligence

N. A. Majeed alhammadi , K. Hameed Zaboon , A. Abdulhadi Abdullah

Recently, technology become an important part of our life, and it is employed to work together with Medicine, Space Science, Agriculture, industry, and more else. Stored the information in the servers and cloud become required. It is a global force that has transformed people's lives with the availability of various web applications that serve billions of websites every day. However, there are many types of attacks that could be targeting the internet, and there is a need to recognize, classify and protect thesis types of attack.  Due to its important global role, it has become important to ensure that web applications are secure, accurate, and of high quality. One of the basic problems found on the Web is DDoS attacks. In this work, the review classifies and delineates attack types, test characteristics, evaluation techniques; evaluation methods, and test data sets used in the proposed Strategic Strategy methodology. Finally, this work affords guidance and possible targets in the fight against creating better events to overcome the most dangerous Cyber-attack types which are DDoS attacks.

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Doi: https://doi.org/10.54216/FPA.070101

Vol. 7 Issue. 1 PP. 08-14, (2022)

Earthworm Optimization with Deep Transfer Learning Enabled Aerial Image Classification Model in IoT Enabled UAV Networks


Unmanned aerial vehicles (UAVs) can be placed effectively in offering high-quality services for Internet of Things (IoT) networks. It finds use in several applications such as smart city, smart healthcare, surveillance, environment monitoring, disaster management, etc. Classification of images captured by UAV networks, i.e., aerial image classification is a challenging task and can be solved by the design of artificial intelligence (AI) techniques. Therefore, this article presents an Earthworm Optimization with Deep Transfer Learning Enabled Aerial Image Classification (EWODTL-AIC) model in IoT enabled UAV networks. The major intention of the EWODTL-AIC technique is to effectually categorize different classes of aerial images captured by UAVs. The EWODTL-AIC technique initially employs AlexNet model as feature extractor for producing optimal feature vectors. Followed by, the hyperparameter values of the AlexNet model are decided by the utilization of earthworm optimization (EWO) algorithm. At last, the extreme gradient boosting (XGBoost) model is employed for the classification of aerial images. The experimental validation of the EWODTL-AIC model is performed using benchmark dataset. The extensive comparative analysis reported the better outcomes of the EWODTL-AIC technique over the other existing techniques. 

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Doi: https://doi.org/10.54216/FPA.070104

Vol. 7 Issue. 1 PP. 41-52, (2022)

Support System Based Computer-Aided Detection for Skin Cancer: A Review

Nechirvan Asaad Zebari , Mehmet Emin Tenekeci

According to the American Society of Clinical Oncology, Computer-Aided Diagnosis (CAD) techniques have the tremendous possibility for the screening and early identification of melanoma. They are evaluated in terms of their current state-of-the-art, as well as current practices, challenges, and prospects in the areas of image screening, pre-processing of an image, segmentation of Region of Interest (ROI), feature extraction, feature selection, and classification of dermoscopic images. It is stated in this study that statistical information and outcomes from the most major implementations that have been reported to date are presented. We investigated the evaluation performance of many classifiers that had been developed specifically for the diagnosis of skin cancer. The fundamental aim of this paper is to develop a framework that will serve as a complete guideline for choosing relevant techniques for various elements of an automatic detection technique.

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Doi: https://doi.org/10.54216/FPA.070103

Vol. 7 Issue. 1 PP. 30-40, (2022)

Text Classification Using Convolutional Neural Networks

Sara Muslih Mishal , Murtadha M. Hamad

Most of the information (more than 80%) is stored as text, and text mining is a very important process as it is an initial step in the process of text classification, and this is especially the case in the Arabic language. The Aim of The Study is to classify Arabic texts according to specific categories using advanced performance indicators We used Data Templates as a platform for managing and organizing Apache Spark to solve big data challenges. Apache Spark offers several integrated language APIs. nlp lib was used for text processing. The data is pre-processed through several steps, namely separating the words into one text on the basis of the space between words, cleaning the text of unwanted words, restoring the words to their roots, as well as the feature selection process is a critical step. in text classification. It is a preprocessing technology. In this paper, one way to determine which TF attributes are used how often each feature appears in the document is that they consider the first level of the feature selection process. Then we use TF-IDF to determine the significance of the feature in the document, and this is the last step in the preprocessing Outcomes Text classification . Results were evaluated using advanced performance indicators such as accuracy, Precision and recall. A high accuracy of 96.94% was achieved.The main objective of this paper is to classify basic texts quickly and accurately, according to the results as long as the feature size is suitable, the most advanced technology is superior to other pass rate methods due to the reasonable reliability and perfect pruning level.

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Doi: https://doi.org/10.54216/FPA.070105

Vol. 7 Issue. 1 PP. 53- 65, (2022)