Data in the time of COVID-19: a general methodology to select and secure a NoSQL DBMS for medical data

ElDahshan, Kamal A.; AlHabshy, Abd Allah A.; Abutaleb, Gaber E.

doi:10.7717/peerj-cs.297

Cited by 12 publications

(20 citation statements)

References 18 publications

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“…The challenges associated with the storage and security of COVID-19 patients’ data were highlighted by ElDahshan, AlHabshy & Abutaleb (2020) . The authors pointed out that the variety, volume, and variability of COVID-19 patients data required storage in NoSQL database management systems (NoSQL DBMS).…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling a deep transfer learning framework for the classification of COVID-19 radiology dataset

Fayemiwo

Olowookere

Arekete

et al. 2021

PeerJ Computer Science

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Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Coronavirus-2 or SARS-CoV-2), which came into existence in 2019, is a viral pandemic that caused coronavirus disease 2019 (COVID-19) illnesses and death. Research showed that relentless efforts had been made to improve key performance indicators for detection, isolation, and early treatment. This paper used Deep Transfer Learning Model (DTL) for the classification of a real-life COVID-19 dataset of chest X-ray images in both binary (COVID-19 or Normal) and three-class (COVID-19, Viral-Pneumonia or Normal) classification scenarios. Four experiments were performed where fine-tuned VGG-16 and VGG-19 Convolutional Neural Networks (CNNs) with DTL were trained on both binary and three-class datasets that contain X-ray images. The system was trained with an X-ray image dataset for the detection of COVID-19. The fine-tuned VGG-16 and VGG-19 DTL were modelled by employing a batch size of 10 in 40 epochs, Adam optimizer for weight updates, and categorical cross-entropy loss function. The results showed that the fine-tuned VGG-16 and VGG-19 models produced an accuracy of 99.23% and 98.00%, respectively, in the binary task. In contrast, in the multiclass (three-class) task, the fine-tuned VGG-16 and VGG-19 DTL models produced an accuracy of 93.85% and 92.92%, respectively. Moreover, the fine-tuned VGG-16 and VGG-19 models have MCC of 0.98 and 0.96 respectively in the binary classification, and 0.91 and 0.89 for multiclass classification. These results showed strong positive correlations between the models’ predictions and the true labels. In the two classification tasks (binary and three-class), it was observed that the fine-tuned VGG-16 DTL model had stronger positive correlations in the MCC metric than the fine-tuned VGG-19 DTL model. The VGG-16 DTL model has a Kappa value of 0.98 as against 0.96 for the VGG-19 DTL model in the binary classification task, while in the three-class classification problem, the VGG-16 DTL model has a Kappa value of 0.91 as against 0.89 for the VGG-19 DTL model. This result is in agreement with the trend observed in the MCC metric. Hence, it was discovered that the VGG-16 based DTL model classified COVID-19 better than the VGG-19 based DTL model. Using the best performing fine-tuned VGG-16 DTL model, tests were carried out on 470 unlabeled image dataset, which was not used in the model training and validation processes. The test accuracy obtained for the model was 98%. The proposed models provided accurate diagnostics for both the binary and multiclass classifications, outperforming other existing models in the literature in terms of accuracy, as shown in this work.

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Section: Literature Reviewmentioning

confidence: 99%

Modeling a deep transfer learning framework for the classification of COVID-19 radiology dataset

Fayemiwo

Olowookere

Arekete

et al. 2021

PeerJ Computer Science

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“…Big data is usually characterized by volume, variety, veracity, value, volatility, and complexity. Big data huge; terabytes and petabytes, and its production is extremely fast from multiple resources which collect data from sensors, social media and/or the website's metadata [45]. Big data needs to be accurate and of high quality to create business value [46].…”

Section: Big Datamentioning

confidence: 99%

“…Big data complexity comes from the dynamic relationship among datasets, where a change in the dataset changes the other datasets. The increasing data availability has resulted in innovative advanced technologies used to analyze and provide business data value; such as, advanced analytics, machine learning, and artificial intelligence tools [45]. Since the scope of big data increases the available attack points, it is more difficult to detect the intrusions in big data environment [25].…”

Section: Big Datamentioning

confidence: 99%

“…SQL databases are developed for data integrity and concurrency control, representing the best option for banking and insurance systems. Data integrity consumes high processing power, reaching the limits of relational databases when it comes to handling data of large volumes [45].…”

Section: Relational Databasementioning

confidence: 99%

“…BASE ensures NoSQL database reliability despite the loss of consistency. Various applications use NoSQL databases such as embedded IR, parallel data processing through distributed systems, explorative analytics on unstructured and semi-structured data, largescale data storage, social networks, search engines, nuclear modeling, geospatial analysis, data warehouses, and caching [45].…”

Section: Nosql Databasesmentioning

confidence: 99%

See 2 more Smart Citations

Distributed Intrusion Detection Systems in Big Data: A Survey

Hameed

AlHabshy

ElDahshan

2021

Al-Azhar Bulletin of Science

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We live in a time where data stream by the second, which makes intrusion detection a more difficult and tiresome task, and in turn intrusion detection systems require an efficient and improved detection mechanism to detect the intrusive activities. Moreover, handling the size, complexity, and availability of big data requires techniques that can create beneficial knowledge from huge streams of the information, which imposes the challenges on the process of both designing and management of both Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) in terms of performance, sustainability, security, reliability, privacy, energy consumption, fault tolerance, scalability, and flexibility. IDSs and IPSs utilize various methodologies to guarantee security, accessibility and reliability of enterprise computer networks. This paper presents a comprehensive study of the Distributed Intrusion Detection Systems in Big Data, and presents intrusion detection and prevention techniques that utilize machine learning, big data analytics techniques in distributed systems of the intrusion detection.

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An Ameliorated Multiattack Network Anomaly Detection in Distributed Big Data System-Based Enhanced Stacking Multiple Binary Classifiers

2022

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Data in the time of COVID-19: a general methodology to select and secure a NoSQL DBMS for medical data

Cited by 12 publications

References 18 publications

Modeling a deep transfer learning framework for the classification of COVID-19 radiology dataset

Modeling a deep transfer learning framework for the classification of COVID-19 radiology dataset

Distributed Intrusion Detection Systems in Big Data: A Survey

An Ameliorated Multiattack Network Anomaly Detection in Distributed Big Data System-Based Enhanced Stacking Multiple Binary Classifiers

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