Evaluating the Role of Big Data in IIOT-Industrial Internet of Things for Executing Ranks Using the Analytic Network Process Approach

Liao, Xiaoqun; Faisal, Mohammad; QingChang, Qing; Ali, Amjad

doi:10.1155/2020/8859454

Cited by 14 publications

(21 citation statements)

References 24 publications

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“…Efficiency is improved due to the execution of each stage of the framework precisely and mostly in the resizing of images to save processing time. The proposed method provides the doctor/cardiologist and patient with a simple and reliable, time-saving diagnostic approach, as well as a health-related approach [ 19 – 21 ].…”

Section: Discussionmentioning

confidence: 99%

Segmentation and Classification of Heart Angiographic Images Using Machine Learning Techniques

Abdullah

Ṣiddiqi

Alhwaiti

et al. 2021

Journal of Healthcare Engineering

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Heart angiography is a test in which the concerned medical specialist identifies the abnormality in heart vessels. This type of diagnosis takes a lot of time by the concerned physician. In our proposed method, we segmented the interested regions of heart vessels and then classified. Segmentation and classification of heart angiography provides significant information for the physician as well as patient. Contradictorily, in the mention domain of heart angiography, the charge is prone to error, phase overwhelming, and thought-provoking task for the physician (heart specialist). An automatic segmentation and classification of heart blood vessels descriptions can improve the truthfulness and speed up the finding of heart illnesses. In this work, we recommend a computer-assisted conclusion arrangement for the localization of human heart blood vessels within heart angiographic imageries by using multiclass ensemble classification mechanism. In the proposed work, the heart blood vessels will be first segmented, and the various features according to accuracy have been extracted. Low-level features such as texture, statistical, and geometrical features were extracted in human heart blood vessels. At last, in the proposed framework, heart blood vessels have been categorized in their four respective classes including normal, block, narrow, and blood flow-reduced vessels. The proposed approach has achieved best result which provides very useful, easy, accurate, and time-saving environment to cardiologists for the diagnosis of heart-related diseases.

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Section: Discussionmentioning

confidence: 99%

Segmentation and Classification of Heart Angiographic Images Using Machine Learning Techniques

Abdullah

Ṣiddiqi

Alhwaiti

et al. 2021

Journal of Healthcare Engineering

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“…A system, which consisted of the seed proteins and has also direct association with their PPI's neighbors, was developed. This system was built using Cytoscape [ 21 ], which is an extremely flexible project designed for the purpose of investigations, operations, and visualization of extensive systems. Not only did this study involve expansion on the system incorporating a giant part but also it included two small separate parts.…”

Section: Methodsmentioning

confidence: 99%

Protein-Protein Interaction Analysis through Network Topology (Oral Cancer)

Khattak

Alhwaiti

Ali

et al. 2021

Journal of Healthcare Engineering

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Oral cancer is a complex disorder. Its creation and spreading are due to the interaction of several proteins and genes in different biological thoroughfares. To study biological pathways, many high-yield methods have been used. Efforts to merge several data found at separate levels related to biological thoroughfares and interlinkage networks remain elusive. In our research work, we have proposed a technique known as protein-protein interaction network for analysis and exploring the genes involved in oral cancer disorders. The previous studies have not fully analyzed the proteins or genes involved in oral cancer. Our proposed technique is fully interactive and analyzes the data of oral cancer disorder more accurately and efficiently. The methods used here enabled us to observe the wide network consists of one mighty network comprising of 208 nodes 1572 edges which connect these nodes and various detached small networks. In our study, TP53 is a gene that occupied an important position in the network. TP53 has a 113-degree value and 0.03881821 BC value, indicating that TP53 is centrally localized in the network and is a significant bottleneck protein in the oral cancer protein-protein interaction network. These findings suggested that the pathogenesis of oral cancer variation was organized by means of an integrated PPI network, which is centered on TP53. Furthermore, our identification shows that TP53 is the key role-playing protein in the oral cancer network, and its significance in the cellular networks in the body is determined as well. As TP53 (tumor protein 53) is a vital player in the cell division process, the cells may not grow or divide disorderly; it fulfills the function of at least one of the gene groups in oral cancer. However, the latter progression in the area is any measure; the intention of developing these networks is to transfigure sketch of core disease development, prognosis, and treatment.

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“…Implementing the packet switching mechanism and introducing the Wireless Access Protocol (WAP) and Multimedia Messaging Services (MMS). EDGE improved GPRS features with an enhancement of its data rates and service reliability [5].…”

Section: Introductionmentioning

confidence: 99%

Cluster-Based Antiphishing (CAP) Model for Smart Phones

Faisal

Abed

2021

Scientific Programming

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Different types of connectivity are available on smartphones such as WiFi, infrared, Bluetooth, GPRS, GPS, and GSM. The ubiquitous computing features of smartphones make them a vital part of our lives. The boom in smartphone technology has unfortunately attracted hackers and crackers as well. Smartphones have become the ideal hub for malware, gray ware, and spyware writers to exploit smartphone vulnerabilities and insecure communication channels. For every security service introduced, there is simultaneously a counterattack to breach the security and vice versa. Until a new mechanism is discovered, the diverse classifications of technology mean that one security contrivance cannot be a remedy for phishing attacks in all circumstances. Therefore, a novel architecture for antiphishing is mandatory that can compensate web page protection and authentication from falsified web pages on smartphones. In this paper, we developed a cluster-based antiphishing (CAP) model, which is a lightweight scheme specifically for smartphones to save energy in portable devices. The model is significant in identifying, clustering, and preventing phishing attacks on smartphone platforms. Our CAP model detects and prevents illegal access to smartphones based on clustering data to legitimate/normal and illegitimate/abnormal. First, we evaluated our scheme with mathematical and algorithmic methods. Next, we conducted a real test bed to identify and counter phishing attacks on smartphones which provided 90% accuracy in the detection system as true positives and less than 9% of the results as true negative.

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Evaluating the Role of Big Data in IIOT-Industrial Internet of Things for Executing Ranks Using the Analytic Network Process Approach

Cited by 14 publications

References 24 publications

Segmentation and Classification of Heart Angiographic Images Using Machine Learning Techniques

Segmentation and Classification of Heart Angiographic Images Using Machine Learning Techniques

Protein-Protein Interaction Analysis through Network Topology (Oral Cancer)

Cluster-Based Antiphishing (CAP) Model for Smart Phones

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