Big Data, Scientific Programming, and Its Role in Internet of Industrial Things: A Decision Support System

Li, Ju; Jan, Muhammad Nazir; Faisal, Mohammad

doi:10.1155/2020/8850096

Cited by 8 publications

(5 citation statements)

References 57 publications

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“…PudMed, OMIM, Drug Bank, and Swiss-Prot are among the few sources of information from which data relevant to the query can be extracted. PolySearch contributes a great deal to extracting information on important biomedical concepts such as genes/proteins, disease, SNPs, drugs, metabolites, pathways, and tissues, which could help in finding the relevant genes of oral cancer [ 20 ]. This method is used for finding genes related to oral cancer.…”

Section: Methodsmentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

“…It is an exploratory database for PPIs. Updated version of STRING, 9.1, contains more than 1100 organisms, covering millions of proteins [ 20 ].…”

Section: Methodsmentioning

confidence: 99%

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

Khattak

Alhwaiti

Ali

et al. 2021

Journal of Healthcare Engineering

Self Cite

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“…Moreover, in recent times, many IOT-based healthcare systems have been proposed [10] which entailed the usage of cardiac data for the effective monitoring of the patient. Furthermore, many studies [11] have underlined the importance of big data in decision support systems. It underlines the core role of big data and how it can be used for various beneficial purposes.…”

Section: Literature Reviewmentioning

confidence: 99%

An Efficient SMOTE-Based Deep Learning Model for Heart Attack Prediction

Waqar

Dawood

et al. 2021

Scientific Programming

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Cardiac disease treatments are often being subjected to the acquisition and analysis of vast quantity of digital cardiac data. These data can be utilized for various beneficial purposes. These data’s utilization becomes more important when we are dealing with critical diseases like a heart attack where patient life is often at stake. Machine learning and deep learning are two famous techniques that are helping in making the raw data useful. Some of the biggest problems that arise from the usage of the aforementioned techniques are massive resource utilization, extensive data preprocessing, need for features engineering, and ensuring reliability in classification results. The proposed research work presents a cost-effective solution to predict heart attack with high accuracy and reliability. It uses a UCI dataset to predict the heart attack via various machine learning algorithms without the involvement of any feature engineering. Moreover, the given dataset has an unequal distribution of positive and negative classes which can reduce performance. The proposed work uses a synthetic minority oversampling technique (SMOTE) to handle given imbalance data. The proposed system discarded the need of feature engineering for the classification of the given dataset. This led to an efficient solution as feature engineering often proves to be a costly process. The results show that among all machine learning algorithms, SMOTE-based artificial neural network when tuned properly outperformed all other models and many existing systems. The high reliability of the proposed system ensures that it can be effectively used in the prediction of the heart attack.

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“…Design is characterized by apparent multi-disciplinary and multield intersection [1]. Product innovation design includes three functional units, namely, the problem, solution, and decision [2], and Analysis-Synthesis-Evaluation (ASE) is one of the typical design processes [3]. Design evaluation and decision-making are important components of modern product innovation design [4].…”

Section: Introductionmentioning

confidence: 99%

Research on the Decision Model of Product Design Based on a Deep Residual Network

Zong

Wang

2022

Scientific Programming

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An artificial intelligence (AI) design decision model is constructed to improve the efficiency of design decision evaluation and avoid the influence of the decision preference on product design and development. Using the concept of AI, the proposed model is based on a data set of product modeling design schemes, and the data set is marked with product modeling semantics. The deep learning residual network (ResNet) algorithm is used to train the data set to improve the accuracy of design decisions, transform the general design decision problem into the semantic recognition problem of design scheme images, and eliminate the design decision preference to the greatest extent. The validity and the feasibility of the proposed AI design decision-making method based on the ResNet algorithm are verified via an example of motorcycle modeling design decision-making.

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Big Data, Scientific Programming, and Its Role in Internet of Industrial Things: A Decision Support System

Cited by 8 publications

References 57 publications

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

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

An Efficient SMOTE-Based Deep Learning Model for Heart Attack Prediction

Research on the Decision Model of Product Design Based on a Deep Residual Network

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