Towards Convergence of IoT and Blockchain for Secure Supply Chain Transaction

Hasan, A S M Touhidul; Sabah, Shabnam; Haque, Rakib Ul; Daria, Apubra; Rasool, Abdur; Jiang, Qingshan

doi:10.3390/sym14010064

Cited by 25 publications

(10 citation statements)

References 35 publications

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“…It is mainly used to predict a binary variable with a large number of independent variables. It is efficient to forecast the probability of being 0 or 1 based on predictors [ 34 , 35 , 36 ]. It can be expressed as (6):

where X is a vector that contains

independent predictor variables;

is the conditional probability of experiencing the event

given the independent variable vector X ; and

is a random error term.…”

Section: Proposed Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis

Rasool

Bunterngchit

Luo

et al. 2022

IJERPH

Self Cite

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Breast cancer death rates are higher than any other cancer in American women. Machine learning-based predictive models promise earlier detection techniques for breast cancer diagnosis. However, making an evaluation for models that efficiently diagnose cancer is still challenging. In this work, we proposed data exploratory techniques (DET) and developed four different predictive models to improve breast cancer diagnostic accuracy. Prior to models, four-layered essential DET, e.g., feature distribution, correlation, elimination, and hyperparameter optimization, were deep-dived to identify the robust feature classification into malignant and benign classes. These proposed techniques and classifiers were implemented on the Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD) datasets. Standard performance metrics, including confusion matrices and K-fold cross-validation techniques, were applied to assess each classifier’s efficiency and training time. The models’ diagnostic capability improved with our DET, i.e., polynomial SVM gained 99.3%, LR with 98.06%, KNN acquired 97.35%, and EC achieved 97.61% accuracy with the WDBC dataset. We also compared our significant results with previous studies in terms of accuracy. The implementation procedure and findings can guide physicians to adopt an effective model for a practical understanding and prognosis of breast cancer tumors.

show abstract

where X is a vector that contains

independent predictor variables;

is the conditional probability of experiencing the event

given the independent variable vector X ; and

is a random error term.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…It is mainly used to predict a binary variable with a large number of independent variables. It is efficient to forecast the probability of being 0 or 1 based on predictors [34][35][36]. It can be expressed as ( 6):…”

Section: Predictive Modelsmentioning

confidence: 99%

Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis

Rasool

Bunterngchit

Luo

et al. 2022

IJERPH

Self Cite

View full text Add to dashboard Cite

show abstract

“…With many independent variables, it is mostly used to predict binary variables. Using predictors to predict whether a value will be 0 or 1 is efficient 23,33,34 . As a result, (6) is true:

y=\pi (X)+\varepsilon

…”

Section: Proposed Methodologymentioning

confidence: 99%

“…Using predictors to predict whether a value will be 0 or 1 is efficient. 23,33,34 As a result, ( 6) is true:…”

Section: Logistical Regressionmentioning

confidence: 99%

An intelligent human‐centric systems to diagnose breast cancer using machine learning and optimized feature selection techniques

Kumar,

Singh,

Mahadev

et al. 2023

Trans Emerging Tel Tech

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All over the world, breast cancer (BC) is the leading cause of cancer mortality among women. Computer‐aided methods can assist in early diagnosis. The proposed approach used SMOTE filter with Ch2 test techniques for class balance and feature section using eight different ML models Gaussian Naive Bayes (GNB), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM) with Linear and Radial Basis Function (RBF), Logistic Regression (LR), K‐nearest neighbor (KNN) and eXtreme Gradient Boosting (XGBoost). A Ch2 test determines the top five features—glucose, HOMA, resistin, BMI, and insulin. Metrics such as accuracy, precision, recall, and F1‐Score are used to compare the performance of models. More than 99% accuracy was achieved by the proposed XGBoost model. Compared to the other breast cancer prediction models, the proposed model had an average accuracy improvement of 9.30%. As a result of our proposed model, breast cancer diagnosis will be more efficient based on risk factors. The proposed prediction model can also predict various breast cancer features. In addition to improving diagnostic decision‐support systems, the proposed model should be able to predict breast cancer disease accurately.

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“…BC is a distributed ledger system that uses a peer-to-peer (P2P) architecture to make all transaction records permanently accessible to all nodes in the network. Without the requirement for a trusted third party, it enables anonymous users to safely deal with one another and broadcast those transactions to others in the network (Hasan et al, 2022). BCs' distributed consensus process is a key component.…”

Section: Blockchainmentioning

confidence: 99%

Adoption of IoT by telecommunication companies in GCC: The role of blockchain

Alarefi¹

2023

10.5267/j.dsl

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The Internet of Things (IoT) has become essential for business. The adoption rate of IoT has dropped recently and this could be due to security, privacy, and trust issues. Blockchain (BC) has the potential to mitigate the risk of security, privacy, and trust. However, few studies examined the integration between IoT and BC in the context of developing countries. The purpose of this study is to examine the predictors of IoT adoption by telecommunication companies in the Gulf Cooperation Council (GCC). In addition, the study aims to examine the moderating role of BC as well as the effect of using IoT and BC on the competitive advantage of companies. Based on technology acceptance model, social exchange theory, and resource-based view, the study proposed that security, privacy, trust, communication quality, perceived ease of use (PEOU), and perceived usefulness (PU) affect positively the adoption of IoT. BC is proposed as a moderating variable and expected with IoT to affect the competitive advantage of companies. The population includes all the telecommunication companies in GCC. Data was collected using purposive sampling from IT professionals. The results of data analysis using SmartPLS showed that security, privacy, trust, PU, and PEOU positively affected the adoption of IoT. BC and IoT adoption have a positive effect on competitive advantage. Further, BC moderated only the effect of security and privacy on the adoption of IoT. Services providers must enhance the security, privacy, and trust of IoT services by deploying BC technology. Effective integration of IoT and BC will lead to the achievement of competitive advantages.

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Towards Convergence of IoT and Blockchain for Secure Supply Chain Transaction

Cited by 25 publications

References 35 publications

Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis

Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis

An intelligent human‐centric systems to diagnose breast cancer using machine learning and optimized feature selection techniques

Adoption of IoT by telecommunication companies in GCC: The role of blockchain

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