Big Data Technology for Developing Learning Resources

Sharma, Kuldeep; Mahesh, T R; Bhuvana, J

doi:10.1088/1742-6596/1979/1/012019

Cited by 14 publications

(4 citation statements)

References 15 publications

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“…Boosting is a sequential method that works on the principle of ensemble. It combines a group of ineffective learners to increase prediction accuracy [29]. Outcomes of the model are weighed at any instant t, based on the results of the previous instant t-1.…”

Section: Ensemble Methods With K-fold Cross Validationmentioning

confidence: 99%

An Efficient Ensemble Method Using K-Fold Cross Validation for the Early Detection of Benign and Malignant Breast Cancer

Mahesh¹,

Kaladevi²,

Balajee³

et al. 2022

IJIE

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In comparison to all other malignancies, breast cancer is the most commonform of cancer, among women. Breast cancer prediction has been studied by a number of researchers, and is considered as a serious threat to women. Clinicians are finding it difficult to create a treatment approach that will help patients live longer, due to the lack of solid predictive models which predicts the outcome in early stages by analyzing history of patient’s data. Rates of this malignancy have been observed to rise, more with industrialization and urbanization, as well as with early detection facilities. It is still considerably more prevalent in very developed countries, but it is rapidly spreading to developing countries as well. The purpose of this work is to offer a report on the disease of breast cancer in which we used available technical breakthroughs to construct breast cancer survivability prediction models. The Machine Learning (ML) techniques, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT) Classifier, Random Forests (RF), and Logistic Regression (LR)are used as base Learners and their performance has been compared with the ensemble method, eXtreme Gradient Boosting(XGBoost). For performance comparison, we employed k-fold cross validation method to measure the unbiased estimate of these prediction models. The results indicated that XGBoost outperformed with an accuracy of 97.81% compared to other ML algorithms.

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Section: Ensemble Methods With K-fold Cross Validationmentioning

confidence: 99%

An Efficient Ensemble Method Using K-Fold Cross Validation for the Early Detection of Benign and Malignant Breast Cancer

Mahesh¹,

Kaladevi²,

Balajee³

et al. 2022

IJIE

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“…It is related to boosting and generalizes decision trees. An AltDTree is made up of a series of decision nodes that indicates a predicate condition and prediction nodes that hold a single number [ 28 ]. Classic DTs, Voted DTs, and Voted Decision Stumps are all generalized into AltDTree.…”

Section: Exploratory Knowledgementioning

confidence: 99%

AdaBoost Ensemble Methods Using K-Fold Cross Validation for Survivability with the Early Detection of Heart Disease

Mahesh

Kumar

et al. 2022

Computational Intelligence and Neuroscience

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As a result of technology improvements, various features have been collected for heart disease diagnosis. Large data sets have several drawbacks, including limited storage capacity and long access and processing times. For medical therapy, early diagnosis of heart problems is crucial. Disease of heart is a devastating human disease that is quickly increasing in developed and also developing countries, resulting in death. In this type of disease, the heart normally fails to provide enough blood to different body parts in order to allow them to perform their regular functions. Early, as well as, proper diagnosis of this condition is very critical for averting further damage and also to save patients’ lives. In this work, machine learning (ML) is utilized to find out whether a person has cardiac disease or not. Both the types of ensemble classifiers, namely, homogeneous as well as heterogeneous classifiers (formed by combining two separate classifiers), have been implemented in this work. The data mining preprocessing using Synthetic Minority Oversampling Technique (SMOTE) has been employed to cope with the imbalance problem of the class as well as noise. The proposed work has two steps. SMOTE is used in the initial phase to reduce the impact of data imbalance and the second phase is classifying data using Naive Bayes (NB), decision tree (DT) algorithms, and their ensembles. The experimental results demonstrate that the AdaBoost-Random Forest classifier provides 95.47% accuracy in the early detection of heart disease.

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“…An effective DWT-SVD is deployed with selfadaptive differential evolution (SDE) algorithm for image watermarking scheme, SDE adjusts the mutation factor F and the crossover rate Cr dynamically in order to balance an individual's exploration and exploitation capability for different evolving phases to achieve invisibility [18][19][20]. In [21][22][23][24], comparative analysis of image compression is done by three transform methods, which are Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) and Hybrid (DCT + DWT) Transform, thereby achieving better invisibility property and good PSNR ratio.…”

Section: Related Workmentioning

confidence: 99%

Efficient Hybrid Blind Watermarking in DWT‐DCT‐SVD with Dual Biometric Features for Images

Mokashi

Bhat

Pujari

et al. 2022

Contrast Media & Molecular Imaging

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In the modern era of virtual computers over the notional environment of computer networks, the protection of influential documents is a major concern. To bring out this motto, digital watermarking with biometric features plays a crucial part. It utilizes advanced technology of cuffing data into digital media, i.e., text, image, video, or audio files. The strategy of cuffing an image inside another image by applying biometric features namely signature and fingerprint using watermarking techniques is the key purpose of this study. To accomplish this, a combined watermarking strategy consisting of Discrete Wavelet Transform, Discrete Cosine Transform, and Singular Value Decomposition (DWT-DCT-SVD) is projected for authentication of image that is foolproof against attacks. Here, singular values of watermark1 (fingerprint) and watermark2 (signature) are obtained by applying DWT-DCT-SVD. Affixing both the singular values of watermarks, we acquire the transformed watermark. Later, the same is applied to cover image to extract the singular values. Then we add these values to the cover image and transformed watermark to obtain a final watermarked image containing both signature and fingerprint. To upgrade the reliability, sturdiness, and originality of the image, a fusion of watermarking techniques along with dual biometric features is exhibited. The experimental results conveyed that the proposed scheme achieved an average PSNR value of about 40 dB, an average SSIM value of 0.99, and an embedded watermark resilient to various attacks in the watermarked image.

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Big Data Technology for Developing Learning Resources

Cited by 14 publications

References 15 publications

An Efficient Ensemble Method Using K-Fold Cross Validation for the Early Detection of Benign and Malignant Breast Cancer

An Efficient Ensemble Method Using K-Fold Cross Validation for the Early Detection of Benign and Malignant Breast Cancer

AdaBoost Ensemble Methods Using K-Fold Cross Validation for Survivability with the Early Detection of Heart Disease

Efficient Hybrid Blind Watermarking in DWT‐DCT‐SVD with Dual Biometric Features for Images

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