Soft Computing and Medical Bioinformatics

Muppalaneni, Naresh Babu; Gurumoorthy, Sasikumar

doi:10.1007/978-981-13-0059-2

Cited by 11 publications

(5 citation statements)

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“…As computational intelligence [64], bioinformatics is the primary role of primate mitogenomics study [9], [65], [66], evolutionary study [67]- [69], phylogeography study [70]- [72], biodiversity study [73]- [75], distribution to conservation plan [76]- [78], as well as help the conservationist and geneticist to work together. Supriatna [79] stated that every single person would help and work together to save the biodiversity, including non-human primates (NHPs) species.…”

Section: Primate As Wildlife Needs Conservation Focusmentioning

confidence: 99%

A Review of Bioinformatics for Primatologists: A Note for Reducing Living Primate Model and Supporting the Conservation

Hakim¹,

Nasution

Rukayah

et al. 2023

JAHIR

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The problem is that bioinformatics, as a part of health informatics, plays a role in studying molecular things and genetic studies. Primatologists who studied primates, as well as non-human primates (NHPs), found a dilemma relating to biomedical studies and conservation. Which one to achieve, the goal of being a model animal or their conservation efforts, of course, has very few boundaries. The solution in this study tries to review how to seek to reduce the quantity of NHPs model animals but not reduce their quality, on the other hand, considerations of conservation efforts. The research contribution in this paper proposed authors' perspectives according to the context of the NHPs model as well as conservation efforts, which are considered for dry or wet materials, which means that the genetic resources come. This study would give the 3Rs principle idea, reducing, which contributes to its conservation efforts. This review method used literature surf, scientific selection, and report writing. The result proposed the discussion about related scientific sources as well as the authors' perspective to solve the problem of animal model uses in NHPs. The conclusion is that NHPs are fortunate to receive special attention outside their studies as animal models, even though they are still used in research to benefit humans. Of course, the benefits and drawbacks of using genetic samples—whether wet or dry—are only relevant when used as research materials for medical and bioinformatics investigations. So that there are no more instances of increasing conservation status brought on by the volume of research on the use of model animals, efforts to conserve NHPs as a component of wildlife in nature must be considered.

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Section: Primate As Wildlife Needs Conservation Focusmentioning

confidence: 99%

A Review of Bioinformatics for Primatologists: A Note for Reducing Living Primate Model and Supporting the Conservation

Hakim¹,

Nasution

Rukayah

et al. 2023

JAHIR

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“…GANs, with their generator and discriminator networks, can synthesise realistic fraud patterns, enhancing the adaptability and robustness of fraud detection systems. These deep-learning approaches have significantly improved the accuracy and efficiency of credit card fraud detection [12][13][14][15].…”

Section: Deep Learning (Dl) In Credit Card Fraud Detectionmentioning

confidence: 99%

Enhancing Credit Card Fraud Detection: An Ensemble Machine Learning Approach

Khalid,

Owoh,

Uthmani

et al. 2023

Preprint

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In the era of digital advancements, the escalation of credit card fraud necessitates the development of robust and efficient fraud detection systems. This paper delves into the application of machine learning models, specifically focusing on ensemble methods, to enhance credit card fraud detection. Through an extensive review of existing literature, we identified limitations in current fraud detection technologies, including issues like data imbalance, concept drift, false positives/negatives, limited generalisability, and challenges in real-time processing. To address some of these shortcomings, we propose a novel ensemble model that integrates a Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Bagging, and Boosting classifiers. This ensemble model tackles the dataset imbalance problem associated with most credit card datasets by implementing under-sampling and the Synthetic Over-sampling Technique (SMOTE) on some machine learning algorithms. The evaluation of the model utilises a dataset comprising transaction records from European credit card holders, providing a realistic scenario for assessment. The methodology of the proposed model encompasses data pre-processing, feature engineering, model selection, and evaluation, with Google Colab computational capabilities facilitating efficient model training and testing. Comparative analysis between the proposed ensemble model, traditional machine learning methods, and individual classifiers reveals the superior performance of the ensemble in mitigating challenges associated with credit card fraud detection. Across accuracy, precision, recall, and F1-score metrics, the ensemble outperforms existing models. This paper underscores the efficacy of ensemble methods as a valuable tool in the battle against fraudulent transactions. The findings presented lay the groundwork for future advancements in the development of more resilient and adaptive fraud detection systems, crucial as credit card fraud techniques continue to evolve.

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“…According to [25], accuracy and receiver operating characteristics (ROC) are compared. The researcher obtained 303 records with 14 sets of variables and divided the data into training and testing.…”

Section: E Data Mining In Heart Disease Predictionmentioning

confidence: 99%

Performance Comparison between Meta-classifier Algorithms for Heart Disease Classification

Zaini¹,

Awang²

2022

IJACSA

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The rise in heart disease among the general population is alarming. This is because cardiovascular disease is the leading cause of death, and several studies have been conducted to assist cardiologists in identifying the primary cause of heart disease. The classification accuracy of single classifiers utilised in most recent studies to predict heart disease is quite low. The accuracy of classification can be enhanced by integrating the output of multiple classifiers in an ensemble technique. Even though they can deliver the best classification accuracy, the existing ensemble approaches that integrate all classifiers are quite resource-intensive. This study thus proposes a stacking ensemble that selects the optimal subset of classifiers to produce meta-classifiers. In addition, the research compares the effectiveness of several meta-classifiers to further enhance classification. There are ten types of algorithms, including logistic regression (LR), support vector classifier (SVC), random forest (RF), extra tree classifier (ETC), naïve bayes (NB), extreme gradient boosting (XGB), decision tree (DT), k-nearest neighbor (KNN), multilayer perceptron (MLP), and stochastic gradient descent (SGD) are used as a base classifier. The construction of the meta-classifier utilised three different algorithms consisting of LR, MLP, and SVC. The prediction results from the base classifier are then used as input for the stacking ensemble. The study demonstrates that using a stacking ensemble performs better than any other single algorithm in the base classifier. The meta-classifier of logistic regression yielded 90.16% results which is better than any base classifiers. In conclusion, we could assume that the ensemble stacking approach can be considered an additional means of achieving better accuracy and has improved the performance of the classification.

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Soft Computing and Medical Bioinformatics

Cited by 11 publications

References 0 publications

A Review of Bioinformatics for Primatologists: A Note for Reducing Living Primate Model and Supporting the Conservation

A Review of Bioinformatics for Primatologists: A Note for Reducing Living Primate Model and Supporting the Conservation

Enhancing Credit Card Fraud Detection: An Ensemble Machine Learning Approach

Performance Comparison between Meta-classifier Algorithms for Heart Disease Classification

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