Intrusion Detection Based on PCA with Improved K-Means

Chapagain, Pralhad; Timalsina, Arun Kumar; Bhandari, Mohan; Chitrakar, Roshan

doi:10.1007/978-981-19-1677-9_2

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…This technique aims to enhance the efficiency and accuracy of hostile activity detection in cloud computing, social networks, and mobile cloud computing, therefore addressing the growing significance of Intrusion Detection Systems (IDS). It attains improved precision and effectiveness in comparison to current techniques.The empirical findings confirm the effectiveness of the suggested methodology and emphasize its potential for practical use in safeguarding network security [21]. This study proposed a hybrid framework to address the challenges of type-2 diabetes prediction, (SMOTE) is employed to balance the imbalanced dataset, PCA to dimensionality reduction, and classification techniques to address the challenges of type-2 diabetes prediction, including logistic regression (LR), naïve Bayes, support vector machine (SVM), and k-nearest neighbors (KNN).…”

Section: Literture Reviewsupporting

confidence: 66%

Hybrid K-means and Principal Component Analysis (PCA) for Diabetes Prediction

Abed Mohammed,

Sumari,

Attabi

2024

IJCDS

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Diabetes is the "silent killer," stealing the lives of millions of people worldwide. There are many reasons for diabetes, such as increasing glucose, Cholesterol, systolic BP, and Age. These are considered to be the four primary causes of diabetes. The challenge in diabetes is predicting the human illness early to start treatment immediately after discovering diabetes; this can be the most challenging thing in diabetes discovery because tens of features may cause diabetes. This study proposes a model consisting of data mining and Machine Learning (ML) algorithms to predict if humans can have diabetes or not in the future. The prediction is made up of compensating two datasets; one dataset is used to reconfirm the other dataset in order to make a more accurate prediction. This can be performed using the k-means-PCA hybrid model and the highest weight selection of features that widely cause diabetes. The selected features help the ML algorithm predict the model's accuracy, which indicates the prediction model's accuracy. Simulation results show that the number of predict-diabetic patients increased from 53 from the original datasets to 142 after applying the proposed model. Simulation outcomes also prove that the Random Forest ML model gives the highest accuracy of other ML models, reaching 95.2%.

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Section: Literture Reviewsupporting

confidence: 66%

Hybrid K-means and Principal Component Analysis (PCA) for Diabetes Prediction

Abed Mohammed,

Sumari,

Attabi

2024

IJCDS

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“…DL models have made significant advances in a variety of fields including, but not limited to, deep fakes [ 22 , 23 ], satellite image analysis [ 24 ], image classification [ 25 , 26 ], the optimization of artificial neural networks [ 27 , 28 ], the processing of natural language [ 29 , 30 ], fin-tech [ 31 ], intrusion detection [ 32 ], steganography [ 33 ], and biomedical image analysis [ 14 , 34 ]. CNNs have recently surfaced as one of the most commonly used techniques for plant disease identification [ 35 , 36 ].…”

Section: Related Workmentioning

confidence: 99%

BotanicX-AI: Identification of Tomato Leaf Diseases Using an Explanation-Driven Deep-Learning Model

et al. 2023

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Early and accurate tomato disease detection using easily available leaf photos is essential for farmers and stakeholders as it help reduce yield loss due to possible disease epidemics. This paper aims to visually identify nine different infectious diseases (bacterial spot, early blight, Septoria leaf spot, late blight, leaf mold, two-spotted spider mite, mosaic virus, target spot, and yellow leaf curl virus) in tomato leaves in addition to healthy leaves. We implemented EfficientNetB5 with a tomato leaf disease (TLD) dataset without any segmentation, and the model achieved an average training accuracy of 99.84% ± 0.10%, average validation accuracy of 98.28% ± 0.20%, and average test accuracy of 99.07% ± 0.38% over 10 cross folds.The use of gradient-weighted class activation mapping (GradCAM) and local interpretable model-agnostic explanations are proposed to provide model interpretability, which is essential to predictive performance, helpful in building trust, and required for integration into agricultural practice.

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“…Min-Max normalization is applied in this research and given by equation 4. (Chapagain, et al, 2022) Min − Max = 𝑉𝑎𝑙𝑢𝑒−𝑀𝑖𝑛 𝑉𝑎𝑙𝑢𝑒 𝑀𝑎𝑥 𝑉𝑎𝑙𝑢𝑒−𝑀𝑖𝑛 𝑉𝑎𝑙𝑢𝑒 (Equation 4)…”

Section: Iqr = Q3 -Q1mentioning

confidence: 99%

Heart Disease Prediction Using Outlier Removal based Max Voting Ensemble Method

Chapagain

2023

Int. J. Engin. Technol.

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Heart disease has emerged as a serious health concern for many individuals due to its high death rate around the world. The routine clinical data analysis has a significant difficulty in the early diagnosis of cardiac disease. The identification of cardiac disease may benefit from the use of machine learning. To improve machine learning models, several studies have previously been conducted. The suggested study uses the maximum voting ensemble technique of classification to effectively identify heart disease. The suggested classifier is a more reliable and accurate approach. To identify and eliminate outliers, conduct Inter quartile range outlier removal and min-max normalization during preprocessing. Accuracy, Precision, Recall, and F1 Score are calculated and evaluated against various models. For the heart disease dataset collected from the Kaggle, the suggested max voting ensemble classifier has an accuracy of 99.22%.

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Intrusion Detection Based on PCA with Improved K-Means

Cited by 5 publications

References 18 publications

Hybrid K-means and Principal Component Analysis (PCA) for Diabetes Prediction

Hybrid K-means and Principal Component Analysis (PCA) for Diabetes Prediction

BotanicX-AI: Identification of Tomato Leaf Diseases Using an Explanation-Driven Deep-Learning Model

Heart Disease Prediction Using Outlier Removal based Max Voting Ensemble Method

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