A Consolidated Decision Tree-Based Intrusion Detection System for Binary and Multiclass Imbalanced Datasets

Panigrahi, Ranjit; Borah, Samarjeet; Bhoi, Akash Kumar; Ijaz, Muhammad Fazal; Pramanik, Moumita; Kumar, Yogesh; Jhaveri, Rutvij H.

doi:10.3390/math9070751

Cited by 116 publications

(52 citation statements)

References 51 publications

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“…Such systems use the smart sensors, controllers and actuators for PQDs automated management. The recent advancements in the domain of machine learning have evolved a variety of automated solutions [7][8][9][10][11][12][13][14]. The same trend is followed by the automated PQDs mitigation techniques [15][16][17].…”

Section: Background and Problem Statementmentioning

confidence: 99%

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Signal-piloted processing and machine learning based efficient power quality disturbances recognition

Qaisar

2021

PLoS ONE

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Significant losses can occur for various smart grid stake holders due to the Power Quality Disturbances (PQDs). Therefore, it is necessary to correctly recognize and timely mitigate the PQDs. In this context, an emerging trend is the development of machine learning assisted PQDs management. Based on the conventional processing theory, the existing PQDs identification is time-invariant. It can result in a huge amount of unnecessary information being collected, processed, and transmitted. Consequently, needless processing activities, power consumption and latency can occur. In this paper, a novel combination of signal-piloted acquisition, adaptive-rate segmentation and time-domain features extraction with machine learning tools is suggested. The signal-piloted acquisition and processing brings real-time compression. Therefore, a remarkable reduction can be secured in the data storage, processing and transmission requirement towards the post classifier. Additionally, a reduced computational cost and latency of classifier is promised. The classification is accomplished by using robust machine learning algorithms. A comparison is made among the k-Nearest Neighbor, Naïve Bayes, Artificial Neural Network and Support Vector Machine. Multiple metrics are used to test the success of classification. It permits to avoid any biasness of findings. The applicability of the suggested approach is studied for automated recognition of the power signal’s major voltage and transient disturbances. Results show that the system attains a 6.75-fold reduction in the collected information and the processing load and secures the 98.05% accuracy of classification.

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Section: Background and Problem Statementmentioning

confidence: 99%

“…Consequently, samples are non-uniformly arranged in time. The process is given by Eq (8). Where dt n is the time elapse among the current instant of sampling, t n , and the prior one, t n-1 .…”

Section: Reconstruction and Signal-piloted Acquisitionmentioning

confidence: 99%

Signal-piloted processing and machine learning based efficient power quality disturbances recognition

Qaisar

2021

PLoS ONE

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“…Various studies have focused on the accuracy enhancement of NCDs diagnostic models concerning feature selection techniques and refined machine-learning classifiers [18][19][20][21][22][23][24][25][26][27][28][29].…”

Section: A Machine Learning Techniques For Non-communicable Diseasesmentioning

confidence: 99%

“…Finally, a random forest (RF) classifier was used to predict cervical cancer. In the study [23], the authors studied a consolidated decision tree-based intrusion detection system for binary and multiclass imbalanced datasets. In their study, an improved version of the random sampling mechanism called supervised relative random sampling proposed to generate a balanced sample from a high class-imbalanced dataset at the preprocessing stage of detector.…”

Section: A Machine Learning Techniques For Non-communicable Diseasesmentioning

confidence: 99%

Explainable Artificial Intelligence Based Framework for Non-Communicable Diseases Prediction

et al. 2021

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The rapid rise of non-communicable diseases (NCDs) becomes one of the serious health issues and the leading cause of death worldwide. In recent years, artificial intelligence-based systems have been developed to assist clinicians in decision-making to reduce morbidity and mortality. However, a common drawback of these modern studies is related to explanations of their output. In other words, understanding the inner logic behind the predictions is hidden to the end-user. Thus, clinicians struggle to interpret these models because of their black-box nature, and hence they are not acceptable in the medical practice. To address this problem, we have proposed a Deep Shapley Additive Explanations (DeepSHAP) based deep neural network framework equipped with a feature selection technique for NCDs prediction and explanation among the population in the United States. Our proposed framework comprises three components: First, representative features are done based on the elastic net-based embedded feature selection technique; second a deep neural network classifier is tuned with the hyper-parameters and used to train the model with the selected feature subset; third, two kinds of model explanation are provided by the DeepSHAP approach. Herein, (I) explaining the risk factors that affected the model's prediction from the population-based perspective; (II) aiming to explain a single instance from the human-centered perspective. The experimental results indicated that the proposed model outperforms various state-of-the-art models. In addition, the proposed model can improve the medical understanding of NCDs diagnosis by providing general insights into the changes in disease risk at the global and local levels. Consequently, DeepSHAP based explainable deep learning framework contributes not only to the medical decision support systems but also can provide to real-world needs in other domains.

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“…Every human that uses any technology has benefited from machine learning. Some of its countless applications can be found in security systems ( Sagar, Jhaveri & Borrego, 2020 ; Panigrahi et al, 2021 ), biometric measurements ( Chaurasia, Kohli & Garg, 2014 ), software developments ( Chandra et al, 2016 ), and fraud news detection ( Hakak et al, 2021 ). One contribution of machine learning that is a continuously developing field is optical character recognition (OCR).…”

Section: Introductionmentioning

confidence: 99%

A Baybayin word recognition system

Pino

Mendoza

Sambayan

2021

PeerJ Computer Science

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Baybayin is a pre-Hispanic Philippine writing system used in Luzon island. With the effort in reintroducing the script, in 2018, the Committee on Basic Education and Culture of the Philippine Congress approved House Bill 1022 or the ”National Writing System Act,” which declares the Baybayin script as the Philippines’ national writing system. Since then, Baybayin OCR has become a field of research interest. Numerous works have proposed different techniques in recognizing Baybayin scripts. However, all those studies anchored on the classification and recognition at the character level. In this work, we propose an algorithm that provides the Latin transliteration of a Baybayin word in an image. The proposed system relies on a Baybayin character classifier generated using the Support Vector Machine (SVM). The method involves isolation of each Baybayin character, then classifying each character according to its equivalent syllable in Latin script, and finally concatenate each result to form the transliterated word. The system was tested using a novel dataset of Baybayin word images and achieved a competitive 97.9% recognition accuracy. Based on our review of the literature, this is the first work that recognizes Baybayin scripts at the word level. The proposed system can be used in automated transliterations of Baybayin texts transcribed in old books, tattoos, signage, graphic designs, and documents, among others.

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A Consolidated Decision Tree-Based Intrusion Detection System for Binary and Multiclass Imbalanced Datasets

Cited by 116 publications

References 51 publications

Signal-piloted processing and machine learning based efficient power quality disturbances recognition

Signal-piloted processing and machine learning based efficient power quality disturbances recognition

Explainable Artificial Intelligence Based Framework for Non-Communicable Diseases Prediction

A Baybayin word recognition system

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