Support Vector Machine based Multi-Class Classification for Oriented Instance Selection

Davuluri, Sandeep Kumar; Srivastava, Deepak; Aeri, Manisha; Arora, Manav; Keshta, Ismail; Rivera, Richard

doi:10.1109/icict57646.2023.10133966

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2023 International Conference on Inventive Computation Technologies (ICICT) 2023

DOI: 10.1109/icict57646.2023.10133966

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Support Vector Machine based Multi-Class Classification for Oriented Instance Selection

Sandeep Kumar Davuluri

Deepak Srivastava

Manisha Aeri

et al.

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2023

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Cited by 2 publications

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A novel approach for lung cancer detection using deep belief networks

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Pranjal,

Rajan

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Due to the increased awareness of lung cancer, researchers have created many algorithms that can recognise the disease in its early stages using a variety of Machine Learning (ML) techniques. Clinicians can manage incidental or screen-found ambiguous pulmonary nodules with the help of machine learning-based models for lung cancer prediction. Such methods might be able to lower the variability in nodule classification, enhance decision making, and eventually decrease the proportion of benign nodules that do not need to be followed. This study proposes a novel lung cancer detection method based on Magnetic Resonance Imaging (MRI). Using ML to classify features in MRI scans, this technology is useful for the early detection of lung cancer. The performance was further enhanced using featureselection methodologies. The images were divided into segments using the FBSO feature selection method, and deep learning techniques were used to analyze the three standard datasets, S1, S2 and S3. In this investigation, 98.9% classifier optimality and 96.7% accuracy were attained. This new approach demonstrated excellent dependability and was found to be the most effective classifier system compared with previous studies.

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A novel approach for lung cancer detection using deep belief networks

Sangeeta,

Pranjal,

Rajan

et al. 2023

JAIM

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A novel methodology for enhancing intrusion detection system

Munish,

Rajan,

Pranjal

et al. 2023

JSE

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An Intrusion Detection System (IDS) monitors network traffic for suspicious activity and alerts when such an activity is discovered. In this study, the NSL-KDD cup 99 dataset was used to evaluate anomaly detection from intruders. Intrusion Detection System, Distributed Denial of Service (DDoS), Deep Belief Network (DBN), Random Forest, Naïve Bayes, Security Attack, Machine Learning. Pre-processing and normalization processes were performed on the dataset with inadequate, noisy, or duplicate data. A hybrid K-means clustering algorithm is used to combine clusters, which are classified using Deep Belief Networks (DBNs), Random Forest and Naïve Bayes. The study analyzed the dataset based on accuracy, precision, F-score, and false alarm rate, among which the DBN showed better performance than the other two ML algorithms.

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Support Vector Machine based Multi-Class Classification for Oriented Instance Selection

Cited by 2 publications

References 20 publications

A novel approach for lung cancer detection using deep belief networks

A novel approach for lung cancer detection using deep belief networks

A novel methodology for enhancing intrusion detection system

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