Computational intelligence based machine learning methods for rule-based reasoning in computer vision applications

Dhivyaprabha, T T; Krishnaveni, M.

doi:10.1109/ssci.2016.7850050

Cited by 15 publications

(2 citation statements)

References 4 publications

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“…Pooling also computes a total or average of all layer neurons [50,51]. In maxpooling [52], the biggest value for each previous layer neuron cluster is chosen [53]. In average pooling [54], the largest value of each previous layer neuron cluster is chosen.…”

Section: Deep Learningmentioning

confidence: 99%

A Review of Unsupervised Machine Learning Frameworks for Anomaly Detection in Industrial Applications

Usmani

Happonen

Watada

2022

Lecture Notes in Networks and Systems

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Unsupervised learning, also known as unsupervised machine learning, analyzes and clusters unlabeled data utlizing machine learning techniques. Without human input, these algorithms discover patterns or groupings in the data. In the domain of abuse and network intrusion detection, interesting objects are often short bursts of activity rather than rare objects. Anomaly detection is a difficult task that requires familiarity and a good understanding of the data and the pattern does not correspond to the common statistical definition of an outlier as an odd item. The traditional algorithms need data preparations while unsupervised algorithms can be prepared so that they can handle the data in war format. Anomaly detection, sometimes referred to as outlier analysis is a data mining procedure that detects events, data points, and observations that deviates from the expected behaviour of a dataset. The unsupervised machine learning approaches have shown potential in static data modeling applications such as computer vision, and their use in anomaly detection is gaining attention. A typical data might reveal critical flaws, such as a software defect, or prospective possibilities, such as a shift in consumer behavior. Currently, academic literature does not really cover the topic of unsupervised machine learning techniques for anomaly detection. This paper provides an overview of the current deep learning and unsupervised machine learning techniques for anomaly detection and discusses the fundamental challenges in anomaly detection.

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Section: Deep Learningmentioning

confidence: 99%

A Review of Unsupervised Machine Learning Frameworks for Anomaly Detection in Industrial Applications

Usmani

Happonen

Watada

2022

Lecture Notes in Networks and Systems

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“…It offers image classification and retrieval [3][4][5][6], object recognition and matching [7][8][9], 3D scene reconstruction [10], robot localization [11], object detection and tracking and video processing. All of these processing systems rely on the presence of stable and meaningful features in the image.…”

Section: Computer Vision Systemmentioning

confidence: 99%

Machine Learning framework for image classification

Loussaief¹,

Abdelkrim²

2018

Adv. sci. technol. eng. syst. j.

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Hereby in this paper, we are going to refer image classification. The main issue in image classification is features extraction and image vector representation. We expose the Bag of Features method used to find image representation. Class prediction accuracy of varying classifiers algorithms is measured on Caltech 101 images. For feature extraction functions we evaluate the use of the classical Speed Up Robust Features technique against global color feature extraction. The purpose of our work is to guess the best machine learning framework techniques to recognize the stop sign images.The trained model will be integrated into a robotic system in a future work.

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Synergistic Fibroblast Optimization Algorithm for Solving Knapsack Problem

Dhivyaprabha,

Subashini

2023

Studies in Computational Intelligence

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Computational intelligence based machine learning methods for rule-based reasoning in computer vision applications

Cited by 15 publications

References 4 publications

A Review of Unsupervised Machine Learning Frameworks for Anomaly Detection in Industrial Applications

A Review of Unsupervised Machine Learning Frameworks for Anomaly Detection in Industrial Applications

Machine Learning framework for image classification

Synergistic Fibroblast Optimization Algorithm for Solving Knapsack Problem

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