Optimization of the Ho-Kashyap classification algorithm using appropriate learning samples

Dezfoulian, Mir Hossein; MiriNezhad, Younes; Mousavi, Seyed Muhammad Hossein; Mosleh, Mehrdad Shafaei; Shalchi, Muhammad Mehdi

doi:10.1109/ikt.2016.7777760

Cited by 9 publications

(3 citation statements)

References 9 publications

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“…After feature selection, there would be binary classification (Dezfoulian et al, 2016) for fire / no fire data which leads to a labeling task for classification. Data is divided into 70 % / 30% for training and testing stages.…”

Section: Fig 8 Lpq Algorithm Workflowmentioning

confidence: 99%

Nature inspired firefighter assistant by unmanned aerial vehicle (UAV) data

Mousavi¹,

Ilanloo²

2023

10.5267/j.jfs

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One of the most hazardous phenomena in forests is wildfire or bush fire and early detection of massive damage prevention is vital. Employing Unmanned Aerial Vehicles (UAV) as a visual and extinguisher tool in order to prevent this tragedy which brings fatal effects on humans and wildlife has high importance. Additionally, using aerial imagery could assist firefighters to recognize fire intensity and localize and route the fire in the forest which shrinks down casualties of firefighters. All these benefits and more is just possible by employing cheap UAVs. The proposed research uses nature-inspired image processing techniques in order to segment and classify fire in color and thermal images. Multiple nature-inspired and traditional computer vision techniques, including Chicken Swarm Algorithm (CSA) intensity adjustment (contrast enhancement), Denoising Convolutional Neural Network (DnCNN), Local Phase Quantization (LPQ) feature extraction, Bees Image Segmentation, Biogeography-Based Optimization (BBO) feature selection, Firefly Algorithm (FA) classification and more are employed to achieve high classification and segmentation accuracy. The system evaluates nine performance metrics including, F-Score, Accuracy, and Jaccard for the segmentation stage and four performance metrics for the classification stage. All experiments are conducted on the two most recent UAV fire datasets of FLAME (2021) and DeepFire (2022). Additionally, fire intensity, fire direction, and fire geometrical calculation are calculated which assists firefighters even more. As smoke shows the location of the fire, a smoke detection workflow is proposed, too. Proposed system Compared with traditional and novel methods for segmentation and classification leading to satisfactory and promising results for almost all metrics. The trained model of this system could be used in most of the current rescue UAVs in real-time applications. For the FLAME dataset (color data), segmentation precision is 95.57 % and classification accuracy is 91.33 %. Also, For the DeepFire dataset segmentation precision is 91.74 % and classification accuracy is 96.88 %.

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Section: Fig 8 Lpq Algorithm Workflowmentioning

confidence: 99%

Nature inspired firefighter assistant by unmanned aerial vehicle (UAV) data

Mousavi¹,

Ilanloo²

2023

10.5267/j.jfs

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“…The authors used the Multi Class Instance Selection (MCIS) algorithm by Chen, Zhang, Xue, and Liu (2013) to obtain the most valuable data from each class and speed up the Widrow-Hoff classification algorithm by Steinbuch and Widrow (1965). A similar method is presented in Dezfoulian, MiriNezhad, Mousavi, Mosleh, and Shalchi (2016). In this case, the authors used MCIS in the first step and the Ho-Kashyap algorithm by Ho and Kashyap (1965) in the classification step.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Mousavi, MiriNezhad, and Mirmoini (2017) proposed a system using K-means clustering and triangular calculations to find support vectors. Mirinezhad, Dezfoulian, Mosleh, and Mousavi (2016) combined MCIS clustering and Widrow-Hoff classification algorithms to obtain the closest samples from each class and speed up the classification process. Table 1 provides a summary of the strengths and weaknesses of our proposed approach versus these other approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

An evolutionary Pentagon Support Vector finder method

Mousavi

Charles

Gherman

2020

Expert Systems with Applications

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In dealing with big data, we need effective algorithms; effectiveness that depends, among others, on the ability to remove outliers from the data set, especially when dealing with classification problems. To this aim, support vector finder algorithms have been created to save just the most important data in the data pool. Nevertheless, existing classification algorithms, such as Fuzzy C-Means (FCM), suffer from the drawback of setting the initial cluster centers imprecisely. In this paper, we avoid existing shortcomings and aim to find and remove unnecessary data in order to speed up the final classification task without losing vital samples and without harming final accuracy; in this sense, we present a unique approach for finding support vectors, named evolutionary pentagon support vector (PSV) finder method. The originality of the current research lies in using geometrical computations and evolutionary algorithms to make a more effective system, which has the advantage of higher accuracy in some data sets. The proposed method is subsequently tested with seven benchmark data sets and the results are compared to those obtained from performing classification on the original data (classification before and after PSV) under the same conditions. The testing returned promising results.

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Bees Local Phase Quantisation Feature Selection for RGB-D Facial Expression Recognition

Mousavi,

Ilanloo

2024

Springer Series in Advanced Manufacturing

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Optimization of the Ho-Kashyap classification algorithm using appropriate learning samples

Cited by 9 publications

References 9 publications

Nature inspired firefighter assistant by unmanned aerial vehicle (UAV) data

Nature inspired firefighter assistant by unmanned aerial vehicle (UAV) data

An evolutionary Pentagon Support Vector finder method

Bees Local Phase Quantisation Feature Selection for RGB-D Facial Expression Recognition

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