Feature decision-making ant colony optimization system for an automated recognition of plant species

Ghasab, Mohammad Ali Jan; Khamis, Shamsul; Mohammad, Faruq; Fariman, Hessam Jahani

doi:10.1016/j.eswa.2014.11.011

Cited by 104 publications

(15 citation statements)

References 25 publications

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“…They showed that their proposed method performs better than GA and PCA. Some researchers have also used particle swarm optimization (Gunasundari et al, 2016;Xue et al, 2014) and ant colony optimization (Ali Jan Ghasab et al, 2015) to deal with the feature selection problem. These research studies either used the accuracy rate or used a weighted sum of the accuracy rate and the number of selected features for evaluating the performance of the individuals in the population.…”

Section: Review Of Feature Selection Methodsmentioning

confidence: 99%

“…In the first stage, we deal with the problem of identifying subsets of important features, which will be used as inputs to the second stage for building a stacking model. In order to perform feature selection, researchers have applied several metaheuristic algorithms, including ant colony optimization (Ali Jan Ghasab et al, 2015;Goodarzi et al, 2009), particle swarm optimization (Gunasundari et al, 2016;Xue et al, 2014), and genetic algorithm (Garc ıa-Nieto et al, 2009;Jiang et al, 2017;Tsai and Hsiao, 2010;Urraca et al, 2015). We have chosen the genetic algorithm as the search strategy for feature selection since it is known for its capability in exploring solution space in combinatorial optimization problems (Gen and Cheng, 2000).…”

Section: Introductionmentioning

confidence: 99%

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A metaheuristic-based stacking model for predicting the risk of patient no-show and late cancellation for neurology appointments

Ahmadi

Garcia-Arce

Masel

et al. 2019

IISE Transactions on Healthcare Systems Engineering

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Patient no-shows and late cancellations for an appointment are common problems in healthcare, which adversely affect the financial performance and quality of service of healthcare organizations. A high rate of patient no-show and late cancellation in a clinic can significantly limit access to healthcare. In general, hospitals create predictive models to assess risk of no-show, and then assign overbooking appointments utilizing those risks. In this paper, by incorporating machine learning and optimization techniques, we proposed a predictive model to assist with the overbooking decision. The model consists of two phases. First, we utilized a metaheuristic optimization technique to explore the best subset of featuresknown as feature selection problemthat can significantly contribute to the prediction outcomes. Second, using the output of the first stage, we proposed a stacking model to improve the prediction performances further. Our extensive computations and comparisons across different classifiers show that formulating the feature selection problem as a multi-objective problem instead of a single-objective problem using random forest classifier yields better results. The proposed model will improve the overbooking at clinics, by increasing the patient access to care. We introduced important new features to the literature that can describe the no-show and late cancellation behavior.

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Section: Review Of Feature Selection Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A metaheuristic-based stacking model for predicting the risk of patient no-show and late cancellation for neurology appointments

Ahmadi

Garcia-Arce

Masel

et al. 2019

IISE Transactions on Healthcare Systems Engineering

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show abstract

“…Typically, as in [9], wavelets have been used to decompose images, fractals to extract features, and artificial neural network to classify leaf images. Ghasab et al [19] used texture features derived from GLCM, namely contrast, correlation, energy, homogeneity, and entropy, and combined them with shape, color, and vein features. In [20], Kadir et al built foliage plant identification systems.…”

Section: Related Workmentioning

confidence: 99%

Plant Leaves Recognition Based on a Hierarchical One-Class Learning Scheme with Convolutional Auto-Encoder and Siamese Neural Network

et al. 2021

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In this paper, we propose a novel method for plant leaves recognition by incorporating an unsupervised convolutional auto-encoder (CAE) and Siamese neural network in a unified framework by considering Siamese as an alternative to the conventional loss of CAE. Rather than the conventional exploitation of CAE and Siamese, in our case we have proposed to extend CAE for a novel supervised scenario by considering it as one-class learning classifier. For each class, CAE is trained to reconstruct its positive and negative examples and Siamese is trained to distinguish the similarity and the dissimilarity of the obtained examples. On the contrary and asymmetric to the related hierarchical classification schemes which require pre-knowledge on the dataset being recognized, we propose a hierarchical classification scheme that doesn’t require such a pre-knowledge and can be employed by non-experts automatically. We cluster the dataset to assemble similar classes together. A test image is first assigned to the nearest cluster, then matched to one class from the classes that fall under the determined cluster using our novel one-class learning classifier. The proposed method has been evaluated on the ImageCLEF2012 dataset. Experimental results have proved the superiority of our method compared to several state-of-the art methods.

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“…These phases essentially include the preparation of the collected leaves, the preparation of some pre-processing to classify their basic features, leaf classification, database compilation, training for identification and the final results assessment. The leaves, while most widely used for plant identification, can be used for automatic processing of stem, flowers, petals, seeds and even the entire plant [15]. Non-botanical experts may use an automated plant identification system to classify plant species easily and without any difficulty [16].…”

Section: Figure 1 Medical Plant Leafmentioning

confidence: 99%

A Group Labelled Classification Model for Accurate Medical Plant Detection Used in Drug Preparation

Thella¹,

Ulagamuthalvi²

2021

RIA

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The use of medical plants in the preparation of medicines has been increased in recent years. Medical plants are an essential component in the production of medicinal products. Medicines are made from root powder or plant leaves. When the herbal medicine is reduced to powder, more experience is required to determine the medicinal product through pharmacognoses. Inaccurate medical plants can cause patients serious health problems. For standardization and quality control of medical drugs the correct identification of the powder shape of medical plants is important. Medical plants are currently classified using a chemical leaf-based assessment, physical assessment and biological assessment. In medicine industry it is extremely necessary to identify the right medicinal plants for the preparation of a medicine. Its leaves form, color and texture are the key features needed to recognize a medicinal plant. In hierarchical clustering technology, the coefficient of inconsistency is used to generate natural clusters. Intra class differences can be seen with the amount of clusters obtained for plant organisms. The aggregate of the corresponding vectors of each sample of a cluster is calculated for one cluster representation. In terms of its leaf samples, therefore, the multiple members of the valued interval type are used to represent the plants in an effective way. The proposed model performs classification of leaf features using group labelled clustering model and then perform locking of labelling. This paper considers a Group Labelled Classification (GLC) Model that examines feature on the front and back of a green leaf, along with morphological characteristics, to achieve a specific optimal combination of features that optimize the recognition rate. The proposed model efficiently extracts the relevant features only from the medical leaf for accurate medical leaf detection. The proposed model is compared with the traditional methods and the results show that the proposed model performance is better.

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Feature decision-making ant colony optimization system for an automated recognition of plant species

Cited by 104 publications

References 25 publications

A metaheuristic-based stacking model for predicting the risk of patient no-show and late cancellation for neurology appointments

A metaheuristic-based stacking model for predicting the risk of patient no-show and late cancellation for neurology appointments

Plant Leaves Recognition Based on a Hierarchical One-Class Learning Scheme with Convolutional Auto-Encoder and Siamese Neural Network

A Group Labelled Classification Model for Accurate Medical Plant Detection Used in Drug Preparation

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