Bunch graph based dimensionality reduction using auto-encoder for character recognition

Bhadoria, Robin Singh; Samanta, Sovan; Pathak, Yadhunath; Shukla, Piyush Kumar; Zubi, Ahmad Ali Al; Kaur, Manjit

doi:10.1007/s11042-022-12907-y

Cited by 7 publications

(2 citation statements)

References 30 publications

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“…An MBFOA [27] that has an improved ftness function computation is employed in the process of selecting the best possible value for each feature [28]. MBFOA required fewer user parameters to be specifed while producing results that were comparable to those of other methods, and it reduced the total number of function evaluations performed.…”

Section: Segmentation and Feature Extractionmentioning

confidence: 99%

Content‐Based Audio Classification and Retrieving Using Modified Bacterial Foraging Optimization Algorithm

Samha

Alshammri

Nuagah

et al. 2023

Computational Intelligence and Neuroscience

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Audio classification and retrieval has been recognized as a fascinating field of endeavor for as long as it has existed due to the topic of identifying and choosing the most useful audio attributes. The categorization of audio files is significant not only in the area of multimedia applications but also in the disciplines of medicine, sound analysis, intelligent homes and cities, urban informatics, entertainment, and surveillance. This study introduces a new algorithm called the modified bacterial foraging optimization algorithm (MBFOA), which is based on a method that retrieves and classifies audio data. The goal of this algorithm is to reduce the computational complexity of existing techniques. Along with the combination of the peak estimated signal, the enhanced mel-frequency cepstral coefficient (EMFCC) and the enhanced power normalized cepstral coefficients (EPNCC) are used. These are then optimized using the fitness function utilizing MBFOA. The probabilistic neural network is used to differentiate between a music signal and a spoken signal from an audio source (PNN). It is next necessary to extract and list the characteristics that correspond to the class that was arrived at as a consequence of the categorization. When compared to other approaches that are somewhat similar, MBFOA demonstrates superior levels of sensitivity, specificity, and accuracy.

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Section: Segmentation and Feature Extractionmentioning

confidence: 99%

Content‐Based Audio Classification and Retrieving Using Modified Bacterial Foraging Optimization Algorithm

Samha

Alshammri

Nuagah

et al. 2023

Computational Intelligence and Neuroscience

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“…For these reasons, it has a direct impact on the entire output of goods, which in turn has an impact on the revenue of farmers and the general health of the world economy. Farmers and the rest of society need food items of high quality at a reasonable price [1]. However, infection in plants can cause a significant amount of damage in the agricultural industry.…”

Section: Detailed Analysis Of Existing Studymentioning

confidence: 99%

Instinctive Recognition of Pathogens in Rice Using Reformed Fractional Differential Segmentation and Innovative Fuzzy Logic-Based Probabilistic Neural Network

Preetham

Ahmad

Wattar

et al. 2022

Journal of Food Quality

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Rice is an essential primary food crop in the world, and it plays a significant part in the country’s economy. It is the most often eaten stable food and is in great demand in the market as the world’s population continues to expand. Rice output should be boosted to fulfil the growing demand. As a result, the yield of plant crops diminishes, creating an environment conducive to the spread of infectious illnesses. To boost the production of agricultural fields, it is necessary to remove plant diseases from the environment. This study presents ways for recognising three types of rice plant diseases, as well as a healthy leaf, in rice plants. This includes image capture, image preprocessing, segmentation, feature extraction, and classification of three rice plant illnesses, as well as classification of a healthy leaf, among other techniques. Following the K-means segmentation, the features are extracted utilising three criteria, which are colour, shape, and texture, to generate a final product. Colour, shape, and texture are the parameters used in the extraction of the features. It is proposed that a novel intensity-based technique is used to retrieve colour features from the infected section, whereas the form parameters of the infected section, such as the area and diameter, and the texture characteristics of the infected section are extracted using a grey-level co-occurrence matrix. The colour features are retrieved depending on the characteristics of the features. All three previous techniques were surpassed by the proposed fuzzy logic-based probabilistic neural network on a range of performance metrics, with the new network obtaining greater accuracy. Finally, the result is validated using the fivefold cross-validation method, with the final accuracy for the diseases such as bacterial leaf blight, brown spot, healthy leaf, and rice blast being 95.20 percent, 97.60 percent, 99.20 percent, and 98.40 percent, respectively, and 95.40 percent for the disease brown spot.

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On the Sanskruti index of graphs

Mondal

Das

2022

J. Appl. Math. Comput.

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Bunch graph based dimensionality reduction using auto-encoder for character recognition

Cited by 7 publications

References 30 publications

Content‐Based Audio Classification and Retrieving Using Modified Bacterial Foraging Optimization Algorithm

Content‐Based Audio Classification and Retrieving Using Modified Bacterial Foraging Optimization Algorithm

Instinctive Recognition of Pathogens in Rice Using Reformed Fractional Differential Segmentation and Innovative Fuzzy Logic-Based Probabilistic Neural Network

On the Sanskruti index of graphs

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