Dimensionality Reduction for Classification and Clustering

Singh, Devender; Leavline, E. Jebamalar

doi:10.5815/ijisa.2019.04.06

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…The optimized VGG22 model demonstrated superior performance across all metrics, proving that deep model modifications can lead to efficient gender classification in crowded settings while meeting the constraints of embedded systems. [1] The research introduced a novel approach for gender identification in chickens using an enhanced ResNet-50 algorithm, incorporating the Squeeze-and-Excitation attention mechanism, Swish activation function, and Ranger optimizer. Trained and tested on a dataset of 960 chicken photos, the model underwent ablation studies to confirm the effectiveness of each component.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In crowded environments, determining a person's gender may be a difficult process owing to a number of variables including occlusions, differences in stances, and distinct physical characteristics [1]. However, effective gender recognition in situations like these has important implications for applications in crowd control, surveillance, and the study of social behavior [2].…”

Section: Introductionmentioning

confidence: 99%

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Detection of Gender in Crowds Using ResNet Model

Rajeev G. Vishwakarma

2024

jes

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The ResNet model is used in this investigation to suggest a gender detection solution for use in congested settings. Due to occlusions, varied stances, and various features, determining a person's gender in crowded surroundings may be a difficult and time-consuming job. The ResNet model, which is a deep convolutional neural network architecture, is used to solve these difficulties because of its capacity to capture detailed characteristics and its efficiency in managing deep network structures. The strategy that has been suggested entails preprocessing the input photos, sending those images through the ResNet model, and then extracting gender-related characteristics from those images. The ResNet model is made up of a number of residual blocks with skip connections, which makes it easier to learn complicated representations. After that, the learnt characteristics are input into fully linked layers, and then softmax activation is used to determine the subject's gender. The usefulness of the technique that was developed was shown by experimental findings on a large dataset, which achieved a high level of accuracy in gender determination. The use of the ResNet model helps the system to handle complicated scenarios and improves the system's ability to accurately recognize gender in situations with a large number of people. The method that has been developed has the potential to find applications in areas such as surveillance, crowd control, and the study of social behavior.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of Gender in Crowds Using ResNet Model

Rajeev G. Vishwakarma

2024

jes

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“…In the articles [8][9][10][11][12][13][14][15][16][17][18] are presented the different approaches to the analytical assessments of the parameters of distributed computer systems and of the information spaces.…”

Section: Literature Surveymentioning

confidence: 99%

Method of Parallel Information Object Search in Unified Information Spaces

Dodonov¹,

Mukhin²,

Zavgorodnii³

et al. 2021

IJCNIS

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The article describes the concept of a unified information space and an algorithm of its formation using a special information and computer system. The process of incoming object search in a unified information space is considered, which makes it possible to uniquely identify it by corresponding features. One of the main tasks of a unified information space is that each information object in it is uniquely identified. For this, the identification method was used, which is based on a step-by-step analysis of object characteristics. The method of parallel information object search in unified information spaces is proposed, when information object search will be conducted independently in all unified information spaces in parallel. Experimental studies of the method of parallel information object search in unified information spaces were conducted, on the basis of which the analysis of efficiency and incoming objects search time in unified information spaces was carried out. There was experimentally approved that the more parameters that describe the information object, the less the time of object identification depends on the length of the interval. Also, there was experimentally approved that the efficiency of the searching of the incoming objects in unified information spaces tends to a directly proportional relationship with a decrease in the length of the interval and an increase in the number of parameters, and vice versa.

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Feature Selection Using Improved Teaching Learning Based Algorithm on Chronic Kidney Disease Dataset

Manonmani

Sarojini

2020

Procedia Computer Science

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Dimensionality Reduction for Classification and Clustering

Cited by 3 publications

References 22 publications

Detection of Gender in Crowds Using ResNet Model

Detection of Gender in Crowds Using ResNet Model

Method of Parallel Information Object Search in Unified Information Spaces

Feature Selection Using Improved Teaching Learning Based Algorithm on Chronic Kidney Disease Dataset

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