Human Gait Recognition: A Single Stream Optimal Deep Learning Features Fusion

Saleem, Faizan; Khan, Muhammad Attique; Alhaisoni, Majed; Tariq, Usman; Armghan, Ammar; Alenezi, Fayadh; Choi, Jung-In; Kadry, Seifedine

doi:10.3390/s21227584

Cited by 36 publications

(20 citation statements)

References 63 publications

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“…The overall performance of the proposed method is much improved compared to already available methods. However, the following improvements will be considered in the future: (1) increase the number of images in the dataset, (2) minimize the identification time through feature optimization algorithms [39][40][41] to implement it in real time, and (3) implement some latest deep learning models [42][43][44][45].…”

Section: Discussionmentioning

confidence: 99%

Mango Leaf Disease Recognition and Classification Using Novel Segmentation and Vein Pattern Technique

et al. 2021

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Mango fruit is in high demand. So, the timely control of mango plant diseases is necessary to gain high returns. Automated recognition of mango plant leaf diseases is still a challenge as manual disease detection is not a feasible choice in this computerized era due to its high cost and the non-availability of mango experts and the variations in the symptoms. Amongst all the challenges, the segmentation of diseased parts is a big issue, being the pre-requisite for correct recognition and identification. For this purpose, a novel segmentation approach is proposed in this study to segment the diseased part by considering the vein pattern of the leaf. This leaf vein-seg approach segments the vein pattern of the leaf. Afterward, features are extracted and fused using canonical correlation analysis (CCA)-based fusion. As a final identification step, a cubic support vector machine (SVM) is implemented to validate the results. The highest accuracy achieved by this proposed model is 95.5%, which proves that the proposed model is very helpful to mango plant growers for the timely recognition and identification of diseases.

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Section: Discussionmentioning

confidence: 99%

Mango Leaf Disease Recognition and Classification Using Novel Segmentation and Vein Pattern Technique

et al. 2021

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“…HAR has emerged as an impactful research area in CV from the last decade [ 22 ]. It is based on important applications such as visual surveillance [ 23 ], robotics, biometrics [ 24 , 25 ], and smart healthcare centers to name a few [ 26 , 27 ]. Several researchers of computer vision developed techniques using machine learning [ 28 ] for HAR.…”

Section: Related Workmentioning

confidence: 99%

Human Action Recognition: A Paradigm of Best Deep Learning Features Selection and Serial Based Extended Fusion

Khan

Alhaisoni

et al. 2021

Sensors

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Human action recognition (HAR) has gained significant attention recently as it can be adopted for a smart surveillance system in Multimedia. However, HAR is a challenging task because of the variety of human actions in daily life. Various solutions based on computer vision (CV) have been proposed in the literature which did not prove to be successful due to large video sequences which need to be processed in surveillance systems. The problem exacerbates in the presence of multi-view cameras. Recently, the development of deep learning (DL)-based systems has shown significant success for HAR even for multi-view camera systems. In this research work, a DL-based design is proposed for HAR. The proposed design consists of multiple steps including feature mapping, feature fusion and feature selection. For the initial feature mapping step, two pre-trained models are considered, such as DenseNet201 and InceptionV3. Later, the extracted deep features are fused using the Serial based Extended (SbE) approach. Later on, the best features are selected using Kurtosis-controlled Weighted KNN. The selected features are classified using several supervised learning algorithms. To show the efficacy of the proposed design, we used several datasets, such as KTH, IXMAS, WVU, and Hollywood. Experimental results showed that the proposed design achieved accuracies of 99.3%, 97.4%, 99.8%, and 99.9%, respectively, on these datasets. Furthermore, the feature selection step performed better in terms of computational time compared with the state-of-the-art.

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“…Different deep learning models are available that perform different tasks such as object detection [ 13 ], visual tracking [ 14 ], semantic segmentation [ 15 ], and classification [ 16 ]. The researchers proposed different models like AlexNet, GoogleNet, ResNet, MobileNet, and EfficientNet to perform the classifications [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

“…The transfer learning [ 13 ] approach is used for improving the efficiency of the training models that are used to perform the classification. This approach makes learning faster and easier.…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Mammograms Classification Using Deep Neural Network and Entropy-Controlled Whale Optimization Algorithm

2022

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Breast cancer has affected many women worldwide. To perform detection and classification of breast cancer many computer-aided diagnosis (CAD) systems have been established because the inspection of the mammogram images by the radiologist is a difficult and time taken task. To early diagnose the disease and provide better treatment lot of CAD systems were established. There is still a need to improve existing CAD systems by incorporating new methods and technologies in order to provide more precise results. This paper aims to investigate ways to prevent the disease as well as to provide new methods of classification in order to reduce the risk of breast cancer in women’s lives. The best feature optimization is performed to classify the results accurately. The CAD system’s accuracy improved by reducing the false-positive rates.The Modified Entropy Whale Optimization Algorithm (MEWOA) is proposed based on fusion for deep feature extraction and perform the classification. In the proposed method, the fine-tuned MobilenetV2 and Nasnet Mobile are applied for simulation. The features are extracted, and optimization is performed. The optimized features are fused and optimized by using MEWOA. Finally, by using the optimized deep features, the machine learning classifiers are applied to classify the breast cancer images. To extract the features and perform the classification, three publicly available datasets are used: INbreast, MIAS, and CBIS-DDSM. The maximum accuracy achieved in INbreast dataset is 99.7%, MIAS dataset has 99.8% and CBIS-DDSM has 93.8%. Finally, a comparison with other existing methods is performed, demonstrating that the proposed algorithm outperforms the other approaches.

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Human Gait Recognition: A Single Stream Optimal Deep Learning Features Fusion

Cited by 36 publications

References 63 publications

Mango Leaf Disease Recognition and Classification Using Novel Segmentation and Vein Pattern Technique

Mango Leaf Disease Recognition and Classification Using Novel Segmentation and Vein Pattern Technique

Human Action Recognition: A Paradigm of Best Deep Learning Features Selection and Serial Based Extended Fusion

Breast Cancer Mammograms Classification Using Deep Neural Network and Entropy-Controlled Whale Optimization Algorithm

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