Multiview Objects Recognition Using Deep Learning-Based Wrap-CNN with Voting Scheme

Balamurugan, D.; Aravinth, S. S.; Reddy, Pundru Chandra Shaker; Rupani, Ajay; Manikandan, A.

doi:10.1007/s11063-021-10679-4

Cited by 51 publications

(7 citation statements)

References 38 publications

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“…Network energy dissipation can be achieved by selecting a sleep schedule that is based on the distance of each hop. TDMA scheduling and an asynchronous duty cycling system can be used to avoid idle node energy consumption by using uneven clustering to improve energy efficiency [18]. The nodes are constantly awakened by the synchronous protocol for the specified period.…”

Section: Related Workmentioning

confidence: 99%

A Novel Routing Protocol for Low-Energy Wireless Sensor Networks

Suresh

Prabhu²,

Parthasarathy

et al. 2022

Journal of Sensors

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The battery power limits the energy consumption of wireless sensor networks (WSN). As a result, its network performance suffered significantly. Therefore, this paper proposes an opportunistic energy-efficient routing protocol (OEERP) algorithm for reducing network energy consumption. It provides accurate target location detection, energy efficiency, and network lifespan extension. It is intended to schedule idle nodes into a sleep state, thereby optimising network energy consumption. Sleep is dynamically adjusted based on the network’s residual energy (RE) and flow rate (FR). It saves energy for a longer period. The sleep nodes are triggered to wake up after a certain time interval. The simulation results show that the proposed OEERP algorithm outperforms existing state-of-the-art algorithms in terms of accuracy, energy efficiency, and network lifetime extension.

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Section: Related Workmentioning

confidence: 99%

A Novel Routing Protocol for Low-Energy Wireless Sensor Networks

Suresh

Prabhu²,

Parthasarathy

et al. 2022

Journal of Sensors

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“…As a result, it is evident that computer-based techniques and machine learning algorithms significantly positively influence on data mining techniques, logistic analysis, and accessibility choices 18 . Additionally, with straightforward data management, robots and computers may do the same work in less time 19 .…”

Section: Introductionmentioning

confidence: 99%

A novel SpaSA based hyper-parameter optimized FCEDN with adaptive CNN classification for skin cancer detection

Ali,

Manikandan,

Lei

et al. 2024

Sci Rep

Self Cite

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Skin cancer is the most prevalent kind of cancer in people. It is estimated that more than 1 million people get skin cancer every year in the world. The effectiveness of the disease’s therapy is significantly impacted by early identification of this illness. Preprocessing is the initial detecting stage in enhancing the quality of skin images by removing undesired background noise and objects. This study aims is to compile preprocessing techniques for skin cancer imaging that are currently accessible. Researchers looking into automated skin cancer diagnosis might use this article as an excellent place to start. The fully convolutional encoder–decoder network and Sparrow search algorithm (FCEDN-SpaSA) are proposed in this study for the segmentation of dermoscopic images. The individual wolf method and the ensemble ghosting technique are integrated to generate a neighbour-based search strategy in SpaSA for stressing the correct balance between navigation and exploitation. The classification procedure is accomplished by using an adaptive CNN technique to discriminate between normal skin and malignant skin lesions suggestive of disease. Our method provides classification accuracies comparable to commonly used incremental learning techniques while using less energy, storage space, memory access, and training time (only network updates with new training samples, no network sharing). In a simulation, the segmentation performance of the proposed technique on the ISBI 2017, ISIC 2018, and PH2 datasets reached accuracies of 95.28%, 95.89%, 92.70%, and 98.78%, respectively, on the same dataset and assessed the classification performance. It is accurate 91.67% of the time. The efficiency of the suggested strategy is demonstrated through comparisons with cutting-edge methodologies.

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“…Deep learning approaches have recently gained popularity because of their ability to beat prior state-of-the-art techniques in various tasks and the amount of complex data from various sources (e.g., visual, auditory, medical, social, and sensor) ( 14 ). Deep learning has made significant advances in a wide range of computer vision tasks, including object recognition ( 15 ), motion tracking ( 16 ), and medical image classification and detection ( 17 , 18 ). Classification of brain tumors for medical specialists is an important field where computer vision and deep learning techniques work together and bring prosperity to patients with non-invasive diagnosis of brain tumors using MRI.…”

Section: Introductionmentioning

confidence: 99%

Brain tumor classification from MRI scans: a framework of hybrid deep learning model with Bayesian optimization and quantum theory-based marine predator algorithm

Ullah,

Khan,

Masood

et al. 2024

Front. Oncol.

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Brain tumor classification is one of the most difficult tasks for clinical diagnosis and treatment in medical image analysis. Any errors that occur throughout the brain tumor diagnosis process may result in a shorter human life span. Nevertheless, most currently used techniques ignore certain features that have particular significance and relevance to the classification problem in favor of extracting and choosing deep significance features. One important area of research is the deep learning-based categorization of brain tumors using brain magnetic resonance imaging (MRI). This paper proposes an automated deep learning model and an optimal information fusion framework for classifying brain tumor from MRI images. The dataset used in this work was imbalanced, a key challenge for training selected networks. This imbalance in the training dataset impacts the performance of deep learning models because it causes the classifier performance to become biased in favor of the majority class. We designed a sparse autoencoder network to generate new images that resolve the problem of imbalance. After that, two pretrained neural networks were modified and the hyperparameters were initialized using Bayesian optimization, which was later utilized for the training process. After that, deep features were extracted from the global average pooling layer. The extracted features contain few irrelevant information; therefore, we proposed an improved Quantum Theory-based Marine Predator Optimization algorithm (QTbMPA). The proposed QTbMPA selects both networks’ best features and finally fuses using a serial-based approach. The fused feature set is passed to neural network classifiers for the final classification. The proposed framework tested on an augmented Figshare dataset and an improved accuracy of 99.80%, a sensitivity rate of 99.83%, a false negative rate of 17%, and a precision rate of 99.83% is obtained. Comparison and ablation study show the improvement in the accuracy of this work.

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Multiview Objects Recognition Using Deep Learning-Based Wrap-CNN with Voting Scheme

Cited by 51 publications

References 38 publications

A Novel Routing Protocol for Low-Energy Wireless Sensor Networks

A Novel Routing Protocol for Low-Energy Wireless Sensor Networks

A novel SpaSA based hyper-parameter optimized FCEDN with adaptive CNN classification for skin cancer detection

Brain tumor classification from MRI scans: a framework of hybrid deep learning model with Bayesian optimization and quantum theory-based marine predator algorithm

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