Efficiency of iterative filtering method for solving Volterra fuzzy integral equations with a delay and material investigation

Yookesh, T. L.; Chithambarathanu, M.; Kumar, E. Boopathi; Sekhar, P. Sudam

doi:10.1016/j.matpr.2021.05.025

Cited by 6 publications

(6 citation statements)

References 19 publications

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“…In this method, these techniques require there to be adequate training data available for each specific image. It is based on computing invariant representations usually cover large areas and require per-pixel labels [12]. Each…”

Section: Dense Connected Convolution Neural Networkmentioning

confidence: 99%

Dense connected convolution neural network for land cover classification

Ragul¹,

Karthikeyan²

2022

ijhs

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Hyperspectral Imaging is employed to monitor the earth regions on basis of spectral continuous data ranges initializing from visible wave infrared region to short wave infrared region of the electromagnetic spectrum. It authorizes the detailed recognition and classification of land cover on account of spectral feature space. Hyperspectral images seemed to be presented by employing traditional unsupervised and supervised classifier with regards to classification. Various problems seemed to cause Hughes phenomenon as it represents the curse of dimensionality issues. In spite of mitigating those challenges, a deep ensemble classification model seemed to be proposed in this work. It process the data features using various convolution layers of the network along modelling the activation function as a simple structure for classification of the hyperspectral data based on the spectral values using Softmax layer and error function to minimize the losses. Dense Connected Convolution Neural Network projected in this work as it has high potential to effectively classify the spectral features with learnt weights from one individual convolution layer to convolution layers. The main idea of Dense Convolution Neural Network is to produce discriminative classification results and to enhance the accuracy and diversity of a classifier simultaneously.

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Section: Dense Connected Convolution Neural Networkmentioning

confidence: 99%

Dense connected convolution neural network for land cover classification

Ragul¹,

Karthikeyan²

2022

ijhs

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“…In this part, segmented abnormal features of the diabetic's retinopathy have been applied to Discriminative Convolution Neural Network. Discriminative Convolution Neural Network [11] identifies retinal disease progression and the severity of the disease using prognostic factor of visual and anatomical outcome in various retinal diseases in several stages on basis of detection of Microaneurysms, Haemorrhages, Hard Exudates and Soft Exudates [12]. AlexaNet architecture is to produce a set of feature map with the smallest resolution and semantic information of the feature gathered from the PCA and ABC techniques.…”

Section: Discriminative Convolution Neural Networkmentioning

confidence: 99%

Discriminative convolution neural network architecture for diagnosis of diabetic retinopathy through classification and progression prediction of lesions grading in color fundus images of retina

Senthilkumar

Bharathi²,

Raja³

2022

ijhs

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Diabetic Retinopathy (DR) is a common complication of diabetes mellitus, which causes lesions on the retina that effect vision. Many researchers have supported the ophthalmologists to diagnosis and classify the stage of the disease in the retinal fundus images using machine learning model. Machine learning models are less impressive for several staging diseases due to clinical grading. In order to develop a system capable of classifying the lesion grading images on disease pathology, a unique deep learning architecture named as discriminative Convolution Neural Network has been employed towards diagnosis of diabetic retinopathy through classification and progression prediction of lesion grading in fundus image of Retina. Initially Image Pre-processing has been carried out using wiener filter to remove the noise and Contrast limited adaptive histogram equalization for image enhancement. Pre-processed image has been processed using Oriented Fast and Rotated Brief for feature descriptors. It contains like Optic distance, Fovea, blood Vessel, Blot haemorrhages, Exudate number, exudates area, Macular Edema, Bifurcation, Shannon entrophy, Kapur Entropy and Renyis Entropy, LBP entropy, LBP energy and Microaneurysm. On these obtained features, feature reduction has to be carried out using principle component analysis to eliminate the irrelevant features before the onset of the process.

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“…Several artificial intelligence-based routing techniques, including as genetic algorithms, particle swarm optimisation, and ant colony optimisation, are used to carry out efficient data transmission [7]. In order to provide reliable and stable data transfer, an unmanned aerial vehicle (sink) has been integrated with VANET in this research.…”

Section: Introductionmentioning

confidence: 99%

Design and Simulation of Efficient Dynamic Data Forwarding Protocol towards Route Optimization in VANET Infrastructure

Maheshwari,

Hemapriyai,

Savithri

2023

ijmst

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The VANET, which consists of the Internet and the Internet of Things (IoT), has recently been implemented with the Internet of Vehicles (IoV). Through the use of a car network and an AdHoc approach, it provides the user with environmental traffic information. Combining the networks of cars, known as VANETs, allows for infrastructure-free data transfer, particularly for entertainment and emergency vehicle notification. The data or packet delivery ratio, data communication latency, data transmission faults, and details of vehicle collisions are among the traffic information provided by the internet of vehicles. In this work, an unmanned aerial vehicle (UAV) is used to solve the problem of route optimisation in VANET topologies for effective data transfer. Many routing protocols are used to improve the effectiveness of the packet or data routing process. In order to alleviate the routing issue and generate an effective way for efficient data transfer with strong network stability, the Optimised Link State Routing (OLSR) Protocol is used in this study with energy-saving techniques on many parameters. To evaluate the efficacy of the suggested routing technique on various network sizes, extensive simulations are carried out on the NS2 simulator with various network parameters. When compared to traditional routing protocols along UAV-based protocols for data transmission, the performance results of the suggested data routing solution employing OLSR protocol achieve higher efficient pathways. Ultimately, the suggested routing system produces superior outcomes in terms of message throughput, message delivery ratio, latency and network overhead on comparing with existing routing protocols.

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Efficiency of iterative filtering method for solving Volterra fuzzy integral equations with a delay and material investigation

Cited by 6 publications

References 19 publications

Dense connected convolution neural network for land cover classification

Dense connected convolution neural network for land cover classification

Discriminative convolution neural network architecture for diagnosis of diabetic retinopathy through classification and progression prediction of lesions grading in color fundus images of retina

Design and Simulation of Efficient Dynamic Data Forwarding Protocol towards Route Optimization in VANET Infrastructure

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