An efficient post-processing adaptive filtering technique to rectifying the flickering effects

Gandam, Anudeep; Sidhu, Jagroop Singh; Verma, Sahil; Jhanjhi, N. Z.; Nayyar, Anand; Abouhawwash, Mohamed; Nam, Yunyoung

doi:10.1371/journal.pone.0250959

Cited by 10 publications

(3 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using the K-Nearest neighbors algorithm uses Euclidean distance of input image to every other point and uses majority voting from K minimum distance points to classify the image [35][36][37]. The generalized equation for calculating the distance is given below 2 where p 1 = (x 1 , y 1 ) and p 2 = (x 2 , y 2 ) Random forest algorithm makes use of an ensemble learning technique with multiple decision trees to classify input images [38,39].…”

Section: Image Classification Machine Learning Algorithmsmentioning

confidence: 99%

Plant Disease Diagnosis and Image Classification Using Deep Learning

Sharma¹,

Singh²,

Kavita³

et al. 2022

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

Indian agriculture is striving to achieve sustainable intensification, the system aiming to increase agricultural yield per unit area without harming natural resources and the ecosystem. Modern farming employs technology to improve productivity. Early and accurate analysis and diagnosis of plant disease is very helpful in reducing plant diseases and improving plant health and food crop productivity. Plant disease experts are not available in remote areas thus there is a requirement of automatic low-cost, approachable and reliable solutions to identify the plant diseases without the laboratory inspection and expert's opinion. Deep learning-based computer vision techniques like Convolutional Neural Network (CNN) and traditional machine learning-based image classification approaches are being applied to identify plant diseases. In this paper, the CNN model is proposed for the classification of rice and potato plant leaf diseases. Rice leaves are diagnosed with bacterial blight, blast, brown spot and tungro diseases. Potato leaf images are classified into three classes: healthy leaves, early blight and late blight diseases. Rice leaf dataset with 5932 images and 1500 potato leaf images are used in the study. The proposed CNN model was able to learn hidden patterns from the raw images and classify rice images with 99.58% accuracy and potato leaves with 97.66% accuracy. The results demonstrate that the proposed CNN model performed better when compared with other machine learning image classifiers such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree and Random Forest.

show abstract

Section: Image Classification Machine Learning Algorithmsmentioning

confidence: 99%

Plant Disease Diagnosis and Image Classification Using Deep Learning

Sharma¹,

Singh²,

Kavita³

et al. 2022

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

show abstract

“…Standard Zigbee has a range of 30 m in urban environment with 1 mW of energy for transmission [86]. A higher range of 90 m is available in Zigbee Pro in the same scenario with an output power of 63 mW, and Pro-900 can cover up to 610 m in urban environment scenario with a power of 250 mW [87]. Healthcare wireless body networks require 1 mW of power and short range for on-body exchange.…”

Section: Zigbeementioning

confidence: 99%

Challenges and Limitation Analysis of an IoT-Dependent System for Deployment in Smart Healthcare Using Communication Standards Features

Upadhyay¹,

Kumar

Upadhyay

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

The use of IoT technology is rapidly increasing in healthcare development and smart healthcare system for fitness programs, monitoring, data analysis, etc. To improve the efficiency of monitoring, various studies have been conducted in this field to achieve improved precision. The architecture proposed herein is based on IoT integrated with a cloud system in which power absorption and accuracy are major concerns. We discuss and analyze development in this domain to improve the performance of IoT systems related to health care. Standards of communication for IoT data transmission and reception can help to understand the exact power absorption in different devices to achieve improved performance for healthcare development. We also systematically analyze the use of IoT in healthcare systems using cloud features, as well as the performance and limitations of IoT in this field. Furthermore, we discuss the design of an IoT system for efficient monitoring of various healthcare issues in elderly people and limitations of an existing system in terms of resources, power absorption and security when implemented in different devices as per requirements. Blood pressure and heartbeat monitoring in pregnant women are examples of high-intensity applications of NB-IoT (narrowband IoT), technology that supports widespread communication with a very low data cost and minimum processing complexity and battery lifespan. This article also focuses on analysis of the performance of narrowband IoT in terms of delay and throughput using single- and multinode approaches. We performed analysis using the message queuing telemetry transport protocol (MQTTP), which was found to be efficient compared to the limited application protocol (LAP) in sending information from sensors.

show abstract

“…Afterwards, vessels are extracted by utilizing K-mean clustering method [53]. Likewise, there are many other suggested works are available in the literature for the improvement and detection health diseases utilizing machine learning [54][55][56][57][58] and deep learning [59][60][61][62][63][64].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Method to Enhance Thick and Thin Vessels for Blood Vessel Segmentation

Dash¹,

Verma

Kavita

et al. 2021

Diagnostics

Self Cite

View full text Add to dashboard Cite

Retinal blood vessels have been presented to contribute confirmation with regard to tortuosity, branching angles, or change in diameter as a result of ophthalmic disease. Although many enhancement filters are extensively utilized, the Jerman filter responds quite effectively at vessels, edges, and bifurcations and improves the visualization of structures. In contrast, curvelet transform is specifically designed to associate scale with orientation and can be used to recover from noisy data by curvelet shrinkage. This paper describes a method to improve the performance of curvelet transform further. A distinctive fusion of curvelet transform and the Jerman filter is presented for retinal blood vessel segmentation. Mean-C thresholding is employed for the segmentation purpose. The suggested method achieves average accuracies of 0.9600 and 0.9559 for DRIVE and CHASE_DB1, respectively. Simulation results establish a better performance and faster implementation of the suggested scheme in comparison with similar approaches seen in the literature.

show abstract

An efficient post-processing adaptive filtering technique to rectifying the flickering effects

Cited by 10 publications

References 41 publications

Plant Disease Diagnosis and Image Classification Using Deep Learning

Plant Disease Diagnosis and Image Classification Using Deep Learning

Challenges and Limitation Analysis of an IoT-Dependent System for Deployment in Smart Healthcare Using Communication Standards Features

A Hybrid Method to Enhance Thick and Thin Vessels for Blood Vessel Segmentation

Contact Info

Product

Resources

About