Optimization of Ultrasound Information Imaging Algorithm in Cardiovascular Disease Based on Image Enhancement

Shao, Yuan‐Hai; Wu, Jue; Ou, Hongping; Pei, Min; Liu, Li; Movassagh, Ali Akbar; Sharma, Ashutosh; Gheisari, Mehdi; Asheralieva, Alia

doi:10.1155/2021/5580630

Cited by 13 publications

(6 citation statements)

References 55 publications

(50 reference statements)

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“…In conclusion, there are different ways a problem can be solved. And for attendance management system the most feasible design seems to be when cloud technologies [31][32][33][34][35][36][37][38][39] and service robots, deep learning etc. [40][41][42][43][44][45][46][47][48][49][50][51][52][53] are used in conjunction.…”

Section: Embedded Systems-based Attendance Systemsmentioning

confidence: 99%

Automation Attendance Systems Approaches: A Practical Review

Jahangir¹,

Atanu²,

Liefa³

et al. 2021

BIJIAM

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Accounting for people is the first step of every manpower-based organization in today's world. Hence, it takes up a signification amount of energy and value in the form of money from respective organizations for both implementing a suitable system for manpower management as well as maintaining that same system. Although this amount of expenditure for big organizations is near to nothing, rather just a formality, it does not hold as much truth for small organizations such as schools, colleges, and even universities to a certain degree. This is the first point. The second point for discussion is that much work has been done to solve this issue. Various technologies like Biometrics, RFID, Bluetooth, GPS, QR Code, etc., have been used to tackle the issues of attendance collection. This study paves the path for researchers by reviewing practical methods and technologies used for existing attendance systems.

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Section: Embedded Systems-based Attendance Systemsmentioning

confidence: 99%

Automation Attendance Systems Approaches: A Practical Review

Jahangir¹,

Atanu²,

Liefa³

et al. 2021

BIJIAM

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“…The medical experts provide the treatment by observing and analysing those images [1]. Different medical imaging techniques being used in daily life are Magnetic Resonance Imaging (MRI), Computerized Tomography, Ultrasonic and X-ray [2,3] which applied individually through variety of sensory systems. Medical imaging also helps doctor in viewing inner portions of the body and diagnose properly.…”

Section: Introduction Various Medicalmentioning

confidence: 99%

Computer-aided Diagnosis applied to MRI images of Brain Tumor using Spatial Fuzzy Level Set and ANN Classifier

Virupakshappa¹,

Veerashetty²,

Ambika³

2022

SCPE

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The most vital organs in the human body are the brain, heart, and lungs. Because the brain controls and coordinates the operations of all other organs, normal brain function is vital. Brain tumour is a mass of tissues which interrupts the normal functioning of the brain, if left untreated will lead to the death of the subject. The classification of multiclass brain tumours using spatial fuzzy based level sets and artificial neural network (ANN) techniques is proposed in this paper. In the proposed method, images are preprocessed using Median Filtering technique, the boundaries of the Brain Tumor are obtained using Spatial Fuzzy based Level Set method, features are extracted using Gabor Wavelet and Gray-Level Run Length Matrix (GLRLM) methods. Finally ANN technique is used for the classification of the image into Normal or Benign Tumor or Malignant Tumor. The proposed method was implemented in the MATLAB working platform and achieved classification accuracy of 94%, which is significant compared to state-of-the-art classification techniques. Thus, the proposed method assist in differentiating between benign and malignant brain tumours, enabling doctors to provide adequate treatment.

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“…The reconfiguration in CR-IoT operates in a dynamic way based on the characteristics of CR-IoT, hence the deployment of the ML method is required to cooperate with the reconfiguration of CR-IoT and selection of better paths by sensing its environment [33][34][35][36][37][38][39][40][41]. It is essential for the present study to check the consistency of the ML method in acting as a prominent approach on analysing the entire characteristics of CR-IoTs.…”

Section: Introductionmentioning

confidence: 99%

An IoT and machine learning‐based routing protocol for reconfigurable engineering application

Natarajan¹,

Srihari

Chandragandhi³

et al. 2021

IET Communications

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With new telecommunications engineering applications, the cognitive radio (CR) networkbased internet of things (IoT) resolves the bandwidth problem and spectrum problem. However, the CR-IoT routing method sometimes presents issues in terms of road finding, spectrum resource diversity and mobility. This study presents an upgradable cross-layer routing protocol based on CR-IoT to improve routing efficiency and optimize data transmission in a reconfigurable network. In this context, the system is developing a distributed controller which is designed with multiple activities, including load balancing, neighbourhood sensing and machine-learning path construction. The proposed approach is based on network traffic and load and various other network metrics including energy efficiency, network capacity and interference, on an average of 2 bps/Hz/W. The trials are carried out with conventional models, demonstrating the residual energy and resource scalability and robustness of the reconfigurable CR-IoT. INTRODUCTIONWireless networks reconfigurable (RWN) is mainly an adaptive network firmware developed to satisfy the demands of modern applications, changing network topologies and changing network conditions. In particular, the RWM can be reconfiguredThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Optimization of Ultrasound Information Imaging Algorithm in Cardiovascular Disease Based on Image Enhancement

Cited by 13 publications

References 55 publications

Automation Attendance Systems Approaches: A Practical Review

Automation Attendance Systems Approaches: A Practical Review

Computer-aided Diagnosis applied to MRI images of Brain Tumor using Spatial Fuzzy Level Set and ANN Classifier

An IoT and machine learning‐based routing protocol for reconfigurable engineering application

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