Data synchronization is a critical aspect in the deployment of Digital Twins within Fog-Edge Cloud environments, ensuring consistency and reliability across distributed systems. However, the security and efficiency of...
详细信息
Access control is a critical component of medical health record management, ensuring that only authorized individuals can access sensitive patient data. Existing access control schemes suffer from limitations such as ...
详细信息
Cooperative Intelligent Transport System(C-ITS)plays a vital role in the future road traffic management system.A vital element of C-ITS comprises vehicles,road side units,and traffic command centers,which produce a ma...
详细信息
Cooperative Intelligent Transport System(C-ITS)plays a vital role in the future road traffic management system.A vital element of C-ITS comprises vehicles,road side units,and traffic command centers,which produce a massive quantity of data comprising both mobility and service-related *** the extraction of meaningful and related details out of the generated data,data science acts as an essential part of the upcoming C-ITS *** the same time,prediction of short-term traffic flow is highly essential to manage the traffic *** to the rapid increase in the amount of traffic data,deep learning(DL)models are widely employed,which uses a non-parametric approach for dealing with traffic flow *** paper focuses on the design of intelligent deep learning based short-termtraffic flow prediction(IDL-STFLP)model for C-ITS that assists the people in various ways,namely optimization of signal timing by traffic signal controllers,travelers being able to adapt and alter their routes,and so *** presented IDLSTFLP model operates on two main stages namely vehicle counting and traffic flow *** IDL-STFLP model employs the Fully Convolutional Redundant Counting(FCRC)based vehicle count *** addition,deep belief network(DBN)model is applied for the prediction of short-term traffic *** further improve the performance of the DBN in traffic flow prediction,it will be optimized by Quantum-behaved bat algorithm(QBA)which optimizes the tunable parameters of *** results based on benchmark dataset show that the presented method can count vehicles and predict traffic flowin real-time with amaximumperformance under dissimilar environmental situations.
A frequent consequence of diabetes and a significant contributor to morbidity and mortality is diabetic foot ulcer (DFU).Early detection and appropriate management of DFUs are essential to prevent complications such a...
详细信息
A frequent consequence of diabetes and a significant contributor to morbidity and mortality is diabetic foot ulcer (DFU).Early detection and appropriate management of DFUs are essential to prevent complications such as infections and lower extremity amputations. In recent years, medical imaging and machine learning have emerged as promising tools for the automated detection and analysis of DFUs. We gathered a sizable foot imaging dataset, including DFU from multiple patients. This paper proposes a novel preprocessing technique based on the Shades of Gray color constancy algorithm to cope with noise and lighting variations in diabetic foot ulcer (DFU) images captured from different devices. The algorithm aims to enhance image quality, improve illumination normalization, and mitigate the impact of noise, thus providing more reliable and accurate DFU analysis and detection. Using the Diabetic Foot Infection Network with the Adam Optimizer (DFINET-AO), features were retrieved after the dataset had been preprocessed and divided. In order to comprehend the normal and pathological spectrum of diabetes, image data and numerical/text data are separated independently. Foot images of patients with aberrant diabetes coverage are separated from each other and classified using Pre-trained Fast Convolutional Neural Network (PFCNN), which has been trained on the U++network. Classification techniques, like foot ulcer analysis, forecast a etiology. This study's primary goal was to establish a novel method for evaluating the likelihood that diabetes individuals may acquire foot ulcers by imaging analysis of existing foot ulcers. The data was preprocessed and segmented after the researchers gathered a collection of foot photographs and medical information from historical records of diabetes patients. The amount of normal and pathological diabetes was then determined from numerical and textual data by extracting characteristics from the segmented data using DFINET-AO. To detect foot ulc
MANETs, as self-configuring networks lacking a fixed infrastructure, are exceptionally vulnerable to a multitude of security threats. One such severe threat is the Wormhole Attack, where malicious nodes create a virtu...
详细信息
Purpose:The present research work is carried out for determining haemoprotozoan diseases in cattle and breast cancer diseases in humans at early *** combination of LeNet and bidirectional long short-term memory(Bi-LST...
详细信息
Purpose:The present research work is carried out for determining haemoprotozoan diseases in cattle and breast cancer diseases in humans at early *** combination of LeNet and bidirectional long short-term memory(Bi-LSTM)model is used for the classification of heamoprotazoan samples into three classes such as theileriosis,babesiosis and ***,BreaKHis dataset image samples are classified into two major classes as malignant and *** hyperparameter optimization is used for selecting the prominent *** main objective of this approach is to overcome the manual identification and classification of samples into different haemoprotozoan diseases in *** traditional laboratory approach of identification is time-consuming and requires human *** proposed methodology will help to identify and classify the heamoprotozoan disease in early stage without much of human ***/methodology/approach:LeNet-based Bi-LSTM model is used for the classification of pathology images into babesiosis,anaplasmosis,theileriosis and breast images classified into malignant or *** optimization-based super pixel clustering algorithm is used for segmentation once the normalization of histopathology images is *** edge information in the normalized images is considered for identifying the irregular shape regions of images,which are structurally ***,it is compared with another segmentationapproach circularHough Transform(CHT).The CHT is used toseparatethe *** Canny edge detection and gaussian filter is used for extracting the edges before sending to ***:The existing methods such as artificial neural network(ANN),convolution neural network(CNN),recurrent neural network(RNN),LSTM and Bi-LSTM model have been compared with the proposed hyperparameter optimization approach with LeNET and *** results obtained by the proposed hyperparameter optimization-Bi-LSTM model showed the accuracy of
In the current scenario, recognizing various objects and tracking their movements in the real-time surveillance footage is the most difficult task. To detect objects, a combination of image processing and computer vis...
详细信息
The development of new technologies and also the provision of great connections are becoming increasingly important in all aspects of our everyday situations. This advanced technology also brings a variety of flaws, m...
详细信息
Advanced Double Input Layered Neural Network has the potential to revolutionize medical diagnostics by solving pressing problems. It improves diagnostic precision by providing a single, unified platform for the examin...
详细信息
ISBN:
(纸本)9798350359756
Advanced Double Input Layered Neural Network has the potential to revolutionize medical diagnostics by solving pressing problems. It improves diagnostic precision by providing a single, unified platform for the examination of both organized and unstructured medical data. It provides real-time decision assistance and data management by utilizing the scalable, secure, and efficient data processing capabilities made possible by cloud computing. Improved patient care, more effective therapy, and higher-quality healthcare are some areas where Advanced Double Input Layered Neural Network can make a difference. Rapid, precise, and secure data analysis;the administration of multiple data sources;and realtime decision support are a few of the difficulties inherent in medical diagnosis. This research proposes an Advanced Double Input Layered Neural Network (ADILNN) with a double input layered neural community, enabling it to examine prepared and unstructured scientific data. This novel technique improves the community's mastering functionality from various data kinds. By centralizing statistics garage, processing, and analysis on the cloud, computing sources may be extra reliably accessed whilst wanted. The network's diagnostic precision and flexibility are each advanced through way of tool gaining knowledge of strategies (MLM). Because of its adaptability, ADILNN can be utilized in diverse medical fields, which include radiology, pathology, cardiology, and genetics. It permits examine genomic information, making recovery alternatives, and analyzing x-ray photos. The technique has numerous ability makes use of in healthcare, improving prognosis accuracy in diverse settings. Simulation analysis is used to gauge ADILNN's capacity by way of gauging its diagnostic accuracy, processing speed, scalability, and records protection. These research validate ADILNN's potential to improve clinical analysis, streamline facts management, and guarantee healthcare records's safe and effectiv
In the current context, integrated distribution networks of the electricity grid to provide reliability during blackouts. Self-healing systems may keep functioning and reconfigure themselves in response to external br...
详细信息
暂无评论