Internet Protocol Security (IPSec) is a protocol suite for securing Internet Protocol (IP) communications by authenticating and encrypting each IP packet of a data stream. . IPSec architecture requires the host to provide confidentiality using Encapsulating Security Payload and data integrity using either Authentication Header or Encapsulating Security Payload and anti-replay protection. IPSec has become the most common network layer security control and, a widely deployed mechanism for implementing Virtual Private Networks (VPNs). This paper presents analysis of IPSec VPN for videoconference in real time traffic over a secure communication links by implementing an IPSec-based VPN technology.
Diabetic Retinopathy is a retinal abnormality which is characterized by progressive damage to the retina, eventually leading to irreversible blindness. In this paper, we propose an aggregated residual transformation‐based model for automatic multistage classification of diabetic retinopathy. The proposed model obtains 99.68% overall classification accuracy, 99.68% sensitivity, 99.89% specificity and 99.68% precision without overfitting on the MESSIDOR dataset. Further, the model obtains an accuracy of 99.89% for stage 0, 99.89% for stage 1, 99.68% for stage 2 and 99.89% for stage 3 of diabetic retinopathy. In comparison to residual network, the model shows an overall accuracy gain of 0.52%. The model also ensures an overall improvement of more than 6% in accuracy, 1.2% in sensitivity and 2.43 % in specificity when compared to best results reported in the literature. The proposed work outperforms the existing methods and achieves state‐of‐the‐art results for the multistage classification of diabetic retinopathy.
Background and Introduction:
Diabetes mellitus is a metabolic disorder that has emerged as
a serious public health issue worldwide. According to the World Health Organization (WHO),
without interventions, the number of diabetic incidences is expected to be at least 629 million
by 2045. Uncontrolled diabetes gradually leads to progressive damage to eyes, heart, kidneys,
blood vessels and nerves.
Method:
The paper presents a critical review of existing statistical and Artificial Intelligence
(AI) based machine learning techniques with respect to DM complications namely retinopathy,
neuropathy and nephropathy. The statistical and machine learning analytic techniques are used
to structure the subsequent content review.
Result:
It has been inferred that statistical analysis can help only in inferential and descriptive
analysis whereas, AI based machine learning models can even provide actionable prediction
models for faster and accurate diagnose of complications associated with DM.
Conclusion:
The integration of AI based analytics techniques like machine learning and deep
learning in clinical medicine will result in improved disease management through faster disease
detection and cost reduction for disease treatment.
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