In this study, a Discriminator Model for Glaucoma Diagnosis (DMGD)using soft computing techniques is presented. As the biomedical images such asfundus images are often acquired in high resolution, the Region of Intere...
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In this study, a Discriminator Model for Glaucoma Diagnosis (DMGD)using soft computing techniques is presented. As the biomedical images such asfundus images are often acquired in high resolution, the Region of Interest (ROI)for glaucoma diagnosis must be selected at first to reduce the complexity of anysystem. The DMGD system uses a series of pre-processing;initial cropping by thegreen channel’s intensity, Spatially Weighted Fuzzy C Means (SWFCM), bloodvessel detection and removal by Gaussian Derivative Filters (GDF) and inpaintingalgorithms. Once the ROI has been selected, the numerical features such as colour, spatial domain features from Local Binary Pattern (LBP) and frequencydomain features from LAWS are generated from the corresponding ROI forfurther classification using kernel based Support Vector Machine (SVM). TheDMGD system performances are validated using four fundus image databases;ORIGA, RIM-ONE, DRISHTI-GS1, and HRF with four different kernels;LinearKernel (LK), Polynomial Kernel (PK), Radial Basis Function (RBFK) kernel,Quadratic Kernel (QK) based SVM classifiers. Results show that the DMGD system classifies the fundus images accurately using the multiple features and kernelbased classifies from the properly segmented ROI.
—Drones are a vital part of our daily lives because of their flexible flying nature and low operation and maintenance costs. Navigation is the most important aspect in the autonomous drone era. With that being said, ...
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Cloud-based SDN(Software Defined Network)integration offers new kinds of agility,flexibility,automation,and speed in the *** and Cloud providers both leverage the benefits as networks can be configured and optimized b...
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Cloud-based SDN(Software Defined Network)integration offers new kinds of agility,flexibility,automation,and speed in the *** and Cloud providers both leverage the benefits as networks can be configured and optimized based on the application *** integration of cloud and SDN paradigms has played an indispensable role in improving ubiquitous health care *** has improved the real-time monitoring of patients by medical ***’data get stored at the central server on the cloud from where it is available to medical practitioners in no *** centralisation of data on the server makes it more vulnerable to malicious attacks and causes a major threat to patients’*** recent days,several schemes have been proposed to ensure the safety of patients’*** most of the techniques still lack the practical implementation and safety of *** this paper,a secure multi-factor authentication protocol using a hash function has been ***(Body Area Network)logic has been used to formally analyse the proposed scheme and ensure that no unauthenticated user can steal sensitivepatient *** Protocol Animator(SPAN)–Automated Validation of Internet Security Protocols and Applications(AVISPA)tool has been used for *** results prove that the proposed scheme ensures secure access to the database in terms of spoofing and *** comparisons of the proposed scheme with other related historical schemes regarding time complexity,computation cost which accounts to only 423 ms in proposed,and security parameters such as identification and spoofing prove its efficiency.
File entropy is one of the major indicators of crypto-ransomware because the encryption by ransomware increases the randomness of file ***,entropy-based ransomware detection has certain limitations;for example,when di...
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File entropy is one of the major indicators of crypto-ransomware because the encryption by ransomware increases the randomness of file ***,entropy-based ransomware detection has certain limitations;for example,when distinguishing ransomware-encrypted files from normal files with inherently high-level entropy,misclassification is very *** addition,the entropy evaluation cost for an entire file renders entropy-based detection impractical for large *** this paper,we propose two indicators based on byte frequency for use in ransomware detection;these are termed EntropySA and DistSA,and both consider the interesting characteristics of certain file subareas termed“sample areas”(SAs).For an encrypted file,both the sampled area and the whole file exhibit high-level randomness,but for a plain file,the sampled area embeds informative structures such as a file header and thus exhibits relatively low-level randomness even though the entire file exhibits high-level *** and DistSA use“byte frequency”and a variation of byte frequency,respectively,derived from sampled *** indicators cause less overhead than other entropy-based detection methods,as experimentally proven using realistic ransomware *** evaluate the effectiveness and feasibility of our indicators,we also employ three expensive but elaborate classification models(neural network,support vector machine and threshold-based approaches).Using these models,our experimental indicators yielded an average Fl-measure of 0.994 and an average detection rate of 99.46%for file encryption attacks by realistic ransomware samples.
This study presents the Normal Discriminant Feature Selection based Regressive Deep Neural MapReduce (NDFS-RDNMR) framework designed for efficient prediction of diabetic chronic diseases using input datasets. The prim...
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Diabetes, influenced by factors like high blood pressure, aging, obesity, and poor lifestyle choices, has become a significant health issue, increasing the risk of heart disease, kidney disease, stroke, and other seri...
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For a parameter Ε ∈ (0, 1), a set of Ε-locality-sensitive orderings (LSOs) has the property that for any two points, p, q ∈ [0, 1)d, there exist an order in the set such that all the points between p and q (in the...
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One of the most effective methods of training a model for intrusion detection requires a very good selection of features from the data and efficient and robust training algorithms to facilitate a better prediction mod...
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Serving generative inference of the large language model is a crucial component of contemporary AI applications. This paper focuses on deploying such services in a heterogeneous and cross-datacenter setting to mitigat...
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Serving generative inference of the large language model is a crucial component of contemporary AI applications. This paper focuses on deploying such services in a heterogeneous and cross-datacenter setting to mitigate the substantial inference costs typically associated with a single centralized datacenter. Towards this end, we propose HEXGEN, a flexible distributed inference engine that uniquely supports the asymmetric partition of generative inference computations over both tensor model parallelism and pipeline parallelism and allows for effective deployment across diverse GPUs interconnected by a fully heterogeneous network. We further propose a sophisticated scheduling algorithm grounded in constrained optimization that can adaptively assign asymmetric inference computation across the GPUs to fulfill inference requests while maintaining acceptable latency levels. We conduct an extensive evaluation to verify the efficiency of HEXGEN by serving the state-of-the-art LLAMA-2 (70B) model. The results suggest that HEXGEN can choose to achieve up to 2.3× lower latency deadlines or tolerate up to 4× more request rates compared with the homogeneous baseline given the same budget. Our implementation is available at https://***/Relaxed-System-Lab/HexGen. Copyright 2024 by the author(s)
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