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Brain Tumour Detection by Gamma DeNoised Wavelet Segmented Entropy Classifier

Computers, Materials & Continua 2021年第11期69卷 2093-2109页

作者： Simy Mary Kurian Sujitha Juliet Devaraj vinodh p.vijayan Karunya Institute of Technology and Science CoimbatoreIndia Albertian Institute of Science and Technology KochiIndia

Magnetic resonance imaging(MRI)is an essential tool for detecting brain ***,identification of brain tumours in the early stages is a very complex task since MRI images are susceptible to noise and other environmental *** order to overcome these problems,a Gamma MAp denoised Strömberg wavelet segmentation based on a maximum entropy classifier(GMDSWS-MEC)model is developed for efficient tumour detection with high accuracy and low time *** GMDSWS-MEC model performs three steps,namely pre-processing,segmentation,and *** the GMDSWS-MEC model,the Gamma MAp filter performs the pre-processing task and achieves a significant increase in the peak signal-tonoise ratio by removing noisy artefacts from the input brain *** pre-processing,Strömberg wavelet transform segmentation is carried out to partition the pre-processed image into a number of blocks based on the features extracted from the ***,the maximum entropy classifier identifies and locates the tumour from the input image based on extracted features with high accuracy and minimal error *** a number of MRI images,experimental evaluation and comparison of the proposed model and existing methods is carried out on the basis of four metrics:peak signalto-noise ratio,tumour detection accuracy,error rate,and tumour detection time with respect to MRI image *** proposed model offers superior performance in terms of all four metrics.

关键词： Gamma MAp filter Strömberg wavelet transform maximum entropy classifier

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Tricube Weighted Linear Regression and Interquartile for Cloud Infrastructural Resource Optimization

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Computer Systems Science & Engineering 2023年第6期45卷 2281-2297页

作者： Neema George B.K.Anoop vinodh p.vijayan Srinivas University MangaloreIndia Mangalam College of Engineering KottayamIndia

Cloud infrastructural resource optimization is the process of precisely selecting the allocating the correct resources either to a workload or *** workload execution,accuracy,and cost are accurately stabilized in opposition to the best possible framework in real-time,efficiency is *** addition,every workload or application required for the framework is characteristic and these essentials change over ***,the existing method was failed to ensure the high Quality of Service(QoS).In order to address this issue,a Tricube Weighted Linear Regression-based Inter Quartile(TWLR-IQ)for Cloud Infrastructural Resource Optimization is introduced.A Tricube Weighted Linear Regression is presented in the proposed method to estimate the resources(i.e.,CpU,RAM,and network bandwidth utilization)based on the usage history in each cloud ***,Inter Quartile Range is applied to efficiently predict the overload hosts for ensuring a smooth *** results show that our proposed method is better than the approach in Cloudsim under various performance *** results clearly showed that the proposed method can reduce the energy consumption and provide a high level of commitment with ensuring the minimum number of Virtual Machine(VM)Migrations as compared to the state-of-the-art methods.

关键词： Cloud infrastructure tricube weighted linear regression inter quartile CpU RAM network bandwidth utilization

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A Combinatorial Optimized Knapsack Linear Space for Information Retrieval

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Computers, Materials & Continua 2021年第3期66卷 2891-2903页

作者： Varghese S.Chooralil vinodh p.vijayan Biju paul M.M.Anishin Raj B.Karthikeyan G.Manikandan Rajagiri School of Engineering&Technology Kochi682039India Mangalam College of Engineering Kottayam686631India Department of CSE Viswajyothi College of Engineering&TechnologyVazhakulam686670India School of Computing SASTRA Deemed To Be UniversityThanjavur613401India

Key information extraction can reduce the dimensional effects while evaluating the correct preferences of users during semantic data ***,the classifiers are used to maximize the performance of web-page recommendation in terms of precision and *** recent method disambiguates contextual sentiment using conceptual prediction with robustness,however the conceptual prediction method is not able to yield the optimal ***-dependent terms are primarily evaluated by constructing linear space of context features,presuming that if the terms come together in certain consumerrelated reviews,they are semantically ***,the more frequently they coexist,the greater the semantic dependency ***,the influence of the terms that coexist with each other can be part of the frequency of the terms of their semantic dependence,as they are non-integrative and their individual meaning cannot be *** this work,we consider the strength of a term and the influence of a term as a combinatorial optimization,called Combinatorial Optimized Linear Space Knapsack for Information Retrieval(COLSK-IR).The COLSK-IR is considered as a knapsack problem with the total weight being the“term influence”or“influence of term”and the total value being the“term frequency”or“frequency of term”for semantic data *** method,by which the term influence and the term frequency are considered to identify the optimal solutions,is called combinatorial ***,we choose the knapsack for performing an integer programming problem and perform multiple experiments using the linear space through combinatorial optimization to identify the possible optimum *** is evident from our experimental results that the COLSK-IR provides better results than previous methods to detect strongly dependent snippets with minimum ambiguity that are related to inter-sentential context during semantic data analysis.

关键词： Key information extraction web-page context-dependent nonintegrative combinatorial optimization knapsack

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