Graph based event measurement for analyzing distributed anomalies in sensor networks

Sherubha, P.; Sasirekha, S.; Sivaram, M.; Gowsic, K.; Mohanasundaram, N.

doi:10.1007/s12046-020-01451-w

Cited by 40 publications

(14 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Voxel techniques [18] evaluate brain MRI by accurately measuring local tissues density (white and grey matter) following rigid normalization of actual brain images [19]- [20]. Sherubha et al [21] suggested identifying volumetric information from specific brain parts from MR scans and then using them to categorize gender and AD.…”

Section: Related Workmentioning

confidence: 99%

Effective Multitier Network Model for MRI Brain Disease Prediction using Learning Approaches

Ravinder¹,

Mohammed²

2022

IJACSA

View full text Add to dashboard Cite

Brain disease prognosis is considered a hot research topic where the researchers intend to predict the clinical measures of individuals using MRI data to evaluate the pathological stage and identifies the progression of the disease. With the lack of incomplete clinical scores, various existing learning-based approaches simply eradicate the score without ground truth score computation. It helps restrict the training data samples with robust and reliable models during the learning process. The major disadvantage of the prior approaches is the adoption of hand-crafted features, as these features are not wellsuited for the prediction process. This research concentrates on modelling a weakly supervised multi-tier dense neural network model (𝒘𝒔 − 𝑴𝑻𝑫𝑵𝑵) for examining the progression of brain disease using the available MRI data. The model helps analyze the incomplete clinical scores. The preliminary ties of the network model initially haul out the distinctive patches from the MRI to extract the global and local structural features (information) and develop a superior multi-tier dense neural network model for task-based image feature extraction and perform prediction in the successive tiers for computing the clinical measures. The loss function is adopted while examining the available individuals even in the absence of ground-truth values. The experimentation is done with the available online Dataset like ADNI-1/2, and the model works effectually with this Dataset compared to other approaches.

show abstract

Section: Related Workmentioning

confidence: 99%

Effective Multitier Network Model for MRI Brain Disease Prediction using Learning Approaches

Ravinder¹,

Mohammed²

2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…Based on these analyses and the best of the knowledge retrieved with the extensive analysis, no existing works have been performed fault tolerance with multi-tier cloud model to guarantee fault-tolerance, elimination of data redundancy, and fairness based resource allocation [24] - [25]. This model intends to predict the system fault and provides the resistance towards the fault by avoiding nonessential data over the cloud environment.…”

Section: Related Workmentioning

confidence: 99%

Modelling an Efficient Three-Tier Fault Tolerance Approach for Resource Provisioning over Cloud

Pawar¹,

Yalla²

2022

EAI Endorsed Transactions on Cloud Systems

View full text Add to dashboard Cite

INTRODUCTION: Nowadays, the cloud computing paradigm encounters newer challenges in offering fault tolerance methods during service provisioning. The failures during service provisioning are unavoidable in the large-scale heterogeneous network. Therefore, the adoption of appropriate fault tolerance techniques can improve the provided service's efficiency and reliability. OBJECTIVES: Thus, fault tolerating metrics give better accuracy to enhance the QoS, where the three-tier fault-tolerance approach is proposed to resolve the various failures in service provisioning. METHODS: Initially, a Collector Model collects the request and ranks it based on the service to be provided. Secondly, the redundancy filter module is designed to filter out the request replication and avoid the unnecessary process to be carried out. Finally, the fairness resource allocation module is designed to perform the prominent request received from the users based on the available resources without service congestion. RESULTS: This three-tier model operates concurrently to handle the multiple requests from the users of various connected nodes. The experimental analysis demonstrates that the three-tier fault tolerance model can enhance the cloud reliability over the large-scale heterogeneous network by ensuring QoS. CONCLUSION: The well-realized fault tolerance approach can efficiently demonstrate the structural model and fault tolerance process over the computing environment, therefore enhancing the cloud extendibility. Moreover, the computing environment's failure is hugely complex, and failure has to be handled efficiently.

show abstract

“…The system for encouraging decision-making was built and developed by Sherubha et al [31], employing different classifiers such as probability-based NB, BPN, LDA, decisions trees, lazy learners k-NN, and techniques for heuristic search categorization. On the Gathered the information, these algorithms' reliability was 78%, 81.5%, 90%, 93% and 76% correspondingly [32] - [35].…”

Section: Related Workmentioning

confidence: 99%

A customized stacked dense network model for chronic kidney disease prediction

Shadadi¹,

Alamer

2022

ijhs

View full text Add to dashboard Cite

Chronic kidney disease (CKD) is still a health concern, even though surgical care and treatment have improved. Recently, academics throughout the globe have been more interested in creating high-performance approaches for diagnosing, treating, and preventing kidney disease by being more knowledgeable about the aspects that the issue is concerned with, designed to provide better. Evaluation of patient records for patients may assist health care providers detects sickness earlier on. Several have tried to construct sophisticated algorithms that forecast CKD by analyzing health data, but their effectiveness needs improvement. An intelligence categorization and linear regression model are suggested in this paper. The kidney-related disorders are predicted using a customized stacked dense network model (). Compared to current models, the testing of the conceptual scheme reveals that it can predict CKD with 98.5% accuracy. Research suggests that utilizing sophisticated deep learning algorithms is advantageous for treatment decisions and may assist in the early diagnosis of CKD and its associated stages, reducing the development of kidney problems.

show abstract

Graph based event measurement for analyzing distributed anomalies in sensor networks

Cited by 40 publications

References 9 publications

Effective Multitier Network Model for MRI Brain Disease Prediction using Learning Approaches

Effective Multitier Network Model for MRI Brain Disease Prediction using Learning Approaches

Modelling an Efficient Three-Tier Fault Tolerance Approach for Resource Provisioning over Cloud

A customized stacked dense network model for chronic kidney disease prediction

Contact Info

Product

Resources

About