Design of a Classifier model for Heart Disease Prediction using normalized graph model

Heart disease is an illness that influences enormous people worldwide. Particularly in cardiology, heart disease diagnosis and treatment need to happen quickly and precisely. Here, a machine learning-based (ML) approach is anticipated for diagnosing a cardiac disease that is both effective and accurate. The system was developed using standard feature selection algorithms for removing unnecessary and redundant features. Here, a novel normalized graph model (n – GM) is used for prediction. To address the issue of feature selection, this work considers the significant information feature selection approach. To improve classification accuracy and shorten the time it takes to process classifications, feature selection techniques are utilized. Furthermore, the hyper-parameters and learning techniques for model evaluation have been accomplished using cross-validation. The performance is evaluated with various metrics. The performance is evaluated on the features chosen via features representation. The outcomes demonstrate that the suggested (n – GM) gives 98 % accuracy for modeling an intelligent system to detect heart disease using a classifier support vector machin

Data Processing and Management in IoT and Wireless Sensor Network

Abstract The deployment of internet over larger scale may introduce huge challenges based on data processing. The enormous amount of IoT based data needs design-based solution for faster data processing and improving its extensibility and adaptability. Based on various IoT based data processing, servicing technologies may provide data-centric models for scalable services. This work concentrates on an extensive review towards the scalable realization and acquisition of data for process. This IoT based services are larger enough to be concentrated. This review gives an insight towards the data processing and management in IoT and sensor-based networking.</jats:p

An Efficient Network Threat Detection and Classification Method using Anp-Mvps Algorithm in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are deployed generally in a hostile environment, where an adversary captures some nodes that are physically connected in the network. It initially reprograms the nodes and makes them replicate into a number of clones, thereby having control over them. In order to provide a distributed solution to resolve the above specified problem specified above, a framework based on Authentic Node Placement based Message Verification and Passing Strategy (ANP-MVPS) is proposed. Some of the solutions offered by existing techniques are not satisfactory due to Energy and Memory constraints. This turns to be a serious drawback for protocols used in WSN’s resource constrained environment. In this work, three diverse factors are considered for investigation. They are: Firstly, modeling of Authentic Node Placement based Message Verification and Passing Strategy (ANP-MVPS) is performed to identify the distributed mechanism of clone in a network and prevent the replication of clone among them. Secondly, the parameter selection Probability of Occurrence of IP, Mean Time Intervals, Time to Live, ACK value, Time Stamp Field, SYN value, Differentiated Service Field and Sequence Number are considered before performing classification. Thirdly, an efficient Naive Bayesian classifier for security analysis based on trust value (NB-TV) is used to estimate the performance metrics like accuracy, sensitivity, specificity, F-measure, Recall etc. This method shows satisfactory results when compared to existing techniques. The simulation was carried out in MATLAB environment. The proposed method shows better trade off in contrast to prevailing techniques.</jats:p

Design of a Classifier model for Heart Disease Prediction using normalized graph model

Heart disease is an illness that influences enormous people worldwide. Particularly in cardiology, heart disease diagnosis and treatment need to happen quickly and precisely. Here, a machine learning-based (ML) approach is anticipated for diagnosing a cardiac disease that is both effective and accurate. The system was developed using standard feature selection algorithms for removing unnecessary and redundant features. Here, a novel normalized graph model (n – GM) is used for prediction. To address the issue of feature selection, this work considers the significant information feature selection approach. To improve classification accuracy and shorten the time it takes to process classifications, feature selection techniques are utilized. Furthermore, the hyper-parameters and learning techniques for model evaluation have been accomplished using cross-validation. The performance is evaluated with various metrics. The performance is evaluated on the features chosen via features representation. The outcomes demonstrate that the suggested (n – GM) gives 98 % accuracy for modeling an intelligent system to detect heart disease using a classifier support vector machin

Investigations on Brain Tumor Classification Using Hybrid Machine Learning Algorithms

The imaging modalities are used to view other organs and analyze different tissues in the body. In such imaging modalities, a new and developing imaging technique is hyperspectral imaging. This multicolour representation of tissues helps us to better understand the issues compared to the previous image models. This research aims to analyze the tumor localization in the brain by performing different operations on hyperspectral images. The tumor is located using the combination of k-based clustering processes like k-nearest neighbour and k-means clustering. The value of k in both methods is determined using the optimization process called the firefly algorithm. The optimization processes reduce the manual calculation for finding K’s optimal value to segment the brain regions. The labelling of the areas of the brain is done using the multilayer feedforward neural network. The proposed technique produced better results than the existing methods like hybrid k-means clustering and parallel k-means clustering by having a higher peak signal-to-noise ratio and a lesser mean absolute error value. The proposed model achieved 96.47% accuracy, 96.32% sensitivity, and 98.24% specificity, which are improved compared to other techniques.</jats:p