There are six main categories of breast cancer be existent. In this paper, we have taken the Type 1 carcinoma cancer to support the decision making. For this, a novel machine learning based cost optimization is applied to make an efficient decision from the samples. Moreover, we have applied our methodology on the real datasets to predict cancer with appropriate parameters using Pearson correlation. This work can be used well on lightweight devices like smartphones or tablets to decide more precisely with primary factors. © 2017 IEEE.

Chandra Segar T

Department of Digital Communications

School of Information Technology and Engineering

Vellore Campus

Rashad manzoor

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

2017 International conference of Electronics, Communication and Aerospace Technology (ICECA)

In the rural side, there is the absence of centers for cardiovascular ailment. Due to this, around 12 million people passing worldwide reported by WHO. The principal purpose of coronary illness is a propensity for smoking. ML classifiers are applied to predict the risk of cardiovascular disease. However, the ML model has some inherent problems like it’s serene to feature selection, splitting attribute, and imbalanced datasets prediction. Most of the mass datasets have multi-class labels, but their combinations are in different proportions. In this paper, we experiment with our system with Cleveland’s heart samples from the UCI repository. Our cluster-based DT learning (CDTL) mainly includes five key stages. At first, the original set has partitioned through target label distribution. From the high distribution samples, the other possible class combination has made. For each class-set combination, the significant features have identified through entropy. With the significant critical features, an entropy-based partition has made. At last, on these entropy clusters, RF performance is made through significant and all features in the prediction of heart disease. From our CDTL approach, the RF classifier achieves 89.30% improved prediction accuracy from 76.70% accuracy (without CDTL). Hence, the error rate of RF with CDTL has significantly reduced from 23.30 to 9.70%. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

Evolutionary Intelligence

Optimal feature selection through a cluster-based DT learning (CDTL) in heart disease prediction

Decision making is used for the selection oriented process. But can we use it in the field of healthcare is the biggest question. The answer is yes here also we can have with machine learning (ML) classifiers like Decision Tree (DT), random forest (RF), support vector machine (SVM), and neural network (NN). In the case of breast cancer (BC) disease, we can predict using machine learning classifiers. For this, original BC samples experiment. Our model named clustering based feature selection (CFS) intends to increase the accuracy and to reduce the feature dimension. We experiment the classifiers in three stages: 1. all features of BC, 2. DT based cluster, and 3. Feature selection. In all feature, RF achieves 99.10% accuracy, in DT based cluster RF, achieves 99.37% accuracy, and finally in cluster-based feature selection (CFS) with only four features the RF achieves 99.50% accuracy. The experimental results of our CFS approach along with DT, RF, SVM, and NN classifier along with its performance are presented. © 2019 IEEE.

2019 Innovations in Power and Advanced Computing Technologies (i-PACT)

Optimized machine learning model using Decision Tree for cancer prediction

2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI)

Accuracy Prediction Using Machine Learning Techniques for Indian Patient Liver Disease

A new variant of RSA has been proposed called Memory Efficient Multi Key (MEMK) generation scheme. For sensitive data, our scheme will aid in exchanging the information between cloud to IoT and IoT to IoT devices. When cryptography belongs to the asymmetric type, then it has public and private keys. For memory efficiency, our scheme reuses the RSA scheme with a Diophantine form of the nonlinear equation. Moreover, our scheme performance comparatively performs well and this mainly due to the use of RSA public key alone. Due to this, our MEMK does not require multiplicative inverse function or Extended Euclid's algorithm. Finally, we have made an experimental result on various phases of MEMK PKC such as key generation, encryption, and decryption by varying the N-bit modulo bits from 1K to 10K. © 2017 IEEE.

2017 Innovations in Power and Advanced Computing Technologies (i-PACT)

Memory efficient multi key (MEMK) generation scheme for secure transportation of sensitive data over cloud and IoT devices

Software metric LOC data analysis for symmetric key cryptographic technique

In the computing world, the digital transformation of data grows exponentially with every year. However, these situations are predominantly tackled by leading enterprises through by adopting a hybrid IT model. This model effectively supports the organization through strategic design to provide standard delivery to customers from independent multi-sourced entities. An experimental design method is applied using hybrid IT architecture with gene-based cryptomata (HITAGC) which is well suited to real-time cloud environments to store and retrieve the cloud space data efficiently. The CSC primarily relies on the service of CSP to keep their confidential data, and this system ultimately uses the PKC crypts. To address this security demand, we propose two different schemes such as HITAGC-PKC and hybrid HITAGC. The first model is designed to generate robust asymmetric keys for the Internet of Things, and the other hybrid HITAGC is for efficient sharing of big data over the cloud in a secure manner. The performance of these crypts is compared with the traditional systems like standard RSA, ESRKGS, SED2, EDCon, and AES. © 2018, Springer-Verlag London Ltd., part of Springer Nature.

Service Oriented Computing and Applications

Hybrid IT architecture by gene-based cryptomata (HITAGC) for lightweight security services

This paper mainly focuses on the design aspects of force balance measurement system for aerodynamic vehicle flying at hypersonic speed. When the speed of vehicle is hypersonic, additional aerodynamic heating creates difficulty in the system and needs change in the design geometry and material type .For the purpose of examining the model we have considered a simulation of blunt cone with following parameters like, length of the string, diameter of string, material of string, drag coefficient, pressure, velocity, density of air, Mach number, Reynolds’s number, stagnation enthalpy were suitably selected. CFD is used as a tool to calculate the drag force generated under the hypersonic conditions. A measurement system was developed for measuring the strain developed in the blunt cone when it is subjected to shock force. The measurement system was calibrated statically using hang weight and dynamically using impact load test performed on the string, from the calibration test impulse response function is calculated. And a fuzzy predication based approach was developed to predict the strain developed in the system.

Materials Today: Proceedings

One-Dimension Force Balance System for Hypersonic Vehicle an experimental and Fuzzy Prediction Approach

IoT devices are everywhere. Shortly, every 'thing' is going to be connected and interact with each other. What are these things? They can be your microwaves, heaters, air conditioners or even your refrigerators. It assessed that there would be 50 billion connected IoT devices by 2020 which is about 7 IoT gadgets for every individual on the planet. IoT devices are low powered devices which have a minimal amount of processing capabilities. With ever increasing number of IoT devices, there is a subsequent need for secure communication among them. In this paper, we aim to propose an encryption algorithm used for secure communication by taking into account the limitations of IoT devices. © 2017 IEEE.

Multi key distribution scheme by diophantine form for secure IoT communications

Decision making is a difficult task over the complex, spirited and competitive environment. A variety of research provides diverse notification for individual failure in making logical decisions under certain circumstances by applying normative approaches. In today's competitive market, the advance of technology is closely related to consumer needs for the extraction of good quality products at less cost which is in rarely finding a model in Consumer to Business (C2B). The aim of the paper is to optimize the C2B model by intuitionistic Fuzzy to resolve multi decision making problems. It derives multi criteria decision making to support complex environments in C2B using membership and non-membership attributes which is built to generate the intuitionistic fuzzy priority weighting vector based on minimum distance and similarity measures. This technique classifies the object type as per the customer needs with a ranking in the optimized way make efficient for the customer satisfaction. © 2017 IEEE.

Journal	Data powered by Typeset2017 International conference of Electronics, Communication and Aerospace Technology (ICECA)
Publisher	Data powered by TypesetIEEE
Open Access	0