Kongu Engineering College

About: Kongu Engineering College is a based out in . It is known for research contribution in the topics: Cluster analysis & Control theory. The organization has 2001 authors who have published 1978 publications receiving 16923 citations.

Unmanned Ground Vehicle for Surveillance

Abstract: Remote worked spy-robots can be immensely helpful in the event that they can be controlled remotely over a bigger working reach. Availability of various modalities for their remote-control movement can furthermore upgrade their abilities and the extent of usages. The human lives are administered by automation industry and robotic world. The robots can be used widely mainly due to its simplicity and their ability to provide solution to the problems. The key intention of this paper is to create an automated vehicle for investigation by using progressive machineries like Wi-Fi. The robot containing the camera can continuously send the video. This class of surveillance robots can be mainly used for spying the enemy in battle.

Fuzzy Relational Equation in Preventing Diabetic Heart Attack

Abstract: Data Mining aims at discovering knowledge out of data and presenting it in a form that is easily compressible to humans. It is a process that is developed to examine large amounts of data routinely collected. Fuzzy Systems are been used for solving a wide range of problems in different application domain Genetic Algorithm for designing. Fuzzy Systems allows us to introduce the learning and adaptation capabilities. The fuzzy set framework has been used in several different process of diagnosis of disease. Fuzzy logic is a computational paradigm that provides a mathematical tool for dealing with the uncertainty and the imprecision typical of human reasoning. Fuzzy relational between symptoms and risks factors for Diabetic based on the expert’s medical knowledge is taken and also related complications or due to some common metabolic disorder it may lead to vision loss, heart failure, stroke, foot ulcer, nerves. In this paper the fuzzy set A is taken as symptoms observed in the patient and fuzzy relation R representing the medical knowledge that relates the symptoms in set S to the diseases in set D, then the fuzzy set B of the possible diseases of the patients can be inferred by means of the compositional rule of inference. Neural Networks are efficiently used for learning membership functions, fuzzy inference rules and other context dependent patterns; fuzzification of neural networks extends their capabilities in applicability. First experts detection is only based on patients articulate that is compared by medical knowledge, that may lead to various modifications and due to patients rejections of certain symptoms may be inappropriate. The proposed detection system uses one committee of Multilayer Perceptron Neural Networks (MLP) for each one of the entity. Using back propagation algorithm the multilayer perceptron works again and again to remove errors in the network.

Hybrid Machine Learning Models for Distributed Biological Data in Multi-Cloud Environment

Abstract: In real-world environment, the term big data is referred to portray the huge volume of complex structured and unstructured data, which is growing exponentially very fast in time. It is mainly applicable for overgrowth of biological data which is processed by cloud source. Cloud computing refers to processing and storing the massive volume of data over the Internet instead of single computer’s hard drive. Various types of services offer to process the data. Cloud provides most of the intelligent services like security, performance, productivity, reliability, scalability, speed, and accurate access. The data is distributed among various places and various sizes. Centralize all the data into single site is to increase the processing speed and memory. A distributed approach employed in the present study is to replace the centralized data environment. The distributed refers to the collection of independent components. To access the data by distributed way, that is, processing the data based on feature selection in data source and getting the data representative based on this, the informative data will be collected into single site. The hybrid machine learning and deep learning models are used to detect the diseases in biological data to improve the computational efficiency and reduce the memory. The hybrid distributed models show the excellent performance in biological research.

Dry Machining Performance Studies on TiAlSiN Coated Inserts in Turning of AISI 420 Martensitic Stainless Steel and Multi-Criteria Decision Making Using Taguchi - DEAR Approach

Abstract: The key objective of this research study is to examine the performance of TiAlSiN coated insert while performing dry machining of AISI 420 martensitic stainless steel on quantified output responses. This paper seeks to optimize process parameters namely speed, feed, and depth of cut during turning process, such as surface roughness, flank wear, and material removal rate simultaneously. TiAlSiN thin film was coated on the carbide tool through high power impulses magnetron sputtering. To confirm the existence of coated elements, SEM and XRD studies were performed. For coated and pure inserts, microhardness was measured, whereas the TiAlSiN coated tool possesses 43.34% higher than pure inserts. The dry machining was performed with three process parameters, each in three phases. The experimentation was performed based on Taguchi’s design of experiments (DoE). In this study, a Multi-Criteria decision making (MCDM) approach encompassing Data Envelopment Analysis based Ranking Methodology (DEAR) with Taguchi’s design was applied. The multi-response performance index (MRPI) was calculated and their impact on the machining parameters was scientifically examined. The parameter combination of cutting speed: 240 m/min; feed rate: 0.20 mm/rev and depth of cut: 0.50 mm was observed to be the optimal input parameters.