Dr.S.Markkandeyan
@ksriet.ac.in
Professor in Information Technology
K S R Institute for Engineering and Technology
EDUCATION
M.E., Ph.D
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering, Information Systems, Computer Science Applications, Artificial Intelligence
Scopus Publications
Scopus Publications
S. Markkandeyan, L. Selvam, K. Tamizharasu, and Senthilkumar Aandi
Informa UK Limited
S. Markkandeyan, P. Kalyanasundaram, U. Muthaiah, Nilavu Niza, and P. Gnanapriya
AIP Publishing
J. Vijayakumar, H. Senthil Kumar, P Kalyanasundaram, S. Markkandeyan, and N Sengottaiyan
IEEE
The genesis of cardiovascular disease is still a global issue that has not been addressed, and the high suffering, impairment, and death rates that are associated with cardiovascular illnesses are the disease's primary features. Therefore, there is a need for artificial intelligence (AI) tools that are both effective and quick in the earlier detection of potential results in individuals who have cardiovascular disease. The Internet of Things (IoT) is growing more pervasive, and this is helping to improve the possibilities of AI technologies. Sensors connected to the internet of things are used to gather data, which is then retrieved and forecasted using technology to predict. Common machine learning technologies that are currently in use are not very good in handling data disparities and have a rather poor level of model accuracy rate. The findings of this article propose a classification algorithm aggregation approach that relies on stackable prototype merging to address this problem. These authors take into account the information will be analyzed and training methodologies used by various algorithms. In order to prevent fitting problem, we utilize a basic linear classifier known as Logistic Regression (LR) as that of the macro classifier. We verified the methodology by utilizing a fused Heart Dataset that was compiled from numerous machine learning libraries at the University of California, Irvine, as well as another Heart Attack Dataset that was made publically accessible, and we compared it to 10 single classifier models. According to the findings of the experiments, the stacking classifier that was developed is superior to other classifiers in terms of both its accuracy and its application.
H. Senthil Kumar, P Kalyanasundaram, S. Markkandeyan, N Sengottaiyan, and J. Vijayakumar
IEEE
The branch of study that focuses on ambient assisted living systems has shown a significant amount of interest in the problem of activity and fall detection. These types of systems make use of a variety of sensing technologies to track human movements and attempt to determine the activity being carried out for the goal of health monitoring as well as other applications. In this regard, in addition to activity identification, fall detection is a very essential role. Falls are a leading cause of injuries and even fatalities, hence it is imperative that falls be detected as soon as possible. This study provides a fall detection and activity identification system that not only takes into account the many activities involved in day-to-day life but also takes into account the detection of falls while taking into consideration the intensity and the direction in which the fall occurred. The data from the Inertial Measurement Unit that is included in the SisFall database is first split into non-overlapping segments that last for three seconds each. Following the appropriate augmentation of the data, exacting the feature with the help of a Convolutional Neural Network, followed by an eXtreme Gradient Boosting (XGB) final step for categorization into the different output groups. The results of the studies demonstrate that the gradient-boosted CNN works far better than previous similar approaches, with an unweighted average recall of 88 percent being achieved.
N Sengottaiyan, J. Vijayakumar, H. Senthil Kumar, P Kalyanasundaram, and S. Markkandeyan
IEEE
The widespread availability of sensing technology, such as detectors, has led to the generation of enormous volumes of time-series data by equipment in manufacturing warehouses and industries. There is a ton of knowledge accessible that may be put to use in order to forecast the failure of machinery and the decline of its quality in a particular plant. The downtime of manufacturing machinery is responsible for significant monetary losses, which may be mitigated by accurately predicting when the system will cease to function by analyzing sensor data. Actual data collection from sensors is now technically viable because to the advances that went into creating the Internet of Things (IoT). Our research has shown that combination research has the potential to provide reliable estimates since it is able to accurately represent the abstract features that are essential to the development of more consistent reading. This makes hybrid modeling a useful tool. In addition, it might be challenging to establish an efficient optimization approach due to the complicated nature of the many sensor data that is seen in real-time circumstances. A technique for nonlinear time prediction for predictive maintenance (PdM) is proposed in this study. The method utilizes a combination of convolution operation, extended memory recall, and skips connections (CNN-LSTM). For the purpose of predicting when machines may break down, we try out several forecasting methods, one at a time, including CNN, LSTM, and CNNLSTM. The data were taken from the case study conducted by Microsoft and utilized in this research. The database contains information on the history of breakdowns, the service background, the error circumstances, as well as the machinery attributes and telemetry, which includes information such as the output power, stress, motion, and roster accelerometer gathered during years 2015 and 2016. The combination CNNLSTM architecture which has been suggested is a program that integrates two distinct end-to-end techniques. In this particular model, the Long Short-Term Memory (LSTM) is put to use to analyze the correlation between various time analysis different factors by making use of its superior memory, and 1-D Convolutional Neural Networks (CNNs) are inclined to take on the task of retrieving raised qualities from of the information in an effective manner. The lengthy structures of the time series are learned by our technique, which does this by isolating the short-term dependence patterns that exist between the multiple factors of the time series. According to our findings, CNN-LSTM was the method that produced the most dependable results and the best forecasting reliability.
In this paper classification models and hybrid feature selection methods are implanted on benchmark dataset on the Mango and Maize. Particle Swarm Optimization–Support Vector Machine (PSO-SVM) classification algorithm for the selection of important features from the Mango and Maize datasets to analysis and also compare with the novel classification techniques. Various experiments conducted on these datasets, provide more generated rules and high selection of features using PSO-SVM algorithm and Fuzzy Decision Tree. The proposed method yield high accuracy output as compared to the existing methods with minimum Error Rate and Maximum Positive Rate.
The World revolves around the web technology at present. Every year, the Web information are exponentially growing and this information are huge and complex. The web users are difficult to classify and extract useful information from the web, because the Webinformation are noisy, redundant and irrelevant and also misclassified.Many researchers don’t have strongknowledge about the process of web page classification, techniques and methods previously used. The objective of this survey is to convey an outline of the modern techniques of Web page classification. In this survey, the recent papers in this area are selected and explored.Thus this study will help the researchers to obtain the required knowledge about the current trends in web page classification
A.B. Arockia Christopher, V. Shunmughavel Vivekanandam, A.B. Antony Anderson, S. Markkandeyan, and V. Sivakumar
Cambridge University Press (CUP)
ABSTRACTData mining is an iterative process in which progress is defined by discovery through either automatic or manual methods. A data cleaning procedure is proposed to improve the quality of classification tasks in the knowledge discovery process by taking into account both redundant and conflicting data. The redundancy check is performed on the original dataset and the resultant dataset is preserved. This resultant dataset is then checked for conflicting data and, if any are found, they are corrected and updated on the original aircraft dataset. This updated dataset is then classified using a variety of classifiers such as Bayes, functions, lazy, MISC, rules and decision trees. The performance of the updated datasets on these classifiers is examine, and the result shows a significant improvement in the classification accuracy after redundancy and conflicts are removed. The conflicts after correction are updated in the original dataset, and when the performance of the classifier is evaluated, great improvement is observed. This paper aims to address how data mining techniques can be used to understand complex system accidents in the aviation domain. Decision trees are considered to be the one of the most powerful and popular approaches in knowledge discovery and data mining. The objective is to develop a classification model for aviation risk investigation and reduction using a decision tree induction method that enhances the ability to form decision trees and thereby proves that the classification accuracy of decision trees is greater. Different feature selectors are used in this study in order to reduce the number of initial attributes.
S. Markkandeyan and M. Indra Devi
Springer Science and Business Media LLC