KOTEESWARAN S
@saec.ac.in
Professor, Department of Computer Science and Engineering (AI and ML)
S.A. Engineering College (Autonomous)
Dr. S. Koteeswaran, B.Tech., M.E., Ph.D. currently working as Professor in the Department of Computer Science and Engineering (AI&ML), S.A. Engineering College, Chennai-600077, TamilNadu, India. He is having 15 years of teaching experience and published more than 50 research articles in various peer reviewed Journals. He is author for two text books and two edited books for Computer Science & Engineering Programme. His research interests include Artificial Intelligence, Machine Learning, Deep Learning, Big Data and Analytics and Internet of Things. He has presented several papers in conference proceedings. He is a reviewer for more than a dozen journals and also organized more than 25 various events such as National and International Conferences, Faculty Development Programs, Workshops, Seminars, National Level Paper Contests, Quiz programmes, 24 Hours IEEE Xtreme Programming Competition and 36 hours Hachathon. He is a Member of ACM, Member of IAEng, Global Member of ISOC.
EDUCATION
Ph.D. (Computer Science and Engineering)
Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology,
Chennai, 2013.
M.E. (Software Engineering)
Vel Tech Engineering College, Anna University, Chennai, 2009.
B.Tech. (Information Technology)
Amrita Institute of Technology and Science, Anna University, Chennai, 2006.
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering, Computer Science, Computer Science Applications, Software
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Woothukadu Thirumaran Chembian, Krishna Murthi Sankar, Seerangan Koteeswaran, Kandasamy Thinakaran, and Periyannan Raman
Institute of Advanced Engineering and Science
Early detection of melanoma skin cancer (MSC) is critical in order to prevent deaths from fatal skin cancer. Even though the modern research methods are effective in identifying and detecting skin cancer, it is a challenging task due to a higher level of color similarity between melanoma non-affected areas and affected areas, and a lower contrast between the skin portions and melanoma moles. For highlighting the aforementioned problems, an efficient automated system is proposed for an early diagnosis of MSC. Firstly, dermoscopic images are collected from two benchmark datasets namely, international skin imaging collaboration (ISIC)-2017 and PH2. Next, skin lesions are segmented from dermoscopic images by implementing a fuzzy based SegNet model which is a combination of both deep fuzzy clustering algorithm and the SegNet model. Then, hybrid feature extraction (ResNet-50 model and local tri-directional pattern (LTriDP) descriptor) is performed to capture the features from segmented skin lesions. These features are given into the normalized stacked long short-term memory (LSTM) network to categorize the classes of skin lesions. The empirical evaluation reveals that the proposed normalized stacked LSTM network achieves 98.98% and 98.97% of accuracy respectively on the ISIC2017 and PH2 datasets, and these outcomes are more impressive than those of the conventional detection models.
S. Koteeswaran, R. Suganya, Chellammal Surianarayanan, E. A. Neeba, A. Suresh, Pethuru Raj Chelliah, and Seyed M. Buhari
Springer Science and Business Media LLC
V K Kavitha and S Koteeswaran
IEEE
A recommendation system aims to suggest certain item or product to specific users based on the user's preference, interest, and rating. It is essential to create associations between objects to deliver the best RS. Today, majority of e-commerce businesses use recommendation algorithms to entice customers to make additional purchases by presenting things that they are likely to like. RS facilitate rapid navigation and information gathering. Reading offers benefits for both individuals and societies as a whole, studies suggest a reduction in reading among young people in particular. The book recommendation systems help readers select the right book for them. The BRS is used by retailers to manage their inventory and boost profits. RS will make it easier to stop this decrease. BRS assists librarians in effectively managing the library catalogue. In this paper a survey on varrious methods applied to BRS is presented. The advantages of adopting a technique, additional techniques that have been used to enhance the BRS and BRS applications are also reviewed. Amazon, Barnes & Noble, Flip cart, Goodread, and other online retailers employ BRS to suggest books that customers might be enticed to purchase because they fit their preferences.
Saiyed Faiayaz Waris and S. Koteeswaran
World Scientific Pub Co Pte Ltd
In recent days, health problems are becoming more prevalent because of changes in lifestyles and inherited factors. Heart disease (HD), in particular, has become increasingly widespread in recent years, putting people’s lives in jeopardy. Blood pressure, cholesterol, and pulse rate are all varied for each person. Normal blood pressure should be 120/90, cholesterol should be 100–129[Formula: see text]mg/dL, pulse rate should be 72, fasting blood sugar level should be 100[Formula: see text]mg/dL, heart rate should be 60–100[Formula: see text]bpm, and ECG should be normal, according to medically established data. The aorta is 25[Formula: see text]mm (1 inch) wide, whereas capillaries are only 8[Formula: see text][Formula: see text]m wide, which signifies HD. This paper is based on a public health dataset that also contains a cardiac dataset. In our previous work, a new conventional neural network (CNN) architecture was used to extract and categorize histopathological images using the [Formula: see text]-means consensus clustering. We achieved good results with the cardiac dataset compared to the existing results. The outcome of the proposed work achieves a precision rate of 97%. In this paper, a novel conventional neural network (CNN) architecture was utilized to identify and characterize histopathology pictures with the help of using the consensus [Formula: see text]-nearest neighbor algorithm (CKNN). The usage of a deep neural network, as well as the selection and recovery of functionality, is an essential step until the dataset is classified. As a result, the training process has pre-formed the dataset. The system evaluates the person’s symptoms as input and provides the disease’s possibility as an output. The suggested approach is linked to temporal data modeling and makes use of a prior HD CNN prediction. In comparison to previous outcomes, we had good outcomes with the current cardiac dataset. The proposed model’s conclusion has brought out accuracy of 99.1%.
Saiyed Faiayaz Waris and S. Koteeswaran
Auricle Technologies, Pvt., Ltd.
Data analysis is important for managing a lot of knowledge in the healthcare industry. The older medical study favored prediction over processing and assimilating a massive volume of hospital data. The precise research of health data becomes advantageous for early disease identification and patient treatment as a result of the tremendous knowledge expansion in the biological and healthcare fields. But when there are gaps in the medical data, the accuracy suffers. The use of K-means algorithm is modest and efficient to perform. It is appropriate for processing vast quantities of continuous, high-dimensional numerical data. However, the number of clusters in the given dataset must be predetermined for this technique, and choosing the right K is frequently challenging. The cluster centers chosen in the first phase have an impact on the clustering results as well. To overcome this drawback in k-means to modify the initialization and centroid steps in classification technique with combining (Convolutional neural network) CNN and ELM (extreme learning machine) technique is used. To increase this work, disease risk prediction using repository dataset is proposed. We use different types of machine learning algorithm for predicting disease using structured data. The prediction accuracy of using proposed hybrid model is 99.8% which is more than SVM (support vector machine), KNN (k-nearest neighbors), AB (AdaBoost algorithm) and CKN-CNN (consensus K-nearest neighbor algorithm and convolution neural network).
N. Malarvizhi, G. Soniya Priyatharsini, and S. Koteeswaran
Springer Science and Business Media LLC
E. A. Neeba, S. Koteeswaran, and N. Malarvizhi
Springer Science and Business Media LLC
Jeevanandam Jotheeswaran and S. Koteeswaran
Bentham Science Publishers Ltd.
Background: Sentiment Analysis (SA) has a big role in Big data applications regarding consumer attitude detection, brand/product positioning, customer relationship management and market research. SA is a natural language processing method to track the public mood on a specific product. SA builds a system to collect/examine opinions on a product in comments, blog posts, re- views or tweets. Machine learning applicable to Sentiment Analysis belongs to supervised classifi- cation in general. Methods: Two sets of documents, training and test set are required in machine learning based classification: Training set is used by classifiers to learn documents differentiating character- istics; it is thus called supervised learning. Results: Test sets validate the classifier’s performance. Se- mantic orientation approach to SA is unsupervised learning because it requires no prior training for mining data. It measures how far a word is either positive or negative. This paper uses a hybrid GA- DE optimization technique for sentiment classification to classify features from movie reviews and medical data. Conclusion: Our research has enhanced the variables on learning rate as well as momentum values which are optimized by genetic approach that in turn improve the accuracy of classification procedure.
S. Koteeswaran, N. Malarvizhi, E. Kannan, S. Sasikala, and S. Geetha
Springer Science and Business Media LLC
Aviation safety management system is a vital component of the aviation industry. Aviation safety inspectors apply a broad knowledge about aviation industry, aviation safety, and the central laws and regulations, and strategies affecting aviation. In addition, they put on severe technical knowledge and skill in the operation and maintenance of aircraft. Data mining methods also have been successfully applied in aviation safety management system. Aviation industry accumulates large amount of knowledge and data. This paper proposes a method that applied data mining technique on the accident reports of the Federal Aviation Administration (FAA) accident/incident data system database which contains accident data records for all categories of civil aviation between the years of 1919 and 2014. In this study, we have investigated the application of several data mining methods on the accidents reports, to arrive at new inferences that could help aviation management system. Moreover correlation based feature selection (CFS) with Oscillating Search Technique is used to select the number of prominent attributes that are potential factors causing maximum number of accidents in aircraft. The principle of this work is to find out the effective attributes in order to reduce the number of the accidents in the aviation industry. This proposed novel idea named "improved oscillated correlation feature selection (IOCFS)" is evaluated against the conventional classifiers like Naïve bayes, support vector machine (SVM), artificial neural network (ANN), k-nearest neighbor (k-NN), Multiclass classifier and decision tree (J48). The selected features are tested in terms of their accuracy, running time and reliability as in terms of true positive rate, false positive rate, precision, recall-measure and ROC. The results are seen to be the best for k-NN classifier on comparing with other conventional classifiers, with the value of k = 5.
E. A. Neeba and S. Koteeswaran
Springer Science and Business Media LLC
A. N. Arularasan, A. Suresh, and Koteeswaran Seerangan
Springer Science and Business Media LLC
Kothandapani Chokkanathan and S. Koteeswaran
Inderscience Publishers
S Ravikumar, K. Antony Kumar, and S Koteeswaran
American Scientific Publishers
P. Suresh, S. Koteeswaran, N. Malarvizhi, and R. H. Aswathy
IGI Global
The physical world entities are communicated via advanced communication technologies without human intervention. Such an evolving advanced version of automation technology is internet of things (IOT), where each smart device is provided with unique identification. The integration part of such technology comprises key elements, protocols, applications, and research challenges. This chapter discusses such terms and addresses the research challenges. The concept of fog computing is analyzed by cognitive approach. Fog computing localizes the processing information and optimizes the communication and storage among enormous smart devices. In addition, it favourably mitigates the need of bandwidth size and delay in communication.
K. Chokkanathan and S. Koteeswaran
Inderscience Publishers
K Chokkanathan and S Koteeswaran
Science Publishing Corporation
C4.5 algorithm is developed by Ross Quinlan which is the extension of ID3 algorithm used for generating a decision trees.Since the tree generated by C4.5 can be used for classification, so it’s also referred to as statistical classifier.Even though the Random Decision Tree is used to avoid the information leakage there are some problems and issues related to privacy maintenance.When we try to instantiate more instances for one class it leads to ambiguity at the same time creating new classes more and more will increase the complexity in RDT. These problems can be resolved by using our C4.5 algorithm.We can have any number of nodes in a network, each node can create its own tree or class and each class can initiate many number of instances for a disseminated classification consuming secure amount or threshold homomorphic encryption. The main objective of this paper is to discuss the ideal nature of the C4.5 algorithm and how they support this algorithm to be utilized in various datamining process.