Senthil Murugan Balakrishnan

Bio: Senthil Murugan Balakrishnan is an academic researcher from VIT University. The author has contributed to research in topics: The Internet & Middleware. The author has an hindex of 5, co-authored 7 publications receiving 58 citations.

A survey on deep learning models for wireless capsule endoscopy image analysis

Abstract: Abdomen Bleeding, Ulcer, Tumour, Crohn's disease, Celiac disease and other diseases in the gastrointestinal tract (GI) are difficult to diagnose, due to the inescapable inherent difficulty in accessing a volute setting in the human body Wireless Capsule Endoscopy (WCE) offers a patient-cordial, non-invasive and painless investigation in the GI tract Automatic detection of anomalies in WCE images using Deep Learning Models improves the detection accuracy but it requires a huge number of labeled data for model training But these deep models suffer from explain-ability and fail to include expert knowledge in the model decision-making process By keeping these aspects in mind, this survey aims to identify the opportunities for using Semi-Supervised deep learning models over supervised deep learning methods in Wireless Capsule Endoscopy (WCE) anomaly detection and classification This paper presents a comprehensive survey on various deep learning solutions for anomaly detection and localization techniques utilized in WCE images in the aspect of performance, complexity, and the quality of the dataset The survey outlined the proposed Attention and Domain Assisted Generative Adversarial Network (ADA-GAN) based Semi-Supervised Model for WCE anomaly image classification along with initial results The result derives the shortcomings of the current literature methods and paves the potential research opportunities in the Semi-Supervised models in Wireless capsule endoscopy image analysis

Aspect Oriented Middleware for Internet of Things: A State-of-the Art Survey of Service Discovery Approaches

Abstract: Internet of Things refers to a paradigm consisting of a variety of uniquely identifiable day to day things communicating with one another to form a large scale dynamic network. The integration of these heterogeneous things in the constrained network is a challenging issue, for which service oriented and architecture based solutions are considered to be useful. The key element of any service oriented paradigm is service discovery which allows the client to get access to the right service at the right time to complete the requested tasks. This paper presented a comprehensive survey of major solutions for service and resource discovery detailing techniques and features used in existing systems. The survey presented the findings in tabular representation and outlined the proposed aspect oriented middleware framework for IoT highlighting context awareness and solving certain pitfalls in the existing service discovery approaches. The survey results might be helpful in deriving the limitations of the existing protocols in literature and identify future research opportunities in service discovery.

Integrated QoUE and QoS approach for optimal service composition selection in internet of services (IoS)

Abstract: Service Oriented Architecture (SOA) approaches are presently getting to be appropriate to embedded devices that feature embedded processing and communication. As a result these services get the ability to be hosted on high end machines to wireless resource constrained devices and on any physical object supported with communication ability. This creates the Internet of Services (IoS) environment. Services from multiple owners can be assembled into a composite service irrespective of their specific Quality of Service (QoS) and related properties for implementing a complex business process. In this context service consumer comes against the problem of selecting best service and for which providing QoS relevant guarantees leads to many challenging issues. One among them is determining a feasible service composition that fulfils a set of conditions while maintaining a good Quality of User Experience (QoUE). The last goal suggests the requirement to enforce an extra optimality prerequisite on the feasibility problem. In this paper, an optimization strategy oriented to efficient composite service selection for IoS model is designed through use of Particle Swarm Optimization (PSO) technique. Furthermore, prior to optimization, the services are assured of rich QoUE, especially trustworthiness in terms of reputation. The proposed work evaluates QoUE using the fuzzy based inference algorithm for identifying QoUE satisfied composite service. Experimental evaluation on a set of real world web services demonstrates the effectiveness of our proposed methodology.

A comprehensive survey on network anomaly detection

Abstract: Nowadays, there is a huge and growing concern about security in information and communication technology among the scientific community because any attack or anomaly in the network can greatly affect many domains such as national security, private data storage, social welfare, economic issues, and so on. Therefore, the anomaly detection domain is a broad research area, and many different techniques and approaches for this purpose have emerged through the years. In this study, the main objective is to review the most important aspects pertaining to anomaly detection, covering an overview of a background analysis as well as a core study on the most relevant techniques, methods, and systems within the area. Therefore, in order to ease the understanding of this survey’s structure, the anomaly detection domain was reviewed under five dimensions: (1) network traffic anomalies, (2) network data types, (3) intrusion detection systems categories, (4) detection methods and systems, and (5) open issues. The paper concludes with an open issues summary discussing presently unsolved problems, and final remarks.

Service Discovery in Pervasive Computing Environments

Abstract: This chapter contains sections titled: Introduction, An Overview of Service Discovery Protocols, Multiprotocol Service Discovery, Context-Aware Service Discovery for Pervasive Environments, Conclusion, Acknowledgment, Notes, References

An Approach towards Increasing Prediction Accuracy for the Recovery of Missing IoT Data Based on the GRNN-SGTM Ensemble.

Abstract: The purpose of this paper is to improve the accuracy of solving prediction tasks of the missing IoT data recovery. To achieve this, the authors have developed a new ensemble of neural network tools. It consists of two successive General Regression Neural Network (GRNN) networks and one neural-like structure of the Successive Geometric Transformation Model (SGTM). The principle of ensemble topology construction on two successively connected general regression neural networks, supplemented with an SGTM neural-like structure, is mathematically substantiated, which improves the accuracy of prediction results. The effectiveness of the method is based on the replacement of the summation of the results of the two GRNNs with a weighted summation, which improves the accuracy of the ensemble operation in general. A detailed algorithmic implementation of the ensemble method as well as a flowchart of its operation is presented. The parameters of the ensemble operation are determined by optimization using the brute-force method. Based on the developed ensemble method, the solution of the task of completing the partially missing values in the real monitoring dataset of the air environment collected by the IoT device is presented. By comparing the performance of the developed ensemble with the existing methods, the highest accuracy of its performance (by the parameters of Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) accuracy) among the most similar in this class has been proved.