Dr. Pradeep Gurunathan
@avccengg.net
Professor and Computer Applications
AVC College of Engineering
RESEARCH INTERESTS
Computer science and Engineering - Information Retrieval, Distributed Computing, Web Services, Sentimental Analysis, IoT
Scopus Publications
Scopus Publications
Aruna. K., , and Pradeep. G.
MECS Publisher
Containers have newly emerged as a potential way to encapsulate and execute programs. In contrast to virtual machines, each container does not have its own kernel and instead shares the host systems. Containers on the other hand are more lightweight, need fewer data to be sent between network nodes and boot up faster than VM. This makes containers a feasible choice, particularly for hosting and extending the services across the fog computing architecture. The major purpose of this paper is to describe the Distributed Intelligent Scheduling based Lightweight Container algorithm (DIS-LC), which is a revolutionary way for container to fog-services integration and resource optimization. In this proposed algorithm is compared to the least connection algorithm, round-robin algorithm and Ant Colony Optimization-based Light Weight Container (ACO-LWC). Operating cost and traffic cost are used to validate the suggested algorithm. Fog node running costs are divided into two categories: CPU and memory. When compared to current algorithms, quantitative research demonstrates that the proposed DIS-LC scheme gets the greatest performance in terms of all metrics. This demonstrate the algorithm is efficient. Finally, the performance of containerized services and resource management systems is evaluated using the iFogSim simulator.
M. Devi Sri Nandhini and Gurunathan Pradeep
IOS Press
Sentiment analysis is the contextual analysis of words to retrieve the social opinion of a brand which aids the business firms/institutions to know the impact of their products/services. It is habitual that users may express different opinions regarding various aspects of the same entity. Therefore, there is a strong demand to extract all the opinion targets may those be explicitly mentioned aspects or implicit aspects which are not directly specified in the reviews. In this context, comparatively less amount of work has been carried out concerning implicit aspect detection. The proposed work has been dedicated solely to extracting the implicit aspects using a dynamic approach based on the type of sentence containing the clues for implicit aspect. A novel aspect pointer compendium (APC) has been developed that catalyzes the task of finding implicit aspects to the maximum extent possible. The APC incorporates the usage of different types of clues such as synonym clues, context clues, phrase clues, and partially implicit aspects that aid in the detection of hidden aspects. Based on this idea, the proposed work classifies the implicit aspect sentences into six types and proceeds with the task in an efficient manner. To strengthen the task of implicit aspect detection, the proposed work utilizes a hybrid technique encompassing APC, domain-specific adjective-noun collocation list (DSANCL), and the explicit aspect-opinion word pairs extracted from the reviews. The experimentation and results reveal that the proposed hybrid approach shows a good improvement in terms of the efficacy of extracting the implicit aspects as compared to the existing baseline models.
Preethi Saroj Srinivasan and Pradeep Gurunathan
Springer Science and Business Media LLC
A. Udhaya Kumar, M. Subhashini, C. Vignes, P. Malathi, and G. Pradeep
IEEE
With the aid of renewable energy, the electric vehicle is charged. In this case, the renewable energy is provided by solar panels. Electric vehicles can be operated in two different ways: either directly at the moment of generation, or by storing energy in batteries for later use. Renewable energy is obtained from the solar panel and transfer it to the solar charge controller. It is possible to immediately charge equipment from the solar charge controller, store the energy in batteries, and then charge electric vehicles using an inverter. By doing this, battery efficiency is improved. The converter can be changed to accomplish it.
K. Aruna and G. Pradeep
IOS Press
Container technology is highly significant in Information and Communication Technology (ICT) systems. To maximize container effectiveness, scaling plays a significant part. Therefore, in the fog computing framework, containers are an ideal solution for hosting and scaling services. Fog networks help to increase the number of connected devices by connecting to external gateways through the Fog of Things (FoT). It is a new approach to designing and implementing fog computing systems for the IoT. The research article aims on a novel Container with a Fog-based Scalable Self-organizing Network (CFSSN) framework and use a Self-Organizing Network based Light Weight Container (SON-LWC) algorithm for moving container services for scaling expansion. This work focuses on how to transfer service or data from container to fog and self-group network. It goes over the most recent container migration methodologies, covering both live and cold migration services. Using intelligent container improves high bandwidth efficiency and provides a solution for a scalable network.
Devi Sri Nandhini M and Pradeep Gurunathan
Springer Science and Business Media LLC
S. Preethi Saroj and Pradeep Gurunathan
IOS Press
Accurate segmentation of brain tumor regions from magnetic resonance images continues to be one of the active topics of research due to the high usability levels of the automation process. Faster processing helps clinicians in identification at initial stage of tumor and hence saves valuable time taken for manual image analysis. This work proposes a Cascaded Layer-Coalescing (CLC) model using convolution neural networks for brain tumor segmentation. The process includes three layers of convolution networks, each with cascading inputs from the previous layer and provides multiple outputs segmenting complete, core and enhancing tumor regions. The initial layer identifies complete tumor, coalesces the discriminative features and the input data, and passes it to the core tumor detection layer. The core tumor detection layer in- turn passes discriminative features to the enhancing tumor identification layer. The information injection through data coalescing voxels results in enhanced predictions and also in effective handling of data imbalance, which is a major contributor in model viewpoint. Experiments were performed with Brain Tumor Segmentation (BraTS) 2015 data. A comparison with existing literature works indicate improvements up to35% in sensitivity, 27% in PPV and 28% in Dice Score, indicating improvement in the segmentation process.
Devi Sri Nandhini M and Pradeep Gurunathan
IOS Press
Since people express their opinions and feelings more openly than ever before, sentiment analysis proves to be a promising research area that effectively analyses the opinion expressed over the entities. In this context, Sentiment analysis is utilized to gather valuable insights from users’ opinions. These insights would benefit a lot for the business concerns and institutions to improve their respective products/services. Aspect-based sentiment analysis (ABSA) is the most robust technique that offers a more fine-grained analysis. The objective of this paper is to improve the efficacy of ABSA by framing a robust and enhanced set of rules. Several experiments were carried out to detect explicit and implicit aspects. The hybrid approach comprising of enhanced rule-based approach (ERBA) and domain-specific lexicon (DSL) is used to improve the solution of the aspect-based sentiment analysis problem. The proposed approach employs a domain-specific adjective-noun collocation list(DSANCL) tailored to the domain for fine-tuning the process of implicit aspect detection(IAD). The proposed model frames a new nine-point scale for measuring the sentiment strength by introducing a ternary classification of intensifiers based on their degree of intensification. The performance of the proposed model is evaluated using the university reviews dataset.
K. Aruna and G. Pradeep
Computers, Materials and Continua (Tech Science Press)
Container technology is the latest lightweight virtualization technology which is an alternate solution for virtual machines. Docker is the most popular container technology for creating and managing Linux containers. Containers appear to be the most suitable medium for use in dynamic development, packaging, shipping and many other information technology environments. The portability of the software through the movement of containers is appreciated by businesses and IT professionals. In the docker container, one or more processes may run simultaneously. The main objective of this work is to propose a new algorithm called Ant Colony Optimization-based Light Weight Container (ACO-LWC) load balancing scheduling algorithm for scheduling various process requests. This algorithm is designed such that it shows best performance in terms of load balancing. The proposed algorithm is validated by comparison with two existing load balancing scheduling algorithms namely, least connection algorithm and round robin algorithm. The proposed algorithm is validated using metrics like response time (ms), mean square error (MSE), node load, largest Transactions Per Second (TPS) of cluster (fetches/sec), average response time for each request (ms) and run time (s). Quantitative analysis show that the proposed ACO-LWC scheme achieves best performance in terms of all the metrics compared to the existing algorithms. In particular, the response time for least connection, round robin and the proposed ACO-LWC algorithm are 58, 60 and 48 ms respectively when 95% requests are finished. Similarly, the error for scheduling 120 requests using least connection, round robin and the proposed ACO-LWC algorithm are 0.15, 0.11 and 0.06 respectively.
M. Devi Sri Nandhini and G. Pradeep
Springer Science and Business Media LLC
K. Aruna and G. Pradeep
Springer Science and Business Media LLC
H. Prabavathi*, , Dr.K. Kavitha, Dr.G. Pradeep, , and
Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
Vehicular networks are significantly improving wireless communication network which provides an intelligent transportation system services among faster moving vehicles with internet and brings safety and comfort drive. From a single source vehicle the multicast routing protocols delivers multicast messages to all members of the multicast group by means of multi-hop communication. Increasing the density of vehicles results in channel overload, which increases the probability of data collision; hence reduction in successful received data will increase the delay. Therefore, delay and energy consumption are the major constraints that should affect the performance of routing. In this paper we suggest a delay and energy efficient multicast routing (DEMR) protocol for vehicular network using a hybrid machine learning algorithm. The DEMR protocol consists of four layers; are vehicle layer, fog layer, OpenFlow switch layer, and SDN controller layer. Moreover, to partition the vehicle layer, and select the optimal multicast path based on multiple constraints improved weed optimization (IWO) algorithm is proposed. IWO algorithm separates the multicast request into emergency, common and police requests. We design a multi-objective lion optimization (MOLO) algorithm among fog nodes for resource management, which increases the utilization of resources in fog layer and decrease the response time of multicast session request. MOLO algorithm removes the unnecessary flow table and session table entries in the controller. The DEMR protocol is implemented in Network Simulator (NS3) tool and simulation results are compared with the protocols as EEMSFV, MABC, and CVLMS. From the simulation results we conclude that the DEMR algorithm is better than EEMSFV, MABC and CVLMS in terms of transmission ratio, overhead load, average end to end delay, packet loss ratio and, energy consumption.
D Daniel, P Raviraj, and G Pradeep
IEEE
The evolution cloud computing have distributed services by combining the exiting clouds with resource and service, convert into hybrid cloud (H-Cloud). In the recent days development in communication and any of its related technology most use multimedia data. Hence the flooding in user request for access and processing of multimedia data to cloud is high and it results in delayed processing of the request, which in turn leads to increase in waiting time of the response and availability of the service to the H-Cloud users. The proposed SK-SVM-FLA (Single Kernel-Support Vector Machine — Fast Lyapunov) algorithm uses SVM classification for media based user request and FLA schedules the user request as jobs and executes the jobs efficiently and based on the optimal waiting time scheduling is adaptively executed on the availability of the computing resource. Comparative analysis is done with existing PSO algorithm and effective scheduling and increase in makespan and throughput is achieved.
Pradeep Gurunathan and Seethalakshmi Pandian
IEEE
In the last few years, several Web-based conference management systems have been developed and used by many international conferences. However, almost all of them were built on stand-alone Web servers. Their fault tolerance, scalability and ability of responding to dispersed users are limited. Aimed at addressing these problems, this paper presents a novel approach for webbased conference management system, whose fault tolerance, scalability and ability of responding to dispersed users are greatly enhanced by Service Oriented Architecture, that aims to achieve interoperability of remotely or locally located homogeneous and heterogeneous applications byutilizing reusable service logic.
Pradeep Gurunathan and Seethalakshmi Pandian
IEEE
The learning technology standardization process is taking the lead role in the research efforts into Web-based education. Web-based educational system (WBES) that involves teaching and learning tasks associated with information distribution, communication, and student assessment. However, teaching and learning activities of WBES require administrative support in the form of tasks such as assessment management, student enrollments, student tracking, student transfers, payment and a variety of other tasks. Standardization is needed for two main reasons: On the one hand, educational resources are defined, structured and presented using different formats. On the other hand, the functional modules that are embedded in a particular learning system cannot be reused by another person in a straightforward way. In order to overcome the issue, we developed a new tool for Web based educational system based on Service Oriented Architecture (SOA), that aims to achieve interoperability of remotely or locally located homogeneous and heterogeneous applications by utilizing reusable service logic.