P. Subbulakshmi

Bio: P. Subbulakshmi is an academic researcher from Hindustan University. The author has contributed to research in topics: Computer science & Cluster analysis. The author has an hindex of 6, co-authored 13 publications receiving 138 citations. Previous affiliations of P. Subbulakshmi include VIT University & Anna University.

Optimization using Artificial Bee Colony based clustering approach for big data

Abstract: As one of the major problems is that the time taken for executing the traditional algorithm is larger and that it is very difficult for processing large amount of data. Clusters possess high degree of similarity among each cluster and have low degree of similarity among other clusters. Optimization algorithm for clustering is the art of allocating scarce resources to the best possible effect. The traditional optimization algorithm is not suitable for processing high dimensional data. The main objective of proposed Artificial Bee Colony (ABC) approach is to minimize the execution time and to optimize the best cluster for the various sizes of the dataset. To deal with this, we are normalizing to distributed environment for time efficiency and accuracy. The proposed ABC algorithm simulates the behavior of real bees for solving numerical optimization problems particularly in clustering. The dataset size is varied for the algorithm and is mapped with its appropriate timings. The result is observed for various fitness and probability value which is obtained from the employed and the onlooker phase of ABC algorithm from which the further calibrations of classification error percentage is done. The proposed ABC Algorithm is implemented in Hadoop environment using mapper and reducer programming. An experimental result reveals that the proposed ABC scheme reduces the execution time and classification error for selecting optimal clusters. The results show that the proposed ABC scheme gives a better performance than PSO and DE in terms of time efficiency.

Performance Evaluation of Multilayer Clustering Network Using Distributed Energy Efficient Clustering with Enhanced Threshold Protocol.

Abstract: In this research, pure deterministic system has been established by a new Distributed Energy Efficient Clustering Protocol with Enhanced Threshold (DEECET) by clustering sensor nodes to originate the wireless sensor network. The DEECET is very dynamic, highly distributive, self-confessed and much energy efficient as compared to most of the other existing protocols. The MATLAB simulation provides aim proved result by means of energy dissipation being emulated in the networks lifespan for homogeneous as well as heterogeneous sensor network, which when contrasted for other traditional protocols. An enhanced result has been obtained for equitable energy dissipation for systematized networks using DEECET.

Design and characterization of corporate feed rectangular microstrip patch array antenna

Abstract: The modern mobile communication systems requires high gain, large bandwidth and minimal size antenna's that are capable of providing better performance over a wide range of frequency spectrum. This requirement leads to the design of Microstrip patch antenna. This paper proposes the design of 4-Element microstrip patch antenna array which uses the corporate feed technique for excitation. Low dielectric constant substrates are generally preferred for maximum radiation. Thus it prefers Taconic as a dielectric substrate. Desired patch antenna design is initially simulated by using high frequency simulation software SONNET and FEKO and patch antenna is designed as per requirements. Antenna dimensions such as Length (L), Width (W) and substrate Dielectric Constant (er) and parameters like Return Loss, Gain and Impedance are calculated using high frequency simulation software. The antenna has been designed for the range 9-11 GHz. Hence this antenna is highly suitable for X-band applications.

Edge Computing-Based Intrusion Detection System for Smart Cities Development Using IoT in Urban Areas

Abstract: The Internet of Things (IoT) provides a more interactional solution for developing smart cities. A larger number of heterogeneous devices are used to extract different volumes of data from different sources to support IOT-based smart cities’ development. Data availability and utilization are major paradigms for different solution providers to utilize them effectively for decision-makers.

Knowledge-Based Systems

Abstract: Knowledge Based Systems (KBS) are systems that use artificial intelligence techniques in the problem solving process. This text is designed to develop an appreciation of KBS and their architecture and to help users understand a broad variety of knowledge based techniques for decision support and planning. It assumes basic computer science skills and a math background that includes set theory, relations, elementary probability, and introductory concepts of artificial intelligence. Each of the 12 chapters are designed to be modular providing instructors with the flexibility to model the book to their own course needs. Exercises are incorporated throughout the text to highlight certain aspects of the material being presented and to stimulate thought and discussion.

Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing.

Abstract: The emergence of the 2019 novel coronavirus (COVID-19) which was declared a pandemic has spread to 210 countries worldwide. It has had a significant impact on health systems and economic, educational and social facets of contemporary society. As the rate of transmission increases, various collaborative approaches among stakeholders to develop innovative means of screening, detecting and diagnosing COVID-19's cases among human beings at a commensurate rate have evolved. Further, the utility of computing models associated with the fourth industrial revolution technologies in achieving the desired feat has been highlighted. However, there is a gap in terms of the accuracy of detection and prediction of COVID-19 cases and tracing contacts of infected persons. This paper presents a review of computing models that can be adopted to enhance the performance of detecting and predicting the COVID-19 pandemic cases. We focus on big data, artificial intelligence (AI) and nature-inspired computing (NIC) models that can be adopted in the current pandemic. The review suggested that artificial intelligence models have been used for the case detection of COVID-19. Similarly, big data platforms have also been applied for tracing contacts. However, the nature-inspired computing (NIC) models that have demonstrated good performance in feature selection of medical issues are yet to be explored for case detection and tracing of contacts in the current COVID-19 pandemic. This study holds salient implications for practitioners and researchers alike as it elucidates the potentials of NIC in the accurate detection of pandemic cases and optimized contact tracing.