Sireesha Rodda

@gitam.edu

PROFESSOR
GITAM (DEEMED TO BE UNIVERSITY)



                    

https://researchid.co/sireesharodda

RESEARCH INTERESTS

Artificial Intelligence, Machine Learning, Data Mining

32

Scopus Publications

314

Scholar Citations

9

Scholar h-index

7

Scholar i10-index

Scopus Publications

  • Comparative Review on Automated Test Failure Detection and Healing Tools
    Nammi Hemanth Kumar and Sireesha Rodda
    Seventh Sense Research Group Journals

  • Enhancing Decision Making Through Aspect Based Sentiment Analysis Using Deep Learning Models
    Deepika Puvvula and Sireesha Rodda
    International Information and Engineering Technology Association

  • AN EMPIRICAL ANALYSIS OF GRAPH ALGORITHM IN CONTEXT OF FREQUENT SUBGRAPH MINING ON GIRAPH SYSTEM
    Sadhana Priyadarshini and Sireesha Rodda
    ENGG Journals Publications
    The Recurrent Subgraph Extraction plays a key role in the Graph Mining field when our data is distributed over networks. This paper emphasizes different types of graph mining algorithms with the Giraph Distributed System to get more desirable and valuable results than existing methods. We discuss how our proposed model MapReduce Geometric Multi-way Advanced Optimized Frequent Subgraph Mining (MGMAOFSM) impacts different graph mining mechanisms for centralized and distributed systems. The comparison is done for different criteria such as memory requirement or execution time with real four datasets (Facebook Social Network, Coronavirus (COVID-19) tweets, Google web graph, Patent Citation Network) with different threshold values. We implement various algorithms such as Triangle Closing, Shortest Path, Connected Components, and PageRank algorithms, and find out our proposed algorithm that requires less memory with the Triangle closing algorithm whereas in the case of PageRank is lowest with all threshold values.

  • Giraph Dynamic Sized Structure Recurrent Subgraph Generation Algorithm for Frequent Subgraph Mining
    Sadhana Priyadarshini and Sireesha Rodda
    IEEE
    Data Mining has a subpart called Frequent Subgraph Mining (FSM) and is a demanding area for the implementation of graph classification and graph clustering which is used in the area of the social network, chemical compounds, and biological datasets, enterprise world. Many research workers have been researching on how to produce an effective and optimized technique to extract the candidate subgraphs by eliminating duplicates for the last few decades. In the case of the Giraph distributed system, a different format for input and output classes is required to take graphs into memory and put graphs after completion of its operation, which leads to excessive memory exhaustion. In this paper, a novel methodology “Giraph Dynamic Sized Structure Frequent Subgraph Mining (GDSSFSM)” has been developed to reduce the memory necessity for FSM in a graph-distributed system. The proposed approach reorganizes the inner input format class (i.e. setEdgeInputFormatClass) without any changes. Hence, it can be used by default in a customized format. The experimental analysis is done on the different datasets with an existing algorithm based on execution time and memory requirements and concludes that it decreases up to on average 52% depending on the dataset and the graph (i.e., PageRank, Connected Components, and Simple Shortest Path) edge-centric algorithm. The proposed algorithm can be used in various fields of graph mining such as social networks, bioinformatics, and web data mining

  • Dynamic Pagerank Frequent Subgraph Mining by GraphX in the Distributed System
    Sadhana Priyadarshini and Sireesha Rodda
    IEEE
    Graph Mining has been the most demanding research area for the last few decades in different fields, such as biological networks, the world wide web, mobile applications, sensors, online, social networks, etc. Frequent Subgraph Mining (FSM) plays a vital role in Graph Mining to exercise, study and generate interesting patterns from graph data. Basically, FSM techniques are classified into two types such as an apriori-based method, and a pattern growth-based method. This technique faces the problems such as the generation of the duplicate frequent subgraph, having no proper technique to rank during candidate generation, and how to map the threshold values. In this proposed system, a Dynamic PageRank GraphX- based Frequent Subgraph Mining (DPRGFSM) model that is able to extract interesting patterns from the distributed system by eliminating duplicates by ranking them to the proper level. In addition, we also use load balancing, pre-punning, and optimization techniques to improve its performance in both memory requirements and time complexity. The potency of methods defined in this paper is evaluated rigorously with different threshold values and comparative studies with different parameters with existing Spark- based Single Graph Mining (SSIGRAM) and A Ranked Frequent pattern Growth Framework (A- RAFF) and found drastic improvement with all four datasets. The proposed methodology is 1.6 times faster than the Spark-based Single Graph Mining (SSIGRAM) model and 50 times faster than the A Ranked Frequent pattern Growth Framework (A- RAFF) for recurrent subgraph extraction.

  • On the convergence and optimality of the firefly algorithm for opportunistic spectrum access
    Shanti Chilukuri, Sireesha Rodda, and Lakshmana Rao Kalabarige
    Inderscience Publishers


  • Face Recognition with Voice Assistance for the Visually Challenged
    G. Chakravarthy, K. Anupam, P. Varma, G. H. Teja and S. Rodda

    Visually impaired people face a lot of challenges in day-to-day life. Having seen the difficulties faced by them, our primary objective is to facilitate confidence and to empower them to lead a life free from threats related to their safety and well-being. The lack of ability to identify known individuals in the absence of auditory or physical interaction cues drastically limits the visually challenged in their social interactions and poses a threat to their security. Over the past few years many prototype models have been developed to aid this population with the task of face recognition. This application will reduce the inherent difficulty for recognition of a person. It will present a facial recognition application with an intuitive user interface that enables the blind to recognise people and interact socially. The carefully designed interface lets the visually challenged to be able to access and use it without any requirement for visual cues as the users are acquainted by a voice assistant to navigate through the application. The entire build is designed to run efficiently on a Raspberry Pi 3 model B module using the Android Things platform. The Open CV library has been used for the detection and recognition of people in this project. This enables the scope for the software to be run on a multitude of devices such as camera embedded glasses to warn users of their surroundings and identify people to interact safely. Since everything in the application is done in real time with no requirement for prior datasets to be hardcoded it drastically improves the versatility of the software. We hope to make the visually impaired feel closer, comfortable and more secure with the world surrounding them through our application.

  • Gideon—An Artificial Intelligent Companion
    M. Pranay, Hari Varshini Rajkumari, Sireesha Rodda, Y. Srinivas, and P. Anuradha
    Springer Singapore
    Technology cannot run essentially without the input of a human. The rate of technological advancement is increasing with time, society is looking to create and develop easier ways to live and lengthen their lives. The internet is a massive source of information that millions of people use and depend on every day. Artificial Intelligence (AI) is intended to do the thinking for us often thinking through things very quickly that we do not have enough information or time to process ourselves. So Gideon will help to do daily task and it acts like artificial companion. As compared to other application Gideon has additional feature face recognition which can able to detect faces in real world and try to recognize known faces, which we may have forgotten. It can able to listen to you and provide appropriate response precisely and quickly.

  • KeyBoard-Less Online Shopping for the Visually Impaired Using Natural Language Processing and Face Recognition Mechanism
    Srija Rallabhandy and Sireesha Rodda
    Springer Singapore
    Online shopping has gained popularity for its omnipresence. However, visually impaired people are not able to make complete use of this e-commerce shopping due to lack of user-friendly nature to the visually impaired. Here, in this paper, we have proposed a solution to make the e-commerce websites more user-friendly to the visually impaired using voice-based assistance. Our solution includes Face Recognition technology using OpenCV for login and registration into the e-commerce website. gTTS (Google Text to Speech) and speech_recognition libraries were used for making it completely speech driven. After the search results, to extract the data from the web page Web Scraping was used and the results were stored in the database to analyse the data and to choose the best-rated products. After selection of the product, the product was added to the cart using Selenium Web Driver.

  • Geometric Multi-Way Frequent Subgraph Mining Approach to a Single Large Database
    Sadhana Priyadarshini and Sireesha Rodda
    Springer Singapore
    In the present time, Graph Mining has become the most research-oriented field in the advance technologies for its importance in many areas, such as citation graphs, web data mining, chemical structures, protein interaction, social networks, etc. The rapid change in Graph Mining research work is fully dependent on the field of Graph Partitioning (GP) as well as Frequent Subgraph Mining (FSM). In this paper, we define Geometric Multi-Way Frequent Subgraph Mining (GMFSM) approach, which is based on Geometric Partition of a Single Large Graph Database with Frequent Subgraph Mining (FSM) approach that uses filtration technique to reduce number of candidate subgraphs. After partitioning the large graph database, we execute FSM algorithm simultaneously on each subparts which produce the desire result much faster (one-third to half) than existing algorithms. In addition, we use two-way partitioning algorithm recursively to obtain multi-way partition which drastically changes the performance of the algorithm.

  • Fuzzy ECOC framework for network intrusion detection system
    Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
    Many aspects of our life now continually rely on computers and internet. Data sharing among networks is a major challenge in several areas, including communication, national security, medicine, marketing, finance and even education. Many small scale and large scale industries are becoming vulnerable to a variety of cyber threats due to increase in the usage of computers over network. We propose Fuzzy-ECOC frame work for network intrusion detection system, which can efficiently thwart malicious attacks. The focus of the paper is to enforce cyber security threats, generalization rules for classifying potential attacks, preserving privacy among data sharing and multi-class imbalance problem in intrusion data. The Fuzzy-ECOC framework is validated on highly imbalanced benchmark NSL_KDD intrusion dataset as well as six other UCI datasets. The experimental results show that Fuzzy-ECOC achieved best detection rate and least false alarm rate.

  • Survey on testing technique for modern web application-rookies vantage point
    M. Vijaya Bharathi and Sireesha Rodda
    Inderscience Publishers
    AJAX build 2.0 web applications depend in light of state full unique client and server correspondence and client side control of the DOM tree, which makes not the same as standard web applications. Provoking to more slip-ups and harder to set. Another methodology for this AJAX named ATUSA based web applications has been perceived for such accuses that can occur in any state and for making the test suite covering the ways. This approach called as ATUSA, realised by using a gadget which offers invariant checking of portions, module instrument. We portray this framework in three phases with six segments and furthermore accuse revealing limits, versatility, manual effort and level of motorisation testing. This paper mainly concentrates on rookie's vantage point of testing modern web application based on so far accomplished potential research done by software practitioners and experts.

  • Survey on techniques of fault detection-rookies vantage point
    M. Vijaya Bharathi and Sireesha Rodda
    Inderscience Publishers

  • Selection of commercially viable areas for taxi drivers using big data
    Kavya Devabhakthuni, Bhavya Munukurthi, and Sireesha Rodda
    Springer Singapore
    Surface transportation in urban cities is inevitable to move from one place to another place for carrying out regular activities. Taxis are assumed as one of the essential parts for transportation in New York. This paper focuses on the selection of the top profitable areas using New York City (NYC) taxi trips dataset. The data used in the current work is captured from the NYC taxi and analyzed using Hadoop Big Data to find the profitable locations for the taxi driver, so that they can increase their income by waiting in most profitable locations.

  • Mobile Sink as Checkpoints for Fault Detection Towards Fault Tolerance in Wireless Sensor Networks
    Pritee Parwekar, Sireesha Rodda, and Parmeet Kaur
    IGI Global
    A WSN consists of a large number of limited computation and storage capability wireless sensor nodes, which communicate wirelessly. These sensor nodes typical communicate in short range and collaborate to accomplish the network function. To increase the range of sensing and with the advent of MEMS, mobile sensors and sinks is the technology the world is moving to. This paper presents a network of mobile sensors and a sink. A mobile sink is selected as check-point to have the recoverability of the network. A Fuzzy Rule based system (FRS) is used to construct and select efficient static sensor nodes having adequate resources as Check Point Storage Nodes (CPSNs). The objective of FRS is to increase the probability of recovery of check-pointed data subsequent to a failure, thereby allowing a distributed application to complete its execution successfully. Simulations show FRS's better recovery probabilities in comparison to a random check-pointing arrangement.

  • Network intrusion detection system to preserve user privacy
    Sireesha Rodda and Uma Shankar Rao Erothi
    Springer Singapore
    A wide range of malicious attacks and threats are increasing day by day with the growth and development of internet and network technologies. Enforcing network security is important to protect data or information in the computer network against attacks from intruders. The right of privacy of the user must be respected even on the network-resident data. This paper evaluates the performance of four different classifiers on a standard network intrusion detection dataset. The original values in the dataset are anonymized in order to protect the user’s privacy. All the experiments were performed on IBM SPSS Premium Modeler. The effectiveness of the techniques is tested using different evaluation measures.

  • Localization of Sensors by base station in Wireless Sensor networks



  • Network intrusion detection systems using neural networks
    Sireesha Rodda
    Springer Singapore
    With the growth of network activities and data sharing, there is also increased risk of threats and malicious attacks. Intrusion detection refers to the act of successfully identifying and thwarting malicious attacks. Traditionally, the help of network security experts is sought owing to their familiarity with the network technologies and broad knowledge. Recently, data mining techniques have been increasingly adopted to perform network intrusion detection. This paper presents the comparison between multi-layer perceptron and radial basis function networks for designing network intrusion detection system. Multi-layer perceptron proved to be more effective than radial basis function when applied on the benchmark NSL_KDD dataset.

  • A study of the optimization techniques for wireless sensor networks (WSNs)
    Pritee Parwekar, Sireesha Rodda, and Neeharika Kalla
    Springer Singapore
    WSN has become one of the important technologies in the present decade. Energy consumption is the major challenge in the field of wireless sensor network. In WSN, there are some hard problems that cannot be solved in deterministic time. These hard problems can be solved by using optimization techniques. Clustering, routing, node localization, maintenance of the nodes, etc., are some of the hard problems that could be addressed. The main aim of these techniques is to provide the solution within specific time and also to minimize the consumption of the energy thus prolonging the lifetime of the network. This paper clearly describes the application of the different published optimization techniques in the field of WSN.

  • A sentimental insight into the 2016 Indian banknote demonetization
    Rajesh Dixit Missula, Shyam Nandan Reddy Uppuluru, and Sireesha Rodda
    Springer Singapore
    On the 8 November 2016, the Government of India effectively demonetized banknotes representing the nation’s two largest and most commonly used denominations: Rs. 500 and Rs. 1000. The abrupt nature of the move and the shortage of cash that followed the announcement invited a lot of polarizing opinions from the public. Social media platforms—which have now become an integral part of daily life, saw an unprecedented inflow of opinions, thereby becoming important repositories of people’s views on demonetization. In this paper, an attempt has been made to understand public consensus on demonetization by utilizing data from one such social media platform—Twitter—and performing a sentimental analysis of the tweets. To this end, the R language was employed in combination with the Twitter Web API. A dictionary-based approach was taken towards classifying tweets as either positive, negative, or neutral.

  • Comparison between genetic algorithm and PSO for wireless sensor networks
    Pritee Parwekar, Sireesha Rodda, and S. Vani Mounika
    Springer Singapore
    One of the most promising algorithms for network optimization is the particle swarm optimization (PSO) and genetic algorithm (GA). The paper is about comparing these two as applied to wireless sensor networks. If a sink is placed at a longer distance from the sensors then the battery life (energy) drains faster, and it reduces the life of the network. Our analysis shows that optimized clustering technique of sensors can minimize the communication distance and can help to increase the network stability. GA and PSO can optimize the cluster formation of sensors. Simulation results have shown us that PSO performs better than GA for clustering algorithms in wireless sensor networks.

  • Performance analysis of NSL_KDD data set using neural networks with logistic sigmoid activation unit
    Vignendra Jannela, Sireesha Rodda, Shyam Nandan Reddy Uppuluru, Sai Charan Koratala, and G. V. S. S. S. Chandra Mouli
    Springer Singapore
    Network intrusion detection system (NIDS) is a software tool that scans network traffic and performs security analysis on it. NIDS performs match operations upon passing traffic with a pre-established library of attacks in order to identify attacks or abnormal behavior. One of the standard data sets used widely for network intrusion systems is the NSL_KDD data set. The current paper aims to analyze the NSL_KDD data set using artificial neural network with sigmoid activation unit in order to perform a metric analysis study that is aimed at discovering the best fitting parameter values for optimal performance of the given data. Evaluation measures such as accuracy, F-measure, detection rate, and false alarm rate will be used to evaluate the efficiency of the developed model.

  • A real time application of web log mining using Hadoop


RECENT SCHOLAR PUBLICATIONS

  • Enhancing Decision Making Through Aspect Based Sentiment Analysis Using Deep Learning Models.
    D Puvvula, S Rodda
    Mathematical Modelling of Engineering Problems 11 (10) 2024

  • Giraph Dynamic Sized Structure Recurrent Subgraph Generation Algorithm for Frequent Subgraph Mining
    S Priyadarshini, S Rodda
    2022 IEEE International Conference on Current Development in Engineering and 2022

  • Dynamic pagerank frequent subgraph mining by GraphX in the distributed system
    S Priyadarshini, S Rodda
    2022 International conference on automation, computing and renewable systems 2022

  • Penguin rider optimization algorithm-based deep recurrent neural network for sentiment classification of political twitter data
    V Harendranath, S Rodda
    International Journal of Web Services Research (IJWSR) 19 (1), 1-25 2022

  • On the convergence and optimality of the firefly algorithm for opportunistic spectrum access
    LR Kalabarige, S Rodda, S Chilukuri
    International Journal of Advanced Intelligence Paradigms 18 (2), 119-133 2021

  • Enhanced dbscan with hierarchical tree for web rule mining
    N Gullipalli, S Rodda
    Scalable Computing: Practice and Experience 21 (2), 189-202 2020

  • Map Reduce Based Optimized Frequent Subgraph Mining Algorithm for Large Graph Database
    S Priyadarshini, S Rodda
    International Journal of Engineering and Advanced Technology 2020

  • Face recognition with voice assistance for the visually challenged
    G Sridhar Chakravarthy, K Anupam, PNS Harish Varma, GH Teja, ...
    Intelligent Computing and Communication: Proceedings of 3rd ICICC 2019 2020

  • Keyboard-less online shopping for the visually impaired using natural language processing and face recognition mechanism
    S Rallabhandy, S Rodda
    Smart Intelligent Computing and Applications: Proceedings of the Third 2020

  • Gideon—An Artificial Intelligent Companion
    M Pranay, HV Rajkumari, S Rodda, Y Srinivas, P Anuradha
    Smart Intelligent Computing and Applications: Proceedings of the Third 2020

  • Optimization of clustering in wireless sensor networks using genetic algorithm
    P Parwekar, S Rodda
    Sensor Technology: Concepts, Methodologies, Tools, and Applications, 822-836 2020

  • Geometric Multi-Way Frequent Subgraph Mining Approach to a Single Large Database
    S Priyadarshini, S Rodda
    Smart Intelligent Computing and Applications: Proceedings of the Third 2019

  • Survey on testing technique for modern web application-rookies vantage point
    MV Bharathi, S Rodda
    International Journal of Networking and Virtual Organisations 21 (2), 277-288 2019

  • Survey on techniques of fault detection-rookies vantage point
    MV Bharathi, S Rodda
    International Journal of Intelligent Systems Technologies and Applications 2019

  • Selection of Commercially Viable Areas for Taxi Drivers Using Big Data
    K Devabhakthuni, B Munukurthi, S Rodda
    Smart Intelligent Computing and Applications: Proceedings of the Second 2019

  • A Roughset Based Ensemble Framework for Network Intrusion Detection System
    S Rodda, US Erothi
    International Journal of Rough Sets and Data Analysis (IJRSDA) 5 (3), 71-88 2018

  • Mobile sink as checkpoints for fault detection towards fault tolerance in wireless sensor networks
    P Parwekar, S Rodda, P Kaur
    Journal of Global Information Management (JGIM) 26 (3), 78-89 2018

  • Data Transformation Technique for Preserving Privacy in Data
    USR Erothi, S Rodda
    International Journal of Computer Sciences and Engineering 6 (5), 42-50 2018

  • Differentiated caching for improved QoS in vehicular content-centric networks
    K Swaroopa, S Rodda, S Chilukuri
    Int J Comput Sci Eng 6 (10), 317-322 2018

  • Prolonging the network life in wireless sensors network–using refined region of interest
    P Parwekar, S Rodda
    International Journal of Advanced Intelligence Paradigms 10 (4), 344-353 2018

MOST CITED SCHOLAR PUBLICATIONS

  • Class imbalance problem in the network intrusion detection systems
    S Rodda, USR Erothi
    2016 international conference on electrical, electronics, and optimization 2016
    Citations: 89

  • Predicting user behavior through sessions using the web log mining
    G Neelima, S Rodda
    2016 International Conference on Advances in Human Machine Interaction (HMI 2016
    Citations: 72

  • A study of the optimization techniques for wireless sensor networks (WSNs)
    P Parwekar, S Rodda, N Kalla
    Information Systems Design and Intelligent Applications: Proceedings of 2018
    Citations: 22

  • An overview on web usage mining
    G Neelima, S Rodda
    Emerging ICT for Bridging the Future-Proceedings of the 49th Annual 2015
    Citations: 22

  • Comparison between genetic algorithm and PSO for wireless sensor networks
    P Parwekar, S Rodda, S Vani Mounika
    Smart Computing and Informatics: Proceedings of the First International 2018
    Citations: 18

  • Optimization of clustering in wireless sensor networks using genetic algorithm
    P Parwekar, S Rodda
    Sensor Technology: Concepts, Methodologies, Tools, and Applications, 822-836 2020
    Citations: 13

  • Keyboard-less online shopping for the visually impaired using natural language processing and face recognition mechanism
    S Rallabhandy, S Rodda
    Smart Intelligent Computing and Applications: Proceedings of the Third 2020
    Citations: 10

  • Network intrusion detection systems using neural networks
    S Rodda
    Information Systems Design and Intelligent Applications: Proceedings of 2018
    Citations: 9

  • Localization of sensors by base station in wireless sensor networks
    P Parwekar, S Rodda
    NISCAIR-CSIR, India 2018
    Citations: 9

  • A normalized measure for estimating classification rules for multi-class imbalanced datasets
    S Rodda, S Mogalla
    Int. J. Eng. Sci. Technol. 3 (4), 3216-3220 2011
    Citations: 7

  • Fault Tolerance in Wireless Sensor Networks: Finding Primary Path
    P Parwekar, S Rodda
    Proceedings of the Second International Conference on Computer and 2016
    Citations: 4

  • Mobile sink as checkpoints for fault detection towards fault tolerance in wireless sensor networks
    P Parwekar, S Rodda, P Kaur
    Journal of Global Information Management (JGIM) 26 (3), 78-89 2018
    Citations: 3

  • Differentiated caching for improved QoS in vehicular content-centric networks
    K Swaroopa, S Rodda, S Chilukuri
    Int J Comput Sci Eng 6 (10), 317-322 2018
    Citations: 3

  • A rough set based associative classifier
    S Rodda, M Shashi
    International Conference on Computational Intelligence and Multimedia 2007
    Citations: 3

  • Dynamic pagerank frequent subgraph mining by GraphX in the distributed system
    S Priyadarshini, S Rodda
    2022 International conference on automation, computing and renewable systems 2022
    Citations: 2

  • On the convergence and optimality of the firefly algorithm for opportunistic spectrum access
    LR Kalabarige, S Rodda, S Chilukuri
    International Journal of Advanced Intelligence Paradigms 18 (2), 119-133 2021
    Citations: 2

  • Map Reduce Based Optimized Frequent Subgraph Mining Algorithm for Large Graph Database
    S Priyadarshini, S Rodda
    International Journal of Engineering and Advanced Technology 2020
    Citations: 2

  • Face recognition with voice assistance for the visually challenged
    G Sridhar Chakravarthy, K Anupam, PNS Harish Varma, GH Teja, ...
    Intelligent Computing and Communication: Proceedings of 3rd ICICC 2019 2020
    Citations: 2

  • A Roughset Based Ensemble Framework for Network Intrusion Detection System
    S Rodda, US Erothi
    International Journal of Rough Sets and Data Analysis (IJRSDA) 5 (3), 71-88 2018
    Citations: 2

  • Performance Analysis of NSL_KDD Data Set Using Neural Networks with Logistic Sigmoid Activation Unit
    V Jannela, S Rodda, SNR Uppuluru, SC Koratala, G Chandra Mouli
    Smart Computing and Informatics: Proceedings of the First International 2018
    Citations: 2