Artificial Intelligence, Machine Learning, Data Mining
39
Scopus Publications
340
Scholar Citations
8
Scholar h-index
8
Scholar i10-index
Scopus Publications
A hybrid transformer architecture for unsupervised aspect-based sentiment analysis Deepika Puvvula, Sireesha Rodda ETRI Journal, 2026 Aspect‐based sentiment analysis focuses on discovering the sentiment polarity of specific aspects within textual data. The subtle complexities of language pose a major challenge, and identifying multiword aspects and their associated sentiment polarities hinders the development of effective models. To address the limitations of supervised approaches, unsupervised clustering can be employed to group similar aspect features. We propose an unsupervised hybrid architecture called the BERT + GPT‐2 fusion model. Our main technical contributions include aspect extraction utilizing the BERTopic model. Our approach is evaluated utilizing silhouette and coherence scores. To identify the optimal number of aspect clusters, cluster quality is measured by calculating the average silhouette and coherence scores. The clusters that maximize the average values are well‐defined clusters. The identified aspects are then used to train the BERT+GPT‐2 fusion model. The proposed technique was evaluated on the 515 K Hotel Reviews Data dataset, demonstrating its efficacy for sentiment analysis. Our fusion model outperformed state‐of‐the‐art approaches, including GPT‐2 and convolutional neural network baselines.
Comparative Review on Automated Test Failure Detection and Healing Tools Nammi Hemanth Kumar, Sireesha Rodda Ssrg International Journal of Electrical and Electronics Engineering, 2025 The main aim of this paper was to evaluate automated test failure detection and healing tools in software test automation. Although Artificial Intelligence and Machine Learning involve creating separate and individual algorithms for accessing data and making sense of it by identifying patterns to form conclusions, these predictions should be used to their full benefit for software testing. Automated test failure detection and healing tools are one approach that makes more of these predictions become a reality under software testing. This paper reviews the existing literature regarding healing tools specifically created for test failure detection and healing, particularly their performance in recognizing User Interface changes and healing the test scripts automatically. The review presents the key characteristics, features, functionalities, and technologies used in the tools, such as Artificial Intelligence, machine learning, visual testing, and integration with popular test automation frameworks. By juxtaposing the sources reviewed above, the review outlines the pros and cons and promising application areas of each and provides suggestions for appropriate uses in highly diverse testing conditions and contexts. Moreover, the review also starts with the gaps and the challenges that the current cutting-edge approaches have faced and gives a future outlook on what directions future research and development have in terms of automated test failure detection and healing. Somewhere It seems like there is no distinctive technique framework or tool available that could support the automated test failure detection and healing and can fulfill all the requirements. Finally, this paper ends with a discussion of the most popular tools available, along with the expressed thought process about the present and forthcoming artificial intelligence for test automation.
Development of IoT Enabled Aqua Pond Monitoring System to Increase the Livelihood of the Aqua Farmers Vijaya Bharathi Manjeti, R Sireesha, D Dhanush, M Abhiram, Bangaru Lakshmi Mahanthi 2nd International Conference on Self Sustainable Artificial Intelligence Systems Icssas 2024 Proceedings, 2024 This research study aims to eliminate the invasion of the Aqua-life. The proposed system monitors the levels of parameters($\mathrm{pH}, \mathrm{DO}$, etc.) by using IoT devices, such as D18B20 water temperature, which can indicate the temperature of the water body, DO sensor by DFRobot, used to display the dissolved oxygen levels in the lake, turbidity sensor to find the dust dissolved in the water, and finally the pH sensor that can tell us about the nature of the pond/lake, whether it is basic in nature or acidic in nature. All of these sensors are connected to a microcontroller, which is Arduino rev2, that collects the data and sends it to the Blynk, where the data is stored and displayed to the user. The data, that is collected will be the analog values and those values, which are between $0-1023$ will be converted to voltage, where the range will be between 0V and 3.3V or 0V and 5.5V, range will depend on the voltage supplied to the particular sensor. After obtaining the voltage values for the data, which includes the values of pH, DO, turbidity and temperature, these values again undergo a process of conversion, that gives out the sensor’s particular value. All of these sensors or can say the system will be placed on the boat, which can controlled by using a mobile or a computer. Making the boat rover over the water can get us the data, to show that the water body is safe for the aqua-life or it is not. Based on this piscators will take the measurements required to save the harvest, that will be yielded soon or later.
AN EMPIRICAL ANALYSIS OF GRAPH ALGORITHM IN CONTEXT OF FREQUENT SUBGRAPH MINING ON GIRAPH SYSTEM Sadhana Priyadarshini, Sireesha Rodda Indian Journal of Computer Science and Engineering, 2022 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.
Dynamic Pagerank Frequent Subgraph Mining by GraphX in the Distributed System Sadhana Priyadarshini, Sireesha Rodda International Conference on Automation Computing and Renewable Systems Icacrs 2022 Proceedings, 2022 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.
Giraph Dynamic Sized Structure Recurrent Subgraph Generation Algorithm for Frequent Subgraph Mining Sadhana Priyadarshini, Sireesha Rodda Proceedings of 2022 IEEE International Conference on Current Development in Engineering and Technology Ccet 2022, 2022 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
On the convergence and optimality of the firefly algorithm for opportunistic spectrum access Shanti Chilukuri, Sireesha Rodda, Lakshmana Rao Kalabarige International Journal of Advanced Intelligence Paradigms, 2021 Meta-heuristic algorithms have been proven to be efficient for engineering optimisation. However, the convergence and accuracy of such algorithms depends on the objective function and also on several choices made during algorithm design. In this paper, we focus on the firefly algorithm for optimal channel allocation in cognitive radio networks. We study the effect of various probability distributions including the Levy alpha stable distribution for randomisation of firefly movement. We also explore various functions for converting firefly positions from the continuous space to the discrete space, as is necessary in the spectrum allocation problem. Simulation results show that in most cases, Levy flight gives better convergence time and results for common optimisation problems such as maximising the overall channel utilisation, maximising the channel allocation for the bottleneck user and maximising proportional fairness. We also note that no single discretisation function gives both good convergence and optimality.
A rough set based associative classifier K.K. Thyagharajan, V. Ramachandran Proceedings International Conference on Computational Intelligence and Multimedia Applications Iccima 2007, 2008
An improved associative classifier B. Nagarajan, P. Balasubramanie Proceedings International Conference on Computational Intelligence and Multimedia Applications Iccima 2007, 2008
RECENT SCHOLAR PUBLICATIONS
A hybrid transformer architecture for unsupervised aspect‐based sentiment analysis D Puvvula, S Rodda ETRI Journal , 2026 2026
A Multi-Modal Automated Driver Alert System Based on Deep Learning NR Dereddi, S Rodda, S Buddharaju Proceedings of Sixth International Conference on Computer and Communication … , 2025 2025
Dental Image Analysis for Caries Recognition Domain: AI/ML D Challakonda, M Raza, CK Koppisetti, A Manikonda, S Rodda International Conference on Machine Learning, IoT and Big Data, 36-47 , 2025 2025
Comparative Review on Automated Test Failure Detection and Healing Tools NH Kumar, S Rodda SSRG Int. J. Electr. Electron. Eng 12 (2), 113-123 , 2025 2025 Citations: 1
Enhancing Decision Making Through Aspect Based Sentiment Analysis Using Deep Learning Models. D Puvvula, S Rodda Mathematical Modelling of Engineering Problems 11 (10) , 2024 2024 Citations: 2
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 2022 Citations: 1
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 2022 Citations: 2
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 2022 Citations: 1
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 2021 Citations: 3
Enhanced dbscan with hierarchical tree for web rule mining N Gullipalli, S Rodda Scalable Computing: Practice and Experience 21 (2), 189-202 , 2020 2020 Citations: 1
Reduct ECOC Framework for Network Intrusion Detection System U EROTHI, S Rodda INTERNATIONAL JOURNAL OF ENGINEERING 9 (3), 258-266 , 2020 2020
Frequent subgraph mining by giraph distributed system S Priyadarshini, S Rodda International Journal of Engineering and Advanced Technology 9, 1267-1275 , 2020 2020 Citations: 3
Map Reduce Based Optimized Frequent Subgraph Mining Algorithm for Large Graph Database S Priyadarshini, S Rodda International Journal of Engineering and Advanced Technology , 2020 2020 Citations: 1
Mobile sink as checkpoints for fault detection towards fault tolerance in wireless sensor networks P Parwekar, S Rodda, P Kaur Sensor Technology: Concepts, Methodologies, Tools, and Applications, 414-425 , 2020 2020 Citations: 3
Optimization of clustering in wireless sensor networks using genetic algorithm P Parwekar, S Rodda Sensor Technology: Concepts, Methodologies, Tools, and Applications, 822-836 , 2020 2020 Citations: 13
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 2019 Citations: 1
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 … , 2019 2019 Citations: 13
Gideon—An Artificial Intelligent Companion M Pranay, HV Rajkumari, S Rodda, Y Srinivas, P Anuradha Smart Intelligent Computing and Applications: Proceedings of the Third … , 2019 2019 Citations: 2
Face recognition with voice assistance for the visually challenged G Sridhar Chakravarthy, K Anupam, PNS Harish Varma, GH Teja, ... International Conference on Intelligent Computing and Communication, 701-709 , 2019 2019 Citations: 2
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 2019 Citations: 1
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 2016 Citations: 92
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 2016 Citations: 77
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 2018 Citations: 24
An overview on web usage mining G Neelima, S Rodda Emerging ICT for Bridging the Future-Proceedings of the 49th Annual … , 2015 2015 Citations: 23
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 … , 2017 2017 Citations: 22
Optimization of clustering in wireless sensor networks using genetic algorithm P Parwekar, S Rodda Sensor Technology: Concepts, Methodologies, Tools, and Applications, 822-836 , 2020 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 … , 2019 2019 Citations: 13
Localization of sensors by base station in wireless sensor networks P Parwekar, S Rodda J Sci Ind Res 77 (2), 83-86 , 2018 2018 Citations: 10
Network intrusion detection systems using neural networks S Rodda Information Systems Design and Intelligent Applications: Proceedings of … , 2018 2018 Citations: 8
A normalized measure for estimating classification rules for multi-class imbalanced datasets S Rodda International Journal of Engineering Science , 2011 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 … , 2015 2015 Citations: 4
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 2021 Citations: 3
Frequent subgraph mining by giraph distributed system S Priyadarshini, S Rodda International Journal of Engineering and Advanced Technology 9, 1267-1275 , 2020 2020 Citations: 3
Mobile sink as checkpoints for fault detection towards fault tolerance in wireless sensor networks P Parwekar, S Rodda, P Kaur Sensor Technology: Concepts, Methodologies, Tools, and Applications, 414-425 , 2020 2020 Citations: 3
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 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 2018 Citations: 3
A rough set based associative classifier S Rodda, M Shashi International Conference on Computational Intelligence and Multimedia … , 2007 2007 Citations: 3
Enhancing Decision Making Through Aspect Based Sentiment Analysis Using Deep Learning Models. D Puvvula, S Rodda Mathematical Modelling of Engineering Problems 11 (10) , 2024 2024 Citations: 2
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 2022 Citations: 2
Gideon—An Artificial Intelligent Companion M Pranay, HV Rajkumari, S Rodda, Y Srinivas, P Anuradha Smart Intelligent Computing and Applications: Proceedings of the Third … , 2019 2019 Citations: 2
