Intelligent Automation in Equity Research: A LangChain-FAISS-LLM Framework for Financial News Understanding and Insight Generation C P Pavan Kumar Hota, Vinod Babu Polinati, Prasanthi Gottumukkala, Surya Pavan Kumar Gudla, S Murali Krishnamachari, P Neelima Conference Proceedings 1st International Conference on Advancing Sustainable Solutions Through Technologies Icasst 2026, 2026 Today, equity research is heavily reliant on the analysis of large amounts of unstructured financial data, such as news reports, earnings releases, and market updates. Traditional approaches are frequently slow, inconsistent, and influenced by human judgment, necessitating greater automation. This study introduces a financial news analysis tool built with modern Natural Language Processing (NLP) methods and tools such as LangChain, FAISS, and Streamlit, as well as Large Language Models. Initially based on OpenAI's GPT models, the tool later shifted to open-source models from Hugging Face to reduce costs and improve scalability. Semantic search with vector embeddings, automatic summarization, and a simple interface for financial queries are among the key features. FAISS allows for quick and relevant information retrieval, whereas LangChain improves response accuracy through effective prompt management. Testing revealed that the system was capable of producing results in milliseconds while extracting financial insights with greater than 80% accuracy. This work presents a practical, efficient, and cost-effective solution for using AI in equity research, providing better support for financial decision-making.
CT-LIVERNET: AN EFFICIENT DEEP LEARNING MODEL FOR LIVER LESION SEGMENTATION AND DIAGNOSIS Journal of Theoretical and Applied Information Technology Journal of Theoretical and Applied Information Technology, 2026 Medicine has undergone substantial changes because of big data analytics combined with deep learning technologies which enable doctors to predict diseases better while monitoring patients and diagnosing conditions as well as delivering better treatments. Liver disease stands as a worldwide significant health challenge attributed to its multiple complicated aspects and elevated death rates as well as its multiple pathologic expressions. A combination of wrong medical diagnosis and insufficient medical care decreases survival expectancy dramatically. Detecting liver disease presents a difficult issue because some damaged liver areas can look unblemished due to minimal lesion distribution which causes experts to make incorrect classifications. The assessment of liver conditions with precision at an early stage proves essential for shrinking treatment deficiencies along with enhanced medical results. The progress of deep learning and medical imaging allowed researchers to develop new detection procedures for liver disease analysis. The presented research develops a novel approach to liver disease recognition that adopts YOLO (You Only Look Once) as its feature extractor and pairs this with Random Forest (RF) and XGBoost classifiers for superior classification accuracy. The proposed diagnostic methodology reached a 95% accuracy level which proved superior to Support Vector Machine (SVM) and Logistic Regression and yielded optimal diagnosis and classification results.
Multimodal Deep Learning for Automated Road Accident Detection: Integrating Vision, Sensing, and AI for Smart Transportation Safety C P Pavan Kumar Hota, Polinati Vinod Babu, N V Muralikrishna Raja, Kolagotla Venkateswara Reddy, Preeti Nutipalli, Kvssr Murthy 2025 2nd Asia Pacific Conference on Innovation in Technology Apcit 2025, 2025 This work explores the transformative impact of deep learning on road accident detection, emphasizing the integration of computer vision, artificial intelligence, and advanced sensing technologies to enhance transportation safety and emergency response. It examines the evolution from traditional rule-based and statistical methods to sophisticated deep learning architectures, including convolutional and recurrent neural networks, and hybrid models that capture both spatial and temporal dynamics in complex traffic environments. The study highlights the critical role of diverse data sources such as sensor arrays, UAV-acquired imagery, and crowd sourced social media inputs, alongside robust data cleaning and feature engineering techniques that underpin model accuracy and reliability. Key system design considerations are addressed, covering software architectures, real-time data processing pipelines, scalable backend frameworks, and secure communication protocols essential for effective deployment. Evaluation metrics including accuracy, precision, recall, and F1-score are discussed to assess model performance comprehensively. Applications of these technologies in intelligent transportation system and in smart city design are illustrated to have major benefits in terms of response time in an event of circumstances like emergency, traffic management and predictive analytics of accidents in urban set ups. Superiority issues, including data quality, scalability, the limitations of computing power, and ethical issues, namely, bias and privacy, are reviewed critically. Emergent themes in adaptive learning, multimodal data fusion and policy implications are also provided to situate future efforts in broadening automated accident detection to not only benefit the greater population in public safety, but also to further collaborate on a multidisciplinary approach to improve safety and prevent accidents as a result.
Uncovering Key Cognitive Traits for Placement Success: A Multi-Dimensional Data Mining Approach D V H Venu Kumar, Rapeti Deepthi, Sudhakar Godi, C P Pavan Kumar Hota, Polinati Vinod Babu, V.Priyadarshani Proceedings of 5th International Conference on Evolutionary Computing and Mobile Sustainable Networks Icecmsn 2025, 2025 The process of placement in institutions of higher learning plays a very important role in determining the employability of graduates. This paper is intended to establish the prevailing cognitive variables across different stages in the placement such as Quantitative Aptitude Test (QAT), Verbal and Non-Verbal Reasoning (VNR), Computer Proficiency Test (CPT), Programming and Coding Test (PCT), English Communication Test (ECT), Technical Round (TR) and the HR Round (HR). It suggests a new model, which is a combination of the Mean Difference-Based Clustering algorithm and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to analyze cognitive profiles holistically. This paper analyzes the data of engineering learners, which gathers attributes of multiple intelligences, learning styles, thinking styles, metacognitive awareness, and personality. Once the data has been properly prepared, similarities among students in terms of cognitive abilities and constraints are found through the help of clustering algorithms. This is followed by the ranking of cognitive qualities based on the TOPSIS (Technique order preference by similarity to ideal solution) technique in which the cognitive qualities with the highest influence on the success of placement are emphasized. The comparison shows essential psychological and cognitive traits differentiating successful candidates that can give valuable information to develop a personalized solution. Through the systematic rating of cognitive qualities and the classification of learners according to several dimensions of preparedness, this study offers a structured, data-driven framework to improve placement outcomes. Because the suggested methodology is reproducible and scalable, it may be used in a variety of educational settings and adapts to changing industry demands by matching student cognitive profiles.
Quadrant Analysis of Learner's Performance in Quantitative Aptitude and Coding Surya Pavan Kumar Gudla, N V Murali Krishna Raja, Karri Chandra Sekhar, S. Anand Kumar, B. Ravi Kumar, C P Pavan Kumar Hota Esic 2025 5th International Conference on Emerging Systems and Intelligent Computing Proceedings, 2025 With the aim of improving learning for varied learners in various situations, abstract educational data mining (EDM) entails utilising methodologies, tools, and research to extract significant insights from educational data repositories. The primary objective of this research is to examine the quantitative aptitude and coding performance of 366 engineering undergraduate students. A convenience and random sampling technique was used to gather the dataset, and preprocessing was then performed to guarantee data completeness. Descriptive statistics demonstrate that students do well overall in both areas, with slightly more variability in Quantitative Aptitude scores than in Coding, where performance is more constant. Based on how well the students performed in these two areas, a quadrant analysis was used to classify the students into four groups: Excellent, Good, Average, and Poor. The results of this analysis demonstrated the differences in the way students developed in the two skill areas, highlighting the significance of tailored educational interventions. Quantitative aptitude and coding had a weakly positive link, according to the correlation study, indicating that these abilities are largely independent of one another. The study highlights that in order to help pupils in these areas, specific instructional practices are required. It is advised to connect technology with pedagogy through the Metacognition-aided Technological Pedagogical Content Knowledge (TPACK) framework. This will improve student outcomes in programming courses and enhance their metacognition. The results offer insightful information on the patterns of student performance that can guide the creation of instructional interventions that are more successful.
IoT-Integrated SCNN-OA Framework for Real-Time Water Quality Prediction Kalyan Sagar Kadali, C P Pavan Kumar Hota, G B Christina, N V Muralikrishna Raja, J. Vikranth, Yannam Bhavyasri Proceedings of International Conference on Sustainable Communication Networks and Application Icscn 2025, 2025 Water quality monitoring is important to facilitate the availability of clean and safe water, as it helps identify and prevent possible hazards that can compromise public health. Conventional real-time water quality monitoring practices generally suffer from excessive computational cost, less flexibility toward variations in complicated data, and inefficiency in sensor noise and uncertainty handling. To overcome these shortcomings, this paper proposed a new Internet of Things (IoT)-integrated framework of Real-Time Water Quality Monitoring that combines a Spherical Convolutional Neural Network with Orchard Algorithm (SCNN-OA). Pre-processing starts with Locally-Adaptive Bitonic Filter (L-ABF) for proper data preparation. The SCNN-OA method is a technique for real-time water quality prediction in monitoring systems based on the Orchard Algorithm(OA) and a Spherical Convolutional Neural Network(SCNN). It effectively simulates spatial-temporal sensor information, enhancing optimization and prediction accuracy. Experimental comparison on an IoT water quality monitoring attests to the exceptional performance of the proposed method, with 99.85% accuracy and 99.62% precision, highlighting its strength, scalability, and readiness for real-world deployment in intelligent water monitoring systems.
Automatic Detection and Analysis of Concrete Cracks Using YOLO C P Pavan Kumar Hota, Vvnls Sowjanya, G Ramya Sree, K Neeharika, G Supritha 2024 4th International Conference on Intelligent Technologies Conit 2024, 2024 This research introduces a novel approach for the automated detection and analysis of concrete cracks using deep learning techniques, with a focus on predicting the lifespan of structures. Concrete infrastructure is susceptible to various forms of deterioration, and cracks are early indicators of potential structural issues. Traditional methods for crack detection and analysis are often time-consuming and subjective. In this study, we utilize deep learning calculations, explicitly convolutional neural networks (CNNs), to distinguish and order breaks in substantial surfaces naturally. The proposed model is trained on a diverse dataset of concrete images, enabling it to generalize across different environmental conditions and crack patterns. Furthermore, the research extends beyond crack detection to predict the potential lifespan of concrete structures. By leveraging historical data on concrete deterioration and utilizing advanced predictive modeling. Based on detected cracks, the system seeks to offer insights into the anticipated durability of structures. This predictive capability serves as a proactive tool for maintenance planning and resource allocation, contributing to the long-term sustainability of civil infrastructure. Our experiments’ outcomes show how well the deep learning model works to identify concrete cracks and forecast possible structural degradation.
Enhancing the Scalability of Knowledge Discovery Services for Massive Data Mining on Clouds Vijayakumar Sajjan, Jayam Pradeep Kumar, Vikram Kalvala, Narsaiah Domala, B. Kiran Kumar, C P Pavan Kumar Hota Proceedings of 5th International Conference on Iot Based Control Networks and Intelligent Systems Icicnis 2024, 2024 The traditional data mining techniques do not work well when it comes to the big data and the tremendous growth rate of the digital data also becomes a major challenge to the discovery of knowledge. Cloud computing offers an ideal solution to computationally and storage intensive tasks with scalable and on-demand services that can process big data at short notice. This paper reviews the current models that employ cloud structure for distributed data mining as well as models like CosDic and the Data Mining Cloud Framework (DMCF). The way of processing large datasets is also represented well by the Map Reduce model where it is possible to implement the data processing in parallel on several nodes. Despite the fact that cloud-based systems are very useful when it comes to the management of large amounts of data, challenges such as privacy, data security and data coordination still persist. These innovative methodologies include Edge Computing and Federated Learning that are anticipated to solve the mentioned issues by enhancing the speed of data processing and its safety. Thus, scalable cloud data mining to meet growing needs of knowledge discovery in big data contexts will have to develop new privacy protection mechanisms, integration of cloud models, and complex algorithms.
Next-Gen Classrooms: Augmented and Virtual Reality in Modern Education Srikanth Reddy E, Jayam Pradeep Kumar, S Anand Kumar, D. Purushothaman, Vijayakumar Sajjan, C P Pavan Kumar Hota Iccds 2024 International Conference on Computing and Data Science, 2024 This paper delves into the transformative role of Augmented Reality (AR) and Virtual Reality (VR) in modern education, marking a paradigm shift towards immersive learning environments. The advent of these technologies has ushered in a new era in educational methodologies, transcending traditional boundaries and fostering a more engaging, interactive, and personalized learning experience. The paper begins with a historical overview of AR and VR technologies in educational settings, tracing their evolution and increasing accessibility. It then critically examines recent studies and developments, shedding light on the diverse applications and pedagogical strategies enabled by these tools. Special attention is given to the ways in which AR and VR cater to different learning styles, enhance student engagement, and facilitate complex conceptual understanding across various disciplines. Furthermore, the paper highlights successful case studies from around the globe, illustrating the practical implementation and impact of these technologies in classrooms. Challenges such as technological barriers, cost implications, and the digital divide are discussed, alongside potential solutions and best practices for integration. Emerging trends and future prospects of AR and VR in education are explored, emphasizing the continuous innovation and potential growth areas in this field. The review concludes by underscoring the need for ongoing research, collaboration between educators and technologists, and policy support to fully realize the potential of AR and VR as pivotal tools in the next generation of educational environments. This comprehensive review aims to provide educators, policymakers, and researchers with a deeper understanding of the current state and future possibilities of AR and VR in education, setting the stage for the next generation of interactive and immersive classrooms.
Exploring the Potential of Hybrid Feature-Based Research Topic Generation for Government Science and Technology Projects Vijayakumar Sajjan, Jayam Pradeep Kumar, B. Kiran Kumar, Narsaiah Domala, C P Pavan Kumar Hota, Vikram Kalvala Proceedings of 5th International Conference on Iot Based Control Networks and Intelligent Systems Icicnis 2024, 2024 This research addresses the challenge of identifying relevant research topics for Government Science and Technology (GRB) projects. Traditional methods for identifying research topics often prove inadequate due to the increasing volume and complexity of available data. This study proposes a novel framework that integrates text mining, K-means clustering, and AI optimization techniques to effectively identify and prioritize research topics. By analyzing existing research literature, identifying emerging trends, and considering the specific needs and priorities of GRB, the framework aims to generate a set of impactful and relevant research topics. The proposed framework has the potential to be adapted to other domains, such as healthcare and finance, facilitating the identification of promising research directions in various fields.
Augumented And Virtual Reality As A Teaching Learning Tool In The Class Room:A Review Jayam Pradeep Kumar, Vijayakumar Sajjan, Srikanth Reddy E, C P Pavan Kumar Hota, Mallikarjun Yaramadhi, B Kiran Kumar 2023 IEEE International Conference on Research Methodologies in Knowledge Management Artificial Intelligence and Telecommunication Engineering Rmkmate 2023, 2023
RECENT SCHOLAR PUBLICATIONS
Intelligent Automation in Equity Research: A LangChain–FAISS–LLM Framework for Financial News Understanding and Insight Generation CPPK Hota, VB Polinati, P Gottumukkala, SPK Gudla, SM Krishnamachari, ... 2026 1st International Conference on Advancing Sustainable Solutions through … , 2026 2026
CT-LIVERNET: AN EFFICIENT DEEP LEARNING MODEL FOR LIVER LESION SEGMENTATION AND DIAGNOSIS M SUNEETHA, MK JETTY, VRAO REDDY, K PATHURI10 Journal of Theoretical and Applied Information Technology 104 (1) , 2026 2026
Trustworthy AI for Truth Discovery: An Interpretable SHAP-SVM Framework for Fake News Classification S Anusha, R Ravi, PV Babu, CPPK Hota, G Kalyani, V Priyadarshini 2026 International Conference on Smart Futuristic Technology, 1-5 , 2026 2026
IoT-Integrated SCNN-OA Framework for Real-Time Water Quality Prediction KS Kadali, CPPK Hota, GB Christina, NVM Raja, J Vikranth, Y Bhavyasri 2025 International Conference on Sustainable Communication Networks and … , 2025 2025
Deep Learning for Inclusive Communication: A CNN-Based Approach to Sign Language Recognition BJ Jaidhan, PV Babu, S Ketha, DVHV Kumar, CPPK Hota, ... 2025 OITS International Conference on Information Technology (OCIT), 749-753 , 2025 2025
Uncovering Key Cognitive Traits for Placement Success: A Multi-Dimensional Data Mining Approach DVHV Kumar, R Deepthi, S Godi, CPPK Hota, PV Babu, V Priyadarshani 2025 5th International Conference on Evolutionary Computing and Mobile … , 2025 2025
Multimodal Deep Learning for Automated Road Accident Detection: Integrating Vision, Sensing, and AI for Smart Transportation Safety CPPK Hota, PV Babu, NVM Raja, KV Reddy, P Nutipalli, K Murthy 2025 2nd Asia Pacific Conference on Innovation in Technology (APCIT), 1-5 , 2025 2025
Quadrant Analysis of Learner’s Performance in Quantitative Aptitude and Coding SPK Gudla, NVMK Raja, KC Sekhar, SA Kumar, BR Kumar, CPPK Hota 2025 International Conference on Emerging Systems and Intelligent Computing … , 2025 2025
Exploring the Potential of Hybrid Feature-Based Research Topic Generation for Government Science and Technology Projects V Sajjan, JP Kumar, BK Kumar, N Domala, CPPK Hota, V Kalvala 2024 International Conference on IoT Based Control Networks and Intelligent … , 2024 2024
Enhancing the Scalability of Knowledge Discovery Services for Massive Data Mining on Clouds V Sajjan, JP Kumar, V Kalvala, N Domala, BK Kumar, CPPK Hota 2024 International Conference on IoT Based Control Networks and Intelligent … , 2024 2024 Citations: 1
Automatic Detection and Analysis of Concrete Cracks Using YOLO CPPK Hota, V Sowjanya, GR Sree, K Neeharika, G Supritha 2024 4th International Conference on Intelligent Technologies (CONIT), 1-6 , 2024 2024 Citations: 2
Next-Gen Classrooms: Augmented and Virtual Reality in Modern Education S Reddy, JP Kumar, SA Kumar, D Purushothaman, V Sajjan, CPPK Hota 2024 International Conference on Computing and Data Science (ICCDS), 1-5 , 2024 2024 Citations: 3
Augumented And Virtual Reality As A Teaching Learning Tool In The Class Room: A Review JP Kumar, V Sajjan, S Reddy, CPPK Hota, M Yaramadhi, BK Kumar 2023 International Conference on Research Methodologies in Knowledge … , 2023 2023 Citations: 4
A Comprehensive Analysis of Relationship among Cognitive Traits, Learning Styles, and Academic Performance in Computer Programming CPPK Hota, V Asanambigai, D Lakshmi Chinese Journal of Computational Mechanics, 424-430 , 2023 2023 Citations: 1
Monitoring Indoor and Outdoor Air Quality Using Raspberry PI Processor V Sajjan, ES Reddy, CPPK Hota, BK Kumar 2023 9th International Conference on Advanced Computing and Communication … , 2023 2023 Citations: 2
Predicting academic grades of students in computer programming using classification algorithms CPPK Hota, V Asanambigai, D Lakshmi 2023 9th International Conference on Advanced Computing and Communication … , 2023 2023 Citations: 6
Metacognition-Driven Approach to Enhance Computer Programming Skills: A Multi-Criteria Decision Making Analysis C P Pavan Kumar Hota, Dr.V. Asanambigai, Dr.D.Lakshmi Tuijin Jishu/Journal of Propulsion Technology 44 (4), 2039-2045 , 2023 2023
Investigation Of Metacognitive Awareness In Learning Programming Course Using Multiple Criteria Decision Making Algorithm: Topsis. CP Hota, V Asanambigai, D Lakshmi Journal of Pharmaceutical Negative Results 13 , 2022 2022 Citations: 6
Investigation on Impact of Applying Metacognitive strategies and Activity-Based Learning in Higher Education CPPK Hota, V Asanambigai, D Lakshmi Journal of Optoelectronics Laser 41 (6), 597-604 , 2022 2022
A Bi-model Interrelationship analysis of Learners’ Psychometric Assessments in Engineering Education C P Pavan Kumar Hota, Dr.V. Asanambigai, Dr.D.Lakshmi NeuroQuantology 20 (5), 3854-3868 , 2022 2022 Citations: 1
MOST CITED SCHOLAR PUBLICATIONS
Predicting academic grades of students in computer programming using classification algorithms CPPK Hota, V Asanambigai, D Lakshmi 2023 9th International Conference on Advanced Computing and Communication … , 2023 2023 Citations: 6
Investigation Of Metacognitive Awareness In Learning Programming Course Using Multiple Criteria Decision Making Algorithm: Topsis. CP Hota, V Asanambigai, D Lakshmi Journal of Pharmaceutical Negative Results 13 , 2022 2022 Citations: 6
Augumented And Virtual Reality As A Teaching Learning Tool In The Class Room: A Review JP Kumar, V Sajjan, S Reddy, CPPK Hota, M Yaramadhi, BK Kumar 2023 International Conference on Research Methodologies in Knowledge … , 2023 2023 Citations: 4
Next-Gen Classrooms: Augmented and Virtual Reality in Modern Education S Reddy, JP Kumar, SA Kumar, D Purushothaman, V Sajjan, CPPK Hota 2024 International Conference on Computing and Data Science (ICCDS), 1-5 , 2024 2024 Citations: 3
Automatic Detection and Analysis of Concrete Cracks Using YOLO CPPK Hota, V Sowjanya, GR Sree, K Neeharika, G Supritha 2024 4th International Conference on Intelligent Technologies (CONIT), 1-6 , 2024 2024 Citations: 2
Monitoring Indoor and Outdoor Air Quality Using Raspberry PI Processor V Sajjan, ES Reddy, CPPK Hota, BK Kumar 2023 9th International Conference on Advanced Computing and Communication … , 2023 2023 Citations: 2
Enhanced ciphertext-policy attribute-based encryption (ECP-ABE) J Venkata Rao, V Krishna Reddy, CP Pavan Kumar Hota International Conference on Intelligent Computing and Communication … , 2019 2019 Citations: 2
A relative study on traditional ETL and ETL with Apache Hadoop BVSR Y Ramu ,C P Pavan Kumar Hota IJARCSSE 6 (3), 74-78 , 2016 2016 Citations: 2
Enhancing the Scalability of Knowledge Discovery Services for Massive Data Mining on Clouds V Sajjan, JP Kumar, V Kalvala, N Domala, BK Kumar, CPPK Hota 2024 International Conference on IoT Based Control Networks and Intelligent … , 2024 2024 Citations: 1
A Comprehensive Analysis of Relationship among Cognitive Traits, Learning Styles, and Academic Performance in Computer Programming CPPK Hota, V Asanambigai, D Lakshmi Chinese Journal of Computational Mechanics, 424-430 , 2023 2023 Citations: 1
A Bi-model Interrelationship analysis of Learners’ Psychometric Assessments in Engineering Education C P Pavan Kumar Hota, Dr.V. Asanambigai, Dr.D.Lakshmi NeuroQuantology 20 (5), 3854-3868 , 2022 2022 Citations: 1
Intelligent Automation in Equity Research: A LangChain–FAISS–LLM Framework for Financial News Understanding and Insight Generation CPPK Hota, VB Polinati, P Gottumukkala, SPK Gudla, SM Krishnamachari, ... 2026 1st International Conference on Advancing Sustainable Solutions through … , 2026 2026
CT-LIVERNET: AN EFFICIENT DEEP LEARNING MODEL FOR LIVER LESION SEGMENTATION AND DIAGNOSIS M SUNEETHA, MK JETTY, VRAO REDDY, K PATHURI10 Journal of Theoretical and Applied Information Technology 104 (1) , 2026 2026
Trustworthy AI for Truth Discovery: An Interpretable SHAP-SVM Framework for Fake News Classification S Anusha, R Ravi, PV Babu, CPPK Hota, G Kalyani, V Priyadarshini 2026 International Conference on Smart Futuristic Technology, 1-5 , 2026 2026
IoT-Integrated SCNN-OA Framework for Real-Time Water Quality Prediction KS Kadali, CPPK Hota, GB Christina, NVM Raja, J Vikranth, Y Bhavyasri 2025 International Conference on Sustainable Communication Networks and … , 2025 2025
Deep Learning for Inclusive Communication: A CNN-Based Approach to Sign Language Recognition BJ Jaidhan, PV Babu, S Ketha, DVHV Kumar, CPPK Hota, ... 2025 OITS International Conference on Information Technology (OCIT), 749-753 , 2025 2025
Uncovering Key Cognitive Traits for Placement Success: A Multi-Dimensional Data Mining Approach DVHV Kumar, R Deepthi, S Godi, CPPK Hota, PV Babu, V Priyadarshani 2025 5th International Conference on Evolutionary Computing and Mobile … , 2025 2025
Multimodal Deep Learning for Automated Road Accident Detection: Integrating Vision, Sensing, and AI for Smart Transportation Safety CPPK Hota, PV Babu, NVM Raja, KV Reddy, P Nutipalli, K Murthy 2025 2nd Asia Pacific Conference on Innovation in Technology (APCIT), 1-5 , 2025 2025
Quadrant Analysis of Learner’s Performance in Quantitative Aptitude and Coding SPK Gudla, NVMK Raja, KC Sekhar, SA Kumar, BR Kumar, CPPK Hota 2025 International Conference on Emerging Systems and Intelligent Computing … , 2025 2025
Exploring the Potential of Hybrid Feature-Based Research Topic Generation for Government Science and Technology Projects V Sajjan, JP Kumar, BK Kumar, N Domala, CPPK Hota, V Kalvala 2024 International Conference on IoT Based Control Networks and Intelligent … , 2024 2024
