Computer Science, Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design
7
Scopus Publications
Scopus Publications
Advancements in Knowledge-Based Visual Question Answering Using Large Language Models: A Review Noorbhasha Junnu Babu, S.P. Rajamohana Proceedings of the 6th International Conference on Inventive Research in Computing Applications Icirca 2025, 2025 Visual Question Answering (VQA) is a rapidly evolving domain in artificial intelligence, bridging computer vision and natural language processing to enable machines to comprehend and respond to image-based queries. Traditional VQA models primarily rely on visual and textual features, often struggling with questions that demand external knowledge and reasoning. Recent advancements in Knowledge-Based VQA (KB-VQA) address this limitation by integrating explicit knowledge from structured sources like knowledge graphs and implicit knowledge from large language models (LLMs). This paper provides a comprehensive review of state-of-the-art KB-VQA approaches, including Knowledge-Aware Transformers (KAT), Region-based Visual Instruction Tuning (REVIVE), and Heuristic-Prompted Large Language Models (Prophet). These methods enhance reasoning capabilities by fusing multimodal information with external knowledge, significantly improving accuracy on challenging datasets. However, key challenges persist, including effective knowledge alignment, scalability, computational efficiency, and interpretability. This review critically examines these challenges and explores potential directions for future research, emphasizing the need for more sophisticated architectures and efficient knowledge retrieval mechanisms to advance human-like multimodal AI systems.
Deep Learning based Quantification of Knee Osteoarthritis Severity from X Ray images and clinical Data 16th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2025, 2025
Innovative Computational Intelligence Frameworks for Complex Problem Solving and Optimization Noorbhasha Junnu Babu, Vidya Kamma, R. Logesh Babu, J. William Andrews, Tatiraju.V.Rajani Kanth, J. R. Vasanthi International Journal of Computational and Experimental Science and Engineering, 2025 The rapid advancement of computational intelligence (CI) techniques has enabled the development of highly efficient frameworks for solving complex optimization problems across various domains, including engineering, healthcare, and industrial systems. This paper presents innovative computational intelligence frameworks that integrate advanced algorithms such as Quantum-Inspired Evolutionary Algorithms (QIEA), Hybrid Metaheuristics, and Deep Learning-based optimization models. These frameworks aim to address optimization challenges by improving convergence rates, solution accuracy, and computational efficiency. In the context of healthcare, a Deep Learning-based optimization framework was successfully used to predict the optimal treatment plans for cancer patients, achieving a 92% accuracy rate in classification tasks. The proposed frameworks demonstrate the potential for addressing a broad spectrum of complex problems, from resource allocation in smart grids to dynamic scheduling in manufacturing systems. The integration of cutting-edge CI methods offers a promising future for optimizing performance and solving real-world problems in a wide range of industries.
Semantic Threads Enabling Image-Text Retrieval via VQA Transformers Junnubabu Noorbhasha, Rohitha Guddeti, Satvika Lingutla, Sujitha Etikikota, Santhosh Kumkumkari Proceedings 2024 International Conference on Computational Intelligence for Security Communication and Sustainable Development Ciscsd 2024, 2024 The integration of vision and language has propelled the advancement of artificial intelligence systems. Visual Question Answering (VQA) stands at the nexus of computer vision and natural language processing, enabling machines to comprehend and respond to image-related queries. This paper introduces a novel VQA approach harnessing the capabilities of the BLIP (Bootstrapping Language Image Pretrained) model, a transformer-based architecture esteemed for its natural language understanding prowess. The methodology involves image preprocessing, and question translation into a standardized language for efficient processing by BLIP. Mainly, the study integrates multilingual support into the VQA framework, facilitating seamless interaction with users across diverse linguistic backgrounds. Through rigorous experimentation, this paper demonstrates the effectiveness of our approach in accurately answering questions in various languages These findings underscore the robustness and adaptability of the BLIP model in handling multilingual inputs, thereby enhancing accessibility and usability in real-world applications. This research contributes to advancing the state-of-the-art in VQA systems by addressing language barriers and promoting inclusivity in human-machine interaction.
Advancements in Forest Fire Prediction: Techniques and Technologies N Junnu Babu, Murala Praveena, B Nagaraju, Dudekula Rabiya Begum, Peravali Surekha, Kolla Vivek Proceedings of the 4th International Conference on Ubiquitous Computing and Intelligent Information Systems Icuis 2024, 2024 Life-threatening effects, including human casualties, environmental damage, and significant financial damages, are caused by forest fires, which continue to be an issue on a worldwide scale. Predicting and detecting forest fires promptly is crucial for mitigating these risks. More and more people are interested in using state-of-the-art methods for forest fire control as a reaction to this issue and because of the present scenario of humans living in the digital age of Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), the Internet of Things (IoT), and Wireless Sensor Networks (WSN). In this work, a wide range of wildfire prediction models like AI-based, ML-based, DL-based, IoT-based, WSN-based, Remote sensing & Satellite imagery, and Unmanned Aerial Vehicles (UAV)-based, Edge computing-based, Weather-based, Ground-based sensor networks were reviewed. This work also provided the latest techniques and models that can be employed in the future for predicting wildfires. Finally, the study offers recommendations for future research that make use of cutting-edge technologies such as digital twins, edge computing, Liquid Neural Networks (LNN), and eXplainable Artificial Intelligence (XAI), and assesses the possible influence of wildfire prediction models on wildfire management. The study concludes by discussing the current state of wildfire prediction models and their limitations in wildfire management, and by recommending future studies to address these issues so that these models may have the greatest possible positive influence.
Detecting Diseases in Potato Leaves using Deep Learning and Machine Learning Approaches: A Review Harikrishna Bommala, N Junnu Babu, Pothuganti Srikanth, S Kumar Reddy Mallidi, Thota Siva Ratna Sai, Rudrapati Mounika Proceedings of the 4th International Conference on Smart Electronics and Communication Icosec 2023, 2023 As a mostly agricultural country, India is deeply concerned about its crop production rate. When crop yields drop, food prices rise, and people who can't afford even potatoes go hungry. Physical examination is a component of conventional approaches for spotting diseases in plants. This approach is exceedingly costly, laborious, and doesn't yield good outcomes. A unique approach for diagnosing potato leaf ailments should be developed to detect sick leaves at the start of their developmental period, which will assist to boost the output and consequently reduce the financial losses suffered by agriculturalists. Fortunately, new Deep Learning (DL) and Machine Learning (ML) models have flourished as a method of reducing plant disease and increasing crop yields, alleviating some of the burdens on farmers. Deep Neural Networks (DNN), a kind of artificial intelligence technology, may be used to identify disease-infected plants so that they can be treated early on, in the bud stage, before the sickness has spread too far. Based on a comprehensive literature assessment, the authors of this research concluded that Convolution Neural Networks (CNN) are superior to other methods for detecting leaf diseases and it is determined that CNN provides the highest achievable accuracy of 91% to 100%.
A Review on Machine Learning and Deep Learning Methods to Fortify Cyberspace N Junnu Babu, J Bhargav, Vanapalli Mounica, Eedupalli Sai Kumar 2022 1st International Conference on Computational Science and Technology Iccst 2022 Proceedings, 2022 Things that have garnered and sparked a great deal of academic attention in the previous decade are Machine learning (ML) and Deep learning (DL). In most people's daily lives, online communities and social media have taken the lead recently, however, this trend comes with serious societal risks. Protecting sensitive data, data networks, and computers against malicious cyber attacks is a difficult task. Cyber security is essential for the safety of our data. Deep learning and machine learning, two relatively new technologies, are merged with cyber-attacks to give a solution to these issues. This article provides a summary of recent research into applying DL to the problem of cyber security, and it examines the many obstacles that must be overcome before this promising field can be fully implemented.
