Browser Extension for Instant Language Conversion for Video Audio L. Pallavi, B.K. Chinna Maddileti, Nallagutla Kiran Kumar Reddy, N.V. Santosh Varma, Rishikesh Varma, Banoth Sneha 2025 8th International Conference on Circuit Power and Computing Technologies Iccpct 2025, 2025 The world of multimedia content in various languages has been consistently growing, and real-time audio translation has emerged as an essential solution to overcome language barriers. We present a browser extension that can translate live audio from any video you are currently playing using machine learning. Integrating ASR, MT and TTS technologies. The extension daisy-chains audio directly from video content via either the Web Audio API or Chrome’s tab capture API to process and convert it into accurate speech-to-text. Next, the text that has been converted to speech is translated to the target language using a neural machine translation model (in our case, Marian MT). The text is eventually fed into Tacotron 2, which guarantees a high-definition reproduction of its meaning extracted from the multiple contexts it has learned to understand. The system uses real-time data over Web Sockets to reduce latency, thereby maintaining synchrony between the original video and its translated speech while maintaining buffering methods for smooth streaming. The proposed extension provides efficient and scalable real-time accessibility, thereby promoting translation to audiences from various languages. Experimental evaluations validated the strength of the given system in meeting its objective that the translations returned are accurate to content and nearly delay-free for end users.
Deep Convection Network (DCNN) to Precisely Identify Distortions on Just Noticeable Difference (JND) for Assessing the Quality of Photo Compression B.K. Chinna Maddileti, Nuthanakanti Bhaskar, X.S. Asha Shiny, B. Shanthi Proceedings of International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI Icscai 2024, 2024 Essentially, a discernable difference (JND) (DCNN) is the prediction approach generated by the deep convolution network, a unique brain theory. Many techniques are used to compress images while maintaining the original's resolution and quality. The screen contented image (SCI) is distinctive and poses several issues for image quality assessment (IQA) since it consists of a combination of graphical and textual elements. The Human Visual System (HVS) is worn to forecast order-based in sequence, eliminating outstanding uneven unpredictability enabling image approaching and understanding. Deep learning is heavily worn in JND pictures. JND is a vast field that uses undervalued visual and psychological information. The basic goal is to compare the original image by the warped image. No DL or ML technique be able to properly assess the indistinct image. In this study, the JND was identified from the supplied pictures using Dennis the core of the proposed model is the precise identification of distortions after evaluation of quality and photo compression. This type of deformation can mislead the DL or ML technique. The level of quality of the warped image determines how well it performs
Detecting Anomalies in IoT Sensor Data with Robust Principal Component Analysis (RPCA) Kamal Sharma, Jajimoggala Sravanthi, B. K. Chinna Maddileti, S. Sivakami, Laith H. Alzubaidi, Y. Mary Reeja 2nd IEEE International Conference on Iot Communication and Automation Technology Icicat 2024, 2024 This work studies the use of RPCA in IoT sensor data for anomaly detection. However, due to the fact that IoT data is complex, high-dimensional, noisy, and frequently has a dynamic pattern, it is hard to employ regular anomaly detection techniques. RPCA, however, gives a quite potential solution as it decomposes the data into low-rank and sparse matrices, in this way the outlying points can be separated for isolation. The effectiveness of the proposed framework is being validated, compared to the state-of-the-art schemes for IoT, and yields high detection accuracy and low FPR/FNR rates in real-world IoT datasets with noise and different severity of anomalies included. It should also noted that the computational complexity of RPCA is bigger than of traditional methods but it offers much better results. The present work can be considered as a valuable contribution to the field of IoT due to a highly reliable and scalable method that can be used for anomaly detection and recognition of IoT systems vulnerabilities.
RECENT SCHOLAR PUBLICATIONS
Interactive Visual Notes Creation Tool Using Gemini AI BKC Maddileti, S Javed, G Nithin, A Zeba International Conference on Computer Science and Communication Engineering … , 2025 2025.0
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Intelligent Computing and Automation: Proceedings of the 12th International Conference on Frontiers in Intelligent Computing: Theory and Applications (FICTA 2024) V Bhateja, M Dey, M Simic Springer Nature , 2025 2025.0
Detecting Anomalies in IoT Sensor Data with Robust Principal Component Analysis (RPCA) K Sharma, J Sravanthi, BKC Maddileti, S Sivakami, LH Alzubaidi, ... Published in: 2024 International Conference on IoT, Communication and … , 2025 2025.0
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Detecting Anomalies in IoT Sensor Data with Robust Principal Component Analysis (RPCA) K Sharma, J Sravanthi, BKC Maddileti, S Sivakami, LH Alzubaidi, ... 2024 International Conference on IoT, Communication and Automation … , 2024 2024.0 Citations: 2
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MOST CITED SCHOLAR PUBLICATIONS
Detecting Anomalies in IoT Sensor Data with Robust Principal Component Analysis (RPCA) K Sharma, J Sravanthi, BKC Maddileti, S Sivakami, LH Alzubaidi, ... 2024 International Conference on IoT, Communication and Automation … , 2024 2024.0 Citations: 2
Interactive Visual Notes Creation Tool Using Gemini AI BKC Maddileti, S Javed, G Nithin, A Zeba International Conference on Computer Science and Communication Engineering … , 2025 2025.0
Assessment on Healthcare System by Combining SHA 256 Algorithm with Machine Learning C Syamsundar Hybrid Intelligent Systems: 23rd International Conference on Hybrid … , 2025 2025.0
A secured and safe CNN deep learning approach for artificial intelligence based robust intelligent malware detection B Ramji, A Naureen, S Kumar, T Jahan, BKC Maddileti AIP Conference Proceedings 3342 (1), 030053 , 2025 2025.0
A Novel Approach for Effective U-Net for Perceptive Lung Cancer Identification S Ashwini, J Narasimharao, B Prasad Innovations in Data Engineering: Sustainability for Societal and Industrial … , 2025 2025.0
Browser Extension for Instant Language Conversion for Video Audio L Pallavi, BKC Maddileti, NKK Reddy, NVS Varma, R Varma, B Sneha 2025 8th International Conference on Circuit, Power & Computing Technologies … , 2025 2025.0
An Enhanced and More Effective Method for Detecting and Reporting Fake Social Media Profiles Using Extremely Generative Boosting Algorithm Information BK Chinna Maddileti, N Sowjanya, K Priyanka, R Reeja Igneshia Malar, ... International Conference on Frontiers of Intelligent Computing: Theory and … , 2025 2025.0
The Reduction of Hadoop Map While Maintaining the Privacy and Balancing Dynamic Demand Across Data Notes N Bhaskar, MSL Reddy, BKC Maddileti, R Tiwari International Conference on Recent Trends in Machine Learning, IOT, Smart … , 2025 2025.0
Intelligent Computing and Automation: Proceedings of the 12th International Conference on Frontiers in Intelligent Computing: Theory and Applications (FICTA 2024) V Bhateja, M Dey, M Simic Springer Nature , 2025 2025.0
Detecting Anomalies in IoT Sensor Data with Robust Principal Component Analysis (RPCA) K Sharma, J Sravanthi, BKC Maddileti, S Sivakami, LH Alzubaidi, ... Published in: 2024 International Conference on IoT, Communication and … , 2025 2025.0
Deep Convection Network (DCNN) to Precisely Identify Distortions on Just Noticeable Difference (JND) for Assessing the Quality of Photo Compression BKC Maddileti, N Bhaskar, XSA Shiny, B Shanthi 2024 1st International Conference on Sustainable Computing and Integrated … , 2025 2025.0
A Novel Approach for Effective U-Net for Perceptive Lung Cancer Identification in CT Scan Gradient Pictures S Ashwini, J Narasimharao, B Prasad, BKC Maddileti International Conference on Data Engineering and Communication Technology … , 2024 2024.0
An Enhanced Innovative Method for Object Recognition Using Convolution Neural Networks K Madhavilatha, Sripriya, J Narasimharao, K Srinu, BK Chinna Maddileti, ... International Conference on Frontiers of Intelligent Computing: Theory and … , 2024 2024.0
Deep Learning Algorithms for the Recognition of Congestive Heart Failure Through the Analysis of Electrocardiogram (ECG) Data CM Rao, VN Kumar, BKC Maddileti, G Saidulu, KS Raju, M Mallikarjun International Conference on Hybrid Intelligent Systems, 417-426 , 2023 2023.0
Spam Email Filtering System Using NLP Features and Machine Learning Algorithms MV Narasimha, GR Kumar, MBKC Maddileti, PA Patel 2023.0
The Reduction of Hadoop Map While Maintaining the Privacy and Balancing Dynamic Demand Across Data Notes NBMSL Reddy, BKC Maddileti, R Tiwari Proceedings of 6th International Conference on Recent Trends in Machine … , 0
