Data reduction techniques in wireless sensor networks with internet of things Vijaya Gunturu, Chandrshekhar Goswami, Vinay Kumar Nassa, Mounika Mandapuram, Tripti Tiwari, et al. Interdisciplinary Approaches to AI Internet of Everything and Machine Learning, 2024 Since they are used in an array of real-life uses, wireless sensor equipment in Internet of Things (IoT) systems will be among the biggest prolific sources of big data on the connection. The huge quantity of data collected from sensing equipment increases transmission overhead, reducing the short lifespan of IoT sensing equipment. As a result, it is required to cleanse and reduce the sensed information in order to cut transmission costs and conserve power on sensing equipment. A Data Reduction and Cleansing Technique (DRCT) for power consumption in IoT-based wireless sensor networks (WSNs) is suggested in this research. This method depends on 2 levels of data cleansing and reduction: sensing and aggregation.
Medicine Supply through UAV Mandala Bhuvana, Kesireddy Rajashekar Reddy, Varagani Ramu, Bochu Sai Vardhan, Vijaya Gunturu, et al. Handbook of Artificial Intelligence and Wearables Applications and Case Studies, 2024
Development of Language Model on Biomedical Domain to Pretrain Natural Language Processing Vijaya Gunturu, Yadavalli Devi Priya, Gayatri Vijayendra Bachhav, K Praveena, Anandhi R J, et al. Proceedings of International Conference on Communication Computer Sciences and Engineering Ic3se 2024, 2024 Large neural language model like BERT can be pre trained to get extraordinary profits through multiple natural language processing task. Though, General Domain Corpora including web and news wire are focused on pre training efforts. The main specific pre training are benefited from general domain language models is considered as a prevailing assumption. The study focusses on the domain specific language model with abundance of unlabeled text like biomedical natural language processing and pre training from its scratch that results in more gains over the general domain language model. The investigation can be facilitated by compiling of biomedical NLP data sets that are publicly available. The experiment shows the pre training of domain specific model that act as a solid foundation in performing biomedical NLP task in wide range. the model is evaluated for modelling choices including task specific fine tuning and pre training. BERT models have some common practises involving named entity recognition using complex tagging schemes. The research can be accelerated with biomedical NLP for pre training and task specific model for the biomedical community and the leader board is created for biomedical language understanding and reasoning benchmark (BLURB).
Storage Solution and Security Transmission in Image Sensing Using Blockchain Technology in Internet of Things Vijaya Gunturu, Nekkanti Renu, Navdeep Dhaliwal, Ippa Sumalatha, Anandhi R J, et al. Proceedings of International Conference on Communication Computer Sciences and Engineering Ic3se 2024, 2024 The number of Internet of Things (IoT) devices has grown dramatically with the technology's rapid development. Higher security standards have so been proposed for the administration, transfer, and archiving of vast amounts of IoT data. But security problems like data theft and forgeries are likely to happen while IoT data is being transmitted. Furthermore, the majority of data storage options now in use are centralized, meaning that a centralized server handles both data maintenance and storage. The confidentiality of IoT data would be seriously jeopardized once a hostile assault targets the server. Given the aforementioned security concerns, a secure transmission as well as storage solution for blockchain sensing images in the Internet of Things is put forth. Therefore, to enable effective secure data storage in Internet of Things-related smart computing systems, develop and build a novel blockchain-based artificial intelligence model. We also demonstrated the operation of the system framework. Upon conducting a thorough security study, we have determined that our suggested solution possesses a strong potential to address the majority of security issues that conventional systems encounter. Furthermore, our suggested method can be used for any file-changing wireless Internet of things network that requires the exchange of multimedia data, including traffic data from smart cities, wearable device data, healthcare data, etc.
Transfer Learning in Biomedical Image Classification Vijaya Gunturu, Niladri Maiti, Babacar Toure, Pankaj Kunekar, Shaik Balkhis Banu, et al. Proceedings 3rd International Conference on Advances in Computing Communication and Applied Informatics Accai 2024, 2024 Transfer learning has emerged as a highly effective method for classifying biomedical images, as it entails the use of pre-trained neural networks on large and diverse datasets. The efficacy of models is substantially enhanced by this method. The challenges associated with training deep learning models from inception, such as the limited availability of annotated data and high computational costs, are circumvented by this strategy. Transfer learning expedites the training process by enhancing the precision and applicability of pre-trained models through the use of domain-specific biological imagery. By examining the various applications and techniques of transfer learning in biomedical imaging, this study investigates its potential future, drawbacks, and benefits.
Enhancement of a Biomedical Instrument using Machine Learning Nada Tahani, Shayaan Hussain, Kunta Nithya Sri, Vijaya Gunturu International Conference on Sustainable Computing and Smart Systems Icscss 2023 Proceedings, 2023 Automated detection in medical diagnostics such as imaging has become an emergent field. There is always a risk of exposing patients to issues brought on by human error in all areas of medicine. Among the most promising Machine Learning (ML) applications in the medical field is the analysis of chest X-rays (CXR). Yet, because of the complex structure of radiographs, the accurate identification and classification of specific diseases in CXR collections is still a challenging problem. A study reported that the level of the range of inaccuracy in radiology differs based on the medical examinations and is between 2 and 30%. The likelihood of errors also increases during the night shift hours, whereas a lower chance during the day shift hours. These mistakes may have devastating consequences for falsely diagnosed patients. This underlines the necessity for automated abnormality identification in x-rays, which might reduce the possibility of mistakes and produce reliable findings. This research study intends to enhance a medical instrument with good accuracy and specificity by assessing the results of different ML techniques.
Biomedical Engineering Impacting Community Service with Embedded Systems Mandala Bhuvana Reddy, Rajashekar Reddy, Varagani Ramu, Bochu Vardhan, Vijaya Gunturu 2023 4th International Conference on Electronics and Sustainable Communication Systems Icesc 2023 Proceedings, 2023 Drones have emerged as a promising solution to deliver medicines and healthcare supplies to remote and inaccessible areas. This research study focuses on the use of drones to supply medicines to remote areas. The paper discusses the benefits of using drones, including their ability to reach areas with poor road infrastructure, reduce delivery times, and improve healthcare access for underserved communities. Also, this study analyses the challenges in implementing drone delivery systems, such as regulatory barriers, technical limitations, and public perception. Finally, case studies of successful drone delivery programs for medical supplies are presented and the potential for scaling up these initiatives in the future are discussed. Overall, this study argues that drones have the potential to revolutionize the delivery of medicines and healthcare supplies to remote areas and that further research and investment in this area are necessary to fully realize their potential.
