Professor, School of Computing and Electrical Engineering and Chairperson of the Centre for Human Computer Interaction, Indian Institute of Technology (IIT) , India
Authors - Hector Rafael Morano Okuno Abstract - Mechatronics is an interdisciplinary field that draws on mechanics, electronics, and computer science. In recent years, the term biomechatronic has been used with increasing frequency; it is also a multidisciplinary field that in volves biological sciences and, therefore, bioinformatics. With the development of AI, bioinformatics provides data to biomechatronic systems, enabling appli cations ranging from agriculture to medicine. This article explores how bio mechatronics and CFD simulations can help monitor a person's health status. The objectives of this research were: 1) to determine whether, using biomarkers such as hemoglobin, fibrinogen, and low-density lipoprotein (LDL), among others, and CFD simulations, it is possible to obtain blood flow velocity pro files; and 2) to investigate whether the information from CFD simulations can be used to feed a biomechatronic system to monitor a person's health condi tions. Among the results, it was found that it is necessary to have models that allow relating the main biomarkers to determine the state of health of a person, as well as with suitable sensors to measure each variable according to the orien tation of the application that is to be developed, for example, for physical train ing or for the monitoring of nutrition.
Authors - Radha Gawande, Supriya Nara Abstract - Complicated nature of the intensive care unit (ICU), immediate and accurate decision-making is vital to the survival of the patient. The problems that healthcare providers are struggling with are the overload of information, slowness of the decision making process, and the human factor due to growing amount of various patient information. Recent development in artificial intelligence (AI) offers promising solutions since they facilitate effective analysis of data, pattern detection and predictive modelling. This changes the provision of critical care. In this paper, the changing application of AI in ICUs is discussed. It talks about its usage, merits and demerits, as well as technological basis. It also discusses AI methods such as machine learning (ML), deep learning (DL), natural language process (NLP), and expert system, predictive analytics, early sepsis detection, clinical decision support system, automated monitoring and insight-based treatments by documentation fueled by natural language processing, are but a few of the practical methods of applying AI. The advantages of automation and robotics to enhance productivity and patient care are also discussed, which are AI-based medication delivery system and robotics helper. Nonetheless, a number of challenges to implement AI in critical care units are a lack of consensus, algorithm bias, understanding model decisions, and various data, personalized AI-driven care in the ICU, integration of edge computing and internet of medical things (IoMT), reinforcement learning in adapting patient management are some of the future prospects[1].
Authors - Priyanka Patel, Ashvi Padshala, Moxa Patel Abstract - This paper surveys recent advances in the application of data analysis, machine learn ing, artificial intelligence, and big data techniques for climate pattern detection. It covers sources of climate data, analytical methods, computational architectures, key challenges, and emerging trends. The focus is on identifying how integrated data-driven methods enhance the understanding, prediction, and interpretability of climate phenomena.
Authors - Rohan Dafare, Supriya Narad Abstract - The quick spread of big data and the rising need for instant analytics have shown the built-in limits of old-school relational database management systems (RDBMS). NoSQL ("Not SQL") databases give schema-less design, side-to-side growth, and adaptable data shaping making them a better fit for handling messy and semi-messy data on a big scale. This paper looks at the edge NoSQL has over SQL systems by checking out key traits like how flexible the data model is how well it works under high output how easy it is to grow sideways, and how well it fits with cloud-native setups. Using a careful review of NoSQL teaching and use, we boil down real-world findings and suggest ways to pick the right database tech based on what the app needs. Our talk ends with a plan to help pros and teachers get when and why to use NoSQL fixes instead of, or along with classic SQL databases. Modern data intensive workloads driven by real time analytics, large scale user interactions, IoT streams, and unstructured content. It demands storage system capable of delivering high throughput, scalability and flexible data models. Traditional SQL databases continue to offer strong consistency, ACID guarantees and structured schema support, making them ideal for transactional applications and environments requiring strict data integrating. However, as data volume, variety and velocity increase, NOSQL databases have emerged as powerful alternative, providing horizontal scalability, schema-less design and optimized performance for distributed and semi-structured data processing.
Authors - Anshuman Prajapati, Madhav Desai, Priyanka Patel Abstract - Analysis of facial skin conditions is essential for both dermatological and cosmetic evaluation; however, inter-class similarity and localized texture variations make multi-label classification of characteristics like wrinkles, dark circles, enlarged pores, hyperpigmentation, pimples, and fine lines difficult. The effectiveness of transfer learning for this task is examined in this paper, and an attention-enhanced framework based on EfficientNet-B0 is proposed. In order to highlight the importance of pre-trained feature representations, we first assess a bespoke convolutional neural network (CNN) as a baseline. Using the Convolu tional Block Attention Module (CBAM), which combines channel and spatial attention processes to enhance discriminative feature localization while maintain ing computational efficiency, we build upon this by using EfficientNet-B0 as the transfer learning backbone. According to experimental data, our CBAM augmented EfficientNet achieves better class-balanced performance in macro-F1 score than both the baseline EfficientNet and the bespoke CNN. Consistent in creases are confirmed by per-class analysis and confusion matrices, even for dif ficult settings. Additionally, Grad-CAM visualizations show that by concentrat ing activation on pertinent facial regions, the attention mechanism improves in terpretability. These results imply that a promising avenue for multi-label derma tological image analysis is attention-guided transfer learning.
Professor, School of Computing and Electrical Engineering and Chairperson of the Centre for Human Computer Interaction, Indian Institute of Technology (IIT) , India
