Authors - Sheela S. J, Rajeshwari B. S, Harsha M, Subhash T. D, Tejas H. S, Thanmaya Ganesh C. S, Harsha S. M, Keerthana T. V Abstract - One of the leading causes of death Globally cardiovascular diseases (CVDs). 2019 key Statistics on CVDs is as follows: Total Deaths: 17.9 million people, 32% of global deaths, 85% of CVD deaths which approximately 15.2 million deaths from heart attacks and strokes. Hence, early diagnosis plays a crucial role in reduction of heart related diseases. Usually, the healthcare professionals collect the initial cardiac data using their quintessential instrument called stethoscope. Traditionally, these stethoscopes have significant drawbacks such as weak sound enhancement and limited noise filtering capabilities. Moreover, the low frequency signals such as below 50 Hz may not be heard because of the variation in sensitivity of a human ear. Hence, the usage of conventional stethoscopes requires experienced medical practitioners. In order to overcome these limitations, it is necessary to develop a device which is more sophisticated than conventional stethoscopes. In this context, the proposed work aims in the development of digital stethoscope which has the capability of displaying heart and lungs sound separately. Further, the proposed digital stethoscope permits to document, convert and transmit heart and lungs sounds to dB range digitally thereby reducing unnecessary travelling to medical facilities. The proposed stethoscope results are compared and validated with conventional techniques.
Authors - Prem Gaikwad, Parth Masal, Mandar Kulkarni, Mousami P. Turuk Abstract - Visual Language Models (VLMs) are an emerging technology that integrates computer vision with natural language processing, offering transformative potential for healthcare. VLMs significantly enhance disease detection, diagnosis, and report generation by enabling automated analysis and interpretation of medical images. These models are designed to support healthcare professionals by streamlining workflows, improving diagnostic accuracy, and assisting in clinical decision-making. Applications include early disease detection through image analysis, automated report generation, and integration with electronic health records (EHR) for personalized medicine. Despite their promise, challenges such as data privacy, interpretability, and the scarcity of labelled datasets remain. However, ongoing advancements in AI-driven medical systems and the integration of multimodal data can potentially revolutionize patient care and operational efficiency in healthcare settings. Addressing these challenges is crucial for realizing the full potential of VLMs in clinical practice.
Authors - Kamini Solanki, Nilay Vaidya, Jaimin Undavia, Jay Panchal Abstract - Polycystic ovary disease (PCOD) is a condition in which the ovaries of women of childbearing age produce too many immature or partially mature eggs. As time passes, these eggs develop into cysts within the ovaries. These cysts can lead to enlargement of the ovaries and an elevated production of male hormones (androgens). Consequently, this hormonal imbalance can result in a range of issues like fertility challenges, irregular menstrual cycles, unanticipated weight gain, and various other health complications. The associated symptoms often exert a long-term impact on both the physical and mental well- being of affected women. Statistics indicate that approximately 34% of individuals facing PCOD also grapple with depressive symptoms, while almost 45% experience anxiety. The primary object of this proposed framework is to detect and classify PCOD disease from standard X-ray pictures with assistance of volume datasets using deep learning model. Polycystic Ovary Disease (PCOD) significantly affects women's reproductive health, leading to various long-term complications. This work introduces a novel framework for automated PCOD detection using integrating Convolutional Neural Networks (CNN) with deep learning, applied to ultrasound imaging. Unlike traditional diagnostic methods, which rely on manual interpretation and are prone to subjectivity, the proposed system leverages the powerful feature extraction capabilities of CNNs to classify infected and non-infected ovaries with 100% accuracy. This high level of precision outperforms existing models and can be seamlessly integrated into clinical workflows for real-time diagnosis during sonography, facilitating early detection and improved fertility management. By focusing on a deep learning approach, this work provides a scalable, reliable, and automated solution for PCOD diagnosis, marking a significant advancement in the use of medical imaging with artificial intelligence.
Authors - Poornima E. Gundgurti, Shrinivasrao B. Kulkarni Abstract - Latent fingerprints play a crucial role in forensic investigations, driven by both public demand and advancements in biometrics research. Despite substantial efforts in developing algorithms for latent fingerprint matching systems, numerous challenges persist. This study introduces a novel approach to latent fingerprint matching, addressing these limitations through hybrid optimization techniques. Recognizing latent fingerprints as pivotal evidence in law enforcement, our comprehensive method encompasses fingerprint pre-processing, feature extraction, and matching stages. The proposed latent fingerprint matching utilizes a novel approach named as, Randomization Gravity Search Forest algorithm (RGSFA). Acknowledging the shortcomings of traditional techniques, our method enhances convergence speed and performance evaluation by integrating weighted factors. Precision, recall, F-measure, and recognition rate serve as performance metrics. The proposed approach has a high recognition rate of 99.9% and is successful in identifying and matching latent fingerprints, furthering the development of biometric-based personal verification techniques in forensic science and law enforcement. Experimental analyses, using publicly accessible low-quality latent fingerprints from FVC-2004 datasets, demonstrate that the proposed framework outperforms various state-of-the-art approaches.
Authors - Krunal Maheriya, Mrugendra Rahevar, Martin Parmar, Deep Kothadiya, Arpita Shah Abstract - Plant diseases pose a significant threat to agricultural output, causing food insecurity and economic losses. Early detection is crucial for effective treatment and control. Traditional diagnosis methods are labor intensive, time-consuming, and require specialized knowledge, making them unsuitable for large scale use. This study presents a novel approach for classifying cassava leaf diseases using stacked convolutional neural networks (CNNs). The proposed model leverages pre-trained ResNet-18 features to enhance feature learning and classification accuracy. The dataset includes images of cassava leaves with various diseases, such as Cassava Mosaic Disease (CMD), Cassava Green Mottle (CGM), Cassava Bacterial Blight (CBB), and Cassava Brown Streak Disease (CBSD). Our method begins with data preparation, including image augmentation to increase robustness and variability. The ResNet-18 model is then used to extract high-level features, which are then fed into a stacked CNN architecture made up of pooling layers, several convolutional layers, and non-linear activation functions. A fully connected layer is then used for classification. Experimental results demonstrate high accuracy in categorizing cassava leaf diseases. The proprietary stacked CNN architecture combined with pre-trained ResNet-18 features offers a significant improvement over conventional machine learning and image processing methods. This study advances precision agriculture by providing a scalable and effective method for early disease identification, enabling farmers to control diseases more accurately and promptly, thereby increasing crop yield. The findings point to the promise of deep learning techniques in agricultural applications and provide directions for further study to create more complex models for the classification and diagnosis of plant diseases.
Authors - Ruchi Tripathi, Anjan Mishra, Subrata Mondal, Arunangshu Giri, Dipanwita Chakrabarty, Wendrila Biswas Abstract - Agricultural product shares a significant part of retail industry. The growing popularity of digital ecosystem can immensely affect agricultural sector as well. The consumers and retailers both can get benefited from Internet of Things or IoT, as it has a vast application in agricultural product retailing. IoT helps a retailer to establish an efficient supply chain with minimum wastage without compromising with quality. On the other side, it delivers authentic real-time information to the consumers, so that they can take efficient decision. This study has identified some factors that yields decision satisfaction to the consumers through application of IoT in agricultural product retailing sector.
Authors - Khush Zambare, Amol Wagh, Sukhada Mahale, Mayank Sohani Abstract - In the all-digital world of today, the search engines are more of entry points to knowing most things. In this regard, most search engines often service the general user; most other needs, specific to a profession, go unattended. Use of "Amazon" will return results for the e-commerce giant, even when the user is the environmental scientist looking for something about the Amazon rainforest or the cloud developer searching for Amazon Web Services (AWS). This generic approach leads to inefficiencies as users need to sift through lots of useless information. This paper allows for a browser extension that personalizes the result of search on Artificial Intelligence and Machine Learning, with the aim of catering to individual users, based on their profession, interests, and specific needs. The solution dynamically re-ranks the search results as it learns from user behavior and search patterns to provide the most relevant information to save the precious time of the users. The paper will discuss current trends in SEO, AIML applications, and personalization techniques to outline how this solution can revolutionize the search engine experience.
Authors - Suruchi Pandey, Hemlata Vivek Gaikwad Abstract - The rapid shift in the integration of AI in various sector for more personalized and efficient training. This research explores into the potential of AI in various training methods, the challenges and vast opportunities of learning and growth while using it. The potential for AI-driven training is vast, spanning fields like corporate, healthcare, education, and the military. This study examines how emerging technologies like virtual reality, augmented reality, and simulation-based training can personalize learning experiences, enhance skill development, and provide real-time feedback. It also addresses critical challenges to implementing AI in training, such as costs and data privacy concerns. Additionally, the paper discusses how AI-enabled training could transform traditional learning and development practices, opening up new possibilities for advanced, adaptive learning methods.
Authors - Shripad Kanakdande, Atharva Kanherkar, Ayush Dhoot, P.B.Tathe Abstract - Efficient inventory forecasting and waste management are essential for streamlining supply chains and cutting expenses, particularly in sectors like retail and food services where inadequate stock management can lead to large losses and environmental damage. This study presents a data-driven approach to inventory prediction that makes use of sophisticated machine learning models that evaluate past data, sales patterns, and seasonal fluctuations. The model seeks to increase demand forecasting accuracy by utilizing predictive skills, which would ultimately result in improved stock management and customer satisfaction. In order to help organizations reduce waste and increase resource efficiency, it also focuses on improving waste management through real-time monitoring and forecasting of surplus inventory. Furthermore, combining sustainable practices with predictive analytics promotes long-term corporate viability while minimizing environmental harm. In addition to increasing operational effectiveness, this all-encompassing strategy supports more general environmental sustainability goals. The suggested framework gives businesses a practical way to optimize and streamline their supply chain operations while fulfilling sustainability goals by offering a complete solution that can minimize the ecological footprint and the costs associated with keeping inventory.
Authors - U.Sakthi, Aman Parasher, Akash Varma Datla Abstract - This work seeks to classify various ship categories on the high-resolution optical remote sensing dataset known as FGSC-23 using deep learning models. The dataset contains 23 types of ships, but for this study, six categories are selected: Medical Ship, Hovercraft, Submarine, Fishing Boat, Passenger Ship and Liquified Gas Ship. The adopted ship categories were thereafter used to train four deep learning models which included VGG16, EfficientNet, ResNet50v2, and MobileNetv2. The accuracy, precision, and AUC parameters were used to evaluate the models where the best one, the ResNet50v2, was set up as accurate. Using these models, it should be possible to achieve a practical deployment aiming at fine-grained classification of ships that will contribute to enhancing maritime surveillance techniques. ResNet50v2 model had the highest precision of 0.9058 and on the other hand MobileNetv2 had the highest AUC of 0.9932. The analysis of the identified models is performed further in this work to illustrate their advantages and shortcomings in adherence to fine-grained ship classification tasks. Based on this research, the practical implications transcend theoretical comparisons of performance metrics, as useful information is provided to improve security applications in the maritime domain, surveillance, and monitoring systems. Categorization and identification of ships is a very important process in going global maritime business because it is used in decision-making processes in fields like security and surveillance, fishing control, search and rescue and conservation of the environment. The models highlighted are namely ResNet50v2 as well as MobileNetv2, proved to be robust in real-time applications such scenarios because of their ability to accurately and proficiently distinguish the differences between the ship types. In addition, this study suggests the luminal possibility of doing further improvement on these models using data enhancement strategies like transfer learning, data augmentation, and hyperparameter optimization which would enable it to perform impressively on any other maritime datasets. Therefore, the outcomes are beneficial for furthering work in automated ship detection and classification and is important toward enhancing the overall effectiveness and safety of navies across the globe.
