Authors - Aarti Agarkar, Ayush Sasane, Amankumar Kumare, Aditya Kadlag, Gaurang Khanderay Abstract - This paper presents a highly accurate intelligent traffic sign recognition system, which has potential to greatly improve safety on roads and make autonomous driving possible. It uses Convolutional Neural Networks (CNN) to detect and classify traffic signs, accurately robustly in real-time systems. The process involves pre-processing a large number of images, training the model and performing real-time detection with OpenCV in Python. The Node MCU Microcontroller is integrated to make the communication and responses more stable and can be set automatically after recognizing traffic signs. The findings show improved precision and stability of the properties that are essential for consideration in autonomous vehicle systems, where harmonious driving is needed to upgrade latest architectures.
Authors - Vidisha Deshpande, Gauri Shelke, Bhakti Kadam Abstract - Advancements in deep learning are fundamentally transforming assistive technologies, providing visually impaired users with unprecedented access to information and enhanced interaction with their surroundings. This paper comprehensively surveys traditional and emerging assistive technologies, focusing on real-time image caption generation systems. The modern advancements that bridge sensory limitations and digital interaction by covering a range of technologies such as Optical Character Recognition (OCR)-based text readers, object detection systems, image captioning systems, and intelligent haptic feedback devices are highlighted. In particular, the critical role of vision-language models and multimodal systems, which enable real-time auditory descriptions of visual scenes is studied. The survey also identifies significant gaps in real-world applications, particularly in terms of adaptability, cost, and inclusivity. These findings emphasize the need for more accessible, affordable, and real-time solutions that cater to the diverse needs of visually impaired individuals.
Authors - Renuka Sandeep Gound, Farhan Mujawar, Niraj Dhakulkar, Payal Rathod, Saish Bhise, Kavita Moholkar Abstract - With the growing interest in naturopathy and holistic health, it is more important to make information about medicinal plants easily accessible. The Virtual Healing Garden is designed to do just that, offering a dynamic platform where AYUSH students, professors, and plant enthusiasts can explore a variety of medicinal plants in a hands-on, engaging way. By blending traditional healing knowledge with modern scientific research, the platform brings these plants to life through interactive 3D models. Using cutting-edge algorithms like photogrammetry and 3D modeling, the garden creates realistic representations of the plants, giving users a detailed and immersive experience. The Virtual Healing Garden will feature a rich database of 3D plant models, each paired with detailed information about their medicinal properties. This will provide users with a visually immersive and informative resource, making it easy to explore and learn about various plants in a more engaging way, whether for education or research. This virtual garden not only raises awareness of the health benefits of medicinal plants but also makes learning about them interactive and fun.
Authors - Pankaj Chandre, Palash Sontakke, Rajkumar Patil, Bhagyashree D Shendkar, Viresh Vanarote, Dhanraj Dhotre Abstract - In today’s digital landscape, the prevalence of scams, phishing, and malicious attacks poses significant risks to both individuals and organizations. Mitigating these threats requires a comprehensive cybersecurity strategy that begins with user awareness and extends to robust protective measures and incident response protocols. By integrating education, proactive defenses, and responsive actions, personal and organizational cybersecurity can be greatly enhanced. Mitigating scams, phishing, and malicious attacks requires a comprehensive approach to cybersecurity and personal protection. This strategy begins with the User Environment, where devices connected to the internet become vulnerable to threats. Education and Awareness play a crucial role, providing training on recognizing phishing attempts and setting up reporting mechanisms to flag suspicious activities. Building on this, Protective Measures such as strong passwords, multi-factor authentication, regular software updates, and the use of security tools strengthen the defenses against cyber threats. Should an attack occur, Incident Response protocols are activated, including the detection and investigation of incidents, followed by recovery actions to restore security and prevent future attacks. By integrating these layers of defense, individuals and organizations can significantly reduce the risks of cyberattacks and safeguard sensitive information.
Authors - Swati Kiran Rajput, Sunil Gupta Abstract - Diabetes mellitus is the root cause of a disease known as diabetic retinopathy, which is a disorder that affects the retina. In every region of the globe, it is the leading cause of blindness. Early detection and treatment are very necessary in order to delay or avoid the deterioration of vision and the loss of eyesight. The scientific community has proposed a number of artificial intelligence algorithms for the aim of identifying and classifying diabetic retinopathy in fundus retina pictures due to the fact that this is the intended objective. Utilizing a Convolutional Neural Network (CNN), we suggested a method for the identification and early prediction of diabetic retinopathy in this particular piece of research. Using a wide variety of hyper parameters, such as epoch size, batch size, optimized, and so on, the Deep CNN has been used for both training and testing purposes. Examples of normal and abnormal retinal pictures have been included in the MRI dataset. When the findings of the experimental investigation were evaluated using machine learning and deep learning algorithms like SVM, ANN, and CNN, the results were shown to be accurate. In conclusion, the CNN achieves a detection and prediction accuracy of 96.60%, which is superior than that of the SVM and other artificial neural networks.
Authors - Nilesh Deotale, Nafiz Shaikh, Ashwin Katela Abstract - WhatsApp chats consist of various kinds of conversation held among two people or a group of people. This chat consists of various topics. This information can provide a lot of data for the latest technologies such as Machine Learning. The most important things for Machine Learning models are to provide the right learning experience which is indirectly affected by the data we provide to the model. This tool aims to provide in depth analysis of the data which is provided by WhatsApp. Irrespective of whichever topic the conversation is based on, our developed code can be applied to obtain a better understanding of the data. The advantage of this tool is that it is implemented using simple python modules such as pandas, matplotlib, seaborn, streamlit, NumPy, re, emojis and a technique sentiment analysis which are used to create data frames and plot different graphs, where then it is displayed in the streamlit web application which is efficient and less resources consuming algorithms, therefore it can be easily applied to larger dataset. The Accuracy of this project is 75%. In summary, this project makes use of state-of-the-art data analysis technologies such as scikit-learn, Topic Modeling, Named Entity Recognition, Clustering, Word Embeddings, Natural Language Toolkit, and more. Sequence-to-Sequence Models, Text Classification and so forth. Users can better understand how others communicate by using Language Model Fine-Tuning to extract relevant information from WhatsApp discussions.
