Authors - Pinakci Kathotia, Ridhima Rathore, Kush Gupta, Eshita Vijay, Ujwala Kshirsagar Abstract - The developing subject of contactless voltage sensing and its revolutionary effects on the electrical sector are explored here. The basics of contactless voltage detection are first described, along with how it differs from conventional techniques. It then goes into detail on the different uses for this technology, such as non-invasive electrical testing and its employment in high-voltage environments. This paper also discusses the advantages of contactless voltage detection, such as improved efficiency and safety, as well as the difficulties that must yet be overcome for this technology to reach its full potential. Examples from everyday life are utilized throughout the paper to highlight the useful uses for contactless voltage detection.
Authors - Vishalsinh Bais, Mansi Pagdhune, Wamik Khan, Gaurav Maske, Aditya Umredkar, Amol P. Bhagat Abstract - The rise of deepfakes has raised questions about the veracity of digital content. This has led to a lot of research into reliable detection techniques. In this study, we introduce a new deepfakes detection approach based on the mesoNet architecture and the use of convolutional neural networks (CNNs). The proposed model has a multi-layer structure that includes convolutional layers, pooling layers, and dropout techniques to effectively extract and discriminate features. Training on a dataset that includes our own forged images and deepfakes, this model shows promising results in identifying manipulated content. With the activation function of leaky ReLU, our mesoNet model shows great promise in accurately distinguishing deepfakes from real images. Our experimental results demonstrate its effectiveness in distinguishing between forged and real images, demonstrating its value as a powerful tool in the fight against digitally manipulated content.
Authors - Shruti Gandhi, Charmy Patel Abstract - This research presents an innovative framework for developing educational chatbots that redefine student support by integrating advanced natural language understanding (NLU), intent recognition, and pragmatic analysis. Leveraging machine learning techniques, including pre-trained models like BERT, the chatbot achieves state-of-the-art performance in recognizing user intents and delivering contextually relevant responses. By addressing the limitations of traditional systems, such as poor personalization and difficulty in handling nuanced queries, this framework enables dynamic, adaptive, and engaging interactions. The chatbot transcends conventional query handling through pragmatic analysis, allowing it to interpret subtle nuances, emotional states, and real-world contexts. This ensures personalized responses that align with individual learning needs, fostering deeper student engagement and comprehension. Finetuned with diverse datasets and instructional materials, the system is robust and scalable, making it suitable for a wide range of educational applications. This approach also emphasizes human-like interaction, combining emotional intelligence with context-aware capabilities to create a supportive learning environment. By enhancing response accuracy, adaptability, and user engagement, the chatbot sets a new benchmark in educational technology. Ultimately, this research demonstrates transformative potential in creating intelligent, scalable, and highly effective tools for modern education, paving the way for a more personalized and interactive learning experience.
Authors - Komal V. Papanwar Abstract - MorseMate is a user-friendly Morse code converter designed to simplify the process of converting Morse code to alphanumerical values by allowing input through three buttons for three separate functions involved in the transmission of the code. This approach eliminates the need for timing-based input, making Morse code more accessible and easier to use. The device, powered by an ESP8266 microcontroller and featuring a 128x64 Organic Light Emitting Diode (OLED) display, converts Morse code into readable text in real-time. Users can input their sequences with the press of a button, and a long press of the submit button clears the display, allowing for continuous use. Custom I2C configuration provides flexibility in hardware setup, while the compact design ensures portability. This paper elaborates on how the system combines simplicity, efficiency, and practicality, to make Morse code more accessible to a wider audience.
Authors - Tanuja Zende, Ramachandra. Pujeri, Suvarna Pawar Abstract - Human Emotion Recognition (HER) has gained considerable attention in recent years, driven by advances in machine learning, acoustic signal analysis and natural language processing. The advancements in HER using speech as a principal modality is addressed in this survey systematically. The importance of emotional intelligence in HCI, social robotics and mental health assessment in this review comprehends a complete analysis of approaches including feature extraction techniques, classification algorithms and data representation used in the field of speech emotion recognition. Furthermore, in this survey, existing methods into traditional rule-based systems, machine learning algorithms and state-of-the-art deep learning frameworks, stressing on the strengths and limitations are discussed. Additionally, thoughtful challenges such as the density of human emotions, the influence of contextual factors, and the need of annotated datasets to train robust emotion recognition systems find their involvement in this work. Current trends in multimodal emotion recognition (MER) and the incorporation of speech with other modalities are also discussed to provide a complete view. This amalgamation of existing literature aims to notify future research directions in emotion recognition systems, enhancing their pertinency across varied fields.
Authors - Akshat Vashisht, Ishika Tekade, Juhi Shah, Aniket Sawarn, Deependra Singh Yadav, Palash Sontakke, Rajkumar Patil Abstract - Segmenting grape leaf stress condition accurately is a critical step in precision agriculture, as it enables early detection and treatment to mitigate crop losses. In this research, we propose a novel approach leveraging the Segment Anything Model 2 (SAM-2) for precise segmentation of stress condition regions on grape leaves. SAM-2 is a foundation model for promptable visual segmentation in images and videos. This model can generate high-quality masks with minimal user input, which makes it an ideal tool for such tasks. The SAM-2 model was tested on field images and we achieved an accuracy of nearly 70% without fine-tuning. Experimental results demonstrate that SAM-2 outperforms traditional segmentation models like U2Net and V-net. Data augmentation can improve the performance of SAM-2, especially in challenging tasks such as detecting early-stage leaf spots or stress condition symptoms in overlapping leaves. Techniques such as rotation, adjusting brightness, and scaling, can simulate different conditions, balance the dataset and improve generalization. This helps SAM-2 to adapt to different scenarios and improve its ability to detect complex patterns. This shows the potential of SAM-2 in agricultural applications and provides a framework that can be integrated into advanced plant monitoring systems. By automating the segmentation process with minimal user intervention, SAM-2 significantly reduces the labour-intensive task of manual stress condition detection, thus saving time and resources in agricultural operations. Integrating SAM-2 into the grape leaf stress condition detection pipeline enhances the precision and reliability of stress condition identification systems. Furthermore, its adaptability across different segmentation tasks provides a foundation for scalable and automated plant health monitoring systems. This study establishes SAM-2 as a transformative tool for advancing sustainable farming practices and precision agriculture.
Authors - Safwan Ahmed, Rojas Binny, S N Velukutty, Animesh Giri Abstract - Industrial Internet of Things (IIoT) networks have started to play a crucial role in revolutionizing manufacturing and Industry 4.0. However, the distributed nature of IIoT and its relative infancy make it a prime target for cyberattacks. This paper proposes a new approach to address the threats faced by industries using a conversational Artificial Intelligence (AI)-interfaced deep neural network model for detecting attacks on an IIoT network. The proposed approach extends to threat mitigation and has been evaluated using an extensive IIoT network traffic data set, demonstrating 93% accuracy in detecting 14 of the most common threats plaguing the industry. This model introduces the integration of conversational AI with deep learning, offering a user-friendly interface for naive users and accurate threat detection. The broader impact of this work lies in its potential to significantly enhance access to a robust and accurate real-time cyber-threat detection and mitigation system, thus contributing to a more secure and resilient industrial landscape.
