News Data Classification using Natural Language Processing and Large Language Models
In order to arrange and evaluate this enormous amount of data, effective categorization techniques are now essential due to the exponential growth of digital news material. This study investigates the use of Large Language Models (LLMs) and other Natural Language Processing (NLP) approaches for the classification of news data. We study how well LLMs do automatic news article classification into predefined classes or subjects. We show through experimental evaluation that LLM-based techniques are capable of effectively classifying news data, providing valuable information about future directions and possible applications in this field.
Published by: Prabhanjay Singh, Gurpreet Kour
Author: Prabhanjay Singh
Paper ID: V10I3-1156
Paper Status: published
Published: May 15, 2024
Centralized street light monitoring system using IoT
The importance of solar LED street lighting systems in reducing the significant energy consumption of conventional street lights is described in this abstract. It emphasizes how solar panels and IoT technology may be combined for effective energy management and conversion. The primary goals of the system are intelligent light control with motion sensors, problem detection through GSM technology, and real-time monitoring of solar panel and battery performance. Energy efficiency and the use of renewable energy sources are provided by this system, which uses solar radiation during the day to power LED lights at night. It also highlights the project's role in facilitating effective IoT integration and data transfer, which makes real-time energy management and monitoring possible.
Published by: Suriya. A, Vetri. J, S. Chanthini
Author: Suriya. A
Paper ID: V10I3-1148
Paper Status: published
Published: May 14, 2024
Intelligent navigation through recommended floor plans
Navigation is essential in the working of a helper robot. Lack of floor plan hinders the training of the helper bots. A floor plan recommender system will help solve this concern. Creating building floor plans is also very essential in the planning and construction of a building. Moreover, the integration of robotic mobility would provide a real-time understanding of the spatial layouts, which contributes to a more efficient and dynamic design implementation. The goal of this project is to develop a SimGNN-based recommendation system that suggests floor plans that satisfies the client requirements about the spatial relationship. The SimGNN-based model calculates similarity between the graphs after transferring the spatial relationship in the floor plan to the graph. We aim to utilize the recommended floor plans to enable the autonomous navigation of a robot. By mentioning the start and the finish point of the robot, a path is created and the robot will maneuver through the obtained path. Through this integration of recommendation system with the robotic mobility, we aim to optimize the training process of helper bots along with improving the design and construction process of a building.
Published by: Ananya Babu, Jawhara Fathima, Khrithikesh M U, Dr. Elizabeth Isaac
Author: Ananya Babu
Paper ID: V10I3-1146
Paper Status: published
Published: May 14, 2024
The Impact of Artificial Intelligence on Business Growth: A Comprehensive Analysis
Business has been revolutionized by artificial intelligence (AI) as machines can do what was once assigned to the human brain. We examine the effect of AI on productivity improvement and growth in various sectors through this research paper. The document evaluates the impact of AI on optimizing operations, enhancing decision-making, and fostering innovation by arguing that it affects all these areas. For instance, Amazon has made use of AI techniques to improve its efficiency by reducing costs and increasing customer satisfaction levels. In addition to that, this essay also discusses upcoming trends where AI is expected to change a lot about business practices including its uses in politics, education, and the fashion industry. This essay brings out the many-sided advantages of using AI for growing business and maintaining competitiveness with empirical evidence and statistical insights.
Published by: Manhar Shankar
Author: Manhar Shankar
Paper ID: V10I3-1149
Paper Status: published
Published: May 8, 2024
Hybrid Approach Involving Deep Learning Techniques for Recognition Facial Emotions Efficiently
Facial emotion recognition holds paramount importance in various human-centric applications, particularly in human-computer interaction (HCI) systems. This paper delves into the realm of machine vision and artificial intelligence (AI) to explore the methodologies and advancements in facial emotion identification. Leveraging computer vision technologies, coupled with AI algorithms, the research focuses on the recognition of human emotions through facial expressions. In human communication, facial expressions serve as a vital channel for conveying emotional states, playing a significant role in interpersonal understanding. Understanding emotions expressed through facial cues aids in effective decision-making and tailored interactions in human-machine interfaces. Emphasizing the relevance of non-verbal communication, this study investigates the significance of facial expressions in conveying emotional nuances. Deep learning techniques, particularly convolutional neural networks (CNNs), have revolutionized facial emotion recognition by enabling end-to-end learning from raw image data. By minimizing reliance on handcrafted features and pre-processing techniques, CNN-based approaches demonstrate superior performance in emotion detection and classification. Researchers have made substantial strides in developing intricate neural network architectures to enhance the accuracy and efficiency of facial emotion recognition systems. Through a comprehensive review of existing literature and methodologies, this research contributes to the ongoing discourse surrounding facial emotion recognition. Insights gleaned from this study pave the way for the continued advancement of HCI systems, facilitating more nuanced and responsive human-machine interactions.
Published by: Nayak Himanshukumar Dinanath, Dr. Ashish Sarvaiya
Author: Nayak Himanshukumar Dinanath
Paper ID: V10I3-1136
Paper Status: published
Published: May 2, 2024
Brain Tumor Classification Leveraging CNNAnd Grad-CAM For Accurate Tumor Type Identification
Brain tumour segmentation in medical image analysis is a challenging task because precision is crucial in the process of diagnosis and treatment. The current research applies a sophisticated method that utilizes Convolutional Neural Networks (CNNs) in conjunction with gradient-weighted Class Activation Mapping (Grad-CAM) to enhance the detection accuracy of brain tumours. By virtue of the implemented complex architecture of EfficientNetB1, our technique shines at solving the complex problems of medical image data processing. Grad-CAM makes a precious input into CNN by supplying visual interpretations of the attention-paying areas of CNN, empowering doctors to make the right diagnoses. We introduce a model that is based on a great number of brain tumour images with confirmed labels and learns to differentiate different tumour types based on their specific patterns. From our comparative analysis, we can see that there is a significant improvement in tumour detection accuracy, with our model reaching even as high as 99.67%. This one is more effective than the VGG16 model that delivers 85%-90% accuracy and ResNet50 model that has 90%-97% accuracy. In particular, the EfficientNetB1 model provides an accuracy range in the interval of 96%-98%, which clearly shows the efficiency of our proposed technique, since this would result in better treatment outcomes for patients.
Published by: Balamurali Besetty, B. Harshitha, A. Chandu, P. Nandini, D. Jayanth, Sreelahari Vallamsetla
Author: Balamurali Besetty
Paper ID: V10I2-1172
Paper Status: published
Published: May 2, 2024
