Authors - Shruti Anghan, Tirth Chaklasiya, Priyanka Patel Abstract - Technology is an indispensable tool that many industries use to transcend and arrive at the best possible results. A very significant part of the Indian economy constitutes the agricultural sector. Half of the country's workforce is still employed by the agriculture industry. What plays a critical role in affecting the agricultural sector is the natural environment within which it operates, and it throws up many challenges in real farming operations. Most agricultural processes in the country have been old-fashioned and the industry is not ready to step into new technologies. Effective technology can enhance production and reduce the greatest barriers in the field. Today, farmers mostly plant crops not based on soil quality but the market value of the crop and what the crops can return to them. This might impact the nature of the land and the farmer also. Properly applied, modern technologies such as machine learning and deep learning can help revolutionize these industries. It shall show how to apply these technologies properly to give the farmer maximum support in the crop advice field.
Authors - Bimal Patel, Ravi Patel, Jalpesh Vasa, Mikin Patel Abstract - The study delves into Tableau's unique characteristics, including its intuitive interface, robust analytics capabilities, and advanced visualization features. By leveraging these features, Tableau empowers users to transform complex datasets into actionable insights, facilitating data-driven decision-making across various domains. The paper explores the extensive applications of Tableau in key industries such as finance, healthcare, retail, and education. In finance, Tableau aids in risk management and performance analysis, while in healthcare, it enhances patient care and operational efficiency through detailed data visualizations. The retail sector benefits from Tableau's ability to analyze sales performance and customer behavior, and in education, it tracks student performance and engagement metrics. Additionally, this research identifies and addresses common challenges associated with data visualization using Tableau, such as handling large datasets, ensuring data accuracy, and maintaining user engagement. The paper provides practical solutions and best practices to overcome these hurdles, ensuring optimal use of Tableau's capabilities. The paper shows how Tableau can be used to help different industries with their specific needs and problems using real-life examples. This study serves as a valuable resource for professionals and researchers seeking to maximize the potential of Tableau in their respective fields.
Authors - Aditi Zeminder, Vaibhav Patil, Prathamesh Raibhole, S V Gaikwad Abstract - This paper presents a part of the study of a collaborative robot (cobot) designed for optimization of work tasks, focusing on selection and workplace. This project investigates best practices by developing a kinematic editing library and using ROS and RViz to perform simulations to analyze and improve motion planning. Conducted an exhaustive review of the existing research literature on collaborative robot control and efficiency and will examine the usage of commercial collaborative software, such as Elephant Robotics' myCobot and Dobot, in introducing the interface design. The Kivy-based control interface was designed to allow users to effectively interact with the robots and adjust parameters to complete tasks. This paper provides an overview of the process adopted, the challenges encountered during development and initial testing, and lays the groundwork for future developments including hardware integration and additional kinematic optimization.
Authors - Eshwari Khurd, Shravani Kamthankar, Avani Kelkar, Ravinder B. Yerram Abstract - One of the major challenges encountered when it comes to speech recognition, medical imaging, and multimedia processing for radar or weather forecasting applications, is noise interference in audio and image signals that invariably affect algorithmic precision and dependability. Denoising is responsible for removing unwanted noise while keeping intact the necessary details in the signal. An effective denoising method for audio and image signals is under continuous research across multiple parameters taken into consideration giving priority to signal-to-noise ratio (SNR). In this paper, we have surveyed various such denoising methods with a focus on the ones using Principal Component Analysis (PCA) and Ensemble Empirical Mode Decomposition (EEMD).
Authors - Renuka Deshmukh, Babasaheb Jadhav, Srinivas Subbarao Pasumarti, Mittal Mohite Abstract - In response to the issue of growing garbage, researchers, foundations, and businesses worldwide developed concepts and created new technology that sped off the procedure. Trash comes from a variety of sources, including municipal solid trash (such as discarded food, paper, cardboard, plastics, and textiles) and industrial garbage (such as ashes, hazardous wastes, and materials used in building and demolition). Contemporary waste management methods often take sociological factors into account in addition to technological ones. This review paper's goal is to talk about the potential applications of cutting-edge digital technology in the waste disposal sector. With reference to smart cities, this study aims to comprehend the environment, including the opportunities, barriers, best practices at present, and catalysts and facilitators of Industry 4.0 technologies. An innovative approach for examining the use of digital technology in smart city transformation is put out in this study. Analysis of the suggested conceptual framework is done in light of research done in both developed and developing nations. The study offers case studies and digital technology applications in trash management. This article will examine the ways in which waste management firms are utilizing cutting-edge technology to transform waste management and contribute to the development of a healthier tomorrow.
Authors - Arvin Nooli, Preethi P Abstract - Recognizing emotional states from electroencephalogram, or Electroencephalogram (EEG), signal data is challenging due to its large dimension and intricate spatial dependencies. Our project illustrates a novel approach to Electroencephalogram (EEG) data analysis in emotion recognition tasks that employ Dynamic Graph Convolutional Neural Networks (DGCNN). Our novel architecture takes advantage of the inherent graph structure of Electroencephalogram (EEG) electrodes to effectively capture spatial relationships and dependencies. Our approach used a refined DGCNN model to process and classify the data into four primary emotional states- Happy, Sad, Fear, and Neutral, we configured the DGCNN with 20 input features per electrode, optimized across 62 electrodes, and utilized multi-layered graph convolutions. The model achieved an overall classification accuracy of 97%, with similarly high macro and weighted average scores for precision, recall, and F1-score, demonstrating its resilience and accuracy.
Authors - Chitraksh Madan Singh, Yash Kumar, Lakshya Gattani, A.Anilet Bala, Harisudha Kuresan Abstract - This study presents an analysis of Instagram reach using Passive Aggressive, Decision Tree, Random Forest, and Linear Regression models. The goal is to predict the impressions generated by posts based on features like likes, saves, comments, shares, profile visits, and follows. Using Instagram data, machine learning algorithms are applied to forecast the post reach, helping marketers optimize content strategies. Quantitative metrics such as Mean Squared Error (MSE) and R-squared (R2) are used to evaluate model performance, with Random Forest showing superior accuracy compared to other models.
Authors - Shreyas Shewalkar, Shweta Autade, Aditi Sonje, M.R. Kale Abstract - With the growing need for automated text recognition and image processing, we have explored techniques that enhance the accuracy of handwritten character recognition while simultaneously addressing image restoration challenges. Handwritten English Character Recognition leverages deep learning (DL) techniques to classify and accurately identify characters from scanned or photographed documents. A deep learning-based approach is employed to recognize the patterns in handwritten text, ensuring high precision in distinguishing between characters despite variances in writing styles. In addition to recognition, colorization of grayscale images has gained attention, where DL models predict and apply realistic colors to black and white images. The recognition process applies CNN (Convolutional Neural Networks) for character identification.
Authors - Sangjukta Halder, Renuka Deshmukh Abstract - This study scrutinizes the impact of ICT-driven financial literacy agendas in India, focusing on their role in promoting financial inclusion and enhancing governance. By leveraging digital tools such as mobile apps, online courses, and e-governance platforms, these programs have effectively increased financial literacy, particularly among underserved populations. The research highlights that while challenges such as the digital divide, language barriers, and varying levels of digital literacy persist, these programs significantly empower citizens to make conversant financial choices and participate more actively with public fiscal management. The incorporation of financial literateness into digital platforms also fosters greater transparency and accountability in governance. For the purpose of improving these programs, legislators, educators, and tech developers may benefit greatly from the insights this research offers. Additionally, it makes recommendations for future research topics to investigate the long-term effects of financial literacy programs powered by ICT on financial behaviours and governance in various socioeconomic situations across India.
Authors - Yasharth Sonar, Piyush Wajage, Khushi Sunke, Anagha Bidkar Abstract - Emotion recognition from speech is a crucial part of human-computer interaction and has applications in entertainment, healthcare, and customer service. This work presents a speech emotion recognition system that integrates machine learning and deep learning techniques. The system processes speech data using Mel Frequency Cepstral Coefficients (MFCC), Chroma, and Mel Spectrogram properties that were extracted from the RAVDESS dataset. A variety of classifiers are employed, including neural network-based multi-layer percept, Random Forest, Decision Trees, Support Vector Machine, and other traditional machine learning models. We have created a hybrid deep learning system to record speech signals' temporal and spatial components. a hybrid model that combines convolutional neural networks (CNN) with long short-term memory (LSTM) networks. With an accuracy of identifying eight emotions—neutral, calm, furious, afraid, happy, sad, disgusted, and surprised—the CNN-LSTM model outperformed the others. This study demonstrates how well deep learning and conventional approaches may be used to recognize speech emotions.
