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Drone-Based Weed And Disease Detection In Agricultural Fields To Maximize Crop Health Using a Yolov8 Approach 7

Drone-Based Weed And Disease Detection In Agricultural Field...

引用

7th IEEE Conference on Information and Communication technology, CICT 2023

作者： Pavithra, S. Kachroo, Darsh Kadam, Vrushali Padala, Hemanth Purbey, Rishit Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology School of Electronics Engineering Chennai India

ISBN: (纸本)9798350305173

The advancement of drone technology has opened up new opportunities for precision agriculture, enabling efficient monitoring and management of agricultural fields. This study introduces a new method that utilizes the YOLOv8 model to detect and classify weeds and diseases in agrarian fields using aerial images captured by drones. The model was trained on a batch size of 32 for 40 to 100 epochs, and the hyperparameters were fine-tuned to get optimal performance. This resulted in a precision of 87% and a recall of 80% for weed detection and 60% precision and 43% recall for disease detection. The results indicate the potential of drone-based weed and disease detection as a valuable tool for farmers and agronomists to gather accurate, timely, and comprehensive data about their fields, facilitating informed decision-making and precise management practices. By leveraging the advantages offered by drones, farmers have the opportunity to enhance the efficiency of resource allocation, increase crop productivity, ensure food security, minimize their ecological footprint, and actively contribute to long-term sustainability farming methods that preserve natural resources for future generations. © 2023 IEEE.

关键词： Drones

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Block Chain-Based Voting System 2

Block Chain-Based Voting System

引用

2nd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2023

作者： Raja Sree, T. Mary Shamala, L. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350348002

A secure fair electronic voting system is required for the smooth working of any democratic process. This paper implements a voting-based system application that runs with the support of blockchain to create a decentralized voting system. The proposed system aims to eliminate the aspect of trust from an election to make it more secure and transparent. © 2023 IEEE.

关键词： Blockchain

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Smart Systems for Faultless Purlieus: Leveraging AI and Machine Learning 13

Smart Systems for Faultless Purlieus: Leveraging AI and Mach...

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13th Annual IEEE Global Humanitarian technology Conference, GHTC 2023

作者： Dixit, Vaishnavi Srinivasan, Makesh Rajani, Dhriti Shah, Vidhi Kumar, Nishant Jaju, Ronith Vydeki, D. Vellore Institute of Technology School of Computer Science & Engineering Chennai India Vellore Institute of Technology School of Electronics Engineering Chennai India

ISBN: (纸本)9798350321326

In today's rapidly evolving world, the imperative of efficient waste management is pivotal for fostering a sustainable future. This paper introduces a holistic solution that combines advanced hardware and software technologies to tackle the multifaceted challenges of waste classification and collection. The proposed initiative leverages state-of-the-art computer vision, robotics, and intelligent systems to revolutionize waste management processes, ultimately contributing to environmental preservation and resource *** innovative project presents an autonomous waste classification and collection system, seamlessly blending cutting-edge hardware components with sophisticated software algorithms. The primary objective is to create an integrated system that not only automates waste sorting but also optimizes resource utilization and minimizes environmental impact. © 2023 IEEE.

关键词： Blending

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Movie Recommendation Systems Using Improved Non-Negative Matrix Factorization

Movie Recommendation Systems Using Improved Non-Negative Mat...

引用

2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Sherly Alphonse, A. Verma, Harshit School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350325188

Currently, there exist effective strategies for providing practical recommendations crucial in many industries, this encompasses a range of online platforms, such as electronic commerce, social networking sites, and various web-based services, as a result of the big data explosion. There are numerous customized movie recommendation systems available today that use publicly accessible movie datasets (like MovieLens) and provide enhanced performance measures (such the Root-Mean-Square Error (RMSE)). The ability to scale and the practicality of usage feedback and verification are determined through real-world implementation and assessment, they are two important difficulties that movie recommendation systems currently struggle with. This research suggests a novel approach using non-negative matrix factorization that resolves these issues through similarity and matrix factorization. Finding two matrices, both consisting of non-negative values, and their resulting product when multiplied together closely resembles the original matrix is the goal of non-negative matrix factorization. Additionally, it places the latent elements under non-negative constraints. The suggested approach offers novel update procedures to discover the latent variables for rating prediction. In contrast to the majority of collaborative filtering techniques, the suggested technique can forecast all the unknowable ratings. Its computational complexity is quite low, and it is simple to implement. Empirical research using the MovieLens shows that the suggested approach is more forgiving of the sparsity and scalability issues and produces good results. © 2023 IEEE.

关键词： collaborative filtering matrix factorization Movie ratings recommendation

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A Framework to Boost Team Effectiveness Through AI based Personality and Skill-Based Assessment 6

A Framework to Boost Team Effectiveness Through AI based Per...

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6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Suganya, G. Kamath, Varun Ramadasan, Manigandan Titiya, Prasham Naik, Ronit Nair, Rishi Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350394702

Choosing the right team members in a project is important as it determines how successful they can get together and productivity of the team as choosing the wrong ones can lead to delays, lack of motivation and other scenarios. One of the important factors that help determine good team members would be their alignment with the project's goals and needs. However, these do not entirely help understand a candidate's personality, which is key to their performance and productivity. This research focuses on implementing Deep Learning to extract the "Big 5 Ocean Traits"of user behavior from carefully designed questions and create groups based on OCEAN trait scores after shortlisting based on their skills. BERT (Bidirectional Encoder Representations from Transformers) was used to determine the personality scores of the person using the answers that they provided for the given questions. An optimized algorithm was developed to form teams based on their OCEAN trait score that will improve the overall efficiency © 2023 IEEE.

关键词： Behavioral research

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AutoDL: Enriching PowerBI AutoML Features with Deep Learning 14

AutoDL: Enriching PowerBI AutoML Features with Deep Learning

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Shankar, Sneha Doshi, Sujay Bhuvaneshwari, R. Praveen Joe, I.R. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350335095

PowerBI, a graphical analytics software developed by Microsoft, has incorporated various AI and ML tools to provide accurate predictions and insights for the data feeded into the software. PowerBI is equipped with the AutoML tool which automates the process of applying machine learning to obtain data driven insights. In recent years, the explosion of data has been phenomenal, and due to the rise in interconnectivity and the age of IoT it has led to the emergence of big data. Though AutoML, which uses machine learning, isn't equipped to handle big data. To tackle the presented challenge of volume and scalability, this study aims to provide a solution, AutoDL, with the usage of deep learning models and algorithms. In this study, we attempt to create a base automated deep learning pipeline providing better results on big data. The pipeline was tested on 2 big data sets and we received affirmative results. © 2023 IEEE.

关键词： Big data

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A Novel AI-based chatbot Application for Personalized Medical Diagnosis and review using Large Language Models

A Novel AI-based chatbot Application for Personalized Medica...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Akilesh, S. Sheik Abdullah, A. Abinaya, R. Dhanushkodi, S. Sekar, Rajeev School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350305708

This research showcases a groundbreaking application in the medical industry by leveraging the cutting-edge OpenAI service's GPT-3.5 model, powered by large language models (LLMs). Through the implementation of a Java-based Android app, it introduces a medical chatbot that redefines the way individuals interact with healthcare systems. By capitalizing on the advanced capabilities of LLMs, the app enables users to effortlessly input their symptoms and receive accurate insights, personalized diagnoses, and actionable medical advice. The utilization of natural language processing and AI algorithms ensures a seamless and intuitive user experience. The significance of this innovation lies in the revolutionary impact of LLMs on the entire medical industry. The integration of LLMs empowers the app to comprehend complex medical queries, interpret contextual information, and deliver reliable and up-to-date medical knowledge to users. This transformative technology facilitates improved access to healthcare information, enhances decision-making processes, and ultimately contributes to the optimization of healthcare services. © 2023 IEEE.

关键词： GPT 3.5 Model Large Language Models Medical Chatbot Natural Language Processing OpenAI Service

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Enhancing Online Education Efficacy: A Multifaceted Analysis and Recommendation System for Online Courses 6

Enhancing Online Education Efficacy: A Multifaceted Analysis...

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6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Vaishnavi, V.V. Narendra, G.O. Divyashree, S. Christy Jackson, J. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350394702

In today's tech-driven world, education has undergone significant changes in schools and colleges worldwide. One of the latest trends in the education sector is online learning. This approach allows students to study over the internet, offering them flexibility and convenience. Online learning has proven to be effective in enhancing students' performance and skills. However, selecting the right online courses can be a challenge for aspiring students. They need to consider various factors to ensure their chosen courses align with their goals. To address this challenge, we propose using data from popular online course platforms like Coursera and Udemy. By analyzing the course statistics available on these platforms, we aim to develop a recommendation system. This system will help students make informed choices by suggesting courses that match their interests and needs. Our study aims to gather and present course data from Coursera and Udemy and build a Course Recommendation algorithm to provide clear guidance for course selection. This will ultimately lead to an improved learning experience for students in the online education landscape. © 2023 IEEE.

关键词： Recommender systems

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Detecting the Political Bias in News Articles and Similarity Using Word Embeddings in American Journalism 1

Detecting the Political Bias in News Articles and Similarity...

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1st International Conference on Computational Intelligence for Information, Security and Communication Applications, CIISCA 2023

作者： Sinha, Apoorv Kumar Kumar Singh, Sanskriti Sanjay Sai, Shreyas School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350339727

Political polarity is on the rise in the world and machine learning tasks are adapting to the same. No longer is it sufficient to simply classify something as positive or negative, for a subject can be polarly opposite like positive or negative, but at the same time, can be switched entirely depending on the perspective. This is why the study of political bias makes up for a very interesting study since the multi-classification task of determining the alignment of a paper is a different take as to the usual understanding of sentiment analysis. Sentiment analysis in general is a natural language processing technique that helps to contextualize whether the natural text has any sort of bias to it. For the purposes of this paper, this polarity will be more aligned to whether a given news article in question is left-leaning, right-leaning or centrist. The outcome is doing so is to gain a knowledge of reporting in the USA as to see whether press organizations are truly neutral in their reporting and exactly how severe is the bias when it comes to particular words along with the other words they are naturally associated with. For the crux of this study, the proposed model will be utilizing three unsupervised document modelling algorithm that utilize document embeddings using paragraph vectors after utilizing Latent Dirichlet Allocation for fuzzy clustering. All three of these models will be used to make a prediction and while their accuracies will be evaluated separately, these three models will be combined towards a hard voting ensemble classifier so that a better classification score can be obtained and to reduce doubts since subjectivity is a large problem when dealing with natural text and especially, articles. © 2023 IEEE.

关键词： Sentiment analysis

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Multi-Modal Deep Learning for Leaf Disease Detection: Integrating Visual and Non-Visual Data Sources 6

Multi-Modal Deep Learning for Leaf Disease Detection: Integr...

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6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Ilavendhan, A. Sreenidhi, R. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350394702

In this paper, a novel technique is presented for detecting leaf diseases using multi-modal deep learning methods that incorporate both visual and non-visual data. The approach involves combining leaf image data with information from environmental sensors, soil moisture levels, and weather data to enhance the accuracy of our models. A vast dataset of leaf images is gathered and associated with non-visual data from various locations and environments, which is used to develop a multi-modal deep learning model capable of effectively integrating both types of data sources to accurately identify leaf diseases. This approach will considerably enhance the accuracy of leaf disease detection and enable effective management of plant diseases. By utilizing multi-modal data sources, we can achieve a more comprehensive understanding of the factors that contribute to leaf diseases and develop more precise models for detecting them. © 2023 IEEE.

关键词： Image enhancement

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