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Generative AI in Agri: Sustainability in Smart Precision Farming Yield Prediction Mapping System Based on GIS Using Deep Learning and GPS

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Procedia computer science 2025年 252卷 365-380页

作者： Senthil G. A S.U. Suganthi L. Prinslin R. Selvi R. Prabha Associate Professor Department of Information Technology Agni College of Technology Chennai India Associate Professor Department of Artificial Intelligence and Data Science Sri Sai Ram Institute of Technology Chennai India Assistant Professor Department of Computer Science and Engineering Agni College of Technology Chennai India Assistant Professor Department of Computing Technologies SRM Institute of Science and Technology kattankulathur Chennai India Associate Professor Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India

Agriculture faces many challenges of precision farming, such as the need for sustainable practices, improving yields, ensuring high yields. In resolution to these challenges, the present research provides an AI-based system that enables the use of deep learning, Global Positioning System (GPS), and Geographic Information System (GIS) technologies to create a highly intelligent smart agricultural precision farming system. Its goal is to monitoring crop health and reduce disease risk, which will lead to improved resource utilization and environmentally sustainability techniques. The proposed framework addresses the urgent need for consistency in agricultural practices, especially as global agriculture deals with pressures from climate change, resource shortages, and increasing demand for food. Traditional agricultural methods for predicting and optimizing crop yields due to increasing factors affecting crop performance Not enough generative AI, especially the use of deep learning models, supports agricultural research in many cases, allowing patterns to be identified and future results to be predicted accurately. The integration of GPS and GIS allows for more accurate mapping, real-time analysis, and effective decision-making. Weather forecasting variability, resource constraints, and demand for more food are isolated from environmental influences using deep learning models, especially Artificial Neural Networks (ANN). By using large data sets, including historical crop yield performance, soil properties, and weather conditions, the system provides highly accurate crop forecasts. Generative Adversarial Networks (GANs) and You Only Look Once (YOLO) hybrid model is playing a key role in generating crop yield and growth potential under different conditions, adjusting model accuracy over time, and this combination of ANN, GANs and YOLO optimization algorithms ensures that the system continuously enhances its predictive accuracy and overall effectiveness. The proposed gene

关键词： Generative AI Deep Learning Agriculture Smart Farming Crop Health Yield Prediction Global Positioning System Geographic Information System Artificial Neural Networks Generative Adversarial Networks Sustainability You Only Look Once (YOLO)

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Comparative Study of Future State Predictions of Unsteady Multiphase Flows Using DMD and Deep Learning 9th

Comparative Study of Future State Predictions of Unsteady Mu...

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9th International and 49th National Conference on Fluid Mechanics and Fluid Power, FMFP 2022

作者： Raj, Neil Ashwin Tafti, Danesh Muralidhar, Nikhil Karpatne, Anuj Department of Mechanical Engineering Virginia Tech Blacksburg United States Assistant Professor Department of Computer Science Stevens Institute of Technology Hoboken United States Department of Computer Science Virginia Tech Blacksburg United States

ISBN: (纸本)9789819971763

Flow across an array of solid obstructions is a common phenomenon observed in many applications such as multiphase flows, heat exchangers, and environmental flows. In this work, we aim to train deep learning models and to predict the time evolution of unsteady flow fields in a domain of randomly arranged 2D cylinders at Reynolds number 50. Two different approaches are used and compared in this paper, dynamic mode decomposition (DMD) which is a dimensionality-reduction algorithm based on singular value decomposition (SVD) and long short-term memory (LSTM) neural networks. In both cases, the model is trained on the first 165 time steps and then is tested on predicting the next 300 time steps. Two flow fields with different spectral characteristics are used to compare the performance of the two techniques. The LSTM architecture owing to its ability to learn nonlinear dynamics performs better than the DMD algorithm in the case with more temporal time scales present. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Dynamic mode decomposition

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Prediction of the Impact of Covid-19 Vaccine on Public Health Using Twitter

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2022年第1期49卷 19-29页

作者： Syed, Ayesha Ayub Gaol, Ford Lumban Suparta, Wayan Abdurachman, Edi Trisetyarso, Agung Matsuo, Tukoro Ph.D. student at the Department of Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Associate Professor at Informatics Engineering and Information System at Bina Nusantara University Jakarta Indonesia Malaysia Associate Professor at the Department of Doctor of Research in Management at Bina Nusantara University Jakarta Indonesia Assistant Professor at the Department of Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Professor at the Graduate School of Industrial Technology Advanced Institute of Industrial Technology Japan and Department of M-Commerce and Multimedia Applications Asia University Taiwan

Social media is a source of big data. Media like Twitter and Facebook has been used for collecting and analyzing user data for different purposes. The data can be used to analyze people opinions towards certain topics and incidents by applying sentiment analysis and then certain useful insights can be drawn from the analyzed data. During the current time of Covid-19, people have been sharing information regarding Covid-19 statistics, vaccines, and discussing the effects of the vaccine concerning public health. The purpose of this study is to analyze tweet data regarding the Covid-19 vaccine by applying sentiment analysis and predicting the impact of the vaccine on public health. Also, the tweets are analyzed for hidden topics by applying Topic Modelling using Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA). The source of data for this study is Twitter API. The coding and data analysis is done using Python programming language in the Spyder (Scientific Python Development Environment) that is an integrated development environment for scientific programming, testing, and data analysis. The results of the study indicate a greater positive sentiment reflecting a healthy public discussion about the Covid-19 vaccine, information, awareness, and public acceptance. With these results, a positive impact of the Covid-19 vaccine on public health is predicted. The results of topic modeling discovered 10 hidden topics from the tweet dataset. © 2022. All Rights Reserved.

关键词： Vaccines

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A Novel Adaptive Fault Tolerance Algorithm Towards Robust and Reliable Distributed Applications to Reuse System Components 10th

A Novel Adaptive Fault Tolerance Algorithm Towards Robust an...

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10th International Conference on Innovations in computer science and engineering, ICICSE 2022

作者： Banothu, Lalu Chandra Mohan, M. Sunil Kumar, Charupalli Professor Department of Computer Science and Engineering JNTUH College of Engineering Hyderabad India Professor & Dean in CSE Apollo University Andhra Pradesh Chittoor India

ISBN: (纸本)9789811974540

Distributed component-based systems relay on components that run in different servers located geographically. In large distributed applications experiencing faults is common due to complexity, heterogeneity and a host of other reasons. Therefore, realizing a reliable and fault-tolerant distributed system is challenging. Existing techniques such as N-version programming and modular redundancy have certain drawbacks such as increased complexity and error prone. In this paper, we proposed an algorithm known as Adaptive Fault Tolerance (AFT) algorithm which evaluates different functions or components in the system and assigns weights to them besides following a novel redundancy strategy. The algorithm exploits function calls and frequency of calls to arrive at finding importance of functions. This know-how helps in identifying functions that are vulnerable and the functions that are reliable. The AFT helps in achieving fault-tolerant distributed component-based applications comprising of untrusted components. Experimental results showed that AFT outperforms many existing fault-tolerant approaches. Thus, the proposed algorithm helps in realizing robust and reliable distributed applications that reuse components and drive home businesses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fault tolerance

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GS-FFO Based Meta-Heuristic Clustering for Discovering Colossal Patterns in High-Dimensional Data 2nd

GS-FFO Based Meta-Heuristic Clustering for Discovering Colos...

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2nd Congress on Control, Robotics, and Mechatronics, CRM 2024

作者： Reddy, T. Sreenivasula Sathya, R. Nuka, Mallikharjuna Rao Research Scholar Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Annamalai Nagar Tamil Nadu Chidambaram608002 India Assistant Professor Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Annamalai Nagar Tamil Nadu Chidambaram608002 India Professor Department of Computer Applications Annamacharya Institute of Technology & Sciences YSR Kadapa Andhra Pradesh Rajampet516115 India

ISBN: (纸本)9789819768097

Colossal pattern mining, which involves deriving important relationships and insights from large and complex datasets, is an essential part of data analysis. Extracting association rules that satisfy preset minimum support and confidence criteria from a given database is the aim of association rule mining (ARM). Nevertheless, ARM often generates an unusually large number of association rules that are hard for end users to comprehend or validate, which lessens the significance of data mining discoveries. Based on the clustering methodology, this research study presents a new mining strategy. The process begins with a method called the Expressed Data Matrix (EDM), which transforms unstructured data into a structured format and enhances the quality and organisation of the information. Primary data cleaning processes are used to eliminate noise and inconsistencies from the data in order to further improve it. To address high-dimensionality issues and boost mining productivity, dimensionality reduction—which lowers the dataset to its most valuable features—is accomplished using Eigen-Principal Component Analysis (PCA). The core of this study is the hybridised clustering approach, or GS-FFO, which combines the Fruit Fly Optimisation technique (FOA) with Glow Swarm Optimisation (GSO). This technique improves the quality of clustering and reduces the probability of local optima convergence by integrating the benefits of both approaches. The suggested GS-FFO model outperforms with four datasets, including accident, pumbs*, retail, and yeast, with an accuracy of up to 98.5%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Dimensionality reduction

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Identification of Autism Spectrum Disorder from Functional MRI Using Deep Learning 4th

Identification of Autism Spectrum Disorder from Functional M...

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4th International Conference on Advances in Information Communication Technology and Computing, AICTC 2022

作者： Pavithra, R. Abirami, S.P. Krithika, S. Sabitha, S. Tharanidharan, P. Assistant Professor Department of Computer Science and Engineering Coimbatore Institute of Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering Coimbatore Institute of Technology Tamil Nadu Coimbatore India

ISBN: (纸本)9789811998874

Autism spectrum disorder (ASD) is a collection of a way of continuing neurodevelopment illnesses identified by limited and repetitive behavioral patterns, as well as social and communication impairments. Despite the fact that symptoms are most common in childhood, diagnosis is sometimes delayed. Because the current ASD diagnostic technique is solely uncertain and questionnaire, requiring the physician to review the behavior and developmental history of a child. It's been suggested that behavioral symptoms in ASD are linked to brain findings of increased short-distance and diminished long-distance connections. The suggested approach makes use of brain imaging data from Autism Brain Imaging Data Exchange (ABIDE), a global multi-site database. For ASD identification, the proposed approach uses functional connectivity characteristics extracted from resting state functional MRI data. ASD patients’ brain connections could be disturbed. The presented method extracts time series from 122 regions of interest defined by the Bootstrap Analysis of Stable Clusters (BASC) and also extracts time series from 48 regions of interest defined by the Harvard Oxford (HO) brain atlas to create an efficient functional connectivity matrix for all individuals. Synthetic Minority Over-sampling Technique (SMOTE) an oversampling procedure employed for artificial data generation. The classification challenge is carried out using a Recurrent Convolutional Neural Network (RCNN) and Convolutional Neural Network (CNN) and results were compared. In both models, fivefold Cross-Validation is applied. The model performed better in the modified experiment, with RCNN achieving an accuracy of nearly 85%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electronic data interchange

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Non-Isolated high gain DC to DC converter using sustainable energy

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Materials Today: Proceedings 2023年 80卷 2513-2517页

作者： Sathiya, R. Arun Noyal Doss, M. Thulasi Raman, N.A. Maniganda Mahaa Prabhu, S. Associate Professor Department of EEE College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Chennai India Department of ECE College of Engineering and Technology SRM Institute of Science and Technology Vadapalani Campus Tamil Nadu Chennai India Department of ECE College of Engineering and Technology SRM Institute of Science and Technology Vadapalani Campus Tamil Nadu Chennai India

Solar energy, wind energy etc. are the main sustainable energy sources used around the world. These sustainable energy sources are used in day-to-day life in various forms for numerous purposes. One of the highly used sustainable energy sources is the solar energy which with the help of photovoltaic (PV) cells can be made use for the generation of electrical power. However, these sources generate less voltage values and thus it requires a high gain converter. In this paper, a modified Luo with flyback DC to DC converter is presented that assures high voltage gain, single switch, less stress across the components and less ripple current. Furthermore, the simulations along with the hardware prototype for the presented converter is carried out and presented in this paper. © 2021

关键词： Wind power

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Decision Making and Recognition of String Characters using NN-Fuzzy Implication System

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Procedia computer science 2025年 258卷 893-902页

作者： Santosh Kumar Henge Avinash Bhagat Sanjeev Kumar Mandal Nikhil Reddy Viraati Ravleen Singh K. Rushi Kedhar Associate Professor Department of Computer Science and Engineering School of Computer Science and Artificial Intelligence SR University Warangal 506371 India Associate Professor School of Computer Applications Lovely Professional University Phagwara Punjab India Assistant Professor Department of CS and IT Jain (Deemed-to-be University) Bangalore India Department of Data Insights Crisp Shared Services 7160 Columbia Gateway Dr Columbia United States of America Assistant Professor Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddewaram AP India BE-CSE RV Institute of Technology and Management Bengaluru India

This research proposes the neural network (NN)-fuzzy logic control (FLC)-based methodology, which is designed with two stages of execution. Stage-1 is composed with the NN approach; it takes the input from the scanned image, the input from the classified clustered group, processes it with hidden layers, and generates the final output, which is input to the FLC in stage 2. The input of stage 2 converts the input into fuzzified data (0s and 1s) and trains the FLC system with fuzzified set values. The trained data has been simulated for further stages in the FLC-based decision-making approach and integrated to identify the character patterns based on the trained dataset. FLC-based planting and normalization techniques are integrated for re-training the data. The dataset has been framed with more than 6200 handwritten image characters, which were written by 235 writers. It aligned with the pre-processing, classification, and filtering stages and achieved a 99.8% recognition rate.

关键词： Fuzzy Logic Control (FLC) Artificial Neural Network (ANN) Multiple Layered Perceptron (MLP) Convolutional Neural Network (CNN) Feed Forward Neural Network (FNN) Optical Character Recognition (OCR) Handwritten (HW)

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Enhancing Object Detection Robustness In Adverse Weather Conditions

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Procedia computer science 2025年 252卷 1014-1024页

作者： Shaik Yacoob D. Phani Kumar G.G.S. Harshitha V.Y. Sai Teja C.H. Satya Veni K. Mahidhar Reddy Senior Assistant Professor Department of Computer Science and Engineering Godavari Global University Rajahmahendravaram-533296 India Assistant Professor Department of Computer Science and Engineering Godavari Global University Rajahmahendravaram-533296 India

Fundamental applications in autonomous cars, robots, and surveillance systems include object detection for safety reasons such as recognizing vehicles, pedestrians, trees, street poles, and buildings. However, one of the major challenges it faces from adverse weather conditions like rain, haze, and fog through deterioration of accuracy and an increase in safety risks such as traffic accidents. This paper proposes an enhanced object detection system to improve its robustness and accuracy in challenging environments. The proposed approach effectively addresses the problem of poor image quality due to adverse weather conditions by using unsupervised domain adaptation and including a Quasi-Translation Network and Feature Calibration Network in the YOLO(You Only Look Once) framework. It attempts to leverage the success made in CNNs(Convolutional Neural Networks) and mass amounts of datasets in order to adapt YOLO-series detectors in the best possible way, achieving object detection under challenging environmental conditions with precision and speed. Experimental results demonstrate that the solution proposed outperforms existing models in terms of adaptability and accuracy, providing a scalable and reliable solution for autonomous systems to enhance overall safety and operational efficiency in real-world environments.

关键词： Convolutional Neural Networks (CNN) Object Detection YOLO Quasi-Translation Network (QTNet) Feature Calibration Network (FCNet)

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Paving the Way for Economically Accepted and Technically Pronounced Smart Radio Environment

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China Communications 2022年第8期19卷 247-266页

作者： Mostafa Salah Andreas Pitsillides Ahmed S.Mubarak Department of Electrical Engineering Sohag UniversitySohagEgypt Department of Computer Science University of CyprusNicosiaCyprus Visiting Professor University of JohannesburgDepartment of Electrical and Electronic Engineering ScienceJohannesburgSouth Africa Department of Electrical Engineering Aswan UniversityAswanEgypt

Recently,Reconfigurable Intelligent Surfaces(RISs)have been introduced to provide the necessary flexibility for the design of the Smart Radio Environment(SRE),which can be optimally shaped to facilitate efficient signal *** line with the concept of smart cities,SRE needs to be carefully designed with respect to the city infrastructure and utilization of *** this paper,we provide our vision on RIS integration into future Smart Cities by highlighting the potential technical,environmental,and economic motivations of RIS deployment in harmony with various ecosystems at a city level such as buildings *** this end,we are pointing out some scenarios for mitigating the conflict between RIS realization and the existing building facade’s ecosystems such as advertising display and solar cells on building ***,in this fashion,the proposed vision supports a win-win relationship between all stakeholders of different *** study presents guidelines for not only enabling seamless economically accepted RIS widespread utilization but,also more technically sounding SRE by supporting enhanced RIS features and more advanced applications that cannot be attained by traditional passive ***,based on the current research directions,we offer promising insights for cost-effective mass pro-duction through motivating two scenarios of“all on silicon”and“all as metasurface”fabrication *** this study,we aim to encourage the metasurface researchers,that for a broad deployment of the technical solution,economic,environmental,and other commercial requirements should be planned together,early on in the design phase.

关键词： smart radio environment(SRE) reconfigurable intelligent surface(RIS) 6G passive/active metasurface

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