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smart Farming for Agriculture Management Using IOT 9

Smart Farming for Agriculture Management Using IOT

引用

9th International Conference on advanced Computing and Communication systems, ICACCs 2023

作者： Rao, G Balu Narasimha Rao, K Venkateswara Kamarajugadda, Raviteja Reddy, Avuthu Avinash Rani, P Padmini Vignan's Foundation of Science Technology and Research Department of CSE Vadlamudi Guntur India Vignan's Lara Institute of Technology and Science Department of CSE Vadlamudi Guntur India Lakireddy Bali Reddy College of Engineering Department of IT Mylavaram Vijayawada India Vignan's Foundation of Science Technology and Research Department of Advanced Computer Science and Engineering Vadlamudi Guntur India

ISBN: (纸本)9798350397376

smart farming is an raising idea because IOT sensors are giving information about agricultural fields and take action based on the given input. The scarcity of food and huge growth of population are the most provocation facing the growth of global. Latest technologies like Internet of Things (IoT), Artificial Intelligence(AI) and the high speed internet are providing the practical solutions to the new challenges facing by the global. smart farming creates a feasible to record and identify many assorted and scientific arguments through latest tools and devices to trace the operations performed by the end users. The article of this paper contain evolution smart farming is a raising idea because internet of things (IOT) sensors are efficient of a technique which can measure temperature, pH level and condition of the soil. It sends an sMs alert to the farmer mobile through GsM module so the farmers can keep track of the fields condition from any place. It also helps in maintaining water by automatically providing water to the fields depending on the water requirements. It is mostly used in fields, lawns and parks. In addition to this, effect of environment is reduced and enlarges the quality & quantity and more importantly overall viability will increase. © 2023 IEEE.

关键词： Facings

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Enhancing Multi-label text classification using adaptive promptify concepts

Enhancing Multi-label text classification using adaptive pro...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： Veerababu Reddy Usha Rani Uppukonda N. Veeranjaneyulu Dept. of Computer Science & Engineering Vignan’s Foundation for Science Technology & Research Guntur India Dept. of Computer Science & Engineering Vignan’s Lara Institute of Technology & Science College Guntur India Dept. of Information Technology Vignan’s Foundation for Science Technology & Research Guntur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In present days, Multi Label Text Classification (MLTC) has become an important research area in Natural Language Processing (NLP). Existing models of MLTC have been facing domain specificity and fine-tuning challenges. Hence a model is developed to overcome the above said challenges by introducing Prompt engineering, GPT FineTuning methods and Pipeline algorithms. The experimental results are obtained based on the present Generative pre trained data produces labels for various text documents and files with an exceptional accuracy of 85%. The outcomes of the experiments reveal notable enhancements over the baseline models.

关键词： surveys Accuracy social networking (online) Text categorization Pipelines Medical services Natural language processing Prompt engineering Optimization sorting

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Development and Analysis of a High-Efficiency T-Type Inverter based 3.7 kW Wireless EV Charging Applications

Development and Analysis of a High-Efficiency T-Type Inverte...

引用

2025 IEEE International students' Conference on Electrical, Electronics and computer science, sCEECs 2025

作者： Venugopal, R. Balaji, C. shanmugam, Yuvaraja Srm Institute of Science and Technology Dept. of Electrical and Electronics Engineering Tamil Nadu Chennai603203 India Vignan's Foundation for Science Technology and Research Department of Eee Andhra Pradesh Vadlamudi522213 India

ISBN: (纸本)9798331529833

This paper presents the design and performance analysis of a T-type inverter optimized for wireless charging applications in electric vehicle (EV) batteries, aiming to enhance efficiency and reduce power losses. Traditional H-bridge inverter-based wireless power transfer (WPT) systems face challenges such as complex circuitry, voltage spikes, switching noise, and losses from switching, which impact overall efficiency. The proposed T-type inverter addresses these issues by improving voltage control and reducing switching losses, resulting in enhanced system efficiency. The system employs a series-series (s-s) compensation technique that provides better transmission control and performance, making it more adaptable to diverse load conditions. This compensation method, combined with optimized alignment tolerance and a high coupling coefficient, allows the system to efficiently use rectangular coils, which generate a more uniform magnetic field and minimize current loss. Designed for a 3.7 kW output at an operating frequency of 85 kHz, the T-type inverter-based WPT system was validated through MATLAB/simulink simulations and a laboratory prototype. Results demonstrate an efficiency of 95.20% in simulation and 93.248% in hardware testing. Additionally, DC-DC efficiencies achieved are 94.78% in simulation and 96.177% in hardware. This research confirms the potential of T-type inverters in high-efficiency wireless EV charging systems. © 2025 IEEE.

关键词： Inductive power transmission

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sensor Enabled Vehicle Parking system using Blynk IoT Application 5

Sensor Enabled Vehicle Parking System using Blynk IoT Applic...

引用

5th International Conference on smart Electronics and Communication, ICOsEC 2024

作者： sekar, G. Benson Mansingh, P.M. Prasad, J. Britto Manoj, A. Vsb College of Engineering Department of Electronics and Communication Engineering Technical Campus Coimbatore India Vignan's Foundation for Science Technology and Research Department of Advanced Computer science and Engineering Guntur India Kpr Institute of Engineering and technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798331504403

In the current scenario, finding the parking in full slot area is very difficult and also there is no online platform to check those parking slot availability details. The proposed system provides a parking slot availability information over phone and reducing a waiting time to check the availability. Using the 'smart parking system' the parking slot availability can be accessed easily over the internet. This system can completely automate the entry and exit of car and also payment process. The Node MCU EsP8266 is connected with IR sensors. The IR sensors will show the availability of the parking slot. The entry and exit gates are connected with servo motors. RFID scanner used to show the information of the user while entering. Online service software was used (Blynk app) it shows the slot through your mobile world. © 2024 IEEE.

关键词： sensor nodes

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Comparative Analysis of Multi-Class Classification Models for Obesity Risk Prediction

Comparative Analysis of Multi-Class Classification Models fo...

引用

Modeling, simulation & Intelligent Computing (MosICom), International Conference on

作者： Ds Bhupal Naik Mohammad salafsha Afrin P srinivasa Rao Bhavanam Amarendra Reddy Advanced Computer Science and Engineering Vignan’s foundation for Science Technology and Research Guntur India

ISBN: (数字)9798331533311

ISBN: (纸本)9798331533328

Obesity is a global health issue associated with increased risks of various chronic diseases, such as diabetes, cardiovascular disease, and certain cancers. Accurate prediction of obesity risk can enable early interventions, improve patient outcomes, and alleviate healthcare burdens. This study performs a comparative analysis of several multiclass classification models to predict obesity risk levels based on demographic, lifestyle, and health-related features. we aimed to predict obesity risk in individuals using various machine learning algorithms, with a particular focus on the Light Gradient Boosting Machine (LGBM) classifier. Obesity is a growing global health issue linked to numerous serious diseases. We evaluated several algorithms, including LGBM, Random Forest, Decision Tree, support Vector Classifier (sVC), Logistic Regression, and AdaBoost algorithms to determine their effectiveness in accurately classifying obesity risk. Our findings revealed that the LGBM classifier outperformed the others with an impressive accuracy of over $\mathbf{9 0 \%}$. Random Forest followed closely, while Decision Tree, sVC, Logistic Regression, and AdaBoost showed progressively lower accuracies. The LGBM algorithm’s superior performance highlights its potential as a valuable tool for early obesity risk detection and intervention, enabling healthcare providers to take proactive steps in managing and preventing obesity.

关键词： Obesity Logistic regression Accuracy Machine learning algorithms Computational modeling static VAr compensators Predictive models Prediction algorithms Classification algorithms Random forests

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Unveiling House Price with Machine Learning Algorithm

Unveiling House Price with Machine Learning Algorithm

引用

2025 International Conference on Multi-Agent systems for Collaborative Intelligence, ICMsCI 2025

作者： Kusuma, Rambabu Kumar, Kalagadda Kiran Pillutla, siva Ramakrishna saikumar, Aruru Vignan's Foundation For Science Technology and Research Department of Computer Science and Engineering Andhra pradesh Guntur India School of Electronics Engineering VIT-AP University Amaravati522241 India Vnr Vjiet Dept of Ece Bachupally India

ISBN: (纸本)9798331509828

A frequently overlooked and yet very critical factor in having a high quality of life is the perfecting of one's living environment. A house should be quiet, unique, and instill peace in the soul of its residents, attaining individual needs across many aspects. Therefore, finding such a house with great architecture and making the right choice is essential. Beyond location, the structure and cost of a house are key elements that significantly influence the suitability of a place to live. Accurate price predictions are crucial to address potential future concerns. This study presents a model designed to help individuals select an ideal home based on their financial circumstances. It evaluates various machine learning models, including the Random Forest Regressor and Gradient Boosting Regressor, to ensure reliable predictions. The proposed model aims to present estimates as closely as possible aligned with ideal conditions, considering individual preferences and demands. © 2025 IEEE.

关键词： Adversarial machine learning

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CNN Models Transfer Learning Based ship Classification for Intelligent ship Routing

CNN Models Transfer Learning Based Ship Classification for I...

引用

computer Vision and Machine Intelligence (CVMI), International Conference on

作者： Brij Kishor Tiwari Rohini sai Munagala sai Dheeraj Mannarugudi Department of Advanced Computer Science and Engineering Vignan’s Foundation for Science Technology and Research Guntur India

ISBN: (数字)9798350376876

ISBN: (纸本)9798350376883

Collision free autonomous navigation is challenging area of interest for the shipping industry, and an accurately classifying the ships according to their size and type classes is a crucial undertaking in marine operations. Accurate prediction and identification will improve the maritime security and navigation safety. However, automatic ship classification is a difficult subject because of the wide variety of ship designs and the complexity of visual data. We developed a deep learning model using transfer learning from pre-trained models in order to classify five different kinds of ships. We performed the experiment using different pre-trained convolution neural network model of the deep learning. In the transfer learning we considered the training of the dataset for 100 epochs. In this study, we investigated four deep learning model ResNet 50, VGG 16, VGG 19, and Inception V3. We carried out the identical procedure for training of each model by removing the top layer of pre-trained model and adding three dense layers for the top layers. The neuron counts are 1024, 512, 256 respectively followed by dropout layer of 0.5. Results of our model shows the maximum obtained accuracies are 86.66 % and from these we can observed that the developed model can be used for ship identification and classification and to take the decisions for collision free and smooth navigation.

关键词： Deep learning Training Visualization Accuracy Navigation Transfer learning Data models security Marine vehicles Tuning

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Exploring Deep Learning Techniques for Automated Cataract Detection in Digital Camera Imagery

Exploring Deep Learning Techniques for Automated Cataract De...

引用

Advances in Modern Age Technologies for Health and engineering science (AMATHE), International Conference on

作者： sujatha Kamepalli Veeranjaneyulu N Jyostna Devi Bodapati Dept. Department of Information Technology And Computer Application Vignan's Foundation for Science Technology and Research Guntur India Dept. Department of Advnced Computer Science and Engineering Vignan's Foundation for Science Technology and Research Guntur India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

Cataracts, a prevalent eye condition impacting vision, demand accurate and prompt identification for effective risk reduction and blindness prevention. Traditional methods of cataract diagnosis rely heavily on manual examination by ophthalmologists, which can be time-consuming and subject to human error. With the advent of digital imaging technology, there is an opportunity to enhance the efficiency of cataract detection. The deep neural network models VGG19 and ResNet-50 are recommended in this paper for automatic cataract identification on digital camera images. To optimize network performance, adjustments to loss and activation functions were made, incorporating minimal kernels, reduced training parameters, and layers. This resulted in a ResNet-50 model with significantly decreased computational cost and average operating time compared to the cutting-edge models. The proposed ResNet 50 model achieved an average accuracy of 93.41%, outperforming the VGG 19 model, which had an accuracy of 91.13%. This research contributes to the evolution of cataract detection methodologies, emphasizing the potential of deep learning models, particularly ResNet-50, in improving diagnostic accuracy and operational efficiency for enhanced eye health outcomes.

关键词： Cataracts Deep learning Training Accuracy Computational modeling Prevention and mitigation Medical services

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Youtube Transcript summarizer

Youtube Transcript Summarizer

引用

Mobile Computing and sustainable Informatics (ICMCsI), International Conference on

作者： Vijaya Babu Panthagani Vijaya Deepika Reddy Duggempudi Naga Lakshmi Kommera Nikhita Vakkalagadda Dept. Computer Science and Engineering Vignan’s Foundation for Science Technology and Research Guntur A.P India

In the modern world, a video transcript summarizer offers a wide range of applications. It draws attention to the crucial points in the video. YouTube videos are watched in large quantities by people for a variety of reasons, including enjoyment, education, obtaining crucial information, and pursuing personal hobbies. Finding a video with any meaningful information about a subject is incredibly tough because most videos are just buffers for unimportant information. The main goal in the majority of situations is to get useful information from the video. In order to generalize YouTube video transcripts for abstractive text summary without sacrificing the essential details and substance, this research has developed a video summarization system based on Natural Language Processing and Machine Learning. The goal of this project is to make the writing for the videos shorter.

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Fast Convergent Unified Richardson Detection Technique for Massive MIMO systems 1

Fast Convergent Unified Richardson Detection Technique for M...

引用

1st International Conference on Recent Trends in Microelectronics, Automation, Computing and Communications systems, ICMACC 2022

作者： Yatagiri, Manasa Telagathoti, Pitchaiah Vignan's Foundation for Science Technology and Research Dept. of Electronics and Communication Engineering Guntur India

ISBN: (纸本)9781665496049

Massive multiple-input multiple-output or massive MIMO, is an expansion of MIMO that brings several antennas at the transmitter and receiver to enhance throughput and spectrum efficiency. Because of its potential to increase the volume of antennas, this technique has become an important part of wireless standards. Nevertheless, as Massive MIMO works with large number of antennas it requires progressive detecting techniques. Many linear conventional detecting techniques like Zero Forcing and MMsE techniques gives optimal BER performance. But both techniques gives rise to high computational complexity because of exact matrix inversion operations. Many iterative inversion methods like Nuemann series, Newton Iteration method and Richardson methods were introduced to reduce that complexity. Paper proposes a fast convergent Richardson method which gives the near best BER performance as that of MMsE in less number of iterations. © 2022 IEEE.

关键词： MIMO systems

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