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Rubber Buddy: A Mobile application to Empower Rubber Planters of Sri Lanka

Rubber Buddy: A Mobile Application to Empower Rubber Planter...

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Advancements in Computing (ICAC), international conference on

作者： Aparna Jayawardena Kasuni Ganegoda Sakuni Imbulana Gavin Gunapala Nuwan Kodagoda thilini Jayasinghe Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398106

this research was conducted to develop a mobile application that provides expert solutions for the common problems faced by rubber planters in Sri Lanka. the application developed consists of four components, namely, identification of pests in immature rubber plantations and rubber nurseries; leaf disease identification; cover crop identification; and weed identification. Images taken using the mobile phone cameras are recognized using machine learning models developed using several convolutional neural network (CNN) architectures such as mobile net version 2 (MobileNet v2), VGG 16, VGG19, and residual networks (ResNet). After the images were recognized, the application will provide expert solutions and management strategies to the rubber planters. As most of the rubber plantations are located in areas with low network coverage, the application was designed to be operated in offline mode using TensorFlow lite technology.

关键词： Image recognition Crops machine learning computer architecture Mobile handsets Mobile applications Rubber

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Design of Porcelain Insulator Defect Recognition System Based on UAV Line Inspection Image 4th

Design of Porcelain Insulator Defect Recognition System Base...

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4th EAI international conference on 6G for Future Wireless Networks, 6GN 2021

作者： Li, Zhaoyu Zheng, Zhong Shi, Shuo Rui, E. School of Electronic and Information Engineering Harbin Institute of Technology Heilongjiang Harbin150001 China International Innovation Institute of HIT in Huizhou Guangdong Huizhou516000 China Heilongjiang Polytechnic Heilongjiang Harbin150001 China

ISBN: (纸本)9783031042447

With the progress of technology and the improvement of equipment quality, the coverage of China’s transmission network is expanding rapidly. Large power grids across complex and volatile terrain and dangerous high-voltage transmission lines are also being extended. therefore, the traditional method of checking the circuit manually is no longer feasible due to its low efficiency, low precision, high risk and high cost. However, unmanned aerial vehicles (uavs) are a perfect way to circumvent these problems by inspecting transmission lines instead of workers. this paper takes the application of unmanned aerial vehicle in power line patrol as the research background, takes the porcelain vase in power transmission as an example, and realizes the image recognition and damage judgment system of the porcelain vase with specific target. Based on the image processing technology of machine learning and MATLAB, the target detection method of YOLO v3, the semantic segmentation method of Deeplab v3+, and the improved damage analysis method of ellipse fitting were respectively used to make the damage judgment and analysis of porcelain vats based on the intelligent image recognition interception, contour extraction and semantic segmentation. In the actual site of 166 porcelain bottles damage detection, damage detection accuracy reached 86.7%. Finally, the identification system of porcelain vase defect is realized. © 2022, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Antennas

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the Role of IOT in Creating SC’S through Ultra Fast Updation of the Status for Accurate Action Plan

The Role of IOT in Creating SC’S through Ultra Fast Updatio...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： S Hariharasudhan S. Subasree M. Bhuvaneswari Mekvin Victor Rintu Kumar K. Sambath Kumar Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Department of computer science and Engineering Nehru Institute of Engineering and Technology T.M. Palayam Tamil Nadu India Department of Biomedical Engineering Karpagam Academy of Higher Education Coimbatore TN School of Business and Management Christ University Bangalore Department of Mech Engg IES Institute of Technology and Management IES University Bhopal M.P. India Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology (Deemed to be University) Avadi Chennai Tamil Nadu

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

the idea of a smart city includes the merging of technologies and advances aimed at improving urban efficiency, scientific progress, the preservation of the environment, and social inclusion. Coined in the year 2000, the term became widely used in politics, business, management, and urban planning groups to drive tech-based changes in urban areas. It reacts to the difficulties posed by postindustrial communities handling problems such as pollution to the environment, demographic changes, population growth, health care monetary crises, and resource shortages. Beyond technical answers, the smart city idea includes non-technical innovations for healthy urban life. Particularly encouraging is the application that uses Internet of the circumstances (IoT)based sensors in healthcare, applying machine learning for effective data management. this paper discusses the application of AI-powered Ai and Wireless Sensor Networks, more commonly known as the field of health care, acting as a basic study to understand the impact of IoT in smart cities, especially in healthcare, for the sake of future research.

关键词： Technological innovation Accuracy Smart cities Sociology Medical services machine learning Blockchains

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Performance Evaluation Of machine learning Algorithms For Rainfall Prediction Using Dimensionality Reduction Techniques

Performance Evaluation Of Machine Learning Algorithms For Ra...

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international conference on Computing, Mathematics and Engineering Technologies (iCoMET)

作者： Sapna Kumari Muhammad Owais Raza Arsha Kumari Dept. of Computer Science Shaheed Zulfikar Ali Bhutto Institute of Science and Technology Hyderabad Pakistan Dept. of Information and Computing University of Sufism and Modern Sciences Bhitshah Pakistan Dept. of Electronics Engineering Mehran University of Engineering & Technology Jamshoro Pakistan

ISBN: (数字)9798350335316

ISBN: (纸本)9798350335323

In the last few decades, tremendous change is observed in rainfall patterns which are majorly influenced by two major factors 1) climate change and 2) CO2 emission. Erratic rainfall patterns caused catastrophic effects on agriculture and human life in developing countries like Pakistan, where major economic growth is largely dependent on agriculture. the main objective of this study is to evaluate a performance different machine learning algorithms for forecasting rainfall patterns using dimensionality reduction techniques on climate change indicators. For this purpose rainfall data was collected for Pakistan. Principle component analysis (PCA), Pearson correlation, and Greedy search algorithms were used for feature selection and the evolution of models was performed using Root Mean Square error (RMSE), Root Absolute Error (RAE), and Coefficient of determination metrics. Results show that features obtained using the Pearson correlation produced the least error and Bayesian linear regression performed with the highest accuracy followed by Neural Network regression.

关键词： Climate change Rain Pattern recognition Predictive models machine learning Neural networks

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Self-Driving Car Simulation Using Reinforcement learning and Xception Model Tuning

Self-Driving Car Simulation Using Reinforcement Learning and...

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Smart Electronics and Communication (ICOSEC), international conference on

作者： Aparna Balasubramaniam Rabindra Kumar Singh Amit Kumar Tyagi School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamilnadu India Department of Fashion Technology National Institute of Fashion Technology New Delhi Delhi India

the main idea is to have the environment (server) and then agents (clients). the server/client architecture means that the user can of course run both the server and client locally on the same machines, but it could also run the environment (server) on one machine and multiple clients on multiple other machines. For training and predicting at the same time, reinforcement learning is used. To obtain a convincing result in the training process would mean promising improvement in the field of automation and automotives. this simulation will be very helpful in all manners when developed with more real-life objects and circumstances that get into the simulation asset. the improvement in self-driving cars comes along with the advancement in the field of deep learning and data handling. Due to the sheer amount of multidimensional data that a self-driving car could create, a more efficient data handling methods are required to compute better results. there is always a chance to add layers to this particular deep learning framework and make it more efficient as the data captured evolves. the data may evolve in the fields of addition in new sensors, new obstacle identification, addition of more real time features. Progressing further into this study and implementation of a deep learning framework for self-driving cars, it is observed that the sensor data provides sufficient parameters for the estimation of new class variables. the model accuracy and results are compared in a way that is projected towards higher volume of data along with high computational power. this study recommends using cloud service to accommodate the graphical and CPU requirements as the computation for this particular multidimensional data could be very demanding. the understanding of how autonomous vehicles and how well it would be accepted is discussed below, along with the complications it would bring. It's a social change which many will oppose in the beginning but will slowly adopt as science impro

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A review for identification of mask using machine learning Technique

A review for identification of mask using Machine Learning T...

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： Juhi Kisanrao Chavan Narendra Kandoi Department of CSE S.S.G.M. College of Engineering Shegaon

In computer field security and protection, the recognition of object is very important to take particular action becomes very necessary for making decisions. Due to increase in scientific development in the information, computer vision and programming techniques, which make an image a very important role in multiple zone such as medicine media, education, design, industry, security etc. this proposed system make use of face identification on the basis of dataset and helps in the scenario for detection mask on face, which helps to prevent the spreading of virus at certain level with specific application.

关键词： Industries Pandemics Face recognition machine learning Logic gates Media Maintenance engineering

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Deep learning Based Recognition of Plant Diseases 1

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4th international conference on Recent Trends in Advanced Computing - computer Vision and machine Intelligence Paradigms for Sustainable Development Goals, ICRTAC-CVMIP 2021

作者： Parthiban, Swetha Moorthy, Sneha Sabanayagam, Sribalaji Shanmugasundaram, Shobana Naganathan, Athishwaran Annamalai, Mohan Balasubramanian, Sabitha Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9789811971693

ISBN: (纸本)9789811971686

Every country's primary requirement is agricultural products. Infected plants have an impact on the agricultural production and economic resources of a country. Early detection of plant diseases minimizes the risk of crop loss by allowing farmers to adopt curative and preventative actions to avoid further damage. Citrus is a large plant that is primarily grown in tropical areas of the world because of its high vitamin C and other key nutrient content. Citrus plant diseases play a vital role in causing a great financial loss to the farmers and affect the economy of the country. the tomato ranks among the most economically important vegetable crops, with a global production rate of around 200 million tonnes. But due to diseases occurring in the tomato plants, the farmers are facing a great loss. To prevent these losses and provide an immediate cure, we create a model to detect plant diseases. To get a better performance model, the data augmentation process is used, and we got 18,392 images. In our model, we used a convolutional neural network (CNN) to classify the citrus and tomato leaf diseases. the diseases are Mancha Graxa, Citrus Canker, Tomato Early Blight, Tomato Septoria Leafspot, Tomato Spider Mite Two-spotted spider mite (Tetranychus urticae), Tomato mosaic virus (Tobamovirus), Tomato yellow leaf curl virus (Begomovirus), Tomato Target Spot (Corynespora cassiicola). Convolution Neural Network (CNN) is stacked by multiple convolutions and pooling layers to detect plant leaf diseases. We train a model with augmented images. After the model gets trained, it is thoroughly tested to ensure that the results are accurate. thereby we were able to achieve high validation accuracy with a rate of (97.02%) for this dataset used. As the results showed, applying the deep CNN model to identify diseases could greatly impact the accurate identification of diseases, and could even prove useful in detecting diseases in real-time agricultural systems. © 2023, the Author(s), unde

关键词： Losses

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Research on the dynamic application of trademark similarity determination based on dynamic time regularization and XGBoost 23

Research on the dynamic application of trademark similarity ...

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proceedings of the 2023 7th international conference on computer Science and Artificial Intelligence

作者： Hong Zhang Xiaojuan Wang Yaping Xu Guijiang Hu Guizhou China Tobacco Industry Co. Ltd. China

ISBN: (纸本)9798400708688

In order to improve the possibility of successful trademark registration in the tobacco industry and monitor the problem of whether the 34 categories of trademarks are preempted, this paper conducts an experimental design based on dynamic time regularization and XGBoost algorithm. the main innovation points are firstly, the similarity is calculated by the Dynamic Time Wrangling (DTW) algorithm by combining the information of text structure and location, and also as a comparison with the accuracy of machine learning prediction results; secondly, both Chinese similarity and English similarity are incorporated into the training set as input features; thirdly, the weekly updated data training results are checked and used as the test set to enhance the accuracy and adaptability of the model. this experiment is applied to automatically output the determination of trademark similarity results, and then take measures to adjust the trademark design or raise objections to the infringing trademark after obtaining the similarity results, which greatly reduces the workload of manual comparison and improves the accuracy rate of comparison at the same time. the experimental design of this paper evaluates the accuracy rate, and the experimental results are in the acceptable range, which can be applied to the whole tobacco industry for screening similar brand names, and can be extended to other text similarity determinations, and this study can continue to optimize the extraction of more features as input, which will be helpful in providing accuracy rate.

关键词： DTW Data application machine learning Trademark publication notification

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Comparative Analysis of machine learning Techniques for Concrete Compressive Strength Prediction

Comparative Analysis of Machine Learning Techniques for Conc...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), international conference on

作者： Fuad Musleh Ranyah Taha Abdel Rahman Musleh Civil Engineering Department University of Bahrain Isa Town Kingdom of Bahrain Computer Department Al Iman School Isa Town Kingdom of Bahrain Electrical Engineering Department University of Bahrain Isa Town Kingdom of Bahrain

ISBN: (数字)9798350369786

ISBN: (纸本)9798350369793

the compressive strength of concrete is of utmost importance in ensuring the structural stability and integrity of construction projects. With increasing importance placed on this aspect, there is a demand for intelligent and efficient techniques to accurately predict and evaluate compressive strength. machine learning methods (ML) present a promising approach to developing accurate models for assessing the compressive strength of concrete. By leveraging the power of data analysis and pattern recognition, these techniques enable the construction industry to obtain reliable and robust concrete for a wide range of applications. ML models can identify the key factors influencing compressive strength, allowing for more informed decision-making, and ensuring the construction of structurally sound and durable projects. this article explores the assessment and comparison of four machine learning (ML) methods, namely Gradient Boosting, Categorical Gradient Boosting CatBoost, Random Forest (RF), and extreme gradient boosting (XGBoost) regressors, for predicting concrete compressive strength. Among these techniques, the CatBoost Regressor demonstrated superior performance in terms of accuracy, R-squared coefficient of determination, and Root Mean Square Error RMSE, surpassing the other three methods.

关键词： Radio frequency Data analysis Accuracy Boosting Concrete Structural engineering Pattern recognition

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4th international conference on Data Management, Analytics and Innovation, ICDMAI 2020

4th International Conference on Data Management, Analytics a...

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4th international conference on Data Management, Analytics and Innovation, ICDMAI 2020

ISBN: (纸本)9789811556159

the proceedings contain 33 papers. the special focus in this conference is on Data Management, Analytics and Innovation. the topics include: A Novel Framework for Data Acquisition and Retrieval Using Hierarchical Schema Over Structured Big Data;Predicting the Cricket Match Outcome Using ANFIS Classifier for Viewers Opinions on Twitter Data;Extraction of Tabular Data from PDF to CSV Files;railway Complaint Tweets Identification;identification of Entities in Scientific Documents;a machine learning Model for Review Rating Inconsistency in E-commerce Websites;NLIDB Systems for Enterprise Databases: A Metadata Based Approach;a Data Science Approach to Analysis of Tweets Based on Cyclone Fani;A GPU Unified Platform to Secure Big Data Transportation Using an Error-Prone Elliptic Curve Cryptography;an IoT-Based Smart Parking Framework for Smart Cities;an Empirical Analysis of Classifiers Using Ensemble Techniques;force of Gravity Oriented Classification Technique in machine learning;machine learning Classifiers for Android Malware Detection;Designing a Model to Handle Imbalance Data Classification Using SMOTE and Optimized Classifier;an Analysis of Computational Complexity and Accuracy of Two Supervised machine learning Algorithms—K-Nearest Neighbor and Support Vector machine;a Survey on application of machine learning Algorithms in Cancer Prediction and Prognosis;augmented Reality Building Surveillance System;interpretation and Segmentation of Chart Images Using h-Means Image Clustering Algorithm;innovative Techniques for Student Engagement in Cybersecurity Education;a Feasibility Study of Service Level Agreement Compliance for Start-Ups in Cloud Computing;open-Source Software Challenges and Opportunities;smart Waste Monitoring Using Internet of things;security Issue in Internet of things.

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