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IoT and Cloud based automated Irrigation System

IoT and Cloud based Automated Irrigation System

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Artificial Intelligence For Internet of Things (AIIoT), international conference on

作者： K G Anandha Jothi T A Vignesh A Gokul Nittin M Vinoth B Janani Shruthi V Abhishek Neeraj Srinivasan K Govardhan School of Electronics Engineering Vellore Institute of Technology Vellore India VIT School of Agricultural Innovations and Advanced Learning Vellore Institute of Technology Vellore India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Agriculture is the most important sector in our country, employing more than 50% of our population and always necessitating innovative solutions for resource utilization and enhanced productivity. In this novel agricultural project, we introduce a versatile and intelligent IoT-based irrigation system. By integrating soil moisture sensors, water level detectors, and weather prediction APIs, our system empowers farmers with real-time insights and automated control over irrigation processes. A key aspect of our strategy is incorporating cloud services, where all data collected by the sensors are sent and analyzed. With a user-friendly mobile app built on Flutter, users can easily supervise and manage the watering system from a distance. Additionally, by utilizing weather predictions obtained through API requests, the system automatically adjusts watering times, ensuring the best conditions for plant growth. One notable feature of our system is its ability to adjust to different types of crops. By employing artificial intelligence algorithms, the system intelligently determines the specific water needs for each crop chosen by the farmer. This personalized technique not only optimizes resource usage but also boosts crop *** summary, this project offers a practical solution for automatic crop irrigation by considering real-time weather forecasts. The proposed device improves irrigation performance and promotes climate-resistant cropping by combining climate monitoring, weather forecasting, and automatic pumping.

关键词： Irrigation Soil moisture Crops Weather forecasting Water conservation Real-time systems Internet of Things

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3rd international conference on Computer Science, engineering and Education Applications, ICCSEEA 2020

3rd International Conference on Computer Science, Engineerin...

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3rd international conference on Computer Science, engineering and Education Applications, ICCSEEA 2020

ISBN: (纸本)9783030555054

The proceedings contain 59 papers. The special focus in this conference is on Computer Science, engineering and Education Applications. The topics include: Construction of Individual learning Scenarios;technology-Enhanced Financial Education and Sustainability Goals;health Status Recognition System for Communication Equipment Based on data Mining;use of Sustainable Innovation in Information System: Integrate Individual’s Spirit and Team Climate;relations of the Genetic Code with Algebraic Codes and Hypercomplex Double Numbers;research on Logistics Center Location-Allocation Problem Based on Two-Stage K-Means Algorithms;operational Intelligence Software Concepts for Continuous Healthcare Monitoring and Consolidated data Storage Ecosystem;method of Recognition and Indexing of People’s Faces in Videos Using Model of Machine learning;deduplication Method for Ukrainian Last Names, Medicinal Names, and Toponyms Based on Metaphone Phonetic Algorithm;two-Layer Perceptron for Voice Recognition of Speaker’s Identity;significant Parameters of the Keystroke for the Formation of the Input Field of a Convolutional Neural Network;detection of Ventricular Late Potentials in Electrocardiograms Using Machine learning;smart Prothesis: Sensorization, Humane Vibration and Processor Control;optimization of Stock Basing on Improved Grey Prediction Model: A Case Study on Garment Supply Chain;improved Post-quantum Merkle Algorithm Based on Threads;improvement of Merkle Signature Scheme by Means of Optical Quantum Random Number Generators;integrated Water Management and Environmental Rehabilitation of River Basins Using a System of Non-linear Criteria;distribution of Resources Between Composite Applications in a Hyperconverged System;the Possibilistic Gustafson-Kessel Fuzzy Clustering Procedure for Online data Processing;information System Penetration Testing Using Web Attack automated Simulation.

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Classification of gender from face images and voice 3rd

Classification of gender from face images and voice

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3rd international conference on Big-data and Cloud Computing, ICBDCC 2019

作者： Poornima, S. Sripriya, N. Preethi, S. Harish, Saanjana Department of Information Technology SSN College of Engineering Chennai India

ISBN: (纸本)9789811552847

automated acknowledgement of a human gender is pivotal for various frameworks like data recovery, human–machine collaboration that processes human source data. Automatic gender classification has become relevant to an increasing amount of applications, particularly since the rise of social platforms and social media. Gender identification has been a widely researched area, but face attribute recognition from facial images still remains challengeable. We propose a methodology for automatic gender classification based on feature extraction from facial images. Our methodology includes three main iterations: preprocessing, feature extraction, and classification. Texture features, geometric moments, and histogram values of facial images are used to train the system. The eminent and efficient features are selected and trained using suitable machine learning technique classifiers, namely SVM, AdaBoost and random forest. The developed model is used to identify the gender of an individual with an accuracy of approximately 95% and can be deployed in various scenarios like hospital registration process, sports selection process, and airports to identify the gender of a person. © Springer Nature Singapore Pte Ltd 2021.

关键词： Adaptive boosting

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Chronic Kidney Disease (CKD) Prediction Using data Mining Techniques

Chronic Kidney Disease (CKD) Prediction Using Data Mining Te...

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3rd international conference on intelligent Computing and Optimization, ICO 2020

作者： Pathak, Abhijit Asma Gani, Most. Tasin, Abrar Hossain Sania, Sanjida Nusrat Adil, Md. Akter, Suraiya Department of Computer Science and Engineering BGC Trust University Bangladesh Chittagong Bangladesh Department of Pharmacy BGC Trust University Bangladesh Chittagong Bangladesh

ISBN: (纸本)9783030681531

In the past decade, the rapid growth of digital data and global accessibility through the modern internet has seen a massive rise in machine learning research. In proportion to it, the medical data has also seen a massive surge of expansion. With the availability of structured clinical data, researchers have attracted scores to study clinical disease detection automation with machine learning and data mining. Chronic Kidney Disease (CKD), also known as the renal disorder, has been such a field of study for quite some time now. Therefore, our research aims to study the automated detection of chronic kidney disease using several machine learning classifiers with clinical data. The purpose of this research work is to diagnose kidney disease using a number of machine learning algorithms such as the Support Vector Machine (SVM) and the Bayesian Network (BN) and to select the most effective one to assess the extent of CKD patients. The amount of expertise in the medical field in relation to CKD is limited. Many patients have to wait a long to get their test results. The experience of medical staff is declining in value. Upon retirement, new employees replace them. It helps professional doctors or medical staff in their diagnosis of CKD. This paper’s primary purpose is to present a clear view of Chronic Kidney Disease (CKD), its symptoms, and the process of early detection that may help humanity be safe from this life-threatening disease. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data mining

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Depression anatomy using combinational deep neural network 3rd

Depression anatomy using combinational deep neural network

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3rd international conference on Innovative Computing and Communication, ICICC 2020

作者： Rustagi, Apeksha Manchanda, Chinkit Sharma, Nikhil Kaushik, Ila Bhagwan Parshuram Institute of Technology Delhi India HMR Institute of Technology and Management Delhi India Krishna Institute of Engineering and Technology GhaziabadUttar Pradesh India

ISBN: (纸本)9789811551475

Depression is a temperament syndrome that causes a tenacious emotion of wretchedness and loss of interest in any activity. It is a supreme root of mental illness, which has established the growth in the risk of early death and economic burden to a country. Traditional clinical analysis procedures are subjective, complex and need considerable contribution of professionals. The turn of the century saw incredible progress in using deep learning for medical diagnosis. Though, prediction and implementation of mental state can be remarkably hard. In this paper, we present Combinational Deep Neural Network (CDNN) for automated depression detection with facial images and text data using amalgamation of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Thereafter, combining the prediction scores of both CNN and RNN model and level of depression is decided on the basis of the range of the predefined depression-level scores. Simulation outcomes based on real-field channel measurements show that the proposed model can significantly predict depression with superior performance. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.

关键词： Recurrent neural networks

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Development Approach of Formation of Individual Educational Trajectories Based on Neural Network Prediction of Student learning Outcomes 3rd

Development Approach of Formation of Individual Educational ...

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3rd international conference of Artificial Intelligence, Medical engineering, Education (AIMEE)

作者： Zaporozhko, Veronika V. Parfenov, Denis, I Shardakov, Vladimir M. Orenburg State Univ 13 Prospect Pobedy Orenburg 460018 Russia

ISBN: (纸本)9783030391621;9783030391614

The study proposed a neural network approach to solving the problem of predicting the results of mastering the educational programs of online learning. The need for prediction is an important component of the decision support system for the intelligent management of the educational process. The proposed approach allows you to make a learning outcomes prediction of the for each student and, if necessary, to dynamically adjust its educational trajectory promptly. The results of the computational experiment to solve the problem of predicting the outcome based on the input data using a multilayer perceptron are presented. The research materials can be used in the design and creation of information systems in which the personalization of the learning process and the automation of the formation process of individual educational trajectories are provided.

关键词： Neural network technologies Artificial neural network Supervised learning Prediction Online learning automated learning Individual educational trajectory

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Industrial Control intelligent Operation and Maintenance System Based on Zigbee Wireless Communication Technology

Industrial Control Intelligent Operation and Maintenance Sys...

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international conference on Artificial Intelligence and Advanced Manufacturing (AIAM)

作者： Jiaqi Fan College of Electronic and Information Southwest Minzu University Chengdu Sichuan China

ISBN: (纸本)9781665417334

In order to solve a series of problems of the existing industrial control equipment based on the traditional operation and maintenance system, based on Zigbee networking technology, artificial intelligence and heartbeat detection technology, a set of intelligent operation and maintenance management system of automated industrial control system has been constructed. Through the mutual detection of equipment network and hardware, data analysis and machine learning, the shortcomings of traditional operation and maintenance systems are supplemented, and the cost of manpower and material resources for maintaining equipment and testing equipment operation status is reduced. Improve the reliability of industrial automation operation and maintenance.

关键词： Wireless communication Costs Heart beat Industrial control Zigbee Maintenance engineering Servers

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Skin lesion classification using deep learning and image processing 3

Skin lesion classification using deep learning and image pro...

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3rd international conference on intelligent Sustainable Systems, ICISS 2020

作者： Jibhakate, Atharva Parnerkar, Pranav Mondal, Sahil Bharambe, Vastav Mantri, Shamla Dr. Vishwanath Karad MIT World Peace University Department of Computer Engineering and Technology Pune India Symbiosis Skills and Professional University Department of Mechatronics Engineering Pune India

ISBN: (纸本)9781728170893

Skin Cancer is the most common (accounting for 40% of cancer cases globally) and potentially life-threatening type of cancers. It was diagnosed in about 5.6 million individuals last year. automated classification of skin lesions through images has been a challenge throughout the years because of fine variability in their appearance. Deep learning techniques exhibit potential in tackling fine-margined image-based analysis and manage to provide accurate results. The three modelling stages include data collection and augmentation, model architecture and finally prediction into 7 different types of skin cancer namely actinic keratoses, basal cell carcinoma, benign keratosis-like lesions, dermatofibroma, melanoma, melanocytic nevi and vascular lesions. A Convolutional Neural Network was fabricated (using TensorFlow) obtaining an accuracy of 81.24%. Further Transfer learning Approach was implemented in PyTorch, which yielded accuracies of 96.40%, 98.20%, 98.70% and 99.04% respectively for Wide Resnet101, Resnet50, Densenet121 and VGG19 with batch normalization, which are all trained end-to-end from images directly, to proliferate the scalability of these models and curtail initial diagnostic costs. The aim of this research paper is to render non-invasive skin cancer screening a common norm, making it simpler. © 2020 IEEE.

关键词： Convolutional neural networks

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21st international conference on intelligent data engineering and automated learning

作者： Pereira, Danilo Santos, Flavio Matos, Leonardo N. Novais, Paulo Zanchettin, Cleber Ludermir, Teresa B. Univ Fed Pernambuco Recife PE Brazil Univ Fed Sergipe Sao Cristovao SE Brazil Univ Minho Braga Portugal

ISBN: (纸本)9783030623647;9783030623654

Knowledge transfer is the task of transferring the knowledge learned by a model A to a new model B. This task is essential in Deep learning, since there are complex models with excellent results, but computationally costly to be executed. The Similarity Knowledge Transfer (SKT) method proposes an approach to transfer the knowledge layer-by-layer between a donor model and a receiver model. This transfer is carried out through the representations learned by the layers from the teacher model. Despite presenting good results, the SKT method proposes just a way to transfer knowledge between two models. Therefore, this work presents the Similarity Knowledge Transfer Ensemble (SKTE) method, a generic form of SKT that allows the transfer from several teachers to a single student model. We carried out experiments with the CIFAR10 benchmark, where the results obtained showed promising results in this activity.

关键词： Knowledge transfer Ensembles Deep learning

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Anticipating Maintenance in Telecom Installation Processes 21st

Anticipating Maintenance in Telecom Installation Processes

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21st international conference on intelligent data engineering and automated learning

作者： Costa, Diana Pereira, Carlos Peixoto, Hugo Machado, Jose Univ Minho Campus Gualtar P-4710 Braga Portugal NOS Senhora Da Hora Portugal Univ Minho Algoritmi Ctr Campus Gualtar P-4710 Braga Portugal

ISBN: (纸本)9783030623647;9783030623654

Improving customer experience is crucial in any industry, especially in telecommunications, where competition is a constant factor. Today, all telecommunications companies rely on the massive amount of data generated daily to get to know the customer or study their behavior and thus create new effective strategies for their business. Within the most varied user experiences, the process of installing new services can be an event that raises doubts about their operation, degrade the user experience, or, in extreme cases, lead to maintenance interventions. Therefore, the use of advanced predictive models that can predict such occurrences become vital. With this, the company can anticipate the cases that will be problematic and reduce the number of negative experiences. The main objective of this work is to create a predictive model that, through all the available data history, can predict which customers will contact the customer service with problems derived from the installation process and have a following maintenance intervention. After analyzing an unbalanced dataset with approximately 560K entries from a Portuguese telecommunications company, and resorting to the CRISPDM methodology for modeling, the best results were found with Light-GBM which obtained an AUPRC of 0.11 and AUROC of 0.62. The best trade-off between precision and recall was found with a threshold model of 0.43 in order to maximize recall while still avoiding a large number of false negatives.

关键词： Customer data mining Installation Machine learning Predict Service Telecommunications

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