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Study of IOT Enabled Water Logging Detection and Control in Drainage System for smart City

Study of IOT Enabled Water Logging Detection and Control in ...

引用

Big data analytics in Bioinformatics (DABCon), international conference on

作者： Subhalaxmi Chakraborty Ankan Sarkar Gourab Das Shreya Chatterjee Shamik Kanjilal Pritam Kundu Department of CST & CSIT Institute of Engineering and Management (IEM) University of Engineering and Management Kolkata India Computer Science and Technology University of Engineering and Management Kolkata Kolkata India Computer Science and Information Technology University of Engineering and Management Kolkata Kolkata India AI.Cloud (AWS) Tata Consultancy Services Limited Kolkata India

ISBN: (数字)9798350378047

ISBN: (纸本)9798350378054

The intelligent Urban Drainage System (IUDS) is a cutting-edge infrastructure designed for smart cities to efficiently manage stormwater runoff, wastewater, and flood control, utilizing the ESP32 controller board with Wi-Fi and Bluetooth connectivity to control and monitor the system. The Blynk server cloud platform enables remote monitoring and data analysis, providing real-time insights into system performance. The IUDS features a suite of sensors, and MQ2 natural gas and smoke detector sensor, which transmit data to the ESP32 board, activating the servo motor actuator to control drainage gates, valves, and pumps, ensuring optimal water flow, and minimizing flood risk. The automation control of the system and monitoring capabilities enable real-time water level monitoring and predictive maintenance, optimized drainage performance and reduced flood risk, enhanced water quality and environmental sustainability, and remote monitoring and data analysis through the Blynk server cloud platform, making the IUDS a resilient, efficient, and adaptable drainage system for smart cities, leveraging advanced technologies and IoT connectivity to create a sustainable and futuristic urban infrastructure.

关键词： Cloud computing data analysis smart cities Sensor phenomena and characterization Real-time systems Servers Floods Internet of Things Remote monitoring Gas detectors

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Distributed Ledger Model for Agriculture Product Supply Chain

Distributed Ledger Model for Agriculture Product Supply Chai...

引用

international KES conference on Human Centred intelligent Systems, KES HCIS 2022

作者： Rahman, M. Nordin A. Kadir, M. Fadzil A. Awang, M. Khalid Safei, Suhailan Saany, Syarilla Iryani A. Kamarudin, A. Syazni Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Besut Campus Terengganu Besut22200 Malaysia Faculty of Bioresources and Food Innovation Universiti Sultan Zainal Abidin Besut Campus Terengganu Besut22200 Malaysia

ISBN: (纸本)9789811934544

computing technology solutions are increasingly being applied to agricultural product supply chains. Information sharing and analytics, market access, finance access and product tracking are the common applications developed in smart agriculture systems. Normally, agriculture product data is stored in a centralized database managed by government agencies and the integrity of data is not audited properly. In collaborative digital transaction, data validity is the main concern in product supply chain. This article proposes a disruptive approach in managing agriculture product supply chain. The integration of digital information management platform and Blockchain technology is applied in developing a secured distributed ledger for managing the transaction flows of agriculture product chain. The integrated model is presented in structured and formal approach. The Blockchain platform and its distributed ledger framework are discussed in details. With this condition, all information regarding to agriculture product are trusted, low risk of fraud, removes marketing overhead and time saving. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Supply chains

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AI-Based Energy Consumptions Predictions in smart Grids

AI-Based Energy Consumptions Predictions in Smart Grids

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Artificial Intelligence, Blockchain, Cloud computing, and data analytics (ICoABCD), international conference on

作者： Raissa Raffi Darmawan Jordan Pascal Wijaya Nadia Franz Adeta Junior Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350368451

ISBN: (纸本)9798350368468

smart Grid is an electrical network technology that can regulate electricity supply and maintain its operations. smart Grid can help Indonesia overcome the excess energy supply that occurred as a result of the creation of mega-electricity projects aimed at meeting Indonesia's electricity needs. However, due to COVID-19, electricity demand in Indonesia has decreased, resulting in an excess supply of electrical energy. Therefore, the aim of this research is to optimize existing electrical energy. To be able to eliminate wasted energy and eliminate losses that occur. The methods we use are artificial neural networks, convolutional neural networks, recurrent neural networks, and long short-term memory. The samples we got from the weather dataset from Indonesia in 2018–2020 from Badan Meteorologi, Klimatologi, and Geofisika (BMKG) Indonesia. We use this dataset to be able to consider the dependency between energy use and weather data. Comparison of (ANN), (CNN), (RNN), and (LSTM). The experimental results of the data set we obtained were that the CNN model outperformed any model. Through experiments and research that the method (CNN) reduces overfitting better than other methods. This approach can be proposed to help predict peak demand periods and automatically control smart equipment to reduce consumption and reduce energy consumption costs for users.

关键词： Energy consumption Recurrent neural networks Electricity Computational modeling Predictive models data models smart grids Convolutional neural networks Long short term memory Meteorology

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Developing smart Precision Farming Using Big data and Cloud-Based intelligent Decision Support System

Developing Smart Precision Farming Using Big Data and Cloud-...

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international conference on ICT For smart Society (ICISS)

作者： Aldian Nurcahyo Haryono Soeparno Ford Lumban Gaol Yulyani Arifin Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

This study discusses the development of smart precision farming systems using big data and cloud-based intelligent decision support systems. Big data plays an important role in collecting, storing, and analyzing large amounts of data from various sources related to agriculture, including data from weather stations, soil sensors, satellite imagery, crop yield records, pest and disease reports, and other sources. This study highlights the differences between smart farming and precision farming. This study describes key techniques and system architecture, including data collection, processing, analysis, and decision support components. Utilizing a cloud platform enables scalability and optimized performance, which lowers costs and makes it safer and easier to manage. The integration of big data and Alibaba cloud computing in smart precision farming can improve farming productivity by providing timely information and recommendations to farmers for better decision-making. Finally, the system produces smart precision farming, which provides cost-effective real-time monitoring and predictive analytics to increase agricultural production and sustainability.

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10th international conference on Innovations in Computer Science and Engineering, ICICSE 2022

10th International Conference on Innovations in Computer Sci...

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10th international conference on Innovations in Computer Science and Engineering, ICICSE 2022

ISBN: (纸本)9789811974540

The proceedings contain 62 papers. The special focus in this conference is on Innovations in Computer Science and Engineering. The topics include: Machine Learning-Based Indian Stock Market’s Price Movement Prediction and Trend Analysis;Machine Learning-Based Mortality Prediction of COVID-19 Patients;smart Computer Monitoring System Using Neural Networks;using Deep Learning to Perform Payload Classification;malicious Domain Detection Using Memory Augmented Deep Autoencoder;graph Analysis Using Page Rank Algorithm to Find Influential Users;hate Speech and Offensive Language Detection in Twitter data Using Machine Learning Classifiers;secrecy Rate Optimization for Energy Efficient Cognitive Relay Networks;avian Influenza Prediction Using Machine Learning;An Explainable AI Approach for Diabetes Prediction;prediction and Comparison of Diabetes with Logistic Regression, Naïve Bayes, Random Forest, and Support Vector Machine;bone Cancer Detection Using Deep Learning;energy and Buffer Size-Based Routing Protocol for Internet of Things;v-Shaped Binary Version of Whale Optimization Algorithm for Feature Selection Problem;an Energy-Efficient Deep Neural Network Model for Photometric Redshift Estimation;deep Learning-Based Diabetic Retinopathy Screening System;artificial Intelligence-Based data analytics Techniques in Medical Imaging;ensuring data Protection Using Machine Learning Integrating with Blockchain Technology;evaluation and Language Training of Multinational Enterprises Employees by Deep Learning in Cloud Manufacturing Resources;development of a Cognitive Question Answering System to Learn Concepts for Placement Assistance;classification of Cervical Cells Using Deep Learning Feature Extraction;cervical Cancer Prediction Using Optimized Meta-Learning;an Ensemble Deep Closest Count and Density Peak Clustering Technique for Intrusion Detection System for Cloud computing;voice-Based intelligent Virtual Assistant for Windows.

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Transfer Learning-Enabled IoT System for Continuous Prediction of Vehicle CO2Concentration 9

Transfer Learning-Enabled IoT System for Continuous Predicti...

引用

9th IEEE international smart Cities conference, ISC2 2023

作者： Al Selek, Mohammad Tena-Gago, David Alcaraz-Calero, Jose M. Wang, Qi School of Computing Engineering & Physical Sciences University of the West of Scotland Paisley United Kingdom

ISBN: (纸本)9798350397758

In this paper, we present the design, implementation, and deployment of an IoT-based system for machine learning (ML)-based real-time prediction of CO2 exhaust concentrations in road vehicles, by means of transfer learning. The system offers a scalable and cost-effective solution for monitoring and predicting CO2 exhaust concentrations from a vehicle fleet. Moreover, the system is specifically designed to be installed in a low-powered microcontroller unit (MCU) to collect and process data from a CO2 sensor, as well as to perform ML-based emission prediction without a sensor. To accomplish this, we have developed an artificial neural network (ANN) model able to generate training labels to further train subsequent ANN models via transfer learning mechanisms. The resulting models are sent to the IoT devices installed in the vehicle to perform the on-board predictions. Empirical experiments show very promising results in terms of high prediction accuracy and satisfactory transfer learning loop execution time. © 2023 IEEE.

关键词： Carbon dioxide

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An Efficient intelligent Edge Diagnosis System Based on WDCNN in the Application of Equipment Fault Classification 3

An Efficient Intelligent Edge Diagnosis System Based on WDCN...

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3rd international conference on Sensing, Measurement and data analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Wu, Qihang Ding, Xiaoxi He, Qingbo Xiang, Hao Tan, Huaping Shao, Yimin College of Mechanical and Vehicle Engineering Chongqing University Chongqing400044 China School of Mechanical and Power Engineering Shanghai Jiao Tong University Shanghai200240 China Chongqing University State Key Laboratory of Mechanical Transmission Chongqing400044 China

ISBN: (纸本)9781665492812

Edge computing has been extensively used in industries to solve problems such as data explosion. However, in the field of mechanical equipment fault diagnosis, there are still problems such as the large amount of data transmission and storage and the difficulty of applying intelligent algorithms to equipment edge fault diagnosis. Therefore, an intelligent edge diagnosis system (IEDS) is designed and applied to mechanical equipment fault diagnosis, which includes an edge diagnosis unit (EDU) and an edge network model. The EDU uses the STM32F405 chip as the core and acquires vibration signals by MEMS acceleration sensor. Using Deep convolutional neural networks with wide first-layer kernels (WDCNN) as the edge network model. Due to the limitation of hardware resources, this paper uses a fixed-point quantification method to minimize the hardware resource usage of WDCNN. The proposed method can efficiently implement the edge fault diagnosis of mechanical equipment, which is reflected by the fact that IEDS can be deployed directly at the edge of equipment to accomplish efficient data reduction and accurate fault diagnosis results output. The proposed method provides a new solution for intelligent fault diagnosis of mechanical equipment. © 2022 IEEE.

关键词： deep learning edge computing Equipment fault classification intelligent diagnosis

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Construction of smart campus under the background of big data

Construction of smart campus under the background of big dat...

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international conference on Application of intelligent Systems in Multi-modal Information analytics, MMIA 2020

作者： Feng, Peilu Hohhot Minzu College Hohhot010051 China

ISBN: (纸本)9783030515553

Since the beginning of the 21st century, the Internet has not only been widely used in various industrial networks, but has gradually extended to every aspect of people’s lives. In recent years, the Internet technology based on big data has been extended to campus, which has put forward higher requirements for the construction of smart campus. Cloud computing and big data technology are typical representatives of information operation efficiency, and new technologies emerge one after another. This paper is based on big data and cloud computing, and carries out research on the construction of smart campus. The purpose of this paper is to analyze the construction strategy of smart campus service system in the context of big data, and to analyze and apply big data technology from a deeper level to provide support for expanding the application space of big data. Based on the comprehensive comparison of existing research results, this paper proposes a smart campus system model based on big data analysis to improve the efficiency of information operation in campus. The experimental results of this paper show that this model has a strong ability to store and process data, and can fully realize the sharing and construction of resources in the smart campus. © Springer Nature Switzerland AG 2021.

关键词： Big data

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Cloud-Based Perspective for intelligent Transportation and Crime Prevention: A Web Deployment Solution

Cloud-Based Perspective for Intelligent Transportation and C...

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international conference on Computer, Information and Telecommunication Systems (CITS)

作者： Denis Nedeljkovic Manar Jammal School of IT York University Toronto Canada

ISBN: (数字)9798350359091

ISBN: (纸本)9798350359107

In the cutting edge domain that is the ever so expanding smart city, the need for development tools that can keep up with such an urban environment is ever so prevalent. Moreover, the rapid expansion of such metropolitan areas requires even more specialists than are currently available. This study presents a pioneering cloud-based solution for intelligent transportation and crime prevention, emphasizing the seamless integration of machine learning techniques within a web deploy-ment framework. Utilizing data from sources like Automated Speed Enforcement, police crime statistics, and traffic moni-toring programs, our approach employs advanced predictive analytics to accurately identify potential crime hotspots and optimize traffic management. A significant innovation of this research is the development of a scalable Software as a Service model, which allows for the effective predictions of traffic sensors across urban settings. The proposed system features a user-friendly graphical user interface and employs Dynamic Load Balancing to enhance computational efficiency, making it accessible to a wide range of users. By harnessing cloud computing, our solution offers a versatility for government officials, law enforcement, and researchers, promising improvements in road safety, crime prevention, and the overall quality of life.

关键词： Cloud computing Law enforcement smart cities Prevention and mitigation Software as a service Transportation Machine learning

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Forecasting the potential influence of Covid-19 using data Science and analytics

Forecasting the potential influence of Covid-19 using Data S...

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2021 IEEE international conference on Innovative computing, intelligent Communication and smart Electrical Systems, ICSES 2021

作者： Murale, Monish Devi, N. Gokul, Ar Guru Leela Rani, P. Navishvardanaa, S. Sri Venkateswara College of Engineering Department of Information Technology Sriperumbudur India

ISBN: (纸本)9781665435215

The major purpose of the study topic is to use data science to anticipate the future effect of COVID-19 using existing data. The goal of this research is to use data science and analytics to generate precise forecasts of the number of substantiations and deaths. LSTM, GRUs, and Prophet are the major models created and tested for the solution. An LSTM model is a type of Recurrent Neural Network that is used to forecast datasets with increasingly changing patterns. Gated recurrent units only has two gateways: reboot and update. The prophet is best suited for forecasting assignments involving observation swith at least a year of history. The various models discussed above were used to the covid-19 data set to forecast the number of positive cases, active cases, and deaths associated with covid-19. We trained the model using data from April and May 2021 to demonstrate a comparison between the observed and expected number of positive events. To assume the future happing of COVID-19 by applying models which are in use, so that we will be able to calculate the impact of the disease's potential spread throughout the human being, preparing our selves to make proper planning and idea to prevent further transmission and equip health systems to manage the disease properly and battle the worldwide pandemic. © 2021 IEEE.

关键词： Speech recognition

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