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Identifying Water Consumption Patterns in Education Buildings Before, During and After COVID-19 Lockdown Periods 7

Identifying Water Consumption Patterns in Education Building...

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7th IEEE international conference on smart computing (smartCOMP)

作者： Zecchini, Marco Griesi, Alessandra Anna Chatzigiannakis, Ioannis Amaxilatis, Dimitrios Akrivopoulos, Orestis Sapienza Univ Rome Rome Italy Spark Works Ltd Galway Ireland

ISBN: (纸本)9781665412520

Adopting modern emerging technologies of smart metering based on IoT that extend existing water distribution systems is of paramount importance for improving the operational efficiency of the water supply system in the cities and rural communities. Real-world data collected by smart water meters allows the analysis of consumption of water. In this work, data collected over a period of 28 months is analyzed on weekly, daily and hourly basis to identify usage patterns in relation to the restrictions imposed by the local authorities as a response to the COVID-19 emergency taking place during 2020 and 2021. The evaluation of water consumption before, during and after the lockdown periods highlights the impact of human actions. A novel data-driven method is presented to profile buildings that can help identify similarities and highlight differences across water usage patterns.

关键词： smart Water Grid smart Water Meters IoT COVID-19 Usage analysis Usage Profiling

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Scalable Remote Cloud data Center for Vessel Equipment Predictive Maintenance Service-as-a-Product (SaaP) 27

Scalable Remote Cloud Data Center for Vessel Equipment Predi...

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27th international conference on Knowledge Based and intelligent Information and Engineering Sytems, KES 2023

作者： Chit, Tan Wei Toro, Carlos Lim, Ho Choon Muthu, Raguram 3 CleanTech Loop 01-01 CleanTech Two Singapore637143 Singapore NTT Data Europe and Latam Santiago De Chile Chile

Predictive maintenance is gradually replacing conventional preventive maintenance, through an informed decision-making process for a fleet operator to proactively monitor the health status of the sea-based equipment and machinery. As a result, the fleet operator will only need to ask for on-time repair and maintenance services instead of periodic maintenance, which saves both time and cost significantly. While moving into this new maritime business model, which is also known as Service-as-a-Product (SaaP), both fleet operators and maintenance, repair, and overhaul (MRO) companies could also build a stronger collaboration in the maritime industries. With the advent of digital transformation towards Industry 4.0 (I4.0), the Industrial Internet-of-Things (IIoT) renders the massive collection of operational and machinery process data from the vessel equipment through sensorization, where these collected real-time information are useful for advanced analytics to predict equipment failure and to avoid unplanned downtime. Unlike other industries, there are a few challenges in the maritime industries when developing a centralized smart vessel equipment monitoring platform. One of the key challenges, is the lack of a feasible data management system, requires the centralized host must be able to handle and manage the telemetry IoT data that is transmitted over the satellite communication, by complying with all cybersecurity considerations and regulations in maritime sector. In this paper, we present a scalable, hybrid cloud-based data management framework that can connect to multiple edge systems where each system is deployed on a physical vessel, to establish a scalable SaaP business model. Hence, the ship-to-shore sensorized data can be processed and monitored via a real-time visualization dashboard through the centralized cloud-based platform. We argue that our approach, would be a turn-key solution that can be implemented for mostly all types of marine equipment and machi

关键词： data Management System Industry 4.0 applications Serverless computing Service-as-a-Product (SaaP)

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Design and smart Audit System Using PSO Algorithm

Design and Smart Audit System Using PSO Algorithm

引用

data analytics, computing and Artificial Intelligence (ICDACAI), international conference on

作者： Yiqiang Wu Tianbo Sun Da Chang State Grid Shanghai Electric Power Company Shanghai China Beijing Xintong Accenture Information Technology Co. Ltd. Beijing China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

Internal audit is an important function of enterprises, and its effective operation is directly related to the healthy development of enterprises. In the network environment, the process of enterprise informatisation is accelerating, and the internal audit of enterprises can face more and more complex multi-source data. In such an environment, the traditional audit mode can no longer meet the needs of the new situation. Using a variety of deep learning methods such as web crawler, visual analysis, recurrent neural network natural language processing, convolutional neural network image processing, etc., it can establish an intelligent auditing system, break the information barrier and data barrier of management, assets, business and other aspects, quickly and accurately locate and describe the problems found by auditing, do the most basic work in the auditing business, improve the efficiency of business processing, and reduce the number of Auditor's mechanical man-hours, which is an inevitable trend. In the field of auditing, traditional methods face challenges in handling big data and complex calculations. This study designed an intelligent audit system using the Particle Swarm Optimization (PSO) algorithm, which can automate data analysis and risk assessment. By combining audit knowledge and machine learning techniques, it not only improve the efficiency of auditing, but also enhance the accuracy of decision-making. Research has shown that PSO optimization methods are more accurate in identifying abnormal projects. This new audit system has important practical and theoretical value in improving audit quality and efficiency.

关键词： Visualization Machine learning algorithms Accuracy data analysis Recurrent neural networks Big data Risk management Particle swarm optimization Faces Business

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HealthX: smart Health Record Management System with Speech Input

HealthX: Smart Health Record Management System with Speech I...

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Advanced computing Technologies (ICoACT), international conference on

作者： Sachin Ware Chudaman Sukte Swati Patil Tarun Patil Shreya Keripale Yuvraj Nikam Information Technology B.R.A.C.T's Vishwakarma Institute Of Information Technology Pune India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

Maintaining personal health records is an essential aspect of modern healthcare, this helps ensure accessibility, efficiency, and solutions that need to be provided. The entire study will enable HealthX, an intelligent health document management system, to manage and store health data using voice commands. The platform engages patients and healthcare providers with the voice-to-text feature-file uploads by how they manage medical records including data entry by voice and usability improvement. Searching through the catalogue of doctors, scheduling an appointment, accessing first-aid information, and securely storing patient history data are the other interesting features. In this case, the system allows digital literacy to be accessed by users with multiple skill levels. The system underwent evaluation by 500 users under different conditions. During this process, the system was found to be 99% accurate in quiet environments, with performance decreasing in noisy settings and strong accents. To address the issue of data security and privacy, HealthX nominates access based on the user roles that are currently defined. Those roles allow patients to view their records, healthcare providers to access only authorized data, and governmental authorities to analyze anonymized data for public health policy development globally. In this way, data safety is guaranteed by accompanying rules and norms by HIPAA, GDPR, and NDHM. The project has its foundation in MongoDB, Express, and ***. With the coming of other updates, technologies like speech recognition, wearable device support, and AI-powered analytics for real-time health monitoring, multilingual support, and remote patient care will be the main focus of HealthX, which will thus become an intelligent, secure and patient-centric healthcare solution.

关键词： smart healthcare Medical services Speech recognition Real-time systems Safety History Noise measurement Wearable devices Usability Public healthcare

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Leveraging IoT and Machine Learning for intelligent Water Quality Monitoring Systems

Leveraging IoT and Machine Learning for Intelligent Water Qu...

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intelligent Control, computing and Communications (IC3), international conference on

作者： Gaurav Baranwal Kaushtubh Singh Ayush Gupta Harsh N Tiwari Tukur Gupta Department of Electronics and communication Engineering Ajay Kumar Garg Enginnering College Ghaziabad India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Water quality monitoring is a crucial aspect of public health, particularly in mitigating the risks associated with waterborne diseases and ensuring safe drinking water. Traditional monitoring methods are often labor-intensive, costly, and lack real-time capabilities, making them unsuitable for widespread and rural deployment. This paper examines the integration of advanced technologies, specifically the Internet of Things (IoT) and Machine Learning (ML), to design a sophisticated, low-cost water quality monitoring system. Leveraging an array of sensors, including pH, turbidity, temperature, total dissolved solids (TDS), and dissolved oxygen (DO), combined with the advanced embedded processor, the system ensures seamless data acquisition and processing. IoT enables continuous remote monitoring, while ML algorithms provide predictive analytics and intelligent anomaly detection to identify potential contaminants and trends. These value-driven solutions underscore the transformative potential of advanced technologies in revolutionizing water quality assessment. This review article carefully reviews the available literature on water quality assessment in order to lay out an assortment of conclusions on the concerns, problems, challenges, and research gaps that have emerged during the last few years. Research gaps are identified by thoroughly examining the previously suggested methodologies. The study presented in this article primarily identified the problems with the accuracy of the models, the generation of data collection mechanisms, and the types of data that were used in the suggested frameworks. Subsequently, research avenues towards smart water quality monitoring are offered by examining key concepts crucial to the expansion of this field of research.

关键词： Temperature sensors Machine learning Turbidity Water pollution Sensor systems Internet of Things Monitoring Water resources intelligent sensors Sensor arrays

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A Task Offloading Method for smart Instruments Based on Edge computing 7

A Task Offloading Method for Smart Instruments Based on Edge...

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7th international conference on Computer and Information Processing Technology, ISCIPT 2022

作者： Liu, Yang Zhang, Tianshi Xiao, Jinchao Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110000 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

The development of edge computing technology has enabled the promotion of traditional industrial instruments to smart instruments with collaborative working capabilities. smart instruments are widely distributed in industrial production sites and can monitor working conditions in real time. Implementing multi-instruments collaborative computing on the edge side will help improve the responsiveness of industrial systems and reduce instrument maintenance costs. This paper proposes a task offloading method for smart instruments based on edge computing. Firstly, the instrument capability is evaluated based on the characteristics of the smart instrument. Then, the task scheduling of smart instruments is optimized based on Lyapunov algorithm. Finally the simulation verification is carried out. The experimental results show that the algorithm in this paper has a significant effect on improving computing power and reducing resource occupancy. © 2022 Copyright for this paper by its authors.

关键词： Edge computing

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Design and Implementation of intelligent Transportation System Based on Big data

Design and Implementation of Intelligent Transportation Syst...

引用

Computers, Information Processing and Advanced Education (CIPAE), 2020 international conference on

作者： Lingxue Zhang Jilin Communication Polytechnic College of Traffic Information Changchun China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

The rise of big data technology brings new development opportunities for intelligent transportation systems. This paper introduces the design and implementation of an intelligent transportation system based on big data. The system architecture consists of data layer, computing layer, service layer, and application layer. It integrates heterogeneous traffic data through data fusion module, utilizes machine learning algorithms for traffic flow analysis, road condition prediction, etc., and provides decision support services. In terms of implementation, it elaborates on development environment configuration, key algorithm implementation, and system testing. Finally, taking Beijing as an example, it demonstrates the application cases of using the system for traffic status evaluation, future road condition prediction, and traffic management decision-making. Practice proves that big data-driven intelligent transportation systems help improve urban traffic operation efficiency and are an important part of building smart cities.

关键词： Machine learning algorithms smart cities Roads Buildings data integration Transportation Systems architecture Big data Timing intelligent transportation systems

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smart Farming

Smart Farming

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international conference on Recent Trends in Machine Learning, IOT, smart Cities and Applications, ICMISC 2020

作者： Sardal, Nihar Patel, Ankit Sawant, Vinaya Department of Information Technology Dwarkadas J. Sanghvi College of Engineering Mumbai India

ISBN: (纸本)9789811572333

Agriculture plays a vital role in the development of an agricultural country like India. As the population soars from today’s 1.3 billion to an estimated 2 billion by 2050, the demand for food is expected to more than double. In an agricultural process, the farmer aims to achieve increased yield at the least cost. The number of factors affecting the farm is high which complicates the decision-making process. The proposed system aims to assist farmers in selecting the crop for cultivation using sensor data collected from the field (Shirsath et al. in 2017 international conference on intelligent computing and control (I2C2), Coimbatore, pp 1–5 2017 [1]). Sensors are connected to the Cloud using IoT. analytics is performed on the real-time data stored in the cloud to analyze sensor data and identify any outliers. In the cloud, Machine Learning based real-time analytics is performed to analyze sensor data and identify any outliers. This system uses Machine Learning and IoT to develop an intelligent and affordable farming product. The techniques incorporated within this system improve the precision of the result and automate crop monitoring thus reducing human involvement. Real-time data collected can be utilized to predict disease using machine learning algorithms. The real-time update will alert the farmer by indicating which crop is in trouble, so the expenses on insecticides, pesticides will reduce. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Crops

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The smart BEAR Project: An Overview of Its Infrastructure 7th

The SMART BEAR Project: An Overview of Its Infrastructure

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7th and 8th international conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2021 and ICT4AWE 2022

作者： Su, Qiqi Peretokin, Vadim Basdekis, Ioannis Kouris, Ioannis Maggesi, Jonatan Sicuranza, Mario Acebes, Alberto Bucur, Anca Mukkala, Vinod Jaswanth Roy Pozdniakov, Konstantin Kloukinas, Christos Koutsouris, Dimitrios D. Iliadou, Elefteria Leontsinis, Ioannis Gallo, Luigi De Pietro, Giuseppe Spanoudakis, George Department of Computer Science City University of London London United Kingdom Philips Research Eindhoven Netherlands SPHYNX Technology Solutions AG Zug Switzerland Biomedical Engineering Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Computer Science Department Università degli Studi di Milano Milan Italy Institute for High-Performance Computing and Networking National Research Council of Italy ICAR - CNR Naples Italy Atos Research and Innovation Madrid Spain 1st Otolaryngology University Department National and Kapodistrian University of Athens Athens Greece 1st Cardiology Clinic Medical School National and Kapodistrian University of Athens Athens Greece

ISBN: (纸本)9783031374951

The paper describes a cloud-based platform that utilizes Artificial Intelligence (AI) and Explainable AI techniques to deliver evidence-based, personalized interventions to individuals over 65 suffering or at risk of hearing loss, cardiovascular disease, cognitive impairments, balance disorders, or mental health issues, while supporting efficient remote monitoring and clinician-driven guidance. As part of the smart BEAR integrated project, this platform has been developed to support its large-scale clinical trials. The platform consists of a standards-based data harmonization and management layer, as well as a security component, a Big data analytics system, a Clinical Decision Support system, and a dashboard component to facilitate efficient data collection across pilot sites. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Artificial intelligence

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smart IoT-integrated weather analytics system

Smart IoT-integrated weather analytics system

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international conference on Green Energy, computing and intelligent Technology 2024 (GEn-CITy 2024)

作者： Lim Shi Xun Suresh Merugu Himani Maheshwari Department of Computer Science School of Computer Science University of Southampton Malaysia 79100 Iskandar Puteri Johor Malaysia School of Computing Graphic Era Hill University Dehradun India

ISBN: (数字)9781837242863

This paper proposes an IoT-based weather monitoring system utilizing Raspberry Pi 4 Model B as the core component to collect extensive weather data, including ambient temperature, humidity, atmospheric pressure, altitude, rainwater, ultraviolet rays, and ambient light intensity, utilizing appropriate sensors. A contemporary, user-friendly website serves as the interface, offering functionalities such as real-time data visualization and email notifications. The paper begins with an overview of the IoT and weather monitoring domains to underscore their importance and the necessity for advancements in these fields. A comprehensive literature review is conducted to identify trends, achievements, and existing gaps in previous IoT-based weather monitoring research. Following this, a detailed design strategy is outlined, covering all development phases from initial planning to final execution. This design framework is applied during the implementation phase, where the system's development and testing processes are meticulously recorded and analyzed. The results section provides an in-depth analysis of the collected data and evaluates the system's performance, demonstrating its effectiveness and reliability. The paper concludes with a summary of key findings, discussing the contributions to the field, identifying limitations, and proposing recommendations for future improvements and extensions to enhance the system's capabilities, thus addressing the gaps identified in the literature review.

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