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Renewal of Cloud-Based Services in smart Grids

Renewal of Cloud-Based Services in Smart Grids

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international conference on Power Energy, Environment and intelligent Control (PEEIC)

作者： Alisha Sikri Gobi N Devendra Kumar Doda Department of Computer Science and Engineering (AIML) Noida Institute of Engineering & Technology Greater Noida Uttar Pradesh India Department of Computer Science Jain (Deemed to be University) Bangalore India Department of Electrical Engineering Vivekananda Global University Jaipur India

Scalable solutions are required to handle fluctuating workloads in modern distribution grids in order to manage the flood of data from many sources, execute dynamic algorithms as needed, and effectively operate sparse actuators. In order to meet these needs, cloud computing technologies have surfaced as a suitable answer, facilitating effective data sharing and interchange. With semantic information integration playing a crucial role, this is especially important for fully exploiting the advantages of cloud computing in enabling sophisticated analytics and mining for smart grids. In order to analyze grid data for power distribution, this essay proposes integrating the semantic web into a cloud-based research platform. The idea recommends using a service restoration technique to reenergize loads after an electrical failure. This presentation is implemented using FIWARE, a platform composed of modular, open-source building pieces designed for future internet services and applications. Moreover, the SARGON ontology-which was created especially for the energy domain-is used here. We test the integration of semantic data into the cloud-based service restoration application using the IEC61850 data model. This integration provides an access to the distribution grid's field devices. The platform's performance test, measured in terms of computing time and network latency, validates the viability of the suggested solution. The results of these experiments provide as a standard against which to measure the performance of smart energy platforms in the future, therefore advancing the field.

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ML Techniques for Employee Performance Prediction

ML Techniques for Employee Performance Prediction

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Innovative computing, intelligent Communication and smart Electrical Systems (ICSES), international conference on

作者： Brajesh Kumar Prateek Agrawal Dipesh Uike Melanie Lourens Indira Bhati M. Chandrakumar Peter Department of Computer Science & Engineering Dr. APJ Abdul Kalam Women's Institute of Technology Darbhanga Bihar Gurukula Kangri (Deemed to be University) Dr. Ambedkar Institute of management studies and research Nagpur Maharashtra Faculty of Management Sciences Durban University of Technology South Africa Department of Management MMIM Maharishi Markandeshwar (Deemed to be University) Department of Software Engineering PeriyarManiammai Institute of Science and Technology Thanjavur Tamilnadu

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

When it comes to maximizing the effectiveness of a business and promoting professional growth, employee performance prediction is an extremely important factor. This research article investigates the use of machine learning (ML) approaches to forecast employee performance, with a particular emphasis on the incorporation of different algorithms from different sources to improve accuracy and dependability. Methods that have been used for a long time, such as linear regression and decision trees, are evaluated alongside more recent approaches, such as ensemble methods and deep learning. To refine the prediction models, our research makes use of approaches such as feature selection and dimensionality reduction. These techniques are utilized by utilizing previous performance data, demographic information, and behavioral indicators. It has been demonstrated through the analysis that sophisticated machine learning techniques, in particular ensemble and deep learning models, are superior to traditional methods when it comes to anticipating employee performance. Our findings offer HR departments concrete insights that can be used to adopt data-driven methods for performance management, which will eventually contribute to improved organizational outcomes and increased employee satisfaction. This paper offers opportunities for future development in predictive analytics within the field of human resources and emphasizes the potential of machine learning to alter performance evaluation systems.

关键词： Deep learning Performance evaluation Accuracy Machine learning algorithms Linear regression Predictive models Prediction algorithms Feature extraction Ensemble learning Predictive analytics

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Securing FIWARE with TEE Technology 22

Securing FIWARE with TEE Technology

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22nd international conference on New Trends in intelligent Software Methodologies, Tools and Techniques, SoMeT 2023

作者： Coppolino, Luigi Nardone, Roberto Petruolo, Alfredo Romano, Luigi University of Naples 'Parthenope' Isola C4 Centro Direzionale Naples80143 Italy Trust UP Via Francesco Petrarca 80 Naples80122 Italy ICAR-CNR Via Pietro Castellino 111 Naples80131 Italy

ISBN: (纸本)9781643684307

An important objective being pursued by the European Commission is the establishment of a unified data market where stakeholders can safely and confidently share and exchange data in standardized formats. This trend is supported by numerous initiatives, promoting the creation of European Common data spaces, and it is already in full swing in several sectors, such as energy and health. Among the many initiatives for building common data spaces, FIWARE appears to be one of the most promising. FIWARE promotes the use of Digital Twin technology to build distributed infrastructures for facilitating real-Time data sharing in collaborative environments. By fostering an open and collaborative approach to software development and providing several building blocks of IT architectures for a number of domains (specifically: smart AgriFood, smart Cities, smart Energy, smart Industry, and smart Water), FIWARE facilitates the creation of Digital Twins of real-world Industry 4.0 setups in a shared data space, which is typically hosted in the cloud. This paper addresses the security issues in a typical functional FIWARE architecture and provides a detailed description of a reference solution which ensures data confidentiality and integrity throughout the data life cycle, i.e. from the generation to the consumption phase. The proposed solution strongly relies on Commercial Off The Shelf Trusted Execution Environment technologies (namely: Intel SGX and Arm TrustZone) to provide effective protection of data-in-use. Protection of data-At-rest and data-in-Transit is achieved by means of advanced cryptographic techniques and secure communication protocols, respectively. © 2023 IOS Press. All rights reserved.

关键词： Cloud computing architecture

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CNN-Based Emotional Stress Classification using smart Learning dataset 15

CNN-Based Emotional Stress Classification using Smart Learni...

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2022 IEEE Congress on Cybermatics: 15th IEEE international conferences on Internet of Things, iThings 2022, 18th IEEE international conferences on Green computing and Communications, GreenCom 2022, 2022 IEEE international conference on Cyber, Physical and Social computing, CPSCom 2022 and 8th IEEE international conference on smart data, smartdata 2022

作者： Andreas, Andreou Mavromoustakis, Constandinos X. Song, Houbing Batalla, Jordi Mongay Department of Computer Science University of Nicosia University of Nicosia Research Foundation Nicosia Cyprus Security and Optimization for Networked Globe Laboratory Department of Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Daytona Beach FL32114-3900 United States Warsaw University of Technology National Institute of Telecommunications Warsaw Poland

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

smart learning analytics aims to support researchers investigating mental health by improving the interpretation of the datasets acquired from physiological biomarkers. The key enabler for emotional stress classification are Machine Learning (ML) methods in conjunction with Online Transfer Learning (OTL). The knowledge of high-level characteristics at the top layers is obtained through an optimized Convolutional Neural Network (CNN)-based on emotional stress datasets. Nevertheless, the lack of performance in a real-time environment and the temporal patterns of data acquisition complications and their interpretation motivated us to contribute by tackling these concerns. Therefore, we propose an innovative procedure based on the aforementioned orientation through our research work. Considering mining data streams with concept drifts, we enable the ensemble classifiers. For evaluation, we compare the proposed classification, the LIBrary for Large LINEAR classification (LIBLINEAR) and the Deep Belief Network with Transfer Learning (DBNTL) model. Furthermore, we utilized a multimodal dataset of physical and biological characteristics obtained by fifteen individuals during a lab study. Finally, our framework based on the extracted results has presented more accuracy in classifying an individual's sense of stress. Hence, the proposed method achieves higher efficiency than the state-of-the-art models. © 2022 IEEE.

关键词： Classification (of information)

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smarteria: intelligent Cafeteria Using Multimodal data 14th

Smarteria: Intelligent Cafeteria Using Multimodal Data

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14th international conference on Computer Science and its Applications, CSA 2022 and the 16th KIPS international conference on Ubiquitous Information Technologies and Applications, CUTE 2022

作者： Seo, Yuri Sun, Teh-Jen Roh, Seol Choi, In-hun Hong, Gi-dae Jeong, Hong-Ju Jeong, Seung-Woo Hong, Choong Seon Park, Seong-Bae Huh, Eui-Nam Department of Artificial Intelligence Kyung Hee University Yongin-si17104 Korea Republic of Department of Computer Science and Engineering Kyung Hee University Yongin-si17104 Korea Republic of

ISBN: (纸本)9789819912513

Convergence technologies including the Internet of Things, Big data, and Artificial Intelligence can detect and respond to dangerous situations through real-time monitoring. Meanwhile, the cafeteria environment has required safety management as it is exposed to various accident risks. Therefore, we conducted a study that applied multimodal data and artificial intelligence for the real-time safety management of the cafeteria environment. We show that multimodal data can improve accuracy in real-time detection. Thus, combining Artificial Intelligence with a multimodal sensors environment will be effective in various real-time safety managements. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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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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2025 international conference on intelligent Control, computing and Communications, IC3 2025

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

ISBN: (纸本)9798331527495

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. © 2025 IEEE.

关键词： Damage detection

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Development and Risk of smart City

Development and Risk of Smart City

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international conference on Machine Learning and Big data analytics for IoT Security and Privacy, SPIoT 2020

作者： Chen, Yanping Long, Minghui Wuhan Donghu University Hubei China

ISBN: (纸本)9783030627423

smart city is the development direction of future urban construction and the main driving force to promote sustainable urban development. In this paper, starting from the concept of intelligent city, profound interpretation of the wisdom of the city development and technical support. At the same time, in view of the wisdom urban construction existing in the information security risk, weak top-level design and problems of traditional urban management discussion, and explore the corresponding Suggestions and countermeasures, in order to promote the construction of wisdom city better and faster. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： smart city

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smart Drip Irrigation using IoT Enabled Water Management in Agriculture

Smart Drip Irrigation using IoT Enabled Water Management in ...

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Ubiquitous computing and intelligent Information Systems (ICUIS), international conference on

作者： Sankari M Pamidi Sri Sarayu Kamatchi K S Palanki Anjali Saraswathi Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Effective water management is a critical component of sustainable agriculture, particularly in light of escalating water scarcity and climate challenges. This paper presents a modern solution integrating precision irrigation, soil moisture sensors, and real-time data analytics to optimize agricultural water usage. The system ensures significant water savings by delivering water directly to the root zones of crops through drip irrigation, thereby minimizing evaporation and runoff. Real-time soil moisture monitoring facilitates dynamic water distribution, achieving up to 92% irrigation efficiency and reducing water consumption by 50%. Additionally, the system demonstrates a 30% improvement in crop yields, affirming its capability to support sustainable farming practices. By leveraging IoT technologies, machine learning algorithms, and water conservation techniques, this framework offers a robust approach to addressing water management challenges. Field testing confirms that the system is a viable, efficient, and environmentally friendly alternative to traditional irrigation methods, promoting a balance between resource conservation and agricultural productivity.

关键词： Productivity Irrigation Soil moisture Water conservation Ubiquitous computing Real-time systems Water resources Crop yield Monitoring Testing

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The Impact of COVID-19 Experience on smart City and Future Mobility 9th

The Impact of COVID-19 Experience on Smart City and Future ...

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9th international conference on smart Cities and Green ICT Systems, smartGREENS 2020, and 6th international conference on Vehicle Technology and intelligent Transport Systems, VEHITS 2020

作者： Gusikhin, Oleg Global Data Insight and Analytics Ford Motor Company 22001 Michigan Avenue DearbornMI United States

ISBN: (纸本)9783030891695

This paper discusses the challenges and opportunities of the existing technologies and practices in the context of smart City and Future Mobility to cope with pandemics. The overview shows a need to accelerate the deployment of smart City and smart Mobility technologies and revisit strategic supply chain planning models. It shows that advancements in IoT, communication, AI and analytics play a crucial role in enabling rapid innovation in order to address immediate needs raised by COVID-19. The overview and discussion focus on five areas: smart City Technologies, Robust Supply Chain of critical items, smart Mobility, Connected and intelligent Vehicles, and Ambient Intelligence. © 2021, Springer Nature Switzerland AG.

关键词： Supply chains

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Cloud Based intelligent Nutrient Management System for Precision Agriculture Using CNN

Cloud Based Intelligent Nutrient Management System for Preci...

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Sustainable computing and smart Systems (ICSCSS), international conference on

作者： M. Shafiya Banu Arshadh Ariff Mohamed Abuthahir R. Mohandas D. Antony Joseph Rajan B. Meenakshi N. Mohankumar Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai Tamil Nadu India Riazulhameed Architect - Data Engineer Blackstraw Technologies Pvt Ltd Chennai Tamil Nadu India Department of Electronics and Communication Engineering Chenai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai Tamil Nadu India Symbiosis Institute of Technology Symbiosis International (Deemed University) Nagpur Maharashtra India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Precision agriculture struggles with scalability, data integration, and decision-making, prompting innovative solutions. Recent advancements like CNNs and cloud computing provide interesting answers but confront challenges. data quality, processing needs, interoperability, and real-time decision-making are issues. These difficulties demand innovative data synchronization, model dependability, and system scalability methods. This project uses sophisticated analytics and cloud technology to improve nutrient management systems. Improving scalability, accuracy, and efficiency while presenting unique data fusion and user-friendly interfaces are goals. Clear problems and goals guide the study's scope and purpose. Wireless soil nutrient sensors can capture real-time data on important soil factors such as pH levels, the amount of nitrogen, phosphate, and potassium present, and more. This information is then sent to a platform hosted in the cloud, where it is processed using sophisticated data analytics and machine learning algorithms, in addition to previous crop performance data and agronomic models. The program will create tailored suggestions for nutrient management, including topics such as the kinds of fertilizer, application rates, and the best time. These proposals give farmers more agency, allowing them to make educated decisions via an easily navigable interface. Because of the system's ability to react to changing circumstances and its continual monitoring, nutrient management techniques are kept in sync with those changes throughout the crop's life cycle.

关键词： Precision agriculture Cloud computing Scalability Decision making Soil Nutrients Real-time systems

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