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Efficiency and Sustainability in Apartment Management: IoT Leak Detection using data analytics 2

Efficiency and Sustainability in Apartment Management: IoT L...

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2nd international conference on Advancements in smart, Secure and intelligent computing, ASSIC 2024

作者： Raman, Ramakrishnan Datta, Mitul Maharashtra Pune India Maharashtra Pune India

ISBN: (纸本)9798350370188

Reduce Water-related leak damage, lower operational costs, and boost sustainability in apartment complexes, this research explores using Internet of Things (IoT) technology to detect water leaks in management. This technology's real-time monitoring and alerts may quickly fix Leaks. To do this, Internet of Things (IoT) sensors are strategically placed in high-risk areas for water leaks, such as bathrooms and kitchens. This research's importance lies in its many benefits, including the avoidance of repair costs, the protection of buildings and other property, the conservation of water supplies, and the increase in tenant contentment. A more cost-effective and efficient apartment management system may be maintained via early detection and correcting problems. The proactive method is consistent with sustainability standards since it reduces water use. However, factors including sensor placement, false alarm risk, and integration with other systems, privacy concerns, and regular maintenance must be considered. Despite these obstacles, IoT-based water leak detection has a lot of room to grow into a vital instrument in the management of modern residences, benefiting both property and the environment. © 2024 IEEE.

关键词： Internet of things

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AI-Based Energy Consumptions Predictions in smart Grids

AI-Based Energy Consumptions Predictions in Smart Grids

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2024 international conference on Artificial Intelligence, Blockchain, Cloud computing, and data analytics, ICoABCD 2024

作者： Darmawan, Raissa Raffi Wijaya, Jordan Pascal Nadia Junior, Franz Adeta School of Computer Science Bina Nusantara University Computer Science Department Jakarta15143 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350368451

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. © 2024 IEEE.

关键词： Deep Learning Forecasting smart Grid

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Implementation of an intelligence-based framework for anomaly detection on the demand-side of sustainable compressed air systems 5

Implementation of an intelligence-based framework for anomal...

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5th international conference on Industry 4.0 and smart Manufacturing (ISM)

作者： Mallia, Jasmine Francalanza, Emmanuel Xuereb, Peter Borg, Massimo Refalo, Paul Univ Malta Fac Engn Dept Ind & Mfg Engn MSD-2080 Msida Malta Univ Malta Fac Informat & Commun Technol Dept Comp & Informat Syst MSD-2080 Msida Malta

The implementation of intelligent techniques produces good results in automating fault finding and predicting future outcomes. These approaches have been on the increase in the past years, especially so to detect faults within Compressed Air Systems (CASs). With the use of intelligent techniques, one could minimise the manual and time-consuming aspect of CAS maintenance, improve the environmental impact of the system, while minimising downtime. This paper proposes a general framework for the implementation of intelligent analysis techniques within a real-world system. Such an approach has been implemented on the demand-side of a CAS. In literature, no open datasets are available for use by artificial intelligence models. Hence, as part of this research, a fault generating and monitoring system has been connected to an existing production machine in a manufacturing site to collect the required data. Two classification machine learning methods were implemented and compared across a number of performance metrics. Both the general framework, and its implementation, provide a stepping stone in integrating smart systems with real-time intelligent data analytics for the demand-side of a CAS. These systems would provide a sustainable CAS operation through the effective detection of anomalies and their timely repair. (c) 2023 The Authors. Published by ELSEVIER B. V.

关键词： pneumatic system intelligent data analysis classification fault detection and diagnosis sustainability

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ML Techniques for Employee Performance Prediction

ML Techniques for Employee Performance Prediction

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2024 international conference on Innovative computing, intelligent Communication and smart Electrical Systems, ICSES 2024

作者： Kumar, Brajesh Agrawal, Prateek Uike, Dipesh Lourens, Melanie Bhati, Indira Peter, M. Chandrakumar Dr. Apj Abdul Kalam Women's Institute of Technology Department of Computer Science & Engineering Bihar Darbhanga India India Dr. Ambedkar Institute of management studies and research Maharashtra Nagpur India Durban University of Technology Faculty of Management Sciences South Africa Department of Management India PeriyarManiammai Institute of Science and Technology Department of Software Engineering Tamilnadu Thanjavur India

ISBN: (纸本)9798331543617

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. © 2024 IEEE.

关键词： Predictive analytics

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data Mining for Predictive analytics and Optimization of Treatment Plans in Cardiovascular Disease Management using Neural Networks 2

Data Mining for Predictive Analytics and Optimization of Tre...

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2nd international conference on Advancements in smart, Secure and intelligent computing, ASSIC 2024

作者： Kaliappan, Seeniappan Balaji, V. Bharathi, G. Bhavani Aluvala, Srinivas Kcg College of Technology Department of Mechatronics Engineering Karapakkam Tamil Nadu Chennai600097 India Loyola Institute of Technology Department of Mechanical Engineering Tamil Nadu Chennai600123 India School of Computer Science & Artificial Intelligence Sr University Telangana Warangal506371 India

ISBN: (纸本)9798350370188

This section presents an integrated method that merges data mining techniques and neural networks to augment predictive analytics and optimize treatment strategies for managing cardiovascular diseases. Algorithm 1 focuses on data preprocessing and feature selection, essential for refining the patient data. It standardizes the input data by normalization and selects critical features using information gain, enhancing the subsequent neural network's efficiency in predicting disease progression. Algorithm elucidates the training of a neural network, utilizing the pre-processed data and disease progression labels. The neural network undergoes iterative training to minimize the loss function, resulting in a trained model capable of accurate predictions. Algorithm emphasizes the utilization of the trained neural network to optimize personalized treatment plans for cardiovascular disease management. By predicting disease progression and evaluating treatment options, it tailors' treatment plans to individual patient needs, thereby enhancing the effectiveness of healthcare interventions. The proposed method is demonstrated to surpass traditional techniques such as Linear Regression, Decision Trees, Support Vector Machines, K-Nearest Neighbors, Random Forest, and Logistic Regression in terms of critical performance metrics. Leveraging the power of neural networks, the proposed method showcases superior accuracy, precision, recall, Fl score, and AUC-ROC. Moreover, a comprehensive evaluation involving sensitivity, specificity, Matthews Correlation Coefficient (MCC), Negative Predictive Value (NPV), Youden's Index, and Cohen's Kappa consistently emphasizes its advanced performance, validating its potential to revolutionize cardiovascular disease management. © 2024 IEEE.

关键词： data mining

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Application of Artificial Bee Colony Algorithm in intelligent Price Supervision System of Digital Economy 2

Application of Artificial Bee Colony Algorithm in Intelligen...

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2nd international conference on data analytics, computing and Artificial Intelligence, ICDACAI 2023

作者： Li, Weijun Wuxi Vocational Institute of Commerce School of Tourism & Culinary Arts Jiangsu Wuxi214100 China

ISBN: (纸本)9798350339765

With With the rapid development of the digital economy and the continuous expansion of the market, the demand for intelligent price regulation systems has become increasingly urgent, and relying solely on traditional regulatory methods is no longer sufficient to meet the market demand. Therefore, this article proposed an intelligent price supervision method based on artificial bee colony algorithm. Through the monitoring and analysis of the market price, the artificial bee colony algorithm can quickly find the rule and trend of the market pricing, and adjust the price through the optimization algorithm to achieve a good regulatory effect. This article designed and analyzed the system, and used data comparison to study the price intelligent supervision system. Through experimental verification, the security of the intelligent price supervision system based on artificial bee colony algorithm reached a maximum of 98%, which can meet the system requirements. © 2023 IEEE.

关键词： Optimization

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Query-driven Edge Node Selection in Distributed Learning Environments 39

Query-driven Edge Node Selection in Distributed Learning Env...

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IEEE 39th international conference on data Engineering (ICDE)

作者： Aladwani, Tahani Anagnostopoulos, Christos Kolomvatsos, Kostas Alghamdi, Ibrahim Deligianni, Fani Univ Glasgow Sch Comp Sci Glasgow Scotland Univ Thessaly Dept Informat & Telecommunicat Volos Greece Al Baha Univ Dept Comp Sci Fac Sci & Arts Baljurashi Al Baha Saudi Arabia

ISBN: (纸本)9798350322446

computing nodes in Edge computing environments share unlimited data. Such data are exploited to locally build Machine Learning (ML) models for applications such as predictive analytics, exploratory analysis, and smart applications. This edge node-centric local learning reduces the need for data transfer and centralization, which is affected by different factors such as data privacy, data size, communication overhead, and computing resource limitations. Therefore, a collaborative learning fashion at the network edge has appeared as a promising paradigm that enables multiple distributed (edge) nodes to train and deploy ML models cooperatively without infringement of data privacy. Nevertheless, the variety, distribution and quality of data vary between edge nodes. Hence, selecting unsuitable edge nodes can have a negative impact on the ML model performances. We have devised (i) an intelligent node selection mechanism per analytics query based on the range of the availability of required training data at the edge and (ii) variants of collaborative learning processes engaging the most suitable nodes for models training and inference. We evaluate the efficiency of our selection mechanism and collaborative learning and provide a comparative assessment with other methods found in the literature using real data. The results showcase that our mechanism significantly outperforms baseline approaches and existing node selection mechanisms in distributed computing environments.

关键词： Query-driven node selection Distributed learning data overlapping.

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Semantics-Driven Cloud-Edge Collaborative Inference A Case Study of License Plate Detection 2

Semantics-Driven Cloud-Edge Collaborative Inference A Case S...

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2nd international conference on Image Processing, Computer Vision and Machine Learning, ICICML 2023

作者： Gao, Yuche Zhang, Beibei University College London Department of Statistical Science London United Kingdom Zhejiang Lab Intelligent Software Research Center Hangzhou China

ISBN: (纸本)9798350331417

With the proliferation of video data in smart city applications like intelligent transportation, efficient video analytics has become crucial but also challenging. This paper proposes a semantics-driven cloud-edge collaborative approach for accelerating video inference, using license plate recognition as a case study. The method separates semantics extraction and recognition, allowing edge servers to only extract visual semantics (license plate patches) from video frames and offload computation-intensive recognition to the cloud or neighboring edges based on load. This segmented processing coupled with a load-aware work distribution strategy aims to reduce end-to-end latency and improve throughput. Experiments demonstrate significant improvements in end-to-end inference speed (up to 5x faster), throughput (up to 9 FPS), and reduced traffic volumes (50% less) compared to cloud-only or edge-only processing, validating the efficiency of the proposed approach. The cloud-edge collaborative framework with semantics-driven work partitioning provides a promising solution for scaling video analytics in smart cities. © 2023 IEEE.

关键词： cloud computing edge computing license plate recognition smart cities video analytics

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Fog Assisted smart Video Surveillance System using Semantic Analysis for Abnormal Motion Detection 10

Fog Assisted Smart Video Surveillance System using Semantic ...

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10th international conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Amshavalli, R.S. Kalaivani, J. Srm Institute of Science and Technology Department of Computing Technology Chennai Kattankulathur India

ISBN: (纸本)9798350384369

Recent Research in creating intelligent smart Closed Circuit Television (CCTV) surveillance systems that can counteract the rising levels of insecurity has increased dramatically as a result of the growing demand for video surveillance systems. However, the storage and processing capacity of analytical applications has been exceeded by the enormous volume of video data produced by these devices. By localizing data to the network's edges, this article suggests a smart surveillance system that exhibits high performance in terms of response time and bandwidth. In modern IoT era, the need for transforming the traditional system into smart surveillance system is high due to its demand for instant detection of anomalous occurrences by processing huge amount of video data on the go. Bringing the smartness into the system, makes it more automated and reliable. The challenging phases in making the system smart is storing and analyzing the video surveillance data since it calls for event recognition, visual interpretation, and the identification of relevant context. We employ sliding technique and adaptive contour based algorithms for detailed preprocessing layer that performs key frame extraction and background elimination respectively. And to address the issue of storage and instant processing, we also propose fog based smart surveillance framework that acts as the intermediator component between cloud and end users. The next major challenge is analyzing the incoming video data on the go. It can be addressed through semantic based algorithm for detecting the abnormal events. We propose an ontology based algorithm for detecting the unusual occurrences. Experiments are conducted on a real-time dataset to evaluate the suggested technique. The computed results demonstrate the superiority of the suggested fog based semantic analytics monitoring system over the traditional cloud-based monitoring solutions by attaining high prediction and accuracy rate coupled with low latency in

关键词： Ontology

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Empowering Education: AI and IoT Integration for smart Learning Environments 15

Empowering Education: AI and IoT Integration for Smart Learn...

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15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Sharma, Divya Gupta, Shruti Dagar, Savita Manav Rachna International Institute of Research and Studies Faridabad India

ISBN: (纸本)9798331300579

To improve learning environments and educational outcomes, this article examines how artificial intelligence (AI) and the Internet of Things (IoT) meet in the context of educational settings. Teachers may design intelligent learning environments that cater to the requirements of each individual student, offer individualised learning experiences, and support more productive teaching methods by combining AI and IoT technologies. This paper explores the possible advantages of AI and IoT integration in education, such as better learning analytics, more efficient resource allocation, and more student engagement, through a thorough analysis of previous research and case studies. Additionally, the study addresses issues like digital literacy, privacy, and data security that come with implementing AI and IoT in educational settings. By combining the most recent understanding and perspectives. © Grenze Scientific Society, 2024.

关键词： Contrastive Learning

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