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IoT Services and Intelligence: Empowering the Internet of Things with Real-Time data analytics and decision-making

IoT Services and Intelligence: Empowering the Internet of Th...

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Trends in Quantum computing and Emerging Business Technologies (TQCEBT), international conference on

作者： Mrudula Kilaru P. Maheswari Eswararao Boddepalli K. Venkataramana Jaksan D Patel M. K. Sharma Velagapudi Ramakrishna Siddhartha Engineering College Department of Commerce Faculty of Science and Humanities SRM Institute of Science and Technology Ramapuram Chennai Bradely Department of ECE Virginia Tech. Blacksburg United States of America Department of CSE VEMU Institute of Technology Chittoor A.P Mechanical Engineering Merchant Engineering College Basna Maheshna Gujarat Mathematics Chaudhary Charan Singh University Meerut Uttar Pradesh India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

This empirical study examines the revolutionary relationship between intelligence and Internet of Things (IoT) services, with a focus on intelligent decision-making and real-time data analytics. The IoT ecosystem is made possible by a complex architecture that spans edge, communication networks, including cloud platforms, producing a significant amount of raw data. IoT services and intelligence combine to provide a real-time, uninterrupted service that defies traditional paradigms. The paper explores the importance of edge intelligence, which reduces latency as well as improves security by bringing decision-making closer to the data source. More complex decision-making processes could potentially be centrally controlled with the use of cloud-based intelligence. Advanced analytics methods, machine learning algorithms, along information visualization tools are all part of the technological approach used to extract valuable insights. As the IoT system's lifeblood, real-time data analytics develops a dynamic awareness of the changing physical environment. Real-time data analytics and edge computing have been especially revolutionary because they lower latency while enhancing responsiveness. Case studies highlight practical applications in smart cities, businesses, and healthcare, demonstrating the value of IoT intelligence and services. Cybersecurity, ethical issues, and communication interoperability are among the difficulties. Nonetheless, these difficulties offer chances for cooperation, creativity, and development. The promise of a responsive, intelligent, and interconnected world is highlighted in the paper's conclusion, along with the necessity of meticulously handling obstacles, seizing opportunities, and placing ethical concerns first to effectively harness the potential of the Internet of Things for a peaceful future.

关键词： Ethics data analysis smart cities Navigation Decision making Ecosystems Standardization

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Innovating smart Buildings and Cities Through Agent-Based Modeling, Discrete Event Simulation and Sensor-Based data analytics

Innovating Smart Buildings and Cities Through Agent-Based Mo...

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IEEE international Workshop on intelligent data Acquisition and Advanced computing Systems: Technology and Applications

作者： Juliane Lopinski Sabine Sachweh Claudia Müller IDiAL Institute University of Applied Sciences and Arts Dortmund Dortmund Germany University Siegen Siegen

The paper emphasizes the significance of sensor-based data analytics (SBDA) for modeling and simulation in a smart city, using the methods discrete event simulation (DES) and agent-based modeling (ABM). Moreover, the project reviewing paper presents several selected project examples, such as CitySim, AIRE, smart Grid, and the Gotland project that demonstrate the potential of SBDA in smart buildings and urban systems and analyse possible advantages of the application of the SBDA method. Furthermore, the result table demonstrates the possibilities of using SBDA in ABM and DES in other sectors, thus obtaining accurate results in a modeling system in the future. These findings have significant implications for policymakers, urban planners, and stakeholders in smart city development who seek to enhance the quality of life for urban citizens. The paper contributes to a better understanding of advantages of using sensor-based data analysis in ABM and DES and the method's potential benefit for other sectors. It provides a basis for analysis on some methodologies in the field of smart cities and buildings. It is intended to serve as an overview for future researchers and to contribute to the understanding of general concepts of research institutions.

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AI-Powered data-Driven Approaches to Fraud Detection in Finance

AI-Powered Data-Driven Approaches to Fraud Detection in Fina...

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

作者： P. Sundara Bala Murugan R. Satish Prabakaran Paranthaman R. Muzhumathi Department of Management Studies St. Joseph's Institute of Technology Chennai Tamil Nadu India Department of Management Sciences Velammal Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Despite detection techniques have advanced, financial fraud is still a serious problem. Conventional rule-based approaches frequently suffer from high false positive rates and missed fraudulent activity because of their rigidity and vulnerability to changing fraud tactics. To improve fraud detection in finance, the study suggests a data-driven, AI-powered method. The system continuously adapts to new fraud tendencies by dynamically analyzing transactional data and utilizing machine learning (ML) algorithms for anomaly identification and predictive analytics. When it comes to real-time monitoring, essential elements include feature engineering, data preprocessing, data collecting, and model training. Its effectiveness in reducing financial risks is demonstrated by testing it against Existing systems and finding superior performance metrics in precision (0.95), recall (0.92), F1-score (0.93), and AUC-ROC (0.97). With the ever-changing financial landscape, the scalable and real-time fraud detection capabilities of the proposed system are essential for preserving transaction security and integrity

关键词： Training Measurement Finance Prediction algorithms Real-time systems data models Fraud Security Predictive analytics Testing

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The Future of smart Factories: Real-Time Automation and Big data analytics on the Cloud

The Future of Smart Factories: Real-Time Automation and Big ...

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international conference on computing and Networking Technology (ICCNT)

作者： K. Ranjith Singh Arpit Arora Dilip Goud Krovvidi S B Ambika Sandeep V Priyanka More Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India NNRG Engineering College India Department of CSE-AI&ML & DS ADITYA COLLEGE OF ENGINEERING AND TECHNOLOGY Surampalem School of Engineering and Technology Mechaical Engineering JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The evolving future of manufacturing will be all about intelligent factories featuring state-of-the-art technologies. smart factories combine connected automation and big data in the cloud to streamline production, increase productivity, and lower operational costs. Real-time automation utilizes state-of-the-art sensors, internet of Things (IoT) devices, and artificial intelligence (AI) technology to monitor and control machines or processes in real time independently. This results in faster potential decision-making and bright maintenance works, thus decreasing downtime risk and increasing overall productivity. Big data analytics in the cloud enables real-time collection and analysis of extensive amounts of production processes, machine performance, and supply chain management-related information. The data can then be utilized to discover areas that need improvement, predict maintenance requirements, and even optimize the level of inventories in the sense that it would save costs while at the same time becoming more efficient. Also, real-time automation combined with significant cloud data analytics results in improved communication and collaboration from one production stage to another, hence smooth operations. In other words, it helps create an even more agile and adaptable manufacturing environment where processes can be fine-tuned on the fly to deal with dynamic market shifts.

关键词： Productivity Cloud computing Automation Costs Supply chains Companies Big data Real-time systems intelligent sensors smart manufacturing

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Enhanced Video Classification System with Convolutional Neural Networks Using Representative Frames as Input data

Enhanced Video Classification System with Convolutional Neur...

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international conference on data analytics, intelligent computing, and Cyber Security, ICDIC 2020

作者： Jayasree, K. Idicula, Sumam Mary Department of Computer Engineering Model Engineering College Kerala Ernakulam682021 India Department of Computer Science Muthoot Institute of Technology and Science Kerala Ernakulam682308 India

ISBN: (纸本)9789811941610

Convolutional neural networks are extensively used in video classifications systems, which uses video action recognition data set which consists of 13,320 videos grouped to 101 classes. In this proposed method, we are using a promising way of speeding up the training by using representative frames from each video for the entire video data set. The advantage is that the training time can be drastically decreased with the compact input video data. Here we are using the inceptionv3 pretrained model for classification where the weights and architecture can be used for similar types of inputs. Instead of giving the entire video frames for video classification, the proposed method, selects representative frames only, still producing comparable results. When used with ‘action recognition data set’ having 101 classes and 13,320 videos, while using full frames, it gives a F1-score of 0.717 and while using representative frames, it results with a 0.704 value which is comparable. When all the frames of the videos are given as input to the pretrained model, the performance is considerably high. However, there is a direct implication on the cost as far as time is concerned. The main objective of this research work is to enhance the computational efficiency of the video classifier by using the representative frames for speeding up the process of training the data set. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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A Study on Supply Chain 4.0 Value Drivers With Respect to Automobile Manufacturing Industry in India 15

A Study on Supply Chain 4.0 Value Drivers With Respect to Au...

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

作者： Kurian, Jinu Parekh, Nishita Faculty Operations Management Mukesh Patel School of Technology Management & Engineering NMIMS Mumbai400 056 India

ISBN: (纸本)9798331300579

Traditional supply chains consist of linear models where information flows from suppliers to producers to distributors to customers and back whereas supply chain 4.0 is a model which uses fully automated technologies that includes Internet of Things, modern robotics, 3-D manufacturing, blockchain, advanced analytics and others. (Ali, 2022). In today's globalised and complex ecosystem, the linear models often cause delays in information or material flow in both upstream and downstream in the chain. Hence, themodern world supply chain ecosystem must shift towards the digital supply chain, supply chain 4.0, by creating circular models that can improve information sharing and visibility thereby leading to improved strategic outcomes. It must start by identifying the essential building blocks of the digital supply chain, and then using them according to the desired supply chain model. Some dimensions like strategic, legal, ethical, technological and organisational, affecting industry 4.0 implementation in supply chains which triggered competitiveness and impacted the strategic business decisions, were identified by researchers (Ghadge et. al 2020). The present study is an attempt to test the impact of some of these dimensions in the Indian automobile sector. This paper focuses on identifying the value drivers for the strategic outcome of supply chain 4.0. It considers Technological support, Leadership Support, data-Driven Services, Supply Chain Collaboration, Strategy, and Process as value drivers for supply chain 4.0. The paper is based on a survey conducted on managers of the automobile manufacturing industry in Maharashtra, India. A questionnaire designed for the study was administered to 200 managers, from which 167 valid responses were analysed using structural equation modelling. The findings of the study reveals that the proposed model satisfies all the validity and reliability tests and the results indicate that Technological support, Leadership Support, data-Dri

关键词： smart manufacturing

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Edge-computing FogFlow Framework For Solar Generation Prediction Exploiting Federated Learning

Edge-Computing FogFlow Framework For Solar Generation Predic...

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2023 international conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Samaras, Thodoris Dimara, Asimina Tzallas, Petros Papaioannou, Alexios Bezas, Napoleon Krinidis, Stelios Anagnostopoulos, Christos-Nikolaos Ioannidis, Dimosthenis Tzovaras, Dimitrios Thessaloniki Greece Thessaloniki Greece Intelligent Systems Lab Mytilene Greece Management Science and Technology Kavala Greece

ISBN: (纸本)9798350369359

The integration of renewable energy sources into residential power systems, accompanied by the growth of edge computing, presents a dynamic landscape for energy management. In this paper, an innovative solution for solar generation forecasting is introduced, effectively addressing issues of data privacy, operational efficiency, scalability, and network optimization. A Federated Learning Gradient Boosting (FedXGB) framework integrated with edge computing is applied, significantly amplifying energy efficiency and sustainability in residential environments. Powered by FogFlow, the orchestration and optimization of IoT ecosystems, particularly within smart homes, are advanced, and data privacy is ensured. Remarkable results are observed, especially in mid-term and long-term solar forecasting, highlighting the framework's transformative potential in reshaping energy management. This innovation holds the promise of revolutionizing how renewable energy is harnessed within homes and communities, paving the way for a more sustainable and efficient future. With this comprehensive approach, the intersection of renewable energy and edge computing takes a significant step forward in enhancing energy management. © 2023 IEEE.

关键词： Edge computing

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

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

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Advancements in smart, Secure and intelligent computing (ASSIC), international conference on

作者： Ramakrishnan Raman Mitul Datta Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune Maharashtra India Symbiosis Law School (SLS) Pune & Symbiosis International (Deemed University) Pune Maharashtra India

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.

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A De-singularity Subgradient Approach for the ExtendedWeber Location Problem 33

A De-singularity Subgradient Approach for the ExtendedWeber ...

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33rd international Joint conference on Artificial Intelligence (IJCAI)

作者： Lai, Zhao-Rong Wu, Xiaotian Fang, Liangda Chen, Ziliang Jinan Univ Coll Informat Sci & Technol Dept Math Jinan Peoples R China Jinan Univ Coll Informat Sci & Technol Dept Comp Sci Jinan Peoples R China Peng Cheng Lab Res Inst Multiple Agents & Embodied Intelligence Shenzhen Peoples R China Jinan Univ Guangdong Inst Smart Educ Jinan Peoples R China

ISBN: (纸本)9781956792041

The extendedWeber location problem is a classical optimization problem that has inspired some new works in several machine learning scenarios recently. However, most existing algorithms may get stuck due to the singularity at the data points when the power of the cost function 1 <= q < 2, such as the widely-used iterative Weiszfeld approach. In this paper, we establish a de-singularity subgradient approach for this problem. We also provide a complete proof of convergence which has fixed some incomplete statements of the proofs for some previous Weiszfeld algorithms. Moreover, we deduce a new theoretical result of superlinear convergence for the iteration sequence in a special case where the minimum point is a singular point. We conduct extensive experiments in a real-world machine learning scenario to show that the proposed approach solves the singularity problem, produces the same results as in the non-singularity cases, and shows a reasonable rate of linear convergence. The results also indicate that the q-th power case (1 < q < 2) is more advantageous than the 1-st power case and the 2-nd power case in some situations. Hence the de-singularity subgradient approach is beneficial to advancing both theory and practice for the extended Weber location problem.

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Machine Learning Insights for Predicting Customer Behavior in Online Retail

Machine Learning Insights for Predicting Customer Behavior i...

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

作者： N. Reena Catherine Ragul. R Deepika. M Pradeepkumar. R Shyam Prasanth. A Department of Commerce Dr.SNS Rajalakshmi College of Arts and Science Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The research work reports a comprehensive analysis of predictive analytics of customer behavior on e-commerce based on high-end data science and artificial intelligence techniques. The sourced information for customer demographics, transaction details, and purchase history was extracted from Kaggle. Machine learning models' applications were utilized to predict the attrition of the customers, which indicated that the accuracy of Random Forest was 87%, while that of XGBoost was 89%. The performance of the model was tested in terms of precision, recall, and F1-score. Precisely and recall for XGBoost were both 0.88. Additionally, different consumer types were identified by applying K-Means clustering to classify customers into different kinds based on their attributes of Recency, Frequency, and Monetary. Its findings indicate that predictive analytics can play a very significant input in making advertisements very effective and in keeping clients in this very dynamic world of e-commerce. In the last few years, there has been an impressive rise in online shopping due to more and more people gaining access to the internet, a growing preference for convenience, and technology's ever-changing nature of digital platforms.

关键词： Consumer behavior Soft sensors Organizations Predictive models data science data models Electronic commerce History Predictive analytics Random forests

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