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Prediction of Marketing Information intelligent System Based on 5G Network

Prediction of Marketing Information Intelligent System Based...

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Artificial Intelligence and Autonomous Robot Systems (AIARS), international conference on

作者： Di Kang Haiyan Hao School of Smart Tourism Hainan College of Software Technology Qionghai China School of Computing and artificial intelligence Hainan College of Software Technology Qionghai China

ISBN: (数字)9798350376173

ISBN: (纸本)9798350376180

To succeed in the market, it is necessary to have an accurate and timely grasp of the market situation and development, and to formulate plans and allocate resources based on current market demand and development trends, in order to meet market needs and gain competitive advantages. The original market planning and decision-making relied on manual writing and experience, making it difficult to meet the requirements of the constantly developing market economy. Therefore, with marketing as the mainstay and a pragmatic attitude, the station has established a 5G network based analysis and prediction system for goods sales information, following the path of technological innovation. The system uses computers to input, store, manage, retrieve, process, analyze, and predict sales information. Its biggest feature is the organic integration of market information both inside and outside the site. A comprehensive and efficient analysis and processing were conducted to meet the needs of actual work, and the future development direction was predicted, truly achieving a targeted understanding of oneself. The experimental results showed that the data transmission speed of the marketing information intelligent system based on 5G technology for prediction and analysis was at least about 125Mbps, while the data transmission speed of the marketing information intelligent system based on traditional systems for prediction and analysis was always maintained at around 25Mbps.

关键词： Computers Technological innovation 5G mobile communication Decision making Writing Market research data communication intelligent systems Predictive analytics Pragmatics

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ENCL: Empirical Evaluation of Consumer Behaviour Analysis Model Using Internet of Things (IoT) Based Enhanced Neural Classification Logic

ENCL: Empirical Evaluation of Consumer Behaviour Analysis Mo...

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

作者： S. Sudha Merlin K. Malathy Ravi S Arun M Raut S.P H. Fawareh Department of Electrical and Electronics Engineering DMI College of Engineering Palanchur Chennai Department of MCA S.A. Engineering College Chennai Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Dept of Computer Science and Engineering Kings College of Engineering Punalkulam Thanjavur Department of Civil Engineering Yeshwantrao Chavan College of Engineering Nagpur India Software Engineering Dept Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The proliferation of consumer data can be attributed to “the Internet of Things (IoT),” which makes it easier to access and more detailed than before. In this research our purpose is to evaluate the effectiveness of a consumer behavior analysis model using data from IoT. The quality of the model is evaluated on multiple parameters - accuracy, predictability and business sense for marketing. It uses the analytics of huge amounts for data from IoT devices to predict patterns, customer preferences and trends. The proposed method called as Enhanced Neural Classification Logic (ENCL) is cross-validated with the already existing method, which is termed as Consumer behavior Forecasting Strategy (CBFS), and helps users to share gather or process information in a fast and cost-effective way through the proposed method. The results are useful to grasp the promise of IoT in more effective consumer behavior analysis. The research portrays the strengths, weaknesses and possible areas of improvement in this model which will be beneficial for both academic and industry practitioners. As such, the objective of this study is to show how an IoT-based consumer behavior analysis can increase business growth and customer satisfaction. The experimental evaluations demonstrate significant improvements in predictive performance over traditional methods, providing actionable insights for targeted marketing and strategic decision-making. The results highlight ENCL's potential to transform consumer behavior analysis by delivering more precise and nuanced understandings of market dynamics.

关键词： Analytical models Consumer behavior Accuracy Computational modeling Neural networks Predictive models data models Internet of Things Logic Business

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Three-Channel Graph Convolutional Network 24

Three-Channel Graph Convolutional Network

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24th IEEE international conference on High Performance computing and Communications, 8th IEEE international conference on data Science and Systems, 20th IEEE international conference on smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Application, HPCC/DSS/smartCity/DependSys 2022

作者： Meng, Lei Ye, Zhonglin Zhao, Haixing Yang, Yanlin Chen, Yang Wang, Zhaoyang College of Computer Qinghai Normal University Tibetan Information Processing Engineering Technology and Research Center of Qinghai Province The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810008 China

ISBN: (纸本)9798350319934

At present, as an emerging direction of artificial intelligence, graph neural networks (GNNs) have developed rapidly and can solve many graph data problems efficiently. The real world is composed of a large number of networks. Compared with the widely studied single-relationship feature information networks, the real-world networks are mostly composed of multi-relationship feature information networks in which different types of objects are interconnected through complex relationships. Complex structural information and rich semantic information are included by multi-relationship feature information networks, thereby the real network can be restored more realistically. However, one type of relational feature of the network is merely considered by most of the existing GNNs, and the information of multi-relationship features and the hidden information between multi-relationship features are ignored in the network. In order to deal with these restrictions, a novel graph convolutional network (TCGCN) based on an attention mechanism is proposed, which aims to extract different relational features in multi-relationship feature networks and the hidden information between them more comprehensively, thereby improving the performance of GNNs in downstream tasks. In this paper, an improved attention mechanism is used to aggregate different types of single relational features, and the aggregated multiple single relational feature network information is fused with the mixed relational feature network information into the final node representation. The proposed TCGCN outperforms most other methods in extensive experiments conducted on three real-world datasets. © 2022 IEEE.

关键词： Graph neural networks

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Public Opinion Guidance with New Government Media in the smart City

Public Opinion Guidance with New Government Media in the Sma...

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

作者： Ji, Yunqing Shandong University Finance and Economics Jinan China

ISBN: (纸本)9783030627423

Internet changed people’s life, the emergence of new media is changing the people’s habit of reading the news and information, the government has also gradually increased the construction of new government media, they hope to take use of the new media platform to guide the network public opinion. This paper studies the new characteristics of public opinion in new media age, analyzes the problems of the new government media in the public opinion guiding, and puts forward some optimization strategy. © 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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Mobilenet and Deep Residual Network for Object Detection and Classification of Objects in IoT Enabled Construction Safety Monitoring

Mobilenet and Deep Residual Network for Object Detection and...

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

作者： Leena R. Jadhav Manoj Hudnurkar Faculty of Computer Studies (FoCS) Symbiosis Center for Research and Innovation (SCRI) Symbiosis International University (SIU) Pune Maharashtra India Vidyalankar School of Information Technology Wadala Mumbai Operations and Analytics Symbiosis Centre for Management and Human Resource Development (SCMHRD) Symbiosis International University (SIU) Pune Maharashtra India

Construction areas are extremely dangerous since the dynamic communication amongst workers and moving tools along with wide fatality rates occurred by collision. Falls, struck-bys, and caught-in-between are the general kinds of accidents that happen in work area. In spite of having their unique benefits, more number of day–to-day accident prevention events respond passively to situations where workers or tools enter predefined unsafe areas. To prevent these types of accidents, a new scheme is introduced for object detection (OD) and classification of objects in IoT-enabled constructing safety monitoring named Mobile-Residual Network (MR-Net), where MR-Net is merged by MobileNet and Deep Residual Network (DRN). This module is composed of three sub-systems, prevention of backover accidents, smart alerting for probable risk avoidance, and middleware IoT platform. The first technique has IoT network with sensor unit. The second technique has cloud server employed to provide smart alerts. The third one is detection progress carried out by image processing with the aid of collected images. First, IoT nodes are simulated, wherein IoT image sensors are responsible for collecting data. At base station, the collected data is pre-progressed by utilizing Non- Local Means (NLM) filter. After that, object is detected by you only live once v3 (YOLO v3) where the recognized objects, like person, machines, vehicles and safety cones are detected. If the detected object is person then classify the objects like Hardhat, Mask and Safety Vest worn using MR-Net. The analytic measures utilized for MR-Net such as accuracy, True Positive Rate (TPR), True Negative Rate (TNR), Positive predictive value (PPV) and Negative predictive value (NPV) gained maximal values of 0.912, 0.905, 0.923, 0.909 and 0.919.

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Design of an IoT-based smart Farming System 2

Design of an IoT-based Smart Farming System

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2nd international conference on computing Advancements: Age of computing and Augmented Life, ICCA 2022

作者： Sheham, Nursari Hasan Hassan, Md Tonim Imran, Al Ahmed, Towkir Fahim Parvez, Md. Shahariar Ahmed, Abir Bangladesh

ISBN: (纸本)9781450397346

Farming is a very vital sector for any developing country like Bangladesh. Therefore, smartly operating the farm is essential nowadays to get maximum productivity from the farm. In this paper, an automatic system is designed for the farm which is operated via the internet of things (IoT) technology. This proposed system is developed to implement advanced technology into the traditional farming system. The proposed approach incorporates feed and water level indicator control systems, health monitoring systems, fire and gas detection systems, temperature-based fan control systems, intelligent tracking systems, and cow dung cleaning mechanisms. Finally, different sensors will collect all the data and eventually be controlled by Arduino Nano micro-controller, which incorporates IoT features. Therefore, the users can handle their farm from any place and get real-time data from the farm through mobile. Furthermore, this proposed system is eco-friendly as well as cost-effective. © 2022 ACM.

关键词： Internet of things

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Improving Security in Vehicle Cloud Technologies

Improving Security in Vehicle Cloud Technologies

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

作者： Kunal Dey Ritesh Kumar Balaji K A Department of Data Analytics and Mathematical Sciences Jain (Deemed to be University) Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Selection Grade Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India

Recently, the development of vehicle computer technology has changed the conventional idea of a car from a simple means of transportation to an intelligent, safe smart vehicle. Additionally, cloud computing has been effortlessly incorporated into the intelligent Transportation System, also known as ITS, features, which include traffic accident avoidance and live traffic information providing. ITS services have grown to include effective traffic management, accident avoidance, and convenience services in the context of Vehicular Cloud computing (VCC). Be that as it may, the ongoing arrangement of vehicle benefits for the most part centers on utilizing information from in-vehicle sensors and misses the mark with regards to collaborating with outside foundation. Moreover, the utilization of Bluetooth networks in VCC settings makes security gambles with that could uncover private data held in vehicle sensors, including the personality of the driver and installment data during exchanges. We present the Virtual computing Center Service-oriented Privacy Framework (VCC-SSF) to settle these limitations and security issues present in VCC-based administrations. As well as ensuring the smooth conveyance of viable and easy to use VCC administrations, this total engineering puts security first. New elements are incorporated, for example, precautionary mishap the executives benefits and further developed installment organization. The VCC-SSF additionally has solid security components that incorporate confirmation, validation, access control, encryption, honesty, secrecy affirmation, and protection assurances for both client private data and in-vehicle information.

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Ultra-fast Machine Learning Classifier Execution on IoT Devices without SRAM Consumption

Ultra-fast Machine Learning Classifier Execution on IoT Devi...

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19th IEEE international conference on Pervasive computing and Communications (IEEE PerCom)

作者： Sudharsan, Bharath Patel, Pankesh Breslin, John G. Ali, Muhammad Intizar NUI Galway Data Sci Inst Confirm SFI Res Ctr Smart Mfg Galway Ireland Dublin City Univ Sch Elect Engn Dublin Ireland

ISBN: (纸本)9781665404242

With the introduction of edge analytics, IoT devices are becoming smart and ready for AI applications. A few modern ML frameworks are focusing on the generation of small-size ML models (often in kBs) that can directly be flashed and executed on tiny IoT devices, particularly the embedded systems. Edge analytics eliminates expensive device-to-cloud communications, thereby producing intelligent devices that can perform energy-efficient real-time offline analytics. Any increase in the training data results in a linear increase in the size and space complexity of the trained ML models, making them unable to be deployed on IoT devices with limited memory. To alleviate the memory issue, a few studies have focused on optimizing and fine-tuning existing ML algorithms to reduce their complexity and size. However, such optimization is usually dependent on the nature of IoT data being trained. In this paper, we presented an approach that protects model quality without requiring any alteration to the existing ML algorithms. We propose SRAM-optimized implementation and efficient deployment of widely used standard/stable ML-frameworks classifier versions (e.g., from Python scikit-learn). Our initial evaluation results have demonstrated that ours is the most resource-friendly approach, having a very limited memory footprint while executing large and complex ML models on MCU-based IoT devices, and can perform ultra-fast classifications while consuming 0 bytes of SRAM. When we tested our approach by executing it on a variety of MCU-based devices, the majority of models ported and executed produced 1-4x times faster inference results in comparison with the models ported by the sklearn-porter, m2cgen, and emlearn libraries.

关键词： Offline Inference intelligent Microcontrollers Edge AI Multi-class Classifiers Efficient Model Deployment

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Task Offloading for Edge-Fog-Cloud Interplay in the Healthcare Internet of Things (IoT)

Task Offloading for Edge-Fog-Cloud Interplay in the Healthca...

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IEEE international conference on Omni-Layer intelligent Systems (IEEE COINS)

作者： Firouzi, Farshad Farahani, Bahar Panahi, Ehsan Barzegari, Mojtaba Duke Univ Dept Elect & Comp Engn Durham NC 27706 USA Shahid Beheshti Univ Cyberspace Res Inst Tehran Iran Univ Tehran Dept Elect & Comp Engn Tehran Iran KU Leuven Univ Dept Mech Engn Biomech Sect Leuven Belgium

ISBN: (纸本)9781665431569

The smart and Connected Health (SCH) revolution is characterized by the convergence of technologies - from edge computing to cloud computing, Artificial Intelligence (AI), and Internet of Things (IoT)- blurring the lines between the physical and digital worlds. Although these are distinct technologies that evolved independently over time, they are becoming increasingly more intertwined in a way that the capabilities of the technologies are aligned in the best possible way. The public embracement of wearables and the integration of IoT provide greater availability, accessibility, personalization, precision, and lower-cost delivery of healthcare services. Bringing the power of AI offers the ability to wring insights from health data more quickly and accurately. Cloud-IoT has emerged to address some of the major challenges of IoT related to analytics, big data storage, scalability, management, reliability, and heterogeneity. Acting on real-time data compels a move towards edge/fog technology to meet the strict computing time requirement addressing the main drawbacks of Cloud-based IoT solutions. Although the convergence of the edge-fog-cloud in the age of IoT can potentially be a promising paradigm shift, its adoption is still in its infancy phase, suffering from various issues, such as lack of consensus towards any reference models or best practices. Thereby, this paper presents a holistic approach and reference architecture to address the interplay of edge-fog-cloud IoT for healthcare applications. Moreover, a Reinforcement Learning (RL) based offloading technique is presented to distribute the load across edge, fog, and cloud. Finally, a novel case study, ECG-based arrhythmia detection, is presented to better demonstrate and evaluate the efficiency of the proposed model.

关键词： Internet of Medical Things (IoMT) Internet of Things (IoT) Healthcare Edge computing Fog computing Cloud computing Offloading

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A Distributed data Mining and Cloud Analysis for Predictive Gas Level, Dynamic Booking, and smart Energy Optimization

A Distributed Data Mining and Cloud Analysis for Predictive ...

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Advances in data Engineering and intelligent computing Systems (ADICS), international conference on

作者： D. Sugumar R. Ramadevi J. Prasad N. Mohankumar D.C. Joy Winnie Wise S. Velmurugan Department of Commerce Care College of Arts and Science Trichy Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Saveetha University Saveetha Nagar Chennai Tamil Nadu India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering B.V. Raju Institute of Technology Telangana India Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering T.J.S. Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

The state-of-the-art Internet of Things (IoT) innovation known as the Autonomous Gas Reservoir Management System (AGaRMS) totally changes the most common way of overseeing gas supplies. AGaRMS uses sensors that monitor gas levels and a distributed architecture that forecasts patterns in gas use to ensure that gas refills occur at the right time. Complex AI calculations examine verifiable information, current weather patterns, and usage patterns to estimate when gas will run out. This, then, prompts robotized booking sales to be transported off the supplier. Likewise, AGaRMS appeals to the savvy progression of energy, which empowers strong gas usage. The framework's flexibility and versatility make it reasonable for use in private and business settings, further developing production network productivity, reducing working expenses, and improving the client experience. The AGaRMS system offers a smart method for managing gas leaders that work with gathering effective power energy practices and streamlining gas provisioning processes. AGaRMS optimizes the gas supply chain and improves the user experience by catering to a wide range of residential and industrial applications with its flexible and scalable design.

关键词： Supply chains Weather forecasting Reservoirs Aerodynamics User experience Internet of Things Predictive analytics

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