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smart Manufacturing Maintenance through LoRaWAN-based Ecosystem 3

Smart Manufacturing Maintenance through LoRaWAN-based Ecosys...

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3rd IEEE international Mediterranean conference on Communications and Networking, MeditCom 2023

作者： Kornaros, Georgios Bakoyiannis, Dimitrios Tomoutzoglou, Othon Hellenic Mediterranean University Electrical and Computer Engineering Dept. Iraklio Crete Greece

ISBN: (纸本)9798350333732

Low-Power Wide Area Networks (LPWANs) allow for cost-effective data transmission from a large number of sensors throughout the whole industrial system while using the least amount of energy and infrastructure. Through smart automated preventative maintenance, remote data gathering via these adap table Internet of Things (IoT), LoRaWAN-connected platforms may continuously monitor and update machinery in a timely way. We propose a fully functional, sustainable LoRaWAN-based solution, based on STM32 ultra-low power microcontrollers, which allows for trusted real-time monitoring and alerting machinery for preventive maintenance via a centralized cloud dashboard. As the massive usage of Edge computing and IoT devices can cause the rising of a large number of services affected by errors and exposure to various cyber-attacks, software updates of end-devices and gateways is an emerging need to retain functionality, services, security, and protocols up to the most recent version. For this purpose, we propose an NFC-based efficient solution for firmware upgrading of IoT devices. © 2023 IEEE.

关键词： Internet of things

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Predictive Analysis for Sales Forecasting Using Machine Learning: Leveraging data-Driven Insights for Enhanced Business Planning and Decision-Making

Predictive Analysis for Sales Forecasting Using Machine Lear...

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

作者： Jonardan Koner Shailesh Shivaji Deore S. Roja Kalyani Nilesh Sahastrabuddhe Prashant Kalshetti Anthony Rose NICMAR University Pune Department of Computer Engineering SSVPS Bapusaheb Shivajirao Deore College of Engineering Dhule Maharashtra Department of Computer Science Engineering and Applications Bharathidasan University Trichy-23 Dr. D. Y. Patil Institute of Management and Research Pimpri Pune Dept. of BBA Dr. D Y Patil Vidyapeeth Global Business School and Research Centre Pune Institute of Management and Entrepreneurship Development Pune Maharashtra India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Within the modern trade scene, leveraging prescient investigation for deal estimating has developed as a basic procedure for organizations pointing to optimize their decision-making forms. This consideration centres on the application of the XG Boost calculation, a well-known machine learning method, to tackle data-driven experiences for upgraded deals determining, thus encouraging prevalent commerce arranging. Through an in-depth investigation of authentic deals information, the inquiry about illustrates the noteworthiness of consolidating machine learning strategies, especially the XG Boost calculation, for exact and solid deal forecasts. By leveraging a comprehensive dataset including different variables affecting deals, such as client socioeconomics, regular patterns, showcasing activities, and financial markets, this exhibits the algorithm's capability to capture complex connections inside the information and produce exact figures. Besides, the investigation emphasizes. interpretability of the XGBoost calculation, empowering partners to comprehend the fundamental components affecting deal expectations. This straightforwardness cultivates a more profound understanding of the driving powers behind deal patterns, encouraging educated decision-making and proactive commerce arranging.

关键词： Machine learning algorithms Decision making Machine learning Organizations Solids Prediction algorithms Planning Predictive analytics Forecasting Socioeconomics

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Intrusion Detection in smart Cities: Leveraging Long Short-Term Memory Networks for Enhanced Cybersecurity

Intrusion Detection in Smart Cities: Leveraging Long Short-T...

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computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Afnan Mansour A Alduraibi Mnaouer Kachout College of Computer Science and Engineering Ha'il University Hail Saudi Arabia Innov'COM Sup 'Comp Carthage University Tunis Tunisia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

The use of the internet is continuously expanding, and data has become a crucial asset in today's digital landscape. smart Cities leverage technology to develop innovative solutions for addressing urban challenges, utilizing communication and information frameworks. However, smart City applications are susceptible to various cyberattacks, such as Probing (Prob), Denial of Service (DOS), User to Root (U2R) and Remote to Local (R2L) attacks. Prominent examples for smart Cities include New York City, Dubai, London, Barcelona, Singapore, and Tokyo. This study introduces a deep learning-based way for attacks detection in smart Cities by Long Short-Term Memory (LSTM) networks. The NSL-KDD dataset is utilized to assess the suggested solution, while the Synthetic Minority Over-sampling Technique (SMOTE) is activated to stem class imbalance. Experimental findings show that the model delivers high-performance metrics, achieving an accuracy of over 98.48%, a precision of 99%, a recall of 99%, and an F1-score of 98% on normalized data utilizing the SMOTE technique. These results focal point the validity of the proposed model in identifying attack traffic in smart City environments.

关键词： Deep learning Measurement Accuracy smart cities Intrusion detection NSL-KDD Convolutional neural networks Information technology Long short term memory intelligent sensors

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Automatic Feature Extraction and Traffic Management Using Machine Learning and Open CV Model

Automatic Feature Extraction and Traffic Management Using Ma...

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2021 IEEE international conference on Innovative computing, intelligent Communication and smart Electrical Systems, ICSES 2021

作者： Prakash, M. Saravanakumar, C. Lakshmi, S. Kanaga Rose, J Dafni Praba, B. Data Science and Analytics Center Karpagam College of Engineering Coimbatore India St. Joseph's Institute of Technology Department of Information Technology Chennai India Sri Muthukumaran Institute of Technology Department of Management Studies Chennai India Loyola-ICAM College of Engineering and Technology Department of Information Technology Chennai India

ISBN: (纸本)9781665435215

Artificial intelligence covers a vast area of the real time domain which supports humans for all activities. Machine learning (ML) techniques learn the data and react based on the properties of these data. The properties are identified by extracting the features from the extracted data. Image and video processing methods are essentials in real time application due the IoT (Internet of Things) devices. The data of these types of data is more complex and also high dimensional in nature. These dimensions are reduced by performing reduction techniques before performing the classification process. The proposed ML model targets the traffic management by automating the traffic light based on the flow in the road. The traffic priority is assigned based on the congestion level on the road. The traffic classification is done by considering different features and infrastructure maintained by the city. Existing system suffers the problem due to the following reasons such as traffic congestion, longer waiting time, improper maintenance of the traffic signal, and high carbon emission and so on. The objective of the proposed model is to reduce the traffic congestion by performing traffic flow conditions and make the people comfortable level during the travel. © 2021 IEEE.

关键词： Image enhancement

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Predicting the stock market index using GRU for the year 2020

Predicting the stock market index using GRU for the year 202...

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Emerging Systems and intelligent computing (ESIC), international conference on

作者： S. Narayana Siddabathula Naga Divya Sri Seelam Rohith Kumar Tappiti Ajay Shaik Sapa Vasiq Dept. of CSE SR Gudlavalleru Engineering College Gudlavalleru Andhra Pradesh India

Predicting the behavior of the stock exchange is crucial, since it ranks as one of the foremost frequently studied fields nowadays. It is challenging to forecast the financial sector that requires a careful analysis of historical trends. The precision of conventional methods for predicting changes in stock prices was insufficient. In order to anticipate the values of stocks, deep learning becomes particularly popular technique. Deep Learning is a particularly popular method for anticipating stock prices because of the efficiency in patterns that are not linear. Recurrent neural networks were used to increase prediction accuracy since share price movements are dependent on sequence of time. Within Recurrent Neural Networks (RNNs), their pivotal strength lies in their adeptness at managing sequential data and grasping offered a distinct challenge as well as a chance to investigate sophisticated forecasting algorithms. Nonlinear stock price movements cannot be accurately predicted by traditional data analytics approaches. Owing to its effectiveness in capturing intricate, nonlinear patterns within time-series data, deep learning stands out as the predominant method employed for predicting stock prices. A 25% drop in global stock prices and a 30% reduction in G20 countries market prices were recorded in March 2020. Many businesses experience significant losses, and it may be very challenging for them to recover. While many stockholders suffered losses, several businesses managed to continue making money throughout this period. whether the Gated Recurrent Unit (GRU) model, exhibits superior predictive capabilities for significant fluctuations in stock prices during the year 2020 compared to the LSTM model. This research involves a comparative analysis between GRU and LSTM using diverse stock indices, with historical data sourced from the Yahoo Finance API. Notably, the Mean Absolute Percentage Error (MAPE) values demonstrate that GRU outperforms LSTM in forecasting accur

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Harnessing Deep Learning for Fault Detection in Industry 4.0: A Multimodal Approach

Harnessing Deep Learning for Fault Detection in Industry 4.0...

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IEEE international conference on Cognitive Machine Intelligence (CogMI)

作者： Jialie Shen Marie Morrison Haiyan Miao Fengshou Gu Department of Computer Science City St Georgia’s University of London London United Kingdom School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom

ISBN: (数字)9798350386721

ISBN: (纸本)9798350386738

The advent of Industry 4.0 has reshaped the modern industries (e.g., Manufacturing, Automotive, Aerospace and Defense), driven by the rapid development of artificial intelligence, smart sensing technologies, and interconnected cyber-physical systems. One of the most important goal for Industry 4.0 is the improvement and enhancement in productivity, reduction of production losses, and operational efficiency. High quality fault detection within different environments is the foundation to achieve these goals. Industry 4.0 emphasizes the use of interconnected systems, smart sensors, and advanced analytics, which generate large volumes of data from production environments. The data complexity and scale, product by the widespread deployment of sensors and IoT devices, demands sophisticated Machine Learning (ML) and data-driven methods. In this paper, we argue that the future of accurate and reliable fault detection lies in the seamless fusion of edge-cloud computing, explainable AI, and adaptive machine learning algorithms capable of processing high-frequency, multimodal data streams in real time. Different kinds of advantages for multimondal learning and core enabling techniques are also discussed and revise. This vision paper outlines the future trajectory of multimodal learning based fault detection, via identifying critical research gaps and promising directions for future study.

关键词： Productivity Machine learning algorithms Fault detection Real-time systems Fourth Industrial Revolution Trajectory Internet of Things intelligent sensors Streams Machine intelligence

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A Novel smart Healthcare System Design for Internet of Health Things

A Novel Smart Healthcare System Design for Internet of Healt...

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2021 IEEE international conference on Innovative computing, intelligent Communication and smart Electrical Systems, ICSES 2021

作者： Jain, Pulkit Chawla, Paras Chandigarh University Department of ECE Mohali India

ISBN: (纸本)9781665435215

COVID-19 might be devastatingly affecting our enterprises, public activities and individual prepping norms and principles but it has also sparked a digital revolution of innovation in different fields. The objective of this paper is to understand the in-depth role of the Internet of Things (IoT) in eHealth to mitigate the impact of COVID-19. This paper covers numerous applications of IoT in healthcare starting from research, telemedicine, teleconsultation via chatbots and virtual assistants providing instantaneous medical help online. Telemedicine and remote patient monitoring is the need of the hour to avoid direct contact with the patients which have been made possible via IoT and its associated tools like Artificial Intelligence, Machine Learning, Blockchain technology and Cloud computing. With such high volumes and diversity of data being generated from IoT there is a strong need for connectivity and streaming analytics thus 5G technology and its applications have been discussed like smart 5G connected ambulances and smart 5G based hospitals. Long Range Radio is another promising technology which due to its low power operation and long-distance data transmission at higher speeds is turning out to be the defacto technology for IoT networks across the globe especially in areas with poor network coverage. Seeing the demand for both ventilators and skilled medical professionals due to lack of proper medical infrastructure worldwide, a review of IoT-based smart ventilators has also been carried out. The paper concludes with possible solutions to IoT challenges in healthcare by proposing a smart healthcare model design. Moreover keeping in mind the situation of Covid-19 Pandemic the module also comprises a UVC Disinfection box that would help in eliminating the risk of the virus entering our homes. © 2021 IEEE.

关键词： COVID-19

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Design of Visualization Platform for intelligent Distribution Room Based on 3D Modeling Technology 7

Design of Visualization Platform for Intelligent Distributio...

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7th international conference on Image, Vision and computing, ICIVC 2022

作者： Zhang, Zhifeng Kuang, Jinhui Cui, Xiao Zhao, Zhe Ma, Junxia Pang, Bin Zhengzhou University of Light Industry College of Software Engineering Zhengzhou China Henan University Fine Arts College Kaifeng China

ISBN: (数字)9781665467346

ISBN: (纸本)9781665467346

Distribution room is an important part of the power system and plays an important role in the daily operation of the power system. With the smart grid becoming the main direction and trend of the future development of power system, distribution rooms are gradually changing from manual monitoring mode to unattended mode, which also has a higher requirement for the visualization level of distribution rooms. However, at this stage, there are some problems in visual display methods, such as poor interactivity, data display is not intuitive, and so on. Based on this, this paper chooses three-dimensional software modeling technology to model urban distribution room and puts forward a lightweight method of three-dimensional model of distribution room. By slicing the model file and reducing the number of triangular surfaces, the model can be loaded and browsed quickly. The visualization platform of intelligent distribution room built in this paper realizes functions of three-dimensional model display, real-time display of electrical data and operation state monitoring of distribution room, meets the requirements of operation and maintenance personnel to monitor the operation state of distribution room, and improves the intelligence level of distribution room management system. © 2022 IEEE.

关键词： smart power grids

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Modified AI-based learning Approach to Secure data using Deep learning algorithms

Modified AI-based learning Approach to Secure Data using Dee...

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intelligent computing and Emerging Communication Technologies (ICEC), international conference on

作者： Venkata Sai Swaroop Reddy Nallapareddy Department of Software Engineering Sony Interactive Entertainment Austin

ISBN: (数字)9798331508432

ISBN: (纸本)9798331508449

intelligent cyber security systems and policies of today rely on deep learning, a technology derived from artificial neural networks (ANNs). Cyber risk analytics that use artificial intelligence (AI) to boost business resilience and understand cyber security risk: pros and cons. Make use of deep learning tools like generative adversarial networks, deep belief networks, deep transfer learning, and deep reinforcement learning to intelligently combat cyber threats. There is also a set of tools that includes generic adversarial networks, auto-encoders, multilayer perceptrons, convolutional neural networks, recurrent neural networks (also known as long short-term memory), and so on. It is also possible to use these networks in ensembles or hybrid techniques. The goal of the backpropagation method is to convert inputs into outputs by making the most of the network weights. When training, a number of optimisation methods are employed, including Adam, Stochastic Gradient Descent, and Limited Memory BFGS. A wide range of cybersecurity issues could be addressed by these neural networks. Building reliable Internet of Things (IoT) systems, detecting harmful botnet traffic, analyzing malware, and conducting security threat analyses are all possible with MLP-based networks. Finding a computationally expensive solution to a complex security model may be difficult with MLP since the model is hyperparameter sensitive and needs adjustment of many different parameters, including the number of hidden layers, neurons, and iterations.

关键词： Deep learning Training Computational modeling smart contracts Transfer learning data models Internet of Things Computer crime Biological neural networks Long short term memory

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Large-Scale Farmland Management on Improving Production Efficiency Under the Background of smart Agriculture-Based on Panel Quantile Regression

Large-Scale Farmland Management on Improving Production Effi...

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

作者： Wu, Bangzheng School of Economics Shanghai University Shanghai China

ISBN: (纸本)9783030627454

With the rise of big data and intelligent manufacturing in recent years, agricultural production in the traditional way of operation has been linked with wisdom gradually. Especially after the rise of the rural revitalization strategy, the key to achieving rural revitalization lies in solving the problems of agriculture, rural areas and farmers. It is inseparable from the intelligent management model and advanced technology to improve the efficiency of agricultural production. It has changed the agricultural production by establishing an intelligent management platform, which can accelerate land transfer and form an intensive agricultural land management model. This paper uses Chinese national panel data from 2002 to 2017 combining quantile regression to examine whether farmland scale can improve production efficiency in the background of smart agriculture. Finally, we will propose relevant policy recommendations based on empirical test results. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Production efficiency

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