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检索条件"任意字段=2024 International Conference on Smart Computing, IoT and Machine Learning, SIML 2024"

共 2275 条记录，以下是71-80 订阅

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iot-Based smart Inventory Management System Using machine learning Techniques 3

IoT-Based Smart Inventory Management System Using Machine Le...

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international conference on Advances in computing, Communication and Applied Informatics (ACCAI)

作者： Manoharan, Geetha Kumar, Vipin Karthik, A. Asha, V Mohan, Chinnem Rama Nijhawan, Ginni SR Univ Sch Business Warangal Telangana India BK Birla Inst Engn & Technol Dept Math Pilani 333031 Rajasthan India Inst Aeronaut Engn Hyderabad India New Horizon Coll Engn Dept Comp Applicat Bangalore Karnataka India Narayana Engn Coll Dept Comp Sci & Engn Nellore 524004 Andhra Pradesh India Lovely Profess Univ Phagwara India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

This paper will show you how to handle your inventory effectively so that it meets all of your needs, whether you are in charge of solid or liquid *** using iot to handle our inventory, we can cut down on the work that needs to be done by hand and automate everything from measuring to placing orders. These steps will make product management more effective. The proposal uses an ultrasonic transducer and an Internet-connected computer, like a Raspberry Pi, to keep track of inventory. It also shows the current stock levels on a webpage that our system hosts and sends an email to the supplier and/or corporate staff to place an order.

关键词： iot (Internet of Things) smart Inventory Management machine learning Predictive Analytics Real-time Data RFID (Radio-Frequency Identification) Sensor Networks

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smart Agriculture System Using Internet of Things (iot) and machine learning 6

Smart Agriculture System Using Internet of Things (IoT) and ...

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6th IEEE international conference on Emerging smart computing and Informatics, ESCI 2024

作者： Anand, Nitipriya Pundir, Harsh Singh, Kapil Bisht, Kartik Singh Chauhan, Rahul Kapruwan, Akanksha Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350306613

One of the biggest factors for economic growth of any nation is agriculture. India, being one of the mass-producing countries in the world of different crops, still relies on traditional techniques rather than advancement over fields. Various advancements in field of agriculture have increased competence of certain farming activities making them reliable and convenient. Farmers face problems in coping with the changing climatic conditions as India has different weather and soil conditions in different regions. The introduction of iot and machine learning-based smart agriculture aims to solve this issue and assist farmers in increasing yield quality and productivity. In addition to providing real-Time agricultural monitoring assistance, it also offers suggestions for crops and fertilizers. The major goal of this study is to propose an Internet of Things (iot) based smart Agriculture system that would assist farmers in receiving suggestions based on a variety of parameters, such as temperature, pH, humidity. © 2024 IEEE.

关键词： Arduino ide DHT11 ESP8266 Node MCU iot

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smart Healthcare Systems: A Survey of iot and AI Technologies for Heart Disease Prediction

Smart Healthcare Systems: A Survey of IoT and AI Technologie...

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2024 international conference on Advancements in Power, Communication and Intelligent Systems, APCI 2024

作者： Vishnu, K. Sebastian, Thomas Binu, P.K. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Applications Amritapuri India

ISBN: (纸本)9798350363289

The surge in heart-related issues is linked to lifestyle changes and increased fast food consumption. The convergence of cloud computing, iot and machine learning for the prediction of cardio vascular disease is examined in this survey. In order to combat the worldwide increase in Cardio Vascular Disease, researchers are investigating novel strategies that make use of iot sensors, ensemble approaches and machine learning for ongoing health monitoring. By combining these technologies, the iot-ML-Cloud paradigm seeks to improve the accuracy of Cardio Vascular Disease prediction. It also emphasizes the importance of cloud computing and data mining for large-scale medical data analysis, as well as key findings from various ML algorithms in Cardio Vascular Disease diagnosis. © 2024 IEEE.

关键词： Electronic health record

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RLAD: Reinforcement learning based Anomaly Detection system for iot devices in smart homes 15

RLAD: Reinforcement Learning based Anomaly Detection system ...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Chadha, Angela Raj Reddy, Kethamreddy Karthikeya Sountharrajan, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

In the era of the Internet of Things (iot), smart homes have become a cornerstone of modern living, offering convenience, energy efficiency, and security. The extensive connectivity within these networks brings forth security challenges, making the detection of anomalies a critical aspect of iot network security. Anomalies encompass a wide array of unexpected events, from irregular data patterns to communication anomalies, power fluctuations, behavioral deviations, security breaches, and environmental variations. This paper underscores the pivotal role of anomaly detection in securing iot networks, highlighting its significance in identifying both known threats and emerging risks. Anomaly detection in iot relies on machine learning and statistical techniques, encompassing data collection, preprocessing, feature extraction, model training, real-time anomaly detection, and responsive actions. Reinforcement learning(RL), a subset of machine learning, emerges as a formidable tool in enhancing iot network security. RL models continuously observe and adapt to device behavior, identifying anomalies and security breaches in real-time. RL offers adaptability in rapidly changing iot environments, proactive responses to anomalies, and the ability to learn and evolve against new threats. RL strengthens network security without compromising individual device data. Our advanced security system that refines its understanding of the smart home environment, collectively learns from anomalies, and bolsters network security. Synergy of iot and RL is reshaping iot network security, promising not only seamless device functionality but also the safeguarding of sensitive data and personal spaces. © 2024 IEEE.

关键词： Anomaly Intrusion Metadata Stream Reinforcement learning

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Data-Driven End-to-End Lighting Automation Based on Human Residential Trajectory Analysis

Data-Driven End-to-End Lighting Automation Based on Human Re...

引用

international conference on smart Applications, Communications and Networking (smartNets)

作者： Zhu, Jack Tan, Jingwen Wu, Wencen San Jose State Univ Comp Engn Dept San Jose CA 95192 USA

ISBN: (纸本)9798350385328;9798350385335

smart home automation, particularly in lighting, holds the potential to significantly improve comfort, energy efficiency, and security by centralizing control over internet-of-things (iot) devices. This paper introduces a smart lighting automation system that analyzes human movement trajectories using machine learning, deep learning, and reinforcement learning techniques integrated into the Home Assistant platform. In particular, we introduce a transformer-based deep neural network architecture with reward-based tuning as our backbone model. The system predicts the user's next location and adjusts the lighting accordingly based on the anticipated movement trajectories derived from data collected by iot devices distributed throughout a residential area. This enhances both convenience and energy efficiency. We deployed the system in a residential setting and conducted experiments to validate its accuracy.

关键词： smart Home Automation machine learning Transformer

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Integrating iot, Edge, and Cloud computing for Early Prediction of Type 2 Diabetes: A machine learning-Driven smart Healthcare Approach 15

Integrating IoT, Edge, and Cloud Computing for Early Predict...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Kashyap, Tarun Neware, Dewashish Ingle, Rajesh Lovanshi, Mayank Kumar, Santosh Deptt. of CSE IIIT Naya Raipur Raipur India

ISBN: (纸本)9798350370249

As diabetes continues to be one of the most prevalent chronic illnesses worldwide, affecting approximately 537 million individuals according to the IDF Diabetes Atlas 2021 report, there is a critical need for enhanced awareness and prediction capabilities. Leveraging the Internet of Things (iot) and edge computing offers a promising approach for early disease detection through real-time analysis. This study introduces "HealthEdge," a comprehensive smart healthcare system that integrates machine learning with iot and edge computing to detect Type 2 Diabetes. "HealthEdge" employs machine learning models at both edge and cloud levels to identify early signs of Type 2 Diabetes using real-time patient health data collected from iot sensors. By prioritizing real-time processing at the edge to minimize latency and utilizing cloud resources for comprehensive data analysis and model training, "HealthEdge" addresses privacy concerns through robust encryption, demonstrating promising results in early diabetes diagnosis while emphasizing scalability, efficiency, and accessibility. Moreover, the proposed solution has been implemented and validated on real-world patient health data, showcasing impressive accuracy of 98% using the random forest algorithm on the original diabetes dataset. This solution can be deployed on various intelligent edge devices, paving the way for improved diabetes management and healthcare delivery. ©2024 IEEE.

关键词： Electronic health record

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Advancements in smart Agriculture: A Comprehensive Review of machine learning and iot Approaches

Advancements in Smart Agriculture: A Comprehensive Review of...

引用

2024 international conference on Communication, computing and Internet of Things, IC3iot 2024

作者： Shanthakumari, G. Vignesh, A. Harish, R. Roshan Karthick, R. Sri Sairam Engineering College Department Of Electronics And Communication Engineering Chennai India

ISBN: (纸本)9798350352689

In contemporary agriculture, optimizing crop yield necessitates efficient weed management and precise irrigation. This review amalgamates insights from ten studies, centering on the integration of machine learning, deep learning, and iot technologies to address these challenges. The weed detection segment underscores advanced solutions like attention-based deep networks and visual transformers, tackling crop-weed discrimination challenges, albeit contending with issues such as high computation costs. Transitioning to precision agriculture, the paper explores machine and deep learning techniques for superior weed identification and control, offering viable integration with herbicide applicators. Challenges encompass interoperability and standardization of sensing and control protocols. The narrative then shifts to smart irrigation systems, presenting iot-based solutions for real-time monitoring and automatic irrigation. Systems like Arduino Mega 2560 and dynamic irrigation scheduling boost crop productivity, with advantages including farmer-friendly interfaces and limitations in potential implementation costs on larger scales. In the results analysis, a collaborative impact is evident, envisioning a seamless integration of advanced technologies for sustainable and productive agriculture. Collectively, these studies signal a promising trajectory in leveraging technology to address challenges, paving the way for a new era of precision farming amid the evolving agricultural landscape. © 2024 IEEE.

关键词： Irrigation

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smartHealth: machine learning based smart Diabetes Prediction in an iot System 2

SmartHealth: Machine Learning based Smart Diabetes Predictio...

引用

2nd IEEE international conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Ghosh, Monalisa Saha, Supriya Garai, Swagata Baraha, Satyakam Institute of Engineering and Management India Sri Sivasubramaniya Nadar College of Engineering Chennai India

ISBN: (纸本)9798350360523

The development of machine learning and Internet-Of-Things has helped to break down barriers in health and medical facilities. In this paper, we propose smartHealth, a smart diabetes prediction framework in an Internet-Of-Things (iot) system based on machine learning. The factors (attributes) affecting diabetes are gathered from individuals using a collection of iot devices and recorded in our designed smartHealth mobile application. We design the machine learning models using different algorithms, such as k-Nearest Neighbour, Logistic Regression, Random Forest, Decision Tress, and Multi Layer Perceptron and determine the best performing model among them. This model has been pre-trained and is used to predict the result of the individuals based on the collected data (attributes) that is uploaded in the edge server. Different results and plots are obtained that convey the performance of our smartHealth framework. © 2024 IEEE.

关键词： Edge computing

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Research on Improving Production Efficiency in smart Manufacturing using iot 4

Research on Improving Production Efficiency in Smart Manufac...

引用

4th international conference on machine learning and Intelligent Systems Engineering (MLISE)

作者： Jiang, Yuxin Northeastern Univ Sch Mat Sci & Engn Shenyang 110000 Liaoning Peoples R China

ISBN: (纸本)9798350375084;9798350375077

In the context of the highly competitive and digital transformation of the manufacturing industry, the Internet of Things (iot) plays a crucial role in enhancing production efficiency in smart manufacturing. This paper delves into how iot improves production efficiency in smart manufacturing through real-time data collection, monitoring, automated decision-making, remote maintenance and services, and supply chain optimization. At the same time, we explore various challenges encountered in this process, including technical, economic, and regulatory and policy challenges. Despite these challenges, we firmly believe that by deepening the research on the application of iot in smart manufacturing, we can overcome these difficulties, further improve the production efficiency of smart manufacturing, and promote the continuous development of the manufacturing industry.

关键词： Internet of Things smart Manufacturing Production Efficiency Technical Challenges Economic Challenges Regulatory and Policy Challenges

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Evaluation of Occupancy Detection with Distributed Environmental Sensors for iot Applications 20

Evaluation of Occupancy Detection with Distributed Environme...

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20th Annual international conference on Distributed computing in smart Sensor Systems and the Internet of Things (DCOSS-iot)

作者： Bovo, Matteo Agyeman, Rockson Arif, Muhammad Rinner, Bernhard Univ Udine Dipartimento Matemat Informat & Fis Udine Italy Univ Klagenfurt Inst Networked & Embedded Syst Klagenfurt Austria AAU SAL USE Lab Silicon Austria Labs Klagenfurt Austria

ISBN: (纸本)9798350369458;9798350369441

Occupancy refers to the presence of people in rooms and buildings. It is an essential input for iot applications, including controlling lighting, heating, access, and monitoring space limitation policies. Occupancy information can also be used to improve users' comfort and to reduce energy waste in buildings. This paper evaluates the performance and resource consumption of recent machine learning techniques for occupancy detection and measurement by exploiting data from distributed environmental sensors. This evaluation is founded on a dataset captured by our dedicated sensor network for indoor monitoring, comprising temperature, humidity, and carbon dioxide (CO2) sensors. Using different sensor modalities and spatio-temporal data selections, we compare eight classification algorithms based on the accuracy achieved and the required runtimes. Binary classification for occupancy detection (OD) achieves accuracies over 90% for individual modalities and close to 100% for modality combinations. Multi-class classification for occupancy measurements (OM) shows as clear ranking of the sensor modalities, and gradient boosting algorithms are superior when combining sensor modalities and fusing data from multiple sensors.

关键词： sensor network indoor environments multiclass classification sensor fusion smart rooms

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