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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 2312 条记录，以下是151-160 订阅

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A machine learning-Based Temperature Control and Security Protection for smart Buildings 10

A Machine Learning-Based Temperature Control and Security Pr...

引用

international conference on smart computing (smartCOMP)

作者： Zaman, Mostafa Al Islam, Maher Zohrabi, Nasibeh Abdelwahed, Sherif Virginia Commonwealth Univ Dept Elect & Comp Engn Richmond VA 23220 USA Penn State Univ Brandywine Departnient Engn Media PA USA

ISBN: (纸本)9798350349955;9798350349948

With the advent of loT technology, smart building management has been transformed, leading to significant improvements in energy efficiency and occupant comfort. Indoor room temperature control is crucial as it affects both building performance and occupant quality of life. Nevertheless, stringent cybersecurity measures are required due to the increasing susceptibility to cyber attacks with more loT links in smart buildings. Identifying and managing unusual temperature readings is essential to keep the system running smoothly, efficiently, and safely. By integrating classical control methods such as PID with anomaly detection and LSTM modeling, this approach enables proactive anomaly identification and accurate temperature forecasts, rendering sustainable and resilient living conditions. This integration optimizes resource usage and mitigates cyber risks. This paper presents a holistic method that combines PID control, LSTM forecasting, and anomaly detection for smart building applications. The proposed integrated approach successfully addresses aberrant temperature variations and enhances building performance, as shown through experimental validation.

关键词： smart Building Anomaly Detection machine learning PID Controller Random Forest Classifier FDI

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Design and Optimization of Central Air Conditioning Systems Based on Digital Twin Technology 24

Design and Optimization of Central Air Conditioning Systems ...

引用

4th international conference on Internet of Things and machine learning

作者： Peng, Fan Hebei Univ Technol Internet Things Engn Tianjin Peoples R China

ISBN: (纸本)9798400710353

With the rapid development of Internet technologies, the Internet of things (iot) has become a crucial bridge connecting the physical and digital worlds. As a significant branch of iot applications, smart homes focus on automating and intelligently controlling the home environment through the interconnection and data exchange of smart devices. However, as the complexity of smart home systems increases, ensuring their efficient operation and meeting users' personalized needs has become a focal point of research. Digital twin technology, as an innovative solution, involves creating virtual replicas of physical entities to simulate, analyze, and optimize systems without disrupting their operations. This paper proposes a smart home control system design based on digital twins, aiming to achieve precise monitoring and intelligent control of the smart home environment through a highly integrated system architecture and modular design. This system not only improves energy efficiency but also autonomously learns from user behavior and preferences, thus offering more personalized services.

关键词： Digital twin smart home Central Air Conditioning System Design Energy Efficiency Optimization User Behavior Analysis

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iot-Cloud based Intelligent System for Crop Yield Prediction: A Review 15

IoT-Cloud based Intelligent System for Crop Yield Prediction...

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

作者： Shalini, G. Beena, B.M. Khanna, Manju Department of Computer Science & Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798331300579

Crop yield prediction is a crucial and challenging task in agriculture due to the complex and non-linear parameters involved. It mainly depends on three fluctuating factors namely soil, weather and environment hence traditional farming methods and statistical methods are no longer feasible to predict the yield precisely therefore smart farming is gaining more attention with the advent of cutting edge technologies like iot, Cloud and AI frameworks. Crop yield prediction involves Crop selection, Disease Detection, Weed detection, pest/insect Recognition and Water requirement level to assess the crop quality. This paper intends to provide a literature review on existing techniques to predict the yield of crops using iot, cloud and Artificial intelligence. The study of 26 selected papers based on exclusion criteria are reviewed chronologically from the year 2017 to 2023 then, outlines the use of different sensors, datasets used in the works considered, simulation platforms, state-of-the art machine learning and Deep learning algorithms applied as well as the performance metrics used to measure the errors. Finally, the research gaps and challenges faced using these cutting edge technologies are defined. © Grenze Scientific Society, 2024.

关键词： smart agriculture

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smart Fish Feeding in iot-based Aquarium: A Comprehensive Approach Integrating Computer Vision, machine learning and Sensor Technology 4

Smart Fish Feeding in IoT-based Aquarium: A Comprehensive Ap...

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4th international conference on Next Generation computing Applications, NextComp 2024

作者： Cassy, Ryan Bekaroo, Girish Middlesex University Mauritius Flic-en-Flac Mauritius

ISBN: (纸本)9798331517403

With the increasingly busy lifestyles of people, advanced maintenance systems such as automated feeding has become of utmost importance to aquarium owners. Despite existing technologies, there remains a gap in smart feeding solutions tailored to the specific dietary requirements of different fish species and population sizes. This paper investigates smart fish feeding through automated detection, counting, classification and feeding of fishes within aquariums. As part of this paper, a smart population-driven aquarium maintenance system, named AquaTrack, is proposed that integrates Internet of Things (iot), computer vision, machine learning, and sensor technologies. The system detects, classifies, and counts fish, adjusting feeding amounts based on species and population to mitigate the risks of overfeeding and underfeeding, which can significantly affect water quality. The system was evaluated through four research questions, where a general accuracy of 76.9% in fish species identification and 77.1% in fish counting were found. While promising outcomes were observed under specific conditions, variability and limitations in performance across different scenarios were also noted. Environmental factors such as lighting, reflections, shadows, and particulate matter affected the accuracies, particularly in scenarios involving smaller fish quantities. Based on these findings, the study recommends further refinement of the system to enhance its capabilities. © 2024 IEEE.

关键词： Fish

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smart Farming Sloutions for Crop Disease Prediction and Control using iot and ML

Smart Farming Sloutions for Crop Disease Prediction and Cont...

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2024 IEEE international conference on Intelligent computing and Emerging Communication Technologies, ICEC 2024

作者： Purushotham, Bhurneni Sai Chandra, Pamulapati Satish Biju, Terry K Kumbhkar, Mohit Kumar SRM Institute of Science and Technology Department of Electronics and Communication Engineering Kattankulathur Chennai India

ISBN: (纸本)9798331508432

The agricultural domain stands as a pivotal sector, underpinning not only economic stability but also fundamental human sustenance. Nowadays, it's challenging to get the right output from agriculture because crops are afflicted with multiple diseases during growth, which frequently require the farmer's notice, in the poor production of crops. Over a period of thousands of years, agriculture has changed by including new technologies and techniques that increase production, sustainability, and efficiency. This study focuses on new approaches to smart farming that combine machine learning (ML) and the Internet of Things (iot) to predict and handle crop diseases and correction. The objective is to collect real-time data on crop conditions by utilizing iot devices that relate to sensors for temperature, moisture, humidity, NPK, PIR and combine ML output with these readings. The input information is analyzed using machine learning (ML) methods to compare the effectiveness of ResNet50, VGG16, and Convolutional Neural Networks (CNN) in the management and prediction of disease. As per the analysis, ResNet50 performs more effectively compared to the other algorithms due to its lower loss, which indicates a more precise detection of diseases. shown in this work how iot and ML can transform the farming process by giving farmers a more precise and efficient way of managing and monitoring crops. © 2024 IEEE.

关键词： smart agriculture

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smart Grid Solutions: iot-Integrated Hedge Systems for Reliable Solar Power Generation 2

Smart Grid Solutions: IoT-Integrated Hedge Systems for Relia...

引用

2nd international conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

作者： Shankar, S. Ravi Ilango, V Dineshkumar, G. Saravanan, S. Muthayammal Engn Coll Dept Elect & Elect Engn Namakkal Tamil Nadu India CMR Inst Technol Dept Comp Applicat Bengaluru Karnataka India

ISBN: (纸本)9798331540661;9798331540678

Solar power is prominent among the renewable energy sources that the world is moving towards. On the other hand, grid stability and reliability are threatened by solar energy's intermittent nature. To solve this problem, we present a new method in this research that uses smart grids that combine iot with hedge systems. The suggested system uses Internet of Things (iot) sensors and actuators to dynamically modify hedge systems in response to changes in solar power generation. The technology maximizes the efficiency of hedge systems by utilizing machine learning algorithms and predictive analytics to reduce the impact of unpredictability on solar power output. As a result, grid stability is improved, and solar power generation reliability is increased.

关键词： Renewable energy smart grid Internet of things Solar power generation Hedging systems Grid stability Predictive Analytics machine learning

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Empowering smart Cities through Federated learning An Overview 28

Empowering Smart Cities through Federated Learning An Overvi...

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28th international conference on System Theory Control and computing

作者： Jarour, Ayah Natl Univ Sci & Technol POLITEHN Bucharest Comp Sci Dept Bucharest Romania

ISBN: (纸本)9798350364309;9798350364293

This paper provides the role of Federated learning (FL) in empowering smart cities, with an emphasis on technology, applications, future directions, approaches and strategies, and the FL environment as a part of this overview, through investigating and discussing the latest research on applying FL in a variety of fields in smart cities by using the literature review as a methodology in the paper. FL is a decentralized machine learning technique that addresses data collecting, privacy, and security issues. Additionally, FL offers potential solutions for problems in smart cities, including enhancing data privacy, real-time anomaly detection, resource optimization, environmental monitoring, and smart grid management. Moreover, the paper presents some applications and relevant work in the FL in smart cities fields and summarizes them to introduce the main limitations of their work and future directions.

关键词： smart Cities Federated learning Privacy and Security Internet of Things.

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Blynk-Powered iot System with machine learning for Personalized Plant Care 8

Blynk-Powered IoT System with Machine Learning for Personali...

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8th international conference on Inventive Systems and Control, ICISC 202

作者： Pappula, Madhavi Myneni, Jaswanth Chowdary Gandra, Manasa Tadigadapa, Raghavendra Akella, Bala Krishna Murthy Lakireddy Balireddy College of Engineering AI&DS AP Mylavaram India

ISBN: (纸本)9798350386578

This research explores the development of a prototype Internet of Things (iot)-enabled smart irrigation system and investigates its potential integration with machine learning (ML). The primary mechanism, constructed using Blynk, facilitates real-time sensor data monitoring (temperature, humidity, and moisture) and enables manual irrigation control via a mobile application. The secondary mechanism includes the integration of machine learning (ML) model, facilitating data-driven predictions for optimal watering schedules. This ML integrated iot system has the potential to revolutionize irrigation practices, promoting water conservation and potentially enhancing plant health through precise watering based on real-time sensor data and historical trends. © 2024 IEEE.

关键词： smart agriculture

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Proceedings - 2024 IEEE conference on Dependable, Autonomic and Secure computing, DASC 2024

Proceedings - 2024 IEEE Conference on Dependable, Autonomic ...

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22nd IEEE international conference on Dependable, Autonomic and Secure computing, DASC 2024

ISBN: (纸本)9798331522728

The proceedings contain 19 papers. The topics discussed include: hybrid iot programming with data flow diagrams and mruby;early detection of user dynamics overheating through frequency analysis of time-series data;smart parking: detecting vacant spots from an angled viewpoint;crew management support application for operation managers and boosting system security;benchmarking deep learning algorithms for intrusion detection iot networks;a novel framework of constructing on water pipeline networks by node embedding approach;and a pervasive, efficient and private future: realizing privacy-preserving machine learning through hybrid homomorphic encryption.

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Incident Response in smart Agriculture: An MQTT Case Study 15

Incident Response in Smart Agriculture: An MQTT Case Study

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

作者： Rudrakar, Santoshi Rughani, Parag H. Sadineni, Lakshminarayana National Forensic Sciences University Gandhinagar India Manipal University Jaipur India School of Cyber Security & Digital Forensics National Forensic Sciences University Gandhinagar India Department of CCE Manipal University Jaipur India

ISBN: (纸本)9798350370249

The integration of Internet of Things (iot) technology into agriculture has significantly enhanced farm management and productivity. The Message Queuing Telemetry Transport (MQTT) protocol, pivotal in this transformation, facilitates communication among iot devices and decision making systems. Being a cost-effective and time-effective system, Ag-iot is prone to attacks due to its inherent vulnerabilities and weak security policies. When security incidents happen, robust response mechanisms help to contain the impact of the incident to prevent severe impacts on the network. This article presents an incident response model for a smart agriculture system to address incident response challenges. We conducted a case study on MQTT based agriculture system with the help of two publicly available iot datasets based on the proposed model. We conducted a series of experiments to statistically analyze the datasets for more insights and also used four widely used machine learning algorithms to detect malicious traffic. This study also proposes a set of Quality of Service based recommendations for effective incident response. Finally, we believe that this work provides an insightful approach to incident response in smart agriculture systems to address the prevailing security incidents. ©2024 IEEE.

关键词： smart agriculture

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