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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131-140 订阅

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Future Internet Architecture for iot Frameworks Implementing the Synapse Algorithm for Intelligent Energy Management in smart Home Automation

Future Internet Architecture for IoT Frameworks Implementing...

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2024 international conference on Innovative computing, Intelligent Communication and smart Electrical Systems, ICSES 2024

作者： Shalini, M. Nagarajan, K.K. Prince Shri Venkateshwara Padmavathy Engineering College Electronics and Communication Engineering Chennai India Sri Sivasubramaniya Nadar College of Engineering Chennai India

ISBN: (纸本)9798331543617

This paper proposes a novel Internet architecture for iot frameworks that integrates the Synapse Algorithm for intelligent energy management in smart homes. Current iot energy management systems often face challenges such as inefficient energy consumption, poor adaptability to user preferences, and limited scalability in managing multiple interconnected devices. These issues are exacerbated by the inability to process large datasets effectively and in real-time. To address these challenges, the proposed framework, Intelligent Energy Management using the Synapse Algorithm (IEM-SA), offers a sophisticated solution. The framework leverages the Synapse Algorithm to optimize energy consumption by analysing data from interconnected devices in real- time, learning user behaviour patterns, and dynamically adjusting energy usage based on predicted demand. This not only enhances the system's energy efficiency but also enables seamless control and adaptability across different environments and devices. The proposed method improves energy efficiency, scalability, and adaptability within smart home environments. By integrating real-time data processing and machine learning, it supports the seamless operation of multiple iot devices while significantly reducing energy waste. Findings indicate that the IEM-SA framework outperforms existing methods in terms of energy savings, system responsiveness, and scalability. Through simulation results, the proposed framework demonstrates its potential to optimize energy usage while improving the overall smart home automation experience. © 2024 IEEE.

关键词： smart homes

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Securing smart Homes: machine learning Solutions for iot Cyber Threats

Securing Smart Homes: Machine Learning Solutions for IoT Cyb...

引用

2024 IEEE international conference on Innovation and Novelty in Engineering and Technology, INNOVA 2024

作者： Khan, Aadil Gupta, Deepali Dutta, Monica Lande, Jayapal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science & Artificial Intelligence SR University Warangal India

ISBN: (纸本)9798331505134

smart home technologies simplify light and temperature management, making life easier. These technologies provide smartphone-based home management. iot improves smart home equipment efficiency and customization. iot gadgets are linked, so hackers may access security cameras, personal data, and smart locks and thermostats, endangering people and property. A solid cybersecurity policy helps manage these security issues. Cyber threat detection early allows rapid reaction and mitigation, minimizing harm and preventing escalation. Active security breach detection reduces sensitivity and improves cybersecurity resilience by limiting consequences, forbidding illegal access, and protecting vital data. To increase smart home security, this initiative uses Aposemat iot-23 dataset and machine learning methods. We tested XGBoost, Simple Neural Network, GaussianNB, and Logistic Regression for illegal access and data injection detection. XGBoost and Simple Neural Network show their abilities to safeguard smart home networks by identifying and preventing dangerous activity with over 99% accuracy. These results illustrate how efficiently machine learning algorithms cover iot devices in smart homes, hence delivering a safer and more trustworthy environment for consumers. © 2024 IEEE.

关键词： smart homes

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machine learning Algorithm for Predicting Soil Classification using smart Agriculture 3

Machine Learning Algorithm for Predicting Soil Classificatio...

引用

3rd international conference on Sentiment Analysis and Deep learning, ICSADL 2024

作者： Ponkumar, D. David Neels Kumar, Kolipaka Pushpa Krupa Himesh Ramesh, S. Kumar, J. Arun Nallasivan, G. Jayachandra, A. Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chengalpattu India Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350396157

The advent of Internet of Things (iot) technology and data analytics has brought about a significant transformation in contemporary agricultural practices, sometimes referred to as smart agriculture. One of the fundamental uses of this technology is the prediction of soil classification, a critical factor in the optimization of crop management strategies and the allocation of resources. The present work investigates the use of machine learning techniques to achieve precise and efficient soil categorization in the context of smart agriculture. The study utilizes a comprehensive dataset consisting of several soil characteristics, including pH levels, moisture content, texture, and nutrient composition. These attributes were obtained via the use of Internet of Things (iot) sensors and Unmanned Aerial Vehicles (UAVs). Several machine learning techniques, such as Random Forest, Support Vector machine, and Neural Networks, are assessed in terms of the classification accuracy in identifying soil types using the given characteristics. The findings indicate that machine learning models has the capability to accurately forecast soil categorization, hence enabling the use of precision agricultural techniques. These models assist farmers in making informed choices based on data analysis pertaining to crop selection, irrigation, and fertilization, resulting in enhanced agricultural productivity and the adoption of sustainable resource management practices. Additionally, this research investigates the interpretability of the chosen machine learning models, with the aim of ensuring that farmers are able to grasp and place faith in the forecasts. The proper implementation of smart agricultural systems also involves addressing ethical problems related to data privacy and security. This study makes a valuable contribution to the progress of sustainable agriculture via the use of machine learning and Internet of Things (iot) technology. By leveraging these technologies, the research a

关键词： Soils

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Proceedings of the 3rd international conference on Applied Artificial Intelligence and computing, ICAAIC 2024

Proceedings of the 3rd International Conference on Applied A...

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3rd international conference on Applied Artificial Intelligence and computing, ICAAIC 2024

ISBN: (纸本)9798350375190

The proceedings contain 308 papers. The topics discussed include: machine vision-driven semantic segmentation for autonomous navigation;face recognition based automated smart attendance using hybrid machine learning algorithms and computer vision;DDoS attacks detection in iot networks using Naive Bayes and random forest;data analysis and visualization of master sample management and due date alert tool;recent developments in designing internet of things architectures;a study on hybrid electric vehicles (HEV) safety and industrial control network security;detection of cyber-attacks in network traffic using machine learning algorithm;smart grid protection with AI and cryptographic security;deep learning based cotton plant pest detection and fertilizer recommendation system;and interactive tactile book framework for visually challenged community.

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A smart Forest Fire Detection and Notification System using iot and machine learning

A Smart Forest Fire Detection and Notification System using ...

引用

2024 international conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Masram, Bharati Mendhe, Anish Mishra, Ayush Pathade, Aniket Shindekar, Nakshatra Kangale, Yash Yeshwantrao College of Engineering Department of Electronics & Telecommunication Engineering Nagpur India Datta Meghe Institute of Higher Education and Research Jawaharlal Nehru Medical College Wardha India

ISBN: (纸本)9798350378092

In this paper, the proposed system presents a two-stage forest fire detection system that first uses a wireless sensor network of MQ sensors and a Node MCU and then a YOLOv8 forest fire detection model to reduce false alarms. The entire process is seamlessly integrated with the Raspberry Pi, which immediately triggers an email notification for quick action upon receiving confirmation of a fire. As the frequency and severity of wildfires continue to escalate, it is imperative that we take swift action to address their devastating impact. The YOLO model was able to achieve an accuracy of 92% with low power and memory consumption on the MCU. The method serves as a demonstration of the potential of combining innovative technologies to solve environmental problems and contributes to the development of effective and proactive fire detection systems using iot and machine learning. © 2024 IEEE.

关键词： Sensor nodes

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iot Based smart Plant Growing and Caring Platform for Urban Environments 9

IOT Based Smart Plant Growing and Caring Platform for Urban ...

引用

9th international conference on Information Technology Research

作者： Ilangaweera, La S. Naramada, S. E. M. H. Sajith, N. T. M. Aashiq, M. N. M. Hinas, M. N. A. South Eastern Univ Sri Lanka Dept Elect & Telecomunicat Engn Oluvil Sri Lanka South Eastern Univ Sri Lanka Dept Comp Sci & Engn Oluvil Sri Lanka

ISBN: (纸本)9798331511425;9798331511432

This paper explores the design, implementation, and performance evaluation of an innovative iot enabled automatic plant growing and caring platform tailored for urban environments. The proposed system utilizes supervised and unsupervised machine learning algorithms to make decisions toward enhancing the growth and health of the plants on the platform with minimum human intervention. Increasing urbanization and busy lifestyles have led to increased interest in smart gardening solutions that optimize plant care and resource utilization. This research delves into the integration of iot technologies and machine learning algorithms to address challenges faced with urban gardening and contribute to the ever-expanding body of literature on iot applications for smart cities. The proposed platform comprises multiple sensors placed to collect vital parameters such as soil moisture, ambient temperature, and humidity. A simple camera collects images of the plants daily. The collected data is transmitted via Wi-Fi to an online database for storage and analysis. A machine learning algorithm analyses the collected environmental data to make informed decisions such as predicting the optimal watering schedule. A pump integrated into the platform is activated based on the schedule generated by the algorithm. The schedule prediction is based on plant species and real-time environmental data. Also, an unsupervised algorithm is used to determine the health status of the plants. The images taken daily are analyzed by the unsupervised algorithm to detect simple abnormalities in the plants to alert the owners to attend to the issues. The system was able to monitor the vital parameters and care for the plants independently.

关键词： iot machine learning Raspberry-pi smart Farming Automation

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Securing smart Homes: machine learning Approaches for Detecting Reconnaissance Attacks

Securing Smart Homes: Machine Learning Approaches for Detect...

引用

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

作者： Sharma, Anshika Babbar, Himanshi Vats, Amit Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Abes Institute of Technology Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798331508432

The widespread use of smart home gadgets has resulted in heightened susceptibilities and the possibility of being exploited by cybercriminals, specifically through reconnaissance attacks that preceded more severe invasions. This study investigates the effectiveness of machine learning (ML) models in identifying recon attacks in smart home applications. The BoT-iot dataset is used to assess four major ML algorithms: Random Forest (RF), Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting machine (LightGBM). This dataset captures a wide range of network traffic events and attack kinds. The dataset undergoes preprocessing to increase the importance of features and decrease the number of dimensions, resulting in optimal performance of the model. Conducting thorough experimentation and cross-validation evaluates each model's accuracy, precision, recall, and F1-score in detecting reconnaissance operations. The results suggest that ensemble models, specifically XGBoost and LightGBM, surpass conventional models in terms of accuracy and computing economy. This makes them reliable options for promptly detecting attacks in smart home situations. The findings highlight the capacity of sophisticated ML methods to improve the security and robustness of smart home networking against surveillance and subsequent attacks. © 2024 IEEE.

关键词： Adversarial machine learning

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Design of Greenhouse System with Internet of Things and machine learning 10

Design of Greenhouse System with Internet of Things and Mach...

引用

10th international conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Pari Pugazh, M. Arul Xavier, V.M. Shyni, S.S. Karunya Institute of Technology and Sciences Division of Cse Tamil Nadu Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

The traditional greenhouse system lacks significant shortcomings in resource management, automation, and active monitoring for diseases and pest attacks. Currently, these greenhouses run with suboptimal resource efficiency, relying heavily on manual labor. The coming together of machine learning (ML), and Internet of Things (iot) has sparked revolutionary solutions spanning various domains. This paper proposes a system by utilizing the potential of these technologies to develop a smart greenhouse model that integrates ML, and iot to revolutionize the conventional farming practices. Equipped with sensors and a computer, this system collects environmental data within greenhouse structures, offering actionable insights for farmers. Utilizing this data, the proposed system autonomously adjusts conditions to align with crop needs, including intelligent crop rotation suggestions. The goal is to optimize and automate farming processes, reduce resource consumption, and minimize human effort. The proposed system also facilitates pest and disease detection, contributing to improved yields. This streamlined approach aims to elevate traditional greenhouse technology, fostering efficient, resource-conscious, and sustainable agriculture practices. © 2024 IEEE.

关键词： smart agriculture

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smart Farm Management Using machine learning 2

Smart Farm Management Using Machine Learning

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2nd IEEE international conference on iot, Communication and Automation Technology, ICICAT 2024

作者： Prabakaran, S. Dharun, T. Gowsik, H. Anu Prakash, K. Kirubakaran, K. V.S.B. Engineering College Department of Computer Science and Engineering India

ISBN: (纸本)9798350368109

This article describes the integration of machine learning (ML) with smart agriculture management to improve agriculture through data-driven insights and automation. machine learning algorithms analyze data from a variety of sources, including sensors and drones, providing farmers with recommendations on crop management, resource allocation and pesticides. Tracking learning helps crop classification and disease detection, while learning from tracking predicts yield and finds anomalies. Reinforcement learning makes robotic farming possible. The challenges include data quality, security and integration, but the benefits are huge;increased usage, decreased resource usage, and increased revenue. © 2024 IEEE.

关键词： smart agriculture

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smart Energy Power Consumption 6

Smart Energy Power Consumption

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

作者： Mishra, Bharatoday Mathew, Rejo BITS Pilani Software System Rajasthan India University Mumbai India

ISBN: (纸本)9798350306613

The new 5G technology was recently launched in several Indian states. Undoubtedly, it will change the human lifestyle, businesses, and technology, such as iot-based devices. Based on iot and its features of scalability, connectivity, machine learning, security, dynamic nature, endpoint management, integration, analysis, and the compact nature of devices, the System can achieve system run time analysis and reduce overhead latency and cost. The topic of 'smart Electric Power Consumption (SEPC)' would likely focus on ways that cutting-edge technology can be applied to increase efficiency and control how much electricity is being consumed in buildings, homes, and industries. smart electricity system management decisions can be made with confidence with an accurate prediction of the consumption load. The inability of the current load forecasting method to handle time-series data, which leads to poor stability and poor forecasting accuracy, must be overcome. Additionally, the symmetric mean absolute percentage error (SMAPE) to carry out accurate forecasting of the temporary power load based on the LSTM model network over the gated recurrent units (GRU) recurrent neural network. The main characteristics of input data are crucial for increasing load forecasting prediction accuracy for smart electricity consumption. The research is based on basic units of iot like INA219 and ACS712 sensors and provides time-serious data to SEPC. Finally, LSTM and GRU predict the overall electricity consumption based on historical data and current data. © 2024 IEEE.

关键词： Deep learning Edge computing Energy Storage Systems iot smart Meters Web and Mweb

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