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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61-70 订阅

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Traffic Data Augmentation Using GANs for ITS 20

Traffic Data Augmentation Using GANs for ITS

引用

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

作者： Dabboussi, Abdul Hamid Jammal, Manar York Univ Elect Engn & Comp Sci Toronto ON Canada York Univ Sch Informat Technol Toronto ON Canada

ISBN: (纸本)9798350369458;9798350369441

Intelligent Transportation Systems (ITS) play a pivotal role in shaping the foundation of smart cities, providing data-driven solutions for traffic management, prediction, and safety. However, these applications often face a significant challenge data scarcity. Insufficient data limits the effectiveness of machine learning models in the context of ITS. To address this issue, this paper presents a novel data augmentation solution using Generative Adversarial Networks (GANs). By collecting sensor-based traffic speed data with contextual labels and training a GAN-based model to generate realistic traffic data for specific days and times, this research successfully proposes a solution to the problem of data scarcity. The generated data undergoes comprehensive qualitative and quantitative evaluations, demonstrating its potential to enhance ITS applications. Furthermore, the generated data is utilized to augment the training data for multiple traffic prediction models, effectively enhancing their performance. This approach opens new avenues for the development of intelligent and sustainable transportation systems, ultimately contributing to the advancement of smarter and more resilient cities.

关键词： Intelligent Transportation Systems iot smart Cities GAN Wasserstein GAN Traffic Prediction machine learning

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siml 2024 Committees

SIML 2024 Committees

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smart computing, iot and machine learning (siml), international conference on

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siml 2024 List Reviewer Page

SIML 2024 List Reviewer Page

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smart computing, iot and machine learning (siml), international conference on

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iot-Based Plant Disease Detection Using machine learning: A Systematic Literature Review

IoT-Based Plant Disease Detection Using Machine Learning: A ...

引用

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

作者： Mohammad, Abdallah Eleyan, Derar Eleyan, Amna Bejaoui, Tarek Palestine Tech Univ Kadoorie Dept Comp Sci Dept Invest & Cybercrime Tulkarem Palestine Palestine Tech Univ Kadoorie Dept Comp Sci Tulkarem Palestine Nablus Univ Tech & Vocat Educ Fac Telecomm & Informat Technol Nablus Palestine Manchester Metropolitan Univ Dept Comp & Math Manchester Lancs England Univ Carthage Dept Comp Engn Carthage Tunisia

ISBN: (纸本)9798350385328;9798350385335

Agriculture has a huge benefit on the global landscape due to rapid population growth and the subsequent increase in demand for food. Hence, there is an urgent need to enhance crop productivity. One of the primary factors contributing to low crop productivity is the spread of diseases caused by bacteria, spores, fungi, and viruses. However, this problem can be mitigated by applying plant disease detection methods, Recently, the field of machine learning and deep learning has achieved remarkable results when combined with iot-based technology, especially in the field of plant disease recognition. This scientific article aims to clarify the prevailing methodologies used in iot-based smart agriculture, with a clear focus on the use of iot and machine learning. In this context, the manuscript presents an innovative model for plant disease detection and recognition, drawing on the fields of machine learning and deep learning, ultimately leading to increased accuracy and efficiency. In essence, this article provides a comprehensive review and overview of various machine learning and deep learning techniques used in plant disease detection, using an iot approach.

关键词： Internet of Things (iot) smart Farming machine learning and Deep learning

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Exploring the Potential of iot, machine learning, and Cloud computing in smart Home Applications

Exploring the Potential of IoT, Machine Learning, and Cloud ...

引用

2024 international conference on Advances in computing Research on Science Engineering and Technology, ACROSET 2024

作者： Mathur, Garima Savita, Pankaj Gaur, Neeraj Vishwakarma, Bhavana Tripathi, Vaibhav Sagar Institution of Science and Technology Computer Science & Engineering Bhopal India Itm University Computer Science & Engineering Gwalior India Oriental Institute of Science & Technology CSE-AIML Bhopal India

ISBN: (纸本)9798350388800

This study investigates using machine learning (ML), the Internet of Things (iot), and cloud computing to predict cardiovascular diseases. Integrating these advanced technologies in healthcare enables the collection, analysis, and real-time response to patient data, enhancing early diagnosis and personalised treatment plans. This paper presents various case studies demonstrating the successful application of ML algorithms, iot devices, and cloud platforms in cardiovascular disease prediction. The findings highlight significant improvements in prediction accuracy, patient monitoring, and overall healthcare efficiency. Additionally, the study addresses the challenges, opportunities, and future research directions in the field, aiming to provide a comprehensive understanding of the potential of these technologies in transforming cardiovascular healthcare. © 2024 IEEE.

关键词： Cloud computing Intelligent Homes iot machine learning

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iot-Based smart Home Healthcare Monitoring System Using machine learning Algorithms 3

IoT-Based Smart Home Healthcare Monitoring System Using Mach...

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

作者： Jamalpur, Bhavana Saseendran, Arun K. Vani, V. Divya Raj, Vijilius Helena Veeramalai, G. GinniNijhawan SR Univ Sch CS & AI Warangal Telangana India Sree Narayana Arts & Sci Coll Dept Econ Kottayam Kerala India Inst Aeronaut Engn Hyderabad India New Horizon Coll Engn Dept Appl Sci Bangalore Karnataka India M Kumarasamy Coll Engn Dept Math Karur India Lovely Profess Univ Phagwara India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

The purpose of this study is to demonstrate an Internet of Things (iot)-based smart home healthcare monitoring system that is run by machine literacy algorithms. An opportunity to update healthcare monitoring has arisen as a result of the spread of Internet of Things (iot) devices. This is especially true when it comes to monitoring patients within the comfort of their own homes. To gather real-time data on vital signs and the conditioning of diurnal life from cases, our proposed system makes use of Internet of Things detectors. In addition, machine learning algorithms are utilized to analyze this data, which enables the implementation of proven and forward-thinking healthcare monitoring. The system can adapt to the activities of individual cases by continuously learning, which allows it to identify anomalies and provide predictions about implicit health problems. The incorporation of machine literacy not only improves the precision of health monitoring but also makes it easier to provide early intervention and preventative care, which ultimately leads to the resolution of patient difficulties and a reduction in the expenses of healthcare. This investigation makes a crucial contribution to the rapidly developing field of Internet of Things (iot)-)-enabled healthcare systems, illustrating the potential for technology to transform conventional approaches to the delivery of healthcare.

关键词： iot smart Home Healthcare Monitoring machine learning

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iot and AI Based smart Plant Watering System 3

IoT and AI Based Smart Plant Watering System

引用

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

作者： Parepalli, Ramanamma Sreejith, S. Talari, Abhijeeth Divya, Mukthapuram Mohan, Gokul Meghana, Cheekuru New Horizon Coll Engn Bengaluru Dept Elect & Commun Engn Bengaluru India New Horizon Coll Engn Dept Artificial Intelligence & Machine Learning E Bengaluru India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

It enhances household plant care through the integration of iot and AI technologies. It utilizes a 3x3 arrangement of plant pots with soil moisture sensors positioned at each end of every row or column. A central microcontroller is employed to activate solenoid valves which is connected to android app, ensuring precise watering of plants and minimizing water wastage. This system has three modes manual, automatic and AI. The AImode incorporates a machine learning algorithm that considers various factors such as temperature, humidity, soil moisture, andwater usage. This algorithm dynamically adjusts watering parameters to the user, promoting sustainable water usage practices. By doing so, the watering system for plants contributes to efficient resource utilization and addresses water conservation *** scalable system is adaptable to various indoor settings, showcasing the effectiveness of technology in meeting the diverse needs of plant care. The fusion of iot and AI not only optimizes plant health but also underscores the role of technology in fostering environmentally conscious practices.

关键词： AI machine learning iot smart plant watering Kodular Python Sensors Hardware Sustainable

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Sustainable Agriculture Using machine learning and iot 15

Sustainable Agriculture Using Machine Learning and IoT

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

作者： Badgujar, Vishakha Panse, Aarya Mohite, Manali Bedse, Siddhant Lanjulkar, Pranjal Kumbhar, Vijayalaxmi Tekade, Arti Andhare, Maithili PCCOER Department of ENTC Pune India

ISBN: (纸本)9798350370249

This paper presents a novel approach to sustainable agriculture, integrating machine learning (ML), Deep learning (DL), and the Internet of Things (iot). It employs iot devices with cameras for real-time monitoring of plant health, enabling early detection of diseases and pests. Through disease and pest detection machine learning models, the system provides farmers with timely alerts and tailored solutions via a user-friendly web application. Additionally, an iot-based spraying mechanism delivers targeted treatments, reducing the overuse of pesticides and promoting sustainability. It provides precision agriculture, offering an efficient solution to enhance crop management and productivity while minimizing environmental impact. © 2024 IEEE.

关键词： smart agriculture

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AI-enhanced Healthcare iot System: Advanced ML Detection and Classification Algorithms for Real-Time Cardiovascular Monitoring 20

AI-enhanced Healthcare IoT System: Advanced ML Detection and...

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

作者： Christodoulou, Lakis Chari, Andreas Georgiades, Michael Biomed Med Syst R&D Technol & Innovat Paphos Cyprus Neapolis Univ CS ISLab Paphos Cyprus Infostrada Commun Paphos Cyprus

ISBN: (纸本)9798350369458;9798350369441

Cardiovascular diseases (CVDs), including coronary heart disease, cerebrovascular disease, rheumatic heart disease, and other conditions affecting the heart and blood vessels, are identified by both the Pan American Health Organization and the World Health Organization as the leading cause of global mortality, underscoring their significant impact on health worldwide. In this paper we utilize emerging technologies in the fields of Internet of Medical Things (IoMT) and AI/machine learning (ML) and propose an end-to-end prototype platform for real-time detection, classification, prediction, and monitoring of cardiovascular anomalies. In addition we introduce Cardio-ECG-Heart Arrhythmia Algorithms using advanced AI/ML, combining mathematical-statistical and computational techniques to intelligently detect critical conditions related to CVDs and arrhythmias. The proposed innovative AI/ML Peak Detection algorithm based on adaptive thresholds, coupled with the ML Decision Tree Classification algorithm, has been tested with numerous ECG signals and exhibits remarkable performance, achieving exceptional precision, accuracy, sensitivity, specificity, and F1 Score rates between 99% and 100%.

关键词： Electro-Cardio-Gram (ECG) Vital Signs AI machine learning (ML) iot IoMT adaptive digital signal processing (DSP) smart Healthcare Monitoring (SHM) Detection Classification PQRST System on Chip (SoC)

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An Adaptive Drone-based Multigenerational Sensor System for Monitoring Large Ecosystems 20

An Adaptive Drone-based Multigenerational Sensor System for ...

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

作者： Mukkamala, Ravi Olariu, Stephan Aljohani, Meshari Sunkara, Mohan Aldabagh, Hind Old Dominion Univ Dept Comp Sci Norfolk VA 23529 USA

ISBN: (纸本)9798350369458;9798350369441

With the increase in the use of sensors and drones to monitor soil and crop conditions in vast agricultural lands as well as for situational-monitoring of natural disasters such as forest fires and increasing sea levels, there is a need for systems that can support long-term monitoring with minimal cost. In this paper, we propose a novel architecture that combines sensor, drone, blockchain, and machine learning technologies to support a long-term monitoring system for vast areas of land. Long-term monitoring is achieved by employing multigenerational sensors that have been deployed at the start but are woken up in stages as and when needed to replace or reinforce existing sensors. A swarm of drones periodically scans the landscape to identify areas that need immediate attention (e.g., dry soil, pests or insects, humidity, temperature, etc.). Drones store and process the data using onboard units and make real-time decisions regarding changing path plans or changing scanning frequencies. Optionally, data is sent to the base station that fuses the information from multiple drones in the swarm, and inputs the fused data to a machine-learning model that recommends the needed actions. The actions may include obtaining additional data, awakening more sensors, actions to handle any situation by signaling for external actions such as cloud seeding, controlled chemical spraying from a drone, etc., in agricultural applications. Using simulated scenarios, we have employed three prediction algorithms-the Prophet model, CNN, and LSTM-to determine the effectiveness of these algorithms in predicting sensors values in the simulated scenarios. The proposed architecture is scalable, supports long-term monitoring, and is effective in predicting future conditions to enable preemptive actions.

关键词： Agriculture Sensors Drones UAVs machine learning smart contract Blockchain Blackbox

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