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IOT Based Smart Irrigation Water Management System 4

IOT Based Smart Irrigation Water Management System

引用

4th International conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Baranitharan, B. Sekhar Dhanush, Ch. Chandra Yuvaraj, K. Gowtham, Bireddy Mahalakshmi, R. Madasamy, K. Kalasalingam Academy of Research and Education Department of Agricultural Engineering Virudhunagar India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Virudhunagar India Kalasalingam Academy of Research and Education Department of Electronics and Communication and Engineering Virudhunagar India

ISBN: (纸本)9798331523923

The world's most urgent problems are water scarcity and ineffective resource management brought on by population increase, climate change, and aging infrastructure. Using IoT technology, the 'IoT-Based Smart Water Management System' integrates sensors and microcontrollers to monitor water levels, flow rates, and quality in real-time. Using data analytics and automation guarantees effective water distribution, leak detection, predictive maintenance, and optimize usage, minimizing waste. Precise irrigation based on soil and meteorological data is supported by essential components like the Arduino Uno and ultrasonic sensors, which allow for smooth automation and remote connectivity through mobile or web applications. This scalable system provides a strong framework for water resource conservation, encourages sustainable practices, and improves agricultural efficiency. © 2025 ieee.

关键词： Smart agriculture

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A Contribution for Vehicule Routing Problem in a Warehouse Environnement 11

A Contribution for Vehicule Routing Problem in a Warehouse E...

引用

11th International conference on Mechatronics and Robotics engineering, ICMRE 2025

作者： Trouillet, Benoît Univ. Lille Cnrs Centrale Lille Umr 9189 CRIStAL Lille France

ISBN: (纸本)9798331509293

In a warehouse context, the goal of the research project is to prove the effectiveness of a fully automated, infrastructure-free guidance system for pallet transfer within warehouses. The means of transfer used is a fleet of Automatic Handling Units (AHUs). The goal is warehouse automation, which has become a priority for the largest players in the logistics sector. The growth in the volume of goods handled is a consequence of the globalisation of the world economy and the increasing demand for fast and efficient supply chain solutions. Thus, it forces warehouses to adapt to the growing complexity of operations. Competition between warehouses and the need to reduce lead times for goods handling require operators to increase overall productivity and reduce operating costs while guaranteeing maximum security. This article addresses the scientific challenge of automatic and optimized fleet management of AHUs. It involves proposing a routing system for the AHU fleet without deadlocks that allow regularly submitted missions to be carried out. © 2025 ieee.

关键词： AHU Routing Heuristics Logistics Optimization Warehouse automation

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IoT and Blockchain for Efficiency in Smart Home automation with BiLSTM-RNN Model 3

IoT and Blockchain for Efficiency in Smart Home Automation w...

引用

3rd International conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Mahendran, A. Vasa, Yeshwanth Devi, S. Rukmani Dubey, Vivek Ravishankar Rao, Gali Nageswara Nishant, Neerav Sona College of Arts and Science Department of Computer Science Salem India Department of Information Technology Woodside Ct Franklin United States Saveetha College of Liberal Arts and Sciences Simats Deemed to be University Department of Computer Science Chennai India Jai Narain College of Technology Department of Computer Science and Engineering Bhopal India Aditya Institute of Technology and Management Department of Information Technology Srikakulam India Maharishi University of Information Technology Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798331508456

One of the numerous things made easier by the rapid advancement of technology is home automation, which has been growing in popularity over the past few years. Mechanization and automation have permeated nearly every facet of human existence. Implementing a network that can link different home automations through sensors, actuators, and other data sources is the main objective of this approach. Sensors, preprocessing, feature engineering, and training the model are the three stages that make up the suggested method. Sensors are useful because they can gather information about the environment and then process it to produce results that are correct relative to that environment. The raw data on energy usage is normalized and outliers are removed as part of the preparation steps in the suggested methodology. Label encoding methods were used in feature engineering to convert the input into a feature vector. For training the model, the RNN-BiLSTM-CNN was employed. Compared to alternatives such as RNN and LSTM, the suggested model obtains an average accuracy rate of 95.06%. © 2025 ieee.

关键词： Long short-term memory

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Advanced Fire Detection and Extinguishing Rover with YOLOv8

Advanced Fire Detection and Extinguishing Rover with YOLOv8

引用

2025 ieee International Students' conference on Electrical, Electronics and Computer science, SCEECS 2025

作者： Shah, Aaryan Kumar Yadav, Anil Awal, Sanika Kashyap, Deekshit Pinge, Ninad Kaushik, Shreya Vit Bhopal University School of Computing Science Engineering and Artificial Intelligence Madhya Pradesh Sehore India

ISBN: (纸本)9798331529833

The project 'Advanced Fire Detection and Extinguishing Rover utilizing YOLOv8' introduces a cutting-edge, fire-detection and suppression system that uses deep learning and robotic automation to improve safety. The system uses the YOLOv8 object identification model, which was trained on several fire-related datasets, to effectively identify fire dangers in real-time using live video feeds processed using Python and OpenCV. When the system detects a fire with a high confidence score, it labels the impacted regions and sends the rover driven by the remote controlled and Arduino microcontroller. The rover's servo motor independently directs an inbuilt fire extinguisher to extinguish flames. Bluetooth allows remote monitoring and control, ensuring that everything runs smoothly and securely. This combination of artificial intelligence, computer vision, and robotics provides a revolutionary method for proactive fire-risk mitigation that aims to reduce damage, improve safety, and save lives. © 2025 ieee.

关键词： Fire extinguishers

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Multi-Agent hierarchical workflow for autonomous code generation with Large Language Models

Multi-Agent hierarchical workflow for autonomous code genera...

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2025 ieee International Students' conference on Electrical, Electronics and Computer science, SCEECS 2025

作者： Akilesh, S. Sekar, Rajeev Om Kumar, C.U. Prakalya, D. Suguna, M. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331529833

This paper presents a multi-agent hierarchical workflow tailored for automating data analysis, code generation, and visualization, focusing specifically on user-provided CSV datasets. The workflow integrates AlphaCodium with LangChain, LangGraph, and GPT, enabling autonomous code generation, unit test development, and debugging. The system operates by analyzing the uploaded dataset, assigning agents that work sequentially: a programmer agent generates code using the Skeleton-Of-Code approach, a unit test agent verifies the code, and an executor agent runs the code. A debugging agent is also included to identify and resolve any issues. The AI agents involved are capable of dynamically accessing online resources, including documentation and references to enhance their decision-making processes. This work attempts to exemplify the application of AI in automating not only code execution but also the planning and validation stages by writing unit tests in the software development process to reflect the increasing role of AI in advancing automation for rapid code generation within the software industry. © 2025 ieee.

关键词： Software design

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Using Convolutional Neural Networks for Image Recognition in IoT Applications

Using Convolutional Neural Networks for Image Recognition in...

引用

2025 International conference on Pervasive Computational Technologies, ICPCT 2025

作者： Rajababu, Durgam Veerraju, Sampenga Kumar, K. Suresh Gupta, Sonali Thurai Raaj, V.B. Joshi, Rahul Sr University School of Engineering Department of Eee Telangana Warangal India Dadi Institute of Engineering and Technology Department of Electronics and Communications Engineering Andhra Pradesh Anakapalli Visakhapatnam531002 India Panimalar Engineering College Mba Department Varadarajapuram Poonamallee Chennai600123 India J C Bose University of Science and Technology Ymca Faridabad India Madanapalle Institute of Technology & Science Electrical and Electronics Engineering Andhra Pradesh Madanapalle India Manav Rachna International Institute of Research and Studies Department of Journalism & Mass Communication SMeH Haryana India

ISBN: (纸本)9798331508685

Convolutional Neural Networks (CNNs) have grown into a powerful picture identification tool. They are an important part of making Internet of Things (IoT) apps. In this regard, CNNs make it possible to analyse visual data gathered from networked devices, like cameras and sensors, effectively and accurately. With an emphasis on its potential for real-time applications for example smart surveillance, healthcare monitoring, autonomous cars, and industrial automation, this study investigates the incorporation of CNN-based image identification into IoT contexts. We tackle important issues such as the requirement for low-latency processing, energy economy, and computing limitations on edge devices. In order to maximise CNN performance in the resource-constrained IoT ecosystem, strategies like model compression, edge computing, and distributed architectures are covered. The ability to interpret large volumes of visual data is improved by the integration of CNNs with IoT, providing creative solutions for automation and intelligent decision-making across numerous industries. © 2025 ieee.

关键词： Convolution

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Virtual Commissioning of Mass Personalisation Manufacturing System Based on Digital Twins in Smart Factory Environment 11

Virtual Commissioning of Mass Personalisation Manufacturing ...

引用

11th International conference on Mechatronics and Robotics engineering, ICMRE 2025

作者： Al-Obaidi, Yaser Al-Shibaany, Zeyad Leon, Marianthi School of Engineering University of the West of England Bristol United Kingdom

ISBN: (纸本)9798331509293

The growing demand for mass personalisation in manufacturing has necessitated integrating advanced technologies that enable flexible, efficient, and scalable production systems. This paper presents the virtual commissioning of a mass personalisation manufacturing system in a smart factory environment, leveraging digital twin technology. The study explores the application of virtual commissioning to validate and optimise measuring and drilling Stations in a smart factory environment, highlighting the use of Digital Twins, the Internet of Things, and automation tools to streamline the transition from design to implementation. © 2025 ieee.

关键词： Flexible manufacturing systems

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From Efficiency to Innovation: LLM 5.0 and the Future of Industry

From Efficiency to Innovation: LLM 5.0 and the Future of Ind...

引用

2025 International conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Gantayat, Pradosh Kumar Kaur, Tejinder Majhi, Madhusmita Jena, Uttam Kumar Das, Swatismita Soni Department of Artificial Intelligence and Data Science Telangana Hyderabad501203 India Department of Mmictbm Mullana Haryana Ambala133207 India Department of Computer Science and Engineering Telangana Hyderabad501203 India Raajdhani Engineering College Biju Patnaik University of Technology Department of Computer Science and Engineering Odisha Bhubaneswar India Raajdhani Engineering College Bhubaneswar India

ISBN: (纸本)9798331509828

Industry 5.0 is a new way of thinking that is consistent with the ideas of Industry 4.0 but places greater emphasis on sustainability, sustainability, and human-centricity. Unlike Industry 4.0, which emphasizes the efficient integration of advanced technologies such as the Internet, AI, robots, and automation, Industry 5.0 seeks to balance human-machine collaboration to create more inclusive, meaningful, and sustainable systems. In Industry 5.0, LLM has a bigger role than automating similar activities. LLMs make sectors more resilient, adaptable and aligned with social values by fostering innovation, improving human-centred processes and promoting sustainable practices. Industry 5.0 will create a revolutionary force in the global industrial landscape thanks to a combination of emerging technologies that will further enhance the synergies between human creativity and machine intelligence. © 2025 ieee.

关键词： Human-AI Collaboration Industrial Efficiency Industry 5.0 Innovation LLM 5.0

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Formula 1 Race Winner Prediction Using Random Forest and SHAP Analysis

Formula 1 Race Winner Prediction Using Random Forest and SHA...

引用

2025 International conference on Control, automation, and Instrumentation, IC2AI 2025

作者： El Haber, Elias Sawaya, Elie Attieh, Maroun Tannous, Aldo Ghazaly, Weam Owayjan, Michel American University of Science & Technology Faculty of Engineering and Computer Science Beriut Lebanon Nantes University Saint-Nazaire France

ISBN: (纸本)9798331529604

Predicting the outcomes of Formula 1 (F1) races presents a significant challenge due to the complex interplay of numerous factors, including driver skill, vehicle performance, team strategy, and unpredictable race-day conditions. This paper investigates the application of the Random Forest algorithm enhanced with SHAP (Shapley Additive Explanations) analysis to forecast race winners while providing interpretability to the model's predictions. By leveraging extensive historical data encompassing driver metrics and race conditions, we aim to build a model that achieves high predictive accuracy and offers valuable insights into the most influential factors affecting race outcomes. Our approach demonstrates how integrating machine learning with explainable AI techniques can assist F1 teams in making data-driven decisions, ultimately optimizing their strategies in the highly competitive environment of professional racing. © 2025 ieee.

关键词： Random forests

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Real-Time Drone System for Detecting, Tracking, and Following of a Mobile Robot in ROS/Gazebo

Real-Time Drone System for Detecting, Tracking, and Followin...

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2025 International conference on Control, automation, and Instrumentation, IC2AI 2025

作者： Salman, Ali Dika, Mohammad Diab, Hassan Owayjan, Michel Achkar, Roger American University of Science & Technology Faculty of Engineering and Computer Science Beriut Lebanon Nantes University Saint-Nazaire France

ISBN: (纸本)9798331529604

Surveillance drones equipped with video transmission capabilities play a crucial role in modern security systems, with the integration of OpenCV for object detection marking a significant advancement. This study evaluates the detecting accuracy by varying the distance between the drone and the target object, addressing gaps in current research through a detailed practical insight. This research explores key aspects of drone-based video surveillance, including object detecting, tracking and real-time following, aiming to enhance understanding and methodology in Computer Vision (CV) applications with Unmanned Aerial Vehicles (UAVs). The Simulation of a surveillance drone system in Gazebo simulator within the ROS2 framework. The drone is programmed to dynamically follow and track a mobile robot, enabling systematic analysis of the object detection algorithm's performance. The findings contribute to advancing the reliability and effectiveness of drone-based video surveillance systems for future innovations in security and monitoring applications. © 2025 ieee.

关键词： Drones

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