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检索条件"任意字段=3rd International Conference on Automation, Robotics and Computer Engineering, ICARCE 2024"

共 803 条记录，以下是21-30 订阅

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Advancing Bilingual SMS Spam Detection: A Comparative Exploration of Machine Learning and Deep Ensemble Techniques 3

Advancing Bilingual SMS Spam Detection: A Comparative Explor...

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3rd IEEE international conference on robotics, automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Sultana, Hafsa Tanvin, Jamal Uddin Sharmin, Nusrat Arefin, Mohammad Shamsul Rahman, Md. Mahbubur Military Institute of Science and Technology Mirpur Cantonment Department of Computer Science and Engineering Dhaka1216 Bangladesh Chittagong University of Engineering and Technology Department of Computer Science and Engineering Chittagong4349 Bangladesh

ISBN: (纸本)9798331534400

Spam messages are increasingly becoming a major issue in digital communication, creating significant challenges for both users and organizations. With the rise in text-based interactions, especially in low-resource languages like Bangla-English SMS, there is a growing need for more effective spam detection systems. This paper presents BESpam, a unique bilingual dataset that contains 5,730 Bangla-English SMS messages, specifically designed to address this challenge. In the literature, various machine learning and deep learning techniques align with feature extraction techniques from natural language processing used for spam SMS detection. Still, no studies have been found that focus on comparing deep and machine learning approaches. As a further contribution, we propose an ensemble model that combines advanced transformer architectures with FastText to improve the accuracy of spam classification. Our results demonstrate that this ensemble approach outperforms both traditional machine learning models and individual transformer-based methods. The stacking ensemble achieved an accuracy of 99%, exceeding the performance of standalone models such as mBERT (98%) and XLM-RoBERTa (98%), as well as traditional classifiers such as logistic regression and SVM, both of which also scored 98%. This work provides insight analysis of machine learning and deep learning in terms of bilingual SMS spam detection, in addition to advancing spam detection methodologies. © 2024 IEEE.

关键词： Contrastive Learning

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SE(3) Pose Estimation for Simultaneous Localization of Objects and Sensors in Dynamic Environments 3

SE(3) Pose Estimation for Simultaneous Localization of Objec...

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3rd international conference on automation, robotics and computer engineering, icarce 2024

作者： Lei, Jiajun Wei, Jiaxiang Zhang, Zhihao College of mechanical and electrical engineering Chengdu University of Technology Chengdu China College of management Sanya University Sanya China

ISBN: (纸本)9798331529505

Visual Simultaneous Localization and Mapping (SLAM) technology offers considerable advantages for enhancing cognitive abilities for mobile robots, especially under complex, dynamic environments. The creation of robust computer vision methods is essential to optimize the understanding of rigid object motion while ensuring precise localization of sensors or cameras. In this study, we introduce a tightly-integrated SLAM approach (MOT), designed to accurately capture SE (3) pose data for sensors/cameras and motion details of objects in the environment. The method initiates by building a streamlined EKF-tracker to determine and associate the real-time motion states of objects. Then, features are separated into static background and object-specific categories based on both instance mask and state information. Static features facilitate the initialization of camera poses, while dynamic features provide SE(3) pose data for objects under observation. Finally, we introduce a novel graph optimization technique to co-optimize the static 3D landmarks, object landmarks, camera poses, and moving object poses within the scene. The proposed approach has shown effectiveness on the KITTI and OMD datasets. © 2024 IEEE.

关键词： SLAM robotics

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i-Glove: An Intelligent Glove System Based on Deep Learning to Support Deaf-blind Individuals in Recognizing Banknotes 3

i-Glove: An Intelligent Glove System Based on Deep Learning ...

引用

3rd IEEE international conference on robotics, automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Hossain, Fahad Mohammad Alavi, Rafit Safat, Yeamin Sarker, Gobinda Chandra Noor, Nafisa Hossain, Ekram Mymensingh Engineering College Department of Computer Science and Engineering Mymensingh Bangladesh University of Dhaka Department of Robotics and Mechatronics Engineering Dhaka Bangladesh

ISBN: (纸本)9798331534400

The deaf-blind community faces significant challenges in identifying banknotes due to simultaneous hearing and vision impairments, which complicate their ability to conduct daily financial transactions. Recent advances in Artificial Intelligence (AI)-based assistive technologies offer promising potential to address these challenges. This study presents the "i-Glove,"a wearable device specifically developed to assist the deaf-blind community in Bangladesh with accurate banknote identification. The i-Glove is designed to recognize nine common Bangladeshi banknotes (e.g., BDT 20, 50, 100) through a camera module integrated on the palmar side of the glove, which captures real-time images of the banknotes. These images are then processed by a convolutional neural network (CNN) model to accurately classify each banknote. Identified notes are translated into unique vibration patterns that are delivered to the user through a shaftless vibration motor embedded in the glove, providing intuitive, non-visual communication. Compared to previous approaches, our model demonstrates superior performance even with a compact dataset, achieving an overall F1-score accuracy of 96.55%, with class-specific precision, recall, and F1-score metrics ranging from 90% to 100%. Additionally, a dedicated application is developed which supports real-time banknote detection and interfaces seamlessly with the glove. The proposed system represents a significant step forward in assistive technology for the deaf-blind community, empowering the users with greater autonomy in financial transactions. © 2024 IEEE.

关键词： Convolutional neural networks

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Internet of Things Based Health Monitoring System Using Smart Glove 3

Internet of Things Based Health Monitoring System Using Smar...

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3rd IEEE international conference on robotics, automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Ahmed, Mohammad Faisal Hasan, Nowshad Ullah, Mohammad Woli Maskat Sharif, Md. Rahaman Khan, Md Tohedur Fahad Khan, Md Rokibul Bahar, Anamul Chittagong University of Engineering and Technology Department of Electronics and Telecommunication Engineering Chittagong Bangladesh International Islamic University Chittagong Department of Electronics and Telecommunication Engineering Chittagong Bangladesh University of London Department of Computer Science Enginnering London United Kingdom

ISBN: (纸本)9798331534400

The current healthcare and emergency response systems face significant challenges in preventing fatalities and serious injuries among elderly individuals and those living alone due to falls, delayed emergency care, and insufficient continuous health monitoring. This research proposes the development and evaluation of a cost-effective smart hand glove designed to enhance the safety and health monitoring of these vulnerable populations. The smart hand glove is built using an Arduino Uno microcontroller, an ESP8266 Wi-Fi module, two MPU6050 sensors for fall detection and gesture recognition, and a MAX30100 sensor for heart rate and SpO2 monitoring. These components work together to detect falls, recognize specific hand gestures, and continuously monitor vital signs. Upon detecting a fall or receiving a gesture input, the system triggers automatic SMS and email alerts to designated contacts. Additionally, health monitoring data is transmitted to a secure, password-protected website accessible by healthcare providers. The smart hand glove demonstrated its potential in improving safety and health outcomes for elderly individuals and those living alone by enabling timely medical intervention and continuous health monitoring. This innovative solution addresses critical gaps in emergency response and healthcare for vulnerable populations, ensuring better safety and wellbeing. © 2024 IEEE.

关键词： Fall detection

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Multi-UAV Maneuvering Targets Tracking Based on Mobility-Focused Multi-Agent Deep Deterministic Policy Gradient 3

Multi-UAV Maneuvering Targets Tracking Based on Mobility-Foc...

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3rd international conference on automation, robotics and computer engineering, icarce 2024

作者： Wang, Yewei Zhang, Yong Jia, Lu School of Computer Science and Information Engineering Hefei University of technology Hefei China

ISBN: (纸本)9798331529505

This paper investigates the problem of autonomous tracking of multiple dynamic ground targets by multiple Unmanned Aerial Vehicles (UAVs). The purpose of the multi-UAV system is to maintain continuous coverage of these moving targets. However, when the targets exhibit high mobility, it becomes challenging for the UAVs to effectively track them. To address this, we propose a Mobility-Focused Multi-Agent Deep Deterministic Policy Gradient (MF-MADDPG) model to learn a cooperative multi-UAV policy for tracking multiple targets. Specifically, we focus on the mobility of the UAVs while tracking highly mobile targets and design a mobility reward function to describe the difference of mobility matching between the UAVs and the targets. Performance tests conducted in the Gazebo simulation environment, based on the Robot Operating System (ROS) and the PX4 autopilot, demonstrated that the proposed MF-MADDPG model outperforms the traditional MADDPG model in tracking highly mobile targets. © 2024 IEEE.

关键词： Drones

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Research on Cockpit Aviation Accident Analysis Model 3

Research on Cockpit Aviation Accident Analysis Model

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automation, robotics and computer engineering (icarce), international conference on

作者： Zheyuan Zhang Shanghai Aircraft Design and Research Institute Shanghai China

ISBN: (数字)9798331529505

ISBN: (纸本)9798331529512

In exploring the design of regression models for aviation accident analysis, we need to focus on aspects such as model construction, parameter selection, data processing, and model validation. Aviation accident analysis is a crucial aspect of ensuring aviation safety. Through in-depth analysis of historical accident data, we can uncover the causes and patterns of accidents, leading to effective prevention and improvement measures. Among various analytical methods, regression models are powerful and flexible tools that can quantify the relationships between different factors and aviation accidents, as well as predict potential risks.

关键词： Analytical models automation Computational modeling Prevention and mitigation Predictive models Data processing Data models Safety Robots Accidents

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Effective Disease Recognition in Cucumbers: A Web-Based Application Using Transfer Learning Models 3

Effective Disease Recognition in Cucumbers: A Web-Based Appl...

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3rd IEEE international conference on robotics, automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Rahman, Md Shahidur Ahamed, Asif Pranto, Md Nuruzzaman Islam, Md Ariful Al Masum, Abdullah Al-Sakib, Abdullah Rahman, S.M. Arafat Haque, Rezaul Rahman, Shafiur Westcliff University Dept. of Engineering Management CA92614 United States Westcliff University College of Technology and Engineering CA92614 United States International American University Dept. of Business Administration CA90010 United States Westcliff University Dept. of Information Technology CA92614 United States East West University Dept. of Computer Science and Engineering Dhaka1212 Bangladesh Daffodil International University Dept. of Software Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798331534400

Cucumber farming plays a crucial role in Bangladesh's agricultural economy, significantly contributing to vegetable production. However, diseases like Downy Mildew, Bacterial Wilt, Anthracnose, and Belly Rot threaten crop quality and lead to financial losses. Effective management of these diseases is crucial for maintaining high crop yields and ensuring economic stability. Current disease identification methods are labor-intensive, costly, and dependent on expert knowledge. To address these challenges, this study presents an automated disease categorization system using advanced transfer learning models, including MobileNet, ShuffleNet, InceptionResNet-v2, AlexNet, and Ensemble Transfer Learning (ETL). Utilizing a dataset of 8,000 well-annotated images, enhanced through preprocessing techniques like GLRM feature extraction and InfoMAX-GAN feature selection, the ETL model achieved a remarkable accuracy of 99.79% and an F1-score of 99.78%. We also developed a user-friendly web tool for real-time diagnostics, providing invaluable support to farmers and agricultural specialists. This tool enables rapid disease detection, leading to increased productivity and a transformative approach to cucumber disease management. By reducing the reliance on chemical interventions, it promotes sustainable farming practices and safeguards human and animal health. © 2024 IEEE.

关键词： Feature Selection

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Advanced Dual-Axis Solar Tracking System with IoT-Driven Real-Time Monitoring for Optimized Efficiency 3

Advanced Dual-Axis Solar Tracking System with IoT-Driven Rea...

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3rd IEEE international conference on robotics, automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Shimul, Asadul Islam Hossain, Md Maruf Dipa, Safia Aktar Bangabandhu Sheikh Mujibur Rahman Science and Technology University Department of Electrical and Electronic Engineering Gopalganj8100 Bangladesh University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO65211 United States

ISBN: (纸本)9798331534400

Photovoltaic (PV) cells can transform solar energy into electrical energy. However, variations in light intensity and the changing position of the sun typically compromise their efficiency. Fixed solar panels experience reduced efficiency when not correctly oriented with the sun, resulting in decreased power generation. To optimize efficiency, it is necessary to implement a tracking system that ensures the panels remain aligned toward the sun. To achieve this objective, a prototype dual-axis solar tracking system was developed. This system utilizes Light Dependent Resistor (LDR) sensors and is controlled by an Arduino UNO microcontroller. It enables movement in both vertical and horizontal directions. Four Light Dependent Resistors (LDRs) and two servo motors enable the panels to track sunlight, achieving ideal orientation. Performance monitoring is achieved by integrating the system with the ThingSpeak IoT platform using the Node MCU ESP8266. This integration allows for the tracking of energy data obtained from the sensors. Compared to a fixed-axis device, the dual-axis tracker demonstrated a 21.83% increase in efficiency. A real-time monitoring system offers a practical means to assess the operation of the solar tracker. © 2024 IEEE.

关键词： Resistors

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Lung Cancer Prediction and Risk Assessment: A Machine Learning Approach Integrating Symptoms and Etiological Factors 3

Lung Cancer Prediction and Risk Assessment: A Machine Learni...

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3rd IEEE international conference on robotics, automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Hossain Sarkar, Md. Mosharrof Afrin, Sadiha Reza, Md Tuhin Bokshi, Md Abid Hasan Roni Mim, Sharmin Sultana Faridpur Engineering College Department of Computer Science and Engineering Faridpur Bangladesh Faridpur Engineering College Department of Electrical and Electronic Engineering Faridpur Bangladesh

ISBN: (纸本)9798331534400

Lung cancer requires accurate risk assessment and early detection due to its high prevalence and fatality rate. The simultaneous analysis of clinical symptoms and etiological factors using Machine Learning revolutionizes our understanding of lung cancer and patient outcomes. Machine Learning models are built on comprehensive patient data, including lifestyle, symptoms, and 12 variables. We used ten models for precise lung cancer diagnosis, including Decision Tree (DT), K-nearest neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), XGBoost, Gradient Boost, CatBoost and AdaBoost. At the National Institute of Cancer Research & Hospital (NICRH) in Mohakhali, TB Gate Road, Dhaka, Bangladesh, we collected 346 data directly from patients using questionnaires, with permission from the director. For every model, we performed parameter tuning to determine the ideal ratio of complexity to accuracy. Notably, with the best accuracy of 98.43% and an excellent area under the curve (AUC) of 0.983, the Decision Tree (DT) performed better than any other classifier. The motivation behind this research is to predict the risk assignment of lung cancer using various machine learning models. © 2024 IEEE.

关键词： Lung cancer

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Enhanced CBBA with Multi-Condition Evaluation for Multi-Robot Collaborative Exploration 3

Enhanced CBBA with Multi-Condition Evaluation for Multi-Robo...

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3rd international conference on automation, robotics and computer engineering, icarce 2024

作者： Qian, Wei Zheng, Yonghang Xu, Kang Hu, Jiaying Qiu, Wenwei Xie, Yangmin School of Mechatronics Engineering and Automation Shanghai University Shanghai China

ISBN: (纸本)9798331529505

In multi-robot collaborative exploration of unstructured environments, redundant exploration often occurs, leading to significant consumption of computational resources and thus reducing exploration efficiency. To address this issue, this paper proposed the EC-MCE algorithm, an enhancement of the Consensus-Based Bundle Algorithm (CBBA) algorithm, which integrates a fully connected neural network to evaluate five key factors: travel cost, information gain, loop closure likelihood, revisit probability, and deviation for local benefit evaluation. This approach ensures the efficient allocation of boundary points, which in turn ensures the efficient completion of exploration tasks. The effectiveness of this method was verified on the Airsim simulation platform, with experimental results showing that compared to traditional methods, this approach reduced the total exploration time by about 10% and decreased the number of repeated area explorations by about 20%. These improvements significantly enhance the exploration efficiency of the system while ensuring the safe navigation of multiple mobile robots in unstructured environments. © 2024 IEEE.

关键词： Multipurpose robots

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