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Identification of Ayurvedic Plants Using Deep Learning

Identification of Ayurvedic Plants Using Deep Learning

2024 IEEE International conference on Computing, Semiconductor, mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Kambli, Prashanth Satrasala, Vikas Shivanna, K. Shreyash Sharma, Shaan Neralkar, Yash Nitin M S Ramaiah Institute of Technology Information Science & Engineering Bangalore India Computer Science & Engineering Sahyadri College of Engineering and Management Adyar Mangalore India

ISBN: (纸本)9798331517892

Deep learning algorithms have become pivotal in the accurate identification and classification of Ayurvedic plants, leveraging advanced techniques to distinguish between various species based on leaf images. Among these algorithms, the VGG16 model, a convolutional neural network (CNN), is particularly notable for its robust performance in image recognition tasks. The Ayurvedic plant detection database serves as a comprehensive resource for training and validating these models, facilitating high accuracy in plant detection. The integration of deep learning models presents significant potential for botany and health care applications. By offering reliable and precise identification of Ayurvedic plants, the proposed system supports botanists and healthcare professionals in making informed decisions. This leads to more accurate identification and utilization of traditional medicinal knowledge. These advancements not only improve the efficiency and reliability of plant detection systems but also build confidence in AI-driven solutions within the field of traditional medicine. The potential to revolutionize plant classification and enhance the understanding of medicinal plants underscores the importance of continued research and innovation in this domain. The model proposed ensures faster and easier way to recognize the medicinal plants and give its description to the reader using deep learning methods with the accuracy of 96% identification systems. © 2024 IEEE.

关键词： Medicinal chemistry

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Design of Fire Guidance and Rescue System Based on ROS Platform 7

Design of Fire Guidance and Rescue System Based on ROS Platf...

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7th IEEE Information Technology and mechatronics engineering conference, ITOEC 2023

作者： Li, Xiang Li, BaoYing Pan, Zhenxiang Wang, Yuru School of Information Science and Engineering Dalian Polytechnic University China School of Design Jiangnan University China

ISBN: (纸本)9798350334197

In order to quickly search for disaster victims in a fire and carry out rescue activities while ensuring the safety of rescue personnel, this article designs a fire guidance and rescue system based on ROS platform, with Nano as the core controller, the upper computer as the remote monitoring platform, and multiple sensors. The main function is to use Lidar and apply the Gmapping-Slam algorithm for on-site map information collection. At the same time, a odometer is used to achieve real-time relocation, and a camera is used to transmit on-site images to the upper computer to assist rescue personnel in locating the affected population. Then, based on the marked points on the map, The RRT-Connect algorithm fused with KD-tree is applied for shortest path planning, guiding rescue personnel to achieve short-term rescue. The experiment shows that the system can meet the function of quickly guiding rescue personnel to rescue in case of fire, reduce the time required for rescue, achieve the basic requirement of reducing the possibility of secondary injury to rescue personnel, and meet the expected expectations. © 2023 IEEE.

关键词： Fires

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A Study of Open-Source Leaf Instance Segmentation Approaches Against Diverse Leaf Datasets 30

A Study of Open-Source Leaf Instance Segmentation Approaches...

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30th International conference on mechatronics and Machine Vision in Practice, M2VIP 2024

作者： Abe, John Wesley Ilao, Joel De La Salle University College of Computer Science Manila Philippines

ISBN: (纸本)9798350391916

In the quest for sustainable agriculture and accurate yield prediction, deep learning approaches are utilized for leaf segmentation to automate and enhance quantifying plant characteristics, contributing to efficient agricultural practices. This study compares three deep learning leaf segmentation methods: a bottom-up instance segmentation method, a Segment Anything Model (SAM) with post-processing, and a fine-tuned Mask R-CNN. Their performance is evaluated using standard metrics on three datasets: the CVPPP Leaf Segmentation Challenge, Komatsuna, and Michigan State University Dense Leaves. The fine-tuned Mask R-CNN achieves the highest precision and dice scores, indicating superior performance. The SAM approach demonstrates the potential for zero-shot leaf segmentation, while the bottom-up method struggles with accurate leaf delineation. Exploring efficient tuning techniques and lightweight models is recommended to enhance performance while reducing computational demands. © 2024 IEEE.

关键词： Contrastive Learning

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Deep Learning-Based Self-Supervised Transfer Learning for Medical Image Classification 2

Deep Learning-Based Self-Supervised Transfer Learning for Me...

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2nd International conference on Disruptive Technologies, ICDT 2024

作者： Shaikh, M.Z. Singh, Samender Varshney, Neeraj Saraswat, Birendra Kumar Shree Bhagubhai Mafatlal Polytechnic College Department of Electronics & Telecommunication Maharastra Mumbai India Ajay Kumar Garg Engineering College Department of Computer Science & Engineering UP Ghaziabad India Gla University Department of Computer Engineering and Applications UP Mathura India Raj Kumar Goel Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India

ISBN: (纸本)9798350371055

Self-supervised switch getting to know is a practical approach for the scientific image type. It includes using unlabeled statistics (from non-clinical images) to teach models to categorize medical photos with low attempts and excessive accuracy. This approach has been utilized in numerous duties, including clinical photo segmentation, computer-aided prognosis, and disease category. To summarize the research studies carried out in this discipline, a complete survey of self-supervised switch learning for scientific image type was performed in 2020. The survey evaluations numerous transfer mastering techniques often used in healthcare, which include area adaptation, multitasking studying, and zero/one-shot mastering. It additionally gives an in-intensity analysis of the modern-day challenges and capability solutions. © 2024 IEEE.

关键词： Medical Outperforms Results Unlabeled Utilizes

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A Writer-Dependent Approach for Offline Signature Verification Using Feature Learning and One-Class Support Vector Machine

A Writer-Dependent Approach for Offline Signature Verificati...

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2023 International conference on Electrical, computer, Communications and mechatronics engineering, ICECCME 2023

作者： Tanko, Osman Senkyire, Isaac Baffour Kwao, Kelvin Aluze-Ele, Simon Hilary Ayinedenaba Boahen, Edward Kwadwo Frimpong, Samuel Akwasi Ghana Communication Technology University Information Technology Department Accra Ghana Ghana Communication Technology University Computer Science Department Accra Ghana Public Utilities Regulatory Commision Accra Ghana Ck Tedam University Computer Science Department Navrongo Ghana Ghana Communication Technology University Computer Engineering Department Accra Ghana

ISBN: (纸本)9798350322972

In many administrative and financial entities, signatures are frequently employed to confirm a person's identification as well as the authenticity of various documents. Being a biometric characteristic, a signature is a fairly secure form of identification. Hence, efforts are being made to continually raise the bar for the security signatures, particularly by creating effective procedures for verifying signatures. Both traditional and automatic techniques have been used to verify signatures, whiles the former has recorded moderately accurate results, the latter in recent studies have demonstrated great success in separating authentic signatures from forgeries. The purpose of this work is to explore the implementation and deployment of offline writer-dependent signature verification systems, by proposing a model that can greatly minimize computation while simultaneously increasing accuracy, to achieve this, we propose combining the VGG-16 architecture as a feature extractor with a One-Class Support Vector Machine (OC-SVM) as a writer-dependent classifier. © 2023 IEEE.

关键词： Support vector machines

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A Comprehensive Survey on Intelligent Reflecting Surfaces and Unmanned Aerial Vehicles Integration for Enhanced Communication in Massive Networks 11

A Comprehensive Survey on Intelligent Reflecting Surfaces an...

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

作者： Attar, Hani Ababneh, Jafar Hafez, Mohamed Rachini, Ali Solyman, Ahmed Bahy, Ramy M. Zarqa University Faculty of Engineering Zarqa Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia Zarqa University Faculty of Information Technology Cyber Security Department Zarqa Jordan INTI-IU-University Nilai Faculty of Engineering Feqs Malaysia Department of Computer Science and Information Technology Jounieh Lebanon School of Computing Engineering and Built Environment Glasgow Caledonian University Glasgow United Kingdom Technical Research Center Cairo Egypt

ISBN: (纸本)9798331509293

Intelligent Reflecting Surfaces (IRSs) offer a revolutionary approach to wireless communication by dynamically controlling the properties of electromagnetic waves. By manipulating wavefronts without requiring radio frequency links, IRSs can enhance spectrum and energy efficiency, reduce costs, and improve overall system performance. Unmanned Aerial Vehicles (UAVs) have garnered significant attention due to their mobility and adaptability. However, real-time data transmission between UAVs can be hindered by obstacles and security risks. By combining IRSs and UAVs, we can create unparalleled opportunities in demanding environments. Through coordinated movements and intelligent wave reflection, both systems can optimize wireless signal propagation. This paper provides a comprehensive review of IRS-assisted UAV communications. We explore existing research, introduce novel technologies and applications, and highlight potential research directions for developing low-cost and energy-efficient wireless systems. Additionally, we address current challenges and outline promising areas for future study. © 2025 IEEE.

关键词： Drones

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Low cost and robust control for brushless DC motor 7

Low cost and robust control for brushless DC motor

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7th IEEE Information Technology and mechatronics engineering conference, ITOEC 2023

作者： Huang, Zhiwei Cheng, Yuanhao Wenzhou University College of Mechanical and Electrical Engineering Wenzhou China

ISBN: (纸本)9798350334197

Brushless DC motor (BLDCM) is widely used in aviation, automobile and other fields, due to its high efficiency, reliable, low noise and low maintenance. However, controlling BLDCM is challenging because of its nonlinear, time-varying, and uncertain characteristics. To improve the control performance of motor speed, an adaptive PI controller based on fuzzy logic is proposed in this paper. The principle and mathematical model of BLDCM is analyzed, and a new algorithm that modifies motor speed by combining classic PID and fuzzy PID using a switch module is introduced to realise low cost and robust control. The effectiveness and superiority of the controller are verified by simulation experiment. © 2023 IEEE.

关键词： Brushless DC motors

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Enhancing single-lead electrocardiogram arrhythmia detection with empirical mode decomposition

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Neural Computing and Applications 2025年 1-23页

作者： Issa, Mohamed F. Yousry, Ahmed Tuboly, Gergely Wang, Zeyu Zoltan, Juhasz Selim, Mazen M. AbuEl-Atta, Ahmed H. Department of Scientific Computing Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Electrical Engineering and Information Systems University of Pannonia Veszprem8200 Hungary Department of Mechatronics Delta University for Science and Technology Gamasa35712 Egypt

Cardiac arrhythmias pose a significant challenge to health care, requiring accurate and reliable detection methods to enable early diagnosis and treatment. However, traditional ECG beat classification methods often lack robustness and fail to generalize effectively to diverse clinical scenarios, particularly when using single-lead-I recordings, which are less studied compared to lead II. To address this gap, this paper proposes a novel method that combines deep learning and empirical mode decomposition (EMD) for arrhythmia detection using single-lead-I ECG recordings obtained with electrodes on each hand. The hybrid model comprises 149 layers of one-dimensional convolution, max pooling, and residual blocks. By leveraging EMD, it classifies ECG beats into normal beats, premature ventricular contractions, and atrial premature beats with high sensitivity (99.86%, 99.72%, and 99.84%, respectively) using the first four intrinsic mode functions (IMFs). The proposed method is applicable in clinical and out-of-hospital monitoring scenarios, and an accompanying web application provides real-time analysis and diagnostic results. This approach addresses a critical gap in lead-I ECG analysis, enhancing diagnostic precision and expanding the scope of ECG applications in health care. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Empirical mode decomposition

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Software System for Determining and Visualizing Locations and Routes of Underwater Robots

Software System for Determining and Visualizing Locations an...

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2023 International conference Automatics and Informatics, ICAI 2023

作者： Karova, Milena Penev, Ivaylo University of Varna Department of Computer Science and Engineering Technical Varna Bulgaria

ISBN: (纸本)9798350312911

This paper presents a software system for visualization of location and route of an underwater robot. The visualization is based on photos of seabed, which are taken by the camera of the robot. The robot also takes records for the photographed objects and stores them into KML format. The photos and the KML data are transferred to a device (a laptop or a computer), where the data are processed and the location and the traveled route of the robot are shown on a map in the web interface of the system. The robot and the presented system can be used to research the seabed relief and habitats, thus avoiding participation of divers and decreasing the risks for human lives under water. © 2023 IEEE.

关键词： Location

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A Case Study in Generating 3D Maps with Collaborative Robots 4

A Case Study in Generating 3D Maps with Collaborative Robots

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4th International conference on Electrical, computer, Communications and mechatronics engineering, ICECCME 2024

作者： Grumeza, Theodor-Radu Lazar, Thomas-Andrei Dramnesc, Isabela Kusper, Gabor Papadopoulos, Konstantinos Lefkos, Ioannis Fachantidis, Nikolaos West University of Timisoara Department of Computer Science Timisoara Romania Eszterhazy Karoly Catholic University Department of Computer Science Eger Hungary University of Macedonia LIRES Lab Thessaloniki Greece

ISBN: (纸本)9798350391183

This research presents a case study on the creation of detailed 3D maps using the Agilex Scout Mini robot equipped with a 3D LiDAR, aimed at improving autonomous navigation in indoor environments. ROS (Robot Operating System) is employed for real-Time data acquisition and processing, and the DBSCAN clustering algorithm for segmenting point cloud data. These maps were subsequently integrated into the humanoid robot Pepper, developed by Aldebaran Robotics, enabling Pepper to navigate autonomously and deliver location-based services at the West University of Timisoara. Our results show a point cloud density of approximately 5,000 points per cubic meter, highlighting the LiDAR sensor's capability to capture fine environmental details. Efficient clustering was achieved with an average computation time of 3 seconds per chunk, and path planning demonstrated a high smoothness score of 0.8, improving navigation efficiency. These findings contribute to the field by demonstrating the effectiveness of combining 3D mapping with autonomous robotics for reliable, real-Time navigation. Moreover, the integration of Intel RealSense cameras is proposed as a future enhancement to improve object recognition and colour mapping, further expanding the system's potential applications. This case study not only advances academic understanding of autonomous robotic systems but also presents practical and scalable solutions for real-world navigation challenges, offering significant improvements in both accuracy and efficiency. © 2024 IEEE.

关键词： Collaborative robots

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