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An Interpretable CNN for the Segmentation of the Left Ventricle in Cardiac MRI by Real-Time Visualization

computer Modeling in Engineering & sciences 2023年第5期135卷 1571-1587页

作者： Jun Liu Geng Yuan Changdi Yang Houbing Song Liang Luo Robotics Institute School of Computer ScienceCarnegie Mellon UniversityPittsburghPA15217USA Department of Electrical&Computer Engineering College of EngineeringNortheastern UniversityBostonMA02115USA Security and Optimization for Networked Globe Laboratory(SONG Lab) Embry-Riddle Aeronautical UniversityDaytona BeachFL32114USA School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu610054China

The interpretability of deep learning models has emerged as a compelling area in artificial intelligence *** safety criteria for medical imaging are highly stringent,and models are required for an ***,existing convolutional neural network solutions for left ventricular segmentation are viewed in terms of inputs and ***,the interpretability of CNNs has come into the *** medical imaging data are limited,many methods to fine-tune medical imaging models that are popular in transfer models have been built using massive public Image Net datasets by the transfer learning ***,this generates many unreliable parameters and makes it difficult to generate plausible explanations from these *** this study,we trained from scratch rather than relying on transfer learning,creating a novel interpretable approach for autonomously segmenting the left ventricle with a cardiac *** enhanced GPU training system implemented interpretable global average pooling for graphics using deep *** deep learning tasks were *** included data management,neural network architecture,and *** system monitored and analyzed the gradient changes of different layers with dynamic visualizations in real-time and selected the optimal deployment *** results demonstrated that the proposed method was feasible and efficient:the Dice coefficient reached 94.48%,and the accuracy reached 99.7%.It was found that no current transfer learning models could perform comparably to the ImageNet transfer learning *** model is lightweight and more convenient to deploy on mobile devices than transfer learning models.

关键词： Interpretable graphics training visualization image segmentation left ventricle CNNs global average pooling

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Plant Leaf Diseases Classification Using SVMs, XGBoost, and CNNs Based Texture Features 6

Plant Leaf Diseases Classification Using SVMs, XGBoost, and ...

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6th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2024

作者： Sami, Abdullah Kamal Al-Dulaimi, Khamael Al-Nahrain University Computer Science Dept. Baghdad Iraq School of Electrical Engineering and Robotics Queensland University of Technology Brisbane Australia

ISBN: (纸本)9798350389692

Plant diseases identification is frequently used by physical examination or laboratory investigation which creates delays that, by the time identification is finished, results in yield loss. Diseases may affect different parts of plant, specifically the leaf. Different Research have made significant contributions to this sector and used computer vision technologies and machine learning effectively. Consequently, having a thorough understanding of the most recent advancements in machine learning technology and its applications for the identification of leaf diseases. In this paper, a technique is proposed by employing texture features with SVM, XGBoost and CNN to identify leaf disease of five types of plants: Alstonia, Gauva, Jamun, Lemon, and Mango. We have used Local Binary Pattern to extract texture features from leaf because the diseases can affect the texture of plant, such as hairs, ridges, and waxy coatings. We have used challenging dataset which taken under different conditions, such as humidity, light, nutrition, water, and temperature which affect the texture, shape, colour and size of leaves. The findings show that the CNN based LBP achieved high accuracy in comparing with others models. © 2024 IEEE.

关键词： Machine learning

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Real-Time Operating System for Multitasking Control in the robotics and Automation Industry

Real-Time Operating System for Multitasking Control in the R...

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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Sayyad, Javed Jatti, Ashwini Attarde, Khush Ramesh, B.T. Deokar, Shubhangi Robotics and Automation Department Lavale Maharashtra Pune India Computer Science and Engineering Lavale Maharashtra Pune India

ISBN: (纸本)9781665474511

Real-Time Operating System (RTOS) is an Operating System (OS) that does a task in a constrained time. The RTOS is integrated with the microprocessor or microcontroller for specialized applications where the time-bound completion of the task is very much substantial. ESP32 is a microcontroller that comes with a package of advantageous and suitable features for RTOS. The ability of equipment like motors, sensors, and actuators to multitask is critical in the robotics and Automation (R&A) industry. In industries, a dedicated/centralized system allocated for a particular activity is not economically feasible;yet, when performing several tasks, some delay or lag is introduced in the task. Consequently, the proposed solution for industrial multitasking is cost-effective and efficient. The RTOS is suitable for machines constrained to do painstaking work, like a robotic arm or CNC drilling. In the R&A industry, a hardware interruption is often critical for an emergency, which may also be accomplished using RTOS. In such applications, implementing a software timer is also essential to delay a task for a period of time. Stepper and servo motors are widely employed in the R&A industries for various applications. This article presents the multitasking of four stepper motors and the introduction of a software timer, which will be developed when the stepper motor multitasking is implemented. For multitasking, the FreeRTOS library will be utilized. © 2023 IEEE.

关键词： Microcontrollers

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FerroVibe: Towards a Modular Tactile Device 1

FerroVibe: Towards a Modular Tactile Device

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1st IEEE Conference on Telepresence, Telepresence 2024

作者： Singh, Harsimran Marcilla, Luis Perez Banerjee, Premankur Rothammer, Michael Hulin, Thomas The Institute of Robotics and Mechatronics Munich Germany Dresden51147 Germany University of Southern California Department of Computer Science United States Hapticlabs Germany

ISBN: (纸本)9798331528249

This paper presents the proof of concept of the modular nature of ferrofluid based tactile device, FerroVibe. The modularity of FerroVibe allows for customization in degrees of freedom, force feedback, and vibrational cues by strategically positioning magnetic actuators around the central assembly containing a neodymium magnet and ferrofluid. Three distinct configurations of FerroVibe are introduced, each offering unique actuation mechanisms and physical characteristics that could be tailored to different application requirements. The paper showcases the proof of concept and details the working principle of each configuration, highlighting the strengths and limitations of each design. Initial comparative analysis is conducted to evaluate the trade-offs in terms of power consumption, speed, size, weight, and feedback fidelity. The results demonstrate that FerroVibe's potential modular architecture provides a flexible and effective solution for a wide range of tactile feedback applications, making it a promising candidate for future advancements in haptic technology. © 2024 IEEE.

关键词： Magnetic actuators

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Multi-Label Classification for Low-Resource Issue Tickets with BERT

Multi-Label Classification for Low-Resource Issue Tickets wi...

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2024 IEEE International Conference on Cybernetics and Innovations, ICCI 2024

作者： Sungthong, Chawanee Ruangtanusak, Saksorn Songmuang, Pokpong Thammasat University Faculty Of Science And Technology Department Of Computer Science Pathum Thani Thailand Bedrock Analytics Ai And Robotics Ventures Applied Machine Learning Team Bangkok Thailand

ISBN: (纸本)9798350370096

This study focuses on the problem in the field of issue ticketing systems, where the need for human involvement in classifying work task in the ticket often results in insufficient classification. Our primary focus is on a problem associated with multi-label classification. A significant challenge in this study is the dependency of our dataset on low resources, which is different from the large datasets usually used in current multi-label text classification studies. Therefore, we propose a pre-training approach for our low-resource dataset. Our methodology applies several embedding techniques, including TF-IDF, Word2Vec, and BERT (WangchanBERTa and PhayaThaiBERT). The classification models, such as Logistic Regression, Support Vector Classifiers, Decision Trees, Neural Networks, and Deep Neural Networks, are experimented in this study to assess their performance in a low-resource environment. According to our experimental results, a combination of PhayaThaiBERT and Deep Neural Networks outperforms conventional methods. The combination yielded an F1 score of 0.769 on the test dataset. © 2024 IEEE.

关键词： Deep neural networks

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RoboMamba: Efficient Vision-Language-Action Model for Robotic Reasoning and Manipulation 38

RoboMamba: Efficient Vision-Language-Action Model for Roboti...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Liu, Jiaming Liu, Mengzhen Wang, Zhenyu An, Pengju Li, Xiaoqi Zhou, Kaichen Yang, Senqiao Zhang, Renrui Guo, Yandong Zhang, Shanghang State Key Laboratory of Multimedia Information Processing School of Computer Science Peking University China AI2Robotics China China

A fundamental objective in robot manipulation is to enable models to comprehend visual scenes and execute actions. Although existing Vision-Language-Action (VLA) models for robots can handle a range of basic tasks, they still face challenges in two areas: (1) insufficient reasoning ability to tackle complex tasks, and (2) high computational costs for VLA model fine-tuning and inference. The recently proposed state space model (SSM) known as Mamba demonstrates promising capabilities in non-trivial sequence modeling with linear inference complexity. Inspired by this, we introduce RoboMamba, an end-to-end robotic VLA model that leverages Mamba to deliver both robotic reasoning and action capabilities, while maintaining efficient fine-tuning and inference. Specifically, we first integrate the vision encoder with Mamba, aligning visual tokens with language embedding through co-training, empowering our model with visual common sense and robotic-related reasoning. To further equip RoboMamba with SE(3) pose prediction abilities, we explore an efficient fine-tuning strategy with a simple policy head. We find that once RoboMamba possesses sufficient reasoning capability, it can acquire manipulation skills with minimal fine-tuning parameters (0.1% of the model) and time. In experiments, RoboMamba demonstrates outstanding reasoning capabilities on general and robotic evaluation benchmarks. Meanwhile, our model showcases impressive pose prediction results in both simulation and real-world experiments, achieving inference speeds 3 times faster than existing VLA models. Our project web page: https://***/view/robomamba-web. © 2024 Neural information processing systems foundation. All rights reserved.

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Drone Swarm Coordination Using Reinforcement Learning for Efficient Wildfires Fighting

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SN computer science 2024年第3期5卷 314页

作者： Blais, Marc-Andrė Akhloufi, Moulay A. Dept. of Computer Science Université de Moncton Moncton NB Canada Perception Robotics and Intelligent Machines (PRIME) Université de Moncton Moncton NB Canada

Natural events, such as wildfires, pose a serious threat to the human population and cause significant environmental and economic damage. As climate change increases the frequency and intensity of extreme natural events, more efficient solutions are required to mitigate their impacts. One proposed solution is the usage of a swarm of drones and unmanned ground vehicles to extinguish wildfires. Controlling a swarm of drones is a challenging task that requires a complex control system capable of providing a good coordination between multiple vehicles. Artificial intelligence, particularly reinforcement learning, has been proposed as a solution for the autonomous control of a swarm. In this paper, we propose a conceptual system based on reinforcement learning and swarm robotics to efficiently combat wildfires. Our system consists of an overwatch drone and multiple payload drones which are responsible for carrying fire suppressants. The goal of our research is to guide a swarm of payload drones to a wildfire while preserving a tight swarm grouping. Each drone is individually controlled and aware of the position of the goal and the positions of other agents. To compare different algorithms, we tested a DQN, Rainbow DQN and FQF using a simulation and four AirSim scenarios. With our approach, we achieve impressive results in controlling a swarm of drones towards a common goal. Our research demonstrates the possibility of utilizing reinforcement learning to control a swarm of drones for wildfire fighting. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Drones Intelligent system Reinforcement learning Swarm Wildfires

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Hand Gestures and Machine Learning: A Novel Path to Number Prediction

Hand Gestures and Machine Learning: A Novel Path to Number P...

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2023 International Conference on Recent Advances in science and Engineering Technology, ICRASET 2023

作者： Karegoudra, Shilpa Veeresha, R.K. Department of Computer Science and Engineering Udupi574110 India Department of Robotics and Artificial Intelligence Udupi574110 India

ISBN: (纸本)9798350306927

The gesture-based communication system proves highly valuable for individuals who are almost deaf and discourse disabled. It allows them to convey messages effectively through gestures. The objective of this application is to provide a platform that accurately interprets these signs, enabling regular individuals to understand the gestures. The system detects and recognizes the gestures and displays the corresponding characters. This technology, which utilizes the PCA method, achieves comparable accuracy to other existing systems and methods. The system we have developed plays a crucial role in enabling individuals to understand and communicate like normal people using static gestures. While various proposals exist for gesture systems with specific languages like ASL, ISL, etc., we introduce a new set of static gestures using MATLAB, building upon existing systems. The system involves capturing input through a camera, after which the system applies preprocessing techniques to enhance the captured image. Next, a set of features is extracted from the preprocessed image using PCA (Principal Component Analysis). To identify the gestures, the system compares these features with those in the training sets, using the Euclidean Distance method. Our hand gesture system has been designed to recognize and interpret numbers from 0 to 5. Once a gesture is identified, the system can produce the output in the form of text ensuring effective communication for the users. © 2023 IEEE.

关键词： Principal component analysis

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Graph-Based Feature Learning for Cross-Project Software Defect Prediction

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computers, Materials & Continua 2023年第10期77卷 161-180页

作者： Ahmed Abdu Zhengjun Zhai Hakim A.Abdo Redhwan Algabri Sungon Lee School of Software Northwestern Polytechnical UniversityXi’anChina School of Computer Science Northwestern Polytechnical UniversityXi’anChina Department of Computer Science Hodeidah UniversityPO Box 3114Al-HudaydahYemen Research Institute of Engineering and Technology Hanyang UniversityAnsanKorea Department of Robotics Hanyang UniversityAnsanKorea

Cross-project software defect prediction(CPDP)aims to enhance defect prediction in target projects with limited or no historical data by leveraging information from related source *** existing CPDP approaches rely on static metrics or dynamic syntactic features,which have shown limited effectiveness in CPDP due to their inability to capture higher-level system properties,such as complex design patterns,relationships between multiple functions,and dependencies in different software projects,that are important for *** paper introduces a novel approach,a graph-based feature learning model for CPDP(GB-CPDP),that utilizes NetworkX to extract features and learn representations of program entities from control flow graphs(CFGs)and data dependency graphs(DDGs).These graphs capture the structural and data dependencies within the source *** proposed approach employs Node2Vec to transform CFGs and DDGs into numerical vectors and leverages Long Short-Term Memory(LSTM)networks to learn predictive *** process involves graph construction,feature learning through graph embedding and LSTM,and defect *** evaluation using nine open-source Java projects from the PROMISE dataset demonstrates that GB-CPDP outperforms state-of-the-art CPDP methods in terms of F1-measure and Area Under the Curve(AUC).The results showcase the effectiveness of GB-CPDP in improving the performance of cross-project defect prediction.

关键词： Cross-project defect prediction graphs features deep learning graph embedding

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Competition for roadside camera monocular 3D object detection

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National science Review 2023年第6期10卷 34-37页

作者： Jinrang Jia Yifeng Shi Yuli Qu Rui Wang Xing Xu Hai Zhang Baidu Inc. Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University College of Software Jilin University School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

INTRODUCTION Accurate environment perception is a critical topic in autonomous driving and intelligent *** environmental perception methods mostly rely on on-board ***,limited by the installation height,thereare problems such as blind spots and unstable long-range perception in vehicle perceptual ***,with the rapid improvement of intelligent infrastructure,it has become possible to use roadside cameras for traffic environment *** from the increased height when compared with on-boardsensors,roadside cameras can obtain a larger perceptual field of view and realize long-range observation.

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