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Semantic Segmentation in Medical Image Based on Hybrid Dlinknet and Unet 3

Semantic Segmentation in Medical Image Based on Hybrid Dlink...

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3rd IEEE international conference on Computing, Communication, and intelligent Systems, ICCCIS 2022

作者： Samudrala, Suresh Mohan, C. Krishna Department of Computer Science and Engineering India India

ISBN: (纸本)9781665462006

Medical imagery segmentation has been widely using deep learning approaches, which are quickly evolving in semantic segmentation. Nevertheless, due to their poor performance, newly proposed methods like Fully Convolutional Network, U-Net, LinkNet and SegNet still require enhancement to offer better semantic segmentation while identifying breast cancer. Therefore, this article presents a hybrid encoder and decoder framework in histology images of tissue slides. The novel network is designed with DlinkNet and UNet with a fusion of medical imagery data. An Attention Gate Module (AGM) is inserted into the proposed network to enrich the network learning's capacity. This way, semantic features are extracted and semantically segmented in the tumors in the input image set. The effectiveness of the planned network is investigated concerning various parameters like Maximum Symmetric Surface Distance (MSSD 35.98), Accuracy (99.2%), Relative Absolute Volume Difference (RAVD 3.41), Jaccard index (0.83), Average Symmetric Surface Distance (ASSD 0.629), Sensitivity (91%) and Dice Coefficient (DICE) (0.928) with existing networks of FPN and SENet. © 2022 IEEE.

关键词： Semantic Segmentation

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A Study of Microgrid Scheduling Methods for Bidirectional LSTM Networks Under Constraints 3

A Study of Microgrid Scheduling Methods for Bidirectional LS...

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3rd international conference on Energy, Power and Electrical engineering, EPEE 2023

作者： Fan, Ying Wei, Xiaochuan Wei, Xuehao Cao, Yingshuang Xu, Qin Luo, Ling Song, Chun Lin, Ke Qin, Zhisong State Grid Shanghai Municipal Electric Power Company Shanghai China East China Grid Corporation Shanghai China Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798350318180

Microgrids, as a crucial component of distributed energy systems, encounter intricate optimization and scheduling issues. Aiming to improve the effectiveness of deep learning methods in micro grids when constrained, this paper proposes a solution to enhance the Bidirectional LSTM model. It involves generating new data and training a second Bidirectional LSTM model with added power constraints for devices like energy storage and power generation. These added constraints are incorporated to refine the initial prediction results of the Bidirectional LSTM model. Compared to the model trained only once, the proposed method significantly decreases both the mean square deviation and the average threshold exceeding value of the prediction data. It also reduces the potential risk of scheduling scenarios that exceed the affordability of the microgrid under constrained conditions. © 2023 IEEE.

关键词： deep learning microgrids optimal scheduling

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Kube-CC: Building an Experiment Education Platform for Big data Courses Based on Kubernetes 3

Kube-CC: Building an Experiment Education Platform for Big D...

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3rd international conference on engineering Education and Information Technology, EEIT 2023

作者： Xue, Feiyu Yang, Libin Fan, Bin Jiang, Huijian Huo, Yinqiu Northwest A&f University College of Information Engineering Shaanxi Xianyang712100 China

ISBN: (纸本)9798350326840

To address the challenges of complex and time-consuming deployment, poor environment isolation, and difficult maintenance in Big data experiments, we build an experiment education platform, Kube-Clould Classroom (Kube-CC), specifically designed for Big data courses. It is built on Kubernetes (K8s) and the Go language (Go), which fully utilizes the abundant and affordable computer resources of the existing laboratory. With the proposed platform, students can deploy and recover big data clusters in container form via a one-click process, making the deployment process simple and easy. Meanwhile, teachers are facilitated with a dashboard to monitor resources. In addition, Kube-CC allows for customized deployment in batches, which is tailored to meet the needs of each individual experiment. Furthermore, the platform achieves persistent storage of configuration and data for each cluster, which ensures that students can work with the same data they used in previous experiments, and eliminates the need to reconfigure the environment each time they start a new experiment. We deployed the education platform and compared it to prior work with results and feedback from teachers and students indicating our platform's effectiveness. Overall, we provide Kube-CC, an efficient solution to the challenges of Big data experiments. It increases device utilization, improves the learning experience for students, and support for Big data courses, which is of great significance to educational reform. Code is available at https://***/fanb129/Kube-CC. © 2023 IEEE.

关键词： Big data

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User Recognition based on Gait Pattern via Smartwatch Accelerometer in Unrestricted Environment 3

User Recognition based on Gait Pattern via Smartwatch Accele...

引用

3rd international conference on Communication, Computing and Digital Systems, C-CODE 2023

作者： Khan, Sheharyar Shah, Syed M Adnan Arsalan, Aamir Ahmed, Wakeel University of Engineering and Technology Taxila Department of Computer Science Taxila Pakistan Fatima Jinnah Women University Rawalpindi Department of Software Engineering Rawalpindi Pakistan

ISBN: (纸本)9798350332391

Wearable gadgets are becoming a major constituent of our society due to a wide range of applications like financial transactions, unlocking automobiles, tracking health and fitness, and many more. Personal data is usually required to manage these services, however, most commercially available wearables devices either lack a user authentication mechanism or employ knowledge-based authentication like passwords, PINs, or pattern locks. In this growing digital age, it is really difficult for an individual to memorize such a huge number of passwords. In this research, we put forward a technique for recognizing users from their walking patterns recorded in real-life settings using a smartwatch accelerometer sensor. Ensemble classification techniques (bagging and boosting) are used for the purpose of user identification using the selected time domain features. Our proposed method attained the highest classification accuracy of 88.35% which transcends the existing user recognition schemes available in the literature. © 2023 IEEE.

关键词： Machine learning

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Deep learning-Based Localization Approach for Autonomous Robots in the RobotAtFactory 4.0 Competition 3rd

Deep Learning-Based Localization Approach for Autonomous Rob...

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3rd international conference on Optimization, learning Algorithms and Applications (OL2A)

作者： Klein, Luan C. Mendes, Joao Braun, Joao Martins, Felipe N. de Oliveira, Andre Schneider Costa, Paulo Wortche, Heinrich Lima, Jose Univ Tecnol Fed Parana UTFPR PR Campus Curitiba Curitiba Parana Brazil Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Braganca Portugal Inst Politecn Braganca Lab Sustainabil & Technol Mt Reg SusTEC Braganca Portugal Univ Porto Fac Engn Porto Portugal INESC Technol & Sci Porto Portugal Hanze Univ Appl Sci Inst Engn Sensors & Smart Syst Grp Groningen Netherlands Eindhoven Univ Technol Dept Elect Engn Eindhoven Netherlands

ISBN: (纸本)9783031530357;9783031530364

Accurate localization in autonomous robots enables effective decision-making within their operating environment. Various methods have been developed to address this challenge, encompassing traditional techniques, fiducial marker utilization, and machine learning approaches. This work proposes a deep-learning solution employing Convolutional Neural Networks (CNN) to tackle the localization problem, specifically in the context of the RobotAtFactory 4.0 competition. The proposed approach leverages transfer learning from the pre-trained VGG16 model to capitalize on its existing knowledge. To validate the effectiveness of the approach, a simulated scenario was employed. The experimental results demonstrated an error within the millimeter scale and rapid response times in milliseconds. Notably, the presented approach offers several advantages, including a consistent model size regardless of the number of training images utilized and the elimination of the need to know the absolute positions of the fiducial markers.

关键词： Indoor Localization CNN Robotic Competition

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Big data Analytics for Electrical Systems using Machine learning Algorithms

Big Data Analytics for Electrical Systems using Machine Lear...

引用

Technology, engineering, Management for Societal impact using Marketing, Entrepreneurship and Talent (TEMSMET), IEEE international conference on

作者： Salava V Satyanarayana Pillalamarri Madhavi Electrical and Electronics Engineering Hyderabad Institute of Technology and Management Hyderabad India

Big data Analytics plays a vital role in every electrical industry to analyze the large amount of data received from their dispatch centers. Typically, data retrieved from electrical systems like Generation, Transmission, and distribution will be analyzed using many control systems like SCADA and HMI without any human intervention. To analyze this data as per industry 4.0, Every system should be automated by integrating the data into the internet of things along with cyber security systems. With consideration of industry 4.0 standards, in view of effective optimized operational maintenance of electrical systems in the future, this paper provides intelligent predicted data by analyzing current and existing data that is fetching from electrical systems i.e., Various state electrical utility data files. The available data of electrical systems can be analyzed by using various Supervised Machine learning algorithms and this paper verified the accuracy of various machine learning algorithms by analyzing the predicted data by each algorithm.

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Multivariate Deep learning for Reconstruction of Spatial Missing Climate data 3

Multivariate Deep Learning for Reconstruction of Spatial Mis...

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3rd international conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Yao, Ziqiang Wu, Li Huang, Jianqia Wang, Xiaoying Department of Computer Technology and Application Qinghai University Xining810016 China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

Complete meteorological data is essential for meteorological research. However, due to sensor failure or occlusion, data loss always occurs. In order to deal with this problem, geoscience often uses Kriging and other traditional interpolation methods. Nevertheless, because the traditional method only considers the existing data, it cannot provide accurate reconstruction results when the spatial missing rate is large. Inspired by image inpainting algorithms, we propose a new deep learning method that combines other variables related to missing meteorological variables to provide more effective information for missing regions, and proves the good effect of the method through a series of experiments. To illustrate that our method can maintain good performance under different missing rates, we use the reanalysis data of the European Centre for Medium-Range Weather Forecasts to create a data set with three different missing rates of 30%, 50%, and 70% through the artificial mask. Under different missing rates, the average RMSE of the method based on deep learning decreased by 77% and 31% respectively compared with Kriging and DINEOF. In addition, by combining relevant variables, the accuracy of the deep learning model can be further improved by about 16%. © 2023 SPIE.

关键词： Kriging

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Recyclable and Non-Recyclable Waste Recognition using Image Processing with Machine learning: Recycle Mate

Recyclable and Non-Recyclable Waste Recognition using Image ...

引用

intelligent Technologies (CONIT), international conference on

作者： Amit Nara Smit Panchal Prof. Suchita Dange Department of Computer Engineering A.P. Shah Institute of Technology Thane India

Waste Management is the effort needed to make our world a better place. As solid waste may cause a problem for our surroundings. Nowadays many people don't even know how to distinguish between recyclable and non-recyclable objects, we know only some general things which are recyclable like paper, plastic, cardboard and drinking cans. To deal with this problem a deep machine-learning Convolution Neural Network (CNN) was created to help us all with an achievable waste management task. The automated task works for everyone as it lets down the burden of work from many people’s shoulders. Our model is built using the Keras API. The model categorizes the data into a binary class. It has been tested with many objects which gave an acceptable prediction accuracy.

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Precision compensation method of manipulator based on neural network and particle swarm optimization 3

Precision compensation method of manipulator based on neural...

引用

3rd international conference on intelligent Mechanical and Human-Computer Interaction Technology, IHCIT 2024

作者： Wang, Xi Department of Mechanical and Electronic Engineering Dongguan University of Technology Guangdong Dongguan523808 China

ISBN: (纸本)9781510683105

In the present era, robot industry is experiencing rapid development. Manipulators have emerged as essential robotic systems that play crucial roles in various domains. Ensuring high precision and reliability of manipulators holds paramount significance, particularly within the industrial sector. Numerous approaches have been explored to enhance the precision and reliability of manipulators by calculating and compensating for errors. However, there are some shortcomings in the existing methods. To address the existing issues and enhance the overall performance, based on the modified DH (MDH) model, this paper proposes an error prediction and compensation approach that combines back propagation (BP) neural network and particle swarm optimization (PSO). In this method, the attitude of IRB1410 manipulator is described by MDH model, and the forward and inverse kinematics are simulated by Matlab software. To complete the training of the BP neural network model, a small disturbance is randomly given to the manipulator within the constraint condition to obtain the corresponding data set. The model serves as a simulator for predicting errors, while the particle swarm optimization algorithm is employed to compute the compensation value, thereby circumventing local optima. Combined with the particle swarm optimization algorithm, the root mean square error before and after compensation is reduced by 78.65%. It can improve the accuracy of the manipulator, so that it can better complete people's needs. © 2024 SPIE.

关键词： MATLAB

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Application of Artificial Intelligence to Cluster Soil Behaviour from CPTu data 3rd

Application of Artificial Intelligence to Cluster Soil Behav...

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3rd international conference on Sustainable Civil engineering and Architecture, ICSCEA 2023

作者： Phu, Nhat Truyen Le, Pham Thanh Hieu Le, Ba Vinh Vo, Dai Nhat 268 Ly Thuong Kiet Street District 10 Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Linh Trung Ward Thu Duc City Ho Chi Minh City Viet Nam

ISBN: (纸本)9789819974337

In this study, the authors used many artificial intelligence algorithms to cluster soil behaviour from CPTu data, that includes cone resistance (qc), frictional resistance (fs), dynamic pore pressure (u2), corrected cone resistance (qt) and friction ratio (Rf). The soil behavior type is following Robertson (1986). There are four model are built in this study include: i. Supervised learning with SVM algorithm by qc, fs, u2, qt and Rf;ii. Supervised learning with SVM algorithm by qt and Rf;iii. Unsupervised learning with Kmeans algorithm by qt and Rf with three clusters;and Unsupervised learning with Kmeans algorithm by qt and Rf with nine clusters. To satisfy "Imbalanced data" and Roberson’s chart shape, the raw data are being preprocessing within two steps before clustering with KMeans or continue to divide to 3 minor set for SVM algorithm that includes: training set - 50%, validation set - 20% and test set - rest. The result indicated the Supervised learning with SVM algorithm by qc, fs, u2, qt and Rf is the best model, while unsupervised learning with KMeans does not meet the requirements. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Unsupervised learning

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