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An Analytical Approach to Predict the COVID-19 Death Rate in Bangladesh Utilizing Multiple Regression and SEIR Model

An Analytical Approach to Predict the COVID-19 Death Rate in...

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robotics, automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 ieee international Conference on

作者： Tamal Joyti Roy Md. Ashiq Mahmood Aninda Mohanta Diti Roy Institute of Information and Communication Technology Khulna University of Engineering & Technology (KUET) Khulna Bangladesh Institute of Engineering & Management Kolkata

ISBN: (数字)9781665478694

ISBN: (纸本)9781665478700

The epidemic of COVID-19 has turned out to be a huge fear for the world. There is currently no advisable drug or cure available to treat this condition. According to WHO statistics, COVID-19 has become a progressive lung illness that is spread by respiratory droplets and other forms of contact. According to WHO, there is still no treatment or defensive plan that has risen till the period to encounter the COVID- 19 pandemic that was arisen in China in late 2019. The purpose of our study is to predict the COVID-19 situation by analyzing the death rate, recovery rate, and susceptibility rate with the help of the regression model and SEIR model. Two analytical models (SEIR and Regression) have been used. Our analysis has shown the prediction of the COVID-19 death rate in Bangladesh with the help of a Regression and SEIR model. We have analyzed the instances per million, number of death rates per million from the SEIR and Regression results and compared them with the real-time result. We have used a valid data set of Bangladesh, collected from the Institute of Epidemiology, Disease Control and Research (ICR) from 18 March 2020 to July 18, 2021. Our experimental result shows promising performance. Examples and descriptions are provided to explain the technique.

关键词： COVID-19 Analytical models computational modeling Government Predictive models Real-time systems Standards

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On Improved MRI Segmentation Using Hierarchical computational intelligence Techniques and Textural Analysis of the Discrete Wavelet Transform Domain

On Improved MRI Segmentation Using Hierarchical Computationa...

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ieee international symposium on Intelligent Signal Processing

作者： D. A. Karras Chalkis Institute of Technology Automation Department Hellenic Open University Athens Greece

ISBN: (纸本)1424408296;97

This paper investigates a novel feature extraction approach to MRI segmentation based on identifying the critical image edges by using textural (cooccurrence matrices) analysis of the discrete wavelet transform (DWT) domain. Furthermore, the presented approach is based on formulating the problem as a two-stage unsupervised classification task using a modified Kohonen's self organizing feature map (SOFM) along with independent component analysis (ICA). The main goal of such a research effort is to better identify abrupt textural image changes without increasing the presence of noise in the resulting image. The suggested methodology is based on novel discrete wavelet descriptors involving the discrete k-level 2-D wavelet transform and cooccurrence matrices analysis applied to sliding windows raster scanning the original image. The proposed two-stage classification scheme applied to such textural wavelet descriptors and using a modified vector quantizing self-organizing feature map (SOFM) and ICA analysis is compared with a corresponding two-stage scheme involving PCA analysis and the widely used SOFM, trained with Kohonen's algorithm. The feasibility of this novel two-stage proposed approach is studied by applying it to the edge structure segmentation problem of brain slice MRI images. The promising results presented in the experimental study illustrate a performance favourably compared, also, to that of traditional Sobel edge detectors supported by usual contour tracing methods.

关键词： Discrete wavelet transforms Magnetic resonance imaging computational intelligence Wavelet analysis Wavelet domain Independent component analysis Image texture analysis Image segmentation Image edge detection Image analysis

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VOOM: Robust Visual Object Odometry and Mapping using Hierarchical Landmarks

VOOM: Robust Visual Object Odometry and Mapping using Hierar...

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ieee international Conference on robotics and automation (ICRA)

作者： Yutong Wang Chaoyang Jiang Xieyuanli Chen School of Mechanical Engineering Beijing Institute of Technology China College of Intelligence Science and Technology National University of Defense Technology China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In recent years, object-oriented simultaneous localization and mapping (SLAM) has attracted increasing attention due to its ability to provide high-level semantic information while maintaining computational efficiency. Some researchers have attempted to enhance localization accuracy by integrating the modeled object residuals into bundle adjustment. However, few have demonstrated better results than feature-based visual SLAM systems, as the generic coarse object models, such as cuboids or ellipsoids, are less accurate than feature points. In this paper, we propose a Visual Object Odometry and Mapping framework VOOM using high-level objects and low-level points as the hierarchical landmarks in a coarse-to-fine manner instead of directly using object residuals in bundle adjustment. Firstly, we introduce an improved observation model and a novel data association method for dual quadrics, employed to represent physical objects. It facilitates the creation of a 3D map that closely reflects reality. Next, we use object information to enhance the data association of feature points and consequently update the map. In the visual object odometry backend, the updated map is employed to further optimize the camera pose and the objects. Meanwhile, local bundle adjustment is performed utilizing the objects and points-based covisibility graphs in our visual object mapping process. Experiments show that VOOM outperforms both object-oriented SLAM and feature points SLAM systems such as ORB-SLAM2 in terms of localization. The implementation of our method is available at https://***/yutongwangBIT/***.

关键词： Location awareness Bundle adjustment Visualization Simultaneous localization and mapping Accuracy Object oriented modeling computational modeling

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Development of man machine interface software for an industrial robot

Development of man machine interface software for an industr...

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ieee international symposium on Virtual Environments, Human-Computer Interfaces and Measurement Systems (VECIMS)

作者： E.I. Konukseven A. Abidi Department of Mechanical Engineering Middle East Technical University Ankara Turkey

This paper describes the design and implementation of a man machine interface software for the ABB IRB-2000 industrial robot. The main aim of this software is to provide features which would facilitate on-line/off-line programming of the robot. The software would provide a three dimensional graphical simulation of the robot and its environment, which also allows loading custom designed objects into the simulated environment. The software also offers collision detection capability between graphically simulated objects, an interpreter/editor to interpret/write robot programs in BASIC like language enhanced with robot commands. The facility to control the actual robot through the computer's serial communication port is also provided.

关键词： Man machine systems Computer industry Service robots Robot programming Open source software Robot sensing systems robotics and automation Mechanical engineering computational modeling Object detection

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DeepCerviNet: Enhanced Cervical Cancer Classification Using Supervised Learning Model

DeepCerviNet: Enhanced Cervical Cancer Classification Using ...

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robotics, automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 ieee international Conference on

作者： Md Anikur Rahaman Maksura Binte Rabbani Nuha Md Tahsin Al Hossain Raihan Ul Islam Mohammad Rifat Ahmmad Rashid Ahmed Wasif Reza Shamim H Ripon Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Engineering International Islamic University Chittagong Chittagong Bangladesh

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

Cervical cancer is a female-specific malignancy that poses a substantial threat worldwide, especially in less privileged and underdeveloped regions. Despite continuous progress and advances in medical science research, the rate of death from cervical cancer remains strikingly high among women. Therefore, accurate early-stage cervical cancer identification is imperative for effective diagnosis. It facilitates cost-effective and timely treatment to increase the survival rate of patients, leading to an optimal diagnosis improvement in disease classification. Deep learning approaches demonstrate superior accuracy in medical data classification, motivating us to consider these techniques in this research. In our study, we propose DeepCerviNet, a custom CNN model that achieves 99% precision, which exceeds the accuracy of other deep learning models, MobileNetV2 (97%), ResNET34 (95%), and GoogleNet (94%). Furthermore, to assess our model’s generalizability, we use another Multicancer dataset where the proposed model classifies various cancers with 99% accuracy. The DeepCerviNet model’s strong performance and adaptability can assist in identifying more aggressive or recurrent cervical cancer types, aiding the development of targeted therapeutic strategies.

关键词： Deep learning Adaptation models Analytical models Accuracy computational modeling Supervised learning Robustness Cervical cancer Medical diagnostic imaging Robots

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Generating and converting plans for intelligent manufacturing systems

Generating and converting plans for intelligent manufacturin...

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ieee international symposium on Industrial Electronics (ISIE)

作者： J. Rocha C. Ramos Departamento de Engenharia lnformatica Instituto Superior de Engenharia do Porto Porto Portugal Department Engineering Electrotécnica e Computadores Faculdade de Engenharia da Universidade do Porto Porto Portugal

This paper describes a system named TPMS (Task Planning for Manufacturing Systems). The final goal of this system is to automatically generate programs or sets of commands for robots, AGVs, numerical control machines and other components of the system without the need of spending much time programming the flexible manufacturing system (FMS) components. The sequence of operations to be handled by the manufacturing system depends on the constraints for the task. Three types of constraints are defined and explained: processing constraints; feasibility constraints and geometric constraints. The symbolic plan for the task is represented in the form of a precedence graph. High level operations can be converted into programs or sets of instructions to control the manufacturing system's equipment. The main contributions of TPMS are the following: to put together symbolic planning and execution planning; it is being developed to work for complete manufacturing systems (and not for controlling only one robot); different industrial operations (assembly, welding, drilling, etc.) can be considered at the same time.< >

关键词： Manufacturing systems Flexible manufacturing systems Control systems Robotic assembly Intelligent robots Machine intelligence robotics and automation Computer numerical control Automatic programming Robot programming

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A simple interface to the Virtual Collaboration Arena for MATLAB applications

A simple interface to the Virtual Collaboration Arena for MA...

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international symposium on computational intelligence and Informatics

作者： Dávid Vincze Szilveszter Kovács Péter Korondi Péter Baranyi Department of Information Technology University of Miskolc Miskolc Hungary Department of Mechatronics Optics and Engineering Informatics Budapest University of Technology and Economics Budapest Hungary Computer and Automation Research Institute Hungarian Academy of Sciences Budapest Hungary

The Virtual Collaboration Arena (VirCA) is a modular, easy to use 3D framework supporting the development of augmented (real and virtual) reality applications. To apply the services provided by VirCA, special VirCA interfaces are needed in the actual programming environment. Native interface does not exist for some special environments, like e.g. for MATLAB, which is a commonly used for implementing complex models. The goal of the paper is to introduce a possible method for interconnecting the prototype spatial ETO-MOTOR model implemented in MATLAB with the 3D VirCA augmented reality environment. This is achieved by a newly developed adapter application, which translates between the two environments and uses standardized network protocols for communication. The ETO-MOTOR is a behaviour model based upon ethological studies. In the case of the example application, the type of the ETO-MOTOR is a “spatial ETO-MOTOR”, i.e. it can directly send spatial coordinates or heading directions to the virtual actors of the model. The prototype of the spatial ETO-MOTOR is constructed as a Fuzzy Rule Interpolation (FRI) based fuzzy automaton. In the example of the paper the ETO-MOTOR describes the behaviour of a dog in an unknown environment. In the original version of the model implementation, it has a very simple proof-of-concept user interface, which is sufficient for basic testing only, but very far from real world experiences. The personal interaction with the model thanks to the 3D VirCA augmented reality environment can yield much more direct observations and hence more profit for ethologists. The developed MATLAB-VirCA connector application provided with this paper can be easily modified to suit the needs of other third party applications too.

关键词： Three dimensional displays MATLAB Interpolation Adaptation models Automata Solid modeling Collaboration

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3DCFS: Fast and Robust Joint 3D Semantic-Instance Segmentation via Coupled Feature Selection

3DCFS: Fast and Robust Joint 3D Semantic-Instance Segmentati...

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ieee international Conference on robotics and automation (ICRA)

作者： Liang Du Jingang Tan Xiangyang Xue Lili Chen Hongkai Wen Jianfeng Feng Jiamao Li Xiaolin Zhang Institute of Science and Technology for Brain-Inspired Intelligence Fudan University China Chinese Academy of Sciences Shanghai Institute of Microsystem and Information Technology Shanghai China School of Computer Science Fudan University China

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

We propose a novel fast and robust 3D point clouds segmentation framework via coupled feature selection, named 3DCFS, that jointly performs semantic and instance segmentation. Inspired by the human scene perception process, we design a novel coupled feature selection module, named CFSM, that adaptively selects and fuses the reciprocal semantic and instance features from two tasks in a coupled manner. To further boost the performance of the instance segmentation task in our 3DCFS, we investigate a loss function that helps the model learn to balance the magnitudes of the output embedding dimensions during training, which makes calculating the Euclidean distance more reliable and enhances the generalizability of the model. Extensive experiments demonstrate that our 3DCFS outperforms state-of-the-art methods on benchmark datasets in terms of accuracy, speed and computational cost. Codes are available at: https://***/Biotan/3DCFS.

关键词： Semantics Task analysis Three-dimensional displays Feature extraction Logic gates Training Euclidean distance

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A survey of management interfaces for eucalyptus cloud

A survey of management interfaces for eucalyptus cloud

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international symposium on Applied computational intelligence and Informatics ( SACI)

作者： A.M. Lonea D.E. Popescu O. Prostean Automation and Applied Informatics Department Politehnica University of Timisoara Timisoara Romania Computer Engineering Department University of Oradea Oradea Romania

Cloud Computing is a technology which is susceptible to a continuous development, because of its strong advantages of accessing data from any place in the world over the Internet without concerning about the infrastructure used and the problems involved by the installation and maintenance processes. The purpose of this article is to provide an overview of several management interfaces for Eucalyptus cloud by addressing the taxonomy and evaluation of the cloud management interfaces. The taxonomy proposed in this paper results from the work accomplished by experimenting the Eucalyptus community cloud. First, an evaluation of the Eucalyptus architecture is presented, in order to emphasize the Eucalyptus platform involved in cloud management process, which facilitates the deployment, management and execution of Infrastructure-as-a-Service (laaS). The cloud management tools are described from two perspectives. The first perspective analyzes the cloud portals from the user roles, while the second perspective addresses them with respect to the type of the tools employed. The taxonomy presented in this paper is related with the 5 elements of the Common Cloud Management Architecture (CCMA), which was provided by Behrendt, et al. (2011) as one of the components of mM Cloud Computing Reference Architecture. Thus, the first category of interfaces (i.e. the cloud portals from the user roles) encompasses the Service Consumer Portal, the Service Provider Portal and the Service Development Portal, while second category of interfaces includes the main components of CCMA: Operational Support Services (OSS) and Business Support Services (BSS). Cloud management is a subject approached by researchers in the community and this can be observed by the big number of third party cloud management providers (i.e. RightScale, enStratus, IMOD Kaavo, CloudWatch, Scarl, Tapin, Cloudkick). This article is motivated by the fact that cloud management is a fundamental support for all users of cloud se

关键词： Fires Taxonomy Local area networks Business Communities

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3D Feedback Motion Planning Of Unmanned Underwater Vehicles Using Sparsity Enhanced Random Sequential Composition*

3D Feedback Motion Planning Of Unmanned Underwater Vehicles ...

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ieee international Conference on automation Science and Engineering (CASE)

作者： Emre Ege Eminalp Koyuncu M. Mert Ankarali Maren Robotics Ankara Türkiye Department of Electrical and Electronics Engineering Center for Robotics and Artificial Intelligence Middle East Technical University Ankara Türkiye

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In this paper, a new, robust, and computationally feasible motion planning algorithm is introduced to solve the path planning, navigation, and probabilistic coverage problems related to the Unmanned Underwater Vehicles (UUV). The approach given in this paper is based on feedback motion planning with random sequential composition (RSC) method. Initially, the RSC method was capable of 2D motion planning with a sparse tree structure. In this work, the sparsity of the RSC method is enhanced using an approach called "circular enlarged funnels," and the algorithm is extended for 3D path planning of UUVs. Then, the proposed method is tested on MATLAB software in a 2D environment with the kinematic model of the UUV. After that, the 3D extension is tested with the realistic dynamical model of the UUV built on ROS/Gazebo environment, with and without environmental disturbances, to show the robustness of the resulting feedback policies. The results of these simulations showed that the proposed method can solve the path planning problem of UUVs in a robust and computationally feasible way with enhanced sparsity.

关键词： Solid modeling Autonomous underwater vehicles Three-dimensional displays Simulation computational modeling Kinematics Probabilistic logic Mathematical models Planning Vehicle dynamics

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