Melanoma is a malignant form of cancer that affects the skin and has a particularly high mortality rate, so it requires early detection to increase the level of safety for users. Diagnosis and detection of skin cancer...
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The mission of classifying remote sensing pictures based on their contents has a range of applications in a variety of *** recent years,a lot of interest has been generated in researching remote sensing image scene **...
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The mission of classifying remote sensing pictures based on their contents has a range of applications in a variety of *** recent years,a lot of interest has been generated in researching remote sensing image scene *** sensing image scene retrieval,and scene-driven remote sensing image object identification are included in the Remote sensing image scene understanding(RSISU)*** the last several years,the number of deep learning(DL)methods that have emerged has caused the creation of new approaches to remote sensing image classification to gain major breakthroughs,providing new research and development possibilities for RS image classification.A new network called Pass Over(POEP)is proposed that utilizes both feature learning and end-to-end learning to solve the problem of picture scene comprehension using remote sensing imagery(RSISU).This article presents a method that combines feature fusion and extraction methods with classification algorithms for remote sensing for scene *** benefits(POEP)include two *** multi-resolution feature mapping is done first,using the POEP connections,and combines the several resolution-specific feature maps generated by the CNN,resulting in critical advantages for addressing the variation in RSISU data ***,we are able to use Enhanced pooling tomake the most use of themulti-resolution feature maps that include second-order *** enablesCNNs to better cope with(RSISU)issues by providing more representative feature *** data for this paper is stored in a UCI dataset with 21 types of *** the beginning,the picture was pre-processed,then the features were retrieved using RESNET-50,Alexnet,and VGG-16 integration of *** characteristics have been amalgamated and sent to the attention layer,after this characteristic has been fused,the process of classifying the data will take *** utilize an ensemble classifier in our classification a
Accurate camera pose estimation is crucial for various applications in robotics and computer vision, enabling precise navigation and interaction with the environment. This paper presents a straightforward approach to ...
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ISBN:
(数字)9798350357509
ISBN:
(纸本)9798350357516
Accurate camera pose estimation is crucial for various applications in robotics and computer vision, enabling precise navigation and interaction with the environment. This paper presents a straightforward approach to camera pose estimation and calibration using ArUco markers. Different Aruco’s is used for getting the camera pose and calibration is performed with a single ArUco marker to determine the camera's intrinsic parameters. To enhance the accuracy of pose estimates, bundle adjustment is applied, minimizing re-projection errors through optimization. Additionally, Networkx is used to build a graph, visualizing the spatial relationships between detected markers. The entire system was tested in the Stonefish ROS simulator and Real Environment, validating the calibration and pose estimation processes. The results demonstrate the effectiveness of our methods, offering a reliable framework for applications that require precise camera pose estimation in robotic systems.
A set of new models that describe the dynamics of battles in strategic computer games is presented. These models not only have descriptive functions, but also allow solving problems of optimizing army control under gi...
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The past two decades witnessed a broad-increase in web technology and on-line *** the broadband confinements is viewed as one of the most significant variables that prompted new gaming *** immense utilization of web a...
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The past two decades witnessed a broad-increase in web technology and on-line *** the broadband confinements is viewed as one of the most significant variables that prompted new gaming *** immense utilization of web applications and games additionally prompted growth in the handled devices and moving the limited gaming experience from user devices to online cloud *** internet capabilities are enhanced new ways of gaming are being used to improve the gaming *** cloud-based video gaming,game engines are hosted in cloud gaming data centers,and compressed gaming scenes are rendered to the players over the internet with updated *** such systems,the task of transferring games and video compression imposes huge computational complexity is required on cloud *** basic problems in cloud gaming in particular are high encoding time,latency,and low frame rates which require a new methodology for a better *** improve the bandwidth issue in cloud games,the compression of video sequences requires an alternative mechanism to improve gaming adaption without input *** this paper,the proposed improved methodology is used for automatic unnecessary scene detection,scene removing and bit rate reduction using an adaptive algorithm for object detection in a game *** a result,simulations showed without much impact on the players’quality experience,the selective object encoding method and object adaption technique decrease the network latency issue,reduce the game streaming bitrate at a remarkable scale on different *** proposed algorithm was evaluated for three video game *** this paper,achieved 14.6%decrease in encoding and 45.6%decrease in bit rate for the first video game scene.
In today’s modern world, digital technology has advanced social networking with revolutionary changes. People use some most popular platforms like Facebook, YouTube and some other social media platforms to engage wit...
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ISBN:
(数字)9798350357509
ISBN:
(纸本)9798350357516
In today’s modern world, digital technology has advanced social networking with revolutionary changes. People use some most popular platforms like Facebook, YouTube and some other social media platforms to engage with each other for business, information sharing and so on. Attacking someone on social media platforms through comments, in response to their speech, has become a common occurrence in Bangladesh as well as worldwide. Very little research has been done in this related field for Bengali NLP. In this study we primarily focus on classifying these comments into two categories: extremist and non-extremist. Comments were collected from two of the most popular platforms, YouTube and Facebook, used in Bangladesh to build our "BEC (Bangla Extremist Comments)" dataset. To classify the comments, a CNN + Bi-LSTM hybrid model was employed considering extremist comments detection systems using deep learning-based sentiment analysis techniques. The proposed model achieved an accuracy rate of 85% in identifying the extremist comments. This research opens opportunities for future researchers to take advantage of contributing and collaboration in this field.
Global stability and robustness guarantees in learned dynamical systems are essential to ensure well-behavedness of the systems in the face of uncertainty. We present Extended Linearized Contracting Dynamics (ELCD), t...
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Tasks in Predictive Business Process Monitoring (PBPM), such as Next Activity Prediction, focus on generating useful business predictions from historical case logs. Recently, Deep Learning methods, particularly sequen...
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This study introduces a methodology for the Uncertainty Analysis of the Canadian power system, leveraging ML techniques. Specifically, we seek to uncover nonlinear behaviors and potential bifurcations, which conventio...
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