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检索条件"任意字段=6th International Conference on Generative Programming and Component Engineering"

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Web Based Brain Tumor Detection using Neural Network 6

Web Based Brain Tumor Detection using Neural Network

引用

6th IEEE international conference on Computing, Communication and Automation, ICCCA 2021

作者： Mridha, Krishna Pandey, Aashish Prashad Ranpariya, Akshay Ghosh, Ankush Shaw, Rabindra Nath Marwadi University Computer Engineering Gujarat Rajkot India The Neotia University School of Engineering and Applied Sciences West Bengal India Galgotias University Electrical Electronics Communication Engg. Greater Noida India

ISBN: (纸本)9781665414739

the brain tumor is a major human life concern. One of the main causes of mortality in individuals is brain tumors in recent years. It is tough to manually detect the tumor. Doctors may being confused to detect the tumor since they have been utilized to do image operations in computer programming. It is very crucial to discover the brain tumor early on with its precise diagnosis. the interior architecture of the brain and the diagnosis, monitoring, and treatment of an illness is an essential component of medical research. In medical practice, X-ray is used to diagnose the body's human component via several types of imaging technologies, such as CT-scan and MRI. MRI is utilized in brain diagnosis or tumor location, tissue volume measurement, tumor size estimation. Because of the tumor variety the detection of the tumor is exceedingly challenging. For the identification and classification of brain tumors, several image processing and neural network algorithms are utilized. In this article, we will present comparisons of several approaches with brain tumor diagnosis. Our study identifies signs of Brain tumor by utilizing the Deep Neural Network (DNN) method based on these symptoms at the very first stage. the hybrid CNN model was utilized to identify brain tumor illness and was demonstrated to outperform it in comparison with standard styles such as InceptionV3, RestNet50, etc. We have built a web-based AI tool to identify Brain tumors using this model. © 2021 IEEE.

关键词： Computerized tomography

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Slicing based on web scrapped concurrent component 6th

Slicing based on web scrapped concurrent component

引用

6th international conference on Information System Design and Intelligent Applications, INDIA 2019

作者： Pujari, Niharika Ray, Abhishek Singh, Jagannath School of Computer Engineering KIIT Deemed to Be University BhubaneswarOdisha India

ISBN: (纸本)9789811553998

In this paper, we have proposed an algorithm for dynamic slicing of concurrent COPs that consist of multiple threads. In order to portray the concurrent COP effectively, an intermediate representation graph called concurrent component-oriented dependency graph (CCmDG) is developed based on the dependencies among all the edges. the intermediate representation graph developed for component-oriented programs consists of system dependence graph (SDG) for individual components, along with that some new dependence edges are introduced that are used to connect each system dependency graph to the interface. Based on the intermediate graph created in first step, a dynamic slicing algorithm is proposed for undertaken case study for the concurrent component-oriented program. the resultant is a graph marking and unmarking the executed nodes in concurrent components dynamic slicing (CCmDS) appropriately during run-time. © Springer Nature Singapore Pte Ltd 2021.

关键词： Concurrent component-oriented programming Dynamic slicing thread connect edge (TCE) Web scrapping

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EigenGRF: Layer-Wise Eigen-Learning for Controllable generative Radiance Fields 29th

EigenGRF: Layer-Wise Eigen-Learning for Controllable Generat...

引用

29th international conference on Neural Information Processing

作者： Yang, Zhiyuan Zhang, Qingfu City Univ Hong Kong Dept Comp Sci Kowloon Tong Hong Kong Peoples R China

ISBN: (纸本)9783031301049;9783031301056

Neural Radiance Fields (NeRF) learn a model for the high-quality 3D-view reconstruction of a single object. Category-specific representation makes it possible to generalize to the reconstruction and even generation of multiple objects. Existing efforts mainly focus on the reconstruction performance including speed and quality. the steerability of generation processes has not been well studied while semantic attributes still exist in 3D neural representations. Inspired by interpreting underlying factors of GANs, this paper proposes a novel method named Eigen-GRF to disentangle the latent semantic subspace in an unsupervised manner. By learning a set of eigenbasis, we can readily control the process and the result of object synthesis accordingly. Concretely, our method brings a mapping network to NeRF by conditioning on a FiLM-SIREN layer. then we use a component analysis method for discovering steerable latent subspaces. Our experiments reveal that the proposed method is powerful for the 3D-aware generation with steerability by both synthetic and real-world datasets.

关键词： Neural Radiance Fields generative Model Disentanglement Learning 3D-Aware Generation

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VAEL: bridging variational autoencoders and probabilistic logic programming 22

VAEL: bridging variational autoencoders and probabilistic lo...

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Proceedings of the 36th international conference on Neural Information Processing Systems

作者： Eleonora Misino Giuseppe Marra Emanuele Sansone Department of Computer Science and Engineering University of Bologna Italy Department of Computer Science KU Leuven Belgium

ISBN: (纸本)9781713871088

We present VAEL, a neuro-symbolic generative model integrating variational autoencoders (VAE) with the reasoning capabilities of probabilistic logic (L) programming. Besides standard latent subsymbolic variables, our model exploits a probabilistic logic program to define a further structured representation, which is used for logical reasoning. the entire process is end-to-end differentiable. Once trained, VAEL can solve new unseen generation tasks by (i) leveraging the previously acquired knowledge encoded in the neural component and (ii) exploiting new logical programs on the structured latent space. Our experiments provide support on the benefits of this neuro-symbolic integration both in terms of task generalization and data efficiency. To the best of our knowledge, this work is the first to propose a general-purpose end-to-end framework integrating probabilistic logic programming into a deep generative model.

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A Personalized Teaching Assistant Platform Driven by Large Language Models of Artificial Intelligence 6th

A Personalized Teaching Assistant Platform Driven by Large L...

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6th international conference on Computer Science and Educational Informatization, CSEI 2024

作者： Guo, Fei Duan, Junwen Peng, Xiaoqing Liang, Cheng School of Computer Science and Engineering Central South University Changsha410083 China Center for Medical Genetics and Hunan Key Laboratory of Medical Genetics School of Life Sciences Central South University Changsha410083 China School of Information Science and Engineering Shandong Normal University Jinan250358 China

ISBN: (纸本)9789819637379

Artificial intelligence (AI) is a pivotal technology driving the next industrial revolution and shaping the future of education. this research focuses on developing a learner-centered educational environment that leverages AI to provide personalized, lifelong learning experiences. In response to the evolving educational landscape, we propose an innovative approach that integrates "Internet + Education" principles with AI-driven technologies. Our study explores the deep integration of information technology and education, emphasizing the creation of a new educational paradigm centered on individual learners. By harnessing the power of big data analytics and AI, we aim to revolutionize the teaching environment, enabling truly individualized instruction. the concept of "AI + X" is introduced as a framework for nurturing multidisciplinary talent, combining expertise in AI with knowledge from two or more additional fields. this approach places particular emphasis on programming education across various majors and disciplines, recognizing it as a crucial component of AI integration in education. Our research on programming pedagogy contributes significantly to the broader "AI + X" teaching model, offering valuable insights and methodologies applicable to AI education in interdisciplinary contexts. this study lays the groundwork for a more adaptive, responsive, and personalized educational system that prepares learners for the challenges and opportunities of an AI-driven future. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Teaching

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Learning 3D LiDAR Perception Models for Self-Aware Autonomous Systems

Learning 3D LiDAR Perception Models for Self-Aware Autonomou...

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international conference on Information Fusion

作者： Saleemullah Memon Ali Krayani Pamela Zontone Lucio Marcenaro David Martin Gomez Carlo Regazzoni Department of Engineering and Naval Architecture (DITEN) University of Genova Italy Intelligent Systems Lab University Carlos III de Madrid Spain

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

Intelligent transportation systems (ITSs) provide a paradigm change in perceiving and interacting with transportation networks, leading to enhanced levels of safety, sustainability, and efficiency. Vehicular-to-everything (V2X) communication is the core component in the ITSs. the proprioceptive and exteroceptive sensors allow these vehicles to be aware of the surrounding environment and respond to emergencies by utilizing their abilities to reach a high level of self-awareness. In this paper, we propose a self-awareness approach to learn a generative dynamic Bayesian network (G-DBN) from the real-time LiDAR perception. Without reducing the dimensionality, we perform offline training and online testing phases on the three-dimensional (3D) point clouds. In the offline training phase, initially, the raw point clouds are preprocessed using a joint probabilistic data association filter (JPDAF) to obtain the 3D tracks of the multiple vehicles in space. then, we perform an unsupervised clustering on all the generalized states (GSs) containing positions and velocities (a 6D vector) by considering the growing neural gas (GNG) technique, thus achieving a trained model from the 3D LiDAR point clouds. In the online testing phase, the high-dimensional Markov jump particle filter (HD-MJPF) utilizes the G-DBN’s probabilistic information to predict the positions of multiple vehicles and to detect the abnormalities at the discrete and continuous levels in normal and abnormal scenarios. Our proposed approach is useful for learning high-dimensional generative models and provides a way to meet the current curse of dimensionality challenges, that machine learning models are suffering.

关键词： Point cloud compression Training Solid modeling three-dimensional displays Laser radar Transportation Probabilistic logic Bayes methods Vehicle dynamics Testing

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Multi-Functional RIS-Aided Anti-Jamming Communication in Semantic Integrated Satellite-Ground Networks

Multi-Functional RIS-Aided Anti-Jamming Communication in Sem...

引用

international conference on Wireless Communications and Signal Processing (WCSP)

作者： Yifu Sun Kang An Yuanzhi He Haotong Cao Xudong Zhong Kefeng Guo Hongbo Zhu Sixty-third Research Institute National University of Defense Technology Nanjing China Institute of Systems Engineering Academy of Military Sciences Beijing China College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China School of Space Information Space Engineering University Beijing China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

Integrated satellite-ground network (ISGN) emerges as a promising key component of the sixth-generation (6G) wireless networks due to its potential capabilities in providing ubiquitous connectivity for global coverage and reliable services. However, the inevitable existences of high-volume data, uncontrollable propagation environment, and malicious jamming attacks pose three significant bottlenecks for enabling efficient ISGN. With these focuses, we propose a novel framework of multi-functional reconfigurable intelligent surface (MF-RIS) aided semantic anti-jamming communication in ISGN. Under this framework, a semantic transceiver exhibits inherent robustness and data compression capability, and MF-RIS can customize the full-space wireless environment by leveraging its signal reflection, refraction, amplification, and energy harvesting functions, thereby achieving substantial global coverage, reliable connectivity, and high-rate transmission. Based on our proposed framework, we formulate a total semantic rate maximization problem considering the impacts of jammer's channel state information (CSI) imperfection, while maintaining the semantic similarity requirement, semantic rate target, and MF-RIS's self-sustainability. then, by transforming the imperfect CSI into a worst-case one by exploiting a discretization method, we propose a fast-converging monotonic optimization algorithm that is combined with decoupling second-order cone programming to obtain a globally optimal solution with fewer feasibility evaluations. Numerical simulations demonstrate the superiority of our proposed framework and algorithms compared to various benchmarks.

关键词： Wireless networks Simulation Semantics Signal processing algorithms Data compression Benchmark testing Transceivers Robustness Jamming Optimization

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Development of a Wireless Semi-Autonomous Gas Contaminant Detection Robot Using Raspberry Pi 13

Development of a Wireless Semi-Autonomous Gas Contaminant De...

引用

2021 IEEE 13th international conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2021

作者： Meris, Paolo Rafael V. Manuel, Mark Christian E. Agustin, Gian Marko C. Bernal, Chris Dane B. Cruz, Jennifer C. Dela Tud, Roderick C. Mapúa University School of Mechanical and Manufacturing Engineering 2 Manila Philippines Mapúa University School of Electrical Electronics and Computer Engineering Manila Philippines

ISBN: (纸本)9781665401678

this paper presents the development of a robot that can be controlled semi-autonomously and has the capability to detect toxic and combustible gases. the purpose of this research is to develop a semi-autonomous robot that can detect gases that are harmful to human workers. the robot is designed to be able to function properly with little human intervention. It has the ability to detect specified types of gases inside a confined space and move on its own to cover the specified operating area. the study is composed of component designing, programming, calibration, development of the user interface, fabrication, initial gas concentration test, mobility tests, and actual tests. the design of this robot is an improvement of the previous model. the mobility and semi-autonomous function of the robot is programmed using Raspberry Pi microcomputer and the sensors are programmed using the Arduino microcontroller. the gases that the robot can detect are alcohol (C2H6O), benzene (C6H6), hydrogen (H2), carbon monoxide (CO), LPG, and methane (CH4). the sensors that were used are MQ3 for alcohol and benzene, MQ7 for hydrogen and carbon monoxide, and MQ9 for methane and LPG. the results of the tests showed that the robot can operate semi-autonomously and in an omnidirectional manner easily. the actual testing procedures were done inside a confined parking area, printing shop, and LPG storage area. © 2021 IEEE.

关键词： Carbon monoxide

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Web Based Brain Tumor Detection using Neural Network

Web Based Brain Tumor Detection using Neural Network

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international conference on Computing, Communication & Automation (ICCCA)

作者： Krishna Mridha Aashish Prashad Pandey Akshay Ranpariya Ankush Ghosh Rabindra Nath Shaw Computer Engineering Marwadi University Rajkot Gujarat India Data Scientist Marwadi University Rajkot Gujarat India School of Engineering and Applied Sciences The Neotia University West Bengal INDIA Electrical Electronics& Communication Engg. Galgotias University Greater Noida India

ISBN: (纸本)9781665447539

关键词： Magnetic resonance imaging Volume measurement programming Brain modeling Medical diagnostic imaging Biological neural networks X-ray imaging

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Supply Chain for Perishable Agriculture Products by Possibilistic Linear programming 6

Supply Chain for Perishable Agriculture Products by Possibil...

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6th IEEE international conference on Industrial engineering and Applications (ICIEA)

作者： Jarernsuk, Saran Phruksaphanrat, Busaba Thammasat Univ Fac Engn ISO RU Dept Ind Engn Pathum Thani Thailand

ISBN: (纸本)9781728108513

this paper proposes mathematical programming models for supply chain of perishable products using Possibilistic Linear programming (PLP) and Preemptive Possibilistic Linear programming (PPLP) models. the possible ranges of unit price and unit costs were used to increase flexibility of obtaining the better supply chain imprecise information. three crisp objective functions were generated, which are maximizing the most possible value of profit, minimizing the risk of obtaining the lower profit and maximizing the opportunity of obtaining the higher profit. the objective functions of the presented model looked for the maximum total profit with low risk in getting the lower profit and higher opportunity to get the better profit. Product shelf-life, time of all processes and quantity of products were determined. the numerical experiment was illustrated for an aromatic coconut supply chain. PPLP model is easier to select the preferred solution than PLP model. these models can find the effective solution for the problem.

关键词： component perishable product supply chain possibilistic linear programming preemptive priority

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