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PIEMAP: Personalized Inverse Eikonal Model from Cardiac Electro-Anatomical Maps 11th

PIEMAP: Personalized Inverse Eikonal Model from Cardiac Elec...

11th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2020 held in Conjunction with MICCAI 2020

作者： Grandits, Thomas Pezzuto, Simone Lubrecht, Jolijn M. Pock, Thomas Plank, Gernot Krause, Rolf Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Center for Computational Medicine in Cardiology Institute of Computational Science Università della Svizzera italiana Lugano Switzerland Institute of Biophysics Medical University of Graz Graz Austria BioTechMed-Graz Graz Austria

ISBN: (纸本)9783030681067

Electroanatomical mapping, a keystone diagnostic tool in cardiac electrophysiology studies, can provide high-density maps of the local electric properties of the tissue. It is therefore tempting to use such data to better individualize current patient-specific models of the heart through a data assimilation procedure and to extract potentially insightful information such as conduction properties. Parameter identification for state-of-the-art cardiac models is however a challenging task. In this work, we introduce a novel inverse problem for inferring the anisotropic structure of the conductivity tensor, that is fiber orientation and conduction velocity along and across fibers, of an eikonal model for cardiac activation. The proposed method, named PIEMAP, performed robustly with synthetic data and showed promising results with clinical data. These results suggest that PIEMAP could be a useful supplement in future clinical workflowss of personalized therapies. © 2021, Springer Nature Switzerland AG.

关键词： Heart

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Optimizing Mobile Advertising with Reinforcement Learning and Deep Neural Networks

Optimizing Mobile Advertising with Reinforcement Learning an...

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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Divya Nimma Chamandeep Kaur Gunjan Chhabra V. Selvi Divya Tyagi A. Balakumar University of southern Mississippi Data Analyst in UMMC USA Department of Computer Science & Information Technology Jazan University Jizan Kingdom of Saudi Arabia Department of CSE Graphic Era Hill University Dehradun Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science Mother Teresa Women's University Kodaikanal India Sanskaram University Jhajjar Haryana India Department of ECE K. Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

Mobile advertising is also closely associated with contemporary digital marketing channels, where the projective targeting is critical in regard to businesses’ potential performance. This research makes use of a combination of machine learning and reinforcement learning (RL) to determine the best place to post an ad considering the users’ demographic and behavior. Deepening the neural networks (DNNs) in this regard, the method is expected to advance the efficiency of mobile advertising while delivering more relevant content to the target user. Purchasing tendency predictions in the report are based on Python-based models through a dataset from Kaggle that has incorporated user demographics (Gender, Age, Estimated Salary) and purchasing patterns. The missing values are first handled using the different techniques then normalization of the numerical data and one hot coding of the features such as the Device Type and the Location. Automatic feature are extracted with the help of DNNs which obviate the process of feature extraction. This approach proposed yielded an accuracy level of 98% a higher level as compared to the CNN and LSTM models. For decision-making reinforcement learning is used with the help of Deep Q-Network (DQN) and Policy gradient. DQNs predict Q-values for the actions (ad displays) and choose the best action, while Policy Gradient methods directly learn the policy by modifying the probabilities related to the actions based on the users’ behavior. The training techniques used on both models include backpropagation and stochastic gradient descent, which enables the RL agent in the continuous implementation of better ad placement strategies. Here they used DNN to train the features and RL to optimize the training and then improve the on average click through rates and conversion rates which provides a better strategy towards advertising.

关键词： Training Quantum computing Stochastic processes Reinforcement learning Predictive models Feature extraction Numerical models Remuneration Advertising Long short term memory

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Automated data preprocessing and training interface for machine learning applications

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AIP Conference Proceedings 2024年第1期2942卷

作者： Anagha Challa Akhila Vutukuri Maithreyi Kenguva Sateesh Kumar Kanagala Department of Electronics and Computer Engineering Sreenidhi Institute of Science and Technology Hyderabad India Assistant Professor Department of Electronics and Computer Engineering Sreenidhi Institute of Science and Technology Hyderabad India

Machine learning is a promising tool for analyzing and predicting data. However, it is very time-consuming and challenging to manually process the data. There are also many missing values in the data that can affect the accuracy of the models. We propose an automated approach to pre-process the data. It efficiently performs the various steps involved in the data cleaning process by converting the categorical values to a label encoded value, sampling the data, and replacing the missing values with the appropriate central tendency. This project takes the raw data as input and provides a clean error-free dataset that is suitable for training machine learning models. It also aims to use a web application to automate data pre-processing and training of classification and regression models. It uses multiple algorithms and normalization strategies to get the optimum model based on the desired metric. It allows users to download the clean dataset as well as the trained models after the models have been trained. Multiple datasets, including binary classification and regression, are used to test this model.

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Sketch2PQ: Freeform Planar Quadrilateral Mesh Design via a Single Sketch

arXiv

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arXiv 2022年

作者： Deng, Zhi Liu, Yang Pan, Hao Jabi, Wassim Zhang, Juyong Deng, Bailin School of Data Science University of Science and Technology of China China Internet Graphics Group Microsoft Research Asia China The Welsh School of Architecture Cardiff University United Kingdom School of Mathematical Sciences University of Science and Technology of China China School of Computer Science and Informatics Cardiff University United Kingdom

The freeform architectural modeling process often involves two important stages: concept design and digital modeling. In the first stage, architects usually sketch the overall 3D shape and the panel layout on a physical or digital paper briefly. In the second stage, a digital 3D model is created using the sketch as a reference. The digital model needs to incorporate geometric requirements for its components, such as the planarity of panels due to consideration of construction costs, which can make the modeling process more challenging. In this work, we present a novel sketch-based system to bridge the concept design and digital modeling of freeform roof-like shapes represented as planar quadrilateral (PQ) meshes. Our system allows the user to sketch the surface boundary and contour lines under axonometric projection and supports the sketching of occluded regions. In addition, the user can sketch feature lines to provide directional guidance to the PQ mesh layout. Given the 2D sketch input, we propose a deep neural network to infer in real-time the underlying surface shape along with a dense conjugate direction field, both of which are used to extract the final PQ mesh. To train and validate our network, we generate a large synthetic dataset that mimics architect sketching of freeform quadrilateral patches. The effectiveness and usability of our system are demonstrated with quantitative and qualitative evaluation as well as user studies. Copyright © 2022, The Authors. All rights reserved.

关键词： Large dataset

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Multiagent Reinforcement Learning for Integrated Network: Applying to a Part of the Road Network of Krasnoyarsk City 16

Multiagent Reinforcement Learning for Integrated Network: Ap...

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16th IEEE International Conference on Application of Information and Communication Technologies, AICT 2022

作者： Tislenko, Timofei I. Semenova, Daria V. Sergeeva, Nataly A. Goldenok, Elena E. Kononova, Nadezhda V. Siberian Federal University School of Mathematics and Computer Science Krasnoyarsk Russia Llc R.D.Science Analytics Groups Krasnoyarsk Russia Krasnoyarsk State Med. University Named after Professor V.F. Voyno-Yasenetsky Name of Organization Department of Medical Cybernetics and Informatics Krasnoyarsk Russia Siberian Federal University School of Space and Information Technology Krasnoyarsk Russia

ISBN: (纸本)9781665451628

The article examines a mathematical model of the selecting phases process of traffic light facilities of the road network section. A Markov decision process with a finite number of actions and states is used as a mathematical model, and the minimization problem is reduced to the Multiagent Reinforcement Learning for Integrated Network (MARLIN) problem. A Q-learning algorithm was implemented and a series of computational experiments were conducted in the Anylogic simulation system for a real section of the Krasnoyarsk road network to study the model. © 2022 IEEE.

关键词： Markov processes

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Reduct-based ranking of attributes 24

Reduct-based ranking of attributes

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24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2020

作者： Zielosko, Beata Stanczyk, Urszula Institute of Computer Science University of Silesia in Katowice Bedzi nska 39 Sosnowiec41-200 Poland Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

The paper is dedicated to the area of feature selection, in particular a notion of attribute rankings that allow to estimate importance of variables. In the research presented for ranking construction a new weighting factor was defined, based on relative reducts. A reduct constitutes an embedded mechanism of feature selection, specific to rough set theory. The proposed factor takes into account the number of reducts in which a given attribute exists, as well as lengths of reducts. Two approaches for reduct generation were employed and compared, with search executed by a genetic algorithm. To validate the usefulness of the reduct-based rankings in the process of feature reduction, for gradually decreasing subsets of attributes, selected through rankings, sets of decision rules were induced in classical rough set approach. The performance of all rule classifiers was evaluated, and experimental results showed that the proposed rankings led to at least the same, or even increased classification accuracy for reduced sets of features than in the case of operating on the entire set of condition attributes. The experiments were performed on datasets from stylometry domain, with treating authorship attribution as a classification task, and stylometric descriptors as characteristic features defining writing styles. © 2020 The Authors. Published by Elsevier B.V.

关键词： Classification (of information)

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The Design of IR System for PC Systems

The Design of IR System for PC Systems

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： Jason Gunjan Chhabra Mohammed Al Sailh France Laith H. Jasim Abdul Redha Hussein Sabr Doaa Talib Zaidan Department of computer applications Hindustan institute of technology and science Department of CSE Graphic Era Hill University Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand College of Technical Engineering National University of Science and Technology Tareq Hafdhi Abdtawfeeq Dhi Qar Iraq Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Computer Engineer Department Al-kunooze University College Basra Iraq Department of Laser and Optical Electronics Engineering Kut University College Al Kut Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Collaborative robotics, in conjunction with artificial intelligence (AI), offers a contemporary and effective paradigm for secure machine-human interactions. This synergy branches out into a variety of industries, including education and entertainment, in addition to industrial uses. Two intriguing board games that offer a platform for examining the potential of cooperative robotic systems are chess and checkers. An intelligent and cooperative robotic system designed for use in Italian checkers games is described in this context by the study being presented. To record the game state, the gadget employs a camera. To physically move pieces across the board, a pick-and-place mechanism is used. An algorithm is used to automatically choose the optimal moves that comply with the rules. The system respects the kinematic restrictions of the manipulator while optimizing minimum-time trajectories live for every manipulation, guaranteeing a smooth and dynamic gaming experience. An experimental validation employing a seven-degree-of-freedom Franka Emika arm verifies the effectiveness of the proposed approach in real-world settings.

关键词： Service robots Heuristic algorithms Robot vision systems Collaboration Entertainment industry Games Kinematics

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A New Hybrid Cuckoo Search for the Resources-Constrained Project Scheduling Problem

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2021年第2期48卷 1-12页

作者： Shen, Xin Zhang, Xiaoxia Yu, Ziqiao X. Shen is a Master Student of School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China X. X. Zhang is a Professor of School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan11041 China A Master Student of School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China

Resource-constrained project scheduling problem (RCPSP) is a kind of representative practical engineering problem, the purpose is to schedule activities in the project through rational use of limited resources in the minimum time. This paper proposes an improved hybrid cuckoo algorithm (CS&SA) to solve the RCPSP problem. Firstly, the individual elements are randomly coded into priority vectors, and the population is decoded using serial scheduling to convert the individual into a set of task scheduling sequences. Secondly, the Levy flight is redesigned to change the algorithm from random walk to adaptive as the population fitness changes. Then, this article adds three neighborhood update techniques to meet the update requirements of the algorithm at different stages. Finally, in order to prevent the algorithm from falling into a local optimum, this paper introduces a simulated annealing strategy to allow the algorithm to accept some individuals with poor quality with a certain probability in each iteration. In the testing part, this paper firstly tests the effectiveness and optimization of three different-scale examples in the classic example library PSPLIB for RCPSP problems. Among them, the average error of the CS&SA algorithm in the small-scale J30 is 0.25%, and the average error in the medium-scale J60 is 11.21%, and the average error of the large-scale J120 is 19.83%. Then, by comparing other intelligent optimization algorithms, it is proved that NCS&SA is superior to other algorithms in optimization and accuracy. © 2021. All Rights Reserved.

关键词： Combinatorial optimization

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Optimizing Battery Management Systems For Electric Vehicles: Current Trends And Future Directions

Optimizing Battery Management Systems For Electric Vehicles:...

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Global Conference for Advancement in technology (GCAT)

作者： V.S.Prakash Annamalai Muthu B. Anandan V. Velmurugan T. Alex Stanley Raja Purushothaman V Department of Computer Science Kristu Jayanti College Bengaluru Electrical Section Engineering Department University of Technology and Applied Science Nizwa Oman Department of Mechanical Engineering Siddharth Institute of Engineering & Technology Puttur Andhrapradesh Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Assistant Professor Level III Bannari Amman Institute of Technology Sathyamangalam Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

The rapid global shift towards electric vehicles (EVs) marks a transformative era in transportation, driven by the imperative to reduce carbon emissions and reliance on fossil fuels. Central to the performance and longevity of EVs are Battery Management Systems (BMS), which optimize battery usage, enhance safety, and extend operational life. This comprehensive review explores current trends and future directions in BMS optimization, focusing on key technological advancements and their implications for electric mobility. Battery Management Systems (BMS) serve as critical components in electric vehicles, orchestrating the efficient operation of battery packs through real-time monitoring, state-of-charge (SoC) estimation, thermal management, and fault diagnosis. These systems not only ensure optimal performance but also safeguard against potential hazards, thereby enhancing vehicle reliability and user confidence. Current advancements in BMS technology emphasize enhanced accuracy in SoC estimation, leveraging sophisticated algorithms and sensor integration to provide precise information on battery charge levels. Coupled with innovative thermal management strategies, which include active cooling and heating systems, BMS can mitigate thermal stress and optimize battery performance across diverse environmental conditions. By synthesizing technological advancements, emerging trends, and potential challenges, it offers valuable insights for researchers, engineers, and policymakers aiming to advance the efficiency, reliability, and sustainability of electric mobility solutions worldwide.

关键词： Technological innovation Electric potential Battery management systems Estimation Electric vehicles Thermal management Market research Sustainable development Thermal stresses Optimization

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ML-Aided Dynamic Clustering and Classification of UEs as VBs in D2D Communication Networks

ML-Aided Dynamic Clustering and Classification of UEs as VBs...

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International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)

作者： Iacovos Ioannou Prabagarane Nagaradjane Ala' Khalifeh Christophoros Christophorou Vasos Vassiliou Gundepudi V. Surya Sashank Charu Jain Andreas Pitsillides Department of Computer Science University of Cyprus CYENS - Centre of Excellence Cyprus Department of ECE Sri Sivasubramaniya Nadar College of Engineering Chennai India Department of Electrical Engineering and Information Technology German Jordanian University Department of Electrical & Electronic Engineering Science University of Johannesburg (Visiting Professor)

With the next generation of mobile devices and streaming services such as Virtual Reality, Augmented Reality, and Meta being available worldwide, the network's data rate, latency, and connectivity must be improved. Even though 5G provides the user with the required Quality of Service (QoS) in terms of data rate and reduced delays by transmitting signals in the higher frequencies (called New Radio (NR)), it faces a lot of attenuation, leading to a short communication range. However, to meet goals set by utilising 6G requirements, many technological advancements and components must be incorporated into the network. The dense disposition of small cells will help reduce the network traffic in hotspot areas and increase the coverage and spectral efficiency. Nonetheless, the current deployment of 5G Base Stations (BSs) and small cells is static and cannot move around even though they are deployed in hot spot areas, leading to high operational costs. Furthermore, more than these static deployments of base stations can be required in an unpredictable scenario of extreme crowd movement. To overcome these issues, Device to Device (D2D) Communication with the dynamic deployment of Virtual Base stations (VBSs) can be called upon, which can be achieved by using User Equipment (UE) such as phones or laptops to mimic the functions of a Base Station (BS). Therefore, in this paper, a User Equipment based Virtual Base Station (UE-VBS) is studied, which will act as a secondary base station and, in turn, help alleviate the traffic load in the network. Specifically, as one UE cannot relieve the entire network traffic load, the network area is split into different clusters by using an unsupervised Machine Learning (ML) clustering technique(i.e., K-Means with Mean Shift Clustering), and a single UE is selected to act as a VBS for that cluster with the utilisation of supervised ML classification techniques (i.e., Decision Trees, Logistic Regression, Linear Discriminant Analysis And Quadrat

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