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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Sulaiman Muhammad Rashid Ibrahim Aliyu Abubakar Isah Jihoon Lee Sangwon Oh Minsoo Hahn Jinsul Kim Department of Intelligent Electronics and Computer Engineering Chonnam National University Gwangju S/Korea Department of Computational and Data Science Astana IT University Astana Kazakhstan

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

This paper presents an approach to joint wireless and computing resource management in slice-enabled metaverse networks, addressing the challenges of inter-slice and intra-slice resource allocation in the presence of in-network computing. We formulate the problem as a mixed-integer nonlinear programming (MINLP) problem and derive an optimal solution using standard optimization techniques. Through extensive simulations, we demonstrate that our proposed method significantly improves system performance by effectively balancing the allocation of radio and computing resources across multiple slices. Our approach outperforms existing benchmarks, particularly in scenarios with high user demand and varying computational tasks.

关键词： Wireless communication Metaverse System performance computational modeling Programming Information and communication technology Resource management Standards Optimization Convergence

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Using Video Games as a Tool in Math Education

Using Video Games as a Tool in Math Education

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Smart Information Systems and Technologies (SIST), IEEE International Conference on

作者： Ruslan Omirgaliyev Diyar Mukhidenov Ayazhan Sydyk Department of Computer Engineering Astana IT University Astana Kazakhstan Department of Computational and Data Science Astana IT University Astana Kazakhstan

Today, you can see how children are increasingly spending their free time playing desktop or mobile video games. The question then becomes whether a simple hobby can be turned into a rewarding learning pastime? In the introduction of the article, the current situation and the problem that video games can create and the direction in which it can be solved using video games will be considered. The literature review considers some interesting aspects and conclusions of earlier similar studies. The methodology will present plans for quantitative and qualitative surveys of the population and a proposal for the development of a gaming product for educational purposes. The results will show what the situation is and what people's opinion about the use of video games in education. All the data obtained will help in the development of an educational project. Summing up, we will talk about the meaning and significance of the project and similar ideas in the modern world.

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Building a portable parallel asynchronous PDE solver using Kokkos

Building a portable parallel asynchronous PDE solver using K...

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IEEE International Conference on High Performance Computing Workshops (HiPCW)

作者： Ranjan Bhat Konduri Aditya Department of Computer Science and Engineering Manipal Academy of Higher Education Manipal Institute of Technology Manipal India Department of Computational and Data Sciences Indian Institute of Science Bengaluru India

ISBN: (数字)9798331509118

ISBN: (纸本)9798331509125

In this study, we outline the design and implementation of a portable massively parallel asynchronous solver for time-dependent partial differential equations (PDEs). The solver is implemented using Kokkos library for portability across different node architectures and is coupled with the communication-avoiding algorithm that relaxes the communication across nodes, thus improving the scalability. Asynchrony-tolerant finite difference schemes for spatial derivatives along with the low storage Runge-Kutta scheme for time integration are used to achieve high-order accuracy. The efficacy of the implementation is demonstrated using a mini-app that solves the three-dimensional diffusion problem.

关键词： Scalability Partial differential equations High performance computing Conferences Buildings Computer architecture Benchmark testing Libraries Performance analysis Finite difference methods

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Transfer Learning and Fine-tuned CNN Architecture for Dog Breed Classification

Transfer Learning and Fine-tuned CNN Architecture for Dog Br...

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： K Gunaranjan K Vijay P Naveen Department of Artificial Intelligence and Data Science St. Joseph’s College of Engineering Chennai India Department of computational intelligence SRM institute of science and technology Chennai India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

This study presents a comprehensive approach to dog breed classification, leveraging transfer learning algorithms and Convolutional Neural Networks (CNNs). The dataset utilized for training and evaluation is sourced from the Dog Breed Identification dataset, encompassing 120 distinct breeds. A pre-trained CNN model is employed, and through the application of transfer learning, the proposed models achieve an impressive accuracy rate of 99%. The primary objective is to enable users to submit images of dogs, with the model accurately identifying the specific breed from the diverse dataset. The research underscores the efficacy of transfer learning and pretrained models in achieving exceptional accuracy for the nuanced task of dog breed classification, contributing to the advancements in image recognition within the context of diverse canine breeds.

关键词： Training Adaptation models Accuracy Transfer learning Dogs Classification algorithms Trajectory

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Deep multimodal predictome for studying mental disorders (vol 44, pg 509, 2023)

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HUMAN BRAIN MAPPING 2023年第14期44卷 4967-4967页

作者： Rahaman, M. A. Chen, J. Fu, Z. Lewis, N. Iraji, A. van Erp, T. G. M. Calhoun, V. D. Department of Computational Science and Engineering Georgia Institute of Technology Atlanta Georgia USA Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta Georgia USA Clinical Translational Neuroscience Laboratory Department of Psychiatry and Human Behavior University of California Irvine Irvine California USA Center for the Neurobiology of Learning and Memory University of California Irvine Irvine California USA

Characterizing neuropsychiatric disorders is challenging due to heterogeneity in the population. We propose combining structural and functional neuroimaging and genomic data in a multimodal classification framework to leverage their complementary information. Our objectives are two-fold (i) to improve the classification of disorders and (ii) to introspect the concepts learned to explore underlying neural and biological mechanisms linked to mental disorders. Previous multimodal studies have focused on naïve neural networks, mostly perceptron, to learn modality-wise features and often assume equal contribution from each modality. Our focus is on the development of neural networks for feature learning and implementing an adaptive control unit for the fusion phase. Our mid fusion with attention model includes a multilayer feed-forward network, an autoencoder, a bi-directional long short-term memory unit with attention as the features extractor, and a linear attention module for controlling modality-specific influence. The proposed model acquired 92% (p < .0001) accuracy in schizophrenia prediction, outperforming several other state-of-the-art models applied to unimodal or multimodal data. Post hoc feature analyses uncovered critical neural features and genes/biological pathways associated with schizophrenia. The proposed model effectively combines multimodal neuroimaging and genomics data for predicting mental disorders. Interpreting salient features identified by the model may advance our understanding of their underlying etiological mechanisms.

关键词： functional network connectivity multimodal deep learning resting-state functional and structural MRI saliency schizophrenia classification single nucleotide polymorphism

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Property Inheritance for Subtensors in Tensor Train Decompositions

arXiv

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arXiv 2025年

作者： Cai, HanQin Huang, Longxiu Department of Statistics and Data Science Department of Computer Science University of Central Florida Orlando32816 United States Department of Computational Mathematics Science and Engineering Department of Mathematics Michigan State University East Lansing48824 United States

Tensor dimensionality reduction is one of the fundamental tools for modern data science. To address the high computational overhead, fiber-wise sampled subtensors that preserve the original tensor rank are often used in designing efficient and scalable tensor dimensionality reduction. However, the theory of property inheritance for subtensors is still underdevelopment, that is, how the essential properties of the original tensor will be passed to its subtensors. This paper theoretically studies the property inheritance of the two key tensor properties, namely incoherence and condition number, under the tensor train setting. We also show how tensor train rank is preserved through fiber-wise sampling. The key parameters introduced in theorems are numerically evaluated under various settings. The results show that the properties of interest can be well preserved to the subtensors formed via fiber-wise sampling. Overall, this paper provides several handy analytic tools for developing efficient tensor analysis methods. Copyright © 2025, The Authors. All rights reserved.

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Predicting the Cerebral Blood Flow Change Condition during Brain Strokes using Feature Fusion of FMRI Images and Clinical Features 14

Predicting the Cerebral Blood Flow Change Condition during B...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Sharma, Vandana Sinha, Anurag Wiryaseputra, Michael Kumar, Biresh Kumar, Tarun Raj Alkhayyat, Ahmed Christ University Institute of Information Technology Delhi Campus India Ignou Department of Computer Science and Information Technology New Delhi India Soegijapranata Catholic University Semarang Indonesia Amity Univeristy Department of Computer Science Jharkhand India Iit Madras Computational Mathematics & Data Science India The Islamic University College of Technical Engineering Najaf Iraq

ISBN: (纸本)9798350335095

By fusing clinical information with functional magnetic resonance imaging (fFMRI) pictures, this study describes a novel method for predicting changes in cerebral blood flow during brain strokes. The FMRI data and patient-specific variables, such as age, gender, and medical history, are combined via feature fusion in the proposed technique. As a result, the model developed can accurately forecast changes in cerebral blood flow that occur during brain strokes. The efficiency of the suggested strategy is shown by experimental findings. The performance of the model is greatly enhanced when FMRI data and clinical characteristics are combined as opposed to just one data source. The findings of this study have important ramifications for increasing the accuracy of stroke diagnosis and treatment and, eventually, for bettering patient outcomes. The experimental results showed that the proposed method a high level of accuracy in predicting changes in cerebral blood flow after brain strokes. The performance of the model was much enhanced by combining clinical characteristics with FMRI data as opposed to using only one of these data sources. This emphasizes the value of including pertinent clinical information in the diagnosis and management of stroke. © 2023 IEEE.

关键词： Forecasting

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Efficient generation of grids and traversal graphs in compositional spaces towards exploration and path planning

arXiv

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arXiv 2024年

作者： Krajewski, Adam M. Beese, Allison M. Reinhart, Wesley F. Liu, Zi-Kui Department of Materials Science and Engineering The Pennsylvania State University United States Institute for Computational and Data Sciences The Pennsylvania State University United States

Many disciplines of science and engineering deal with problems related to compositions, ranging from chemical compositions in materials science to portfolio compositions in economics. They exist in non-Euclidean simplex spaces, causing many standard tools to be incorrect or inefficient, which is significant in combinatorically or structurally challenging spaces exemplified by Compositionally Complex Materials (CCMs) and Functionally Graded Materials (FGMs). This work explores such problem spaces conceptually, quantifies their computational feasibility, and implements several essential methods specific to simplex spaces through a high-performance open-source library nimplex. Most significantly, we derive and implement an algorithm for constructing a novel n-dimensional simplex graph data structure, which contains all discretized compositions and all possible neighbor-to-neighbor transitions as pointer arrays. Critically, no distance or neighborhood calculations are performed, instead leveraging pure combinatorics and the ordering in procedurally generated simplex grids, keeping the algorithm O(N), enabling construction of graphs with billions of transitions in seconds. Furthermore, we demonstrate how such graph representations can be combined to express path-planning problem spaces and to incorporate prior knowledge while keeping the problem space homogeneous. This enables efficient deployment of existing high-performance gradient descent, graph traversal, and other optimization algorithms. © 2024, CC BY-NC-ND.

关键词： Functionally graded materials

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Crop Yield Prediction Using Spatio Temporal CNN and Multimodal Remote Sensing 2

Crop Yield Prediction Using Spatio Temporal CNN and Multimod...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Arunarani, A.R. Selvanayaki, S. Saleh Al Ansari, Mohammed Ala Walid, Md. Abul Devireddy, Nagamalleswari Keerthi, Murugesan Mohana School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computational Intelligence Kattankulatur Campus Tamilnadu Chennai India Saveetha Engineering College Artificial Intelligence and Data Science Tamilnadu Chennai India College of Engineering University of Bahrain Department of Chemical Engineering Bahrain Department of Cse Khulna9203 Bangladesh Mohan Babu University Department of Freshman Andhra Pradesh Tirupathi India School of Agriculture Sr University Telangana Warangal India

ISBN: (纸本)9798350347579

Crop yield forecasting has been thoroughly studied recently and has become more important in preserving the safety of food. The majority of studies have concentrated on obtaining fixed spatial data from remote sensing images. Crop development, however, is a very complicated attribute influenced by a variety of factors. This work is intended to concurrently use spatial and temporal information from multimodal remotely sensed images in order to fully investigate these diverse characteristics. With the goal to make use of their supportive nature, Spatio temporal Convolutional Neural Networks (STCNN), is proposed as a revolutionary deep learning architecture for agricultural production prediction. To recognize the combined spatial-temporal representation, the STCNN specifically combines a spatial training segment and a temporal dependent segment into the convolutional network. The innovative spatial training segment begins by extracting enough spatial information from the multimodal spatial images. Then, in order to determine the temporal connection from the lengthy time-series images, the temporal dependent segment is concatenated on top of the spatial training segment. Both common machine learning techniques and cutting-edge deep learning techniques are compared to the outcomes of the proposed method. The experimental findings show that the STCNN can offer superior prediction performance of 0.64 RMSE and 12.03 MAPE values compared to the rival methods with minimized errors. © 2023 IEEE.

关键词： Remote sensing

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nimCSO: A Nim package for Compositional Space Optimization

arXiv

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arXiv 2024年

作者： Krajewski, Adam M. Debnath, Arindam Reinhart, Wesley F. Beese, Allison M. Liu, Zi-Kui Department of Materials Science and Engineering The Pennsylvania State University United States Institute for Computational and Data Sciences The Pennsylvania State University United States

nimCSO is a high-performance tool implementing several methods for selecting components (data dimensions) in compositional datasets, which optimize the data availability and density for applications such as machine learning. Making said choice is a combinatorically hard problem for complex compositions existing in highly dimensional spaces due to the interdependency of components being present. Such spaces are encountered, for instance, in materials science, where datasets on Compositionally Complex Materials (CCMs) often span 20-45 chemical elements, 5-10 processing types, and several temperature regimes, for up to 60 total data dimensions. At its core, nimCSO leverages the metaprogramming ability of the Nim language (Rumpf, 2023) to optimize itself at the compile time, both in terms of speed and memory handling, to the specific problem statement and dataset at hand based on a human-readable configuration file. As demonstrated in the Methods and Performance section, nimCSO reaches the physical limits of the hardware (L1 cache latency) and can outperform an efficient native Python implementation over 400 times in terms of speed and 50 times in terms of memory usage (not counting interpreter), while also outperforming NumPy implementation 35 and 17 times, respectively, when checking a candidate solution. nimCSO is designed to be both (1) a user-ready tool, implementing two efficient brute-force approaches (for handling up to 25 dimensions), a custom search algorithm (for up to 40 dimensions), and a genetic algorithm (for any dimensionality), and (2) a scaffold for building even more elaborate methods in the future, including heuristics going beyond data availability. All configuration is done with a simple human-readable YAML config file and plain text data files, making it easy to modify the search method and its parameters with no knowledge of programming and only basic command line skills. © 2024, CC BY.

关键词： Genetic algorithms

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