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SynthBA: Reliable Brain Age Estimation Across Multiple MRI Sequences and Resolutions

SynthBA: Reliable Brain Age Estimation Across Multiple MRI S...

Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), IEEE International Conference on

作者： Lemuel Puglisi Alessia Rondinella Linda De Meo Francesco Guarnera Sebastiano Battiato Daniele Ravì Dept. of Math and Computer Science University of Catania Catania Italy Department of Neuroinflammation University College London London UK School of Physics Engineering and CS University of Hertfordshire Hatfield UK

ISBN: (数字)9798350378009

ISBN: (纸本)9798350378016

Brain age is a critical measure that reflects the biological ageing process of the brain. The gap between brain age and chronological age, referred to as brain PAD (Predicted Age Difference), has been utilized to investigate neurodegenerative conditions. Brain age can be predicted using MRIs and machine learning techniques. However, existing methods are often sensitive to acquisition-related variabilities, such as differences in acquisition protocols, scanners, MRI sequences, and resolutions, significantly limiting their application in highly heterogeneous clinical settings. In this study, we introduce Synthetic Brain Age (SynthBA), a robust deep-learning model designed for predicting brain age. SynthBA utilizes an advanced domain randomization technique, ensuring effective operation across a wide array of acquisition-related variabilities. To assess the effectiveness and robustness of SynthBA, we evaluate its predictive capabilities on internal and external datasets, encompassing various MRI sequences and resolutions, and compare it with state-of-the-art techniques. Additionally, we calculate the brain PAD in a large cohort of subjects with Alzheimer's Disease (AD), demonstrating a significant correlation with AD-related measures of cognitive dysfunction. SynthBA holds the potential to facilitate the broader adoption of brain age prediction in clinical settings, where re-training or fine-tuning is often unfeasible. The SynthBA source code and pre-trained models are publicly available at https://***/LemuelPuglisi/SynthBA.

关键词： Protocols Magnetic resonance imaging Source coding Neural engineering Machine learning Predictive models Metrology Brain modeling Robustness Software

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Ease Delivery: A Next-Gen Delivery Management Solution

Ease Delivery: A Next-Gen Delivery Management Solution

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Saurabh Pratap Singh Rathore Priyanka Kaushik Minakshi Poonia Shailendra Singh Sikarwar Dipti Singh Deepak Jain Management & Research ICAPSR New Delhi India Dept. of CSE (AIML) CSE-APEX Chandigarh University Chandigarh India Dept. of Eng. Math’s & Computing Madhav Institute of Technology & Science Gwalior India Dept. of Computer Science PGV College Jiwaji University Gwalior India Dept. of Management Madhav University Abu Road India IQAC Sandip University Madhubani India

Delivery Tracker" is a comprehensive delivery management system designed to streamline package delivery services within an educational institution. The primary goal of this initiative is to simplify the process for students by allowing them to effortlessly create, monitor, and confirm the delivery of their packages. Leveraging cutting-edge technologies such as Express, Next. js, React, MongoDB, Paytm UPI, and Socket. io, this system promises efficiency and convenience. With "Delivery Tracker," senders can initiate the delivery process by furnishing recipient details, specifying package attributes, and supplying essential information. Each package is allocated a unique identifier, and the system promptly notifies a nearby student who is available to collect and deliver the package to the intended recipient’s address. Real-time tracking capabilities ensure that deliveries are not only prompt but also secure. Upon successful delivery, recipients can confirm the receipt of their package through the system. Furthermore, the system boasts an integrated payment gateway, streamlining the transaction process. "Delivery Tracker" represents a significant leap forward in enhancing the package delivery experience within educational institutions, prioritizing simplicity, efficiency, and transparency.

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Explainable AI for the Metaverse: A Short Survey

Explainable AI for the Metaverse: A Short Survey

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Intelligent Metaverse Technologies & Applications (iMETA), International Conference on

作者： Chemmalar Selvi G Gokul Yenduri Gautam Srivastava Ramalingam M Dasaradharami Reddy K Muhammad Uzair Thippa Reddy Gadekallu Vellore Instituite of Technology Vellore Tamil Nadu India Dept. of Math and Computer Science Brandon University Brandon Canada Institute of Computer - Science University of Tartu Estonia Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

Virtual reality, augmented reality, and immersive technologies have advanced rapidly, giving rise to the concept of the metaverse. As users delve into these virtual environments, it becomes crucial to understand the decision-making processes of intelligent systems within the metaverse. Explainable AI (XAI) provides a framework for interpreting and understanding the outcomes of artificial intelligence, making it an essential component for ensuring transparency, trust, and user engagement within the metaverse. This paper aims to explore the fusion of XAI in the context of the metaverse, including key enabling technologies, the impact of XAI on metaverse applications, integration challenges, and future directions.

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Interaction Between Rogue HI Clouds and the Magnetic Field High Above the Galaxy

arXiv

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arXiv 2024年

作者： Forster, Bailey M. Foster, Tyler J. Kothes, Roland Hill, Alex S. Brown, Jo-Anne C. Dept. of Physics & Astronomy Brandon University Canada National Research Council Canada Herzberg Astronomy and Astrophysics Dominion Radio Astrophysical Observatory KaledenBC Canada Dept. of Physics McGill University Canada Dept. of Computer Science Math Physics & Statistics University of British Columbia KelownaBC Canada Dept. of Physics & Astronomy University of Calgary Canada

Observations of the Milky Way and external galaxies support the idea that large-scale magnetic fields are concentrated in galactic disks, with halo magnetic fields at least an order of magnitude weaker. However, very little is known about the transition between the two. We present the discovery of linearly polarized radio emission at the interface between interacting shells of gas within a well-known grouping of high-velocity clouds (HVCs), the Anti-Center Shell. Faraday rotation of diffuse emission and of background extragalactic compact sources demonstrates an enhancement of the field at the interface. This is the clearest observed example of an HVC altering the large-scale magnetic field at the disk-halo interface and is the first image of magnetic field effects in an HVC. These results demonstrate the possibility of future three-dimensional reconstruction of the Galactic magnetic field and showcase the versatility of the Synthesis Telescope at the Dominion Radio Astrophysical Observatory as one of the few existing telescopes which can exploit this new method of probing Galactic magnetism. © 2024, CC BY.

关键词： Galaxies

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ARPIST: Provably Accurate and Stable Numerical Integration over Spherical Triangles

arXiv

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arXiv 2022年

作者： Li, Yipeng Jiao, Xiangmin Dept. of Applied Math. & Stat. Institute for Advanced Computational Science Stony Brook University Stony BrookNY11794 United States

Numerical integration on spheres, including the computation of the areas of spherical triangles, is a core computation in geomath.matics. The commonly used techniques sometimes suffer from instabilities and significant loss of accuracy. We describe a new algorithm, called ARPIST, for accurate and stable integration of functions on spherical triangles. ARPIST is based on an easy-to-implement transformation to the spherical triangle from its corresponding linear triangle via radial projection to achieve high accuracy and efficiency. More importantly, ARPIST overcomes potential instabilities in computing the Jacobian of the transformation, even for poorly shaped triangles that may occur at poles in regular longitude-latitude meshes, by avoiding potential catastrophic rounding errors. We compare our proposed technique with L’Huilier’s Theorem for computing the area of spherical triangles, and also compare it with the recently developed LSQST method (J. Beckmann, H.N. Mhaskar, and J. Prestin, GEM - Int. J. Geomath., 5:143–162, 2014) and a radial-basis-function-based technique (J. A. Reeger and B. Fornberg, Stud. Appl. math., 137:174–188, 2015) for integration of smooth functions on spherical triangulations. Our results show that ARPIST enables superior accuracy and stability over previous methods while being orders of magnitude faster and significantly easier to implement. Copyright © 2022, The Authors. All rights reserved.

关键词： Integration

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AGV Quality of Service Throughput Prediction via Neural Networks

AGV Quality of Service Throughput Prediction via Neural Netw...

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IEEE International Conference on Big Data

作者： Katarzyna Prokop Dawid Połap Gautam Srivastava Faculty of Applied Mathematics Silesian University of Technology Kaszubska 23 Gliwice Poland Dept. of Math and Computer Science Brandon University Brandon MB Canada

The recent development of Autonomous Guided Vehicles (AGV) use in industry has resulted in the need to model new solutions based on the latest technological achievements. One of the areas worth attention and development is Quality of Service (QoS) in relation to communication between vehicles. QoS makes it possible to divide the bandwidth in such a way that tasks performed by devices are completed with a certain priority. However, in order to manage these resources effectively, it is necessary to anticipate available network throughput. Therefore, this paper presents a neural-based model to ensure throughput prediction for AGV. The proposed solution assumes the use of information on both historical throughput values and data obtained from other sensors that AGV are equipped with. Therefore, the idea is to integrate two neural networks with another network, which is supposed to predict the result based on these two previously obtained predictions. Ultimately, prediction results with a Root Mean Squared Error (RMSE) of 0.1 for the downlink and 1.6 for the uplink were obtained.

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Lightweight CNN based on Spatial Features for a Vehicular Damage Detection System

Lightweight CNN based on Spatial Features for a Vehicular Da...

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IEEE International Conference on Big Data

作者： Dawid Połap Antoni Jaszcz Katarzyna Prokop Gautam Srivastava Faculty of Applied Mathematics Silesian University of Technology Kaszubska 23 Gliwice Poland Dept. of Math and Computer Science Brandon University Brandon MB Canada

Autonomous vehicles are a key element of the automotive industry, where the impact of the human factor on the condition of the vehicle and driving is minimized. An important element is the analysis of vehicular condition, which allows maintainence of its value and correct operation. We propose a system based on the analysis of the image of vehicles, which determines whether there is any damage. For this purpose, we propose a new model of a Convolutional Neural Network (CNN) that has 0. 395M trained values. The architecture of the network is adapted to the analysis of spatial features that allow networks to be adapted to analyze primarily vehicular shape and orientation in relation to other objects. The model also implements spatial dropout and regularization techniques for preventing overtraining and maintaining model generalization. The modeled architecture contributes to obtaining high classification accuracy at 94.78% using a public database and exceeding metrics of known transfer learning models.

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GPT for the Metaverse in Smart Cities

GPT for the Metaverse in Smart Cities

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International Symposium on Wireless Personal Multimedia Communications (WPMC)

作者： Ramalingam M Gokul Yenduri Mohamed Baza Gautam Srivastava Deepti Raj G Chemmalar Selvi G Thippa Reddy Gadekallu Vellore Instituite of Technology Vellore Tamil Nadu India Department of Computer Science College of Charleston Charleston USA Dept. of Math and Computer Science Brandon University Brandon Canada Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

The metaverse is a virtual space that blends elements of augmented reality, virtual reality, and many other technologies, offering a tailored and immersive experience where individuals can communicate with each other and digital objects. Though the metaverse incorporates various advanced technologies, there is still room for enhancement in terms of interactivity and in achieving realism of virtual environments. Generative Pre-trained Transformers can help address these issues. GPTs advanced NLP algorithms that generate dynamic and realistic content in real-time, allowing the creation of interactive non-playable characters, improving NLP for chatbots and voice assistants, and generating realistic virtual environments in the metaverse. The integration of GPT and metaverse is essential to make it a more dynamic, realistic, and engaging workspace. This review paper explores the opportunity of integrating GPTs with the metaverse applications and highlights prospective challenges associated with this integration.

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Enhancing Spatiotemporal Disease Progression Models via Latent Diffusion and Prior Knowledge

arXiv

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arXiv 2024年

作者： Puglisi, Lemuel Alexander, Daniel C. Ravì, Daniele Dept. of Math and Computer Science University of Catania Italy Centre for Medical Image Computing University College London United Kingdom School of Physics Engineering and CS University of Hertfordshire United Kingdom

In this work, we introduce Brain Latent Progression (BrLP), a novel spatiotemporal disease progression model based on latent diffusion. BrLP is designed to predict the evolution of diseases at the individual level on 3D brain MRIs. Existing deep generative models developed for this task are primarily data-driven and face challenges in learning disease progressions. BrLP addresses these challenges by incorporating prior knowledge from disease models to enhance the accuracy of predictions. To implement this, we propose to integrate an auxiliary model that infers volumetric changes in various brain regions. Additionally, we introduce Latent Average Stabilization (LAS), a novel technique to improve spatiotemporal consistency of the predicted progression. BrLP is trained and evaluated on a large dataset comprising 11,730 T1-weighted brain MRIs from 2,805 subjects, collected from three publicly available, longitudinal Alzheimer’s Disease (AD) studies. In our experiments, we compare the MRI scans generated by BrLP with the actual follow-up MRIs available from the subjects, in both cross-sectional and longitudinal settings. BrLP demonstrates significant improvements over existing methods, with an increase of 22% in volumetric accuracy across AD-related brain regions and 43% in image similarity to the ground-truth scans. The ability of BrLP to generate conditioned 3D scans at the subject level, along with the novelty of integrating prior knowledge to enhance accuracy, represents a significant advancement in disease progression modeling, opening new avenues for precision medicine. The code of BrLP is available at the following link: https://***/LemuelPuglisi/BrLP. © 2024, CC BY.

关键词： Brain

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New Version of Fast Automatic Determination of Cluster Numbers for High Dimensional Big Data

New Version of Fast Automatic Determination of Cluster Numbe...

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International Conference on Computational science and Computational Intelligence (CSCI)

作者： Zohreh Safari Dept. Computer Science Math Engineerign Technology Elizabeth City State University Elizabeth City USA

The main issue in clustering algorithms is how to efficiently define number of clusters automatically. Considering both the quality of clustering and efficiency of clustering algorithm during determination of number of clusters can be a trade off that was our main purpose to overcome with. Successfully, In our approach the best number of clusters for a large data set of high dimensional data automatically would be determined with respect to clustering quality and efficiency. We carried out experimental studies on our five previous data sets [43]. and 4 new larger ones by which we found that our procedure has the flexibility of choosing different criteria to determine the optimal K under each of them. The procedure takes the advantages of Bisecting K-Means algorithm, and indicated higher efficiency compared with Ray & Turi method when it comes to find the best number of clusters.

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