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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1472-1487页

作者： Liu, Zhenhong Wang, Xingce Wu, Zhongke Ju, Xiaodong Zhu, YiCheng Frangi, Alejandro F. Beijing Normal University School of Artificial Intelligence Beijing100091 China Peking University Third Hospital Department of Sports Medicine Beijing100096 China Peking Union Medical College Hospital Department of Neurology Beijing100005 China The University of Manchester Christabel Pankhurst Institute Division of Informatics Imaging and Data Sciences School of Health Sciences School of Engineering ManchesterM139PL United Kingdom The University of Manchester Department of Computer Science School of Engineering ManchesterM139PL United Kingdom Department of Cardiovascular Sciences Leuven3000 Belgium KU Leuven Department of Electrical Engineering Leuven3000 Belgium Alan Turing Institute LondonNW1 2DB United Kingdom

Magnetic resonance imaging (MRI) is often limited by noise and low-resolution (LR), which can impact the precision of the diagnosis and treatment of patients. LR images and mixed noise (e.g., Gaussian noise, Rician noise, and Impulse noise) are inherent in MR images, and current approaches typically address image superresolution (SR) reconstruction and denoising separately, resulting in a discrepancy between the actual MRI data distribution and the reconstructed images. This research introduces a new algorithm SRDSNF, the stochastic normalizing flow-based MR image SR and denoising model, which tackles SR and denoising simultaneously through a stochastic normalizing flow. Our method integrates the encoded information of the input image as a conditional variable in each reverse step of the stochastic normalizing flow, ensuring a consistent representation of the spatial distribution between the reconstructed image and the original data. Additionally, we incorporate range-null space decomposition and subsequence sampling techniques to increase the consistency between the original and constructed data and accelerate model generation. To assess the efficacy of our approach, we conducted experiments on the BrainWeb and NFBS datasets, which include simultaneous SR and denoising, standalone denoising, and standalone SR tasks. The results of the experiments illustrate that our method achieves superior SR and denoising performance with fewer sampling steps, closely approximating the ground truths. Furthermore, our results surpass those of existing methods in various tasks, showing improvement of up to 4.64 dB in PSNR and 13.8% in SSIM achieved by our SRDSNF model was contextualized against state-of-the-art approaches such as AMIR, SwinIR, and InstructIR. These methods typically report PSNR improvements ranging from 0.5 to 2 dB and SSIM increases of 3%–6%, underscoring the potential clinical value of our methodology. © 2020 IEEE.

关键词： Stochastic systems

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Weapons Detection for Security and Video Surveillance Using CNN and YOLO-V5s

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computers, Materials & Continua 2022年第2期70卷 2761-2775页

作者： Abdul Hanan Ashraf Muhammad Imran Abdulrahman M.Qahtani Abdulmajeed Alsufyani Omar Almutiry Awais Mahmood Muhammad Attique Mohamed Habib Department of Computer Science Shaheed Zulfikar Ali Bhutto Institute of Science and TechnologyIslamabad44000Pakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia College of Applied Computer Science King Saud University(Almuzahmiyah Campus)Riyadh11543Saudi Arabia Department of Computer Science HITEC UniversityTaxila47080Pakistan College of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Faculty of Engineering Port Said UniversityPort Fuad CityEgypt

In recent years,the number of Gun-related incidents has crossed over 250,000 per year and over 85%of the existing 1 billion firearms are in civilian hands,manual monitoring has not proven effective in detecting *** is why an automated weapon detection system is *** automated convolutional neural networks(CNN)weapon detection systems have been proposed in the past to generate good ***,These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection *** models have a high rate of false negatives because they often fail to detect the guns due to the low quality and visibility issues of surveillance *** research work aims to minimize the rate of false negatives and false positives in weapon detection while keeping the speed of detection as a key *** proposed framework is based on You Only Look Once(YOLO)and Area of Interest(AOI).Initially,themodels take pre-processed frames where the background is removed by the use of the Gaussian blur *** proposed architecture will be assessed through various performance parameters such as False Negative,False Positive,precision,recall rate,and F1 *** results of this research work make it clear that due to YOLO-v5s high recall rate and speed of detection are *** reached 0.010 s per frame compared to the 0.17 s of the Faster *** is promising to be used in the field of security and weapon detection.

关键词： Video surveillance weapon detection you only look once convolutional neural networks

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Modeling Blockage in High Directional Wireless Systems 2

Modeling Blockage in High Directional Wireless Systems

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2nd IEEE Virtual Conference on Communications, VCC 2024

作者： Koutsonas, Evangelos Boulogeorgos, Alexandros-Apostolos A. Trevlakis, Stylianos E. Ali, Tanweer Tsiftis, Theodoros A. University of Western Macedonia Department of Electrical and Computer Engineering Kozani50100 Greece InnoCube P.C. Research and Development Department Thessaloniki55535 Greece Manipal Institute of Technology Manipal Academy of Higher Education Department of Electronics and Communication Engineering Manipal576104 India University of Thessaly Department of Informatics and Telecommunications Lamia35100 Greece University of Nottingham Ningbo China Department of Electrical and Electronic Engineering Ningbo315100 China

ISBN: (纸本)9798331530099

While the wireless word moves towards higher frequency bands, new challenges arises, due to the inherent characteristics of the transmission links, such as high path and penetration losses. Penetration losses causes blockages that in turn can significantly reduce the signal strength at the receiver. Most published contributions consider a binary blockage stage, i.e. either fully blocked or blockage-free links. However, in realistic scenarios, a link can be partially blocked. Motivated by this, in this paper, we present two low-complexity models that are based on tight approximations and accommodates the impact of partial blockage in high-frequency links. To demonstrate the applicability of the derived framework, we present closed-form expressions for the outage probability for the case in which the distance between the center of the receiver plane and the blocker's shadow center follow uniform distribution. Numerical results verify the derived framework and reveal how the transmission parameters affect blockage. © 2024 IEEE.

关键词： Blockage high-frequency communications modeling outage probability

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Exploring Network Dynamics: Community Detection and Influencer Analysis in Multidimensional Social Networks

Exploring Network Dynamics: Community Detection and Influenc...

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IEEE International Conference on Big Data

作者： Andreas Kanavos Gerasimos Vonitsanos Ioannis Karamitsos Khalil Al-Hussaeni Department of Informatics Ionian University Corfu Greece Computer Engineering and Informatics Department University of Patras Patras Greece Rochester Institution of Technology Dubai UAE Computing Sciences Department Rochester Institute of Technology Dubai UAE

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

In the digital era, multidimensional social networks have become integral to daily communication, catering to diverse relational needs, from interpersonal to professional and commercial. This study utilizes two comprehensive datasets from Twitter to explore and visualize user interactions within these networks. Focusing on advanced community detection algorithms, we apply the Louvain and Label Propagation methods to delineate the structure of these communities and identify influential users effectively. Through systematic analysis, our research reveals significant insights into the dynamics of network clusters and the pivotal role of influencers. We demonstrate that community structures significantly influence in formation dissemination and user engagement, providing key data to optimize digital communication strategies in complex environments. The findings underscore the importance of strategic influencer engagement and tailored community management in enhancing interaction within multidimensional social networks. Additionally, our results suggest that understanding the network’s structural nuances can aid in developing targeted interventions that leverage influencer capabilities to maximize communication impact, illustrating potential applications across various sectors, including marketing, politics, and public health.

关键词： Systematics Social networking (online) Heuristic algorithms Blogs Focusing Media Big Data Digital communication Public healthcare Detection algorithms

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Prostate Gleason Score Prediction via MRI using Capsule Network

Prostate Gleason Score Prediction via MRI using Capsule Netw...

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Medical Imaging 2023: computer-Aided Diagnosis

作者： Li, Yuheng Wang, Jing Hu, Mingzhe Patel, Pretesh Mao, Hui Liu, Tian Yang, Xiaofeng Department of Radiation Oncology Winship Cancer Institute Emory University AtlantaGA30322 United States Department of Biomedical Engineering Emory University AtlantaGA30322 United States Department of Computer Science and Informatics Emory University AtlantaGA United States

ISBN: (纸本)9781510660359

Magnetic Resonance imaging (MRI) is a non-invasive modality for diagnosing prostate carcinoma (PCa) and deep learning has gained increasing interest in MR images. We propose a novel 3D Capsule Network to perform low grade vs high grade PCa classification. The proposed network utilizes Efficient CapsNet as backbone and consists of three main components, 3D convolutional blocks, depth-wise separable 3D convolution, and self-attention routing. The network employs convolutional blocks to extract high level features, which will form primary capsules via depth-wise separable convolution operations. A self-attention mechanism is used to route primary capsules to higher level capsules and finally a PCa grade is assigned. The proposed 3D Capsule Network was trained and tested using a public dataset that involves 529 patients diagnosed with PCa. A baseline 3D CNN method was also experimented for comparison. Our Capsule Network achieved 85% accuracy and 0.87 AUC, while the baseline CNN achieved 80% accuracy and 0.84 AUC. The superior performance of Capsule Network demonstrates its feasibility for PCa grade classification from prostate MRI and shows its potential in assisting clinical decision-making. © 2023 SPIE.

关键词： Magnetic resonance imaging

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Retraction Notice: Mobile Application for Cholesterol Detection Using Iridology with Image Processing and Support Vector Machine

Retraction Notice: Mobile Application for Cholesterol Detect...

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International Electronics Symposium (IES)

作者： Entin Martiana Kusumaningtyas Alfian Haidar Setyawan Ali Ridho Barakbah Departement of Informatics and Computer Engineering Electronic Engineering Polytechnic Institute of Surabaya EEPIS Surabaya Indonesia

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489720

Cholesterol is one of the factors than can cause disability and other diseases that is fatal to one’s life. Data from World Health Organization (WHO) said that around 4.4 million people die because of cholesterol related diseases and 40.4 million people are disabled because of cholesterol. To avoid this, people usually check their cholesterol rate so that they can control their cholesterol. That said the current method for checking cholesterol is by doing blood test, which is an inefficient method if we want to check our cholesterol regularly because it is time consuming and money consuming. This paper presents an application based on mobile to detect cholesterol rate using iridology with the use of Image Processing and Support Vector Machine. By taking a picture of someone eye, which will go through the process such as pre-processing, segmentation, normalization, and classification, it will return three kind of output which is "Normal", "Medium", and "High". By analyzing data from Mugi Barokah Clinic, we managed to create a system to extract the feature of an image and a SVM model to classify the said image with the final result of 80% accuracy.

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3D Skull Completion via Two-stage Conditional Diffusion-Based Signed Distance Fields

3D Skull Completion via Two-stage Conditional Diffusion-Base...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Zhenhong Ru, Xudong Wang, Xingce Wu, Zhongke Zhu, Yi-Cheng Zhang, Chong Frangi, Alejandro F. Beijing Normal University School of Artificial Intelligence Beijing China Peking Union Medical College Hospital Department of Neurology Beijing China Bioimage Analysis Barcelona Spain School of Engineering University of Manchester Christabel Pankhurst Institute Division of Informatics Imaging and Data Sciences School of Health Sciences Department of Computer Science United Kingdom Department of Cardiovascular Sciences Department of Electrical Engineering Leuven Belgium Alan Turing Institute London United Kingdom

ISBN: (纸本)9798350386226

A fast and fully automatic design of 3D cranial implants is highly desired in cranioplasty, and is key to the treatment of skull trauma. We have defined the repair of skull defects as a 3D shape completion task by proposing a two-stage diffusion model based on the representation of 3D shapes using signed distance function (SDF). Specifically, we design a diffusion model conditioned on partial shapes, we compress the 3D shape into a compact latent representation using the encoder in the vector quantized variational autoencoder (VQ-VAE) and learn the diffusion model based on this compressed discrete representation. Encoding the latent space with the autoencoder can achieve high-quality 3D cranial shape completion. In order to accurately capture local and fine-grained shape details, the training data is geometrically encoded from a compactly learned code-book. The two-stage diffusion generator with a coarse-to-fine approach possesses precise and expressive structural modeling capabilities to ensure the supplementation of detailed geometric information. Experimental results verified sufficient expressiveness of our model with generating high-fidelity results with fine-grained local details, outperforming the state-of-the-art methods. © 2024 IEEE.

关键词： Signal encoding

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JARVIS-Leaderboard:a large scale benchmark of materials design methods

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npj Computational Materials 2024年第1期10卷 2280-2296页

作者： Kamal Choudhary Daniel Wines Kangming Li Kevin F.Garrity Vishu Gupta Aldo H.Romero Jaron T.Krogel Kayahan Saritas Addis Fuhr Panchapakesan Ganesh Paul R.C.Kent Keqiang Yan Yuchao Lin Shuiwang Ji Ben Blaiszik Patrick Reiser Pascal Friederich Ankit Agrawal Pratyush Tiwary Eric Beyerle Peter Minch Trevor David Rhone Ichiro Takeuchi Robert B.Wexler Arun Mannodi-Kanakkithodi Elif Ertekin Avanish Mishra Nithin Mathew Mitchell Wood Andrew Dale Rohskopf Jason Hattrick-Simpers Shih-Han Wang Luke E.K.Achenie Hongliang Xin Maureen Williams Adam J.Biacchi Francesca Tavazza Material Measurement Laboratory National Institute of Standards and TechnologyGaithersburgMD20899USA Department of Materials Science and Engineering University of Toronto27 King’s College CirTorontoONCanada Department of Electrical and Computer Engineering Northwestern UniversityEvanstonIL 60208USA Lewis-Sigler Institute for Integrative Genomics Princeton UniversityPrincetonNJ 08544USA Ludwig Institute for Cancer Research Princeton UniversityPrincetonNJ 08544USA Department of Physics and Astronomy West Virginia UniversityMorgantownWV 26506USA Materials Science and Technology Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Center for Nanophase Materials Science Oak Ridge National LaboratoryOak RidgeTN 37831USA Computational Sciences and Engineering Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Department of Computer Science and Engineering Texas A&M UniversityCollege StationTX 77843USA Globus University of ChicagoChicagoIL 60637USA Data Science and Learning Division Argonne National LabLemontIL 60439USA Institute of Nanotechnology Karlsruhe Institute of TechnologyKaiserstraße 1276131 KarlsruheGermany Institute of Theoretical Informatics Karlsruhe Institute of TechnologyKaiserstraße 1276131 KarlsruheGermany Department of Chemistry and Biochemistry and Institute for Physical Science and Technology University of MarylandCollege ParkMD 20742USA Department of Physics Applied Physics and AstronomyRensselaer Polytechnic InstituteTroyNY12180USA Department of Materials Science and Engineering University of MarylandCollege ParkMD20742USA Department of Chemistry and Institute of Materials Science and Engineering Washington University in St.LouisSt.LouisMO63130USA School of Materials Engineering Purdue UniversityWest LafayetteIN47907USA Department of Mechanical Science and Engineering University of Illinois Urbana-ChampaignUrbanaIllinois 61801USA Materials Research Laboratory University of Illinois Urbana-ChampaignUrbanaIL 61801USA Theore

Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many *** science,in particular,encompasses a variety of experimental and theoretical approaches that require careful *** efforts have been developed previously to mitigate these ***,a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still *** work introduces JARVIS-Leaderboard,an open-source and community-driven platform that facilitates benchmarking and enhances *** platform allows users to set up benchmarks with customtasks and enables contributions in the form of dataset,code,and meta-data *** cover the following materials design categories:Artificial Intelligence(AI),Electronic Structure(ES).

关键词： rigorous perfect enable

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Analysis of Large-Scale Human Protein Sequences Using an Efficient Spark-Based DBSCAN Algorithm

Analysis of Large-Scale Human Protein Sequences Using an Eff...

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International Conference on Frontiers in Computing and Systems, COMSYS 2020

作者： Sekhar Bandyopadhyay, Soumyendu Kumar Halder, Anup Chatterjee, Piyali Sroka, Jacek Nasipuri, Mita Basu, Subhadip Department of Computer Science and Engineering Jadavpur University Kolkata India Department of Computer Science and Engineering Netaji Subhash Engineering College Kolkata India Institute of Informatics University of Warsaw Warsaw Poland

ISBN: (纸本)9789811578335

The development of modern high throughput sequencing techniques has resulted in an exponential growth in meta-genomic sequence accumulation that could greatly enhance large-scale functional annotation. Processing of these large and redundant sequences has become a major challenge for researchers. Clustering by similarity is one of the major steps to reduce the redundancy of these enormous resources and analysis of such large biological sequences. The n–gram feature representation, generally used in sequence clustering and classification, results in high dimensional input spaces, for larger values of n. However, it becomes intractable to cluster such large-scale sequences by current algorithms due to a large number of dimensions. Efficiently designed, clustering approach can easily scale to handle large-scale sequences by utilizing the power of parallel computing with high-performance computing systems. In this work, we propose a spark-based DBSCAN algorithm to cluster large-scale human protein sequence data by exploring n-gram (n © 2021, Springer Nature Singapore Pte Ltd.

关键词： Proteins

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Comparative Analysis of Transfer Learning Models for Breast Cancer Classification

arXiv

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arXiv 2024年

作者： Eskandari, Sania Eslamian, Ali Cheng, Qiang Department of Electrical and Computer Engineering University of Kentucky Lexington United States Department of Computer Science University of Kentucky Lexington United States Institute for Biomedical Informatics University of Kentucky Lexington United States

The classification of histopathological images is crucial for the early and precise detection of breast cancer. This study investigates the efficiency of deep learning models in distinguishing between Invasive Ductal Carcinoma (IDC) and non-IDC in histopathology slides. We conducted a thorough comparison examination of eight sophisticated models: ResNet-50, DenseNet-121, ResNeXt-50, Vision Transformer (ViT), GoogLeNet (Inception v3), EfficientNet, MobileNet, and SqueezeNet. This analysis was carried out using a large dataset of 277,524 image patches. Our research makes a substantial contribution to the field by offering a comprehensive assessment of the performance of each model. We particularly highlight the exceptional efficacy of attention-based mechanisms in the ViT model, which achieved a remarkable validation accuracy of 93%, surpassing conventional convolutional networks. This study highlights the promise of advanced machine learning approaches in clinical settings, offering improved precision as well as efficiency in breast cancer diagnosis. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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