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DieT Transformer model with PCA-ADE integration for advanced multi-class brain tumor classification

Intelligence-Based Medicine 2025年 11卷 100192-100192页

作者： Amin, Mohammad Nahar, Khalid M.O. Gharaibeh, Hasan Nasayreh, Ahmad Alsalmanc, Neda'a Alomar, Alaa Malkawi, Majd Alqasem, Noor Smerat, Aseel Zitar, Raed Abu Nusier, Shawd Ezugwu, Absalom E. Abualigah, Laith Department of Information Technology and Computer Sciences Yarmouk University Irbid 211633 Jordan Faculty of Computer Studies Arab Open University P.O.Box 84901 Riyadh 11681 Saudi Arabia Department of Computer Science Faculty of Information Technology Jordan University of Science and Technology Irbid 22110 Jordan Department of Medicine Faculty of Medicine Jordan University of Science and Technology Irbid 22110 Jordan Department of Biomedical Systems and Informatics Engineering Hijjawi Faculty for Engineering Technology Yarmouk University Irbid 21163 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates Department of Health and Clinical Outcomes Research Saint Louis University St. Louis MO United States Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

Early and accurate diagnosis of brain tumors is crucial to improving patient outcomes and optimizing treatment strategies. Long-term brain injury results from aberrant proliferation of either malignant or nonmalignant tissues in the brain. MRIs, or magnetic resonance imaging, are one of the most used approaches for detecting brain tumors. Professionals physically evaluate people after they have had MRI filtering, the process of enhancing MRI scans for radiologist interpretation, to establish if they have a brain tumor. Because different specialists use different frames to make judgments on the same MRI image, their analyses may yield contradictory results. Furthermore, simply detecting a tumor is insufficient. Inconsistent diagnoses can lead to delays in treatment, impacting survival rates and quality of care. It is also crucial to diagnose the patient's tumor so that treatment can begin as soon as possible. In this research, we investigate the multi-class classification of brain tumors utilizing a cutting-edge methodology that includes feature extraction from pictures using the DieT Transformer model, dimensionality reduction with PCA, and feature selection using the ADE algorithm. The proposed model, known in the publication as ADE_DieT, obtained an accuracy of 96.09 %. In addition, this article analyzes the performance of various pre-trained models, including MobileNetV3, NasNet, ResNet50, VGG16, VGG19, and DeiT. The proposed approach shortens the time required for manual diagnosis by clinicians by assisting in the rapid and accurate identification of brain tumors using MRI data. In oncology, this is important since it allows for early treatment. Integrating ADE_DieT into clinical workflows can support radiologists by reducing diagnosis time and enhancing diagnostic consistency. © 2024 The Authors

关键词： Brain tumor Medical image analysis MRI Vision Transformer

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OPINION MANIPULATION ON FARSI TWITTER

arXiv

引用

arXiv 2022年

作者： Farzam, Amirhossein Moradi, Parham Mohammadi, Saeedeh Padar, Zahra Siegel, Alexandra A. Department of Political Science Duke University DurhamNC27708 United States Department of Engineering Sciences and Applied Mathematics Northwestern University EvanstonIL60208 United States Max Planck Institute for Mathematics in the Sciences Leipzig04103 Germany Center for Complex Networks and Social Data Science Tehran Iran Department of Physics Shahid Beheshti University Tehran Iran School of Mathematics and Statistics University College Dublin BelfieldDublin 4 Ireland Department of Computer Science University of Freiburg Freiburg Germany Department of Political Science University of Colorado BoulderCO80309 United States

For Iranians and the Iranian diaspora, the Farsi Twittersphere provides an important alternative to state media and an outlet for political discourse. But this understudied online space has become an opinion manipulation battleground, with diverse actors using inauthentic accounts to advance their goals and shape online narratives. Examining trending discussions crossing social cleavages in Iran, we explore how the dynamics of opinion manipulation differ across diverse issue areas. Our analysis suggests that opinion manipulation by inauthentic accounts is more prevalent in divisive political discussions than non-divisive or apolitical discussions. We show how Twitter's network structures help to reinforce the content propagated by clusters of inauthentic accounts in divisive political discussions. Analyzing both the content and structure of online discussions in the Iranian Twittersphere, this work contributes to a growing body of literature exploring the dynamics of online opinion manipulation, while improving our understanding of how information is controlled in the digital age. © 2022, CC BY.

关键词： Social networking (online)

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Robust Self-Expression Learning with Adaptive Noise Perception

SSRN

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SSRN 2023年

作者： Wang, Yangbo Zhou, Jie Lu, Jianglin Wan, Jun Gao, Can Lin, Qingshui College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China SMILE Lab Department of ECE College of Engineering Northeastern University Boston02115 United States School of Information and Safety Engineering Zhongnan University of Economics and Law Wuhan430073 China Basic Teaching Department Liaoning Technical University Huludao125105 China

Self-expression learning methods often obtain a coefficient matrix to measure the similarity between pairs of samples. However, directly using the raw data to represent each sample under the self-expression framework may not be ideal, as noise points are inevitably involved in the process of representing clean samples. To address this issue, this work proposes a novel self-expression model called robust Self-Expression learning with adaptive Noise Perception (SENP). SENP decomposes each sample into a clean part and a noisy part, and samples with large self-expression losses can be recognized as the noise points. A reliable coefficient matrix can then be learned by using only the clean points to reconstruct the clean part of each sample. By simultaneously detecting the noisy part of each sample and noise points, and adaptively mitigating their negative impacts, the representative ability of the generated coefficient matrix is improved. Moreover, inspired by the solution of non-negative matrix factorization (NMF), an effective algorithm is formed to optimize SENP. Extensive experiments on well-known benchmark datasets demonstrate the superiority of SENP compared to several state-of-the-art methods. © 2023, The Authors. All rights reserved.

关键词： Non-negative matrix factorization

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Proof methods for robust low-rank matrix recovery

arXiv

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arXiv 2021年

作者： Fuchs, Tim Gross, David Jung, Peter Krahmer, Felix Kueng, Richard Stöger, Dominik Department of Mathematics Technical University of Munich Germany Institute for Theoretical Physics University of Cologne Germany Communications and Information Theory Group Technische Universität Berlin Germany Data Science in Earth Observation Technical University of Munich Germany Institute for Integrated Circuits Johannes Kepler University Linz Austria Ming Hsieh Department of Electrical and Computer Engineering University of Southern California United States

Low-rank matrix recovery problems arise naturally as mathematical formulations of various inverse problems, such as matrix completion, blind deconvolution, and phase retrieval. Over the last two decades, a number of works have rigorously analyzed the reconstruction performance for such scenarios, giving rise to a rather general understanding of the potential and the limitations of low-rank matrix models in sensing problems. In this article, we compare the two main proof techniques that have been paving the way to a rigorous analysis, discuss their potential and limitations, and survey their successful applications. On the one hand, we review approaches based on descent cone analysis, showing that they often lead to strong guarantees even in the presence of adversarial noise, but face limitations when it comes to structured observations. On the other hand, we discuss techniques using approximate dual certificates and the golfing scheme, which are often better suited to deal with practical measurement structures, but sometimes lead to weaker guarantees. Lastly, we review recent progress towards analyzing descent cones also for structured scenarios—exploiting the idea of splitting the cones into multiple parts that are analyzed via different techniques. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

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Air quality data prediction using data analytics

引用

AIP Conference Proceedings 2024年第1期2742卷

作者： Senthil Kumar Sidharthan Kalaiarasan Nancy Anvar Shathik Kavietha Dept. of Computer Science and Egg Chennai Institute of Technology Chennai Tamilnadu India Department of Information Technology Sri Krishna College of Engineering and Technology Coimbatore Tamilnadu India Saveetha School of Engineering Saveetha Institute of Medical and technical sciences(Deemed to be university) Chennai India Department of Cloud Technology & Data Science iNurture Education Solutions Srinivas University College of Engineering & Technology Bangalore India. Department of Computer Science and Engineering Sri Sairam Institute of Technology Chennai India.

Air pollution is usually described as the release into the atmosphere of contaminants which are harmful to human health and the environment. It may be described as one of the most serious risks human beings have ever faced because to the growing more catastrophic problem of air pollution in the globe. A description is provided of how our air quality sensor system is designed, implemented and optimised, which provides a live and fine grain air quality map of the area under surveillance. The average joint failure in the generated real-time air quality map should be maintained to a minimal when unmeasured data values as part of the objective are deducted. In terms of studying the quality issues, Big data Technology has been recommended to be utilised in order to organise the air quality sensing equipment sensing tasks appropriately. In addition, the prediction of air quality based on climatic conditions is explored using various machine learning techniques for the data given.

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Weisfeiler and Leman go Machine Learning: The Story so far

arXiv

引用

arXiv 2021年

作者： Morris, Christopher Lipman, Yaron Maron, Haggai Rieck, Bastian Kriege, Nils M. Grohe, Martin Fey, Matthias Borgwardt, Karsten Department of Computer Science RWTH Aachen University Aachen Germany Meta AI Research Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel NVIDIA Research Tel Aviv Israel AIDOS Lab Institute of AI for Health Helmholtz Zentrum München and Technical University of Munich Munich Germany Faculty of Computer Science University of Vienna Vienna Austria Research Network Data Science University of Vienna Vienna Austria Kumo.AI Mountain ViewCA United States Machine Learning & Computational Biology Lab Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland

In recent years, algorithms and neural architectures based on the Weisfeiler–Leman algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a powerful tool for machine learning with graphs and relational data. Here, we give a comprehensive overview of the algorithm’s use in a machine-learning setting, focusing on the supervised regime. We discuss the theoretical background, show how to use it for supervised graph and node representation learning, discuss recent extensions, and outline the algorithm’s connection to (permutation-)equivariant neural architectures. Moreover, we give an overview of current applications and future directions to stimulate further research. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Vehicle Re-Identification by Deep Feature Embedding and Approximate Nearest Neighbors

Vehicle Re-Identification by Deep Feature Embedding and Appr...

引用

International Joint Conference on Neural networks (IJCNN)

作者： Artur O. R. Franco Felipe F. Soares Aloísio V. Lira Neto José A. F. de Macêdo Paulo A. L. Rego Fernando A. C. Gomes José G. R. Maia Federal University of Ceará (UFC) Group of Computer Networks Software Engineering and Systems (GREat) Brazilian Federal Highway Police Insight Data Science Lab Virtual UFC Institute

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Disorganized urban growth has led cities to chaos, which has brought countless challenges for their development in several sectors, such as traffic organization, public safety, and transportation. Vehicular re-identification (ReID) technologies have become increasingly important in this context since these allow to produce insights capable of benefiting many areas. As recently, methods frequently resorted to Deep Learning and Convolutional Neural networks (CNNs), especially in the design of loss functions capable of improving the learning capacity of CNNs. Couple and triplet loss techniques have gained prominence, but their effectiveness depends on the mining of samples to converge properly. In this paper, we investigate a new simple approach for vehicle ReID by combining sample mining strategy and approximate nearest neighbor (ANN) method to improve retrieval quality. By relying only on relatively low-dimensional deep features, we were able to obtain state-of-the-art performance on the VeRi-776 dataset in terms of mAP, HIT@1, and HIT@5 metrics, but using relatively simple CNN and ANN methods, which are feasible in CPU for real-time scenarios.

关键词： Measurement Cameras Training Licenses Image color analysis Safety Urban areas

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Narrow-linewidth optical frequency comb reference to a fiber delay line

Narrow-linewidth optical frequency comb reference to a fiber...

引用

Conference on Lasers and Electro-Optics (CLEO)

作者： Haochen Tian Fei Meng Baike Lin Shiying Cao Zhanjun Fang Youjian Song Minglie Hu Ultrafast Laser Laboratory Key Laboratory of Opto-Electronic Information Technical Science of Ministry of Education School of Precision Instrument and Opto-Electronics Engineering Tianjin University Tianjin China National Institute of Metrology Beijing China Key Laboratory of Advanced Optical Communication System and Networks School of Electronics Engineering and Computer Science Peking University Beijing China

A fully-stabilized Er:fiber optical frequency comb referenced to a km-long fiber delay line is presented. The comb mode’s fractional frequency stability is at 10 −12 level in 12.8 ms. The absolute linewidth is 587 Hz.

关键词： Phase measurement Laser noise Laser mode locking Measurement by laser beam Delay lines Lasers and electrooptics Optical harmonic generation

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An integrated GIS-based multivariate adaptive regression splines-cat swarm optimization for improving the accuracy of wildfire susceptibility mapping

引用

Geocarto International 2023年

作者： Hai, Tao Theruvil Sayed, Biju Majdi, Ali Zhou, Jincheng Sagban, Rafid Band, Shahab S. Mosavi, Amir School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Guizhou Duyun China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Selangor Shah Alam Malaysia Department of Computer Science Dhofar University Salalah Oman Department of Building and Construction Technologies Engineering Al-Mustaqbal University College Hilla Iraq Department of Computer Technology Engineering Technical Engineering College Al-Ayen University Thi-Qar Iraq Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou Taiwan John von Neumann Faculty of Informatics Obuda University Budapest Hungary German Research Center for Artificial Intelligence Oldenburg Germany Institute of the Information Society University of Public Service Budapest Hungary

A hybrid machine learning method is proposed for wildfire susceptibility mapping. For modeling a geographical information system (GIS) database including 11 influencing factors and 262 fire locations from 2013 to 2018 is used for developing an integrated multivariate adaptive regression splines (MARS). The cat swarm optimization (CSO) algorithm tunes the parameters of the MARS in order to generate accurate susceptibility maps. From the Pearson correlation results, it is observed that land use, temperature, and slope angle have strong correlation with the fire severity. The results demonstrate that the prediction capability of the MARS-CSO model outperforms model tree, reduced error pruning tree and MARS. The resulting wildfire risk map using MARS-CSO reveals that 20% of the study areas is categorized in the very low wildfire risk class, whereas 40% is under the very high class of fire hazard. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： artificial intelligence cat swarm optimization geospatial machine learning natural hazard Wildfire susceptibility

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Towards software-defined-networking-based IoT: A systematic literature review on management frameworks and open challenges

TechRxiv

引用

TechRxiv 2021年

作者： Siddiqui, Shahbaz Hameed, Sufian Shah, Syed Attique Ahmad, Ijaz Draheim, Dirk Dustdar, Schahram The Department of Computer Science NUCES Karachi75160 Pakistan The Data Systems Group Institute of Computer Science University of Tartu Delta Centre Narva mnt 18-3123 Tartu51009 Estonia The VTT Technical Research Centre of Finland Finland The Information Systems Group Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia The Distributed Systems Group TU Wien Austria

Internet of Things (IoT) is characterized as one of the leading actors for the next evolutionary stage in the computing world. IoT-based applications have already produced a plethora of novel services and are improving the living standard by enabling innovative and smart solutions. However, along with its rapid adoption, IoT technology also creates complex challenges regarding the management of IoT networks due to its resource limitations (computational power, energy, and security). Hence, it is urgently needed to refine the IoT-based application’s architectures to robustly manage the overall IoT infrastructure. Software-defined networking (SDN) has emerged as a paradigm that offers software-based controllers to manage hardware infrastructure and traffic flow on a network effectively. SDN architecture has the potential to provide efficient and reliable IoT network management. This research provides a comprehensive survey investigating the published studies on SDN-based frameworks to address IoT management issues in the dimensions of fault tolerance, energy management, scalability, load balancing, and security service provisioning within the IoT networks. We conducted a Systematic Literature Review (SLR) on the research studies (published from 2010 to 2021) focusing on SDN-based IoT management frameworks. We provide an extensive discussion on various aspects of SDN-based IoT solutions and architectures. We elaborate a taxonomy of the existing SDN-based IoT frameworks and solutions by classifying them into categories such as network function virtualization, middleware, OpenFlow adaptation, and blockchain-based management. We present the research gaps by identifying and analyzing the key architectural requirements and management issues in IoT infrastructures. Finally, we highlight various challenges and a range of promising opportunities for future research to provide a roadmap for addressing the weaknesses and identifying the benefits from the potentials offered by SD

关键词： Software defined networking

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