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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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An evaluation of lightweight deep learning techniques in medical imaging for high precision COVID-19 diagnostics

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Healthcare Analytics 2022年 2卷 100096-100096页

作者： Ukwandu, Ogechukwu Hindy, Hanan Ukwandu, Elochukwu Department of Computer Communication and Information Systems School of Engineering and Built Environment Glasgow Caledonian University United Kingdom Computer Science Department Faculty of Computer and Information Sciences Ain Shams University Cairo 11566 Egypt Department of Applied Computing and Engineering Cardiff School of Technologies Cardiff Metropolitan University Cardiff CF5 2YB United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom

Timely and rapid diagnoses are core to informing on optimum interventions that curb the spread of COVID-19. The use of medical images such as chest X-rays and CTs has been advocated to supplement the Reverse-Transcription Polymerase Chain Reaction (RT-PCR) test, which in turn has stimulated the application of deep learning techniques in the development of automated systems for the detection of infections. Decision support systems relax the challenges inherent to the physical examination of images, which is both time consuming and requires interpretation by highly trained clinicians. A review of relevant reported studies to date shows that most deep learning algorithms utilised approaches are not amenable to implementation on resource-constrained devices. Given the rate of infections is increasing, rapid, trusted diagnoses are a central tool in the management of the spread, mandating a need for a low-cost and mobile point-of-care detection systems, especially for middle- and low-income nations. The paper presents the development and evaluation of the performance of lightweight deep learning technique for the detection of COVID-19 using the MobileNetV2 model. Results demonstrate that the performance of the lightweight deep learning model is competitive with respect to heavyweight models but delivers a significant increase in the efficiency of deployment, notably in the lowering of the cost and memory requirements of computing resources. © 2022 The Author(s)

关键词： COVID-19 detection Diagnostic analytics Lightweight deep learning techniques Machine learning Point-of-care Resource-constrained devices

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Integrating V2X solutions in intelligent green cities: an AI-driven point exchange system approach

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Neural Computing and Applications 2025年

作者： Talaat, Fatma M. Shaban, Warda M. ZainEldin, Hanaa Badawy, Mahmoud Elhosseini, Mostafa Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science & Engineering New Mansoura University Gamasa35712 Egypt Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence and informatics Horus University New Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Computer Science and Information Department Applied College Taibah University Madinah42353 Saudi Arabia College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia

With rapid urbanization, smart cities have become essential for enhancing urban management and sustainability by integrating technological, social, and institutional innovations. Among these innovations, vehicle-to-everything (V2X) communications and electric vehicles (EVs) play a critical role in reducing carbon emissions and optimizing urban mobility. To address the existing gaps in holistic V2X integration, this paper presents a novel eco-assistive fog-based traffic management system (EAFTMS) that leverages a four-tier architecture (IoT, fog, cloud, and application) for scalable, real-time traffic optimization. A key innovation of this system is the AI-driven point exchange system (PES), designed to incentivize sustainable behaviors such as reducing unnecessary vehicle usage and promoting green lifestyle choices. Unlike conventional models, the proposed framework incorporates real-time behavioral monitoring, rewards-based sustainability programs, and V2X-enabled dynamic traffic control. Empirical validation demonstrates that EAFTMS outperforms existing models, including support vector regression (SVR), achieving a 30% reduction in latency, a 40% improvement in response time, a 25% increase in traffic flow efficiency, and a 35% reduction in CO2 emissions. These results highlight the framework’s potential to set new standards in intelligent green cities by offering scalable, practical, and environmentally impactful solutions to urban transportation challenges. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Urban transportation

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Promising new Techniques for Computer Network Traffic Classification: A Survey

Promising new Techniques for Computer Network Traffic Classi...

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International Conference on Advanced Computer Information Technologies (ACIT)

作者： Michal Konopa Jan Fesl Jan Janeček Institute of Applied Informatics Faculty of Science at University of South Bohemia České Budějovice Czech Republic Department of Computer Systems Czech Technical University in Prague Praha Czech Republic

ISBN: (数字)9781728167602

ISBN: (纸本)9781728167619

This paper aims to give an overview of the application of image processing to network traffic analysis, including a description of the essence of the most important works in the last 15 years. The importance of efficient, automated analysis of network traffic is growing especially today, when huge volumes of diverse data need to be quickly processed. With the rapid development of artificial intelligence in the field of image processing, it seems logical to use it for analyzing network traffic image data. Recent results on this topic are very promising.

关键词： Telecommunication traffic IP networks Monitoring Feature extraction Neural networks Complexity theory

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Modern Computing: Vision and Challenges

arXiv

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arXiv 2024年

作者： Gill, Sukhpal Singh Wu, Huaming Patros, Panos Ottaviani, Carlo Arora, Priyansh Pujol, Victor Casamayor Haunschild, David Parlikad, Ajith Kumar Cetinkaya, Oktay Lutfiyya, Hanan Stankovski, Vlado Li, Ruidong Ding, Yuemin Qadir, Junaid Abraham, Ajith Ghosh, Soumya K. Song, Houbing Herbert Sakellariou, Rizos Rana, Omer Rodrigues, Joel J.P.C. Kanhere, Salil S. Dustdar, Schahram Uhlig, Steve Ramamohanarao, Kotagiri Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Center for Applied Mathematics Tianjin University Tianjin China Raygun Performance Monitoring Wellington New Zealand Department of Computer Science York Centre for Quantum Technologies University of York York United Kingdom Microsoft Schiphol Netherlands Distributed Systems Group Vienna University of Technology Vienna Austria Detecon International GmbH Munich Germany Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Department of Computer Science University of Western Ontario London Canada Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Institute of Science and Engineering Kanazawa University Japan Tecnun School of Engineering University of Navarra Spain Department of Computer Science and Engineering College of Engineering Qatar University Doha Qatar Bennett University Greater Noida India Machine Intelligence Research Labs AuburnWA United States Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Baltimore United States Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia COPELABS Lusófona University Lisbon Portugal Sydney Australia The University of Melbourne Victoria Australia Laboratory School of Computing and Information Systems The University of Melbourne Australia

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress. © 2024, CC BY.

关键词： Obsolescence

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Preface

Lecture Notes in Networks and Systems

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Lecture Notes in Networks and systems 2024年 1076 LNNS卷 v-vii页

作者： Biele, Cezary Kacprzyk, Janusz Kopeć, Wieslaw Możaryn, Jakub Owsiński, Jan W. Romanowski, Andrzej Sikorski, Marcin National Information Processing Institute Warszaw Poland Polish Academy of Sciences Systems Research Institute Warsaw Poland Polish-Japanese Academy of Information Technology Warsaw Poland Institute of Automatic Control and Robotics Faculty of Mechatronics Warsaw University of Technology Warsaw Poland Institute of Applied Computer Science Faculty of Electrical Electronic Computer and Control Engineering Lódż University of Technology LODZ Poland Department of Applied Informatics in Management Faculty of Management and Economics Gdańsk University of Technology Gdańsk Poland

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AB1556 MACHINE LEARNING APPROACH FOR CLASSIFICATION OF ARTHRITIS ACTIVITY STATE, USING DATA FROM A SINGLE ACCELEROMETER

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Annals of the Rheumatic Diseases 2023年 82卷 2012-2012页

作者： P. Mielnik A. Myhre Hjelle A. Traseth K. Tokarz B. Pollen M. Fojcik Helse Forde Section for Rheumatology； Department for Neurology Rheumatology and Physical Medicine Forde Norway Institute of Informatics Silesian University of Technology Department of Graphics Computer Vision and Digital Systems Gliwice Poland Western Norway University of Applied Sciences Faculty of Engineering and Science Forde Norway

Background Machine learning (ML) potential is not fully exploited in diagnostics and follow up of autoimmune inflammatory rheumatic disease (AIIRD). It is despite the broader use of ML in, e.g. imaging diagnostics. The specific tools for AIIRD are lacking. Objectives This is an interim analysis of data from the first checkpoint of the proof-of-concept study on using accelerometer (ACC) data in follow up of patients with AIIRD. The main goal of the study was to investigate the value of single ACC data in the classification of arthritis activity status. Methods Subjects with AIIRD are enrolled in the study when they start of new treatment due to the disease activity. Several comorbidities, such as severe neurological and cardiological disorders, as wells as impaired mobility, are part of the protocol exclusion criteria. Volunteers without AIIRD who fulfil other inclusion/exclusion criteria are included as controls. The study was approved by local competent authorities and informed consent was given by all participants. We used data collected up till the first study checkpoint. This analysis covers nine patients with AIIRD (5 rheumatoid and four psoriasis arthritis) and 13 controls. Five patients had 3 study visits, 1 had 2 visits and 3 only one. Controls have only one visit per protocol. We analysed ACC data from 3 minutes of clapping using a home-brewed device based on Arduino nano 33 BLE with 6-axis MTU by Nordic Semiconductor. Data was divided into 6-second chunks that were found optimal in our prior study. We conducted binary classification between any/ no arthritis in any of the upper extremities. We used accuracy and area under curve (AUC) as an efficacy function derived from the receiver operating characteristic curve (ROC). We extracted 54 features from 3 ACC axis signal. The features encompassed, among others, Fourier's components, autoregression coefficients, median absolute deviation (MAD), variance, Fourier's entropy, etc. We built linear discriminant anal

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Performance evaluations of a cloud computing physical machine with task reneging and task resubmission (feedback) 27th

Performance evaluations of a cloud computing physical machin...

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27th International Conference on Computer Networks, CN 2020

作者： Kuaban, Godlove Suila Soodan, Bhavneet Singh Kumar, Rakesh Czekalski, Piotr Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland School of Mathematics Shri Mata Vaishno Devi University KatraJammu and Kashmir182320 India Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

ISBN: (纸本)9783030507183

Cloud service providers (CSP) provide on-demand cloud computing services, reduces the cost of setting-up and scaling-up IT infrastructure and services, and stimulates shorter establishment times for start-ups that offer or use cloud-based services. Task reneging or dropping sometimes occur when a task waits in the queue longer than its timeout or execution deadline, or it is compromised and must be dropped from the queue or as an active queue management strategy to avoid tail dropping of tasks when the queues are full. Reneged or dropped tasks could be resubmitted provided they were not dropped due to security reasons. In this paper, we present a simple M/M/c/N queueing model of a cloud computing physical machine, where the interarrival times and the services times are exponentially distributed, with N buffer size and c virtual machines running in parallel. We present numerical examples to illustrate the effect of task reneging and task resubmission on the queueing delay, probability of task rejection, and the probability of immediate service. © Springer Nature Switzerland AG 2020.

关键词： Cloud computing

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Super stable CsPbBr3@SiO2 tumor imaging reagent by stress-response encapsulation

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Nano Research 2020年第3期13卷 795-801页

作者： Wentao Song Yiming Wang Bing Wang Yingfang Yao Wenguang Wang Jinhui Wu Qing Shen Wenjun Luo Zhigang Zou Eco-materials and Renewable Energy Research Center(ERERC) College of Engneering and Applied SciencesNanjing UniversityNo.22 Hankou RoadNanjing210093China Jiangsu Key Laboratory for Nano Technology National Laboratory of Solid State MicrostructuresDepartment of PhysicsNanjing UniversityNo.22 Hankou RoadNanjing210093China Collaborative Innovation Center of Advanced Microstructures Nanjing UniversityNo.22 Hankou RoadNanjing210093China Faculty of Informatics and Engineering The University of Electro-CommunicationsTokyo182-8585Japan Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao999078China

Great photoelectric properties can herald the high potentials of CsPbBr3 nanocrystals(NCs)to function as the fluorescent probes for early tumor ***,the intrinsic water vulnerability of CsPbBr3 NCs highly restricts their biomedical *** conquer this challenge,we herein introduce a nature inspired"stress-response"method to tightly encapsulate CsPbBr3 into SiO2 nano-shells that can dramatically improve the water stability of CsPbBr3@SiO2 nanoparticles for over 48 *** further highlighted the advantageous features of CsPbBr3@SiO2 by using them as the fluorescent probes for CT26 tumor cell imaging with their high water stability,biocompatibility,and low *** work for the first time exhibited the potential of lead halide perovskite NCs for tumor diagnosis,and can highly anticipate the further in vivo biomedical applications that light up live cells.

关键词： CsPbBr3 nanocrystals stress response encapsulation cancer diagnose

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PerFail 2024: Third International Workshop on Negative Results in Pervasive Computing - Welcome and Committees

2024 IEEE International Conference on Pervasive Computing an...

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2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2024 2024年 38-39页

作者： Peltonen, Ella Mohan, Nitinder Zdankin, Peter Josten, Malte Shreedar, Tanya Shreedhar, Tanya Bayhan, Suzan Berrocal, Javier Chakraborty, Sandip Crowcroft, Jon Ding, Aaron Yi Ferlin, Simone Gasser, Oliver Holz, Ralph G. Kangasharju, Jussi Kolcun, Roman Nicklas, Daniela Ott, Jörg Schiele, Gregor Sigg, Stephan Solmaz, Gürkan Eleni, Eirini Weis, Torben University of Oulu Finland Technical University of Munich Germany University of Duisburg-Essen Germany University of Edinburgh United Kingdom University of Edinburgh United Kingdom University of Twente Netherlands University of Extremadura Spain Indian Institute of Technology Kharagpur India University of Cambridge United Kingdom TU Delft University of Helsinki Netherlands Red Hat and Karlstad University Sweden Max Planck Institute for Informatics Germany University of Münster Germany University of Helsinki Finland University of Bamberg Faculty Information Systems and Applied Computer Science Germany Technische Universität München Germany Aalto University Finland NEC Laboratories Europe Germany Tsiropoulou University of New Mexico United States Universität Duisburg-Essen Germany

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