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

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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

Machine Learning Approach for Identifying Anatomical Biomarkers of Early Mild Cognitive Impairment

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arXiv 2024年

作者： Ahmad, Alwani Liyana Sanchez-Bornot, Jose Sotero, Roberto C. Coyle, Damien Idris, Zamzuri Faye, Ibrahima Department of Fundamental and Applied Sciences Faculty of Science and Information Technology Universiti Teknologi PETRONAS Perak Malaysia Department of Neurosciences Hospital Universiti Sains Malaysia Kelantan Malaysia Brain and Behaviour Cluster School of Medical Sciences Universiti Sains Malaysia Kelantan Malaysia Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Magee campus Derry LondonderryBT48 7JL United Kingdom Department of Radiology Hotchkiss Brain Institute University of Calgary CalgaryAB Canada The Bath Institute for the Augmented Human University of Bath BathBA2 7AY United Kingdom Department of Neurosciences School of Medical Sciences Universiti Sains Malaysia Kelantan Malaysia Universiti Teknologi PETRONAS Perak Malaysia Universiti Teknologi PETRONAS Perak32610 Malaysia

Background. Alzheimer's Disease (AD) represents a significant challenge in neurodegenerative disorders, necessitating early detection for effective intervention. Among neuroimaging methods, magnetic resonance imaging (MRI) is widely used because it is easy to apply in clinical practice and cost-effective, making it crucial for studying AD. Objective. This study aims to perform a comprehensive analysis of machine learning (ML) methods used in MRI-based biomarker selection and classification analysis. The goal is to study AD-related early cognitive decline by discriminating between healthy control (HC) participants who stayed stable and those unstable (uHC) who developed mild cognitive impairment (MCI) within five years. Methods. We utilized 3-Tesla (3T) MRI data from the Alzheimer's Disease Neuroinformatic Initiative (ADNI) and the Open Access Series of Imaging Studies 3 (OASIS-3), focusing on HC and uHC. Freesurfer’s recon-all, among other tools, was used to extract MRI-based anatomical biomarkers corresponding to semi-automatic segmented subcortical and cortical brain regions. We applied various ML techniques to select features and classify the data. These included methods from preliminary analysis performed in the MATLAB Classification Learner (MCL) app and more sophisticated methods like nested cross-validation and Bayesian optimization implemented in a customized pipeline to enhance classification performance for balanced and imbalanced datasets. Our pipeline was applied to both original imbalanced and randomly balanced datasets within a Monte Carlo analysis. Moreover, we implemented data harmonization approaches based on polynomial regression that enhanced the performance of ML and statistical methods. Complementary performance metrics, such as Accuracy (Acc), area under receiver operating characteristic curve (AROC), F1 score, and Matthew’s correlation coefficient (MCC), were used to evaluate the assessed methodologies. Results. In feature selection analyses,

关键词： Neurodegenerative diseases

Factors affecting team social networking and performance: The moderation effect of team size and tenure

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Journal of Open Innovation: Technology, Market, and Complexity 2023年第2期9卷

作者： Kurdi, Barween Al Alshurideh, Muhammad Turki Akour, Iman Alzoubi, Haitham M. Obeidat, Zaid Mohammad Hamadneh, Samer Joghee, Shanmugan Department of Marketing Faculty of Business The Hashemite University Zarqa Jordan Department of Marketing School of Business The University of Jordan Amman 11942 Jordan Department of Management College of Business Administration University of Sharjah Sharjah 27272 United Arab Emirates Information Systems Department College of Computing & Informatics University of Sharjah Sharjah United Arab Emirates School of Business Skyline University College Sharjah United Arab Emirates Applied Science Research Center Applied Science Private University Amman 11937 Jordan Department of Marketing School of Business The University of Jordan Amman Jordan School of Business Skyline University College Sharjah United Arab Emirates

As it derives from information technology, the internet revolution, and their consequences for business operations, social networking has received wide research interest. Therefore, this study aims to analyze the factors influencing team social networking and performance with the moderating effect of team size and tenure in SMEs in Jordan. Using a survey questionnaire, the study collected the data from this sector and analyzed it with SamartPLS to test the proposed research model. The results showed a mostly significant positive effect from seven factors (team communication, team conflict, team power, team regulation, team collaboration, team engagement, and team motivation). The study also revealed a moderating effect of both team size and tenure in the relationship between team social networking and SME performance in Jordan. The contributions provided through this work expand understanding of the factors’ importance while addressing the issue of team social networking and performance. In practice, this enables team leaders to focus on team size and tenure to develop their business performance. © 2023

关键词： Moderation Performance Social networking Team size Team tenure

Multiobjective Forecasting Model Based on the Interval Discrete Type-2 Fuzzy Sets and Genetic Algorithm 34

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Multiobjective Forecasting Model Based on the Interval Discr...

34th International Conference on information Technologies, InfoTech 2020

作者： Demidova, Liliya Anatolievna Stepanov, Maksim Anatolievich Institute of Integrated Safety and Special Instrumentation Russian Technological University - MIREA Department of Intelligent Systems of Information Security Vernadsky Avenue78 Moscow119454 Russia Faculty of Computing Engineering Ryazan State Radio Engineering University Named after V.F. Utkin Department of Computational and Applied Mathematics Gagarin Str. 59/1 Ryazan390005 Russia

ISBN: (纸本)9781728169149

The forecasting task for the short time series has been investigated. The main problem of this task is the availability of a small amount of relevant information. Such tasks can be considered as the tasks in the conditions of uncertainty, fuzziness of the initial data. We suggest to develop the multiobjective forecasting model based on the interval discrete type-2 fuzzy sets. As the criteria which should be minimized, it is proposed to use the average relative forecasting error and the tendency mismatch indicator. The search for the optimal parameters values of the forecasting model is implemented using the genetic algorithm. The examples showing the possibilities of the proposed forecasting model have been discussed. © 2020 IEEE.

关键词： Genetic algorithms

Techniques in Pan-Tilt-Zoom Operations of IP Network Camera for Visual IoT

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Techniques in Pan-Tilt-Zoom Operations of IP Network Camera ...

International Symposium on Wireless Personal Multimedia communications (WPMC)

作者： Yuki Murakami Ken T. Murata Ryozo Yamazaki Kazutaka Kikuta Yoshihide Kagatani Toshiki Aoki Takamichi Mizuhara Kazunori Yamamoto Tomohiro Kawanabe Tsutomu Nagatsuma Junji Tokairin Michiharu Niimi Kazuki Kobayashi Keiichiro Fukuzawa Social Innovation Unit National Institute of Information and Communications Technology (NICT) Tokyo Japan Resilient ICT Reserach Center National Institute of Information and Communications Technology (NICT) Sendai Japan GEO Solutions Inc. Nishinomiya Japan CLEALINK TECHNOLOGY Co. Ltd. Kyoto Japan Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology (Kyutech) Fukuoka Japan Academic Assembly Shinshu University Nagano Japan Academic Center for Computing and Media Studies (ACCMS) Kyoto University Kyoto Japan

ISBN: (纸本)9781665427616

One of the objectives of Visual IoT is to realize safe and secure society via remote video transmission. Low-cost cameras play an important role since we need to distribute many cameras in the society. To achieve wide coverage of monitoring with single IP network camera, remote PTZ (Pan-Tilt-Zoom) function is one of the preferable solutions that is installed in IP network camera at high location such as at the top of mountains. In this paper we propose techniques of PTZ remote control of low-cost IP cameras for Visual IoT systems over mobile networks. At first, one of the regulations of PTZ remote-control, ONVIF (Open Network Video Interface Forum) Profile S, is introduced for remote operations over IP networks. We then design and implement remote ONVIF commands in our Visual IoT system. Finally the system performances are examined in both laboratory environment and outdoor fields. The remote PTZ operations is practically successful while issues are discussed to achieve area monitoring with a high precision using low-cost IP network cameras.

关键词： Wireless communication Visualization Costs Surveillance Laboratories Cameras Regulation

Cramér-rao bound optimization for joint radar-communication design

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arXiv 2021年

作者： Liu, Fan Liu, Ya-Feng Li, Ang Masouros, Christos Eldar, Yonina C. The Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematicsand Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The School of Information and Communications Engineering Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’anShaanxi710049 China The Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom The Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot Israel

In this paper, we propose multi-input multi-output (MIMO) beamforming designs towards joint radar sensing and multi-user communications. We employ the Cramér-Rao bound (CRB) as a performance metric of target estimation, under both point and extended target scenarios. We then propose minimizing the CRB of radar sensing while guaranteeing a pre-defined level of signal-to-interference-plus-noise ratio (SINR) for each communication user. For the single-user scenario, we derive a closed form for the optimal solution for both cases of point and extended targets. For the multi-user scenario, we show that both problems can be relaxed into semidefinite programming by using the semidefinite relaxation approach, and prove that the global optimum can always be obtained. Finally, we demonstrate numerically that the globally optimal solutions are reachable via the proposed methods, which provide significant gains in target estimation performance over state-of-the-art benchmarks. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal to noise ratio

Rate-Splitting for Cell-Free Massive MIMO: Performance Analysis and Generative AI Approach

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arXiv 2024年

作者： Zheng, Jiakang Zhang, Jiayi Du, Hongyang Zhang, Ruichen Niyato, Dusit Dobre, Octavia A. Ai, Bo The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China The Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong The College of Computing and Data Science Nanyang Technological University Singapore Faculty of Engineering and Applied Science Memorial University Canada The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-speed Railway System China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China

Cell-free (CF) massive multiple-input multiple-output (MIMO) provides a ubiquitous coverage to user equipments (UEs) but it is also susceptible to interference. Rate-splitting (RS) effectively extracts data by decoding interference, yet its effectiveness is limited by the weakest UE. In this paper, we investigate an RS-based CF massive MIMO system, which combines strengths and mitigates weaknesses of both approaches. Considering imperfect channel state information (CSI) resulting from both pilot contamination and noise, we derive a closed-form expression for the sum spectral efficiency (SE) of the RS-based CF massive MIMO system under a spatially correlated Rician channel. Moreover, we propose low-complexity heuristic algorithms based on statistical CSI for power-splitting of common messages and power-control of private messages, and genetic algorithm is adopted as a solution for upper bound performance. Furthermore, we formulate a joint optimization problem, aiming to maximize the sum SE of the RS-based CF massive MIMO system by optimizing the power-splitting factor and power-control coefficient. Importantly, we improve a generative AI (GAI) algorithm to address this complex and non-convexity problem by using a diffusion model to obtain solutions. Simulation results demonstrate its effectiveness and practicality in mitigating interference, especially in dynamic environments. Copyright © 2024, The Authors. All rights reserved.

关键词： Genetic algorithms

Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation

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arXiv 2022年

作者： Nan, Yang Del Ser, Javier Tang, Zeyu Tang, Peng Xing, Xiaodan Fang, Yingying Herrera, Francisco Pedrycz, Witold Walsh, Simon Yang, Guang National Heart and Lung Institute Imperial College London London United Kingdom Department of Communications Engineering University of the Basque Country UPV/EHU Bilbao Spain Derio Spain Department of Informatics Technical University of Munich Germany University of Granada Granada Spain Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Department of Electrical and Computer Engineering Faculty of Engineering King Abgudulaziz University Jeddah Saudi Arabia The Systems Research Institute Polish Academy of Sciences Warsaw Poland Royal Brompton Hospital Sydney Street London United Kingdom

Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases, while its manual delineation is unduly burdensome. To alleviate this time-consuming and potentially subjective manual procedure, researchers have proposed methods to automatically segment airways from computerized tomography (CT) images. However, some small-sized airway branches (e.g., bronchus and terminal bronchioles) significantly aggravate the difficulty of automatic segmentation by machine learning models. In particular, the variance of voxel values and the severe data imbalance in airway branches make the computational module prone to discontinuous and false-negative predictions. especially for cohorts with different lung diseases. Attention mechanism has shown the capacity to segment complex structures, while fuzzy logic can reduce the uncertainty in feature representations. Therefore, the integration of deep attention networks and fuzzy theory, given by the fuzzy attention layer, should be an escalated solution for better generalization and robustness. This paper presents an efficient method for airway segmentation, comprising a novel fuzzy attention neural network and a comprehensive loss function to enhance the spatial continuity of airway segmentation. The deep fuzzy set is formulated by a set of voxels in the feature map and a learnable Gaussian membership function. Different from the existing attention mechanism, the proposed channel-specific fuzzy attention addresses the issue of heterogeneous features in different channels. Furthermore, a novel evaluation metric is proposed to assess both the continuity and completeness of airway structures. The efficiency, generalization and robustness of the proposed method have been proved by training on normal lung disease while testing on datasets of lung cancer, COVID-19 and pulmonary fibrosis. © 2022, CC BY-NC-ND.

关键词： COVID-19

Review of e-learning service framework based on cloud computing

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AIP Conference Proceedings 2023年第1期2706卷

作者： Purwono Hendradi Moh Khanapi Abd Ghani Siti Nur Mahfuzah Mohamad Karima Aissaoui Uky Yudatama Teknik Informatika Universitas Muhammadiyah Magelang Magelang Indonesia Biomedical Computing and Engineering Technologies (BIOCORE) Applied Research Group Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka Melaka Malaysia Department of Interactive Media Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka Melaka Malaysia Artificial Intelligence Data Science and Emergent Systems Laboratory ENSAF Sidi Mohammed Ben Abdellah University FEZ Morocco

The impact of Covid-19 on education is the result of online learning from home as part of the Work from Home (WFH) protocol. There are at least three essential roles in WFH, namely Institutions, Teachers and Students, each of which uses personal resources. This literature begins with a review of the primary literature with the theme of e-learning frameworks and cloud computing. It is found that two crucial things are Integration and measuring the level of success. On that basis, in this literature review research, keywords are taken from the theme, including the word equation by using AND OR operators on the research database site over a span of the last five years. Each literature will be explored in stages focusing on the problem of Integration and or Measurement Level measurement. These results become material for systematic discussion so that an overview is obtained for the development of an E-learning Service Framework based on Cloud computing using a six-layer model with the Business Process layer being the layer used in system integration, both with other service systems and similar systems in the education ecosystem.

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