检索结果-内蒙古大学图书馆

Journal of engineering Research (Kuwait) 2024年

作者： Shuwandy, Moceheb Lazam Jouda, A.S. Ahmed, M.A. Salih, Mahmood M. Al-qaysi, Z.T. Alamoodi, A.H. Garfan, Salem Albahri, O.S. Zaidan, B.B. Albahri, A.S. Computer Science Department College of Computer Science and Mathematics Tikrit University (TU) Tikrit Iraq Salahaddin Health Office Ministry of Health Tikrit Iraq MEU Research Unit Middle East University Amman Jordan Faculty of Computing and Meta-Technology (FKMT) Universiti Pendidikan Sultan Idris (UPSI) Perak Malaysia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Victorian Institute of Technology Australia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Applied Science Research Center Applied Science Private University Amman Jordan SP Jain School of Global Management Lidcombe Sydney 2141 NSW Australia International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Technical College Imam Ja'afar Al-Sadiq University Baghdad Iraq Iraqi Commission for Computers and Informatics (ICCI) Baghdad Iraq

With the widespread usage of smartphones, users not just using their smartphone for calls and messaging only, but they are using it for a variety of purposes such as banking transactions, e-mailing, chatting, online shopping, video conferencing, health monitoring, and many more. Most of these activities store personal and sensitive data of the user on the device, reaching this confidential information by unauthorized person may cause huge losses and bad consequences. Therefore, securing the smartphone's accessibility from unauthorized people became an extremely essential. With the advanced development in the area of sensor-based smartphone authentication methods, different methods were developed to be easily used by users through specific criteria. However, many aspects haven't been discovered yet in the area of sensor-based smartphone authentication methods. On the basis of that, this study aims to review the area of sensor-based smartphone authentication methods systematically to provide a comprehensive understanding of this new area and to discuss the current challenges and issues. To conduct this systematic review, four scientific digital databases were utilized;scienceDirect, IEEE Xplore, Web of science, and Scopus. The total number of studies found was n = 256 articles, only n = 46 articles were included based on our inclusion criteria. To provide a comprehensive understanding of the included studies, taxonomy was drawn. Also, this study provides a discussion on the current motivations, challenges and issues, and recommendations in sensor-based smartphone authentication methods. This review enlightens, encourage, and direct researchers to develop authentication solutions and reduce the current gaps in this area. © 2024 The Authors

关键词： Authentication MHealth Microphone Sensor Password Privacy Touch Sensor

来源：评论

学校读者我要写书评

暂无评论

Exploring the integration of multi criteria decision analysis in the clean energy biodiesels applications: A systematic review and gap analysis

引用

engineering Applications of Artificial Intelligence 2024年 133卷

作者： Alamoodi, A.H. Garfan, Salem Al-Zuhairi, Omar Zaidan, B.B. Zaidan, A.A. Albahri, O.S. Y.Y. Ahmaro, Ibraheem Albahri, A.S. Yussof, Salman Magableh, Aws A. Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Malaysia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Perak Malaysia Applied Science Research Center Applied Science Private University Amman Jordan Low Dimensional Materials Research Centre Department of Physics Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia SP Jain School of Global Management Lidcombe SydneyNSW2141 Australia International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Victorian Institute of Technology Australia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Computer Science Department College of Information Technology Hebron University Hebron Palestine Technical College Imam Ja'afar Al-Sadiq University Baghdad Iraq Department of Information Systems Yarmouk University Irbid Jordan Baghdad Iraq Department of Computing College of Computing and Informatics Universiti Tenaga Nasional Malaysia Departments of Software Engineering Prince Sultan University Riyad Saudi Arabia

Biodiesel has gained tremendous attention from researchers due to its environmentally friendly and renewable fuel manufactured domestically from vegetable oils or animal fats. Multiple factors, including emission and fuel efficiency, measure the performance of biodiesel. These characteristics make the process of evaluation and benchmarking a multi-criteria decision-making (MCDM) problem. This research attempts to survey the literature for multi-criteria decision-making applications on biodiesel. This systematic review was carried out on four scientific databases: PubMed, scienceDirect, IEEE Xplore, and WoS. The collected articles were summarized and mapped into a coherent taxonomy, while the main challenges, motivations, and recommendations were collected and categorized for better understanding and finding out essential characteristics. The final set of included articles was 33, and they were categorized into six different categories. Most articles (n=15/33) investigated the evaluation and selection of biodiesel alternatives using MCDM. Furthermore, a state-of-art bibliometric analysis is provided. The analysis demonstrates several challenges in this area, such as source selection, optimum fuel blending, and uncertainty. This article is expected to assist future researchers in addressing the current gaps and contribute to advancing this area. © 2024 Elsevier Ltd

关键词： Biodiesel

来源：评论

学校读者我要写书评

暂无评论

Nonlinear feedforward enabling quantum computation

arXiv

引用

arXiv 2022年

作者： Sakaguchi, Atsushi Konno, Shunya Hanamura, Fumiya Asavanant, Warit Takase, Kan Ogawa, Hisashi Marek, Petr Filip, Radim Yoshikawa, Jun-Ichi Huntington, Elanor Yonezawa, Hidehiro Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan Department of Optics Palacký University 17. listopadu 1192/12 Olomouc77146 Czech Republic Centre for Quantum Computation and Communication Technology and Energy and Materials Engineering College of Engineering and Computer Science Australian National University CanberraACT2600 Australia Centre for Quantum Computation and Communication Technology School of Engineering and Information Technology University of New South Wales CanberraACT2600 Australia

Measurement-based quantum computation with optical time-domain multiplexing is a promising method to realize a quantum computer from the viewpoint of scalability. Fault tolerance and universality are also realizable by preparing appropriate resource quantum states and electro-optical feedforward that is altered based on measurement results. While a linear feedforward has been realized and become a common experimental technique, nonlinear feedforward was unrealized until now. In this paper, we demonstrate that a fast and flexible nonlinear feedforward realizes the essential measurement required for fault-tolerant and universal quantum computation. Using non-Gaussian ancillary states we observed 10% reduction of the measurement excess noise relative to classical vacuum ancilla. © 2022, CC BY.

关键词： Fault tolerance

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

PUBLISHING NEURAL NETWORKS IN DRUG DISCOVERY MIGHT COMPROMISE TRAINING DATA PRIVACY

arXiv

引用

arXiv 2024年

作者： Krüger, Fabian P. Östman, Johan Mervin, Lewis Tetko, Igor V. Engkvist, Ola AstraZeneca R&D Discovery Sciences Molecular AI Mölndal431 83 Sweden Technical University of Munich TUM School of Computation Information and Technology Department of Mathematics Munich80333 Germany Helmholtz Munich Molecular Targets and Therapeutics Center Institute of Structural Biology Neuherberg85764 Germany AI Sweden Gothenburg41756 Sweden AstraZeneca R&D Discovery Sciences Molecular AI CambridgeCB2 0AA United Kingdom Chalmers University of Technology Department of Computer Science and Engineering Gothenburg412 96 Sweden

This study investigates the risks of exposing confidential chemical structures when machine learning models trained on these structures are made publicly available. We use membership inference attacks, a common method to assess privacy that is largely unexplored in the context of drug discovery, to examine neural networks for molecular property prediction in a black-box setting. Our results reveal significant privacy risks across all evaluated datasets and neural network architectures. Combining multiple attacks increases these risks. Molecules from minority classes, often the most valuable in drug discovery, are particularly vulnerable. We also found that representing molecules as graphs and using message-passing neural networks may mitigate these risks. We provide a framework to assess privacy risks of classification models and molecular representations. Our findings highlight the need for careful consideration when sharing neural networks trained on proprietary chemical structures, informing organisations and researchers about the trade-offs between data confidentiality and model openness. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

Behavior and properties of water in silicate melts under deep mantle conditions

Research Square

引用

Research Square 2021年

作者： Karki, Bijaya B. Ghosh, Dipta B. Karato, Shun-Ichiro School of Electrical Engineering and Computer Science Department of Geology and Geophysics Center for Computation and Technology Louisiana State University Baton RougeLA70803 United States Dpeartment of Earth and Planetary Sciences Yale University New HavenCT06520 United States

Water (H2O) as one of the most abundant fluids present in Earth plays crucial role in the generation and transport of magmas in the interior. Though hydrous silicate melts have been studied extensively, the experimental data are confined to relatively low pressures and the computational results are still rare. Moreover, these studies imply large differences in the way water influences the physical properties of silicate magmas, such as density and electrical conductivity. Here, we invesigate the equation of state, speciation, and transport properties of water dissolved in Mg1-xFexSiO3 and Mg2(1-x)Fe2xSiO4 melts (for x = 0 and 0.25) as well as in its bulk (pure) fluid state over the entire mantle pressure regime at 2000 to 4000 K using first-principles molecular dynamics. The simulation results allow us to constrain the partial molar volume of the water component in melts along with the molar volume of pure water. The predicted volume of silicate melt+water solution is negative at low pressures but becomes zero above 15 GPa implying ideal mixing at higher pressures. Consequently, the hydrous component tends to lower the melt density to similar extent over much of the mantle pressure regime irrespective of composition. Our results also show that hydrogen diffuses fast in silicate melts and enhances the melt electrical conductivity in a way that differs from electrical conduction in the bulk water. The speciation of the water component varies considerably from the bulk water structure as well. Water is dissolved in melts mostly as hydroxyls at low pressure and as -O-H-O-, -O-H-O-H- and other extended species with increasing pressure. On the other hand, the pure water behaves as a molecular fluid below 15 GPa, gradually becoming a dissociated fluid with further compression. On the basis of modeled density and conductivity results, we suggest that partial melts containing a few percent of water may be gravitationally trapped both above and below the upper mantle-transiti

关键词： Iron compounds

来源：评论

学校读者我要写书评

暂无评论

Multimodal Whole Slide Foundation Model for Pathology

arXiv

引用

arXiv 2024年

作者： Ding, Tong Wagner, Sophia J. Song, Andrew H. Chen, Richard J. Lu, Ming Y. Zhang, Andrew Vaidya, Anurag J. Jaume, Guillaume Shaban, Muhammad Kim, Ahrong Williamson, Drew F.K. Chen, Bowen Almagro-Perez, Cristina Doucet, Paul Sahai, Sharifa Chen, Chengkuan Komura, Daisuke Kawabe, Akihiro Ishikawa, Shumpei Gerber, Georg Peng, Tingying Le, Long Phi Mahmood, Faisal Department of Pathology Mass General Brigham Harvard Medical School BostonMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States Helmholtz Munich German Research Center for Environment and Health Munich Germany School of Computation Information and Technology Technical University of Munich Munich Germany Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Department of Pathology Pusan National University Busan Korea Republic of Department of Pathology and Laboratory Medicine Emory University School of Medicine AtlantaGA United States Harvard Data Science Initiative Harvard University CambridgeMA United States Department of Systems Biology Harvard Medical School BostonMA United States Department of Preventive Medicine Graduate School of Medicine The University of Tokyo Tokyo Japan Division of Pathology National Cancer Center Exploratory Oncology Research & Clinical Trial Center Chiba Japan

The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests (ROIs) into versatile and transferable feature representations via self-supervised learning (SSL). However, translating these advancements to address complex clinical challenges at the patient and slide level remains constrained by limited clinical data in disease-specific cohorts, especially for rare clinical conditions. We propose TITAN, a multimodal whole slide foundation model pretrained using 335,645 WSIs via visual self-supervised learning and vision-language alignment with corresponding pathology reports and 423,122 synthetic captions generated from a multimodal generative AI copilot for pathology. Without any finetuning or requiring clinical labels, TITAN can extract general-purpose slide representations and generate pathology reports that generalize to resource-limited clinical scenarios such as rare disease retrieval and cancer prognosis. We evaluate TITAN on diverse clinical tasks and find that TITAN outperforms both ROI and slide foundation models across machine learning settings such as linear probing, few-shot and zero-shot classification, rare cancer retrieval and cross-modal retrieval, and pathology report generation. The model is publicly accessible at https://***/mahmoodlab/TITAN © 2024, CC BY-NC-ND.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Exploring nontrivial topology at quantum criticality in a superconducting processor

arXiv

引用

arXiv 2025年

作者： Tan, Ziqi Wang, Ke Yang, Sheng Shen, Fanhao Jin, Feitong Zhu, Xuhao Ji, Yujie Xu, Shibo Chen, Jiachen Wu, Yaozu Zhang, Chuanyu Gao, Yu Wang, Ning Zou, Yiren Zhang, Aosai Li, Tingting Bao, Zehang Zhu, Zitian Zhong, Jiarun Cui, Zhengyi Han, Yihang He, Yiyang Wang, Han Yang, Jianan Wang, Yanzhe Shen, Jiayuan Liu, Gongyu Song, Zixuan Deng, Jinfeng Dong, Hang Zhang, Pengfei Jian, Shao-Kai Li, Hekang Wang, Zhen Guo, Qiujiang Lin, Hai-Qing Song, Chao Yu, Xue-Jia Wang, H. Wu, Fei College of Computer Science and Technology Zhejiang University Hangzhou310027 China School of Physics ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang Key Laboratory of Micro-nano Quantum Chips and Quantum Control Zhejiang University Hangzhou310000 China Institute for Advanced Study in Physics School of Physics Zhejiang University Hangzhou310058 China Department of Physics and Engineering Physics Tulane University New OrleansLA70118 United States Hefei National Laboratory Hefei230088 China Department of Physics Fuzhou University Fujian Fuzhou350116 China Fujian Key Laboratory of Quantum Information and Quantum Optics College of Physics and Information Engineering Fuzhou University Fujian Fuzhou350108 China

The discovery of nontrivial topology in quantum critical states has introduced a new paradigm for classifying quantum phase transitions and challenges the conventional belief that topological phases are typically associated with a bulk energy gap. However, realizing and characterizing such topologically nontrivial quantum critical states with large particle numbers remains an outstanding experimental challenge in statistical and condensed matter physics. Programmable quantum processors can directly prepare and manipulate exotic quantum many-body states, offering a powerful path for exploring the physics behind these states. Here, we present an experimental exploration of the critical cluster Ising model by preparing its low-lying critical states on a superconducting processor with up to 100 qubits. We develop an efficient method to probe the boundary g-function based on prepared low-energy states, which allows us to uniquely identify the nontrivial topology of the critical systems under study. Furthermore, by adapting the entanglement Hamiltonian tomography technique, we recognize two-fold topological degeneracy in the entanglement spectrum under periodic boundary condition, experimentally verifying the universal bulk-boundary correspondence in topological critical systems. Our results demonstrate the low-lying critical states as useful quantum resources for investigating the interplay between topology and quantum criticality. Copyright © 2025, The Authors. All rights reserved.

关键词： Topology

来源：评论

学校读者我要写书评

暂无评论

AdjointBackMapV2: Precise Reconstruction of Arbitrary CNN Unit’s Activation via Adjoint Operators

arXiv

引用

arXiv 2021年

作者： Wan, Qing Cheung, Siu Wun Choe, Yoonsuck School of Computer Science and Technology Zhejiang Gongshang University Zhejiang Province 310018 China Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA94550 United States Department of Computer Science & Engineering Texas A&M University College StationTX77843 United States

Adjoint operators have been found to be effective in the exploration of CNN’s inner workings [1]. However, the previous no-bias assumption restricted its generalization. We overcome the restriction via embedding input images into an extended normed space that includes bias in all CNN layers as part of the extended space and propose an adjoint-operator-based algorithm that maps high-level weights back to the extended input space for reconstructing an effective hypersurface. Such hypersurface can be computed for an arbitrary unit in the CNN, and we prove that this reconstructed hypersurface, when multiplied by the original input (through an inner product), will precisely replicate the output value of each unit. We show experimental results based on the CIFAR-10 and CIFAR-100 data sets where the proposed approach achieves near 0 activation value reconstruction error. Copyright © 2021, The Authors. All rights reserved.

关键词： Chemical activation

来源：评论

学校读者我要写书评

暂无评论

Methodology for creation of labeled image datasets of entrained air voids and aggregates in concrete surfaces using confocal laser scanning microscopy

引用

Advanced engineering Informatics 2025年 67卷

作者： Viktor Kostic Qadeer Khan Daniel Cremers Jithender Timothy Thomas Kränkel Christoph Gehlen TUM School of Engineering and Design Department of Materials Engineering Chair of Materials Science and Testing Centre for Building Materials (cbm) Technical University of Munich Munich 81245 BY Germany TUM School of Computation Information and Technology Computer Vision Group Technical University of Munich Munich 81245 BY Germany Munich Center for Machine Learning Munich 81245 BY Germany

Several strategies using neural network algorithms have been established to automate the process of evaluating parameters regarding the freeze–thaw resistance of hardened concrete from image data. However, the performance of such models is dependent on the quality of the datasets used for training. Hand-labeling concrete image data is challenging, where especially small micro air voids (<300 μ m) can be overlooked. This potentially leads to algorithms that are unable to accurately detect the micro air void content, despite being required by current standards (e.g. DIN EN 480-11). To account for this, a method was developed using confocal laser scanning microscopy and the “Segment Anything” model. Acquiring surface height-data through confocal laser scanning microscopy expendites the labeling process and creates more reliable training data due to the detectability of air voids through height differences. Furthermore, by combining the height-data with RGB-images it was possible to accurately distinguish between air voids, aggregates and cement paste with high repeatability between repeated labeling iterations. Keeping high magnification levels (120-480x) enabled the detection of air voids down to 5 μ m. The framework and dataset presented in this study are fundamental for further development of new neural network based algorithms, specialized for the characterization and evaluation of the freeze–thaw resistance of concrete.

关键词： Concrete Freeze–thaw resistance Air voids Labeling Confocal laser scanning microscopy Segment anything model

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：