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2nd International Conference on Computing and Information Technology, ICCIT 2022

作者： Shahid, Muhammad Rehman Munir, Asim Ifraheem, Layeba Aldabbas, Hamza Wadood, Abdul Alwada'n, Tariq University of Faisalabad Departmental of Computational Sciences Faislabad Pakistan International Islamic University Department of Computer Science and Software Engineering Islamabad Pakistan Al-Balqa Applied University Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology Al-Salt Jordan School of Computing Engineering Digital Technologies Teesside University Middlesbrough United Kingdom

ISBN: (纸本)9781665436052

A forensic autopsy is a surgical process in which experts collect a deceased body's internal and external information. These death certificates are the source of timely warnings of an increase in disease activity. It's only helpful if accurate and quantitative data is available. Therefore, the Classification of plain text medical autopsy reports reduces the time consumption and irregularities. The motive is to design an automatic text classification system that classifies plain text autopsy reports. Therefore, a methodology proposes using different Automatic Text Classification Techniques (ATC). This technique has embedded Feature Extraction, Feature Representation, and Feature Reduction techniques. These techniques use for the construction of classification models that classify the text of autopsy reports. Data sets collected from these types will be helpful in future experiments. Finally, the performance of the classifier measures by using different Evaluation parameters. These Evaluation Measures are Precision, Recall, Accuracy, and F-measure. © 2022 IEEE.

关键词： Support vector machines

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Sparsity Aware of TF-IDF Matrix to Accelerate Oblivious Document Ranking and Retrieval

Sparsity Aware of TF-IDF Matrix to Accelerate Oblivious Docu...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Zeshi Zhang Guangping Xu Hongzhang Yang Yulei Wu School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Ministry of Education Key Laboratory of Computer Vision and System China Faculty of Engineering University of Bristol Bristol U.K.

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Due to cloud security concerns, there is an increasing interest in information retrieval systems that can support private queries over public documents. It is desirable for oblivious document ranking and retrieval in public cloud at lower cost and faster speed without revealing query-related information. Currently, the term frequency-inverse document frequency (TF-IDF) and private information retrieval (PIR) techniques are used to solve this problem, but the encryption operation time is over dominant. Motivated by the observation of the sparsity of the TF-IDF matrix, we propose an efficient approach for oblivious document ranking and retrieval, called E-Coeus. It takes advantage of the high sparsity of the TF-IDF matrix to rearrange the matrix. Our method accelerates the speed of PIR inadvertently retrieving documents and reduces the user retrieval delay time. In a stand-alone experiment for a TF-IDF matrix of 1.2M rows and 64K columns with the sparsity of 10%, E-Coeus improves the document ranking and retrieval performance by 23% over the state-of-the-art approach, Coeus. With cluster of 64 machines, E-Coeus improves the performance by 34% over Coeus when the TF-IDF matrix sparsity is 30%.

关键词： Time-frequency analysis Privacy Costs Cloud computing security Information retrieval Encryption Delays

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Heterogeneous Multi-commodity Network Flows over Time 1

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17th International computer science Symposium in Russia, CSR 2022

作者： Li, Yifen Bei, Xiaohui Qiao, Youming Tao, Dacheng Chen, Zhiya School of Traffic and Transportation Engineering Central South University Changsha China School of Physical and Mathematical Sciences Nanyang Technological University Singapore Singapore Centre for Quantum Software and Information University of Technology Sydney Ultimo Australia UBTECH Sydney AI Centre School of Computer Science Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia

ISBN: (数字)9783031095740

ISBN: (纸本)9783031095733

In the 1950’s, Ford and Fulkerson introduced dynamic flows by incorporating the notion of time into the network flow model (Oper. Res., 1958). In this paper, motivated by real-world applications including route planning and evacuations, we extend the framework of multi-commodity dynamic flows to the heterogeneous commodity setting by allowing different transit times for different commodities along the same edge. We first show how to construct the time-expanded networks, a classical technique in dynamic flows, in the heterogeneous setting. Based on this construction, we give a pseudopolynomial-time algorithm for the quickest flow problem when there are two heterogeneous commodities. We then present a fully polynomial-time approximation scheme when the nodes have storage for any number of heterogeneous commodities. The algorithm is based on the condensed time-expanded network technique introduced by Fleischer and Skutella (SIAM J. Comput., 2007). © 2022, Springer Nature Switzerland AG.

关键词： Polynomial approximation

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EEG Feature Extraction with Fast Fourier Transform for Investigating different Brain regions in Cognitive and Reasoning Activity 5

EEG Feature Extraction with Fast Fourier Transform for Inves...

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5th IEEE International Symposium in Robotics and Manufacturing Automation, ROMA 2022

作者： Amin, Hafeez Ullah Hafeez, Yasir Reza, Mohammed Faruque Adil, Syed Hasan Hasan, Rumaisa Abu Ali, Syed Saad Azhar University of Nottingham Malaysia School of Computer Science Faculty of Science and Engineering Semenyih43500 Malaysia Universiti Sains Malaysia Dept. of Neurosciences Kota Bharu Kubang Kerian Malaysia Iqra University Software Engineering Department Karachi Pakistan Universiti Teknologi Petronas Centre for Intelligent Signal and Imaging Research Electrical and Electronics Engineering Department Perak Seri Iskandar32610 Malaysia King Fahd University of Petroleum and Minerals Aerospace Engineering Department Dhahran31261 Saudi Arabia

ISBN: (数字)9781665459327

ISBN: (纸本)9781665459327

In this study, cortical brain activity during a pattern matching task (PMT) was measured by employing electroencephalography (EEG). The EEG data were recorded from 128 scalp locations during a pattern-matching task and in rest conditions (eyes open and eyes closed). Spectral Analysis of EEG frequency bands reflected a significant (p © 2022 IEEE.

关键词： Fast Fourier transforms

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A Fuzzy Logic-Based Computational Framework for Precision Triage in Androgenetic Alopecia: A Simulated Biomarker-Driven Approach

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International Journal of Computational Intelligence Systems 2025年第1期18卷 1-30页

作者： Al-Samarraay, Mohammed S. Magableh, Aws A. Mahmood, Rana I. Joudar, Shahad Sabbar Zahid, Idrees A. Al-Obaidi, Jameel R. Al-Saffar, Ali Z. Albahri, A. S. Albahri, O. S. Alamoodi, A. H. Abdullah, Mohd Faizal Nizam Lee Electrical and Electronic Engineering Department College of Engineering Gulf University Sanad Bahrain Faculty of Information Technology and Computer Sciences Department of Information Systems Yarmouk University Irbid Jordan Department of Software Engineering Prince Sultan University Riyadh Saudi Arabia Department of Biomedical Engineering College of Engineering Al-Nahrain University Jadriya Baghdad Iraq University of Technology Baghdad Iraq Department of Biology Faculty of Science and Mathematics Universiti Pendidikan Sultan Idris Tanjong Malim Malaysia Applied Science Research Center Applied Science Private University Amman Jordan Department of Molecular and Medical Biotechnology College of Biotechnology Al-Nahrain University Baghdad Iraq Technical Engineering College Imam Ja’afar Al-Sadiq Baghdad Iraq University of Information Technology and Communications (UOITC) Baghdad Iraq Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Victorian Institute of Technology Melbourne Australia GUST Engineering and Applied Innovation Research Center (GEAR) Gulf University for Science and Technology Mishref Kuwait Department of Mathematics Faculty of Science and Mathematics Universiti Pendidikan Sultan Idris Tanjong Malim Malaysia

Androgenetic alopecia (AGA) is a common cause of hair loss affecting both men and women. Although its precise etiology remains uncertain, genetic and hormonal factors are recognized as major contributors. This study introduces a computational intelligence framework employing fuzzy logic and multi-criteria decision-making (MCDM) to simulate a robust triage system for AGA management. Using a simulated dataset of 100 AGA patients, we applied the fuzzy-weighted zero-inconsistency (FWZIC) method to assign weights to 11 bioactive criteria associated with AGA. These weights informed a novel triage procedure for alopecia patients (TPAP), which stratified patients into seven severity levels (level 1: minor; level 7: severe). This study presents a computationally intelligent triage model tailored for AGA, emphasizing the applicability of fuzzy MCDM techniques in medical decision support. The TPAP framework can assist in resource allocation and treatment planning, paving the way for personalized and timely interventions in hair loss management.

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Towards Survivable In-Memory Stores with Parity Coded NVRAM

Towards Survivable In-Memory Stores with Parity Coded NVRAM

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Zhixuan Wang Guangping Xu Hongzhang Yang Yulei Wu School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China Faculty of Engineering University of Bristol Bristol U.K.

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Erasure codes have been widely applied to in-memory key-value storage systems for high reliability and low redundancy. In distributed in-memory key-value storage systems, update operations are relatively frequent, especially the partial-stripe update, which makes data update more challenging. Recently, existing research has been based on appending logs to accelerate parity data write. However, its logs are stored on disks, which decreases the system performance significantly. Therefore, we propose a novel in-memory key-value storage architecture, DNVPL, which utilizes NVRAM to log parity data. Our main idea is to design an appending-only update scheme to tradeoff the memory cost and the update overhead. We implement DNVPL with an in-memory key-value storage prototype, called LogKV. We evaluate it with different workloads. The experiments show that our scheme achieves high update performance from different metrics. Our scheme can reduce update latency by up to 49% and save storage space by 48% compared to the state-of-the-art schemes.

关键词： Privacy Nonvolatile memory System performance Redundancy Random access memory Prototypes computer architecture

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Multi-View Ensemble Learning for Robust Graph Convolutional Networks

SSRN

引用

SSRN 2022年

作者： Wu, Tao Luo, Junhui Xiao, Min Xian, Xingping Wang, Chao Yuan, Lin Qiao, Shaojie Song, Xiuli School of Cybersecurity and Information Law Chongqing University of Posts and Telecommunications Chongqing China School of Computer and Information Science Chongqing Normal University Chongqing China School of Software Engineering Chengdu University of Information Technology Chengdu China

Graph neural networks (GNNs) have received extensive attention owing to the remarkable expressive power of graphs in various research areas. However, recent studies have shown that they are vulnerable to adversarial attacks, which significantly limits their application in safety-critical applications. Despite some attempts to design robust GNNs against adversarial attacks, there are no effective defense methods in the literature. Previous studies have established that effective adversarial perturbations generated by adversarial attacks usually have low feature similarity. Our empirical study shows that adversarial perturbations tend to have low structural similarity. Thus, a natural idea to defend GNNs against adversarial attacks is to aggregate high-order robust information that is immune to the effects of adversarial attacks in the input graph. Therefore, in this study, we construct views with high structure and feature similarity and propose a multi-view ensemble learning-based robust graph convolutional network (MV-RGCN). Each base model in the MV-RGCN aggregates information from the original graph, as well as the constructed view, through an adaptive aggregation mechanism, thereby eliminating the impact of adversarial attacks. Then, the robust representations of the base models are integrated using an adaptive ensemble to generate predictions. Extensive experiments on multiple datasets demonstrate that the MV-RGCN outperforms the state-of-the-art methods and achieves satisfactory performance without affecting the accuracy on clean graph data. This code is available at https://***/thomaslok0516/MVRGCN. © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

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Single Loop Control of a Common DC-Bus-Configured Traction Motor Emulator Using State Feedback Linearization Method

Single Loop Control of a Common DC-Bus-Configured Traction M...

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2021 IEEE Southern Power Electronics Conference, SPEC 2021

作者： Kadam, Arvind H. Williamson, Sheldon S. Group Faculty of Engineering and Applied Science Department of Electrical Computer and Software Engineeirng 2000 Simcoe Street North OshawaONL1G 0C5 Canada

ISBN: (纸本)9781665436236

Nowadays, the LCL (inductor-capacitor-inductor) filter is an integral part of every power electronic converter system operating on current control. Though higher order, the LCL filter due to its high harmonic suppression capability are widely adopted in back-to-back converter systems or grid connected converter systems. Normally, with LCL filter, the control system follows double closed loop with outer loop controlling DC voltage and inner loop controlling AC current. However, for some special applications like common DC-bus-configured motor emulator (ME) system, the DC bus voltage is not required to be controlled. In addition, the presence of LCL filter introduces cross coupling of flux and torque component in case of AC motor emulation. The double closed loop control is generally adopted to decouple torque and flux components, however the controller design is more complex process. Therefore, this paper presents a single loop control of a common DC-bus-configured ME system with LCL filter based on state feedback linearization method. The decoupling equations in direct and quadrature axis are derived here. The simulation results presented for a 2.0 kW PMSM motor validate the stability as well as closed loop operation of the system with single loop control. © 2021 IEEE.

关键词： Synchronous motors

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Comparison of Epilepsy Induced by Ischemic Hypoxic Brain Injury and Hypoglycemic Brain Injury using Multilevel Fusion of Data Features

arXiv

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arXiv 2024年

作者： Kadem, Sameer Sami, Noor Elaraby, Ahmed Alyousif, Shahad Jalil, Mohammed Altaee, M. Almusawi, Muntather Ghany, Ismaeel A. Kareem, Ali Kamil Kamalrudin, Massila Ftaiet, Adnan Allwi Dijlah University College Baghdad Iraq Department of Computer Engineering Techniques Mazaya University College Th Qar Iraq Department of Computer Science Faculty of Computer Science and Information South Valley University Qena Egypt Department Of Cybersecurity College of Engineering and Information Technology Buraydah Private Colleges Buraydah Saudi Arabia Department of Electrical and Electronic Engineering College of Engineering Gulf University Sanad26489 Bahrain Research centre Northampton University Faculty of Engineering Department of Electrical and Electronics Engineering University Drive Northampton NN1 5PH United Kingdom Department of Computer Science Alturath University College Baghdad Iraq Department of Medical Engineering Techniques Alfarahidi University Baghdad Iraq College of Technical Engineering The Islamic University Najaf Iraq Computer Technology Engineering College of Engineering Technology Al-Kitab University Iraq Department of Biomedical Engineering Al-Mustaqbal University College Hillah51001 Iraq Faculty of Information & Communication Technology Universiti Teknikal Malaysia Melaka Durian Tunggal Melaka75450 Malaysia Medical Instruments Engineering Techniques National University of Science and Technology Thi Qar Iraq

The study aims to investigate the similarities and differences in the brain damage caused by Hypoxia-Ischemia (HI), Hypoglycemia, and Epilepsy. Hypoglycemia poses a significant challenge in improving glycemic regulation for insulin-treated patients, while HI brain disease in neonates is associated with low oxygen levels. The study examines the possibility of using a combination of medical data and Electroencephalography (EEG) measurements to predict outcomes over a two-year period. The study employs a multilevel fusion of data features to enhance the accuracy of the predictions. Therefore this paper suggests a hybridized classification model for Hypoxia-Ischemia and Hypoglycemia, Epilepsy brain injury (HCM-BI). A Support Vector Machine is applied with clinical details to define the Hypoxia-Ischemia outcomes of each infant. The newborn babies are assessed every two years again to know the neural development results. A selection of four attributes is derived from the Electroencephalography records, and SVM does not get conclusions regarding the classification of diseases. The final feature extraction of the EEG signal is optimized by the Bayesian Neural Network (BNN) to get the clear health condition of Hypoglycemia and Epilepsy patients. Through monitoring and assessing physical effects resulting from Electroencephalography, The Bayesian Neural Network (BNN) is used to extract the test samples with the most log data and to report hypoglycemia and epilepsy patients non-invasively. The experimental findings demonstrate that the suggested strategy improves accuracy by 95.05% and reduces the error rate to 0.41 when comparing diseases. © 2024, CC0.

关键词： Electroencephalography

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Design and FPGA Implementation of the Henon Chaotic Map for Image Encryption

Design and FPGA Implementation of the Henon Chaotic Map for ...

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International Conference on Information and Communication Systems (ICICS)

作者： Eyad Taqieddin Dheya Mustafa Ahmed Shatnawi Osama Al-Khaleel Christos Papachristou Network Eng. and Security Department Jordan University of Science and Tech. Irbid Jordan Dept. Of Computer Engineering Faculty of Engineering The Hashemite University Zarqa Jordan Software Engineering Department Jordan University of Science and Tech. Irbid Jordan Computer Engineering Department Jordan University of Science and Tech. Irbid Jordan ECSE Department Case Western Reserve University Cleveland Ohio USA

Due to the ever increasing usage of multimedia content sharing and the inherent nature of the insecure links used for transmission, it is quite necessary to provide a fast and secure encryption algorithm. Current symmetric ciphers possess properties that may reveal some content of the plaintext images, especially when a block cipher is used without any chaining. Chaotic encryption has been proposed to allow for hiding inherent properties of the images and to generate almost uniform histograms of the cipher image (i.e.; hiding any visual properties). To that end, we propose the use of hardware to implement chaos-based ciphers for faster execution time. The results of this work show that such an implementation is feasible, and even favorable, when compared to software-based image encryption.

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