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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Al Noman, Abdullah Fardin, Hasib Chhabra, Gunjan Sultana, Shayma Haque, Rezaul Ahmed, Md. Redwan Hasibunnahar, Swarna Kaushik, Keshav Westcliff University Department of Information Technology United States Westcliff University Department of Engineering Management United States Graphic Era Hill University Department of Cse Uttarakhand Dehradun India Graphic Era Deemed to be University Uttarakhand Dehradun India International American University Department of Management Information System United States East West University Department of Computer Science and Engineering Bangladesh Huzhou University Department of Life Sciences China Amity University Amity School of Engineering and Technology Punjab Mohali India

ISBN: (纸本)9798350350067

Monkeypox is a viral disease found in Central and West Africa, and it presents notable public health challenges due to its clinical similarity to smallpox. Recent outbreaks in over 40 countries have underscored the urgent need for quick and accurate diagnosis, especially in settings with limited resources where traditional diagnostic methods may be inadequate. This study explores the application of deep learning techniques to classify four specific types of monkeypox skin lesions. Transfer learning was employed to evaluate the performance of different models using a dataset of 770 skin lesion images. Various methods, including data augmentation and image preprocessing, were utilized to improve the effectiveness of the models. The results indicate that the custom SqueezeNet model was the most successful, achieving an accuracy of 98.85% and an F1 score of 98.89%. These results surpass previous research benchmarks across four key evaluation measures. Additionally, the model was integrated into a web-based diagnostic tool capable of reliably detecting different forms of monkeypox. This study represents a significant advancement in utilizing artificial intelligence for disease detection, offering a practical approach for early and accurate monkeypox diagnosis. The proposed system has thex potential for seamless integration into clinical practice, aiding healthcare professionals in timely interventions. © 2024 IEEE.

关键词： Contrastive Learning

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Performance Analysis of Higher Order Tensor Storages for Highly Sparse Multidimensional Data 5

Performance Analysis of Higher Order Tensor Storages for Hig...

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5th International Conference on Electrical Information and Communication Technology, EICT 2021

作者： Safayet Hossain, Md. Azharul Hasan, K.M. Tsuji, Tatsuo Department of Computer Science and Engineering Khulna Bangladesh University of Fukui Faculty of System Design Engineering Fukui Japan

ISBN: (纸本)9781665409063

To compute multidimensional data, tensor algebra is a powerful tool and it has many applications in machine learning and data analytics. This multidimensional data which is represented with higher-order tensors for multi-Aspect data analysis is very sparse for an actual dataset. The degree of data sparsity increases with the increase of dimensionality. This is because the volume of the tensor increases very fast with the increase of dimensionality but the actual data to the tensor does not increase at such a rate for high-performance scientific computing. Storing and applying operations on this highly sparse multidimensional data is a challenge for data scientists. Therefore, an efficient storage scheme for such sparse tensor is very important. There are many storage schemes in the literature for storing sparse tensors. In this paper, we analyze and compare different tensor storage formats using synthetic and actual datasets. We found that no single storage scheme is efficient for all the operations or applications of tensors. We calculated the memory required for per-nonzero elements among these tensor formats and compared them for different parameters. © 2021 IEEE.

关键词： Tensors

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XU-NetI: Simple U-shaped encoder-decoder network for accurate imputation of multivariate missing data

Franklin Open

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Franklin Open 2024年 8卷

作者： Firdaus, Firdaus Nurmaini, Siti Tutuko, Bambang Rachmatullah, Muhammad Naufal Islami, Anggun Darmawahyuni, Annisa Sapitri, Ade Iriani Karim, Muhammad Irfan Fachrurrozi, Muhammad Zarkasi, Ahmad Intelligent System Research Group Faculty of Computer Science Universitas Sriwijaya South Sumatera 30139 Indonesia Department of Informatic Engineering Faculty of Computer Science Universitas Sriwijaya Palembang 30139 Indonesia Department of Computer Engineering Faculty of Computer Science Universitas Sriwijaya Indonesia

Patients in intensive care unit (ICU) often have multiple vital signs monitored continuously. However, missing data is common in ICU settings, negatively impacting clinical decision-making and patient outcomes. In this study propose a multivariate data imputation method based on simple U-Shaped encoder-decoder network imputation (XU-NetI) method to learn the underlying patterns in the data and generate imputations for missing values of vital signs data with ICU patients. To evaluate the performance of this study's imputation methods, this study employed a publicly available database such the medical information mart for intensive care III (MIMIC III) v1.4. In this study proposed model has been developed to analyze 219.281 vital sign worth of data, focusing on eight essential vital sign features: body temperature, heart rate, respiration rate, systolic blood pressure, diastolic blood pressure, mean blood pressure, oxygen saturation, and glucose. The evaluation results demonstrates the effectiveness of the imputation techniques in improving the accuracy of predictive models. This study compared the XU-NetI approach to other state-of-the-art imputation methods including Autoencoder and Convolutional Neural Networks. As a result found, the technique using XU-NetI architecture outperformed them, in terms of root mean square error (RSME) by approximately 0.01, mean absolute error (MAE) by approximately 0.009, and R square (R2) by approximately 0.99. The XU-NetI method has the potential to enhance clinical decision-making and improve patient outcomes. © 2024 The Author(s)

关键词： Convolutional neural network Deep Learning Missing data imputation U-shaped encoder-decoder network

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Influence of early through late fusion on pancreas segmentation from imperfectly registered multimodal magnetic resonance imaging

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Journal of Medical Imaging 2025年第2期12卷

作者： Remedios, Lucas W. Liu, Han Remedios, Samuel W. Zuo, Lianrui Saunders, Adam M. Bao, Shunxing Huo, Yuankai Powers, Alvin C. Virostko, John Landman, Bennett A. Vanderbilt University Department of Computer Science NashvilleTN United States Johns Hopkins University Department of Computer Science BaltimoreMD United States National Institutes of Health Department of Radiology and Imaging Sciences BethesdaMD United States Vanderbilt University Department of Electrical and Computer Engineering NashvilleTN United States Vanderbilt University Medical Center Department of Medicine Division of Diabetes Endocrinology and Metabolism NashvilleTN United States VA Tennessee Valley Healthcare System NashvilleTN United States Vanderbilt University Department of Molecular Physiology and Biophysics NashvilleTN United States University of Texas at Austin Dell Medical School Department of Diagnostic Medicine AustinTX United States University of Texas at Austin Livestrong Cancer Institutes Dell Medical School AustinTX United States University of Texas at Austin Department of Oncology Dell Medical School AustinTX United States University of Texas at Austin Oden Institute for Computational Engineering and Sciences AustinTX United States Vanderbilt University Department of Biomedical Engineering NashvilleTN United States

Purpose: Combining different types of medical imaging data, through multimodal fusion, promises better segmentation of anatomical structures, such as the pancreas. Strategic implementation of multimodal fusion could improve our ability to study diseases such as diabetes. However, where to perform fusion in deep learning models is still an open question. It is unclear if there is a single best location to fuse information when analyzing pairs of imperfectly aligned images or if the optimal fusion location depends on the specific model being used. Two main challenges when using multiple imaging modalities to study the pancreas are (1) the pancreas and surrounding abdominal anatomy have a deformable structure, making it difficult to consistently align the images and (2) breathing by the individual during image collection further complicates the alignment between multimodal images. Even after using state-of-the-art deformable image registration techniques, specifically designed to align abdominal images, multimodal images of the abdomen are often not perfectly aligned. We examine how the choice of different fusion points, ranging from early in the image processing pipeline to later stages, impacts the segmentation of the pancreas on imperfectly registered multimodal magnetic resonance (MR) images. Approach: Our dataset consists of 353 pairs of T2-weighted (T2w) and T1-weighted (T1w) abdominal MR images from 163 subjects with accompanying pancreas segmentation labels drawn mainly based on the T2w images. Because the T2w images were acquired in an interleaved manner across two breath holds and the T1w images on one breath hold, there were three different breath holds impacting the alignment of each pair of images. We used deeds, a state-of-the-art deformable abdominal image registration method to align the image pairs. Then, we trained a collection of basic UNets with different fusion points, spanning from early to late layers in the model, to assess how early through lat

关键词： Image segmentation

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The Power of Bamboo: On the Post-Compromise Security for Searchable Symmetric Encryption 30

The Power of Bamboo: On the Post-Compromise Security for Sea...

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30th Annual Network and Distributed system Security Symposium, NDSS 2023

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

ISBN: (纸本)1891562835

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2023 30th Annual Network and Distributed system Security Symposium, NDSS 2023. All Rights Reserved.

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Streamflow Prediction in the Mekong River Basin Using Deep Neural Networks

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IEEE Access 2023年 11卷 97930-97943页

作者： Nguyen, Thi-Thu-Ha Vu, Duc-Quang Mai, Son T. Dang, Thanh Duc National Taiwan University Department of Bioenvironmental Systems Engineering Taipei10617 Taiwan National Central University Department of Computer Science and Information System Taoyuan320317 Taiwan Thai Nguyen University of Education Thai Nguyen250000 Viet Nam Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom Institute for Computational Science and Artificial Intelligence Van Lang University Laboratory of Environmental Sciences and Climate Change Ho Chi Minh City70000 Viet Nam Van Lang University Faculty of Environment Ho Chi Minh City70000 Viet Nam

In recent years, the Mekong River Basin (MRB), one of the largest river basins in Southeast Asia, has experienced severe impacts from extreme droughts and floods. Streamflow forecasting has become crucial for effective risk management strategies in the region. However, this task presents significant challenges due to rapid climate changes and the presence of numerous newly constructed upstream dams, which disrupt the natural flow. In this paper, we develop multiple deep learning models (incl. Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long short-term Memory (LSTM), and Transformer) to predict streamflow with different lead time forecasts based on observed meteorological variables and climatic indices (i.e., discharge, water level, precipitation, and temperature) from 1979 to 2019. The results indicate that LSTM obtains high performance for streamflow prediction in both dry and wet seasons while Transformer is not recommended for long-term prediction, especially in the dry season. The proposed deep learning models capture well the fluctuation of river flow in the MRB during the period of high-dam development, especially LSTM (NSE ≥ 0.8). The models' performances are enhanced with the adding of temperature for short-term prediction while precipitation was the most sensitive variable for long-term one. Such proposed models are essential for government agencies to plan mitigation and adaptation strategies at different periods, which can range from days to years. © 2013 IEEE.

关键词： Floods

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Smart Home Automation system controlling Through computer Vision

Smart Home Automation System controlling Through Computer Vi...

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Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE International Conference on

作者： Md. Sabbir Hossain Ummay Khadiza Rumpa Shatabdi Sharma Mamunur Rashid Habibur Rahman Sazzadul Islam Shovon Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Information System Central Michigan University Michigan United States of America

ISBN: (数字)9798331529529

ISBN: (纸本)9798331529536

We are dwelling in the golden age of science development. Smart domestic automation has now ended up in the middle of enchantment in the lookup field. The guide machine of switching every so often motives an excessive strength loss. Keeping that in mind, we tried to make a computerized domestic which will now not require any remote, Bluetooth, or any kind of controlling device. In this paper, we have introduced a benchmark contrast of current some hardware the usage of the equal algorithm. For that, we tried to use some sensors, haar cascade documents, and some OpenCV codes for our design. After doing some processes we have chosen a layout that is convenient. In our chosen layout we tend to use a camera, laptop, ARDUINO and made human detection feasible the use of laptop vision. Through the digital camera, rooms are being monitored always, and from that actual time, the image processing presence of human beings is being determined by using the machine automatically. Finally, the microcontroller triggers in accordance with the decision, when it no longer locates any human in the room, it turns the lights, followers, off and on the opposite, it turns the lights, followers, on.

关键词： Home appliances computer vision Portable computers Costs Bluetooth Layout Switches Smart homes Control systems Monitoring

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Resilient Cooperative Control of Multiple DC Microgrids With Interconnection Networks Against Cyber Attacks

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical systems 2025年 3卷 116-126页

作者： Xiao, Feng Liu, Shiyu Wei, Bo Fang, Fang Qin, Jiahu North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing102206 China North China Electric Power University School of Control and Computer Engineering Beijing102206 China North China Electric Power University New Energy Generation National Engineering Research Center Beijing102206 China University of Science and Technology of China Department of Automation Hefei230027 China

The two control objectives of voltage regulation and precise current sharing are conflicting in multi-bus DC microgrids. In this paper, a distributed control strategy is proposed, and it can achieve accurate current sharing and regulate the voltage of a critical node to the rated value. However, such a distributed control system is susceptible to potential cyber attacks that could deteriorate microgrids performances by corrupting communication links or actuators. To overcome these difficulties, we design the interconnected communication networks to establish a resilient cooperative system of multiple microgrids against unknown bounded injection attacks. The proposed resilient control strategy can achieve the control objectives of maintaining voltage regulation and current sharing within each microgrids under different operating conditions, and the interconnected system consisting of multiple microgrids cooperates to mitigate the adverse effect of cyber attacks. The dynamics and stability of the overall close-loop system of the DC microgrids are analyzed, in the present or absent of cyber attacks. Simulation results validate the effectiveness of the proposed approach in two 4-bus DC microgrids. © 2023 IEEE.

关键词： Cyber attacks

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Medical Image Analysis of Lung Tumor Diagnosis Based on Generalized Fuzzy C-Means Clustering Algorithm

Medical Image Analysis of Lung Tumor Diagnosis Based on Gene...

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International Conference on Emerging Electronic and Automation Technology 2022, ICEEAT 2022

作者： Zwaid, Jassim Mohammed Ahmed Murad, Masrah Azrifah Azmi Khalid, Fatimah Binti Manshor, Noridayu Al-Jumaily, Abdulmajeed Faculty of Computer Science and Information Technology University Putra Malaysia UPM Serdang 43400 Malaysia Affiliated to University of Anbar Ramadi Iraq Department of Software Engineering and Information System Faculty of Computer Science and Information Technology University Putra Malaysia UPM Serdang 43400 Malaysia Multimedia Department Faculty of Computer Science and Information Technology University Putra Malaysia UPM Serdang 43400 Malaysia Department of Signal Theory and Communications Universidad Carlos III de Madrid Leganés Madrid28911 Spain

In recent years, the rapid development of medical imaging technology has brought medical image analysis into the era of big data. CT imaging technology is one of the most common imaging methods for disease screening. This paper introduces medical image analysis of lung tumor diagnosis based on Generalized fuzzy c - means clustering algorithm was proposed. Brief introduce lung CT imaging to diagnose lung tumors requires a huge workload, which is often accompanied by long-term image reading and subjective evaluation. The experimental data comes from 15 patient samples which include two groups of samples are selected as test objects. The result shows the positive rate is high, resulting in misdiagnosis. In addition, great success of deep learning technology in the field of computer vision has made it possible to realize computer-aided diagnosis and screening of lung cancer. © Published under licence by IOP Publishing Ltd.

关键词： Deep learning

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Feature Selection with Stacked Autoencoder Based Intrusion Detection in Drones Environment

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computers, Materials & Continua 2022年第12期73卷 5441-5458页

作者： Heba G.Mohamed Saud S.Alotaibi Majdy M.Eltahir Heba Mohsen Manar Ahmed Hamza Abu Sarwar Zamani Ishfaq Yaseen Abdelwahed Motwakel Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

The Internet of Drones(IoD)offers synchronized access to organized airspace for Unmanned Aerial Vehicles(known as drones).The availability of inexpensive sensors,processors,and wireless communication makes it possible in real time *** several applications comprise IoD in real time environment,significant interest has been received by research *** IoD operates in wireless environment,it is needed to design effective intrusion detection system(IDS)to resolve security issues in the IoD *** article introduces ametaheuristics feature selection with optimal stacked autoencoder based intrusion detection(MFSOSAEID)in the IoD *** major intention of the MFSOSAE-ID technique is to identify the occurrence of intrusions in the IoD *** do so,the proposed MFSOSAE-ID technique firstly pre-processes the input data into a compatible *** addition,the presented MFSOSAEID technique designs a moth flame optimization based feature selection(MFOFS)technique to elect appropriate ***,firefly algorithm(FFA)with stacked autoencoder(SAE)model is employed for the recognition and classification of intrusions in which the SAE parameters are optimally tuned with utilize of *** performance validation of the MFSOSAE-ID model was tested utilizing benchmark dataset and the outcomes implied the promising performance of the MFSOSAE-ID model over other techniques with maximum accuracy of 99.72%.

关键词： Internet of drones unmanned aerial vehicles security intrusion detection machine learning

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