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ExpressionHash: Securing Telecare Medical Information Systems Using BioHashing

computers, Materials & Continua 2021年第6期67卷 2747-2764页

作者： Ayesha Riaz Naveed Riaz Awais Mahmood Sajid Ali Khan Imran Mahmood Omar Almutiry Habib Dhahri SEECS National University of Science and TechnologyIslamabadPakistan College of Applied Computer Science King Saud University(Almuzahmiyah Campus)RiyadhSaudi Arabia Department of Software Engineering Foundation University IslamabadPakistan

The COVID-19 outbreak and its medical distancing phenomenon have effectively turned the global healthcare challenge into an opportunity for Telecare Medical Information *** systems employ the latest mobile and digital technologies and provide several advantages like minimal physical contact between patient and healthcare provider,easy mobility,easy access,consistent patient engagement,and *** leakage or unauthorized access to users’medical data can have serious consequences for any medical information *** majority of such systems thus rely on biometrics for authenticated access but biometric systems are also prone to a variety of attacks like spoong,replay,Masquerade,and stealing of stored *** this article,we propose a new cancelable biometric approach which has tentatively been named as“Expression Hash”for Telecare Medical Information *** idea is to hash the expression templates with a set of pseudo-random keys which would provide a unique code(expression hash).This code can then be serving as a template for *** expressions would result in different sets of expression hash codes,which could be used in different applications and for different roles of each *** templates are stored on the server-side and the processing is also performed on the *** proposed technique is a multi-factor authentication system and provides advantages like enhanced privacy and security without the need for multiple biometric *** the case of compromise,the existing code can be revoked and can be directly replaced by a new set of expression hash *** well-known JAFFE(The Japanese Female Facial Expression)dataset has been for empirical testing and the results advocate for the efcacy of the proposed approach.

关键词： Biometrics TMIS biohashing multifactor authentication medical information system

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Birth Mode Prediction Using Bagging Ensemble Classifier: A Case Study of Bangladesh

Birth Mode Prediction Using Bagging Ensemble Classifier: A C...

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2021 International Conference on Information and Communication Technology for Sustainable Development, ICICT4SD 2021

作者： Bin Alam, Md. Sakib Patwary, Muhammed J. A. Hassan, Maruf Asian University for Women Department of Computer Science Bangladesh Big Data Institute College of Computer Science and Software Engineering Shenzhen University China International Islamic University Chittagong Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9781665414609

Maternal mortality and childbirth complications are major delivery issues in most developing countries, especially in rural areas. The proper identification of risks associated with the delivery of an expecting woman at an earlier stage can substantially reduce the mortality rate. A few studies have been conducted on using Machine Learning (ML) techniques for predicting birth mode i.e. caesarean section or normal delivery. The most commonly used methods are Decision Tree (DT), K-Nearest Neighbour (KNN), Naive Bayes (NB) and Support Vector Machine (SVM). In this study we have implemented Bagging Ensemble Classifiers based on these traditional machine learning algorithms, which is a novel approach in the area of birth mode prediction. This paper examines the performance of four ML algorithms, with bagging ensemble classifiers (DT-Bagging, KNN-Bagging, NB-Bagging, SVM-Bagging). The result shows that bagging ensemble models outperformed the traditional models in this domain. Besides, we have identified the association between important factors and caesarean section. This study may later be used to create a decision support system by extracting knowledge from the hidden patterns in data to reduce the rate of caesarean delivery in Bangladesh. © 2021 IEEE.

关键词： Decision trees

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A spectrum-enhanced attention model for semantic segmentation of remote sensing images

A spectrum-enhanced attention model for semantic segmentatio...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Xin Xu, Feng Tao, Feifei Tong, Yao Lyu, Xin Zhong, Jianyi Kaup, André College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology Ministry of Water Resources Hohai University Nanjing China School of Computer Engineering Jiangsu Ocean University Lianyungang China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen-Nuremberg Erlangen Germany

ISBN: (纸本)9798350368741

Semantic segmentation of remote sensing images (RSIs) is essential for applications such as environmental monitoring, urban planning, and disaster management. Convolutional Neural Networks (CNNs) and their variants struggle to capture comprehensive spectral context for learning discriminative representations. In this paper, we propose a Spectrum-Enhanced Network (SPENet) that leverages the Frequency Transformer Block (FTB) to capture rich spectral context. FTB integrates Spectrum-Enhanced Attention (SEA) with Multi-Head Frequency Self-Attention (MH-FSA), incorporating more informative contextual cues. Specifically, SEA aggregates spectral statistics through covariance matrix normalization before applying channel-wise attention. By projecting feature maps onto the frequency domain, MH-FSA provides the network with a broader context, extending beyond the low-frequency focus of standard self-attention mechanisms. Extensive experiments on the ISPRS Potsdam and LoveDA datasets show that SPENet significantly outperforms state-of-the-art methods. Besides, the proposed SEA module notably rises average F1-score/overall accuracy/mean insert over union wiht more than 2.5/2.6%/2.3%, as demonstrated by ablation study. © 2025 IEEE.

关键词： frequency transformer remote sensing images Semantic segmentation spectrum enhancement

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Smart System to Optimize Organic Crop Rotation Using Precision Agriculture Data

Smart System to Optimize Organic Crop Rotation Using Precisi...

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International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

作者： Ransinghe R.A.D. M Samarakoon S.M.A. D Waththaladeniya N. M Ihalagedara I.H.U. B Udara Srimath S. Samaratunge Arachchilllage N. Kodagoda Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

Optimizing organic crop rotation and effectively managing pests and diseases remain a significant challenge for farmers. In this research, we propose a Smart System to Optimize Organic Crop Rotation using Precision Agriculture Data. The system leverages Internet of Things (IoT) technology, machine learning (ML) algorithms, and cloud computing to enhance decision-making and improve productivity in organic farming. The proposed system integrates multiple components to provide farmers with informed decisions for optimized crop rotation. Soil sensors collect data on soil health, which is then analyzed using a multi-objective optimization technique to determine the best crop for a given soil sample. Real-time weather data is incorporated to enable climate-resilient farming practices and help farmers make educated choices in selecting crops and implementing rotation plans. Additionally, a cloud computing-based model is developed for pest and disease identification, providing farmers with effective solutions to combat these challenges in organic methodologies. By leveraging IoT, ML, and cloud computing, our system proposes farmers a more efficient and effective approach to managing their crops. The system provides real-time data-driven recommendations on fertilizer selection and crop rotation, leading to improved crop growth, increased yields, and reduced environmental impact. Moreover, by adopting organic farming practices, the system contributes to the sustainable development of the agricultural sector.

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Collaborative Teams Recruitment Based on Dual Constraints of Willingness and Trust for Crowd Sensing 25

Collaborative Teams Recruitment Based on Dual Constraints of...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Song, Nianyun Lu, Dianjie Shi, Yepeng Zhang, Guijuan Liu, Hong School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology State Key Laboratory of High-end Server and Storage Technology Shandong Jinan China

ISBN: (纸本)9781665405270

As crowd sensing tasks become more complex, it is increasingly needed for participants to form teams to collaborate interactively in order to complete tasks more efficiently. It is valuable to fully understand user behavior, the organizational principles of crowd collaboration teams and incorporate them into the user recruitment process. In fact, there is a two-way process of subjective and objective selection for building a team in crowd sensing. From the participant's perspective, the willingness of users to participate in the task is a subjective factor;from the platform's perspective, the trust relationship between users is an objective factor. However, existing work has not well recognized the value of including collaborative teams in the crowd sensing recruitment process and has not considered the willingness and trust relationships between participants simultaneously, which results in poor quality of service (QoS) among recruited users. To address this problem, we propose a willingness and trust-based collaborative team recruitment method (WT-CTRM). First, we construct a willingness network (WN) and a trust network (TN) to describe the interaction of willingness and trust among the collaborated users respectively. Based on these networks, we then predict the unknown willingness and trust relationships between users for collaboration by using graph convolutional networks (GCNs). Finally, we build collaborative teams for the user recruitment process of crowd sensing based on the consensus constraints of the predicted willingness network and trust network. Simulation results show that the recruitment scheme significantly improves the QoS in task scenarios with collaborative requirements. © 2022 IEEE.

关键词： Quality of service

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Masked Face Recognition using Deep Learning Model 3

Masked Face Recognition using Deep Learning Model

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3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021

作者： Kumar, Manoj Mann, Rachit Delhi Technological University Department of Computer Science and Engineering Delhi India Delhi Technological University Department of Software Engineering Delhi India

ISBN: (纸本)9781665438117

Face Masks have become part of our day-to-day activities. But these create a problem with the existing face recognition techniques. Existing face recognition techniques vary from simple ML techniques such as SVM, PCA, etc. to state-of-the-art models such as ResNet, VGG, etc. In this paper, we are studying the effects of masked faces on the performance of face recognition techniques. Face recognition itself is divided into face verification and face identification. This study is performed for the face identification task using different deep learning models. In particular, we are studying the pre-trained deep learning models that are trained using transfer learning for face identification tasks. In this study, a custom dataset is used consisting of 65 subjects. The custom dataset is a part of the VGGFace2 dataset. Subject faces are augmented with masks. The dataset doesn't consist the faces without masks. In this study, we are using different popular pre-trained models such as VGG16, InceptionV3, etc. We are re-training those pre-trained models on a custom dataset and analyzing the gathered results. Apart from the pre-trained models, a new model has been proposed for the masked face identification task. © 2021 IEEE.

关键词： Face recognition

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Galliot: Path Merging Based Betweenness Centrality Algorithm on GPU 42

Galliot: Path Merging Based Betweenness Centrality Algorithm...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Zheng, Zhigao Zhao, Chen Xie, Peichen Dum, Bo School of Computer Science Wuhan University Wuhan 430072 China National Engineering Research Center for Multimedia Software Wuhan University Wuhan 430072 Chin. Institute of Artificial Intelligence Wuhan University Wuhan 430072 Chin. Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan 430072 China Hubei Luojia Laboratory Wuhan 430072 China

ISBN: (纸本)9798350334142

Betweenness centrality (BC) is widely used to measure a vertex's significance by using the frequency of a vertex appearing in the shortest path between other vertices. However, most recent algorithms in BC computation suffer from the problem of high auxiliary memory consumption. To reduce BC computing's memory consumption, we propose a path-mergingbased algorithm called Galliot to calculate the BC values on GPU, which aims to minimize the on-board memory consumption and enable the BC computation of large-scale graphs. The proposed algorithm requires O(n) space and runs in O(mn) time on unweighted graphs. We present the theoretical principle for the proposed path merging method. Moreover, we propose a locality-oriented policy to maintain and update the worklist to improve GPU data locality. In addition, we conducted extensive experiments on NVIDIA GPUs to show the performance of Galliot. The results show that Galliot can process the larger graphs, which have 11.32× more vertices and 5.67× more edges than the graphs that recent works. Moreover, Galliot can achieve up to 38.77× speedup over the existing methods. © 2023 IEEE.

关键词： Graphics processing unit

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Improving Confidence of Uncertain Knowledge Graphs by Crowdsourcing with Limited Budget

Improving Confidence of Uncertain Knowledge Graphs by Crowds...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Haodi Zhang Wenxi Huang Chenyu Xu Zhengbang Yang Hongxin Zhou Fengtian Qi Chen Zhang Kaishun Wu School of Computer Science and Software Engineering Shenzhen University Department of Computing Hong Kong Polytechnic University

Knowledge graphs (KGs), either constructed automatically from texts or collected manually from crowdsourcing workers, may contain uncertainty. The uncertainty may propagate into the knowledge graph embedding and downstream tasks, which is potentially harmful, especially for those confidencesensitive applications such as medical diagnostic suggestion. Crowdsourcing workers with domain knowledge can help improve the data quality of knowledge graphs, by knowledge checking. However, due to the large scale of knowledge graphs and the limitation of adequate crowdsourcing workers, it is unrealistic to check all triplets in a knowledge graph to improve the data quality. Therefore, in this paper, we propose a crowdsourcing framework that efficiently improves the confidence of knowledge graphs with limited budget. We instantiate the framework in the medical domain and conduct a series of experiments with realworld medical data. We deploy the framework for knowledge graph embedding UKGE and corresponding downstream tasks. The experimental results show that the proposed method efficiently improves the quality of the knowledge graphs, and hence improves the performance of probabilistic knowledge graph embedding in the downstream tasks.

关键词： Crowdsourcing Uncertainty Data integrity Distributed databases Knowledge graphs Probabilistic logic Medical diagnosis

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EUIEF: Enhancement of Underexposed Images via Exposure Fusion 1

EUIEF: Enhancement of Underexposed Images via Exposure Fusio...

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1st Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021

作者： Nazir, Daniyal Dawood, Hassan Butt, Ammar Mohsin Dawood, Hussain University of Engineering and Technology Department of Software Engineering Taxila47050 Pakistan University of Engineering and Technology Department of Computer Engineering Taxila47050 Pakistan University of Jeddah Computer Science Department Jeddah21577 Saudi Arabia

ISBN: (纸本)9781665424134

Images captured in inappropriate light conditions can often result in flat color saturation, bad contrast and weakly illuminated images. In this paper, we propose a framework named as Enhancement of underexposed images via exposure fusion (EUIEF), to solve the problems associated with underexposed images. First, the enhanced input image is obtained by proposed Luminous Transform (LT) which enhances the low light images on the basis of their luminous value. Secondly, an enhanced input image is generated by Invert-Image enhancement method, which works by inverting a weakly illuminated image and then applying optimized enhancement algorithm on the inverted low light image. The enhancement of inverted low light image is carried out with the help of global atmospheric light, intensity of original scene objects and the amount of light reflected by the objects. The final enhanced low light image is the result of fusion technique which blends both enhanced images. Our work was evaluated on publicly available LOL dataset. The results suggest that the images enhanced by proposed EUIEF method possess better quality in terms of contrast enhancement, naturalness level and detail enhancement compared to existing methods. To validate the EUIEF, both qualitative and quantitative comparisons are conducted. © 2021 IEEE.

关键词： Image enhancement

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Quantum correlations in multimode nonlinear optics

Quantum correlations in multimode nonlinear optics

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CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Uddin, Shiekh Zia Sloan, Jamison Rivera, Nicholas Pontula, Sahil Salamin, Yannick Birk, Michael Sidorenko, Pavel Kaminer, Ido Soljačić, Marin Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Harvard University CambridgeMA02138 United States Department of Electrical and Computer Engineering Solid State Institute Technion Israel Institute of Technology Haifa32000 Israel

We develop a framework describing quantum noise propagation in highly spatially multimode nonlinear optical systems. We predict quantum deviations of the spatial intensity noise distribution from the spatial power dis... 详细信息

关键词： Quantum noise

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