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Liver Tumor Decision Support System on Human Magnetic Resonance Images:A Comparative Study

computer Systems Science & engineering 2023年第8期46卷 1653-1671页

作者： Hiam Alquran Yazan Al-Issa Mohammed Alslatie Isam Abu-Qasmieh Amin Alqudah Wan Azani Mustafa Yasmin Mohd Yacob Department of Biomedical Systems and Informatics Engineering Yarmouk University 556Irbid21163Jordan Department of Biomedical Engineering Jordan University of Science and TechnologyIrbid22110Jordan Department of Computer Engineering Yarmouk University 556Irbid21163Jordan The of Biomedical Technology King Hussein Medical CenterRoyal Jordanian Medical ServiceAmman11855Jordan Faculty of Electrical Engineering&Technology Campus Pauh PutraUniversiti Malaysia PerlisArau02600PerlisMalaysia Faculty of Electronic Engineering&Technology Campus Pauh PutraUniversiti Malaysia PerlisArau02600PerlisMalaysia Advanced Computing Centre of Excellence(CoE)Universiti Malaysia PerlisArau02600PerlisMalaysia

Liver cancer is the second leading cause of cancer death *** tumor detection may help identify suitable treatment and increase the survival *** imaging is a non-invasive tool that can help uncover abnormalities in human *** Resonance Imaging(MRI),in particular,uses magnetic fields and radio waves to differentiate internal human organs ***,the interpretation of medical images requires the subjective expertise of a radiologist and ***,building an automated diagnosis computer-based system can help specialists reduce incorrect *** paper proposes a hybrid automated system to compare the performance of 3D features and 2D features in classifying magnetic resonance liver tumor *** paper proposed two models;the first one employed the 3D features while the second exploited the 2D *** first system uses 3D texture attributes,3D shape features,and 3D graphical deep descriptors beside an ensemble classifier to differentiate between four 3D tumor *** top of that,the proposed method is applied to 2D slices for comparison *** proposed approach attained 100%accuracy in discriminating between all types of tumors,100%Area Under the Curve(AUC),100%sensitivity,and 100%specificity and precision as well in 3D liver *** the other hand,the performance is lower in 2D *** maximum accuracy reached 96.4%for two classes and 92.1%for four *** top-class performance of the proposed system can be attributed to the exploitation of various types of feature selection methods besides utilizing the ReliefF features selection technique to choose the most relevant features associated with different *** novelty of this work appeared in building a highly accurate system under specific circumstances without any processing for the images and human input,besides comparing the performance between 2D and 3D *** the future,the presented work can be extended to be

关键词： Liver tumors ensemble classifier 3D shape features 3D cooccurrence matrix ResNet101

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Impact Assessment of NILM Methods for an Enhanced Observability of Low Voltage Distribution Networks 8

Impact Assessment of NILM Methods for an Enhanced Observabil...

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8th IEEE International Smart Cities Conference, ISC2 2022

作者： Moreno Jaramillo, Andres F Lopez-Lorente, Javier Brogan, Paul Laverty, David Del Rincon, Jesus Martinez Foley, Aoife School of Electronics Electrical Engineering and Computer Science Queen's University Belfast Belfast United Kingdom Pv Technology Laboratory Foss Research Centre for Sustainable Energy University of Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus Applications Strategist Phasora Ltd Belfast United Kingdom School of Mechanical and Aerospace Engineering Queen's University Belfast Belfast United Kingdom

ISBN: (数字)9781665485616

ISBN: (纸本)9781665485616

The migration to low-carbon technologies in secondary (low-voltage) distribution networks relies on high integration of distributed energy resources (DER), including distributed generation and low-carbon loads. The electrification of power and transport sectors has led to an increased deployment of solar photovoltaic systems and electric vehicles causing unconventional dynamics in distribution networks. Thus, distribution network operators are facing new technical challenges such as reverse power flow, higher load peaks, and power quality issues. This has attracted research interest to investigate the implementation of innovative techniques such as non-intrusive load monitoring (NILM) methods. These techniques contribute to an enhanced and scalable monitoring of DER in electrical networks. Current literature based NILM models have achieved significantly high performance for both electromobility and photovoltaic systems, which makes them eligible for deployment from a technical standpoint. However, a critical analysis of the considerations for a feasible implementation of these methodologies has not been covered. Thus, this work studies the techno-economic implications for the large-scale implementation of NILM methods at the customer level and low voltage side of distribution transformers. While a substantial investment is associated with a better monitored low voltage distribution network, there are multiple benefits related to technical, economic, social, and environmental aspects. © 2022 IEEE.

关键词： Electric power transmission networks

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High-Rate Measurement-Device-Independent Quantum Communication without Optical Reference Light

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Physical Review X 2025年第2期15卷 021066-021066页

作者： Shan-Feng Shao Lai Zhou Jinping Lin Mariella Minder Chengfang Ge Yuan-Mei Xie Ao Shen Zhengyu Yan Hua-Lei Yin Zhiliang Yuan Beijing Academy of Quantum Information Sciences Beijing 100193 China National Laboratory of Solid State Microstructures and School of Physics Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing 210093 China School of Physics and Beijing Key Laboratory of Opto-Electronic Functional Materials and Micro-Nano Devices Key Laboratory of Quantum State Construction and Manipulation (Ministry of Education) Renmin University of China Beijing 100872 China Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol 3036 Cyprus

In the realm of long-distance quantum communication, asynchronous measurement-device-independent quantum key distribution (AMDI-QKD) stands out for its experimental simplicity and high key rate generation. Despite these advantages, there exists a challenge in finding a balance between simplifying the laser system further and achieving high key rates. To address this challenge, we have devised a postmeasurement compensation scheme to accurately estimate the mutual frequency offset between two compact lasers using just the announced quantum-signal detection results, thereby obviating the need for optical reference light. As a result, we demonstrate an AMDI-QKD system operating at 2.5 GHz and achieving secure key rates (SKRs) of 537 and 101 kbit/s at distances of 100 and 201 km, respectively, showcasing a significant key rate improvement compared to similar setups. By leveraging ultrastable lasers, we achieve the highest SKRs with measurement-device-independent security within the 100–400-km range. Over 100 km, we reach a remarkable key rate of 1.03 Mbit/s, which could enable real-time one-time-pad video encryption. These findings render AMDI-QKD as a promising contender for the establishment of high performance and cost-effective large-scale intercity quantum networks.

关键词： Quantum communication

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Virtual healthcare services and digital health technologies deployed during coronavirus disease 2019(COVID-19)pandemic in South Africa:a systematic review

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Global Health Journal 2022年第2期6卷 102-113页

作者： Elliot Mbunge John Batani Goabaone Gaobotse Benhildah Muchemwa Department of Computer Science Faculty of Science and EngineeringUniversity of EswatiniKwaluseniManziniEswatini Department of Information Technology Faculty of Accounting and InformaticsDurban University of TechnologySouth Africa Faculty of Engineering and Technology Botho UniversityLesotho Department of Biological Sciences and Biotechnology Faculty of ScienceBotswana International University of Science and TechnologyBotswana

Aims:To identify virtual healthcare services and digital health technologies deployed in South Africa during coronavirus disease 2019(COVID-19)and the challenges associated with their ***:To determine the status of digital health utilization during COVID-19 in South Africa,the preferred reporting items for systematic reviews and meta-analyses model was used to perform a systematic and in-depth critical analysis of previously published studies in well-known and trusted online electronic databases using specific search keywords words that are relevant to this *** selected published peer-reviewed articles available from the onset of COVID-19 to July ***:Total of 24 articles were included into this *** study revealed that South Africa adopted digital technologies such as SMS-based solutions,mobile health applications,telemedicine and telehealth,WhatsApp-based systems,artificial intelligence and chatbots and robotics to provide healthcare services during COVID-19 *** innovative technologies have been used for various purposes including screening infectious and non-infectious diseases,disease surveillance and monitoring,medication and treatment compliance,creating awareness and *** study also revealed that teleconsultation and e-prescription,telelaboratory and telepharmacy,teleeducation and teletraining,teledermatology,teleradiology,telecardiology,teleophthalmology,teleneurology,telerehabilitation,teleoncology and telepsychiatry are among virtual healthcare services delivered through digital health technologies during COVID-19 in South ***,these smart digital health technologies face several impediments such as infrastructural and technological barriers,organization and financial barriers,policy and regulatory barriers as well as cultural ***:Although COVID-19 has invigorated the use of digital health technologies,there are still some *** outbreak of pandemics like COVID-19 in t

关键词： Digital health technology Virtual healthcare Coronavirus disease 2019(COVID-19) Health systems South Africa

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Performance Evaluation of Multi-Contrast Dixon MRI and CT for Abdominal Fat and Muscle Segmentation Using a UNet CNN

Performance Evaluation of Multi-Contrast Dixon MRI and CT fo...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Heller, Andrew Liu, Andrew Chang, Lin-Ching Cohen, Gregg Jones, Elizabeth C. Hsu, Li-Yueh National Institutes of Health Clinical Center BethesdaMD United States Catholic University of America Department of Electrical Engineering and Computer Science WashingtonDC United States New Jersey Institute of Technology Department of Informatics NewarkNJ United States

ISBN: (纸本)9798350362480

We evaluate the performance of a deep learning framework for segmenting abdominal fat and muscle using multi-contrast Dixon magnetic resonance (MR) and computed tomography (CT) images. We aim to compare MR image segmentation by testing Dixon fat-only, water-only, and combination of both types of images and comparing the results with CT images. Nineteen subjects underwent abdominal CT and Dixon MR imaging on the same day. For each participant, three pairs of matched axial images from both CT and MR were selected at the intervertebral levels of L2-L3, L3-L4, and L4-L5 for analysis. References labels were generated through semi-automated segmentation of subcutaneous adipose tissue, visceral adipose tissue, and muscle areas. They were then used to train and evaluate a U-Net based Convolutional Neural Network (CNN) framework with a 3-fold cross-validation to compare the segmentation performance across CT, Dixon fat-only and water-only MR images. Combining the fat-only and water-only MR image inputs produced superior results in all labels. Our study demonstrates that CNN-based segmentation performance for abdominal fat and muscle improves with the inclusion of additional input channels, such as combining Dixon fat-only and water-only MR images. While CT results represent the gold standard in abdominal image segmentation, increasing the number of input image channels used in MR segmentation can approach, and even match, the results of CT. © 2024 IEEE.

关键词： Image segmentation

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Enhancing Intrusion Detection Systems Using RNN, LSTM, and Hybrid RNN-LSTM Models

Enhancing Intrusion Detection Systems Using RNN, LSTM, and H...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Sahar Ebadinezhad Nooshin Nooraei Nia Nasratullah Shirzad Nwabueze Kenneth Osemeha Department of Computer Information Systems Computer Information Systems Research and Technology Center (CISRTC) Near East University Nicosia Turkey Artificial Intelligence Near East University Nicosia North Cyprus Software Engineering Department Near East University Nicosia North Cyprus

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Due to The lack of comparison studies and practical applications of RNN, LSTM, and hybrid RNN-LSTM models for intrusion detection systems, especially when managing class imbalances in complex network datasets, represents a gap in the literature. This study investigates the application of deep learning techniques to improve the detection capabilities of IDS. For this study, we applied and assessed Recurrent Neural Network, Long Short-Term Memory, and Hybrid RNN-LSTM models on a UNSW-NB15 dataset, and to redress class imbalance we trained and validated our models using synthetic minority over-sampling. The RNN-LSTM model was shown to perform the best with a 9 4. 0 0 % accuracy in comparison to other models. These experimental results signify the possibility of enhancing IDS performance and a dependable method of detecting various network intrusions with the use of hybrid models comprised of RNN & LSTM.

关键词： Deep learning Recurrent neural networks Accuracy Computational modeling Intrusion detection computer architecture Network security Hyperparameter optimization Network intrusion Long short term memory

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MAX CUT in Weighted Random Intersection Graphs and Discrepancy of Sparse Random Set Systems 32

MAX CUT in Weighted Random Intersection Graphs and Discrepan...

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32nd International Symposium on Algorithms and Computation, ISAAC 2021

作者： Nikoletseas, Sotiris Raptopoulos, Christoforos Spirakis, Paul Computer Engineering and Informatics Department University of Patras Greece Computer Technology Institute Patras Greece Computer Engineering and Informatics Department University of Patras Greece Department of Computer Science University of Liverpool United Kingdom

ISBN: (纸本)9783959772143

Let V be a set of n vertices, M a set of m labels, and let R be an m × n matrix of independent Bernoulli random variables with probability of success p;columns of R are incidence vectors of label sets assigned to vertices. A random instance G(V, E, RT R) of the weighted random intersection graph model is constructed by drawing an edge with weight equal to the number of common labels (namely [RT R]v,u) between any two vertices u, v for which this weight is strictly larger than 0. In this paper we study the average case analysis of Weighted Max Cut, assuming the input is a weighted random intersection graph, i.e. given G(V, E, RT R) we wish to find a partition of V into two sets so that the total weight of the edges having exactly one endpoint in each set is maximized. In particular, we initially prove that the weight of a maximum cut of G(V, E, RT R) is concentrated around its expected value, and then show that, when the number of labels is much smaller than the number of vertices (in particular, m = nα, α Θ(1)n), we show that with high probability, a majority type randomized algorithm outputs a cut with weight that is larger than the weight of a random cut by a multiplicative constant strictly larger than 1. Then, we formally prove a connection between the computational problem of finding a (weighted) maximum cut in G(V, E, RT R) and the problem of finding a 2-coloring that achieves minimum discrepancy for a set system Σ with incidence matrix R (i.e. minimum imbalance over all sets in Σ). We exploit this connection by proposing a (weak) bipartization algorithm for the case m = n, p = Θ(1)n that, when it terminates, its output can be used to find a 2-coloring with minimum discrepancy in a set system with incidence matrix R. In fact, with high probability, the latter 2-coloring corresponds to a bipartition with maximum cut-weight in G(V, E, RT R). Finally, we prove that our (weak) bipartization algorithm terminates in polynomial time, with high probability, at least w

关键词： Graph theory

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Efficient and Confidentiality-Preserving Bloom Filter-Encoded Video Search

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IEEE Internet of Things Journal 2025年

作者： Yang, Xu Zhao, Hongguang Qi, Saiyu Li, Ke Wang, Qiuhao Qi, Yong Wei, Wei Mumtaz, Shahid School of Computer Science and Technology 710049 China Xi’an University of Technology School of Computer Science and Engineering Xi’an710048 China Shaanxi University of Technology School of Mathematics and Computer Science Hanzhong723000 China Silesian University of Technology Department of Applied Informatics Akademicka 16 Gliwice44-100 Poland Nottingham Trent University Department of Computer Sciences Nottingham United Kingdom

Content based video search services find extensive applications across various domains including video surveillance and object detection. In recent times, researchers have increasingly turned their attention towards enhancing the security of video search over outsourced encrypted videos. Nonetheless, prior researchers often leverage cost-expensive techniques like Homomorphic encryption or Order-preserving encryption to ensure privacy preservation. To reduce the overhead, Bloom Filter (BF)-encoded keyword search is a promising technology for retrieving encrypted videos with image queries. However, it generally suffers from serious data privacy leakage since it will reveal the inclusion relationship between "1" and "0" in the BF. Fortunately, the privacy-preserving bloom filter-based search scheme (PBKS) was recently proposed to achieve secure and effective search while protecting the values in BFs, but it still has two limitations. One is the size of a search token is very large in some cases and the other is the cloud server can infer the true value of each bit in the BF by doing a few operations. In this paper, we propose an efficient and confidentiality-preserving bloom filter-encoded video search (ECVS) scheme for retrieving encrypted videos with image queries. We first design a new CPRF (prefix-constrained pseudorandom function)-based token compression method to reduce the size of the search token and reduce the communication cost largely. Furthermore, we customize a periodic refresh mechanism to conceal the true value of each bit in the BF while avoiding excessive computational pressure on resource-limited users. Security analysis and experiments confirm the security and efficiency of our schemes. © 2014 IEEE.

关键词： Privacy by design

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Exploring Children’s Strategies in Response to Robot’s Advice During a Group Task with iCub and Nao 16th

Exploring Children’s Strategies in Response to Robot’s A...

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16th International Conference on Social Robotics, ICSR + AI 2024

作者： Pusceddu, Giulia Sangineto, Mariapia Cocchella, Francesca Bogliolo, Michela Belgiovine, Giulia Lastrico, Linda Casadio, Maura Rea, Francesco Gena, Cristina Sciutti, Alessandra Robotics Brain and Cognitive Science Department Italian Institute of Technology Genoa Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Department of Computer Science University of Turin Turin Italy Cognitive Architecture for Collaborative Technologies Unit Italian Institute of Technology Genoa Italy Scuola di Robotica Genoa Italy

ISBN: (纸本)9789819635184

This study investigates children’s trust in two humanoid robots, Nao and iCub, through a cooperative game designed to elicit spontaneous behaviors and group dynamics. We investigate whether participants change their choices after hearing advice from a robot, considering rounds where the advice is correct or incorrect, with the robot apologizing for its mistakes. Results reveal that, in most cases, participants prefer not to change their moves after the robot’s advice. Additionally, after the robots made mistakes, participants are less likely to actively follow their advice and more likely to reject it. No significant differences in strategies among the iCub and Nao samples are evidenced. Furthermore, we examine the influence of group dynamics, identifying "Proactive Players" who consistently initiated moves. A significant association between the strategies of this player and their teammates is found, suggesting potential influence. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Short-term multi-step-ahead sector-based traffic flow prediction based on the attention-enhanced graph convolutional LSTM network (AGC-LSTM)

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Neural Computing and Applications 2024年 1-20页

作者： Zhang, Ying Xu, Shimin Zhang, Linghui Jiang, Weiwei Alam, Sameer Xue, Dabin College of General Aviation and Flight Nanjing University of Aeronautics and Astronautics Nanjing China State Key Laboratory of Air Traffic Management System Nanjing China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Air Traffic Management Research Institute School of Mechanical & ampAerospace Engineering Nanyang Technological University Singapore Singapore Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic University Hong Kong

Accurate sector-based air traffic flow predictions are essential for ensuring the safety and efficiency of the air traffic management (ATM) system. However, due to the inherent spatial and temporal dependencies of air traffic flow, it is still a challenging problem. To solve this problem, some methods are proposed considering the relationship between sectors, while the complicated spatiotemporal dynamics and interdependencies between traffic flow of route segments related to the sector are not taken into account. To address this challenge, the attention-enhanced graph convolutional long short-term memory network (AGC-LSTM) model is applied to improve the short-term sector-based traffic flow prediction, in which spatial structures of route segments related to the sector are considered for the first time. Specifically, the graph convolutional networks (GCN)-LSTM network model was employed to capture spatiotemporal dependencies of the flight data, and the attention mechanism is designed to concentrate on the informative features from key nodes at each layer of the AGC-LSTM model. The proposed model is evaluated through a case study of the typical enroute sector in the central–southern region of China. The prediction results show that MAE reduces by 14.4% compared to the best performing GCN-LSTM model among the other five models. Furthermore, the study involves comparative analyses to assess the influence of route segment range, input and output sequence lengths, and time granularities on prediction performance. This study helps air traffic managers predict flight situations more accurately and avoid implementing overly conservative or excessively aggressive flow management measures for the sectors. © The Author(s) 2024.

关键词： Forecasting

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