检索结果-内蒙古大学图书馆

Spectroscopic detection of pigments in tissues: correlation with tissue aging and cancer development

学校读者我要写书评

暂无评论

Spectroscopic detection of pigments in tissues: correlation ...

2022 International Conference Laser Optics, ICLO 2022

作者： Oliveira, L.M. Goncalves, T.M. Botelho, A.R. Martins, I.S. Silva, H.F. Carneiro, I. Carvalho, S. Henrique, R. Tuchin, V.V. Porto Portugal Center of Innovation in Engineering and Industrial Technology Isep Porto Portugal Department of Electrical and Computer Engineering Porto University-School of Engineering Porto Portugal Department of Pathology and Cancer Biology and Epigenetics Group Portuguese Oncology Institute of Porto Porto Portugal Porto Portugal Viana do Castelo Portugal Dept. of Pathol. and Molecular Immunology Porto University-Institute of Biomedical Sciences Abel Salazar Porto Portugal Science Medical Center Saratov State University Saratov Russia Laboratory of Laser Molecular Imaging and Machine Learning National Research Tomsk State University Tomsk Russia Laboratory of Laser Diagnostics of Technical and Living Systems Institute of Precision Mechanics and Control Frc-Saratov Scientific Centre-of the Russian Academy of Sciences Saratov Russia

ISBN: (纸本)9781665466646

The direct calculation of the absorption coefficient spectra of various tissues from spectral measurements allowed to retrieve the contents of melanin and lipofuscin. In the rabbit brain cortex, 1.8 times higher melanin content is explained by the neuron degeneration process. Similar melanin and lipofuscin contents were found in the rabbit pancreas as a result of the tissue aging process. The conversion of 83 % of the melanin in the human normal kidney into lipofuscin in the cancer kidney indicates that lipofuscin can be considered a kidney cancer marker in humans. © 2022 IEEE.

关键词： Tissue

UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithms

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Hajijamali, Atefeh Hu, Peng Zhu, Yeying The Dept. of Statistics and Actuarial Science University of Waterloo ONN2L 3G1 Canada Department of Electrical and Computer Engineering University of Manitoba The Faculty of Mathematics University of Waterloo ONN2L 3G1 Canada Digital Technologies Research Center National Research Council Canada

Recent technological advancements in space, air, and ground components have made possible a new network paradigm called "space-air-ground integrated network" (SAGIN). Unmanned aerial vehicles (UAVs) play a key role in SAGINs. However, due to UAVs’ high dynamics and complexity, real-world deployment of a SAGIN becomes a significant barrier to realizing such SAGINs. UAVs are expected to meet key performance requirements with limited maneuverability and resources with space and terrestrial components. Therefore, employing UAVs in various usage scenarios requires well-designed planning in algorithmic approaches. This paper provides an essential review and analysis of recent learning algorithms in a UAV-assisted SAGIN. We consider possible reward functions and discuss the state-of-the-art algorithms for optimizing the reward functions, including Q-learning, deep Q-learning, multi-armed bandit, particle swarm optimization, and satisfaction-based learning algorithms. Unlike other survey papers, we focus on the methodological perspective of the optimization problem, applicable to various missions on a SAGIN. We consider real-world configurations and the 2-dimensional (2D) and 3-dimensional (3D) UAV trajectories to reflect deployment cases. Our simulations suggest the 3D satisfaction-based learning algorithm outperforms other approaches in most cases. With open challenges discussed at the end, we aim to provide design and deployment guidelines for UAV-assisted SAGINs. Copyright © 2022, The Authors. All rights reserved.

关键词： Particle swarm optimization (PSO)

Expert-driven Rule-based Refinement of Semantic Segmentation Maps for Autonomous Vehicles

学校读者我要写书评

暂无评论

Expert-driven Rule-based Refinement of Semantic Segmentation...

IEEE Symposium on Intelligent Vehicle

作者： Eric L. Manibardo Ibai Laña Javier Del Ser Alexander Carballo Kazuya Takeda Tecnalia Basque Research and Technology Alliance (BRTA) Derio Spain University of the Basque Country (UPV/EHU) Bilbao Spain Dept. of Electrical Electronic and Computer Engineering Gifu University Gifu Japan Institute of Innovation for Future Society Nagoya University Nagoya Japan Tier IV Inc Nagoya University Open Innovation Center Nagoya Japan Graduate School of Informatics Nagoya University Nagoya Japan

Semantic segmentation aims at assigning labels to every pixel of a given image. In the context of autonomous vehicles, semantic segmentation models should be trained with data collected from the traffic network through which vehicles are expected to circulate. Road regulation, weather conditions, and other context features may differ between regions, making local semantic segmentation datasets extremely valuable. However, the high ground truth annotation costs represent a hindrance to the development of such models. The upsurge of powerful feature learning architectures leaves room for semantic segmentation models trained on an unsupervised fashion. This observation vertebrates the purpose of this work: to produce coarse segmentation maps for scene understanding without the need of annotated data. We depart from an unsupervised model that yields low-quality results. The proposed methodology establishes a set of guidelines for the enhancement of segmentation maps. Obtained results expose an improvement of the segmentation quality thanks to the application of our devised guidelines, paving the way for the automatic generation of semantic segmentation datasets.

关键词：

Advancing Predictive Security for Consumer Applications in Beyond 5G/6G Networks With Annotated Datasets

学校读者我要写书评

暂无评论

IEEE Transactions on Consumer Electronics 2025年

作者： Xylouris, George Vekraki, Athina Christopoulou, Maria Kourtis, Michail Alexandros Markakis, Evangelos K. Trakadas, Panagiotis National Center for Scientific Research "Demokritos" Athens Greece Kapodistrian University of Athens Greece University of Peloponnese Dept. of Informatics and Telecommunications Tripoli Greece Hellenic Mediterranean University Electrical and Computer Engineering Dept Heraklion Greece

The evolution of Beyond 5G (B5G) and 6G networks introduces new opportunities for consumer-centric applications, requiring robust predictive security measures to maintain reliability. A critical component in the B5G landscape is the Network Data Analytics Function (NWDAF), a Network Function (NF) of the 5G Core introduced by the 3rd Generation Partnership Project (3GPP) in Rel. 15, designed to provide data analytics capabilities to the cellular network. This study focuses on detecting Distributed Denial of Service (DDoS) attacks, leveraging a comprehensive dataset collected from a 5G testbed. We evaluate deep learning models-Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Multi-Layer Perceptron (MLP)-and compare their performance with eXtreme Gradient Boosting (XGBoost), a machine learning technique based on gradient boosting, and Z-Score, a statistical method that quantifies how far a data point deviates from the mean. Results demonstrate that XGBoost achieves the highest F1-score of 0.97, precision of 0.96, and recall of 0.98, making it the preferred solution for identifying DDoS attacks from 5G network features, while also offering a computationally efficient solution for real-time applications. To improve interpretability, SHapley Additive exPlanations (SHAP) analysis identifies the network features influencing model decisions. The publicly available dataset used in our study supports further research in anomaly detection and provides valuable insights for future 6G applications, including immersive consumer experiences and autonomous services, while addressing emerging cyber threats. © 1975-2011 IEEE.

关键词： Convolutional neural networks

Information encoding/decoding using the memory effect in fractional-order capacitive devices

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Allagui, Anis Elwakil, Ahmed S. Dept. of Sustainable and Renewable Energy Engineering University of Sharjah PO Box 27272 Sharjah United Arab Emirates Center for Advanced Materials Research Research Institute of Sciences and Engineering University of Sharjah PO Box 27272 Sharjah United Arab Emirates Dept. of Electrical and Computer Engineering University of Sharjah PO Box 27272 Sharjah United Arab Emirates Dept. of Mechanical and Materials Engineering Florida International University MiamiFL33174 United States Nile University Cairo Egypt Dept. of Electrical and Computer Engineering University of Calgary Calgary Canada

In this study, we show that the discharge voltage pattern of a fractional-order supercapacitor from the same initial steady-state voltage into a constant resistor is dependent on the past charging voltage profile. The charging voltage was designed to follow a power-law function, i.e. vc(t) = Vcc (t/tss)p (0 ss), in which tss (charging time duration between zero voltage to the terminal voltage Vcc) and p (0 © 2021, CC BY.

关键词： Signal encoding

Ransomware Attack Modeling and Artificial Intelligence-Based Ransomware Detection for Digital Substations

学校读者我要写书评

暂无评论

Ransomware Attack Modeling and Artificial Intelligence-Based...

IEEE Electronic Power Grid (eGrid)

作者： Syed R. B. Alvee Bohyun Ahn Taesic Kim Ying Su Young–Woo Youn Myung–Hyo Ryu Dept. of Electrical Engineering and Computer Science Texas A&M University-Kingsville Kingsville TX USA Dept. of Computer Science The University of Texas at Austin Austin TX USA Adv. Power Apparatus Researh Center Korea Electrotechnology Research Institute Changwon South Korea

ISBN: (纸本)9781665449809

Ransomware has become a serious threat to the current computing world, requiring immediate attention to prevent it. Ransomware attacks can also have disruptive impacts on operation of smart grids including digital substations. This paper provides a ransomware attack modeling method targeting disruptive operation of a digital substation and investigates an artificial intelligence (AI)-based ransomware detection approach. The proposed ransomware file detection model is designed by a convolutional neural network (CNN) using 2-D grayscale image files converted from binary files. The experimental results show that the proposed method achieves 96.22% of ransomware detection accuracy.

关键词： Substations Conferences Computational modeling Gray-scale Real-time systems Smart grids Ransomware

Cognitive Inspired Sound-Based Automobile Problem Detection: A Step Toward Xai

学校读者我要写书评

暂无评论

SSRN

SSRN 2024年

作者： Hamad, Abdulsattar Abdullah Nasim, Muhammad Fawad Jaffar, Arfan Khalaf, Osamah Ibrahim Ouahada, Khmaies Hamam, Habib Akram, Sheeraz Siddique, Abubakar Faculty of Computer Science and Information Technology The Superior University Lahore54000 Pakistan Lahore55150 Pakistan University of Samarra College of Education Department of physics Iraq Lahore 54000 Lahore55150 Pakistan Department of Solar Al-Nahrain Research Center for Renewable Energy Al-Nahrain University Baghdad Jadriya Iraq School of Electrical Engineering Dept. of Electrical and Electronic Eng. Science University of Johannesburg Johannesburg2006 South Africa Faculty of Engineering Uni de Moncton NBE1A3E9 Canada Hodmas University College Somalia Bridges for Academic Excellence Taleh Area Mogadishu Centre-Ville Tunis Tunisia School of Electrical Engineering University of Johannesburg South Africa Riyadh12571 Saudi Arabia Wellington Institute of Technology Te Pukenga Wellington6022 New Zealand

The automobile has become an essential part of modern human life, and one of the critical factors that determine a vehicle’s value is its reliability. Engine breakdowns are not only hazardous but also financially burdensome. Early detection of engine failure can help save costs and improve the average lifespan of a car. Traditionally, manual inspection of engine sounds by expert mechanics has been used as an effective approach for the early detection of engine problems. There have been recent attempts to develop sound detection-based methods for the automatic diagnosis of engine problems. However, the majority of these techniques are neither robust nor explainable. These systems act like black boxes, making their decision-making process difficult to *** Learning Classifier System, also known as LCS, is a machine learning technique that operates on a set of rules. It has shown promise in producing robust, explainable, and generalizable solutions for many problem domains. The objective of this work is to develop a novel LCS-based system for the automatic detection of engine problems. The developed system’s decision-making process is interpretable, which is a step toward eXplainable artificial intelligence. Using time domain, frequency domain, and time-frequency domain features, the innovative system’s generalizability is assessed. While the developed system’s robustness is evaluated using noisy sound data collected under various normal and abnormal conditions, The experimental results indicate that the novel approach outperformed conventional state-ofthe-art techniques by 2.6% − 6.0% and achieved the highest performance accuracy of 98.6%. © 2024, The Authors. All rights reserved.

关键词： Frequency domain analysis

A Machine Learning based Framework for KPI Maximization in Emerging Networks using Mobility Parameters

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Shodamola, Joel Masood, Usama Manalastas, Marvin Imran, Ali AI4Networks Research Center Dept. of Electrical & Computer Engineering University of Oklahoma United States

Current LTE network is faced with a plethora of Configuration and Optimization Parameters (COPs), both hard and soft, that are adjusted manually to manage the network and provide better Quality of Experience (QoE). With 5G in view, the number of these COPs are expected to reach 2000 per site, making their manual tuning for finding the optimal combination of these parameters, an impossible fleet. Alongside these thousands of COPs is the anticipated network densification in emerging networks which exacerbates the burden of the network operators in managing and optimizing the network. Hence, we propose a machine learning-based framework combined with a heuristic technique to discover the optimal combination of two pertinent COPs used in mobility, Cell Individual Offset (CIO) and Handover Margin (HOM), that maximizes a specific Key Performance Indicator (KPI) such as mean Signal to Interference and Noise Ratio (SINR) of all the connected users. The first part of the framework leverages the power of machine learning to predict the KPI of interest given several different combinations of CIO and HOM. The resulting predictions are then fed into Genetic Algorithm (GA) which searches for the best combination of the two mentioned parameters that yield the maximum mean SINR for all users. Performance of the framework is also evaluated using several machine learning techniques, with CatBoost algorithm yielding the best prediction performance. Meanwhile, GA is able to reveal the optimal parameter setting combination more efficiently and with three orders of magnitude faster convergence time in comparison to brute force approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Benchmarking

A Survey on Explainable Artificial Intelligence for Cybersecurity

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Rjoub, Gaith Bentahar, Jamal Wahab, Omar Abdel Mizouni, Rabeb Song, Alyssa Cohen, Robin Otrok, Hadi Mourad, Azzam King Hussein School of Computing Sciences Princess Sumaya University for Technology Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer and Software Engineering Polytechnique Montréal Montréal Canada Electrical Engineering and Computer Science Department Khalifa University Abu Dhabi United Arab Emirates Department of EECS Khalifa University Abu Dhabi United Arab Emirates David R. Cheriton School of Computer Science University of Waterloo Canada Cyber Security Systems and Applied AI Research Center Dept. of CSM Lebanese American University Lebanon Division of Science New York University Abu Dhabi United Arab Emirates

The "black-box" nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions. In the field of cybersecurity, XAI has the potential to revolutionize the way we approach network and system security by enabling us to better understand the behavior of cyber threats and to design more effective defenses. In this survey, we review the state of the art in XAI for cybersecurity and explore the various approaches that have been proposed to address this important problem. The review follows a systematic classification of cybersecurity threats and issues in networks and digital systems. We discuss the challenges and limitations of current XAI methods in the context of cybersecurity and outline promising directions for future research. © 2023, CC BY.

关键词： Cybersecurity