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arXiv 2024年

作者： Alazab, Moutaz Khurma, Ruba Abu Castillo, Pedro A. Abu-Salih, Bilal Martı´n, Alejandro Camacho, David Faculty of Artificial Intelligence Al-Balqa Applied University Amman Jordan Computer Science Department Faculty of MEU Research Unit Middle East University Amman Jordan Department of Computer Engineering Automation and Robotics University of Granada Granada Spain King Abdullah II School of Information Technology The University of Jordan Amman Jordan Department of Computer Systems Engineering Universidad Politécnica de Madrid Madrid Spain

This paper proposes an Intrusion Detection System (IDS) employing the Harris Hawks Optimization algorithm (HHO) to optimize Multilayer Perceptron learning by optimizing bias and weight parameters. HHO-MLP aims to select optimal parameters in its learning process to minimize intrusion detection errors in networks. HHO-MLP has been implemented using EvoloPy NN framework, an open-source Python tool specialized for training MLPs using evolutionary algorithms. For purposes of comparing the HHO model against other evolutionary methodologies currently available, specificity and sensitivity measures, accuracy measures, and mse and rmse measures have been calculated using KDD datasets. Experiments have demonstrated the HHO MLP method is effective at identifying malicious patterns. HHO-MLP has been tested against evolutionary algorithms like Butterfly Optimization Algorithm (BOA), Grasshopper Optimization Algorithms (GOA), and Black Widow Optimizations (BOW), with validation by Random Forest (RF), XGBoost. HHO-MLP showed superior performance by attaining top scores with accuracy rate of 93.17%, sensitivity level of 89.25%, and specificity percentage of 95.41%. © 2024, CC BY.

关键词： Optimization

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InGaAs/InP Quantum Well Nanowire Array Micro-LEDs at Telecommunication Wavelengths

InGaAs/InP Quantum Well Nanowire Array Micro-LEDs at Telecom...

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2023 Novel Optical Materials and Applications, NOMA 2023

作者： Zhang, Fanlu Su, Zhicheng Li, Zhe Zhu, Yi Gagrani, Nikita Li, Ziyuan Lockery, Mark Li, Li Aharonovich, Igor Lu, Yuerui Tan, Har Hoe Jagadish, Chennupati Fu, Lan Australian Research Council Centre of Excellence for Transformative Meta-Optical Systems Department of Electronic Materials Engineering Research School of Physics The Australian National University CanberraACT2601 Australia School of Electronic Science and Engineering Southeast University Jiangsu Nanjing210096 China Microstructural Analysis Unit University of Technology Sydney SydneyNSW2007 Australia Australian National Fabrication Facility ACT Node Research School of Physics The Australian National University CanberraACT2601 Australia ARC Centre of Excellence for Transformative Meta-Optical Systems Faculty of Science University of Technology Sydney UltimoNSW Australia School of Engineering College of Engineering and Computer Science The Australia National University Canberra CanberraACT2601 Australia

We report the growth, fabrication and characterization of highly uniform p-i-n core-shell InGaAs/InP single quantum well nanowire array light emitting diodes operating at telecommunication wavelengths of ~1.35 and ~1.... 详细信息

关键词： Nanowires

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Exploring Personal Knowledge Ecologies: Dealing with Digital Platform Asymmetries

Exploring Personal Knowledge Ecologies: Dealing with Digital...

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International Conference on computer Supported Cooperative Work in Design

作者： Marcos Antonio De Almeida Jano Moreira De Souza António Correia Daniel Schneider Systems Engineering and Computer Science Program (PESC/COPPE/UFRJ) Rio de Janeiro Brazil Faculty of Information Technology University of Jyväskylä Jyväskylä Finland Postgraduate Program in Informatics PPGI/UFRJ Rio de Janeiro Brazil Tércio Pacitti Institute of Computer Applications and Research NCE/UFRJ Rio de Janeiro Brazil

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The rise of artificial intelligence agents and information technology platforms governed by data-driven algorithms has had profound impacts on the way knowledge work is managed and carried out. Concerning the novel uses of digital work platforms, the arising tensions might suggest that the socio-technical design frameworks adopted by major tech firms are becoming less viable and in some cases ineffective. We propose a conceptual architecture to support a socio-technical framework for exploring Personal Knowledge Ecologies in the context of the digital knowledge economy. This framework aims to increase the degree of individual freedom for the interoperation of these knowledge information infrastructures.

关键词： Knowledge engineering Sociotechnical systems computer architecture Ecology Software Heterogeneous networks Complexity theory

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‘Do I Belong Here, There, or Elsewhere?’: The Materiality and Intangible Benefits of Technology in Digital Nomads' Place-Belongingness

‘Do I Belong Here, There, or Elsewhere?’: The Materiality ...

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International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

作者： Hesam Mohseni Marcos Antonio de Almeida Daniel Schneider António Correia Faculty of Information Technology University of Jyväskylä Jyväskylä Finland Systems Engineering and Computer Science Program (PESC) Federal University of Rio de Janeiro Rio de Janeiro Brazil Postgraduate Program in Informatics (PPGI) Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (数字)9798350354423

ISBN: (纸本)9798350354430

The advent of digital technology has contributed to experiencing liquid lifestyles and flexible work arrangements with different mobility routes and targets. Escaping the exhausting routine of commuting to and from work has been a key driver of digital nomadism, a phenomenon that has become increasingly widespread across the globe and is often centered around cross-border travel and expatriate settlement. Due to their frequent mobility, digital nomads face unique challenges in fostering a sense of place-belongingness compared to most other types of workers. Despite the popularity of digital nomadic work/life in the current public discourse, there is no standardized guidebook for understanding how digital technology already supports or could potentially support the sense of place-belongingness among these voluntary migrants. Motivated by this line of research, in our paper, we propose an initial guide with practical methodological considerations on how to investigate this phenomenon using qualitative and quantitative materials. Such studies can assist strategists and organizations in attracting new talent and fostering more inclusive environments.

关键词： Liquids Standards organizations Organizations Mediation Faces

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Collective excitations of a Bose-condensed gas: Fate of second sound in the crossover regime between hydrodynamic and collisionless regimes

arXiv

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arXiv 2023年

作者： Hiyane, Hoshu Watabe, Shohei Nikuni, Tetsuro Quantum Systems Unit Okinawa Institute of Science and Technology Graduate University Okinawa Onna904-0495 Japan Department of Physics Tokyo University of Science Shinjuku Tokyo162-8601 Japan Faculty of Engineering Computer Science and Engineering Shibaura Institute of Technology Toyosu Tokyo135-8548 Japan

We develop the moment method for Bose–Einstein condensates at finite temperatures that enable us to study collective sound modes from the hydrodynamic to the collisionless regime. In particular, we investigate collective excitations in a weakly interacting dilute Bose gas by applying the moment method to the Zaremba–Nikuni–Griffin equation, which is the coupled equation of the Boltzmann equation with the generalized Gross–Pitaevskii equation. Utilizing the moment method, collective excitations in the crossover regime between the hydrodynamic and collisionless regimes are investigated in detail. In the crossover regime, the second sound mode loses the weight of the density response function because of the significant coupling with incoherent modes, whereas the first sound shows a distinct but broad peak structure. We compare the result obtained by the moment method with that of the Landau two-fluid equations and show that the collective mode predicted by the Landau two-fluid equations well coincides with the result from the moment method even far from the hydrodynamic regime, whereas clear distinction also emerges in the relatively higher momentum regime. Copyright © 2023, The Authors. All rights reserved.

关键词： Method of moments

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Pothole Detection in Asphalt Roads: A Comprehensive Approach for Enhanced Road Maintenance and Safety with AlexNet Model

Pothole Detection in Asphalt Roads: A Comprehensive Approach...

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Telecommunications Conference (ITC-Egypt), International

作者： Mark Emad Sobhi Abdelmalak Nima Khodadadi Ahmed Mohamed Zaki Marwa M. Eid Faris H. Rizk Abdelhameed Ibrahim Abdelaziz A. Abdelhamid Laith Abualigah El-Sayed M. El-kenawy Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Department of Civil and Architectural Engineering University of Miami Coral Gables FL USA Computer Science and Intelligent Systems Research Center Blacksburg Virginia USA Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura Egypt Faculty of Engineering School of ICT Design and Information & Communications Technology (EDICT) Bahrain Polytechnic Isa Town Bahrain Department of Computer Science Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Computer Science Department Al al-Bayt University Mafraq Jordan MEU Research Unit Middle East University Amman Jordan Jadara Research Center Jadara University Irbid Jordan

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

The research article described in this paper puts forward a novel method of using an integrated software approach and high-end hardware devices for adaptive and intelligent detection of potholes on asphalt roads. The Pothole Detection Dataset is used for the dataset analysis, and we put VGG19Net, ResNet-50, GoogLeNet, and AlexNet among the computer vision models to analyze the applicability of these models. Different types of networks were compared, and AlexNet showed the best results as it achieved 92.15% accuracy, 91.38 % sensitivity (TPR), and a surprisingly high F-score, which reached 96.52%. Furthermore, by using its time of 279.35 seconds, which might be considered very fast, AlexNet shows many strengths in helping to do this, as well as identifying road anomalies, making it a perfect candidate for real-world utilization. This research demonstrates the emergence of sophisticated integrated pothole repair solutions, emphasizing the importance of both software and hardware in developing sophisticated pothole detection. Practices and this research could be an example for further surveying road inspection technologies.

关键词： computer vision Analytical models Adaptation models Asphalt Roads Computational modeling Hardware

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Revolutionizing Oil Spill Detection: A Machine Learning Approach for Satellite Image Classification

Revolutionizing Oil Spill Detection: A Machine Learning Appr...

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Telecommunications Conference (ITC-Egypt), International

作者： Khaled Sherif Faris H. Rizk Ahmed Mohamed Zaki Marwa M. Eid Nima Khodadadi Abdelhameed Ibrahim Abdelaziz A. Abdelhamid Laith Abualigah El-Sayed M. El-Kenawy Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Computer Science and Intelligent Systems Research Center Blacksburg Virginia USA Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura Egypt Department of Civil and Architectural Engineering University of Miami Coral Gables FL USA Faculty of Engineering Design and Information & Communications Technology (EDICT) School of ICT Bahrain Polytechnic Isa Town Bahrain Department of Computer Science Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Computer Science Department Al al-Bayt University Mafraq Jordan MEU Research Unit Middle East University Amman Jordan Jadara Research Center Jadara University Irbid Jordan

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

Identifying and labeling oil spills in satellite imagery is an essential activity of both environmental monitoring and disaster response actions. This work is dedicated to applying an Artificial Neural Network (ANN) model for gathering oil spill data by using a dataset that is specially curated for this reason. Our dataset was developed from satellite pictures of the ocean, some of which depict oil spills and some that do not. The features were extracted from each picture using computer vision algorithms. Our ANN model is trained to distinguish between two classes: The metrics that are looked at consist of accuracy, sensitivity, specificity, PPV, NPV, and statistical significance, and they illustrate how the model performs. As a result, the ANN model gets an accuracy of 96.88% and a sensitivity of 92.86% at the same time, while the specificity is 99.88%. The sensitivity of this diagnostic test is 96.30%, and the specificity is 94.74%. A p-value of 0.985997 means that the reported finding reaches a statistical significance, which is enough to support our hypothesis. This can be concluded from the results of ANN, showing the potential of this model to successfully classify the image patches into two sets, namely the ones covered by oil spills and the oil spill-free ones. The research work is a great contribution to the development of the area of environmental monitoring through the machine learning methods used for quick and appropriate detection of environmental hazards.

关键词： Sensitivity Oils Computational modeling Disasters Artificial neural networks Machine learning Predictive models

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Two-Stage Approach to Cluster Categorical Medical Data 29th

Two-Stage Approach to Cluster Categorical Medical Data

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29th International Conference on systems engineering, ICSEng 2022

作者： Drapala, Jaroslaw Szczepanowski, Remigiusz Świątek, Jerzy Uchmanowicz, Izabella Czapla, Michal Biegus, Jan Reczuch, Krzysztof Guszkowski, Tomasz Department of Computer Science and Systems Engineering Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wyb. Wyspiańskiego 27 Wroclaw50-370 Poland Department of Nursing and Obstetrics Faculty of Health Sciences Wroclaw Medical University Wroclaw51-618 Poland Institute of Heart Diseases University Hospital Wroclaw50-566 Poland Laboratory for Experimental Medicine and Innovative Technologies Department of Emergency Medical Service Wroclaw Medical University Wroclaw51-616 Poland Faculty of Nursing University of La Rioja Logroño26006 Spain Institute of Heart Diseases Medical University Wroclaw50-556 Poland European Centre for Postgraduate Education ECKP Wroclaw Wroclaw50-048 Poland

ISBN: (纸本)9783031274695

Medical datasets, apart from interval data, usually contain variables of categorical kind, either nominal or ordinal, such as symptoms and stage of disease, diagnosis, medications used, gender, etc. The attempts to cluster such data brings specific challenges that are not feasible with sets consisting of interval variables only, because assumptions behind most clustering algorithms, such as Gaussianity of noise, are violated, and the idea of a prototype vector (centroid) to represent a cluster becomes unclear. A reliable clustering technique could be extremely useful e.g. in diagnostics of cardiological illnesses. Here, we proposed overcoming the issues mentioned above using a two-stage clustering procedure. The first stage was to project multidimensional categorical vectors into a 2D continuous space. Then, in the second stage, clustering was performed on continuous-value vectors. The additional advantage of this procedure was visual inspection of the data distribution, facilitating the selection of clusters. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Clustering algorithms

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Comparative Analysis of Machine Learning Models in Online Payment Fraud Prediction

Comparative Analysis of Machine Learning Models in Online Pa...

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Intelligent Methods, systems, and Applications (IMSA)

作者： Ahmed Younes Shdefat Mennatallah Mohamed Shahd Khaled Farrah Hany Hanaa Fathi Diaa Salama AbdElminaam College of Engineering and Technology American University of the Middle East Egaila Kuwait Faculty of Computer Science Misr International University Cairo Egypt Applied Science Research Center Applied Science Private University Amman Jordan Jadara Research Center Jadara University Irbid Jordan Information Systems department Faculty of Computers and Artificial Intelligence Benha University Benha Egypt MEU Research Unit Middle East University Amman Jordan

ISBN: (数字)9798350362633

ISBN: (纸本)9798350362640

With the substantial increase in Cybercrime, the pressing need for effective solutions in the light of the rising amount of corporate financial losses, is a concern f or many researchers. The widespread adoption of ecommerce and diverse online payment methods has contributed to the surge in a major form of cybercrime—online payment fraud. This further highlights the critical need to control and reduce the widespread danger of fraudulent behaviour on the internet. However, predicting online payment fraud is difficult as fraudsters are constantly changing their tactics, transactions happen quickly and in large volumes, transactions involve different regions and countries, stolen data is often used and other factors. Our goal is to improve the accuracy and usability of online payment fraud prediction through the use of machine learning. A well-established and widely recognized dataset specifically designed for studying online payment fraud was used. The six proposed algorithms trained and tested were (SVM), Decision Tree, Naïve Bayes, Random Forest, (KNN) and Logistic Regression. After rigorous testing, Decision Tree presented the highest accuracy in performance of 98.58%. However, as ten-fold cross validation was implemented on the dataset, Decision Tree suffered a loss while Random Forest had an increase in accuracy making it the best model with accuracy of 98.47%. As shown in this paper, machine learning has proven to be a reliable and ever-evolving approach for the prediction of Online Payment Fraud and results can be further improved in collaboration with financial and cybercrime specialists.

关键词： Support vector machines Accuracy Machine learning algorithms Predictive models Prediction algorithms Fraud Bayes methods

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Advanced Discrete Tracking Control for Nonlinear systems: Development and Application

Advanced Discrete Tracking Control for Nonlinear Systems: De...

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European Control Conference (ECC)

作者： Samia Charfediine Sondoss Ben Aoun Faiçal Hamidi Catalin Dimon Research Unit of Photovoltaic Wind and Geothermal Systems University of Gabès Tunisia Department of Computer Engineering College of Computer Science and Engineering University of Ha’il Hail Saudi Laboratory “Modélisation Analyse et Commande des Systèmes” University of Gabes Gabes Tunisia Faculty of Automatics and Computers University Politehnica of Bucharest Bucharest Romania

An innovative fuzzy discrete control technique is presented in this paper. An analytical discrete tracking control strategy offering high dynamic efficiency is developed using the gain scheduling methodology combined with the feedback linearization theory. For the stability analysis study, an enlarged basin of attraction around operating points is estimated using a non-Lyapunov method. To ensure a high trajectory tracking accuracy with specified dynamics, a numerical approach is implemented with the use of fuzzy logic reasoning. While optimizing the running time of the designed algorithm, the control strategy is shown to be easy to implement. As a result, the trajectory tracking objective is achieved with satisfactory dynamic characteristics. Through numerical simulation analysis, the efficiency of the designed strategy is established.

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