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29th International Conference on Software, Telecommunications and computer Networks, SoftCOM 2021

作者： Cichosz, Pawel Kozdrowski, Stanislaw Sujecki, Slawomir Department of Computer Science Warsaw University of Technology Warsaw Poland Telecommunications and Teleinformatics Dep. University of Science and Technology Wroclaw Poland

ISBN: (纸本)9789532901092

Telecommunication optical networks grow steadily in response to increasing traffic. Thus, to accommodate a steady traffic growth, network operators need to increase the number of active optical channels and reconfigure networks constantly. These tasks cannot be successfully performed without an assessment of the quality of transmission prior to any changes made to an optical network. In this contribution a machine learning-based method for the assessment of the quality of transmission in a Dense Wavelength Division Multiplexing (DWDM) network is proposed. The proposed machine learning method uses a database, which was created only on the basis of information that is available to a network operator via the DWDM network control plane. Particular focus of this study is given to one-class classification algorithms: the one-class support vector machine, one-class naive Bayes classifier and isolation forest algorithms. This is to spare scarcely available examples of unsuccessful channel configurations for the purpose of model evaluation and learn exclusively from successful channel examples. The results obtained show that one-class classification algorithms present a viable alternative to the more common binary classification approach. © SoftCOM 2021. All rights reserved.

关键词： Dense wavelength division multiplexing

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Bayesian Network Structure Discovery Using Antlion Optimization Algorithm.

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2022年第1期7卷 46-65页

作者： Awla, Hoshang Qasim Kareem, Shahab Wahhab Mohammed, Amin Salih Computer science department Faculty of Science Soran University Erbil Iraq Dep. of Technical Information Systems Engineering Erbil Technical Engineering College Erbil Poly-technic University Erbil Iraq Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Erbil Iraq

Bayesian networks have recently been used for discovering an optimal learning structure in machine learning. Bayes networks can describe possible dep.ndencies of explanatory variables. As a novel approach to studying the structure of a Bayesian network, the authors present the Antlion Optimization Algorithm (ALO). In the algorithm;deletion, rewind, insertion, and change are utilized to produce ALO to reach the best hull solution. Essentially, the technique used in the ALO algorithm imitates the antlions’ behaviors while hunting. The suggested approach is contrasted with simulated Annealing, Simulated Annealing Hybrid Bee, Greedy Search Hybrid Bee, optimization inspired by Pigeon, and greedy search using the BDe Score function. The researchers also studied the representation of the confusion matrix of these techniques using different reference data sets. The findings of the assessments reveal that the proposed algorithm works better than the other algorithms and has better consistency and score values. As shown by the experimental evaluations, the proposed method has a more reliable performance than other algorithms (including the production of excellent scores and accuracy values). © 2022. All Rights Reserved.

关键词： Antlion Optimization Bayesian Network Hunting Search Local & Global Search Metaheuristics Search and Score Structure Learning

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Internet of Things (IoT): survey of most important security risks

Internet of Things (IoT): survey of most important security ...

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Sciences and Techniques of Automatic Control and computer Engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Ahmed Alaskri Nuredin Ali Salem Ahmed Hala Shaari Software Engineering Dep. Collage of Computer Technology Tripoli Libya Computer Engineering Department Faculty of Engineering Tripoli Libya Web Technologies Department Faculty of Information Tripoli Libya

The Internet of things (IoT) is getting more and more intrusive into our lives until the day comes when everything becomes connected to the Internet. Due to the limited resources and heterogeneous Internet of Things (IoT) devices, the traditional means of protection are useless and cannot be used to protect these devices. The most important security risks, their causes and the consequences of their occurrence have been listed, scheduled and categorized. The study concluded that there are real security risks that cannot be ignored, and they need to find innovative solutions to eliminate them or reduce their damage to a minimum. This paper showed the main risks addressed in previews research, and outlined the gaps in this field of technology, also producing a brief summary information about the most important solution to avoid many threats against the IoT field.

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EEG-Based Stress Assessment During Robot Assisted Surgery. Comparison of Statistical Methods with Machine Learning

EEG-Based Stress Assessment During Robot Assisted Surgery. C...

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Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), IEEE International Conference on

作者： Pasquale Arpaia Irene Del Chicca Matteo De Luca Ludovica Gargiulo Giovanna Mastrati Nicola Moccaldi Elisa Morganti Marco Nalin Mauro Picciafuoco Francesca Pierro Filippo Rigoni Riccardo Rigoni Dep. of Electrical Engineering and Information Technology (DIETI) University of Naples Federico II Naples Italy ab medica S.p.A. Cerro Maggiore MI Italy Dep. of Electronics Information and Bioengineering (DEIB) Polytechnic University of Milan Milan Italy Dep. of Computer Science (D-INFK) ETH Zurich Zurich Switzerland

ISBN: (数字)9798350378009

ISBN: (纸本)9798350378016

This study proposes a adaptive pipeline (feature extraction, feature selection, and classification) for the processing of electroencephalographic (EEG) signals to assess Mental Workload (MWL) during training sessions with a surgical robot. Unlike previous studies in this field, EEG signals were acquired by a dry eight-electrode device to maximize usability and comfort for the users. Fourteen medical students were EEG monitored while performing four different tasks on a simulation platform incorporated into the Da Vinci System. The proposed pipeline was compared with typical machine-learning approaches based on handcraft feature extraction approaches. The results confirmed the effectiveness of the proposed solution as a further contribution to EEG-based monitoring of robotic surgery for adaptive training.

关键词： Training Medical robotics Pipelines Surgery Machine learning Feature extraction Electroencephalography Biomedical monitoring Usability Monitoring

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Deep Learning Technique for Real-Time Forest Fire Detection from Video Streams

Deep Learning Technique for Real-Time Forest Fire Detection ...

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Intelligent Systems and Embedded Design (ISED), International Conference on

作者： Nidhi Gupta Sujata Chakravarty Satyabrata Dash Dept. of Computer Science & Engineering Centurion University of Technology & Management Odisha India Dep. of Computer Science and Engineering Gandhi Institute of Technology & Management (GITAM Deemed to be University) Andhra Pradesh India

Forest fires are currently the most common issue that we are dealing with. The resources that have been lost are irreplaceable. Forest fires are uncontrollable fires that occur in nature as a result of human involvement or any other natural event. Forest fires can be so large that putting them out takes a long time. Contrasting with previous years, there has been a striking surge in the frequency of forest fires. Changes in climatic conditions are the key factor affecting the scope of forest fires. Long dry spells in the spring and summer make the woodlands particularly vulnerable. The extent of forest fires is mostly determined by weather conditions such as precipitation and wind, as well as the terrain's architecture. Long dry spells in the spring and summer make the woodlands particularly vulnerable. The extent of forest fires is mostly determined by weather conditions such as precipitation and wind, as well as the terrain's architecture. The incorporation of a Video Fire Detection model significantly enhances the efficiency of fire detection systems, particularly in large spaces. Establishing a video fire detection system involves focusing on spatial, spectral, and temporal indicators, as well as flame picture segmentation, recognition, tracking, and prediction. Analytical techniques, specifically utilizing ResNet50 and VGG16 models, can be applied to identify pool fire images through these segmentation processes, contributing to more robust fire detection capabilities.

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Machine learning algorithms in Bigdata Analysis and its applications: A review 4

Machine learning algorithms in Bigdata Analysis and its appl...

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4th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2022

作者： Khoshaba, Farah Kareem, Shahab Awla, Hoshang Mohammed, Chnar Erbil Polytechnic University Technical Engineering College Information System Engineering Department Erbil Iraq Lebanese French University College of Engineering and Computer Science Dep. of Information Technology Kurdistan Erbil Iraq Soran University College of Science Department of Computer Science Erbil Iraq

ISBN: (纸本)9781665468350

A wide range of disparate variety of heterogeneous and even disparate data sources has been integrated into the computer science research principles through the assistance of Artificial Intelligence (AI) and Machine Learning (ML) delivering excellent results in accuracy and data quality. Even so, taking machine learning approaches to very broad and dynamic datasets is both logistically and computationally costly. Some people are generating so much data regularly, which means that advanced analytics platforms have greater importance than ever before. Spark Machine Learning has extensive capabilities for a variety of ML activities such as regression, grouping, dimension reduction, and extraction of rules. While it has numerous datasets of high quality and consistency of structure. The specification of the MapReduce programming model was intended for the massive density of large-volume computation. It accomplishes this by splitting the job into several well-defined sub-sized units. There are various sets of data mining frameworks that link to a distributed device. Hadoop serves as the infrastructure for the distributed system. Several resources claim that Mahout will scale to handle massive datasets. This article discusses and demonstrates the use of algorithms and their significant impact on Bigdata, as well as a comparison between their implementations among Apache Spark and Apache Mahout. © 2022 IEEE.

关键词： Big data

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Introduction to the HICSS-56 Software Development for Mobile Devices, the Internet-of-Things, and Cyber-Physical Systems Minitrack

Proceedings of the Annual Hawaii International Conference on...

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Proceedings of the Annual Hawaii International Conference on System Sciences 2023年 2023-January卷 6893-6894页

作者： Majchrzak, Tim A. Grønli, Tor-Morten Kaindl, Hermann University of Agder Kristiansand Norway Kristiania University College Dep. Technology Oslo Norway TU Wien Inst. of Computer Tech. Vienna Austria

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Cybersecurity Threats in the Era of AI: Detection of Phishing Domains Through Classification Rules 2

Cybersecurity Threats in the Era of AI: Detection of Phishin...

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2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Aljaidi, Mohammad Alsarhan, Ayoub Al-Fraihat, Dimah Al-Arjan, Ahmed Igried, Bashar El-Salhi, Subhieh M. Khalid, Muhammad Al-Na'amneh, Qais Zarqa University Faculty of Information Technology Department of Computer Science Zarqa13110 Jordan The Hashemite University Department of Information Technology Jordan Isra University Faculty of Information Technology Department of Software Engineering Amman11622 Jordan The Hashemite University Faculty of Prince AL-Hussein Bin Abdullah Ii of Information Technology Department of Information Technology Jordan The Hashemite University Department of Computer Science and Applications Zarqa Jordan The Hashemite University P. O Box 330127 Zarqa13133 Jordan University of Hull Department of Computer Sciences and Technology Kingston upon Hull United Kingdom Applied Science Private University Dep. of Cyber Security Amman Jordan

ISBN: (纸本)9798350373363

Phishing is a cyber threat where attackers create deceptive websites or emails to exploit individuals for fraudulent purposes such as providing sensitive information like usernames, passwords, or financial details. Hence, detecting phishing emails is of paramount importance to protect sensitive information, preventing financial losses, protecting against identity theft, and maintaining trust. This study employs machine learning techniques to classify and categorize websites that are used for phishing attacks. To achieve this, we proposed a classification model for the detection of phishing domains based on classification rules. These rules were formulated based on analyzing and distributing them into three classifiers: domain name features, search engine opinion (SEO) information of the domain, and security scans by security vendors. A total of eleven classification rules were formulated which were distributed as follows: six rules for the first classifier, four rules for the second classifier, and one rule for the third classifier. Each classifier feeds its results to the main classifier, which then evaluates the received results based on its rules. Domain names from 222 confirmed phishing URLs were used to test the suggested model. The model demonstrated exceptional performance, achieving an impressive accuracy rate of 99.1% during testing. © 2023 IEEE.

关键词： Websites

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Real-time Control of UGV Robot in Gazebo Simulator using P300-based Brain-computer Interface

Real-time Control of UGV Robot in Gazebo Simulator using P30...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Nuaimi, Fatima Ali Al Zeddoug, Jamal Belkacem, Abdelkader Nasreddine Autonomous Robotics Research Center Technology Innovation Institute Abu Dhabi United Arab Emirates CIT United Arab Emirates University Dep. Computer and Network Engineering Al Ain United Arab Emirates

ISBN: (纸本)9781665468190

Brain computer interface (BCI)-based virtual environment control has found broad applications in solving and pursuing factual healthcare issues concerning efficiency, safety, and costs. In this technical paper, an unmanned ground vehicle (UGV) robot with a simulator-equipped BCI system was utilized. The Gazebo simulator was employed to develop a simulated setting. The software CitySim World allowed rendering the simulated milieu more down-to-earth. A non-invasive electroencephalogram (EEG)-based BCI was used to follow the brain signals and extract the P300 component, a kind of simultaneous BCI controlling procedure for safe, fast, and inexpensive implementation. This UGV control system using human brain activity can be beneficial for the real UGV platform control. It enables the discovery of the probable errors in the physical implementation. All the steps implementing our BCI system were appropriately provided (data acquisition system, user interface design, BCI data architecture, ROS/ robot Jackal, and implementation and tests). Furthermore, the project implementation and some solutions to possible issues were posed. The project outcomes were assessed by employing BCI in a simulation;before implementing real-time, to determine errors and resolve the validity of the project scope. © 2022 IEEE.

关键词： Electroencephalography

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An analysis of the Oracles used in Ethereum’s blockchain

An analysis of the Oracles used in Ethereum’s blockchain

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IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Giuseppe Antonio Pierro Honore Mahugnon Dep. of Mathematics and Computer Science University of Cagliari Cagliari Italy National Institute for Research in Digital Science and Technology INRIA Lille France

Smart contracts are programs whose business logic runs in a decentralized architecture named blockchain where each of the executing nodes trusts and agrees with the execution outcomes. Blockchain is an isolated execution environment, so smart contracts cannot access external data by themselves. The blockchain Oracle, or simply Oracle, is the software that allows smart contracts to interact with the outside world. In the past, a wide variety of studies have examined the oracles from various perspectives such as their implementation characteristics and their reliability. Unlike some other studies, this paper analyzes the use of Oracles through a static analysis of smart contracts. First, we analyze the source code of a corpus of over 40K smart contracts to find the most blockchain Oracles used by the Ethereum blockchain. On the basis of previous works, we examine the features of the most used blockchain Oracles in Ethereum. Lastly, we discussed our findings and the possible reasons why some blockchain oracles are more widely used than others. The purpose of this study is to shed light on how and which oracles are actually used by Ethereum smart contracts, as an alternative to other very interesting studies that analysed the blockchain oracle characteristics.

关键词： Source coding Smart contracts Finance Static analysis computer architecture Software Blockchains

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