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IEEE Workshop on High Performance Switching and Routing

作者： Marios Avgeris Meriem Mechennef Aris Leivadeas Ioannis Lambadaris Department of Systems and Computer Engineering Carleton University Ottawa Canada Department of Software and IT Engineering École de Technologie supérieure (ÉTS) Montréal Canada

In the 5G/6G era of networking, computational offloading, i.e., the act of transferring resource-intensive computational tasks to separate external devices in the network proximity, constitutes a paradigm shift for mobile task execution on Edge Computing infrastructures. However, in order to provide firm Quality of Service (QoS) assurances for all the involved users, meticulous planning of the offloading decisions should be made, which potentially involves inter-site task transferring. In this paper, we consider a multi-user, multi-site Multi-Access Edge Computing (MEC) infrastructure, where mobile devices (MDs) can offload their tasks to the available edge sites (ESs). Our goal is to minimize end-to-end delay and energy consumption, which constitute the sum cost of the considered system, and comply with the MDs’ application requirements. To this end, we introduce a two-stage Reinforcement Learning (RL)-based mechanism, where the MDs-to-ES task offloading and the ES-to-ES task transferring decisions are iteratively optimized. The proper operation, effectiveness and efficiency of our proposed offloading mechanism is assessed under various evaluation scenarios.

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Prediction of Solar Energy Production Using Low-Cost Data Logger and ANN Algorithm 9th

Prediction of Solar Energy Production Using Low-Cost Data L...

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9th International Conference on Smart City Applications, SCA 2024

作者： Raif, Mourad Ledmaoui, Younes El Aroussi, Mohamed Saadane, Rachid Jakimi, Abdeslam Chehri, Abdellah Laboratory Engineering System Hassania School of Public Works Casablanca Casablanca Morocco Software Engineering and Information Systems Engineering Team FST of Errachidia Moulay Ismail University Meknes Morocco Department of Mathematics and Computer Science Royal Military College of Canada KingstonONK7K 7B4 Canada

ISBN: (纸本)9783031886522

This article introduces a novel method for predicting solar energy production using an affordable data logger and the Artificial Neural Network (ANN) algorithm. The main goal of this research is to create a precise predictive model for solar energy output, addressing the critical need for accurate forecasting in the renewable energy industry. The study capitalizes on the accessibility and cost-efficiency of a low-cost data logger to collect detailed environmental and solar irradiance data. The ANN algorithm is utilized to analyze the collected dataset, providing a robust and effective approach for forecasting solar energy production. Extensive experiments demonstrate that the developed model achieves an impressive R2 score of 0.95, indicating a high degree of accuracy and reliability in predicting solar energy output. This significant R2 score highlights the proposed methodology’s effectiveness, demonstrating its potential for practical applications in solar energy planning and management. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data logger Machine learning Photovoltaic prediction Renewable energy

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Suggestion Mining from Opinionated Text of Big Social Media Data

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computers, Materials & Continua 2021年第9期68卷 3323-3338页

作者： Youseef Alotaibi Muhammad Noman Malik Huma Hayat Khan Anab Batool Saif ul Islam Abdulmajeed Alsufyani Saleh Alghamdi Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Engineering and Computer SciencesNational University of Modern LanguagesIslamabadPakistan Department of Software Engineering Faculty of Engineering and Computer SciencesNational University of Modern LanguagesIslamabadPakistan Department of Computer Sciences Institute of Space TechnologyIslamabadPakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia

:Social media data are rapidly increasing and constitute a source of user opinions and tips on a wide range of products and *** increasing availability of such big data on biased reviews and blogs creates challenges for customers and businesses in reviewing all content in their decision-making *** overcome this challenge,extracting suggestions from opinionated text is a possible *** this study,the characteristics of suggestions are analyzed and a suggestion mining extraction process is presented for classifying suggestive sentences from online customers’reviews.A classification using a word-embedding approach is used via the XGBoost *** two datasets used in this experiment relate to online hotel reviews and Microsoft Windows App Studio discussion reviews.F1,precision,recall,and accuracy scores are *** results demonstrated that the XGBoost classifier outperforms—with an accuracy of more than 80%.Moreover,the results revealed that suggestion keywords and phrases are the predominant features for suggestion ***,this study contributes to knowledge and practice by comparing feature extraction classifiers and identifying XGBoost as a better suggestion mining process for identifying online reviews.

关键词： Suggestion mining word embedding Naïve Bayes random forest XGBoost dataset

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Security-Oriented Network Intent Placement using Particle Swarm Optimization

Security-Oriented Network Intent Placement using Particle Sw...

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Cyber Security in Networking Conference (CSNet)

作者： Gabriel Landeau Marios Avgeris Aris Leivadeas Ioannis Lambadaris Department of Software and IT Engineering École de technologie supérieure (ÉTS) Montréal Canada Department of Systems and Computer Engineering Carleton University Ottawa Canada

As the network infrastructure grows, its configuration and service provisioning become a tedious process. Accordingly, new paradigms have emerged, such as the Intent-Based Networking (IBN), that envision the automation of the network configuration, while minimizing the human intervention. Specifically, IBN allows users to interact with the network through high-level and declarative requests, called intents, which later can be translated into low-level configurations. IBN can entail different scopes and target network infrastructures, while being domain specific, which can create several challenges in terms of the final activation of the requested intents. To this end, in this paper, we mainly focus on intents that are expressing security and Quality of Service (QoS) network services demands that can be translated into Service Function Chains (SFC) and automatically deployed over a campus network. Our work and depending on the security level expressed in the intent, tries to optimally decide the level of multi-tenancy or complete segregation of the users' services that can be achieved, while satisfying the network provider's objectives. In particular, an artificial intelligence inspired algorithm called Particle Swarm Optimization (PSO) is modeled that automatically tries to find the best placement of the intents, while satisfying the security and QoS requirements of the users issuing the intents.

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Towards Modeling Functional Requirements From Tacit Knowledge

Towards Modeling Functional Requirements From Tacit Knowledg...

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computer Science, computer engineering, & Applied Computing (CSCE), Congress in

作者： Rameen Jafar Maram Fahaad Almufareh Sundas Ashraf Bushra Khan Wasi Haider Butt Mamoona Humayun Department of Computer and Software Engineering National University of Sciences and Technology Pakistan Department of Information Systems College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia

Requirements Elicitation is the process of identifying system needs by talking with stakeholders who have a direct or indirect effect on the requirements. Requirements may be derived from several sources and are one of the most important phases of requirement engineering. We need to perform different elicitation techniques to find out the user's needs. Pre-requirements tracing is the process of determining the origins of a specific demand. Typically, certain needs arise that have no apparent source, but stakeholders will testify to their importance. Such criteria, however, are most likely dependent on tacit or tacit-like information entrenched in the issue domain. Tacit knowledge is one of the most common challenges for requirement analysts. This study contributes to constructing a systematic literature review for exploring multiple ways to model functional requirements using tacit knowledge. We have identified and analyzed 10 studies. In these research papers, different authors have discussed different methods to gather functional requirements from tacit knowledge.

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Application of Two Forms of the Erlang Distribution Law in Queueing Theory 8

Application of Two Forms of the Erlang Distribution Law in Q...

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8th IEEE International Conference on Problems of Infocommunications, Science and Technology, PIC S and T 2021

作者： Bakhareva, Nadezhda Tarasov, Veniamin Povolzhskiy State University of Telecommunications And Informatics Department of Informatics And Computer Engineering Software And Management In Technical Systems Department Samara Russia

ISBN: (纸本)9781665406826

The purpose of this work is to compare two forms of the Erlang distribution law: ordinary and normalized. The ordinary Erlang distribution is a special case of a more general gamma distribution and its mathematical expectation depends on the order of the distribution k. For a normalized distribution, the mathematical expectation will not depend on the order of the distribution k;this is the meaning of the normalization operation. Consequently, these two forms of the Erlang distribution law will differ in their numerical characteristics. The paper considers the problem of how these two forms of the distribution law are applied in the theory of queuing and how they affect the main characteristic of queuing systems (QS)-the average delay of claims arriving for service in the system. The rest of the QS characteristics are derivatives of the average delay. For this, three different QSs are considered, including the Erlang distribution law. The method of spectral decomposition of the solution of the Lindley integral equation was used as a mathematical apparatus for studying the QS. For the practical application of the results obtained, the method of moments of the theory of probability was used. Basically, queueing systems G/G/1 with general distribution laws are in demand for modeling data transmission systems for various purposes, including computer and telecommunication networks. This is especially true since for G/G/1 systems there is no solution for the average latency in the final form for the general case. Therefore, such systems are investigated using examples with particular distribution laws. © 2021 IEEE.

关键词： Laplace transforms

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An Exact Formula for the Hazardous Event Frequency

An Exact Formula for the Hazardous Event Frequency

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Annual Symposium on Reliability and Maintainability (RAMS)

作者： Armin Zimmermann Andreas Hildebrandt Department of Computer Science and Automation Systems and Software Engineering Group Technische Universität Ilmenau Ilmenau Germany Pepperl+Fuchs SE Mannheim Germany

The remaining risk of safety-instrumented systems is an important non-functional requirement that is regulated by international standards. Several ways towards computing the safety as a function of its relevant design parameters have been studied in the literature. However, the standard approach only covers two special cases of high or low demand, which simplify the treatment by either ignoring the effects of demand rate or test interval on the safety. More detailed treatments in the literature derive Markov models, which can be numerically analyzed, or approximate solutions using Taylor series expansions etc. This paper introduces closed-form exact formulas for the average probability of failure on demand (PFD) and the resulting hazardous event frequency (HEF, or accident rate), taking into account demand rate and test interval. It integrates all cases of low, high and medium demand in one formula. The derivation is based on an analysis of the cyclostationary semi-Markov stochastic process of the safety-integrated system and its symbolic transient analysis over the test interval.

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Super-Resolution Based ROI Enhancement Approaches for Palmprint Recognition

Super-Resolution Based ROI Enhancement Approaches for Palmpr...

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International Artificial Intelligence and Data Processing Symposium (IDAP)

作者： Hüseyin Furkan Macan Özkan Bіngöl Salih Türk Murat Ekіncі Department of Management Information Systems Gumushane University Gümüşhane Turkey Department of Software Engineering Gumushane University Gümüşhane Turkey Department of Computer Engineering Karadeniz Technical University Trabzon Turkey

ISBN: (数字)9798331531492

ISBN: (纸本)9798331531508

The sizes of regions of interest (ROI) obtained from images captured by cameras in palmprint recognition may vary depending on the camera specifications and the position of the hand. The regions of interest that recognition systems can use are typically rescaled through interpolation techniques and then defined as inputs to the system. In such cases, there may be some loss of biometric data. This study aims to enhance the quality of the palmprint ROI images, which are critical for palmprint recognition systems. To achieve this, various super-resolution techniques were employed to reconstruct low-resolution ROI images to the target sizes while preserving the distinctiveness of their biometric features. This approach is designed to optimize recognition accuracy and improve data reliability in palmprint recognition processes.

关键词： Interpolation Image recognition Accuracy Palmprint recognition Superresolution Cameras Reliability engineering Data processing Artificial intelligence Image reconstruction

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Simulation-Based Study on False Alarms in Intrusion Detection systems for Organizations Facing Dual Phishing and Dos Attacks

Simulation-Based Study on False Alarms in Intrusion Detectio...

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Annual Modeling and Simulation Conference (ANNSIM)

作者： Jeongkeun Shin L. Richard Carley Kathleen M. Carley Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA United States Software and Societal Systems Department Carnegie Mellon University Pittsburgh PA United States

ISBN: (数字)9781713899310

ISBN: (纸本)9798350350562

Machine learning-based intrusion detection systems (IDS) have attracted considerable attention for their role in proactively identifying intrusion attempts and facilitating swift organizational response. While numerous researchers have conceptually discussed the potential negative impacts of high false alarms in machine learning-based IDS on organizations and have proposed various methods to reduce them, there is a shortage of studies that explore how these false alarms can exacerbate cyberattack damage in different organizational settings and in the face of various cyberattack campaigns. This paper introduces an agent-based modeling and simulation approach to assess false alarm consequences in machine learning-based IDS during dual Denial of Service (DoS) and phishing attacks. The IDS with distinct false positive rates, constructed using the KDD Cup 1999 dataset with diverse machine learning algorithms, were simulated to analyze how these varying false alarm rates affect the extent of damage caused by phishing and DoS attacks.

关键词： Machine learning algorithms Phishing Intrusion detection Organizations Agent-based modeling Cyberattack Faces

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Automated Classification of Medical Images Using Convolutional Neural Networks 15

Automated Classification of Medical Images Using Convolution...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Pillai, N. Muthuvairavan Manimala, S. Rongali, Aruna Sri Gugnani, Anchit Kumar, Senthil A. Sriramakrishnan, G.V. Computer Science and Business Systems R.M.D Engineering College Thiruvallur district Kavarapettai India Computer science and engineering University SJCE JSS Science and technology University Karnataka Mysore India Sr. Software Engineer Department Information Technology Emphasis/Information Technology Ph.D University The country University of The Cumberlands United States Physiotherapy Amity University Rajasthan Jaipur India Artificial Intelligent department Shri Vishnu Engineering College for Women AP Bhimavaram India Tamilnadu Chennai India

ISBN: (纸本)9798350370249

Medical imagery has been increasingly used in diagnosing and treating various diseases, leading to advances in healthcare. While humans usually lead the qualitative interpretation of visual features, Automated Classification of Medical Images Using Convolution Neural Networks (CNNs) has gained traction for accurate image classification. CNNs are a type of deep learning algorithm that attempts to mimic the structure of the human brain and are ideal for image classification due to their convolution layers that can detect patterns within the image. These convolution layers enable the networks to learn the characteristics of the data through a series of convolution and pooling operations. Additionally, they can rapidly identify relationships and patterns within the dataset and extract features from the input image, thus allowing a more accurate classification. With the increasing availability of medical imaging data, advancements in the field of ML/AI has enabled the use of CNNs for automated medical image classification, helping clinicians to diagnose diseases accurately with less time and effort. ©2024 IEEE.

关键词： Convolutional neural networks

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