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Optimal Quad Channel Long Short-Term Memory Based Fake News Classification on English Corpus

computer systems Science & engineering 2023年第9期46卷 3303-3319页

作者： Manar Ahmed Hamza Hala J.Alshahrani Khaled Tarmissi Ayman Yafoz Amal S.Mehanna Ishfaq Yaseen Amgad Atta Abdelmageed Mohamed I.Eldesouki Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The term‘corpus’refers to a huge volume of structured datasets containing machine-readable *** texts are generated in a natural communicative *** explosion of social media permitted individuals to spread data with minimal examination and filters *** to this,the old problem of fake news has *** has become an important concern due to its negative impact on the *** manage the spread of fake news,automatic recognition approaches have been investigated earlier using Artificial Intelligence(AI)and Machine Learning(ML)*** perform the medicinal text classification tasks,the ML approaches were applied,and they performed quite ***,a huge effort is required from the human side to generate the labelled training *** recent progress of the Deep Learning(DL)methods seems to be a promising solution to tackle difficult types of Natural Language Processing(NLP)tasks,especially fake news *** unlock social media data,an automatic text classifier is highly helpful in the domain of *** current research article focuses on the design of the Optimal Quad ChannelHybrid Long Short-Term Memory-based Fake News Classification(QCLSTM-FNC)*** presented QCLSTM-FNC approach aims to identify and differentiate fake news from actual *** attain this,the proposed QCLSTM-FNC approach follows two methods such as the pre-processing data method and the Glovebased word embedding ***,the QCLSTM model is utilized for *** boost the classification results of the QCLSTM model,a Quasi-Oppositional Sandpiper Optimization(QOSPO)algorithm is utilized to fine-tune the *** proposed QCLSTM-FNC approach was experimentally validated against a benchmark *** QCLSTMFNC approach successfully outperformed all other existing DL models under different measures.

关键词： English corpus fake news detection social media natural language processing artificial intelligence deep learning

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A Two-Stage Cooperative Reinforcement Learning Scheme for Energy-Aware Computational Offloading

A Two-Stage Cooperative Reinforcement Learning Scheme for En...

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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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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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Image Classification Analysis to Identify the Tourism Potential Sukabumi on Instagram Using A Deep Learning Approach

Image Classification Analysis to Identify the Tourism Potent...

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International Conference on Informatics and Computing (ICIC)

作者： Falentino Sembiring Rieska Rahayu Ayuningsih Muhamad Muslih Muhammad Andy Maulana Gina Wahdayani Futri Mohd Zainuri Saringat Rachma Siva Ainunnisa Anggun Fergina Information Systems Nusa Putra University Sukabumi Indonesia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

This research focuses on image classification, grouping images based on shared characteristics. The study aims to identify the highest-potential category within various fields, from high to low potential. By understanding existing potential, researchers can inform targeted strategies for future development. The methodology combines Logistic Regression in Orange Data Mining to enhance classification accuracy. Hierarchical Clustering is used to analyze 7,777 image results. Findings reveal that Tourism (C3) has the most potential, followed by Culinary (C2) and Culture (C4), while Handcrafts (C1) exhibit the least potential. In conclusion, logistic regression successfully identifies influential aspects of Sukabumi through Instagram images, aiding potential development

关键词： Deep learning Logistic regression Accuracy Web sites Multimedia communication Data mining Cultural differences Informatics Image classification

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