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Abstractive Arabic Text Summarization Using Hyperparameter Tuned Denoising Deep Neural Network

Intelligent Automation & Soft computing 2023年第11期38卷 153-168页

作者： Ibrahim M.Alwayle Hala J.Alshahrani Saud S.Alotaibi Khaled M.Alalayah Amira Sayed A.Aziz Khadija M.Alaidarous Ibrahim Abdulrab Ahmed Manar Ahmed Hamza Department of Computer Science College of Science and ArtsSharurahNajran UniversitySharurahSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversityMakkahSaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

ive Arabic Text Summarization using Hyperparameter Tuned Denoising Deep Neural Network(AATS-HTDDNN)*** presented AATS-HTDDNN technique aims to generate summaries of Arabic *** the presented AATS-HTDDNN technique,the DDNN model is utilized to generate the *** study exploits the Chameleon Swarm Optimization(CSO)algorithm to fine-tune the hyperparameters relevant to the DDNN model since it considerably affects the summarization *** phase shows the novelty of the current *** validate the enhanced summarization performance of the proposed AATS-HTDDNN model,a comprehensive experimental analysis was *** comparison study outcomes confirmed the better performance of the AATS-HTDDNN model over other approaches.

关键词： Text summarization deep learning denoising deep neural networks hyperparameter tuning Arabic language

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Enhanced Artificial Bee Colony Based Optimization for Mitigating Replication in Large Data for Internet of Things (Iot)

SSRN

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SSRN 2022年

作者： Ramzan, Muhammad Sher Asghar, Anees Ullah, Ata Jhanji, Noor Zaman Alsolami, Fawaz Ahmad, Iftikhar Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science National University of Modern Languages Islamabad Pakistan School of Computer Science and Engineering SCE Taylor’s University Malaysia

Internet of Things (IoT) consists of complex and dynamically aggregated elements or smart entities that needs decentralized supervision for data exchanging throughout the different networks. A most commonly used swarm intelligence based approach named artificial bee colony (ABC) can be utilized in order to figure out the problems like clustering of gigantic data in IoT, complex optimization problems in IoT etc. In standard ABC algorithm the key issue is that every single food site is compared with every other food site to find out the finest solution in the neighboring regions. In this way an extensive number of redundant comparisons will be required which results in delayed convergence rate, more time consumption and increased delay will occur. This paper proposes an enhanced ABC (E-ABC) oriented optimized algorithm for data sharing and replication. E-ABC enhances the prior ABC-based schemes by decreasing the number of redundant comparisons. The proposed algorithm makes a comparison between the finest food sites with the neighboring sites that results in the exclusion of poor sources. The proposed algorithm was implemented in a replication scenario to verify its dominance over existing schemes with respect to convergence rate, data file accessibility and average response time. The results prove the superiority of the E-ABC over counterparts. © 2022, The Authors. All rights reserved.

关键词： Internet of things

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Cloud-Powered Neural Networks for Sustainable Fishing Practices and Stock Management System 4

Cloud-Powered Neural Networks for Sustainable Fishing Practi...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Suresh, M. Xavier Prathik, A. Arun, V. Venugopal, R. Ganeshbabu, T.R. Muthulekshmi, M. School of Advanced Sciences and Languages Vit Bhopal University Department of Physics Madhya Pradesh Bhopal India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India School of Computing Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Kattankulathur Tamil Nadu Chennai India Department of Mathematics Tamil Nadu Salem India Department of Electronics and Communication Tamil Nadu Namakkal India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331540364

Sustainable fishing methods and efficient stock management are crucial considering growing environmental concerns;fishing practices and effective stock management are imperative. This research presents a novel approach that uses neural networks (NN) hosted in the cloud to transform sustainable fishing and stock management. This method trains NN to make predictions by combining several data sources, such as satellite images, weather patterns, and catch records. These models can reliably predict fish stocks' dynamics, giving stakeholders the information needed to make informed decisions. Crucial for real-time processing and storage of massive information is the scalability and flexibility offered by cloud infrastructure. This framework provides the appropriate usage of resources while simultaneously optimizing fishing tactics, which helps protect marine ecosystems. It demonstrates that method promotes sustainable fishing via thorough case studies and simulations. It provides a flexible and scalable solution to the problems confronting marine biodiversity and the fishing industry by combining advanced technology with ecological principles. To ensure a more egalitarian and sustainable future for fisheries management on a global scale, method is a giant leap forward in balancing commercial interests with environmental protection. Integrating data, ensuring sensors are accurate, managing huge datasets, managing regulatory compliance, and encouraging stakeholders to work together for sustainable practices are all challenges. © 2024 IEEE.

关键词： Fisheries

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Towards Optimal Fault Tolerant Scheduling in Computational Grid

Towards Optimal Fault Tolerant Scheduling in Computational G...

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International Conference on Emerging Technologies, ICET

作者： Muhammad Imran Iftikhar Azim Niaz Sajjad Haider Naveed Hussain M. A. Ansari Faculty of Computing Riphah International University Islamabad Pakistan Information Technology Department National University of Modern Languages Islamabad Pakistan Computer Science Department Federal Urdu University of Arts Science & Technology Islamabad Pakistan

ISBN: (纸本)9781424414932

Grid environment has significant challenges due to diverse failures encountered during job execution. Computational grids provide the main execution platform for long running jobs. Such jobs require long commitment of grid resources. Therefore fault tolerance in such an environment cannot be ignored. Most of the grid middleware have either ignored failure issues or have developed adhoc solutions. Most of the existing fault tolerance techniques are application dependant and causes cognitive problem. This paper examines existing fault detection and tolerance techniques in various middleware. We have proposed fault tolerant layered grid architecture with cross-layered design. In our approach Hybrid Particle Swarm Optimization (HPSO) algorithm and Anycast technique are used in conjunction with the Globus middleware. We have adopted a proactive and reactive fault management strategy for centralized and distributed environments. The proposed strategy is helpful in identifying root cause of failures and resolving cognitive problem. Our strategy minimizes computation and communication thus achieving higher reliability. Anycast limits the effect of Denial of Service/Distributed Denial of Service D(DoS) attacks nearest to the source of the attack thus achieving better security. Significant performance improvement is achieved through using Anycast before HPSO. The selection of more reliable nodes results in less overhead of checkpointing.

关键词： Fault tolerance Processor scheduling Grid computing Middleware computer crime Fault detection Particle swarm optimization Environmental management Security Checkpointing

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IEEE Transactions on Evolutionary Computation: Special Issue on Theoretical Foundations of Evolutionary Computation

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IEEE Transactions on Evolutionary Computation 2018年第4期22卷 632-632页

作者： Oliveto, Pietro S. Auger, Anne Chicano, Francisco Fonseca, Carlos M. Department of Computer Science University of Sheffield United Kingdom INRIA Ecole Polytechnique Paris France Department of Languages and Computing Sciences University of Malaga Spain Department of Informatics Engineering University of Coimbra Portugal

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Component Based Proactive Fault Tolerant Scheduling in Computational Grid

Component Based Proactive Fault Tolerant Scheduling in Compu...

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International Conference on Emerging Technologies, ICET

作者： Sajjad Haider Muhammad Imran Iftikhar Azim Niaz Saeed Ullah M. A. Ansari Information Technology Department National University of Modern Languages Islamabad Pakistan Faculty of Computing Riphah International University Islamabad Pakistan Computer Science Department Federal Urdu University of Arts Science & Technology Islamabad Pakistan

ISBN: (纸本)1424414935

Computational Grids have the capability to provide the main execution platform for high performance distributed applications. Grid resources having heterogeneous architectures, being geographically distributed and interconnected via unreliable network media are extremely complex and prone to different kinds of errors, failures and faults. Grid is a layered architecture and most of the fault tolerant techniques developed on grids use its strict layering approach. In this paper, we have proposed a cross-layer design for handling faults proactively. In a cross-layer design, the top- down and bottom-up approach is not strictly followed, and a middle layer can communicate with the layer below or above it [1]. At each grid layer there would be a monitoring component that would decide on predefined factors that the reliability of that particular layer is high, medium or low. Based on Hardware Reliability Rating (HRR) and Software Reliability Rating (SRR), the Middleware Monitoring Component / Cross- Layered Component (MMC/CLC) would generate a Combined Rating (CR) using CR calculation matrix rules. Each grid participating node will have a CR value generated through cross layered communication using HMC, MMC/CLC and SMC. All grid nodes will have their CR information in the form of a CR table and high rated machines would be selected for job execution on the basis of minimum CPU load along with different intensities of check pointing. Handling faults proactively at each layer of grid using cross communication model would result in overall improved dependability and increased performance with less overheads of check pointing.

关键词： Fault tolerance Processor scheduling Grid computing Chromium computer architecture Cross layer design Monitoring Distributed computing High performance computing Nonhomogeneous media

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Nearest Neighbor Algorithms Using xBR-Trees

Nearest Neighbor Algorithms Using xBR-Trees

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Panhellenic Conference on Informatics (PCI)

作者： George Roumelis Michael Vassilakopoulos Antonio Corral Information Systems Open University of Cyprus Nicosia Cyprus Department of Computer Science and Biomedical Informatics University of Central Greece Lamia Greece Department of Languages and Computing University of Almeria Almeria Spain

One of the common queries in spatial databases is the (K) Nearest Neighbor Query that discovers the (K) closest objects to a query object. Processing of spatial queries, in most cases, is accomplished by indexing spatial data by an access method. In this paper, we present algorithms for Nearest Neighbor Queries using a disk based structure that belongs to the Quad tree family, the xBR-tree, that can be used for indexing large point datasets. We demonstrate performance results (I/O efficiency and execution time) of alternative Nearest Neighbor algorithms, using real datasets.

关键词： Algorithm design and analysis Spatial databases Vegetation Nearest neighbor searches Indexing Informatics

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Artificial Bee Colony Based Gabor Parameters Optimizer (ABC-GPO) for Modulation Classification

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Computational Intelligence and Neuroscience 2022年第0期2022卷 9464633页

作者： Aljubayrin, Saad Sarfraz, Mubashar Ghauri, Sajjad A. Amirzada, Muhammad Rizwan Mezgebo Kebedew, Teweldebrhan Department of Computer Science College of Computing and Information Technology Shaqra University Shaqra Saudi Arabia Faculty of Engineering and Computer Science National University of Modern Languages Islamabad Pakistan School of Engineering & Applied Sciences ISRA University Islamabad Pakistan Ethio Telecom Addis Ababa Ethiopia

Modulation classification is one of the essential requirements in the various cognitive radio applications where prior information about the incoming signal is unknown. The modulation classification using a pattern recognition approach can be achieved in 2 modules: first, parameters are extracted from the noisy signal, and then feature selection is carried out using a Gabor filter network (GFN). In the second module, features are exploited for classification purposes. The modulation formats considered for the purpose of classification are BPSK, QPSK, 8PSK, 16PSK, 64PSK, 4FSK, 8FSK, 16FSK, QAM, 8QAM, 16QAM, 32QAM, and 64QAM. The Gabor filter parameters and weights of the adaptive filter are attuned using the Delta rule and recursive least square (RLS) algorithm until the cost function is minimized. In the end, the artificial bee colony (ABC) algorithm is used to optimize the Gabor parameters as well as the classifier's performance. The simulation results show the supremacy of the proposed classifier structure. © 2022 Saad AlJubayrin et al.

关键词： Adaptive filters

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Improved Attentive Recurrent Network for Applied Linguistics-Based Offensive Speech Detection

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computer Systems science & Engineering 2023年第11期47卷 1691-1707页

作者： Manar Ahmed Hamza Hala J.Alshahrani Khaled Tarmissi Ayman Yafoz Amira Sayed A.Aziz Mohammad Mahzari Abu Sarwar Zamani Ishfaq Yaseen 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 UniversityMakkahSaudi 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 Cairo11835Egypt Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Applied linguistics is one of the fields in the linguistics domain and deals with the practical applications of the language studies such as speech processing,language teaching,translation and speech *** ever-growing Online Social Networks(OSNs)experience a vital issue to confront,i.e.,hate *** the OSN-oriented security problems,the usage of offensive language is the most important threat that is prevalently found across the *** on the group targeted,the offensive language varies in terms of adult content,hate speech,racism,cyberbullying,abuse,trolling and *** these,hate speech is the most intimidating form of using offensive language in which the targeted groups or individuals are intimidated with the intent of creating harm,social chaos or *** Learning(ML)techniques have recently been applied to recognize hate speech-related *** current research article introduces a Grasshopper Optimization with an Attentive Recurrent Network for Offensive Speech Detection(GOARN-OSD)model for social *** GOARNOSD technique integrates the concepts of DL and metaheuristic algorithms for detecting hate *** the presented GOARN-OSD technique,the primary stage involves the data pre-processing and word embedding ***,this study utilizes the Attentive Recurrent Network(ARN)model for hate speech recognition and *** last,the Grasshopper Optimization Algorithm(GOA)is exploited as a hyperparameter optimizer to boost the performance of the hate speech recognition *** depict the promising performance of the proposed GOARN-OSD method,a widespread experimental analysis was *** comparison study outcomes demonstrate the superior performance of the proposed GOARN-OSD model over other state-of-the-art approaches.

关键词： Applied linguistics hate speech offensive language natural language processing deep learning grasshopper optimization algorithm

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Automata, languages and Programming 1

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丛书名： Lecture Notes in computer science

1000年

作者： Peter Widmayer Stephan Eidenbenz Francisco Triguero Rafael Morales Ricardo Conejo Matthew Hennessy

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