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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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Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

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Computers, Materials & Continua 2023年第11期77卷 2579-2594页

作者： Abdennasser Dahmani Yamina Ammi Nadjem Bailek Alban Kuriqi Nadhir Al-Ansari Salah Hanini Ilhami Colak Laith Abualigah El-Sayed M.El-kenawy Department of Mechanical Engineering Faculty of Science and TechnologyGIDD Industrial Engineering and Sustainable Development LaboratoryUniversity of RelizaneBourmadiaRelizane48000Algeria Laboratory of Biomaterials and Transport Phenomena(LBMPT) University of MedeaUrban PoleMedea26000Algeria Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of TamanghassetTamanrasset10034Algeria Sustainable Development and Computer Science Laboratory Faculty of Sciences and TechnologyAhmed Draia University of AdrarAdrarAlgeria CERIS Instituto Superior TécnicoUniversidade de LisboaLisbon1049-001Portugal Civil Engineering Department University for Business and TechnologyPristina10000Kosovo Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Engineering and Architecture Faculty Nisantasi UniversityIstanbulTurkey Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia Department of Computing and Information Systems School of Engineering and TechnologySunway University MalaysiaPetaling Jaya27500Malaysia Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running *** into renewable energy sources such as solar energy is being pursued to counter *** energy is one of the most promising renewable energy sources,as it has the potential to meet the world’s energy needs *** study aims to develop and evaluate artificial intelligence(AI)models for predicting hourly global *** hyperparameters were optimized using the Broyden-FletcherGoldfarb-Shanno(BFGS)quasi-Newton training algorithm and STATISTICA *** from two stations in Algeria with different climatic zones were used to develop the *** error measurements were used to determine the accuracy of the prediction models,including the correlation coefficient,the mean absolute error,and the root mean square error(RMSE).The optimal support vector machine(SVM)model showed exceptional efficiency during the training phase,with a high correlation coefficient(R=0.99)and a low mean absolute error(MAE=26.5741 Wh/m^(2)),as well as an RMSE of 38.7045 Wh/m^(2) across all ***,this study highlights the importance of accurate prediction models in the renewable energy,which can contribute to better energy management and planning.

关键词： Renewable energy energy prediction global irradiation artificial intelligence BFGS quasi-Newton training algorithm

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Is Alice Really in Wonderland? UWB-Based Proof of Location for UAVs with Hyperledger Fabric Blockchain

arXiv

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arXiv 2023年

作者： Fu, Lei Morón, Paola Torrico Queralta, Jorge Peña Hästbacka, David Edelman, Harry Westerlund, Tomi Turku Intelligent and Embedded Robotic Systems University of Turku Finland Department of Engineering and Business Turku Univesity of Applied Sciences Finland Computing Sciences Faculty of Information Technology and Communication Sciences Tampere University Finland

Remote identification of Unmanned Aerial Vehicles (UAVs) is becoming increasingly important since more UAVs are being widely used for different needs in urban areas. For example, in the US and in the EU, identification and position broadcasting is already a requirement for the use of drones. However, the current solutions do not validate the position of the UAV but its identity, while trusting the given position. Therefore, a more advanced solution enabling the proof of location is needed to avoid spoofing. We propose the combination of a permissioned blockchain managed by public authorities together with UWB-based communication to approach this. Specifically, we leverage the identity management tools from Hyperledger Fabric, an open-source permissioned blockchain framework, and ultra-wideband (UWB) ranging, leading to situated communication (i.e., simultaneous communication and localization). This approach allows us to prove both the UAV identity and also the location it broadcasts through interaction with ground infrastructure in known locations. Our initial experiments show that the proposed approach is viable and UWB transceivers can be used for UAVs to validate both their identity and position with ground infrastructure deployed in known locations. Copyright © 2023, The Authors. All rights reserved.

关键词： Blockchain

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Segmentation and Multi-facet Classification of Individual Logs in Wooden Piles 18th

Segmentation and Multi-facet Classification of Individual Lo...

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18th International Conference on Computer Aided systems Theory, EUROCAST 2022

作者： Praschl, Christoph Auersperg-Castell, Philipp Forster-Heinlein, Brigitte Zwettler, Gerald Adam Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Hagenberg Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Austria Bluedynamics Auersperg-Castell KG Kritzing 31 Freinberg Austria Faculty of Computer Science and Mathematics University of Passau Passau Germany

ISBN: (纸本)9783031253119

The inspection of products and assessment of quality is connected with high costs and time effort in many industrial domains. This also applies to the forestry industry. Utilizing state-of-the-art deep learning models allows automizing the analysis of wooden piles in a vision-based manner. In this work, a parallel two-step approach is presented for the segmentation and multi-facet classification of individual logs, according to the wood type and quality. The present approach is based on a preliminary log localization step and like this allows determining the quality, volume and also the value of individual logs, respectively the whole wooden pile. Using a YOLOv4 model for wood species classification for douglas firs, pines and larches results in an accuracy of 74.53%, while a quality classification model for spruce logs reaches 86.58%. In addition to that, the trained U-NET segmentation model reaches an accuracy of 93%. In the future, the underlying data set and models will be further improved and integrated to a mobile application for the on site analysis of wooden piles by foresters. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Detecting Covid-19 in Chest X-Ray Images with Convolutional Neural Network 2

Detecting Covid-19 in Chest X-Ray Images with Convolutional ...

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2nd International Conference on Computer Science and Engineering, IC2SE 2021

作者： Rashid, Nurullainy Mat Shuib, Liyana Eke, Christopher Ifeanyi Azmi, Nor A. Faculty of Computer Science and IT University of Malaya Department of Information Systems Kuala Lumpur Malaysia Faculty of Computing Federal University of Lafia Department of Computer Science Nasarawa State Nigeria School of Diagnostic and Applied Health Sciences Faculty of Health Sciences Bangi Malaysia

ISBN: (纸本)9781665400459

The World Health Organization (WHO) had declared Coronavirus disease (COVID-19), a deadly respiratory illness, as a pandemic in March 2020. The number of infected and deaths in COVID-19 positive patients has been rising at a distressing rate. A massive number of chest X-rays to analyse and the exhaustion from monotonous routine work may become prone to the escalation of error rate. For this reason, the use of the artificial intelligence-based tool will be beneficial in detecting subtle COVID-19, as well as to prevent false diagnoses on patients. In this study, several multi-class image classifications were developed using pre-Trained models, namely VGG16, ResNet50, MobbileNetV2, and Xception. A publicly available dataset of 14,220 chest X-ray images comprised of four different classes was used. Extensive evaluation and analysis of the Convolutional Neural Network model in predicting COVID-19 positives have been conducted. The MobileNetV2 model has been demonstrated as the best performing model out of four classifiers with an overall accuracy score of 92.48%. The model also yields 92.67% precision and 92.48% recall score, thus exhibiting a high success in the detection of COVID-19. © 2021 IEEE.

关键词： COVID-19

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Impact of Metaverse and Marketing Innovation on Digital Transformation 2

Impact of Metaverse and Marketing Innovation on Digital Tran...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Alshurideh, Muhammad Turki Akour, Iman A. Kurdi, Barween Al Hamadneh, Samer Alzoubi, Haitham M. University of Sharjah College of Business Department of Management Sharjah27272 United Arab Emirates The University of Jordan School of Business Department of Marketing Amman11942 Jordan University of Sharjah College of Computing and Informatics Department of Information Systems Sharjah27272 United Arab Emirates The Hashemite University Faculty of Economics and Administrative Sciences Department of Marketing Zarqa Jordan Skyline University College School of Business Sharjah United Arab Emirates Applied Science Private University Applied Science Research Center Amman Jordan

ISBN: (纸本)9798350335644

Business stakeholders have expressed a great deal of interest in the idea of boosting the value of their companies by fusing them with the emerging metaverse notion. The metaverse is a brand-new phenomenon that will soon completely transform both the digital and physical environments. In light of this, the author creates a framework for how the metaverse alters the universe. To comprehend the value of the metaverse in relation to digital transformation, this research aimed to examine the effects of metaverse and marketing innovation based on empirical data on how companies use cutting-edge technology in conjunction with marketing initiatives and their impact on digital transformation. The quantitative analysis applied to explore the research model, a descriptive, exploratory and analytical research design applied using convenient sampling. 61 digital marketing agencies were approached located in Dubai. Data collected from 196 respondents were incorporated for analysis using SmartPLS 4.0. The findings suggest a positive significant relationship of metaverse and marketing innovation with digital transformation. © 2023 IEEE.

关键词： Marketing

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Retraction Note: Optimizing radio access in 5G vehicle networks using novel machine learning-driven resource management

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Optical and Quantum Electronics 2024年第10期56卷 1-2页

作者： Natarajan, Rajesh Mahadev, Natesh Alfurhood, Badria Sulaiman Ranjith, Christodoss Prasanna Zaki, John Manu, M. N. Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences-Shinas Shinas Oman Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysore India Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Computer and Systems Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Information Science and Engineering SJBIT Bengaluru India

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Artificial Intelligence-Driven Lung Cancer Detection Model Using Chest Radiography Images

SSRN

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SSRN 2023年

作者： Malebary, Sharaf J. Abulfaraj, Anas W. Dutta, Ashit Kumar Sait, Abdul Rahaman Wahab Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University P.O. Box 344 Rabigh21911 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University P.O. Box 344 Rabigh21911 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Ad Diriyah Riyadh13713 Saudi Arabia Department of Archives and Communication King Faisal University Al Ahsa Hofuf31982 Saudi Arabia

In recent times, medical imaging techniques are widely applied for detecting lung cancer (LC) in the earlier stages. However, there is a lack of an effective LC detection model using chest radiography (CXR) images. The developers face challenges in extracting key features from the CXR images due to their complexities. There is a demand for a framework for handling the complexities of CXR images and generating insight to assist clinicians and radiologists to make a decision. Therefore, the researchers intend to build an LC detection framework using the CXR images. This study proposes an artificial intelligence-driven LC detection model using improved crow search optimization (ICSO) feature selection and the ResNest model. A Cauchy mutation is introduced in the ICSO for updating the position of the image features. The ResNest model’s hyper-parameters are tuned using the Harris Hawks optimization algorithm. To evaluate the proposed model’s performance, the researcher obtained normal and abnormal CXR images from seven healthcare centers across India. The dataset comprises 789 normal and 843 abnormal CXR images. In addition, the researchers employed baseline models for the comparative evaluation. The comparative analysis findings highlighted that the proposed framework obtained an average accuracy, F1-measure, MCC, and Kappa of 98.3, 97.9, 96.4, and 95.6. The area under the receiver operating characteristics and precision-recall curves of 0.99 and 0.98 outlines the significance of the proposed model in detecting LC from the CXR images. The model can assist clinicians and radiologists in making effective decisions and identifying the LC in the earlier stages. © 2023, The Authors. All rights reserved.

关键词： Diseases

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Infrastructural justice for responsible software engineering

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Journal of Responsible Technology 2024年 19卷

作者： Robinson, Sarah Buckley, Jim Ciolfi, Luigina Linehan, Conor McInerney, Clare Nuseibeh, Bashar Twomey, John Rauf, Irum McCarthy, John Lero - SFI Research Centre for Software Ireland School of Applied Psychology University College Cork Ireland School of Computer Science and Information Systems University of Limerick Ireland Open University School of Computing and Communications Walton Hall Milton Keynes United Kingdom

In recent years, we have seen many examples of software products unintentionally causing demonstrable harm. Many guidelines for ethical and responsible computing have been developed in response. Dominant approaches typically attribute liability and blame to individual companies or actors, rather than understanding how the working practices, norms, and cultural understandings in the software industry contribute to such outcomes. In this paper, we propose an understanding of responsibility that is infrastructural, relational, and cultural;thus, providing a foundation to better enable responsible software engineering into the future. Our approach draws on Young's (2006) social connection model of responsibility and Star and Ruhleder's (1994) concept of infrastructure. By bringing these theories together we introduce a concept called infrastructural injustice, which offers a new way for software engineers to consider their opportunities for responsible action with respect to society and the planet. We illustrate the utility of this approach by applying it to an Open-Source software communities’ development of Deepfake technology, to find key leverage points of responsibility that are relevant to both Deepfake technology and software engineering more broadly. © 2024

关键词： Deepfake technology Infrastructure Installed base Responsible software engineering Social connection model of responsibility

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Wind Driven Optimization-Based Medical Image Encryption for Blockchain-Enabled Internet of Things Environment

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Computers, Materials & Continua 2022年第11期73卷 3219-3233页

作者： C.S.S.Anupama Raed Alsini N.Supriya E.Laxmi Lydia Seifedine Kadry Sang-Soo Yeo Yongsung Kim Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Malla Reddy Engineering College(A)Hyderabad500100India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University College4612KristiansandNorway Department of Computer Engineering Mokwon UniversityDaejeon35349Korea Department of Technology Education Chungnam National University 99Daehak-roYuseong-guDaejeon34134Korea

Internet of Things(IoT)and blockchain receive significant interest owing to their applicability in different application areas such as healthcare,finance,transportation,*** image security and privacy become a critical part of the healthcare sector where digital images and related patient details are communicated over the public *** paper presents a new wind driven optimization algorithm based medical image encryption(WDOA-MIE)technique for blockchain enabled IoT *** WDOA-MIE model involves three major processes namely data collection,image encryption,optimal key generation,and data ***,the medical images were captured from the patient using IoT ***,the captured images are encrypted using signcryption *** addition,for improving the performance of the signcryption technique,the optimal key generation procedure was applied by WDOA *** goal of the WDOA-MIE algorithm is to derive a fitness function dependent upon peak signal to noise ratio(PSNR).Upon successful encryption of images,the IoT devices transmit to the closest server for storing it in the blockchain *** performance of the presented method was analyzed utilizing the benchmark medical image *** security and the performance analysis determine that the presented technique offers better security with maximum PSNR of 60.7036 dB.

关键词： Internet of things image security medical images encryption optimal key generation blockchain

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