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applied Linguistics with Mixed Leader Optimizer Based English Text Summarization Model

Intelligent Automation & Soft computing 2023年第6期36卷 3203-3219页

作者： Hala J.Alshahrani Khaled Tarmissi Ayman Yafoz Abdullah Mohamed Manar Ahmed Hamza Ishfaq Yaseen Abu Sarwar Zamani Mohammad Mahzari 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 Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The term‘executed linguistics’corresponds to an interdisciplinary domain in which the solutions are identified and provided for real-time language-related *** exponential generation of text data on the Internet must be leveraged to gain knowledgeable *** extraction of meaningful insights from text data is crucial since it can provide value-added solutions for business organizations and *** Automatic Text Summarization(ATS)process reduces the primary size of the text without losing any basic components of the *** current study introduces an applied Linguistics-based English Text Summarization using a Mixed Leader-Based Optimizer with Deep Learning(ALTS-MLODL)*** presented ALTS-MLODL technique aims to summarize the text documents in the English *** accomplish this objective,the proposed ALTS-MLODL technique pre-processes the input documents and primarily extracts a set of ***,the MLO algorithm is used for the effectual selection of the extracted *** the text summarization process,the Cascaded Recurrent Neural Network(CRNN)model is exploited whereas the Whale Optimization Algorithm(WOA)is used as a hyperparameter *** exploitation of the MLO-based feature selection and the WOA-based hyper-parameter tuning enhanced the summarization *** validate the perfor-mance of the ALTS-MLODL technique,numerous simulation analyses were *** experimental results signify the superiority of the proposed ALTS-MLODL technique over other approaches.

关键词： Text summarization deep learning hyperparameter tuning applied linguistics multi-leader optimizer

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Network-based methods with heterogeneous data to identify severe COVID immune-related genes 26

Network-based methods with heterogeneous data to identify se...

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26th International Computer Science and Engineering Conference, ICSEC 2022

作者： Sagulkoo, Pakorn Suratanee, Apichat Plaimas, Kitiporn Chiang Mai University Center of Biomedical Informatics Department of Family Medicine Faculty of Medicine Chiang Mai50200 Thailand King Mongkut's University of Technology North Bangkok Department of Mathematics Faculty of Applied Science Bangkok10800 Thailand Faculty of Science Chulalongkorn University Advance Virtual and Intelligent Computing Center Department of Mathematics and Computer Science Bangkok10330 Thailand

ISBN: (纸本)9781665491983

Bioinformatics and systems biology play a vital role in the computational prediction of disease-associated genes using multi-omics data. The network-based approach is one of the most potent tools in disease-associated gene prediction. The two commonly used methods are neighborhood-based and network diffusion techniques. However, there is still a lack of studies comparing the performance of these methods, especially in terms of functional pathway discovery. Thus, this study demonstrated the performance comparison of these two techniques in both numerical accuracies based on the area under the receiver operating characteristic curve (AUROC) and biological meaning efficiency based on functional pathway enrichment. In this study, we analyzed data of severe COVID-19 immune-related genes using heterogeneous data. The prediction results of the COVID-19 immune-related genes in the human protein-protein interaction (PPI) network showed that the network diffusion had better performance in both AUROC and pathway enrichment even though it provided a longer computational time than the neighborhood method. © 2022 IEEE.

关键词： COVID-19

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Understanding Comparative Genomics Data Visualization for Biodiversity Domain Through Persona-Based Interviews

Understanding Comparative Genomics Data Visualization for Bi...

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2024 IEEE International Conference on computing, ICOCO 2024

作者： Chan, Vei Siang Mohamed, Farhan Bin Salleh, Faezah Binti Mohd Iglesias, Andres Jamaludin, Najwa Ayuni Binti Mustafa Albakri, Ikmal Faiq Albakri Media and Game Innovation Center of Excellence Institute of Human Centered Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia Department of Biosciences Faculty of Science Universiti Teknologi Malaysia Johor Bahru Malaysia Department of Applied Mathematics and Computational Sciences Universidad de Cantabria Santander Spain Pervasive Computing & Educational Technology Department of Media Interactive Fakulti Teknologi Maklumat dan Komunikasi Universiti Teknikal Malaysia Melaka Melaka Malaysia

ISBN: (纸本)9798331530303

Understanding the needs of genomic data visualization techniques is important to help bioinformaticians to perform their comparative analytical tasks effectively. DNA sequencing datasets consist of a long sequential string of nucleotide base pairs combination and are often involved in multiple sequences alignment for comparative analysis. Therefore, existing challenges include designing suitable visualization layout and the need of user-centered approach to ensure optimal user experience. Existing works also featured heavily on the qualitative evaluation of comparative visualization tools, such as use cases and case studies, which are difficult to quantify and conduct follow up evaluation for tool improvement. Hence, this paper aims to understand the needs of visualization tools in comparative genomic analysis to identify visualization tasks and visual design requirements. The methodology includes the creation of semi-structured interview based on personas template as guiding questions and the synthesis of thematic analysis results. These results presented the requirements of the visualization tasks for the biodiversity domain based on comparative targets. For future work, this research will design, develop, and validate the comparative visualization tool based on the discovered requirements. ©2024 IEEE.

关键词： Genome

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EXISTENCE OF UNDERCOMPRESSIVE TRAVELLING WAVES OF A NON-LOCAL GENERALISED KORTEWEG-DE VRIES-BURGERS EQUATION

arXiv

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arXiv 2024年

作者： Achleitner, F. Cuesta, C.M. Diez-Izagirre, X. Vienna University of Technology Institute for Analysis and Scientific Computing Wiedner Hauptstrasse 8-10 Wien1040 Austria Faculty of Science and Technology Department of Mathematics Aptdo. 644 Bilbao48080 Spain Faculty of Economics and Business Department of Applied Economics Oñati plaza 1 Donostia-San Sebastian20018 Spain

We study travelling wave solutions of a generalised Korteweg-de Vries-Burgers equation with a non-local diffusion term and a concave-convex flux. This model equation arises in the analysis of a shallow water flow by performing formal asymptotic expansions associated to the triple-deck regularisation (which is an extension of classical boundary layer theory). The resulting non-local operator is a fractional type derivative with order between 1 and 2. Travelling wave solutions are typically analysed in relation to shock formation in the full shallow water problem. We show rigorously the existence of travelling waves that, formally, in the limit of vanishing diffusion and dispersion would give rise to non-classical shocks, that is, shocks that violate the Lax entropy condition. The proof is based on arguments that are typical in dynamical systems. The nature of the non-local operator makes this possible, since the resulting travelling wave equation can be seen as a delayed integro-differential equation. Thus, linearisation around critical points, continuity with respect to parameters and a shooting argument, are the main steps that we have proved and adapted for solving this *** Codes 47J35, 26A33, 35C07 © 2024, CC BY.

关键词： Korteweg-de Vries equation

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Classification & Detection of Epilepsy Using IEEG Application 4

Classification & Detection of Epilepsy Using IEEG Applicatio...

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4th IEEE International Conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Kanna, R. Kishore Joshi, Kapil Singh, Kamreed Udham Pacholi, Ambali Thangam, Mathar Mohideen Nagoor Nimasajai, X.S. Blessing Department of Biomedical Engineering Chennai India Uttaranchal Institute of Technology Uttaranchal University Department of Cse Dehradun India School of Computing Graphic Era Hill University Uttarakhand Dehradun248007 India Graphic Era Deemed to Be University Uttarakhand Dehradun248007 India Gwalior India Faculty of Applied Medical Sciences University of Tabuk Department of Nursing Saudi Arabia King Khalid University College of Nursing Khamis Mushayt Saudi Arabia

ISBN: (纸本)9798350360165

More than only cognitive loss and impairment, epileptic seizures may seriously harm one's intellect. Early seizure detection is essential to minimize the harm caused by their possessions. Presently, medical professionals use optical testing of electroencephalographic (EEG) data for project labeling, which may be a labor-intensive and delayed procedure. By resolving EEG data using the best spatial convolutional interconnected system-long temporary thought (1D CNNLSTM) model, this study offers a revolutionary pattern for always identifying seizures. Before the raw EEG dossier is augmented into a 1D CNN that pulls appearance data from the dossier, it is preprocessed and standardized. Hence, worldly faces are extracted using Long Short-Term Memory (LSTM) layers, which are then combined into various suitably linked coatings for capture recognition. The projected model's representation was evaluated using the UCI epileptic capture acknowledgment dataset. For the twofold and five-class taking acknowledgment tasks, respectively, the outcomes show an exceptional acknowledgment veracity of 99.39 % and 82.00 %. The predicted model achieved better act when compared to existing machine intelligence setups and additional deep education models. These rulings highlight the promise of the proposed method for accurately diagnosing seizures in patients who are experiencing muscular spasms and acknowledge the potential for improved impartial outcomes. © 2024 IEEE.

关键词： Electroencephalography

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An Integrated Image Classification Approach to Detect the Flood Prone Areas using Sentinel-1 Images

An Integrated Image Classification Approach to Detect the Fl...

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International Conference on computing for Sustainable Global Development (INDIACom)

作者： Mohammed Siddique Tasneem Ahmed Mohd Shahid Husain Department of Computer Application Advanced Computing Research Lab Integral University Lucknow India Faculty of Information Technology Majan University College Muscat Oman Department of Information Technology CAS-Ibri University of Technology and Applied Sciences Muscat Oman

The untimely monsoon floods and the rise in the level of rivers in the north Indian state of Uttar Pradesh (UP) is a major challenge for coordinating relief and rescue operations. To address this challenge, the experts that are deploying a variety of techniques to evaluate the damages might get useful information from synthetic aperture radar (SAR) images which offer images in close to real-time. The Sentinel-1A satellite images from the European Space Agency provide C-band SAR images that have helped researchers achieve enhanced processes of flood detection and flood mapping in flood-prone areas. In regular cases, the Random Forest (RF) and K-Nearest Neighbor (KNN) supervised classification techniques provide higher accuracies than the unsupervised K-Means classification techniques. The integrated approach with the resultant accuracy provides long-reliable elucidation towards flood damages and assessment control. Thenceforth, the RF and KNN supervised classifications based outcomes were integrated with the outcomes of K-Means iterative clustering unsupervised classification producing an enhanced overall accuracy. The overall accuracy from the integrated approach for the city of Basti and Ayodhya is 97% and 93.8% which is higher than the accuracies derived from the supervised and unsupervised classifications. Further, these processed results when refined by time-series analysis over a number of years along with implementing change detection techniques would help in monitoring the flood situation and undertaking precautionary measures.

关键词： Radio frequency Satellites Time series analysis Urban areas Vegetation mapping Radar polarimetry Rivers

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Data Mining and Text Mining Approach: Query based Information Retrieval using Genetic Algorithm 5

Data Mining and Text Mining Approach: Query based Informatio...

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Ananthi, S. Venkateswaran, R. Vijaya, G. Arun, J. Sathya, R. Sri Eshwar College of Engineering Advanced Research Centre for Future Networks and Digital Twin Coimbatore India College of Computing & Information Sciences University of Technology and Applied Sciences Faculty-Cyber and Information Security Salalah Oman Sri Krishna College of Engineering and Tech Dept. of CSE Coimbatore India Sri Eshwar College of Engineering Dept. of ECE Coimbatore India Kongunadu College of Engg and Tech Department of IT Namakkal India

ISBN: (纸本)9798350367171

Information Retrieval system facilitates the retrieval efficiency of the user's requirement from a collection of information resources. In this research work, Genetic Algorithm (GA) with various fitness functions are implemented for retrieving the relevant document for the given textual query. To increase the efficiency of the retrieval system extended keywords are added to the retrieval system using query reformulation method. Furthermore, the selection procedure namely fitness proportionate selection is used to identify the best fit individual among the chosen population. This selection algorithm assumes that, for the chosen population, the fittest individuals have a greater chance of endurance than weaker ones. The performance of the proposed system is evaluated with three different datasets solely consists of 20,322 documents are analyzed using various matching functions and their results are examined. © 2024 IEEE.

关键词： Content based retrieval

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Enhancing Battery Voltage Prediction with Deep Learning: A Comparative Analysis of LSTM and Traditional Models

Enhancing Battery Voltage Prediction with Deep Learning: A C...

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IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Masoumeh Rostam Niakan Mojtaba Hajihosseini Seyed Saeed Madani Carlos Ziebert Department of Electricity and Energy Economics NRI Tehran Iran Laboratory for Renewable Energy Systems University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia Institute of Applied Materials-Applied Materials Physics Karlsruhe Institute of Technology Karlsruhe Germany

The growing demand for efficient energy storage solutions has sparked increased interest in precise battery voltage prediction. In this study the Long Short-Term Memory networks, are applied in the time series forecasting, to improve battery voltage prediction compared to conventional models. The developed forecasting system includes three main parts: Pre-processing, modeling, and evaluation. In the pre-processing, the voltage and current time series are normalized, and divided to the test and train subsets. Then the windows of consecutive samples are generated for both train and test subsets. The forecasting LSTM models are learnt from training data set, in the modeling phase. Finally, the Mean Absolute Error (MAE) is used as the evaluation criterion. This model is compared to two other neural network models. The study concludes that LSTM outperforms the other models, highlighted by a significantly lower MAE. With a comprehensive methodology and successful experimental framework, this paper demonstrates the efficacy of LSTM in predicting battery voltage, indicating its superiority over simple neural networks for time series forecasting.

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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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Detection of Pneumonia in Chest X-Ray with Bat-Algorithm Selected Deep and Handcrafted Features 8

Detection of Pneumonia in Chest X-Ray with Bat-Algorithm Sel...

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8th International Virtual Conference on Biosignals, Images, and Instrumentation, ICBSII 2022

作者： Suresh Manic, K. Kadry, Seifedine Biju, Roshima Balaji, Gangadharam Department of Electrical and Communication Engineering National University of Science and Technology Oman Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway System Engineer M/S TATA Consultancy Services Limited TCSL Siruseri SEZ Unit SIPCOT I.T. Park TN Chennai603103 India

Pneumonia is one infectious disease caused by viruses/bacteria, and early screening is necessary for the detection and treatment. Furthermore, pneumonia causes severe problems in children and elderly. The proposed work aims to develop a disease screening scheme for efficiently classifying the chest radiograph (X-ray) pictures into the Normal/Pneumonia group. The proposed process has the below phases;(i) Image collecting and resizing, (ii) Deep-feature extraction, (iii) Handcrafted feature extraction, (iv) Bat-Algorithm based feature selection and (v) Classification. In this work, the VGG16 scheme is considered to extract the deep-features and the necessary handcrafted features are mined using the Weighted Local Binary Pattern (WLBP). The necessary feature is then selected using the bat-algorithm supported feature selection. The experimental result of this study proves the accuracy of KNN is healthier (>98%) than other methods. © Published under licence by IOP Publishing Ltd.

关键词： Diseases

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