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9th IEEE International Conference on Progress in Informatics and computing, PIC 2024

作者： Rao, Sana Kashif, Rao Virtual University of Pakistan Department of Computer Science Multan Pakistan National University of Modern Languages Department of Computing Pakistan

ISBN: (纸本)9798350363203

Optical flow estimation is essential in computer vision for analyzing and interpreting motion in image sequences. For agriculture, optical flow can be a powerful tool in predicting plant health, particularly in identifying water-stressed versus well-watered conditions in wilted plant leaves. While traditional methods for optical flow estimation have made significant progress, challenges such as ill-defined edges and blurred motion boundaries remain, particularly when dealing with complex motion patterns like those found in natural leaf movements. This paper investigates the robustness of non-local total variation methods in optical flow estimation for wilted plant leaves. By leveraging the strengths of non-local total variation-based approaches, we aim to improve motion boundary preservation in wilted leaf motion sequences, offering a way to differentiate between water-stressed and well-watered leaves. We review existing non-local total variation methods, analyze their performance on wilted plant datasets, and present a new approach integrating weighted guided filtering to handle edge preservation. The findings suggest that non-local total variation with weighted guided filter achieves robust optical flow estimations, offering potential for precision agriculture applications, particularly in detecting early signs of drought stress. © 2024 IEEE.

关键词： Optical flows

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Robust Optical Flow Estimation for Wilted Plant Leaves Motion: Predicting Water-Stressed and Well-Watered Conditions

Robust Optical Flow Estimation for Wilted Plant Leaves Motio...

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Sana Rao Rao Kashif Department of Computer Science Virtual University of Pakistan Multan Pakistan Department of Computing National University of Modern Languages Pakistan

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

关键词： Precision agriculture Optical filters Filtering Reviews Plants (biology) Image edge detection Estimation Robustness Optical flow Stress

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Modified Sine Cosine Optimization with Adaptive Deep Belief Network for Movie Review Classification

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Intelligent Automation & Soft computing 2023年第7期37卷 283-300页

作者： Hala J.Alshahrani Abdulbaset Gaddah Ehab S.Alnuzaili Mesfer Al Duhayyim Heba Mohsen Ishfaq Yaseen Amgad Atta Abdelmageed Gouse Pasha Mohammed 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 English College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer63311Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Aflaj16828Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Sentiment analysis(SA)is a growing field at the intersection of computer science and computational linguistics that endeavors to automati-cally identify the sentiment presented in *** linguistics aims to describe the fundamental methods utilized in the formation of computer methods for understanding natural *** is classified as a negative or positive assessment articulated through *** can be commonly used for the movie review classification that involves the automatic determination that a review posted online(of a movie)can be negative or positive toward the thing that has been *** learning(DL)is becoming a powerful machine learning(ML)method for dealing with the increasing demand for precise *** this motivation,this study designs a computational intelligence enabled modified sine cosine optimization with a adaptive deep belief network for movie review classification(MSCADBN-MVC)*** major intention of the MSCADBN-MVC technique is focused on the identification of sentiments that exist in the movie review ***,the MSCADBN-MVC model follows data pre-processing and the word2vec word embedding *** the classification of sentiments that exist in the movie reviews,the ADBN model is utilized in this *** last,the hyperparameter tuning of the ADBN model is carried out using the MSCA technique,which integrates the Levy flight concepts into the standard sine cosine algorithm(SCA).In order to demonstrate the significant performance of the MSCADBN-MVC model,a wide-ranging experimental analysis is performed on three different *** comprehensive study highlighted the enhancements of the MSCADBN-MVC model in the movie review classification process with maximum accuracy of 88.93%.

关键词： Computational linguistics movie review analysis sentiment analysis sentiment classification deep learning

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Robust speech perception and classification-driven deep convolutional neural network with natural language processing

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Alexandria Engineering Journal 2025年 123卷 358-368页

作者： Alshammari, Alya Alzaidi, Muhammad Swaileh A. Alrusaini, Othman Alshuhail, Asma Allafi, Randa Yafoz, Ayman Alsini, Raed Khadidos, Alaa O. Department of Applied Linguistics College of Languages Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of English Language College of Language Sciences King Saud University P. O. Box 145111 Riyadh Saudi Arabia Department of Engineering and Applied Sciences Applied College Umm Al-Qura University Makkah Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal University Saudi Arabia Department of Computer Science College of Sciences Northern Border University Arar Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

Speech recognition systems use computer algorithms to interpret and process spoken words and translate them into text. Speech-to-text, or Speech detection, is the proficiency of a program or machine to find spoken words aloud and translate them into plain text. Speech detection systems are the most common complex systems that use wide-ranging research in computer engineering, linguistics, and computer science. In today's significant data era, several machine learning (ML) approaches for processing raw, unlabelled speech data still need to be improved. Deep learning (DL) models, on the other hand, with their powerful modelling ability for extensive data, are capable of directly processing unlabelled data and have become a crucial area of research in the domain of automated speech recognition. The DL model has recently gained popularity for voice disorders and automated speech detection. DL is an ML that exploits artificial neural networks (ANN) to gain meaningful insights from data. The DL approach demonstrated great success and achieved significant attention in different fields comprising speech processing, computer vision (CV), and natural language processing (NLP). Therefore, this article develops a Deep Learning with Natural Language Processing based Robust Speech Detection (DLNLP-RSD) technique. The DLNLP-RSD technique derives features from the mel-frequency cepstral coefficient (MFCC) technique. Followed by the DLNLP-RSD technique employs a one-dimensional deep convolutional neural network (CNN) approach for the detection and classification of speech. Moreover, the parameter tuning model uses the grasshopper fractals optimization algorithm (GOA) approach. The performance evaluation of the proposed technique highlights its significant performance over other models concerning distinct measures. An extensive simulation analysis is executed to examine the improved results of the DLNLP-RSD model. The experimental validation of the DLNLP-RSD technique portrayed a sup

关键词： Convolutional neural networks

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Applied Linguistics with Mixed Leader Optimizer Based English Text Summarization Model

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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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Cloud-Based Intelligent Patient Triage Systems with SVM Classifiers for Emergency departments 1

Cloud-Based Intelligent Patient Triage Systems with SVM Clas...

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1st International Conference on Innovations in Communications, Electrical and computer Engineering, ICICEC 2024

作者： Shibu, N.V. Sujatha, M. Suresh, M. Xavier Sindhuja, K. Raj, T. Saju Srinivasan, C. SRM Institute of Science and Technology Department of Computing Technologies School of Computing Kattankulathur Tamil Nadu Chennai India Easwari Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vit Bhopal University Department of Physics School of Advanced Sciences and Languages Madhya Pradesh Bhopal India J.J College of Engineering and Technology Department of Information Technology Tamil Nadu Tiruchirappalli India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha University Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376517

Efficient patient triage in emergency departments is crucial for the timely and successful allocation of resources and treatment in the constantly changing healthcare environment. To improve patient triage systems, this research suggests a new strategy that makes use of cloud computing and support vector machine (SVM) classifiers. The proposed solution endeavors to enhance patient outcomes in emergency departments by optimizing resource allocation via the use of cloud infrastructure's scalability and accessibility in conjunction with SVM algorithms' strong classification capabilities. Quickly classifying patients according to severity, the system integrates and analyzes data in real-time, allowing healthcare providers to prioritize high-risk situations and distribute resources appropriately. Healthcare practitioners can work together more efficiently and share patient data with cloud-based architecture, which promotes a more comprehensive approach to treatment. Results from the experiments prove that the system works as advertised and can be easily scaled up, highlighting its revolutionary potential to change the way emergency healthcare facilities handle patient triage. The growing body of evidence demonstrates the value of intelligent systems for healthcare delivery optimization and emergency patient care improvement via the use of technology. © 2024 IEEE.

关键词： Data-driven triage Emergency medicine Healthcare efficiency Healthcare optimization Real-time decision support Resource management

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Cloud-Enabled Predictive Vehicle Health Monitoring and Maintenance with LSTM Networks for Time-Series Analysis 1

Cloud-Enabled Predictive Vehicle Health Monitoring and Maint...

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1st International Conference on Innovations in Communications, Electrical and computer Engineering, ICICEC 2024

作者： Vinya, Viyyapu Lokeshwari Manivannan, K.K. Manivannan, S.K. Sathiyamoorthy, C. Jagadeeswaran, M. Meenakshi, B. SRM Institute of Science and Technology Department of Computing Technologies School of Computing Kattankulathur Tamil Nadu Chennai India Easwari Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vit Bhopal University Department of Physics School of Advanced Sciences and Languages Madhya Pradesh Bhopal India J.J College of Engineering and Technology Department of Information Technology Tamil Nadu Tiruchirappalli India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha University Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376517

Maximizing operational efficiency and customer satisfaction in the automobile sector depends on maintaining vehicle health and avoiding maintenance downtime. Vehicle Internet of Things (IoT) devices capture massive volumes of sensor data, enabling predictive maintenance. This research offers cloud-based predictive vehicle health monitoring and maintenance systems using Long Short-Term Memory (LSTM) networks for time-series analysis. System architecture uses IoT sensors in automobiles to gather real-time data on engine temperature, oil pressure, and vibration. This data is stored, analyzed, and predicted on clouds. To assess time-series sensor data and forecast failures or maintenance requirements, LSTM networks, a form of recurrent neural network, are used. The proposed system uses data preprocessing to manage missing values and outliers, feature extraction to find significant sensor data patterns, and LSTM-based predictive modeling to predict vehicle health conditions. Technology may predict future faults by continually monitoring and evaluating the vehicle's state, allowing proactive maintenance to avoid expensive breakdowns and downtime. The cloud-based architecture is scalable and flexible, providing smooth connection with fleet management systems and remote vehicle health monitoring across scattered sites. The suggested method optimizes maintenance schedules and resource allocation using predictive insights from real-time sensor data to improve operational efficiency and save costs. It may improve predictive vehicle health monitoring and maintenance in the automobile sector, improving dependability, safety, and cost. © 2024 IEEE.

关键词： Customer satisfaction

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Knowledge Discovery in COVID-19 Research Literature

Knowledge Discovery in COVID-19 Research Literature

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International Conference on Recent Advances in Natural Language Processing: Deep Learning for Natural Language Processing Methods and Applications, RANLP 2021

作者： Estevanell-Valladares, Ernesto L. Piad-Morffis, Alejandro Estevez-Velarde, Suilan Gutierrez, Yoan Montoyo, Andres Muñoz, Rafael Almeida-Cruz, Yudivián School of Math and Computer Science University of Havana Cuba Department of Languages and Computing Systems University of Alicante Spain

ISBN: (纸本)9789544520724

This paper presents the preliminary results of an ongoing project that analyzes the growing body of scientific research published around the COVID-19 pandemic. In this research, a general-purpose semantic model is used to double annotate a batch of 500 sentences that were manually selected from the CORD-19 corpus. Afterwards, a baseline text-mining pipeline is designed and evaluated via a large batch of 100, 959 sentences. We present a qualitative analysis of the most interesting facts automatically extracted and highlight possible future lines of development. The preliminary results show that general-purpose semantic models are a useful tool for discovering fine-grained knowledge in large corpora of scientific documents. © 2021 Incoma Ltd. All rights reserved.

关键词： COVID-19

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The Combination of Recognition Technology and Artificial Intelligence for Questioning and Clarification Mechanisms to Facilitate Meaningful EFL Writing in Authentic Contexts 6th

The Combination of Recognition Technology and Artificial Int...

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Proceedings of the 6th International Conference on Innovative Technologies and Learning, ICITL 2023

作者： Hwang, Wu-Yuin Nurtantyana, Rio Lai, Yu-Fu Chiang, I-Chin Nonie Ghenia, George Tsai, Ming-Hsiu Michelle Graduate Institute of Network Learning Technology National Central University Taoyuan City Taiwan School of Computing Telkom University Bandung Indonesia Department of Humanities National Open University New Taipei City Taiwan Department of Computer Science Brunel University London United Kingdom Department of Foreign Languages and Literature Feng Chia University Taichung City Taiwan Department of Computer Science and Information Engineering College of Science and Engineering National Dong Hwa University Hualien County Taiwan

ISBN: (纸本)9783031401121

Most studies of English as a Foreign Language (EFL) writing usually used grammar checking to help EFL learners to check writing errors. However, it is not enough since EFL learners have to learn how to create more meaningful content, particularly using their surroundings in authentic contexts. Therefore, we develop one app, Ubiquitous English (UEnglish), with recognition technology, particularly Image-to-Text Recognition (ITR) texts to provide the vocabulary and description from authentic pictures, and generative-AI that can generate meaningful questions and clarifications to trigger EFL learners to write more. In addition, EFL learners need to answer the question from generative-AI before they receive the clarification. Hence, we proposed a Smart Questioning-Answering-Clarification (QAC) mechanism to help EFL writing. A total of 35 participants were assigned into two groups, experimental groups (EG) with 19 learners and control groups (CG) with 16 learners with/without Smart QAC mechanism support, respectively. In this study, the quasi-experiment was conducted over five weeks and we used quantitative analysis methods. The results revealed that the EG with ITR-texts and Smart QAC had a significant difference with CG in the learning behaviors and post-test. Furthermore, EG could write more meaningful words in the assignments. Therefore, the Smart QAC mechanism could facilitate EFL learners to enhance their EFL writing in authentic contexts. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Character recognition

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Sine Cosine Optimization with Deep Learning-Based Applied Linguistics for Sentiment Analysis on COVID-19 Tweets

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computers, Materials & Continua 2023年第6期75卷 4767-4783页

作者： Abdelwahed Motwakel Hala J.Alshahrani Abdulkhaleq Q.A.Hassan Khaled Tarmissi Amal S.Mehanna Ishfaq Yaseen Amgad Atta Abdelmageed Mohammad Mahzari 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 English College of Science and Arts at MahayilKing Khalid UniversityMuhayil63763Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Applied linguistics is an interdisciplinary domain which identifies,investigates,and offers solutions to language-related real-life *** new coronavirus disease,otherwise known as Coronavirus disease(COVID-19),has severely affected the everyday life of people all over the ***,since there is insufficient access to vaccines and no straight or reliable treatment for coronavirus infection,the country has initiated the appropriate preventive measures(like lockdown,physical separation,and masking)for combating this extremely transmittable ***,individuals spent more time on online social media platforms(i.e.,Twitter,Facebook,Instagram,LinkedIn,and Reddit)and expressed their thoughts and feelings about coronavirus *** has become one of the popular social media platforms and allows anyone to post *** study proposes a sine cosine optimization with bidirectional gated recurrent unit-based senti-ment analysis(SCOBGRU-SA)on COVID-19 *** SCOBGRU-SA technique aimed to detect and classify the various sentiments in Twitter data during the COVID-19 *** SCOBGRU-SA technique follows data pre-processing and the Fast-Text word embedding process to accomplish ***,the BGRU model is utilized to recognise and classify sen-timents present in the ***,the SCO algorithm is exploited for tuning the BGRU method’s hyperparameter,which helps attain improved classification *** experimental validation of the SCOBGRU-SA technique takes place using a benchmark dataset,and the results signify its promising performance compared to other DL models.

关键词： Applied linguistics deep learning sentiment analysis COVID-19 pandemic sine cosine optimization twitter

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