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

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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Optical flow estimation technique for hazy scenes 2

Optical flow estimation technique for hazy scenes

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2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Kashif, Rao Rao, Sana Ltd Faculty of Engineering and Computer Science Department of CS/IT National University of Modern Languages Multan Pakistan Xiamen University School of Informatics Xiamen China Fujian Key Laboratory of Sensing and Computing for Smart City Xiamen China

ISBN: (数字)9781665498197

ISBN: (纸本)9781665498197

Hazy scenes limit the visibility and make the optical flow estimation task challenging due to low contrast images. Previous methods are not robust when applied to hazy image sequences because the two assumptions (brightness constancy and smoothness) break down due to the low contrast outdoor effects. Based on the pre-processing work on images, we applied an image dehazing method on image sequences first to handle haze and then estimated optical flow. The proposed method is based on a classical pyramidal technique. Results show that our method performs well on hazy scenes as compared to the other existing method. © 2022 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

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.

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

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Towards an intelligent environment for programming Multi-core computing systems

Towards an intelligent environment for programming Multi-cor...

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Workshops on Parallel Processing, Euro-Par 2008: VHPC 2008, UNICORE 2008, HPPC 2008, SGS 2008, PROPER 2008, ROIA 2008, and DPA 2008

作者： Pllana, Sabri Benkner, Siegfried Mehofer, Eduard Natvig, Lasse Xhafa, Fatos University of Vienna Department of Scientific Computing Nordbergstrasse 15 1090 Vienna Austria NTNU Department of Computer and Information Science Sem Saelands vei 9 NO-7491 Trondheim Norway UPC Department of Languages and Informatics Systems C/Jordi Girona 1-3 08034 Barcelona Spain

ISBN: (纸本)3642009549

In this position paper we argue that an intelligent program development environment that proactively supports the user helps a mainstream programmer to overcome the difficulties of programming multi-core computing systems. We propose a programming environment based on intelligent software agents that enables users to work at a high level of abstraction while automating low-level implementation activities. The programming environment supports program composition in a model-driven development fashion using parallel building blocks and proactively assists the user during major phases of program development and performance tuning. We highlight the potential benefits of using such a programming environment with usage-scenarios. An experiment with a parallel building block on a Sun UltraSPARC T2 Plus processor shows how the system may assist the programmer in achieving performance improvements. © Springer-Verlag Berlin Heidelberg 2009.

关键词： Software agents

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A data warehouse approach to semantic integration of pseudomonas data

A data warehouse approach to semantic integration of pseudom...

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7th International Conference on Data Integration in the Life sciences, DILS 2010

作者： Marrakchi, Kamar Briache, Abdelaali Kerzazi, Amine Navas-Delgado, Ismael Aldana-Montes, José Francisco Ettayebi, Mohamed Lairini, Khalid Rossi Hassani, Badr Din Department of Biology Faculty of Sciences and Techniques University Abdelmalek Essaâdi BP: 416 Tangier Morocco Department of Computer Languages and Computing Science Higher Technical School of Computer Science Engineering University of Málaga 29071 Malaga Spain Department of Biology Faculty of Sciences Dhar-Mahraz Sidi Med Ben Abdellah University B.P. 1796 Atlas Fez Morocco

ISBN: (纸本)3642151191

Biological research and development are routinely producing terabytes of data that need to be organized, queried and reduced to useful scientific knowledge. Even though data integration can provide solutions to such biological problems, it is often problematic due to the sources' heterogeneity and their semantic and structural diversity. Moreover, necessary updates of both structure and content of databases provide further challenges for an integration process. We present a new biological data warehouse for Pseudomonas species "PseudomonasDW" to integrate annotation and pathway data from highly different resources. The combination of knowledge from multiple disciplines and sources should advance the understanding of cellular processes and lead to the prediction of cellular behavior in its entirety. The key aspect of our approach is the combination of a materialized and a virtual data integration to exploit their advantages in a new hybrid approach. The data are extracted from the original data sources using SB-KOM (System Biology Khaos Ontology-based Mediator) and then stored locally in the data warehouse to ensure a fast performance and data consistency. © 2010 Springer-Verlag.

关键词： Ontology

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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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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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Method to support diagnosis of Cervical Intraepithelial Neoplasia (CIN) based on structural analysis of histological images

Method to support diagnosis of Cervical Intraepithelial Neop...

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25th IEEE International Symposium on computer-Based Medical Systems, CBMS 2012

作者： Miranda, Gisele Helena Barboni Soares, Edson Garcia Barrera, Junior Felipe, Joaquim Cezar Department of Computing and Mathematics Faculty of Philosophy Sciences and Languages of Ribeirão Preto University of São Paulo Brazil Department of Pathology Faculty of Medicine of Ribeirão Preto University of São Paulo Brazil Department of Computer Science Institute of Mathematics and Statistics University of São Paulo Brazil

ISBN: (纸本)9781467320511

Histopathology is considered one of the most important tools for diagnosis in medical routine. It is characterized by the study of structural and morphological changes of the cells, caused by diseases in biological tissues. The use of computational techniques in the processing of histopathological images allows the identification of structural elements as well as the determination of inherent characteristics, supporting the study of the structural organization of tissues and their pathological changes. Within this perspective, the overall objective of this work includes the proposal, the implementation and the evaluation of a methodology for the analysis of cervical intraepithelial neoplasia (CIN) from histopathological samples through techniques based on Neighbourhood Graphs theory and Complex Networks. The proposed method was evaluated concerning the detection of the presence of lesions in the tissue. The maximum accuracy obtained in the evaluation of the detection of abnormalities was 88%. Since this method is generic, it can be applied to other types of lesions and tissues. © 2012 IEEE.

关键词： Tissue

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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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A Multiclass Depression Detection in Social Media Based on Sentiment Analysis 17th

A Multiclass Depression Detection in Social Media Based on S...

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17th International Conference on Information Technology: New Generations, ITNG 2020

作者： Mustafa, Raza Ul Ashraf, Noman Ahmed, Fahad Shabbir Ferzund, Javed Shahzad, Basit Gelbukh, Alexander Department of Computer Science COMSATS University Islamabad Islamabad Pakistan IPN - Computing Research Center Mexico City Mexico Yale School of Medicine New HavenCT United States Deptartment of Software Engineering National University of Modern Languages NUML Islamabad Pakistan

ISBN: (纸本)9783030430191

Depression is a common mental health disorder. Despite its high prevalence, the only way of diagnosing depression is through self-reporting. However, 70% of the patients would not consult doctors at an early stage of depression. Meanwhile people increasingly relying on social media for sharing emotions, and daily life activities thus helpful for detecting their mental health. Inspired by these a total of 179 depressive individuals selected from Twitter, who have reported depression and they are on medical treatment. A sample of their recent tweets collected ranges from (200 to 3200) tweets per person. From their tweets, we selected 100 most frequently used words using Term Frequency-Inverse Document Frequency (TF-IDF). Later, we used the 14 psychological attributes in Linguistic Inquiry and Word Count (LIWC) to classify these words into emotions. Moreover, weights were assigned to each word from happy to unhappy after classification by LIWC and trained machine learning classifiers to classify the users into three classes of depression High, Medium, and Low. According to our study, better features selections and their combination will help to improve performance and accuracy of classifiers. © Springer Nature Switzerland AG 2020.

关键词： Sentiment analysis

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