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Visual Lip-Reading for Quranic Arabic Alphabets and Words Using Deep Learning

Computer systems science & Engineering 2023年第9期46卷 3037-3058页

作者： nada Faisal Aljohani Emad sami Jaha Department of Computer science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

The continuing advances in deep learning have paved the way for several challenging *** such idea is visual lip-reading,which has recently drawn many research ***-reading,often referred to as visual speech recognition,is the ability to understand and predict spoken speech based solely on lip movements without using *** to the lack of research studies on visual speech recognition for the Arabic language in general,and its absence in the Quranic research,this research aims to fill this *** paper introduces a new publicly available Arabic lip-reading dataset containing 10490 videos captured from multiple viewpoints and comprising data samples at the letter level(i.e.,single letters(single alphabets)and Quranic disjoined letters)and in the word level based on the content and context of the book Al-Qaida *** research uses visual speech recognition to recognize spoken Arabic letters(Arabic alphabets),Quranic disjoined letters,and Quranic words,mainly phonetic as they are recited in the Holy Quran according to Quranic study aid entitled Al-Qaida *** study could further validate the correctness of pronunciation and,subsequently,assist people in correctly reciting ***,a detailed description of the created dataset and its construction methodology is *** new dataset is used to train an effective pre-trained deep learning CNN model throughout transfer learning for lip-reading,achieving the accuracies of 83.3%,80.5%,and 77.5%on words,disjoined letters,and single letters,respectively,where an extended analysis of the results is ***,the experimental outcomes,different research aspects,and dataset collection consistency and challenges are discussed and concluded with several new promising trends for future work.

关键词： Visual speech recognition lip-reading deep learning quranic Arabic dataset Tajwid

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Improving Date Fruit Classification Using Cycle GAN-Generated Dataset

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Computer Modeling in Engineering & sciences 2022年第4期131卷 331-348页

作者： Dina M.Ibrahim nada M.Elshennawy Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Computers and Control Engineering Department Faculty of EngineeringTanta UniversityTanta37133Egypt

Dates are an important part of human *** are high in essential nutrients and provide a number of health *** fruits are also known to protect against a number of diseases,including cancer and heart *** fruits have several sizes,colors,tastes,and *** are a lot of challenges facing the date *** of the most significant challenges is the classification and sorting of *** there is no public dataset for date fruits,which is a major limitation in order to improve the performance of convolutional neural networks(CNN)models and avoid the overfitting *** this paper,an augmented date fruits dataset was developed using Deep Convolutional Generative Adversarial Networks(DCGAN)and CycleGAN approach to augment our collected date fruit *** augmentation is required to address the issue of a restricted number of images in our datasets,as well as to establish a balanced *** are three types of dates in our proposed dataset:sukkari,Ajwa,and *** dataset augmentation,we train our created dataset using ResNet152V2 and CNN models to assess the classification process for our three categories in the *** train these two models,we start with the original ***,the models were trained using the DCGAN-generated dataset,followed by the CycleGAN-generated *** resulting results demonstrated that when using the ResNet152V2model,the CycleGAN-generated dataset had the highest classification performance with 96.8%accuracy,followed by the CNN model with 94.3%accuracy.

关键词： Dates fruits data augmentation DCGAN CycleGAN,deep learning convolution neural networks

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Land use optimization for sustainable development of mountain regions of western serbia

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Journal of Mountain science 2018年第7期15卷 1471-1480页

作者： Tijana VULEVIC Mirjana TODOsIJEVIC nada DRAGOVIC Miodrag ZLATIC University of Belgrade Faculty of Forestry Kneza Viseslava 1 11000 Belgrade Serbia

This study analyses the possibilities for optimal land use structure in mountainous areas of rural serbia, which is faced with problems such as unfavorable terrain characteristics and inadequate land use patterns causing economic underdevelopment and increasing population migration. In the case of a small privately owned farm in Western serbia, optimal land use pattern is determined using the linear programming model. The objective function-cost-effectiveness or soil erosion reduction, as well as constraints, are formulated using the benefit-cost ratio(BCR) for a 15-year production period and soil losses data calculated using the Universal soil Loss Equation(UsLE). The best results are achieved using the scenario with the aim to minimize soil erosion, considering the BCR in the constraints. This scenario is proposed since it was registered that the benefit for landowners would increase by five times, while the losses of land would decline about 80%. The sensitivity analysis of investments is performed to check whether the price variation affects the cost-effectiveness of proposed land use. Results show that market changes(nine cases are considered) do not lead to significant changes in land use allocation. Proposed land use pattern improves the environmental condition, strengthens the rural economy and thus offers the greatest potential for sustainable development of mountainous rural areas.

关键词： Cost-benefit analysis Linear programming Land use optimization Mountain region soil erosion

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Autosomal Dominant Polycystic Kidney Disease: Epidemiological, Clinical Aspects and Predictive Factors of Poor Renal Prognosis (About 300 Cases)

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Open Journal of Nephrology（肾脏病） 2024年第2期 275页

作者： Rihab Dkhissi nada El Kadiri Tarik Bouattar Loubna Benamar Naima Ouzeddoun

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Prediction of Residential Building’s solar Installation Energy Demand in Morocco Using Multiple Linear Regression Analysis

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Energy Engineering 2022年第5期119卷 2135-2148页

作者： nada Yamoul Latifa Dlimi Baraka Achraf Chakir Department of Physics Faculty of SciencesIbn Tofail UniversityKenitra City14000Morocco Department of Mathematics Faculty of SciencesIbn Tofail UniversityKenitra City14000Morocco

The building sector is one of the main energy-consuming sectors in *** fact,it accounts for 33%of the final consumption of energy and records a high increase in the annual consumption of energy caused by further planned large-scale ***,the energy consumption of the building sector is experiencing a significant acceleration justified by the rapid need for the development of housing stock,wich is estimated at an average increase of 1,5%per year;furthermore,tant is an estimated increase of about 6,4%.In this sense,building constitutes an important potential source for rationalizing both energy consumption and energy savings through the adoption of energy efficiency *** consumption control efforts in the residential building sector involve socio-economic,technological,and environmental concerns that require sophisticated ***,different types of quantitative models have been developed and examined so as to find a solution for the optimizing energy *** this work,we have highlighted the importance of using solar heaters to reduce energy demand in terms of the use of domestic hot *** do this,we have defined the needs and characteristics of a solar installation of a residential building located in Casablanca through a calculating tool“sOLO 2000”in addition to the use of multiple linear regression analysis to deduct the impact of irradiation and solar contributions on the energy demand of the solar installation.

关键词： Residential sector energy simulation prediction models black box solar energy

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Time series Facebook Prophet Model and Python for COVID-19 Outbreak Prediction

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Computers, Materials & Continua 2021年第6期67卷 3781-3793页

作者： Mashael Khayyat Kaouther Laabidi nada Almalki Maysoon Al-zahrani Department of Information systems and Technology Collage of Computer Science and EngineeringUniversity of JeddahJeddah23218Saudi Arabia Department of Computer and Network Engineering Collage of Computer Science and EngineeringUniversity of JeddahJeddah23218Saudi Arabia and University of TunisElmanarTunisia

COVID-19 comes from a large family of viruses identied in 1965;to date,seven groups have been recorded which have been found to affect *** the healthcare industry,there is much evidence that Al or machine learning algorithms can provide effective models that solve problems in order to predict conrmed cases,recovered cases,and *** researchers and scientists in the eld of machine learning are also involved in solving this dilemma,seeking to understand the patterns and characteristics of virus attacks,so scientists may make the right decisions and take specic ***,many models have been considered to predict the Coronavirus outbreak,such as the retro prediction model,pandemic Kaplan’s model,and the neural forecasting *** research has used the time series-dependent face book prophet model for COVID-19 prediction in India’s various ***,we proposed a prediction and analysis model to predict COVID-19 in saudi *** time series dependent face book prophet model is used to t the data and provide future *** study aimed to determine the pandemic prediction of COVID-19 in saudi Arabia,using the Time series Analysis to observe and predict the coronavirus pandemic’s spread daily or *** found that the proposed model has a low ability to forecast the recovered cases of the COVID-19 *** contrast,the proposed model of death cases has a high ability to forecast the COVID-19 ***,obtaining more data could empower the model for further validation.

关键词： COVID-19 time series analysis prediction face book prophet model python

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Improving sentiment Analysis in Election-Based Conversations on Twitter with ElecBERT Language Model

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Computers, Materials & Continua 2023年第9期76卷 3345-3361页

作者： Asif Khan Huaping Zhang nada Boudjellal Arshad Ahmad Maqbool Khan school of Computer science and Technology Beijing Institute of TechnologyBeijing100081China The Faculty of New Information and Communication Technologies University Abdel-Hamid Mehri Constantine 2Constantine25000Algeria Department of IT and Computer science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan

sentiment analysis plays a vital role in understanding public opinions and sentiments toward various *** recent years,the rise of social media platforms(sMPs)has provided a rich source of data for analyzing public opinions,particularly in the context of election-related ***,sentiment analysis of electionrelated tweets presents unique challenges due to the complex language used,including figurative expressions,sarcasm,and the spread of *** address these challenges,this paper proposes Election-focused Bidirectional Encoder Representations from Transformers(ElecBERT),a new model for sentiment analysis in the context of election-related ***-related tweets pose unique challenges for sentiment analysis due to their complex language,sarcasm,*** is based on the Bidirectional Encoder Representations from Transformers(BERT)language model and is fine-tuned on two datasets:Election-Related sentiment-Annotated Tweets(Elecsent)-Multi-Languages,containing 5.31 million labeled tweets in multiple languages,and Elecsent-English,containing 4.75million labeled tweets in *** outperforms othermachine learning models such as support Vector Machines(sVM),Na飗e Bayes(NB),and eXtreme Gradient Boosting(XGBoost),with an accuracy of 0.9905 and F1-score of 0.9816 on Elecsent-Multi-Languages,and an accuracy of 0.9930 and F1-score of 0.9899 on *** performance of differentmodels was compared using the 2020 United states(Us)Presidential Election as a case *** ElecBERT-English and ElecBERT-Multi-Languages models outperformed BERTweet,with the ElecBERT-English model achieving aMean Absolute Error(MAE)of *** paper presents a valuable contribution to sentiment analysis in the context of election-related tweets,with potential applications in political analysis,social media management,and policymaking.

关键词： sentiment analysis social media election prediction machine learning transformers

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Enhanced Best Fit Algorithm for Merging small Files

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Computer systems science & Engineering 2023年第7期46卷 913-928页

作者： Adnan Ali nada Masood Mirza Mohamad Khairi Ishak school of Electrical and Electronic Engineering Universiti Sains Malaysia(USM)Nibong TebalPulau Pinang14300Malaysia University College United Arab Emirates UniversityAl AinUAE

In the Big Data era,numerous sources and environments generate massive amounts of *** enormous amount of data necessitates specialized advanced tools and procedures that effectively evaluate the information and anticipate decisions for future *** is used to process this kind of *** is known to handle vast volumes of data more efficiently than tiny amounts,which results in inefficiency in the *** study proposes a novel solution to the problem by applying the Enhanced Best Fit Merging algorithm(EBFM)that merges files depending on predefined parameters(type and size).Implementing this algorithm will ensure that the maximum amount of the block size and the generated file size will be in the same *** primary goal is to dynamically merge files with the stated criteria based on the file type to guarantee the efficacy and efficiency of the established *** procedure takes place before the files are available for the Hadoop ***,the files generated by the system are named with specific keywords to ensure there is no data loss(file overwrite).The proposed approach guarantees the generation of the fewest possible large files,which reduces the input/output memory burden and corresponds to the Hadoop framework’s *** findings show that the proposed technique enhances the framework’s performance by approximately 64%while comparing all other potential performance-impairing *** proposed approach is implementable in any environment that uses the Hadoop framework,not limited to smart cities,real-time data analysis,etc.

关键词： Big data Hadoop MapReduce small file HDFs

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From social Media to Ballot Box:Leveraging Location-Aware sentiment Analysis for Election Predictions

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Computers, Materials & Continua 2023年第12期77卷 3037-3055页

作者： Asif Khan nada Boudjellal Huaping Zhang Arshad Ahmad Maqbool Khan school of Computer science and Technology Beijing Institute of TechnologyBeijing100081China The Faculty of New Information and Communication Technologies University Abdel-Hamid Mehri Constantine 2Constantine25000Algeria Department of IT and Computer science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan

Predicting election outcomes is a crucial undertaking,and various methods are employed for this purpose,such as traditional opinion polling,and social media ***,traditional polling approaches often struggle to capture the intricate nuances of voter sentiment at local levels,resulting in a limited depth of analysis and *** light of this challenge,this study focuses on predicting elections at the state/regional level along with the country level,intending to offer a comprehensive analysis and deeper insights into the electoral *** achieve this,the study introduces the Location-Based Election Prediction Model(LEPM),which utilizes social media data,specifically Twitter,and integrates location-aware sentiment analysis techniques at both the state/region and country *** predicts the support and opposing strength of each political party/*** determine the location of users/voters who have not disclosed their location information in tweets,the model utilizes a Voter Location Detection(VotLocaDetect)approach,which leverages recent tweets/*** sentiment analysis techniques employed in this study include rule-based sentiment analysis,Valence Aware Dictionary and sentiment Reasoner(VADER)as well as transformers-based sentiment analysis such as Bidirectional Encoder Representations from Transformers(BERT),BERTweet,and Election based BERT(ElecBERT).This study uses the 2020 United states(Us)Presidential Election as a case *** applying the LEPM model to the election,the study demonstrates its ability to accurately predict outcomes in forty-one states,achieving an 0.84 accuracy rate at the state ***,at the country level,the LEPM model outperforms traditional polling *** a low Mean Absolute Error(MAE)of 0.87,the model exhibits more precise predictions and serves as a successful alternative to conventional polls and other *** the extensive social media data,the LEPM model provides nuanc

关键词： sentiment analysis big data machine learning election predictions social media analysis

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Effect of adipose-derived mesenchymal stem cells on hepatocellular carcinoma: In vitro inhibition of carcinogenesis

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World Journal of Gastroenterology 2019年第5期25卷 567-583页

作者： Rim serhal Nagib saliba George Hilal Mayssam Moussa Ghada s Hassan Oula El Atat nada Alaaeddine Regenerative Medicine Laboratory Faculty of Medicine Saint-Joseph University surgery Department Faculty of Medicine Saint-Joseph University and Hotel-Dieu de France Cancer and Metabolism Laboratory Faculty of Medicine Campus of Medical Sciences Saint-Joseph University Laboratoire d’Immunologie Cellulaire et Moléculaire Centre Hospitalier de l’Université de Montréal

AIM To investigate the effect of adipose-derived mesenchymal stem cells(ADMsCs)and their conditioned media(CM) on hepatocellular carcinoma(HCC) cell *** The proliferation rate of HepG2 and PLC-PRF-5 HCC cancer cells was measured using the trypan blue exclusion method and confirmed using the cell-counting kit8(commonly known as CCK-8) assay. Apoptosis was detected by flow cytometry using annexin V-FITC. Protein and mRNA expression was quantified by ELIsA and real time PCR, respectively. Migration and invasion rates were performed by Transwell migration and invasion assays. Wound healing was examined to confirm the data obtained from the migration *** Our data demonstrated that when co-culturing HCC cell lines with ADMsCs or treating them with ADMsC CM, the HCC cell proliferation rate was significantly inhibited and the apoptosis rate increased. The decreased proliferation rate was accompanied by an upregulation of P53 and Retinoblastoma mRNA and a downregulation of c-Myc and hTERT mRNA levels. More notably, ADMsCs and their CM suppressed the expression of the two important markers of HCC carcinogenicity, alpha-fetoprotein and Des-gamma-carboxyprothrombin. In addition, the migration and invasion levels of HepG2 and PLC-PRF-5 cells significantly decreased, potentially through increased expression of the tissue inhibitor metalloproteinases TIMP-1, TIMP-2 and *** These findings shed new light on a protective and therapeutic role for ADMsCs and their CM in controlling HCC invasiveness and carcinogenesis.

关键词： Hepatocellular carcinoma Adipose-derived mesenchymal stem cells Adipose-derived mesenchymal stem cell conditioned media Proliferation Apoptosis Migration Invasion

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