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| 1 | Split-dose menthol-enhanced PEG vs PEG-ascorbic acid for colonoscopy preparation显示文摘AIM:To compare the efficacy and palatability of 4L polyethylene glycol electrolyte(PEG)plus sugar-free menthol candy(PEG+M)vs reduced-volume 2 L ascorbic acid-supplemented PEG(Asc PEG).METHODS:In a randomized controlled trial setting,ambulatory patients scheduled for elective colonoscopy were prospectively enrolled.Patients were randomized to receive either PEG+M or Asc PEG,both splitdosed with minimal dietary restriction.Palatability was assessed on a linear scale of 1 to 5(1=disgusting;5=tasty).Quality of preparation was scored by assignment-blinded endoscopists using the modified Aronchick and Ottawa scales.The main outcomes were the palatability and efficacy of the preparation.Secondary outcomes included patient willingness to retake the same preparation again in the future and completion of the prescribed preparation.RESULTS:Overall,200 patients were enrolled(100patients per arm).PEG+M was more palatable than Asc PEG(76%vs 62%,P=0.03).Completing the preparation was not different between study groups(91%PEG+M vs 86%Asc PEG,P=0.38)but more patients were willing to retake PEG+M(54%vs 40%respectively,P=0.047).There was no significant difference between PEG+M vs Asc PEG in adequate cleansing on both the modified Aronchick(82%vs77%,P=0.31)and the Ottawa scale(85%vs 74%,P=0.054).However,PEG+M was superior in the left colon on the Ottawa subsegmental score(score0-2:94%for PEG+M vs 81%for Asc PEG,P=0.005)and received significantly more excellent ratings than Asc PEG on the modified Aronchick scale(61%vs 43%,P=0.009).Both preparations performed less well in afternoon vs morning examinations(inadequate:29%vs 15.2%,P=0.02).CONCLUSION:4 L PEG plus menthol has better palatability and acceptability than 2 L ascorbic acidPEG and is associated with a higher rate of excellentpreparations;Clinicaltrial.gov identifier:NCT01788709. | Ala I Sharara Ali H Harb Fayez S Sarkis Jean M Chalhoub Rami Badreddine Fadi H Mourad Mahmoud Othman Omar Masri | 2015 | World Journal of Gastroenterology2015，21，6： | 2 |
| 2 | Developmental changes in contraction of gastric smooth muscle cells in rats correlate with their differences in RhoA/ROCK pathway显示文摘Previous research has shown that smooth muscle of the stomach undergoes developmental changes in the intracellular regulatory mechanism responsible for the contractile process. Whether these developmental changes relate to differences in the expression and/or activity of the key enzymes regulating smooth muscle contraction has not been previously evaluated. Therefore, we aimed to examine the expression and activation of the small monomeric G protein 'Rho A' and Rho kinase(ROCK) as well as their correlation with the contraction of gastric smooth muscle cells(GSMCs) in newborn vs. adult rats. Freshly isolated single GSMCs from Sprague-Dawley rats at 1 week(newborn) and 3 months(adult) of age were used in the study. Protein and m RNA expression levels of both ROCK2 and total Rho A were higher in adult compared to newborn rats. Moreover, acetylcholine(ACh)-induced contractions of GSMCs in adult rats were significantly higher than that in newborn animals. Meanwhile,ROCK and Rho activation was higher in adult stomach cells compared to newborn ones. Pretreatment of GSMCs with Y-27632, the ROCK inhibitor, significantly reduced ACh-induced contraction in both groups of cells and greatly abolished contractile differences. In conclusion, our results indicate that Rho A/ROCK pathway and contraction of stomach muscle cells are under developmental regulation. | Othman Al-Shboul Mahmoud Alfaqih Ayman Mustafa Ahmed Al-Dwairi Mohammad Alqudah Saja Haifawi Mohanad Alebbini Mohammad Al-Zaareer Khobaib Alahmar | 2019 | The Journal of Biomedical Research2019，33，5： | 2 |
| 3 | A Saudi Gastroenterology association position statement on the use of tumor necrosis factor-alfa antagonists for the treatment of inflammatory bowel disease显示文摘 | Mahmoud Mosli Othman Al-Harbi Brian Feagan Majid Almadi | 2015 | Saudi Journal of Gastroenterology2015，，4： | 1 |
| 4 | Cervical myelopathy revealing bone fluorosis显示文摘 | Younes M Mahmoud I Ben Othman M | 2008 | Rev Neurol(Paris)2008，164，2： | 1 |
| 5 | Laparoscopy rather than colonoscopy for the diagnosis and treatment of fulminant ischemic colitis显示文摘 | Mahmoud Othman Nadim El-Majzoub Ghattas Khoury Kassem Barada | 2014 | International Journal of Colorectal Disease2014，，11： | 1 |
| 6 | BERT-CNN: A Deep Learning Model for Detecting Emotions from Text显示文摘Due to the widespread usage of social media in our recent daily lifestyles,sentiment analysis becomes an important field in pattern recognition and Natural Language Processing(NLP).In this field,users’feedback data on a specific issue are evaluated and analyzed.Detecting emotions within the text is therefore considered one of the important challenges of the current NLP research.Emotions have been widely studied in psychology and behavioral science as they are an integral part of the human nature.Emotions describe a state of mind of distinct behaviors,feelings,thoughts and experiences.The main objective of this paper is to propose a new model named BERT-CNN to detect emotions from text.This model is formed by a combination of the Bidirectional Encoder Representations from Transformer(BERT)and the Convolutional Neural networks(CNN)for textual classification.This model embraces the BERT to train the word semantic representation language model.According to the word context,the semantic vector is dynamically generated and then placed into the CNN to predict the output.Results of a comparative study proved that the BERT-CNN model overcomes the state-of-art baseline performance produced by different models in the literature using the semeval 2019 task3 dataset and ISEAR datasets.The BERTCNN model achieves an accuracy of 94.7%and an F1-score of 94%for semeval2019 task3 dataset and an accuracy of 75.8%and an F1-score of 76%for ISEAR dataset. | Ahmed R.Abas Ibrahim Elhenawy Mahinda Zidan Mahmoud Othman | 2022 | Computers, Materials & Continua2022，，5： | 0 |
| 7 | Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition显示文摘The recognition of the Arabic characters is a crucial task incomputer vision and Natural Language Processing fields. Some major complicationsin recognizing handwritten texts include distortion and patternvariabilities. So, the feature extraction process is a significant task in NLPmodels. If the features are automatically selected, it might result in theunavailability of adequate data for accurately forecasting the character classes.But, many features usually create difficulties due to high dimensionality issues.Against this background, the current study develops a Sailfish Optimizer withDeep Transfer Learning-Enabled Arabic Handwriting Character Recognition(SFODTL-AHCR) model. The projected SFODTL-AHCR model primarilyfocuses on identifying the handwritten Arabic characters in the inputimage. The proposed SFODTL-AHCR model pre-processes the input imageby following the Histogram Equalization approach to attain this objective.The Inception with ResNet-v2 model examines the pre-processed image toproduce the feature vectors. The Deep Wavelet Neural Network (DWNN)model is utilized to recognize the handwritten Arabic characters. At last,the SFO algorithm is utilized for fine-tuning the parameters involved in theDWNNmodel to attain better performance. The performance of the proposedSFODTL-AHCR model was validated using a series of images. Extensivecomparative analyses were conducted. The proposed method achieved a maximum accuracy of 99.73%. The outcomes inferred the supremacy of theproposed SFODTL-AHCR model over other approaches. | Mohammed Maray Badriyya B.Al-onazi Jaber S.Alzahrani Saeed Masoud Alshahrani Najm Alotaibi Sana Alazwari Mahmoud Othman Manar Ahmed Hamza | 2023 | Computers, Materials & Continua2023，，3： | 0 |
| 8 | An Intelligent Hazardous Waste Detection and Classification Model Using Ensemble Learning Techniques显示文摘Proper waste management models using recent technologies like computer vision,machine learning(ML),and deep learning(DL)are needed to effectively handle the massive quantity of increasing waste.Therefore,waste classification becomes a crucial topic which helps to categorize waste into hazardous or non-hazardous ones and thereby assist in the decision making of the waste management process.This study concentrates on the design of hazardous waste detection and classification using ensemble learning(HWDC-EL)technique to reduce toxicity and improve human health.The goal of the HWDC-EL technique is to detect the multiple classes of wastes,particularly hazardous and non-hazardous wastes.The HWDC-EL technique involves the ensemble of three feature extractors using Model Averaging technique namely discrete local binary patterns(DLBP),EfficientNet,and DenseNet121.In addition,the flower pollination algorithm(FPA)based hyperparameter optimizers are used to optimally adjust the parameters involved in the EfficientNet and DenseNet121 models.Moreover,a weighted voting-based ensemble classifier is derived using three machine learning algorithms namely support vector machine(SVM),extreme learning machine(ELM),and gradient boosting tree(GBT).The performance of the HWDC-EL technique is tested using a benchmark Garbage dataset and it obtains a maximum accuracy of 98.85%. | Mesfer Al Duhayyim Saud S.Alotaibi Shaha Al-Otaibi Fahd N.Al-Wesabi Mahmoud Othman Ishfaq Yaseen Mohammed Rizwanullah Abdelwahed Motwakel | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 9 | Improved Metaheuristics with Deep Learning Enabled Movie Review Sentiment Analysis显示文摘Sentiment Analysis(SA)of natural language text is not only a challenging process but also gains significance in various Natural Language Processing(NLP)applications.The SA is utilized in various applications,namely,education,to improve the learning and teaching processes,marketing strategies,customer trend predictions,and the stock market.Various researchers have applied lexicon-related approaches,Machine Learning(ML)techniques and so on to conduct the SA for multiple languages,for instance,English and Chinese.Due to the increased popularity of the Deep Learning models,the current study used diverse configuration settings of the Convolution Neural Network(CNN)model and conducted SA for Hindi movie reviews.The current study introduces an Effective Improved Metaheuristics with Deep Learning(DL)-Enabled Sentiment Analysis for Movie Reviews(IMDLSA-MR)model.The presented IMDLSA-MR technique initially applies different levels of pre-processing to convert the input data into a compatible format.Besides,the Term Frequency-Inverse Document Frequency(TF-IDF)model is exploited to generate the word vectors from the pre-processed data.The Deep Belief Network(DBN)model is utilized to analyse and classify the sentiments.Finally,the improved Jellyfish Search Optimization(IJSO)algorithm is utilized for optimal fine-tuning of the hyperparameters related to the DBN model,which shows the novelty of the work.Different experimental analyses were conducted to validate the better performance of the proposed IMDLSA-MR model.The comparative study outcomes highlighted the enhanced performance of the proposed IMDLSA-MR model over recent DL models with a maximum accuracy of 98.92%. | Abdelwahed Motwakel Najm Alotaibi Eatedal Alabdulkreem Hussain Alshahrani MohamedAhmed Elfaki Mohamed K Nour Radwa Marzouk Mahmoud Othman | 2023 | Computer Systems Science & Engineering2023，47，10： | 0 |
| 10 | Modified Dragonfly Optimization with Machine Learning Based Arabic Text Recognition显示文摘Text classification or categorization is the procedure of automatically tagging a textual document with most related labels or classes.When the number of labels is limited to one,the task becomes single-label text categorization.The Arabic texts include unstructured information also like English texts,and that is understandable for machine learning(ML)techniques,the text is changed and demonstrated by numerical value.In recent times,the dominant method for natural language processing(NLP)tasks is recurrent neural network(RNN),in general,long short termmemory(LSTM)and convolutional neural network(CNN).Deep learning(DL)models are currently presented for deriving a massive amount of text deep features to an optimum performance from distinct domains such as text detection,medical image analysis,and so on.This paper introduces aModified Dragonfly Optimization with Extreme Learning Machine for Text Representation and Recognition(MDFO-EMTRR)model onArabicCorpus.The presentedMDFO-EMTRR technique mainly concentrates on the recognition and classification of the Arabic text.To achieve this,theMDFO-EMTRRtechnique encompasses data pre-processing to transform the input data into compatible format.Next,the ELM model is utilized for the representation and recognition of the Arabic text.At last,the MDFO algorithm was exploited for optimal tuning of the parameters related to the ELM method and thereby accomplish enhanced classifier results.The experimental result analysis of the MDFO-EMTRR system was performed on benchmark datasets and attained maximum accuracy of 99.74%. | Badriyya BAl-onazi Najm Alotaibi Jaber SAlzahrani Hussain Alshahrani Mohamed Ahmed Elfaki Radwa Marzouk Mahmoud Othman Abdelwahed Motwakel | 2023 | Computers, Materials & Continua2023，76，8： | 0 |
| 11 | Optimal Deep Learning Driven Intrusion Detection in SDN-Enabled IoT Environment显示文摘In recent years,wireless networks are widely used in different domains.This phenomenon has increased the number of Internet of Things(IoT)devices and their applications.Though IoT has numerous advantages,the commonly-used IoT devices are exposed to cyber-attacks periodically.This scenario necessitates real-time automated detection and the mitigation of different types of attacks in high-traffic networks.The Software-Defined Networking(SDN)technique and the Machine Learning(ML)-based intrusion detection technique are effective tools that can quickly respond to different types of attacks in the IoT networks.The Intrusion Detection System(IDS)models can be employed to secure the SDN-enabled IoT environment in this scenario.The current study devises a Harmony Search algorithmbased Feature Selection with Optimal Convolutional Autoencoder(HSAFSOCAE)for intrusion detection in the SDN-enabled IoT environment.The presented HSAFS-OCAE method follows a three-stage process in which the Harmony Search Algorithm-based FS(HSAFS)technique is exploited at first for feature selection.Next,the CAE method is leveraged to recognize and classify intrusions in the SDN-enabled IoT environment.Finally,the Artificial Fish SwarmAlgorithm(AFSA)is used to fine-tune the hyperparameters.This process improves the outcomes of the intrusion detection process executed by the CAE algorithm and shows the work’s novelty.The proposed HSAFSOCAE technique was experimentally validated under different aspects,and the comparative analysis results established the supremacy of the proposed model. | Mohammed Maray Haya Mesfer Alshahrani Khalid A.Alissa Najm Alotaibi Abdulbaset Gaddah AliMeree Mahmoud Othman Manar Ahmed Hamza | 2023 | Computers, Materials & Continua2023，，3： | 0 |
| 12 | Natural Language Processing with Optimal Deep Learning-Enabled Intelligent Image Captioning System显示文摘The recent developments in Multimedia Internet of Things(MIoT)devices,empowered with Natural Language Processing(NLP)model,seem to be a promising future of smart devices.It plays an important role in industrial models such as speech understanding,emotion detection,home automation,and so on.If an image needs to be captioned,then the objects in that image,its actions and connections,and any silent feature that remains under-projected or missing from the images should be identified.The aim of the image captioning process is to generate a caption for image.In next step,the image should be provided with one of the most significant and detailed descriptions that is syntactically as well as semantically correct.In this scenario,computer vision model is used to identify the objects and NLP approaches are followed to describe the image.The current study develops aNatural Language Processing with Optimal Deep Learning Enabled Intelligent Image Captioning System(NLPODL-IICS).The aim of the presented NLPODL-IICS model is to produce a proper description for input image.To attain this,the proposed NLPODL-IICS follows two stages such as encoding and decoding processes.Initially,at the encoding side,the proposed NLPODL-IICS model makes use of Hunger Games Search(HGS)with Neural Search Architecture Network(NASNet)model.This model represents the input data appropriately by inserting it into a predefined length vector.Besides,during decoding phase,Chimp Optimization Algorithm(COA)with deeper Long Short Term Memory(LSTM)approach is followed to concatenate the description sentences 4436 CMC,2023,vol.74,no.2 produced by the method.The application of HGS and COA algorithms helps in accomplishing proper parameter tuning for NASNet and LSTM models respectively.The proposed NLPODL-IICS model was experimentally validated with the help of two benchmark datasets.Awidespread comparative analysis confirmed the superior performance of NLPODL-IICS model over other models. | Radwa Marzouk Eatedal Alabdulkreem Mohamed KNour Mesfer Al Duhayyim Mahmoud Othman Abu Sarwar Zamani Ishfaq Yaseen Abdelwahed Motwakel | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 13 | Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning显示文摘Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital era.Malicious Uniform Resource Locators(URLs)can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their systems.This may result in compromised security of the systems,scams,and other such cyberattacks.These attacks hijack huge quantities of the available data,incurring heavy financial loss.At the same time,Machine Learning(ML)and Deep Learning(DL)models paved the way for designing models that can detect malicious URLs accurately and classify them.With this motivation,the current article develops an Artificial Fish Swarm Algorithm(AFSA)with Deep Learning Enabled Malicious URL Detection and Classification(AFSADL-MURLC)model.The presented AFSADL-MURLC model intends to differentiate the malicious URLs from genuine URLs.To attain this,AFSADL-MURLC model initially carries out data preprocessing and makes use of glove-based word embedding technique.In addition,the created vector model is then passed onto Gated Recurrent Unit(GRU)classification to recognize the malicious URLs.Finally,AFSA is applied to the proposed model to enhance the efficiency of GRU model.The proposed AFSADL-MURLC technique was experimentally validated using benchmark dataset sourced from Kaggle repository.The simulation results confirmed the supremacy of the proposed AFSADL-MURLC model over recent approaches under distinct measures. | Anwer Mustafa Hilal Aisha Hassan Abdalla Hashim Heba G.Mohamed Mohamed K.Nour Mashael M.Asiri Ali M.Al-Sharafi Mahmoud Othman Abdelwahed Motwakel | 2023 | Computers, Materials & Continua2023，，1： | 0 |
| 14 | Anti-stress Effects of Tunisian Cymbopogon schoenanthus L. Ethanol Extract and Some of Its Active Compounds显示文摘 | Mahmoud Ben Othman Mohamed Neffati Hiroko Isoda | 2017 | Journal of Agricultural Science and Technology(A)2017，7，2： | 0 |
| 15 | Deep Transfer Learning Driven Oral Cancer Detection and Classification Model显示文摘Oral cancer is the most commonly occurring‘head and neck cancers’across the globe.Most of the oral cancer cases are diagnosed at later stages due to absence of awareness among public.Since earlier identification of disease is essential for improved outcomes,Artificial Intelligence(AI)and Machine Learning(ML)models are used in this regard.In this background,the current study introduces Artificial Intelligence with Deep Transfer Learning driven Oral Cancer detection and Classification Model(AIDTLOCCM).The primary goal of the proposed AIDTL-OCCM model is to diagnose oral cancer using AI and image processing techniques.The proposed AIDTL-OCCM model involves fuzzy-based contrast enhancement approach to perform data pre-processing.Followed by,the densely-connected networks(DenseNet-169)model is employed to produce a useful set of deep features.Moreover,Chimp Optimization Algorithm(COA)with Autoencoder(AE)model is applied for oral cancer detection and classification.Furthermore,COA is employed to determine optimal parameters involved in AE model.A wide range of experimental analyses was conducted on benchmark datasets and the results were investigated under several aspects.The extensive experimental analysis outcomes established the enhanced performance of AIDTLOCCM model compared to other approaches with a maximum accuracy of 90.08%. | Radwa Marzouk Eatedal Alabdulkreem Sami Dhahbi Mohamed K.Nour Mesfer Al Duhayyim Mahmoud Othman Manar Ahmed Hamza Abdelwahed Motwakel Ishfaq Yaseen Mohammed Rizwanullah | 2022 | Computers, Materials & Continua2022，，11： | 0 |
| 16 | Improved Ant Lion Optimizer with Deep Learning Driven Arabic Hate Speech Detection显示文摘Arabic is the world’s first language,categorized by its rich and complicated grammatical formats.Furthermore,the Arabic morphology can be perplexing because nearly 10,000 roots and 900 patterns were the basis for verbs and nouns.The Arabic language consists of distinct variations utilized in a community and particular situations.Social media sites are a medium for expressing opinions and social phenomena like racism,hatred,offensive language,and all kinds of verbal violence.Such conduct does not impact particular nations,communities,or groups only,extending beyond such areas into people’s everyday lives.This study introduces an Improved Ant Lion Optimizer with Deep Learning Dirven Offensive and Hate Speech Detection(IALODL-OHSD)on Arabic Cross-Corpora.The presented IALODL-OHSD model mainly aims to detect and classify offensive/hate speech expressed on social media.In the IALODL-OHSD model,a threestage process is performed,namely pre-processing,word embedding,and classification.Primarily,data pre-processing is performed to transform the Arabic social media text into a useful format.In addition,the word2vec word embedding process is utilized to produce word embeddings.The attentionbased cascaded long short-term memory(ACLSTM)model is utilized for the classification process.Finally,the IALO algorithm is exploited as a hyperparameter optimizer to boost classifier results.To illustrate a brief result analysis of the IALODL-OHSD model,a detailed set of simulations were performed.The extensive comparison study portrayed the enhanced performance of the IALODL-OHSD model over other approaches. | Abdelwahed Motwakel Badriyya B.Al-onazi Jaber S.Alzahrani Sana Alazwari Mahmoud Othman Abu Sarwar Zamani Ishfaq Yaseen Amgad Atta Abdelmageed | 2023 | Computer Systems Science & Engineering2023，46，9： | 0 |
| 17 | Parameter Tuned Machine Learning Based Emotion Recognition on Arabic Twitter Data显示文摘Arabic is one of the most spoken languages across the globe.However,there are fewer studies concerning Sentiment Analysis(SA)in Arabic.In recent years,the detected sentiments and emotions expressed in tweets have received significant interest.The substantial role played by the Arab region in international politics and the global economy has urged the need to examine the sentiments and emotions in the Arabic language.Two common models are available:Machine Learning and lexicon-based approaches to address emotion classification problems.With this motivation,the current research article develops a Teaching and Learning Optimization with Machine Learning Based Emotion Recognition and Classification(TLBOML-ERC)model for Sentiment Analysis on tweets made in the Arabic language.The presented TLBOML-ERC model focuses on recognising emotions and sentiments expressed in Arabic tweets.To attain this,the proposed TLBOMLERC model initially carries out data pre-processing and a Continuous Bag Of Words(CBOW)-based word embedding process.In addition,Denoising Autoencoder(DAE)model is also exploited to categorise different emotions expressed in Arabic tweets.To improve the efficacy of the DAE model,the Teaching and Learning-based Optimization(TLBO)algorithm is utilized to optimize the parameters.The proposed TLBOML-ERC method was experimentally validated with the help of an Arabic tweets dataset.The obtained results show the promising performance of the proposed TLBOML-ERC model on Arabic emotion classification. | Ibrahim M.Alwayle Badriyya B.Al-onazi Jaber S.Alzahrani Khaled M.Alalayah Khadija M.Alaidarous Ibrahim Abdulrab Ahmed Mahmoud Othman Abdelwahed Motwakel | 2023 | Computer Systems Science & Engineering2023，46，9： | 0 |
| 18 | Enhanced Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection显示文摘Recent developments in computer networks and Internet of Things(IoT)have enabled easy access to data.But the government and business sectors face several difficulties in resolving cybersecurity network issues,like novel attacks,hackers,internet criminals,and so on.Presently,malware attacks and software piracy pose serious risks in compromising the security of IoT.They can steal confidential data which results infinancial and reputational losses.The advent of machine learning(ML)and deep learning(DL)models has been employed to accomplish security in the IoT cloud environment.This article pre-sents an Enhanced Artificial Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection(EAGTODL-CTD)in IoT Cloud Net-works.The presented EAGTODL-CTD model encompasses the identification of the threats in the IoT cloud environment.The proposed EAGTODL-CTD mod-el mainly focuses on the conversion of input binaryfiles to color images,where the malware can be detected using an image classification problem.The EAG-TODL-CTD model pre-processes the input data to transform to a compatible for-mat.For threat detection and classification,cascaded gated recurrent unit(CGRU)model is exploited to determine class labels.Finally,EAGTO approach is employed as a hyperparameter optimizer to tune the CGRU parameters,showing the novelty of our work.The performance evaluation of the EAGTODL-CTD model is assessed on a dataset comprising two class labels namely malignant and benign.The experimental values reported the supremacy of the EAG-TODL-CTD model with increased accuracy of 99.47%. | Fatma S.Alrayes Najm Alotaibi Jaber S.Alzahrani Sana Alazwari Areej Alhogail Ali M.Al-Sharafi Mahmoud Othman Manar Ahmed Hamza | 2023 | Computer Systems Science & Engineering2023，45，6： | 0 |
| 19 | Midwives’views and experiences of providing healthy eating advice to pregnant women:a qualitative content analysis of semi-structured interviews显示文摘Objective:To explore midwives’views on how they provide healthy eating education to pregnant women after attending a healthy eating education workshop/webinar.Methods:A qualitative descriptive approach was utilized.Semi-structured interviews were conducted to explore the views and experiences of midwives on providing healthy eating education for pregnant women.A purposive sample of six midwives was interviewed face-to-face,and one was conducted by telephone interview.Data were analyzed through qualitative conventional content analysis.Results:Midwives described their views and experiences of factors that impacted their role in providing healthy eating educa-tion.They identified three categories:perceived role of midwi ves,health literacy,and model of care.Conclusions:Knowledge and confidence of midwives improved after attending the workshop/webinar on healthy eating education.Findings suggested that midwives perceived their role as important in providing nutrition education.However,time and resources were highlighted as challenges when providing healthy eating education for pregnant women.The availability of health literacy and model of care were significant factors in enabling midwives to adequately provide this education.Midwives acknowledged a need for further education in areas of vegan diet,cultural food preferences for ethnic minority groups,and regular updates on national healthy eating guidelines. | Shwikar Mahmoud Etman Othman Julie-Anne Fleet Mary Steen Rasika Jayasekara | 2020 | Frontiers of Nursing2020，7，4： | 0 |
| 20 | Leveraging Gradient-Based Optimizer and Deep Learning for Automated Soil Classification Model显示文摘Soil classification is one of the emanating topics and major concerns in many countries.As the population has been increasing at a rapid pace,the demand for food also increases dynamically.Common approaches used by agriculturalists are inadequate to satisfy the rising demand,and thus they have hindered soil cultivation.There comes a demand for computer-related soil classification methods to support agriculturalists.This study introduces a Gradient-Based Optimizer and Deep Learning(DL)for Automated Soil Clas-sification(GBODL-ASC)technique.The presented GBODL-ASC technique identifies various kinds of soil using DL and computer vision approaches.In the presented GBODL-ASC technique,three major processes are involved.At the initial stage,the presented GBODL-ASC technique applies the GBO algorithm with the EfficientNet prototype to generate feature vectors.For soil categorization,the GBODL-ASC procedure uses an arithmetic optimization algorithm(AOA)with a Back Propagation Neural Network(BPNN)model.The design of GBO and AOA algorithms assist in the proper selection of parameter values for the EfficientNet and BPNN models,respectively.To demonstrate the significant soil classification outcomes of the GBODL-ASC methodology,a wide-ranging simulation analysis is performed on a soil dataset comprising 156 images and five classes.The simulation values show the betterment of the GBODL-ASC model through other models with maximum precision of 95.64%. | Hadeel Alsolai Mohammed Rizwanullah Mashael Maashi Mahmoud Othman Amani A.Alneil Amgad Atta Abdelmageed | 2023 | Computers, Materials & Continua2023，，7： | 0 |
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