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Adaptive Deep Learning Model to Enhance Smart Greenhouse Agriculture

Computers, Materials & Continua 2023年第11期77卷 2545-2564页

作者： Medhat A.Tawfeek Nacim Yanes Leila Jamel ghadah aldehim Mahmood A.Mahmood Department of Computer Science College of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia Department of Computer Science Faculty of Computers and InformationMenoufia UniversityP.O.Box 6121890MenoufiaEgypt Department of Software Engineering College of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia RIADI Laboratory La Manouba UniversityP.O.Box2010ManoubaTunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems and Technology Faculty of Graduate Studies and ResearchCairo UniversityP.O.Box 3753450GizaEgypt

The trend towards smart greenhouses stems from various factors,including a lack of agricultural land area owing to population concentration and housing construction on agricultural land,as well as water *** study proposes building a full farming adaptation model that depends on current sensor readings and available datasets from different agricultural research *** proposed model uses a one-dimensional convolutional neural network(CNN)deep learning model to control the growth of strategic crops,including cucumber,pepper,tomato,and *** proposed model uses the Internet of Things(IoT)to collect data on agricultural operations and then uses this data to control and monitor these operations in real *** helps to ensure that crops are getting the right amount of fertilizer,water,light,and temperature,which can lead to improved yields and a reduced risk of crop *** dataset is based on data collected from expert farmers,the photovoltaic construction process,agricultural engineers,and research *** experimental results showed that the precision,recall,F1-measures,and accuracy of the one-dimensional CNN for the tested dataset were approximately 97.3%,98.2%,97.25%,and 97.56%,*** new smart greenhouse automation system was also evaluated on four crops with a high turnover *** system has been found to be highly effective in terms of crop productivity,temperature management and water conservation.

关键词： Greenhouse wireless sensor network deep learning Internet of Things strategic crops monitoring smart irrigation

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Smart MobiNet:A Deep Learning Approach for Accurate Skin Cancer Diagnosis

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Computers, Materials & Continua 2023年第12期77卷 3533-3549页

作者： Muhammad Suleman Faizan Ullah ghadah aldehim Dilawar Shah Mohammad Abrar Asma Irshad Sarra Ayouni Department of Computer Science Bacha Khan UniversityCharsaddaPakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Faculty of Computer Studies Arab Open UniversityMuscatOman School of Biochemistry and Biotechnology University of the PunjabLahorePakistan

The early detection of skin cancer,particularly melanoma,presents a substantial risk to human *** study aims to examine the necessity of implementing efficient early detection systems through the utilization of deep learning ***,the existing methods exhibit certain constraints in terms of accessibility,diagnostic precision,data availability,and *** address these obstacles,we put out a lightweight model known as Smart MobiNet,which is derived from MobileNet and incorporates additional distinctive *** model utilizes a multi-scale feature extraction methodology by using various convolutional *** ISIC 2019 dataset,sourced from the International Skin Imaging Collaboration,is employed in this *** data augmentation approaches are implemented to address the issue of model *** this study,we conduct experiments to evaluate and compare the performance of three different models,namely CNN,MobileNet,and Smart MobiNet,in the task of skin cancer *** findings of our study indicate that the proposed model outperforms other architectures,achieving an accuracy of ***,the model exhibits balanced precision,sensitivity,and F1 scores,all measuring at *** model serves as a vital instrument that assists clinicians efficiently and precisely detecting skin cancer.

关键词： Deep learning Smart MobiNet machine learning skin lesion melanoma skin cancer classification

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LexDeep:Hybrid Lexicon and Deep Learning Sentiment Analysis Using Twitter for Unemployment-Related Discussions During COVID-19

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Computers, Materials & Continua 2023年第4期75卷 1577-1601页

作者： Azlinah Mohamed Zuhaira Muhammad Zain Hadil Shaiba Nazik Alturki ghadah aldehim Sapiah Sakri Saiful Farik Mat Yatin Jasni Mohamad Zain Institute for Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA(UiTM)Shah AlamMalaysia College of Computer and Information Sciences Princess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA(UiTM)Shah AlamMalaysia

The COVID-19 pandemic has spread globally,resulting in financialinstability in many countries and reductions in the per capita grossdomestic *** analysis is a cost-effective method for acquiringsentiments based on household income loss,as expressed on social ***,limited research has been conducted in this domain using theLexDeep *** study aimed to explore social trend analytics usingLexDeep,which is a hybrid sentiment analysis technique,on Twitter to capturethe risk of household income loss during the COVID-19 ***,tweet data were collected using Twint with relevant keywords before(9 March2019 to 17 March 2020)and during(18 March 2020 to 21 August 2021)***,the tweets were annotated using VADER(lexiconbased)and fed into deep learning classifiers,and experiments were conductedusing several embeddings,namely simple embedding,Global Vectors,andWord2Vec,to classify the sentiments expressed in the *** performanceof each LexDeep model was evaluated and compared with that of a supportvector machine(SVM).Finally,the unemployment rates before and duringCOVID-19 were analysed to gain insights into the differences in unemploymentpercentages through social media input and *** resultsdemonstrated that all LexDeep models with simple embedding outperformedthe *** confirmed the superiority of the proposed LexDeep modelover a classical machine learning classifier in performing sentiment analysistasks for domain-specific *** terms of the risk of income loss,the unemployment issue is highly politicised on both the regional and globalscales;thus,if a country cannot combat this issue,the global economy will alsobe *** research should develop a utility maximisation algorithmfor household welfare evaluation,given the percentage risk of income lossowing to COVID-19.

关键词： Sentiment analysis sentiment lexicon machine learning imbalanced data deep learning method unemployment rate

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A Fusion of Residual Blocks and Stack Auto Encoder Features for Stomach Cancer Classification

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Computers, Materials & Continua 2023年第12期77卷 3895-3920页

作者： Abdul Haseeb Muhammad Attique Khan Majed Alhaisoni ghadah aldehim Leila Jamel Usman Tariq Taerang Kim Jae-Hyuk Cha Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirut1100Lebanon College of Computer Science and Engineering University of Ha’ilHa’il81451Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428RiyadhSaudi Arabia Department of Management Information Systems College of Business AdministrationPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

Diagnosing gastrointestinal cancer by classical means is a hazardous *** have witnessed several computerized solutions for stomach disease detection and ***,the existing techniques faced challenges,such as irrelevant feature extraction,high similarity among different disease symptoms,and the least-important features from a single *** paper designed a new deep learning-based architecture based on the fusion of two models,Residual blocks and Auto ***,the Hyper-Kvasir dataset was employed to evaluate the proposed *** research selected a pre-trained convolutional neural network(CNN)model and improved it with several residual *** process aims to improve the learning capability of deep models and lessen the number of ***,this article designed an Auto-Encoder-based network consisting of five convolutional layers in the encoder stage and five in the decoder *** research selected the global average pooling and convolutional layers for the feature extraction optimized by a hybrid Marine Predator optimization and Slime Mould optimization *** features of both models are fused using a novel fusion technique that is later classified using the Artificial Neural Network *** experiment worked on the HyperKvasir dataset,which consists of 23 stomach-infected *** last,the proposed method obtained an improved accuracy of 93.90%on this *** is also conducted with some recent techniques and shows that the proposed method’s accuracy is improved.

关键词： Gastrointestinal cancer contrast enhancement deep learning information fusion feature selection machine learning

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