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Information-Centric IoT-Based Smart Farming with Dynamic Data Optimization

Computers, Materials & Continua 2023年第2期74卷 3865-3880页

作者： Souvik Pal Hannah VijayKumar D.Akila N.Z.Jhanjhi omar A.darwish Fathi Amsaad Department of Computer Science and Engineering Sister Nivedita UniversityKolkataIndia Post Doctoral Researcher Sambalpur UniversitySambalpurIndia Department of Computer Science Anna Adarsh College for WomenChennaiIndia Department of Information Technology Vels Institute of ScienceTechnology and Advanced StudiesChennaiIndia School of Computer Science and Engineering SCETaylor’s UniversitySubang Jaya47500SelangorMalaysia FITTaylor’s UniversitySubang Jaya47500SelangorMalaysia Department of Information Security and Applied Computing Eastern Michigan UniversityUSA School of Information Security and Applied Computing Eastern Michigan UniversityUSA

Smart farming has become a strategic approach of sustainable agriculture management and monitoring with the infrastructure to exploit modern technologies,including big data,the cloud,and the Internet of Things(IoT).Many researchers try to integrate IoT-based smart farming on cloud platforms *** define various frameworks on smart farming and monitoring system and still lacks to define effective data management *** IoT-cloud systems involve massive structured and unstructured data,data optimization comes into the ***,this research designs an Information-Centric IoT-based Smart Farming with Dynamic Data Optimization(ICISF-DDO),which enhances the performance of the smart farming infrastructure with minimal energy consumption and improved ***,a conceptual framework of the proposed scheme and statistical design model has beenwell *** information storage and management with DDO has been expanded individually to show the effective use of membership parameters in data *** simulation outcomes state that the proposed ICISF-DDO can surpass existing smart farming systems with a data optimization ratio of 97.71%,reliability ratio of 98.63%,a coverage ratio of 99.67%,least sensor error rate of 8.96%,and efficient energy consumption ratio of 4.84%.

关键词： Smart farming data optimization information management internet of things cloud

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An eFP browser for visualizing strawberry fruit and flower transcriptomes

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Horticulture Research 2017年第1期4卷 227-234页

作者： Charles Hawkins Julie Caruana Jiaming Li Chris Zawora omar darwish Jun Wu Nadim Alkharouf Zhongchi Liu Department of Cell Biology and Molecular Genetics University of MarylandCollege Park20742MDUSA Centre of Pear Engineering Technology Research Nanjing Agricultural UniversityNanjing210095China Department of Computer and Information Sciences Towson UniversityTowson21252MDUSA Irrigated Agriculture Research and Extension Center Washington State UniversityProsserWA 99350USA

Wild strawberry Fragaria vesca is emerging as an important model system for the cultivated strawberry due to its diploid genome and availability of extensive transcriptome data and a range of molecular genetic *** able to better utilize these tools,especially the transcriptome data,will greatly facilitate research progress in strawberry and other Rosaceae fruit *** electronic fluorescent pictograph(eFP)software is a useful and popular tool to display transcriptome data visually,and is widely used in other model organisms including Arabidopsis and *** we applied eFP to display wild strawberry RNA sequencing(RNA-seq)data from 42 different tissues and stages,including various flower and fruit developmental *** addition,we generated eight additional RNA-seq data sets to represent tissues from ripening-stage receptacle fruit from yellow-colored and red-colored wild strawberry *** gene expression analysis between these eight data sets provides additional information for understanding fruit-quality ***,this work greatly facilitates the utility of the extensive transcriptome data for investigating strawberry flower and fruit development as well as fruit-quality traits.

关键词： straw cultivated colored

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Nature-Inspired Level Set Segmentation Model for 3D-MRI Brain Tumor Detection

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Computers, Materials & Continua 2021年第7期68卷 961-981页

作者： Oday Ali Hassen Sarmad omar Abter Ansam A.Abdulhussein Saad M.darwish Yasmine M.Ibrahim Walaa Sheta Department of Computer Techniques Engineering Imam Al-Kadhum College(IKC)WasitIraq University of Information Technology and Communications Media Technology Engineering DepartmentIraq University of Information Technology and Communications Baghdad10081Iraq Department of Information Technology Institute of Graduate Studies and ResearchAlexandria University63 Horreya AvenueEl–ShatbyAlexandria21526Egypt Informatics Research Institute City of Scientific Research and Technology ApplicationsBorg EL Arab21934Egypt

Medical image segmentation has consistently been a significant topic of research and a prominent goal,particularly in computer *** tumor research plays a major role in medical imaging applications by providing a tremendous amount of anatomical and functional knowledge that enhances and allows easy diagnosis and disease therapy *** prevent or minimize manual segmentation error,automated tumor segmentation,and detection became the most demanding process for radiologists and physicians as the tumor often has complex *** methods for detection and segmentation presently exist,but all lack high *** paper’s key contribution focuses on evaluating machine learning techniques that are supposed to reduce the effect of frequently found issues in brain tumor ***,attention concentrated on the challenges related to level set *** study proposed in this paper uses the Population-based Artificial Bee Colony Clustering(P-ABCC)methodology to reliably collect initial contour points,which helps minimize the number of iterations and segmentation errors of the level-set *** proposed model measures cluster centroids(ABC populations)and uses a level-set approach to resolve contour differences as brain tumors vary as they have irregular form,structure,and *** suggested model comprises of three major steps:first,pre-processing to separate the brain from the head and improves contrast ***,P-ABCC is used to obtain tumor edges that are utilized as an initial MRI sequence *** level-set segmentation is then used to detect tumor regions from all volume slices with fewer *** suggest improved model efficiency compared to state-of-the-art methods for both datasets BRATS 2019 and BRATS *** BRATS 2019,dice progress was achieved for Entire Tumor(WT),Tumor Center(TC),and Improved Tumor(ET)by 0.03%,0.03%,and 0.01%*** BRATS 2017,an increase in precision for WT was reac

关键词： 3D-MRI tumor diagnosis bio-inspired clustering ABC optimization multimodal detection

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