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A Combination Ensemble Feature Selection Method for the Prognosis of Recurrent Head and Neck Squamous Cell Carcinoma Scientia Research LibraryJournal of Applied Science And Research2348 - 04162024126 Kwame pages : 1-19 1 In developing nations like Ghana, where the recurrent and mortality rates are extremely high due to poor prognoses, the rapid development of Head and Neck Squamous Cell Carcinomas (HNSCC) cases and their recurrences poses medical challenges to all those involved in their management globally. Numerous efforts to determine the most accurate collection of prognoses linked to HNSCC recurrence were unsuccessful because high dimensional cancer datasets typically contain elements that are redundant or irrelevant. In this sense, feature selection is especially crucial. In this sense, a classification models performance is greatly influenced by the training characteristics that are used to learn the model. due to the possibility of bias, instability, or unreliability when obtaining a feature subset utilizing a single feature selection technique. This work suggested a classification task-oriented ensemble feature selection method called Subsets Summation Frequency Ensemble Feature Selection (SSF-EFS), which is based on subsets summation frequency. The findings (feature subsets) of five single feature selection techniquesGBM, RF, DNN, NB, and GLMwere combined by subset summation specific approach to provide the highest accurate forecast for the recurrent HNSCC dataset. The acquisition of the ideal feature subset was prompted by the integration effect of a specified threshold value. Three of the best classifiersGBM, RF, and NB with outstanding performance were examined in order to assess the resilience of the prognostic performance of feature subsets. In comparison to the single feature selection methods employed in this study, the experimental findings demonstrated that the suggested ensemble feature selection strategy successfully enhanced the classification using accuracy and AUC. Keywords: HNSCCs, Recurrence, Machine learning, Ensemble learning, Feature selection, Prognosis. http://www.scientiaresearchlibrary.com/archive-abs.php?arc=924http://www.scientiaresearchlibrary.com/archive/A Combination Ensemble Feature Selection Method for the Prognosis of Recurrent Head and Neck Squamous Cell Carcinoma.pdf

Response of Certain Novel Triticum aestivum L. (wheat) Varieties to Salinity in the Iraqi Kurdistan Region Scientia Research LibraryJournal of Applied Science And Research2348 - 04162024126 Paul IVCheikho pages:1-6 1 4.5 billion people rely on wheat, the most extensively cultivated crop in the world, for 20% of their daily calories and protein. After rice, it is the most significant food crop in the poor world. New and promising rust-resistant wheat cultivars Charmo, Maroof, and Alla have been evaluated for their ability to withstand salinity through the formation of radicals and plumules, as well as seed water uptake (germination mean and percentage).Using salt solution concentration levels with a control, 0.01, 0.03.0,05, 0.07, and 0.09 molL-1, which correspond to 0.0, 0.58,1.75,9.2, 4.01 and 5.26 gL-1, represented as C0, C1, C2, C3, C4, and C5, the varsities were examined for their level of salt tolerance. Variables had a substantial impact on water uptake, germination percentage, germination mean time, and wet and dry radicals, but not on radical quantity and length, plumule length, or wet dry plumule, according to the data. Regarding how salt levels affect germination characteristics and variety growth, we found that salt significantly affected germination mean, germination percentage, plumule and radical length, and wet radical weight. Additionally, the results indicate that there is a considerable impact of the interaction of varsities and salt levels on dry radical. Salt concentrations from C3 have been shown to significantly affect wet radical germination, but not water uptake, dry radical, or wet and dry plumule. Charmo and maroof are superior to Alla, according to the results of the varieties effects on germination and growth metrics. According to the study, Charmo and Maroof have more tolerance at this level than Alla, which is why it cannot be advised to use them in soil with EC 0.802.50, which corresponds to C2 and is equivalent to 4561425 mgL-1 instead. Keywords: Salinity, New wheat varieties Germination, Wet and dry radical Plumule, Tolerance level. http://www.scientiaresearchlibrary.com/archive-abs.php?arc=925http://www.scientiaresearchlibrary.com/archive/Response of Certain Novel Triticum aestivum L Varieties to Salinity in the Iraqi Kurdistan Region.pdf

Subject Assignment at an Educational Institution Using the HAAR Algorithm Scientia Research LibraryJournal of Applied Science And Research2348 - 04162024126 Abdulaziz pages :1-2 1 Subject allocation is a crucial responsibility for all educational establishments. Allocating subjects is thought to be a key component of high-quality instruction. In this study, I assigned the subjects in a department for the upcoming semester using the fuzzy allocation approach, also known as the HAAR ranking method. Based on the students performance from the previous semester, I used the scores that the faculty members received from the Chairman, the Course Director, and the students themselves. The department will assign the subjects based on performance rather than faculty members preferences. Keywords: HAAR ranking, Hungarian method, Triangular Fuzzy number, Fuzzy assignment, Membership function. http://www.scientiaresearchlibrary.com/archive-abs.php?arc=926http://www.scientiaresearchlibrary.com/archive/Subject Assignment at an Educational Institution Using the HAAR Algorithm.pdf