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jcoeduw-1585
On the Use of the First-Person Pronoun ‘we’ in Final-Year Master Projects of South Algerian EFL Students
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Literature on the use of the first-person pronoun in abstracts and conclusion sections of final-year projects is limited. In case of Algerian Master students, it is too scant. The present paper aims at filling this gap through a study concerned with students’ and engagement in their final projects (memoirs). This quantitative study examines the use of “we” and its various types, “our- us-I, my, the researcher” in memoirs chosen at random from the d-space portal of the University of Adrar, southern Algeria. Sixty-five papers, submitted in the fields of Linguistics or didactics between 2015 and 2020 and representing nearly half of the whole memoirs’ depository at the library’s d-space, constituted the corpus of study. The descriptive analytical analysis of the findings has shown that the pronouns “we” (exclusive), “our” (inclusive), and the ambiguous “us” are highly employed in general conclusions (GCs) than they are in abstracts. The results clearly suggested that the students’ use of the personal pronouns in GCs rather than in abstracts reflects their awareness to their implications in the paper not only as writers, but also as main researchers, thinkers and interpreters. The conclusions and interpretations have ultimately called for further studies with regard to their pedagogical and academic significance.

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Publication Date
Thu Sep 30 2021
Journal Name
Iraqi Journal Of Science
PFDINN: Comparison between Three Back-propagation Algorithms for Pear Fruit Disease Identification
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     The diseases presence in various species of fruits are the crucial parameter of economic composition and degradation of the cultivation industry around the world. The proposed pear fruit disease identification neural network (PFDINN) frame-work to identify three types of pear diseases was presented in this work. The major phases of the presented frame-work were as the following: (1) the infected area in the pear fruit was detected by using the algorithm of K-means clustering. (2) hybrid statistical features were computed over the segmented pear image and combined to form one descriptor. (3) Feed forward neural network (FFNN), which depends on three learning algorithms of back propagation (BP) training, namely Sca

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