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Articles by A. Lotfi
Total Records ( 3 ) for A. Lotfi
  A.A. Vahabi , A. Lotfi , M. Solouki and S. Bahrami
  In this study, molecular and morphological variation of 22 population of Plantago ovata assessed using RAPD, ISJ and agro-botanical markers. Field experiment was conducted in completely randomized block design whit four replicates. Principal component analysis and clustering based on distance between means of 8 morphological traits were used to detect relationship between accessions. Thirty five RAPD primers produced 142 polymorphic bonds, average 4.05 for each primer. Clustering analysis technique based on RAPD using Unweighted Pair Group Method shown that a closely association exist among morphological and RAPD dendrograms while didn`t exist any accordance between ISJ-GS with RAPD and morphological variation. In RAPD–based clustering, all population that was belonged to near area formed closely groups. The ISJ system marker produced 95 DNA fragment with 2.55 polymorphic bands for each semi-random primers. The dendrogram based on ISJ marker did not have accordance with geographical, morphological and RAPD variation. The result of this research verified possibility of use of RAPD and ISJ markers for estimation of genetic diversity, management of genetic resources and determination of repetitive accessions in Plantago ovata.
  B. Kouninef , R. Tlemsani , S.M. Rerbal and A. Lotfi
  Technology progress in the field of learning has evolved especially with the use of e-learning and now with the emergence of a new concept called m-learning due to mobile technologies which are probably the most influential technology for teaching and learning in the next decade. Internet access remains inaccessible to most of our students. But almost all students have access to mobile technology. In this initiative, the students are encouraged to use their phones to send questions to their teachers, see the platform, add notes etc. Nevertheless, the m-learning has some constraints in the use of mobile technologies such as the size of the screen which is too small for reading, battery life that is in constant progress but still low for e-learning applications, user interfaces that are not user-friendly on most mobile phones and diversity of mobile devices and the rapidly changing trends suggest producing “LMS mobiles” adaptable to a wide range of mobiles. To avoid redundancy in the reproduction of content that exists in the e-learning environment, we combined in this approach the use of mobile platform in the e-learning (m-Moodle) to offer to our students the opportunity to be mobile and use the mobile as the medium of information. In this study, the benefits of mobile-learning and its use in the INTTIC’s Moodle platform are presented. A new way to send information to different learners is developed, relying on the multimedia podcasting technology. This latter is still relatively new and not widely known.
  A. Lotfi , K. Mezzoug and A. Benyettou
  This study presents the principle of operation of the Rotated Kernel Neural Network (RKNN) for radar target detection in non-Gaussian noise. This classifier is based on adopting the architecture of standard probabilistic neural networks and using different kernel functions to approximate density functions. The training algorithm for this classifier is more complicated than the original PNN training algorithm but allow better generalization. Performance curves of the Rotated Kernel Neural Network are compared to those of probabilistic neural networks (original), Radial Basis Neural Networks with an expectation maximization training algorithm and Back propagation neural networks for Radar target detection in background noise in terms of probability of detection versus signal-to-noise ratio (SNR). For most cases, the Rotated Kernel Neural Network classifier outperforms other conventional Radar target detection techniques and presents the advantage of resistance to background noise for values of SNR greater than 5 dB.
 
 
 
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