درخت حوزه‌های تخصصی

In order to achieve the sensing, communication and processing tasks of Wireless Sensor Networks, an energy-efficient routing protocol is required to manage the dissipated energy of the network and to minimalize the traffic and the overhead during the data transmission stages. Clustering is the most common technique to balance energy consumption amongst all sensor nodes throughout the network. In this paper, a multi-objective bio-inspired algorithm based on the Firefly and the Shuffled frog-leaping algorithms is presented as a clustering-based routing protocol for Wireless Sensor Networks. The multi-objective fitness function of the proposed algorithm has been performed on different criteria such as residual energy of nodes, inter-cluster distances, cluster head distances to the sink and overlaps of clusters, to select the proper cluster heads at each round. The parameters of the proposed approach in the clustering phase can be adaptively tuned to achieve the best performance based on the network requirements. Simulation outcomes have displayed average lifetime improvements of up to 33.95%, 32.62%, 12.1%, 13.85% compared with LEACH, ERA, SIF and FSFLA respectively, in different network scenarios.

The heavy trends of the Information Society are knowledge that requires the spirit of innovation, creativity, flexibility and critical thinking “more than benign introspection.” In addition, information about each other’s relationships with oneself, the other and the environment is crucial to enable individuals to understand the complex reality. The Moroccan school is part of this societal vision, by renovating its education and training programs, which claim the development of the quality of procedural, cognitive and metacognitive strategies. In this perspective this research work aimed at studying the engineering analysis of investigation approaches in the teaching of SVT for the implementation of a reflexive practice in lycian learners, generating the ability to process information and invest in mechanisms for understanding, conceptualizing and proceduralizing cognitive and metacognitive strategies. Informational investigative capacity is crucial because it informs learning decisions, which can lead to innovations in the delivery of cognitive styles. To do this, we chose to conduct our study, through interview surveys and semi-structured questionnaires. The first among teachers and the second for SVT inspectors from three provincial directorates of National Education. The results show that the majority of those involved in education are involved in the classical informational approach and the experimental approach. The use of investigative analysis approaches remains episodic in some learning units (scientific ecological output and DNA extraction). Teachers are keen to introduce new information technologies to create favourable conditions for learning about investigative approaches, in order to improve the power of analysis, treatment and critical thinking and coping with situations of authentic and novel learning. We are concerned about the ability to understand what and how each of its actors is further shaping.

The condition of the hotel's competitiveness is a strong brand. The introduction of digital marketing in the strategy of hotel branding creates new opportunities for hotels when interacting with guests through digital channels. The purpose of this study is to develop theoretical and practical measures to improve the effectiveness of marketing strategies in hotel branding using digital tools. To achieve the goal of the study was conducted research on targeted branding some of the largest hotel chains. The results of the analysis showed that in the process of branding at each stage the corresponding goals have achieved by means of advertising, marketing, public relations management, personnel selection, corporate culture.This study substantiates the main tools of the strategy of the Digital Marketing and Sales. The brands need to constantly monitor changes in market positions and audience sentiment using all the features and channels. The priorities should be implemented by performing key tasks, in particular such astimely measurements as far as brand experience has a positive effect on customer satisfaction and loyalty.

The purpose of this research was to identify the skills and abilities learners need in a digital learning environment during the Corona crisis. The research approach was applied in terms of objective and the data collected using the mixed-method approach. In the first part, using a qualitative approach and a document search method, 33 components (related to the skills and abilities of e-learners) were identified, and then19 components out of 33 with the highest frequencies were selected. The target population for this study included all MA students of the University of Tehran who had the experience of studying in an online learning environment, then 374 individuals were selected as a sample using the random sampling method. In the second part, using a quantitative approach and a descriptive-analytical research method, the questionnaire developed by the researcher relies on 19 components identified in the previous step. The experts' opinions proved the validity of the questionnaire—moreover, its reliability in 0.94 (Cronbach alpha). The data was analyzed by SPSS and LISREL, and the results of the data analysis demonstrate that the proposed model is a good fit. Among the 19 components surveyed, all are above average. Finally, the results indicated that the most critical components from e-learners’ view which more critical in digital learning were effective communication, use of the computer, active learning, learning skills, resilience, motivation toward e-learning, emotional intelligence, interaction with e-learning system, self-direct learning, time management, flexibility and adaptability, critical thinking, creativity, asking someone for help, leadership skills, stress management, self-organized, cognitive skills and self-assessment

Trust establishment (TE) among sensor nodes has become a vital requirement to improve security, reliability, and successful cooperation. Existing trust management approaches for large scale WSN are failed due to their low cooperation (i.e., dependability), higher communication and memory overheads (i.e., resource inefficient). This paper provides a new and comprehensive hybrid trust estimation approach for large scale WSN employing clustering to improve cooperation, trustworthiness, and security by detecting selfish sensor nodes with reduced resource (memory, power) consumption. The proposed scheme consists of unique features like authentication based data trust, scheduler based node trust, and attack resistant by giving the high penalty and minimum reward during node misbehavior. A task scheduling mechanism is employed for scheduling the significant task to reduce computation overhead. The proposed trust model would be capable to provide security against blackhole attack, grey hole attack, and badmouthing attack. Moreover, the proposed trust model feasibility has been tested with MATLAB. Simulation results exhibit the great performance of our proposed approach in terms of trust evaluation cost, prevention, and detection of malicious nodes with the help of analyzing consistency in trust values and communication overhead.

Deep learning techniques have become very popular among Artificial Intelligence (AI) techniques in many areas of life. Among many types of deep learning techniques, Convolutional Neural Networks (CNN) can be useful in image classification applications. In this work, a hybridized approach has been followed to classify lung nodule as benign or malignant. This will help in early detection of lung cancer and help in the life expectancy of lung cancer patients thereby reducing the mortality rate by this deadly disease scourging the world. The hybridization has been carried out between handcrafted features and deep features. The machine learning algorithms such as SVM and Logistic Regression have been used to classify the nodules based on the features. The dimensionality reduction technique, Principle Component Analysis (PCA) has been introduced to improve the performance of hybridized features with SVM. The experiments have been carried out with 14 different methods. It has been found that GLCM + VGG19 + PCA + SVM outperformed all other models with an accuracy of 94.93%, sensitivity of 90.9%, specificity of 97.36% and precision of 95.44%. The F1 score was found to be 0.93 and the AUC was 0.9843. The False Positive Rate was found to be 2.637% and False Negative Rate was 9.09%.