درخت حوزه‌های تخصصی

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.

Blockchain technology is one of the revolutionary technologies in service-based industries. E-health services are considered as a major economic and social activity in the world in which patients, the medical community and service providers such as insurance organizations play a key role. In Iran, e-health services have been introduced for more than a decade, but it is seriously lagging behind the leading countries. According to the Blockchain Opportunity window, a technological and economic catch-up is possible in e-health services in the country. Decentralization, data transparency, security and privacy are the key features of Blockchain technology. Therefore, in this research, through case study strategy and using documents, scientific articles, evidence, statistics and events and opinions of experts, policy factors were presented to catch-up through the Blockchain technology opportunity window. The dimensions of the proposed policy framework were presented in 4 categories: macro policies, market, financing, and technology learning and development. This study presents technological catch-up policy framework by Blockchain technology to policymakers for using in e-health services.

Detecting occluded faces is a non-trivial problem for face detection in computer vision. This challenge becomes more difficult when the occlusion covers majority of the face. Despite the high performance of current state-of-the-art face detection algorithms, the detection of occluded and covered faces is an unsolved problem and is still worthy of study. In this paper, a deep-learning-face-detection model Niqab-Face-Detector is proposed along with context-based labeling technique for detecting unconstrained veiled faces such as faces covered with niqab. An experimental test was conducted to evaluate the performances of the proposed model using the Niqab-Face dataset. The experiment showed encouraging results and improved accuracy compared with state-of-the-art face detection algorithms

The article presents the results of studies on the development of new statistical and econometric approaches to modeling budget policy. The obtained results are applied on the example of tax revenue modeling. Accounting for various factors, including through the use of mathematical methods, will allow you to plan reforms with a scientific position. In particular, this is especially true in connection with the introduction of multi-year budget planning. To identify the main threats to the tax base, the phenomenon of “tax passes” wasused, which is based on an assessment of the effectiveness of a tax credit. The main participants in the formation of the “gross gap” in the value added tax revenues in Ukraine are shown. A correlation and regression analysis of the natural logarithms of the gross domestic product and tax revenues is carried out. This allowed us to determine the elasticity of tax revenues and GDP in Ukraine. A change in GDP directly affects the amount of tax payments to the budget, and the rate of change of indicators is proportional and changes insignificantly. These results allow us to strategically model the reform of discretionary tax policy mechanisms based on a quantitative assessment of tax gaps and the elasticity of tax payments. The authors were able to substantiate some proposals for reforming the budget policy regarding tax revenues.

Corona Virus (COVID-19) is a major problem among people, and it causes suffering worldwide. Yet, the traditional prediction models are not yet suitably efficient in catching the fundamental expertise as they cannot visualize the difficulty in the health's representation problem areas. This paper states prediction mechanism that uses a model of deep learning called Long Short-Term Memory (LSTM). We have carried this model out on corona virus dataset that obtained from the records of infections, deaths, and recovery cases across the world. Furthermore, producing a dataset which includes features of geographic regions (temperature and humidity) that have experienced severe virus outbreaks, risk factors, spatio-temporal analysis, and social behavior of people, a predictive model can be developed for areas where the virus is likely to spread. However, the outcomes of this study are justifiable to alert the authorities and the people to take precautions.

World of technology provides everyone with a great outlet to give their opinion, using social media like Twitter and other platforms. This paper employs machine learning methods for text analysis to obtain sentiments of reviews by the people on twitter. Sentiment analysis of the text uses Natural language processing, a machine learning technique to tell the orientation of opinion of a piece of text. This system extracts attributes from the piece of writing such as a) The polarity of text, whether the speaker is criticizing or appreciating, b) The topic of discussion, subject of the text. A comparison of the work done so far on sentiment analysis on tweets has been shown. A detailed discussion on feature extraction and feature representation is provided. Comparison of six classifiers: Naïve Bayes, Decision Tree, Logistic Regression, Support Vector Machine, XGBoost and Random Forest, based on their accuracy depending upon type of feature, is shown. Moreover, this paper also provides sentiment analysis of political views and public opinion on lockdown in India. Tweets with ‘#lockdown’ are analysed for their sentiment categorically and a schematic analysis is shown.

The article is devoted to solving the problems of predicting the education of Ukrainian staff in the current COVID-2019 conditions in order to improve the efficiency of their work. Theoretical approaches regarding the characteristics of modeling and prediction of education have been studied, systematic and summarized. Factors that significantly complement the theory and methodology of the scientific provisions of the economy have been identified, taking into account the peculiarities of their work in the modern economic environment, structural changes, and new changes. The pros and cons of studying modeling approaches have been identified. The main features of structural change have been studied and it has been established that the modeling and forecasting of staff education has significant features.

In our research, we provide a statistical syntax parsing method experimented on Kannada texts, which is an official language of Karnataka, India. The dataset is downloaded from TDIL website. Using the Cocke-Younger-Kasami (CYK) parsing technique, we generated Kannada Treebank dataset from 1000 annotated sentences in the first stage. The Treebank generated in this stage contains 1000 syntactically structured sentences and it is used as input to train the syntax parser model in the second stage. We have adopted Probabilistic Context Free Grammar (PCFG) while training the parser model and extracting the Chmosky Normal Form (CNF) grammar from a Treebank dataset. The developed syntax parser model is tested on 150 raw Kannada sentences. It outputs with the most likely parse tree for each sentence and this is verified with golden Treebank. The syntax parser model generated 74.2% precision, 79.4% recall, and 75.3% F1-score respectively. The similar technique may be adopted for other low resource languages.

The purpose of this research study is to measure the impact of governance on the organizational performance of schools in administrative, financial and pedagogical aspects. This governance approach will be approached using a methodology respecting several principles in terms of participation, transparency, accountability and evaluation (CSEFRS, 2015). Furthermore, we added in our frame of reference the primordial aspect of good governance, which is the new technologies. Considering the difficulty of an etymological and polysemic consensus on the concept of governance. It would be wise to set up operational indicators for applying this notion of governance with a perspective to enhance management skills, multi-stakeholder strategic planning, internal collaborative management, effective and efficient steering, control and evaluation. Methodology / approach: Our methodological approach based on a descriptive exploratory study. The researcher carried out an empirical study involving 20 principals of qualifying secondary schools from the Nouaceur-Casablanca region. Our measurement instrument is related to a survey which is appraised as one of the five principles of effective governance (transparency, accountability, participation, evaluation and technology). Findings: data analysis shows difficulties in applying governance principles, influencing organizational performance in administrative management. Additionally, the educational management and financial management of these establishments have seen a positive application of these governance mechanisms. It would be wise to apply the multidimensional aspects of governance, through the harmonious, complete and balanced integration of the three management mechanisms: administrative, pedagogic and financial management. The relationship between the study variables is demonstrated by the application of the five principles of governance, the results of the study recommended that we apply them to contribute significantly to improve the effectiveness of strategic planning, collaborative management and community steering.  

Opportunistic networks in the Internet of Things (IoT) scenario, also known as OppIoT, espouse IoT devices interactions opportunistically in order to improve connectivity, the lifetime of the network, and network reliability. An increase in opportunistic utilization is fostered by IoT applications to find communication opportunities whenever possible to route and deliver data efficiently. In this opportunistic scenario, devising an efficient path for data delivery is a challenging work due to uncertainty in the connection between the nodes and the selection of intermediate forwarder nodes for data delivery towards the destination. Considering the scenario of uncertainty in device location and exploiting IoT devices opportunistically, this paper propounds a routing algorithm for OppIoT called Hybrid Multi-Copy Routing Algorithm (HMCRA). The proposed algorithm finds potential forwarder nodes by using fuzzy logic wherein residual energy, distance, and speed of the nodes are considered as input values while preparing fuzzy rules. Genetic Algorithm (GA) is considered along with fuzzy logic to select an efficient path for data delivery. In GA, the delay is taken as the fitness function to select a reliable path for data delivery. Simulation results of the proposed algorithm perform well in contrast with relative existing routing algorithms with respect to latency, overhead ratio, delivery probability, and hop count. The work uniqueness lies in the selection of potential nodes and finding path having less hop count in an opportunistic IoT network scenario.

In many significant characteristics of the modern world, including particular industry, agriculture, and military applications, Wireless Sensor Networks (WSNs) have begun to emerge. Even though energy consumption is the greatest challenge confronting WSNs, it is essential to analyse the feasibility of the use of mobile components for the data collection on WSN networks. To collect information from their sensing terrain, WSNs use significant numbers of wireless sensor nodes. The method of acquiring data from the sensor nodes and forwarding this data to the node or base station is Data Collection (DC). In such methods, data collection is effective and efficient in data transfer for an extended WSN lifetime. The scientific and investigational analysis demonstrated that Routing Path Selection-DC has the robustness and low energy, is required to reduce the mobile sink's energy demand, which provides better results, and is adaptable dynamically to multiple situations, including the variance between the ground robot and the cost of energy. The research paper developed an approach that creates a data collection schedule that is collision-free where appropriate power limits are allocated and verifies its overall performance of virtual environment network latency.