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This study was conducted to determine the stock portfolio with the best return and low-risk investments using momentum-contrarian strategies (MCSs). The momentum-Contrarian strategy is one of the well-known models to construct the portfolio which suggests buying the stocks with the best performance (the winner stocks) and selling the stocks with the worst performance (the loser stocks). The optimal values of the portfolio's objective function and the weight of all assets in the portfolio that are not necessarily the same are calculated by defining a nonlinear multivariate optimization model combined with momentum-contrarian strategies (MCSs). The return information of companies listed on the Tehran Stock Exchange from 2014 to 2019 was used to select the best optimal portfolio. The results confirmed the stability in the profitability of the contrarian optimal portfolio with minimum risk compared to other optimal portfolios. Furthermore, through MATLAB software the optimal weight of assets in the optimal portfolio is calculated based on statistical data.

An administrator is employed to identify network security breaches in their organizations by using a Network Intrusion Detection and Prevention System (NIDPS), which is presented in this paper that can detect and preventing a wide range of well-known network attacks. It is now more important than ever to recognize different cyber-attacks and network abnormalities that build an effective intrusion detection system plays a crucial role in today's security. NSL-KDD benchmark data set is extensively used in literature, although it was created over a decade ago and will not reflect current network traffic and low-footprint attacks. Canadian Institute of Cyber security introduced a new data set, the CICIDS2017 network data set, which solved the NSL-KDD problem. With our approach, we can apply a variety of machine learning techniques like linear regression, Random Forest and ID3. The efficient IDPS is indeed implemented and tested in a network environment utilizing several machine learning methods. A model that simulates an IDS-IPS system by predicting whether a stream of network data is malicious or benign is our objective. An Enhanced ID3 is proposed in this study to identify abnormalities in network activity and classify them. For benchmark purposes, we also develop an auto encoder network, PCA, and K-Means Clustering. On CICIDS2017, a standard dataset for network intrusion, we apply Self-Taught Learning (STL), which is a deep learning approach. To compare, we looked at things like memory, Recall, Accuracy, and Precision.

This work serves to propose a national electronic health system based on the Big Data approach. First of all, we assessed the practice of health information systems (HIS) in Morocco and their obstacles. We performed a survey that was founded on 24 questions to specify the necessary details on this topic. This study shows that there is a primary need for the establishment of an HIS that facilitates the control, analysis, and management of health data in Morocco. For this reason, we have proposed the implementation of the Moroccan Health Data Bank (MHDB). This system will be based on powerful big data technologies that save, manage, and process health data with greater efficiency. The information present in this proposed system can provide the necessary resources for several actors to exploit this wealth, which is embodied in this massive data. We have developed a general description of the MHDB, its components, its conceptual architecture, and an example of a use case.

Purpose: With the increase of problems and the complexity of organizations, the need to provide better solutions and use more powerful tools of strategic planning is evident because managers with strategic thinking and a better understanding of the strategic plan will perform more effectively. It can be said that strategic thinking is considered as a supplement to strategic planning, a suitable approach to "leadership" of the organization and can help in systematically dealing with problems, understanding opportunities, optimal allocation of scarce resources and achieving the desired results. In view of this importance, the current research aims to design a strategic thinking competence assessment model with the approach of developing organizational flexibility in National Gas Company of Iran.Method: This research is applied in terms of purpose and was done with qualitative approach based on grounded theory method. Sampling of the research was done using snowball method (chain reference). For this purpose, qualitative data was collected, which led to the identification of many aspects of the phenomenon and the possibility of developing a conceptual research model. The data collection method is library studies, perusal of high-level documents, and in-depth and open interviews. The statistical population of this research consisted of specialists and experts in the field of management and strategic thinking, of whom 12 people were investigated. In the process of conducting this research, the interviews continued until theoretical saturation was observed.Findings: The findings showed that six main categories are effective on the evaluation of strategic thinking with an organizational flexibility approach, which are: foresight, strategic determination, systemic attitude, innovation and creativity. The results show that the validity of the model is confirmed and the six obvious variables (six dimensions) explain the latent variables of strategic thinking competency assessment. According to the direction of all the relations between the manifest variables and the latent ones, , all the relations are aligned and direct.Conclusion: According to the amount of standardized factor loadings, the dimension "Foresight (AN)", with a factor loading of 0.88, is the most reliable indicator belonging to evaluation measurement.