Arman Process Journal (APJ)

Abstract Background and Objectives: Wind turbines as one of the means of producing electrical energy from renewable and clean energies have been the focus of many researchers. The discussion of turbine control in order to produce more power and its economical use against fossil fuels has challenged different control methods.

Methods: In the current research, the purpose of using intelligent fuzzy controllers is to improve the output power and stabilize it when necessary due to its robustness. For this purpose, the induction generator with two-way feeding and variable wind was modeled first, then phase controllers will be designed to separately control active and reactive powers, reduce interference and uncertainty effects. that we used the particle swarm algorithm and the best rules and fuzzy parameters of the intelligent fuzzy system based on deep learning to create the rule and interference system to better performance.

Findings: The comparison of the simulation results of intelligent fuzzy and PI controllers shows the better performance and efficiency of the fuzzy controller in terms of more stability, steady state error and less settling time than the PI controller used in the system. The performance accuracy of the fuzzy controller based on deep learning due to rule extraction and optimal PSO design using random forest algorithm for this system is suitable according to the obtained outputs and the system is controlled in less than 0.4 seconds.

Conclusion: Our integrated and hybrid algorithm shows the good performance due to accuracy and precision parameters, applying the deep learning in order to select the effective parameters on system design for rule extraction in fuzzy and create the decision making in PSO leads the novel way to approach the results.

Abstract Introduction: The use of Information Technology (IT) in business model innovation and entrepreneurship in Qazvin province, especially for startups, presents significant opportunities. Technology startups can create new business models by leveraging innovative solutions, leading to increased productivity and economic growth in the region. Given the importance of innovation and the role of IT in facilitating this process, it is essential to examine the existing challenges and opportunities. Providing appropriate solutions can pave the way for the development of entrepreneurship and innovation in Qazvin province. Promoting a culture of innovation and supporting technology-driven startups will contribute to job creation, increased competitiveness, and improved quality of life in the region. The research method is applied and descriptive in nature. The statistical population includes employees and managers of research and development, production, marketing, and sales units of startups in Qazvin province.
Methodology: This research is applied in terms of purpose and quantitative in terms of variables. It is also cross-sectional, examining the status of variables at a specific point in time. In terms of research design, it is descriptive. The sample size was estimated to be approximately 109 individuals using Cochran's formula, selected through simple random sampling. A questionnaire was used for data collection. The data were analyzed using Smart PLS software, and the validity of the questionnaire was assessed through construct validity and reliability using Cronbach's alpha coefficient.
Findings: The analysis of the findings showed a significant relationship between innovative management and entrepreneurial intention, with business model innovation contributing to this relationship. Business model innovation plays a mediating role in the impact of innovative management on entrepreneurial intention. Regarding the formulation of research hypotheses, the main hypothesis test confirmed that "innovative management has a significant impact on entrepreneurial intention through business model innovation," and business model innovation strengthens this relationship. Business model innovation mediates the effect of innovative management on entrepreneurial intention, and innovative management significantly influences entrepreneurial intention through business model innovation.

Abstract Background and Objectives: In recent years, the use of deep learning methods to analyze electroencephalogram and physiological signals for emotion recognition has attracted the attention of many researchers and scholars. However, the lack of expertise in extracting features from electroencephalogram signals and classifying emotions based on these signals is considered one of the main challenges of this discussion.
Methods: In this study, signal-to-image conversion will be used to prepare input data for a pre-trained network. The innovation of the research is in how to prepare the input data to the pre-trained network. The innovation of the proposed method is also the conversion of signal to image as input features to the learning model in the recorded data for emotion recognition. Also, a hybrid architecture based on VGG-16 network and fuzzy layer is used to optimally solve the problem and increase the emotion recognition rate.
Findings: Given the problems and limitations of data collection and the sensitivity of electroencephalogram data to environmental noise and similar problems, recording electroencephalogram signals in an isolated environment is required. Considering the above, it is better to use a dataset whose accuracy has been previously confirmed by researchers and scholars. In this article, the public DEAP dataset was used for research and hypothesis testing.
Conclusion: By examining the results of implementing the proposed method, the emotion recognition rate shows an accuracy of 90.89 percent.

Abstract  
Nowadays, with the increasing use of real-time applications and multimedia content such as online videos and live streaming, the need for optimization-based approaches to manage resources and enhance the quality of end-user experience has become one of the important requirements and main challenges of network service providers. This research first examines the challenges and solutions to improve the quality of user experience in software-defined networks for video streams, and then designs and proposes a new classification perspective and a multi-objective model to evaluate different dimensions of the problem of managing the quality of user experience in common video services in distributed networks. The proposed approach provides a documented and structured framework for optimal resource allocation and efficient management of this network. Covering such approaches in high-level network management not only helps improve the quality of end-user experience, optimize video streams, and reduce latency, but also increases network efficiency and reduces operating costs by creating a multidimensional and comprehensive perspective and optimal use of available resources.

Abstract Blockchain technology, with its unique features such as decentralization, transparency, security, and immutability, has brought about a significant transformation in various technological fields. This article aims to comprehensively review the security aspects of blockchain, its challenges, and future prospects. First, the fundamental concepts of blockchain, various consensus algorithms, and cryptographic techniques used in it are described. Then, the diverse applications of blockchain in fields such as finance, IoT, energy, health, and privacy are analyzed. An important part of the article is dedicated to blockchain security issues, including network attacks, code vulnerabilities, smart contract attacks, and data protection. Finally, security measures and vulnerability analysis tools are introduced, and research trends are presented to develop more scalable and secure systems. The results show that while blockchain has high potential, it cannot be used sustainably and widely without considering the existing security issues and challenges. Therefore, addressing these challenges, along with leveraging new technologies, will be the key to success in the future development of blockchain. This paper can be used as a reference for further research in the field of blockchain security and the development of related technologies.

Abstract Quantum genetic algorithm, which is a combination of quantum mechanics principles and evolutionary algorithms, has been proposed as a new method in the field of optimization. In recent years, numerous applications of these algorithms have been reported in solving complex biological problems such as gene and stem cell structure analysis, protein simulation, and molecular behavior prediction. This article aims to comprehensively review the performance of quantum genetic algorithm in biological problems, and reviews its theoretical foundations, functions, advantages, and challenges. Various studies have shown that quantum genetic algorithm, by utilizing quantum properties such as superposition and entanglement, has been able to improve the inefficient convergence problems of classical algorithms and provide more optimal solutions. Also, in the second part of the article, the architecture and technical mechanisms of quantum genetic algorithm are described and applied examples in biology are analyzed. Finally, the prospects of this technology in biological research are reflected by providing recommendations and perspectives for future developments.