The present study aimed to investigate the determinants of venture investment in companies listed on the Tehran Stock Exchange using the Soccer League Competition Algorithm. The present study is applied in terms of aim and descriptive-correlational in terms of method and nature. The financial data of 100 companies listed on the Iran Stock Exchange for 5 years from 2014 to 2019 were selected as a sample and were analyzed using Excel, Eviews, and Matlab software. First, the determinants of venture investment in the Tehran Stock Exchange (investment volume, rate of export, company tax, disclosure index, rule of law index, diversification of the industry, diversification of the stages of the company's life cycle, type of ownership, number of sectors and subsidiary companies, company investment, company size, and company age) were identified. By using the Soccer League Competition Algorithm, a predictive model was created to determine the investment risk based on the extracted indicators. Then, the accuracy of this model in terms of predicting the riskiness of the investment was compared with the simulated annealing algorithm, the NN neural network, and the SOM neural network. Manuscript profile

In the stock market, predicting the trend of price series is one of the most widely investigated and challenging problems for investors and researchers. Risk managers need to decide when to leave a portfolio unhedged to generate profit and when to hedge in order to control downside risk. There are multiple time scale features in financial time series due to different durations of impact factors and traders’ trading behaviors. While science and technology parks and towns and development centers have been established before 2002 in Iran and their number has been continually increased, there has been only a seminar presented by Tehran Securities and Exchange Organization, in Agricultural Bank, 2001, about venture capital in the country. Gradually, venture capital has been presented as a new mechanism for financing to entrepreneurs and a bill has been represented to the cabinet by Management and Planning Organization to define an annual definite budgetary in a general manner. The industry of venture capital has been under the attention of many organizations and science centers during recent years. The Institute of Elites’ technological development, on behalf of Centre for Innovation and Technology Cooperation, Presidency of the Islamic Republic of Iran, Management and Planning Organization of Iran, Industrial Development and Renovation Organization of Iran, Centre for New Industries of Iran. In this paper, we extend the field of expert systems, forecasting, and model by applying an Artificial Neural Network. ANN model is applied to forecast market volatility. The results show an overall improvement in forecasting using the neural network is compared to the linear regression method. Manuscript profile

Agriculture plays a significant role in the economic sector. Automation in agriculture is the main concern and an emerging subject across the world. The population is increasing tremendously and with this increase, the demand for food and employment is also increasing. The traditional methods which were used by the farmers were not sufficient enough to fulfill these requirements. Thus, new automated methods were introduced. These new methods satisfied the food requirements and also provided employment opportunities to billions of people. Artificial Intelligence in agriculture has brought an agricultural revolution. This technology has protected the crop yield from various factors like climate changes, population growth, employment issues, and food security problems. The main concern of this paper is to audit the various applications of Artificial intelligence in agriculture such as for irrigation, weeding, spraying with the help of sensors and other means embedded in robots and drones. These technologies save the excess use of water, pesticides, herbicides, maintains the fertility of the soil, also helps in the efficient use of manpower and elevate productivity and improve the quality. This paper surveys the work of many researchers to get a brief overview of the current implementation of automation in agriculture, the weeding systems through robots and drones. The various soil water sensing methods are discussed along with two automated weeding techniques. The implementation of drones is discussed, the various methods used by drones for spraying and crop monitoring is also discussed in this paper. Manuscript profile