Amid the previous three decades, the topic of image processing has gained vital name and recognition among researchers because of their frequent look in varied and widespread applications within the field of various branches of science and engineering. As an example, image processing is helpful to issues in signature recognition, digital video processing, remote sensing and finance. Image processing models are used for detecting the face. The aim of this thesis is to solve the face-detection in the first attempt using the Haar-cascade classifier from images containing simple and complex backgrounds. It is one of the preeminent detectors in terms of reliability and speed. We introduced a new method to deal with the frontal face images by using a modified Haar cascade algorithm. By using this algorithm, we can detect the image as well as the coordinates. The main attraction of this paper is to solve different types of images having one object, two objects, and three objects which can’ t be solved by any of the existing methods but can be solved by our proposed method.

Nowadays, the control of the traffic in the urban roads and in the highway has been a big challenge as the number of increase in the auto mobiles. So to overcome this problem we use the detection and tracking the vehicles using the traffic surveillance system. We can manage and control the traffic more easily. It is very complicated and a challenging task to identify the vehicle or a moving object in a complex environment with various background. The ratio detected of such algorithms depends on the quality of the foreground mask generated. Therefore this project is to present the detection and tracking the vehicles and the pedestrians in an efficient method which focus on trajectory motion of the vehicles and the pedestrians. In this proposed method, the pixels in the background are preserved which can be cars, bikes, buses, pedestrian, etc., the rest is discarded as the noise. Hence, our proposed method detects the vehicles and the pedestrians as mentioned and discards the rest noise as well in the same time. Here the quality of the generated foreground mask is more to increase the detection ratio. The performance is compared with other standard methods qualitatively and quantitatively.

The optimal portfolio selection is vital for investment. The risk of portfolio Selection and return is the most critical concern of investment companies and private investors. According to modern portfolio theory, diversification should cover the risk. This theory is based on the normality of assets return. Experimental findings indicate that the assets return non-normality. Higher moments are sed to upgrade traditional models with the primary presumption of a normal distribution in recent years. This study uses a higher moment and the entropy for diversification and selects a portfolio given a non-normality assumption. It is essential to use up-to-date information to increase the model's efficiency, and accordingly, we used the rolling window for new price information. For the financial information method, we use the total index return in the last five working days and weigh the shares of the banking, insurance, and leasing industries on the next working day and evaluate this for three years. Python, math, and NumPy libraries were used to analyze the data. The results show that a much higher moment model can provide better portfolio selection results in most cases.

Background: One of the spine deformities is scoliosis, and Cobb angle is generally used to assess it. Objectives: In this study, a computer-aided measurement system (CAMS) was presented as a new repeatable and reproducible approach to assess the Cobb angle in idiopathic scoliosis patients. Methods: Python libraries, including OpenCV and NumPy were used for image processing and design of the software. To assess the repeatability and reproducibility of the CAMS, a series of 98 anterior-posterior radiographs from patients with idiopathic scoliosis were used. Assessments were done by five independent observers. Each radiograph was assessed by each observer three times with a minimum break of two weeks among assessment. The single measure intraclass correlation coefficient (ICC), the mean absolute difference (MAD), and the standard error measurement (SEM) values were used for intra-and inter-observer reliability. Results: The inter-observer analysis indicated that the ICCs ranged from 0. 94-0. 99, and the MAD between manual and CAMS were less than 3°, . For intra-observer measurements, the combined SEM between all observers for the manual and CAMS was 1. 79°,and 1. 27°, , respectively. An ICC value of 0. 97 with 95% confi, dence interval (CI) was excellent in CAMS for inter-observer reliability. The MAD of CAMS was 2. 18 ,2. 01 degrees. Conclusions: The CAMS is an effective and reliable approach for assessing scoliotic curvature in the standing radiographs of thoraco-lumbar. Moreover, CAMS can accelerate clinical visits, and its calculation results are reliable.

Purpose: Analyzing the subject structure of research in this field can provide valuable information about the current state of the field, including its issues and needs, strengths, gaps, and shortcomings. The present study aims to analyze the global research topics in information science and library science, while also reviewing research topics from previous years and identifying thematic gaps. This analysis will provide a foundation for planning and policy research in this field. Methodology: The present study utilized an automated classification technique. Data analysis was performed on 85, 403 articles indexed on the Web of Science website between 1945 and 2019 in the field of library and information science. The source and tool used for extracting research data was Web of Science. After preparing and pre-processing the data and formulating the classification plan for articles across 31 thematic categories, the logistic regression algorithm has been employed for the automatic classification of articles. This algorithm assigns articles to their respective thematic categories using the Cosine similarity criterion. The various stages of the research were performed using the Python 3. 7 programming language and the Pandas, NumPy, NLTK, and Plotly software packages. Findings: Most of the global research in this field is dedicated to information technology, while the field of organizational management has the least amount of research. The growth trend of research in many thematic areas is increasing. However, there is further growth in areas such as web studies, information technology, information behavior, user studies, information retrieval, and search engines. After the 1990s, popular terms such as social media, big data, virtual library, IoT (Internet of Things), machine learning, deep learning, data mining, open data, artificial intelligence, natural language processing, cloud computing, virtual reality, augmented reality, and machine translation have emerged in research in this field. It appears that the utilization of keywords pertaining to traditional subjects in the field has declined, while the usage of concepts encompassing new topics related to technology and the web has increased. The rapid growth of fields such as web development, technology, artificial intelligence, and data mining has led to the evolution of research topics and increased connections with computer science and information technology, particularly in recent years. The changes in the subjects of this field can be attributed to the development of the web and advancements in technology. Additionally, the concepts and research topics in this field have had an impact on globalization, communication progress, and overall development in various economic, social, and cultural dimensions. Conclusion: The conclusion of the study highlights the thematic trends in library and information science research over different time periods. It also identifies the weaknesses in the scientific output of this field, while outlining the evolutionary process and the path of progress and development in the field of science. Due to the fact that scientific outputs are derived from the issues, needs, and priorities of a society at any given time, studying them can help clarify the most important issues and research needs in the field of library and information science. This, in turn, can provide a foundation for identifying future research needs and subjects in the field. In other words, having a clear understanding of the subject areas of library and information science will help researchers in this field identify the most important issues and needs. This, in turn, will enable them to take appropriate actions when selecting topics for future research. By conducting needs-based research, we can lay the groundwork for policy development and research planning in this field. This approach will also help address potential challenges and problems that may arise. This is despite the fact that if the needs and issues are ignored, research resources and credits will be spent on topics that do not have the necessary priority and importance. It cannot be expected that the findings from these topics will solve the problems and pave the way for the advancement of this field.

Background and Objectives: In recent decades, the complex interaction between environmental factors and public health has attracted the attention of researchers, policy makers, and public health practitioners. Understanding how environmental factors affect human health is very important in dealing with citizens' health challenges. In recent years, geographic information systems (GIS) have been used as practical tools in this field and have enabled the investigation and analysis of complex relationships between environmental variables and health. These systems are a useful tool for zoning diseases, and with the spatial distribution of some diseases, significant results can be achieved. Results such as that some diseases are related to environmental factors. Diagnosing environmental factors in the direction of treatment, prevention and reduction of healthcare costs is a way to develop health. This article deals with the spatial analysis of some environmental factors affecting health in order to provide solutions to prevent the occurrence of pathogenic factors in citizens.Methods: The research method of performing a location analysis includes several steps including data collection, data pre-processing, spatial analysis and integration with decision support systems. Collecting air quality data is one of the main parts of this research. The air quality of Tehran is affected by several factors, including the emission of pollutants by cars, industrial units and natural resources, the amount of each of which is measured using the network of air quality monitoring stations throughout the city of Tehran. This time-spatial data set enables us to investigate trends and changes in air quality in different areas of Tehran. Also, ensuring access to safe water is a fundamental aspect of public health. Therefore, the collection of water quality data is critical for our study. The desired parameters include water acidity or alkalinity (pH), clarity, chemical oxygen demand (COD), biological oxygen demand (BOD) and concentrations of certain pollutants such as heavy metals. Is. The data collected by local health centers have been used to analyze infectious diseases from animals. After collecting the required data, the process of data analysis and processing is done for their spatial analysis, and after the said analysis, the data obtained from different stages of the research are integrated in GIS. This allows us to combine spatial data to more clearly show the connections between environmental factors and diseases. This data integration should be done regularly and carefully so that the results of the analysis are valid. QGIS software was used to perform spatial data analysis and processing. In addition, Pandas and NumPy libraries in Python were used for statistical data analysis.Findings: The data collected from the air quality monitoring stations allowed us to obtain detailed maps of the concentration of pollutants and their spatial changes. These maps are very valuable in monitoring health risks related to air pollution. Analysis of air quality data showed high concentrations of PM2.5 and PM10 particulate matter in densely populated areas. In addition, the concentration of NO2 near the main roads indicates the major contribution of vehicles in the production of this gas. The analysis of water quality in Tehran showed that there are no significant differences in the water quality of different regions. . The analysis of disease data provides important information about carriers, their habitats and behavioral patterns, which leads to a comprehensive understanding of the city's infectious disease ecosystem. Targeted measures to control infectious diseases in high-risk areas and education to the target community are necessary to reduce the risk of diseases.Conclusion: Human health is affected by various environmental factors, including the place of their lives, so that it can be said that health-related issues almost always have spatial dimensions. Investigating the characteristics of these places (including anthropological characteristics and the presence of environmental risk factors) is very important in order to conduct studies. The results of this research showed that GIS has a valuable role in investigating and tracking the spread of diseases and other health issues in The length of time periods and the assessment of environmental risks for the residents of an area. Using GIS is one of the health warning solutions to people at risk. By specifying the location of the disease and the polluted areas of the city, people will become more aware of their surroundings and better understand prevention issues. Also, with the identification of high-risk areas, health costs and expenses will be adjusted because policy makers and health managers will focus on the necessary strategies to prevent and deal with the spread of these types of diseases in a targeted manner.

Water balance looks like a simple function, but its estimation at the watershed level is a complex process. For this purpose, water balance models could be a useful tool for simulating water balance components. The aim of this study was to simulate the surface water balance of the Karun 4 watershed using a model with minimum input data, because one of the problems in mountainous areas is lack of data. Many water balance models have been used in Karun watershed. Therefore, the Exp-Hydro model, which has not been used in Iran so far and has a simple structure and minimum input data, was selected. Exp-Hydro is one of the watershed-scale daily hydrological models that make use of daily data including precipitation, air temperature and potential evapotranspiration as input and simulates the daily streamflow at the watershed outlet. Due to its simple structure in this study, the open-source model, written in Python was used to simulate the daily runoff of the Karun 4 watershed. Methods: In this study, the spatially lumped version of the Exp-Hydro model was used to simulate daily runoff. The script code of the model was written in Python. Python software, NumPy, SciPy, and Matplotlib library functions were installed to run this model. The input data of the model were prepared in separate files in text format. Then the results of Exp-Hydro model were compared with a daily water balance model that was developed in this study. This model was prepared in Excel software. The statistical period of this study was from 2000 to 2020, where two-thirds of the data were used for the calibration period and one-third for the validation period. The WHAT software and recursive digital filter method were used to separate the baseflow from the observed daily flow data. The Exp-Hydro model was calibrated automatically using PSO optimization algorithm and the daily water balance model that developed in this study was calibrated using Goal Seek method. The efficiency of the models were evaluated in this study by means of Kling Gupta criteria (KGE), Nash-Sutcliffe efficiency coefficient (NSE), Correlation coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Results: In the separation process of the base flow values from the total flow of Armand hydrometric station, was done in such a way that in the minimum flows, the separated flow rate corresponds to the minimum values, which in this case, the values of the two constant parameters of the filter parameter and base flow index (ratio of baseflow to the total flow) was chosen as 0.9 and 0.36, respectively. Evaluation of the developed daily water balance model's performance using the KGE, NSE, R, RMSE and MAE showed that these objective functions were 0.76, 0.69, 0.83, 0.25, and 0.11 respectively during the calibration and 0.76, 0.62, 0.85, 0.19, and 0.09 respectively during the validation period. This means the developed daily water balance model in this study has produced good and satisfactory outputs. Also, the evaluation of Exp-Hydro model's performance using the KGE, NSE, R, RMSE and MAE showed that these objective functions were 0.69, 0.37, 0.69, 0.35, and 0.14 respectively during the calibration and 0.6, 0.19, 0.71, 0.28, and 0.11 respectively during the validation period. This means the developed daily water balance model in this study has produced good and satisfactory outputs. This means the Exp-Hydro model has an intermediate performance according to the KGE coefficient in both calibration and validation periods. In addition, the developed daily water balance model in this study estimated the low flows with higher accuracy and both models had underestimated regarding flood flows. Sensitivity analysis of Exp-Hydro model was done manually by changing each parameter of the model and comparing the changes between observation and simulation runoff hydrographs, and the value of efficiency coefficients was also controlled. In this analysis, it was found that since f is the parameter that controls the reduction of runoff depending on the reserve, this parameter has an inverse relationship with simulation runoff value; So that by decreasing the f value, the simulation runoff values increase and become more than the observation runoff values. Also, this parameter was the most sensitive parameter in the Exp-Hydro model. In the developed daily water balance model in this study, m in calculating maximum runoff generated in the catchment bucket using the Reservoir performance exponential function and e in calculating snow melt were the most sensitive parameters. These parameters were the most sensitive parameter in this model; so that by reducing their values, the simulated runoff values of the model decrease and become lower than the observed runoff values. In general, the performance of the developed daily water balance model in this study was better than Exp-Hydro model.