List of Articles Logistic Regression

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  1 - Urban Growth and Its Influencing Factors
       
  The urbanization process and urban growth in different parts of the world result from the interconnected interactions between factors and various socio-economic, political, technological, geographical, cultural, global and local issues. Accordingly, identifying the driv More

  The urbanization process and urban growth in different parts of the world result from the interconnected interactions between factors and various socio-economic, political, technological, geographical, cultural, global and local issues. Accordingly, identifying the driving and shaping factors of the growth of cities is vital for urban planning and sustainable development, especially in developing countries, which is used as a means of predicting future trends, controlling and guiding the growth of the city, organizing the future development of the city and eventually efficiently and purposefully managing the city. The main objective of this study is to identify the driving and shaping factors of the biophysical and socio-economic growth of Tehran metropolis. In order to achieve this goal, five main stages are taken into consideration, including the production of land use maps, land cover to study the land use spatial-temporal changes, land cover in Tehran and its surrounding areas using satellite images, identifying driving factors of urban growth using logistic regression, using a factor ecology approach to investigate the human factors effective on Tehran landscape, calculating spatial metrics for quantization of the structure and characteristics of the landscape pattern in Tehran metropolis using Fragstatas software and investigating the relationship between urban growth pattern and social areas using multivariate regression. The results indicate that the distance from the roads and the commercial center of Tehran in the period 2000 to 2014 is the most significant biophysical factor shaping the growth pattern of Tehran metropolis, and among the obtained five socioeconomic components, the population density and employment components have the most effect on urban growth pattern in Tehran metropolis. Manuscript profile
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  2 - Breast Cancer Classification Approaches - A Comparative Analysis
  Mohan Kumar Sunil Kumar Khatri Masoud Mohammadian
  10.52547/jist.33395.11.41.1
  Cancer of the breast is a difficult disease to treat since it weakens the patient's immune system. Particular interest has lately been shown in the identification of particular immune signals for a variety of malignancies in this regard. In recent years, several methods More

  Cancer of the breast is a difficult disease to treat since it weakens the patient's immune system. Particular interest has lately been shown in the identification of particular immune signals for a variety of malignancies in this regard. In recent years, several methods for predicting cancer based on proteomic datasets and peptides have been published. The cells turns into cancerous cells because of various reasons and get spread very quickly while detrimental to normal cells. In this regard, identifying specific immunity signs for a range of cancers has recently gained a lot of interest. Accurately categorizing and compartmentalizing the breast cancer subtype is a vital job. Computerized systems built on artificial intelligence can substantially save time and reduce inaccuracy. Several strategies for predicting cancer utilizing proteomic datasets and peptides have been reported in the literature in recent years.It is critical to classify and categorize breast cancer treatments correctly. It's possible to save time while simultaneously minimizing the likelihood of mistakes using machine learning and artificial intelligence approaches. Using the Wisconsin Breast Cancer Diagnostic dataset, this study evaluates the performance of various classification methods, including SVC, ETC, KNN, LR, and RF (random forest). Breast cancer can be detected and diagnosed using a variety of measurements of data (which are discussed in detail in the article) (WBCD). The goal is to determine how well each algorithm performs in terms of precision, recall, and accuracy. The variation of each classification threshold has been tested on various algorithms and SVM turned out to be very promising. Manuscript profile