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In this paper, we have extended our literature survey with experimental implementation. Analyzing numerous Artificial Intelligence (AI) techniques in software engineering (SE) can help understand the field better; the outcomes will be more effective when used with it. Our manuscript shows various AI-based algorithms that include Machine learning techniques (ML), Artificial Neural Networks (ANN), Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN), Natural Language Processing (NLP), Genetic Algorithms (GA) applications. Software testing using Ant Colony Optimization (ACO) approach, predicting software maintainability with Group Method of Data Handling (GMDH), Probabilistic Neural Network (PNN), and Software production with time series analysis technique. Furthermore, data is the fuel for AI-based model testing and validation techniques. We have also used NASA dataset promise repository in our script. There are various applications of AI in SE, and we have experimentally demonstrated one among them, i.e., software defect prediction using AI-based techniques. Moreover, the expected future trends have also been mentioned; these are some significant contributions to the research

Data recorded in the thermal band satellites, are commonly used in the thermal characteristics of different phenomena, especially in desert areas. Classification is one of the most important steps in the use of satellite imagery. In this study, using ASTER data TERRA satellite on 22 August 2001, East and North East Kashan Salt Lake, were studied to find the role of quantity bands of this sensor to increase the resolution accuracy of phenomena in the maximum likelihood (MLK) and support vector machine (SVM). For this purpose, after the initial radiometric and geometric corrections, eight approaches were chosen with different band combinations step by step in order to investigate the quantitative role of each band to increase the classification accuracy and then the classification accuracy using Kappa index, user and producer accuracy were evaluated. The classification algorithm results showed that the support vector machine algorithm rather than maximum likelihood algorithm has slightly better results. Generally, the use of all spectral and thermal bands (14 bands) had a highest accuracy for both Kappa support vector machine algorithm (83.04) and maximum likelihood (90.82). While the Kappa accuracy by 15% in both algorithm with elimination of all thermal bands (bands 10, 11, 13.12 and 14) was reduced. The maximum likelihood algorithm had the greatest impact on increasing the accuracy of Kappa index bands of thermal 14 (between 8 to 10 percent) and in the support vector machine algorithm bands of 10 and 14 (7%). Finally, it was proven that ASTER sensor due to wide spectrum range in the wavelengths of thermal infrared has very high potential in increasing accuracy coefficient of classification Kappa.

In this paper, we first simulate ML, MUSIC, Root-MUSIC algorithms and space smoothing in noisy and fiddling environments, and then these algorithms are compared in terms of estimating the accuracy of the angles received from the two sources. By presenting the simulation results of four algorithms in the form of tables that are based on mean, deviation from mean, variance of estimated angles and the percentage of successful experiments, it is observed that Root-MUSIC and MUSIC algorithms in terms of estimation and Detecting close angles is more accurate. In these two methods, the angles close to each other, up to about one degree, can be detected and separated. In addition, simulation results show that spatial smoothing algorithm is the most effective way to identify coherent resources.