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This paper employs machine learning algorithms to detect tax evasion and analyzes tax data. With the development of commercial businesses, traditional algorithms are not appropriate for solving the tax evasion detection problem. Hence, other algorithms with acceptable speed, precision, analysis, and data decisions must be used. In the case of assets and tax assessment, the integration of machine learning models with meta-heuristic algorithms increases accuracy due to optimal parameters. In this paper, intelligent machine learning algorithms are used to solve tax evasion detection. This research uses an improved particle swarm optimization (IPSO) algorithm to improve the multilayer perceptron neural network by finding the optimal weight and improving support vector machine (SVM) classifiers with optimal parameters. The IPSO-MLP and IPSO-SVM models using the IPSO algorithm are used as new models for tax evasion detection. Our proposed system applies the dataset collected from the general administration of tax affairs of West Azerbaijan province of Iran with 1500 samples for the tax evasion detection problem. The evaluations show that the IPSO-MLP model has a higher accuracy rate than the IPSO-SVM model and logistic regression. Moreover, the IPSO-MLP model has higher accuracy than SVM, Naive Bayes, k-nearest neighbor, C5.0 decision tree, and AdaBoost. The accuracy of IPSO-MLP and IPSO-SVM models is 93.68% and 92.24%, respectively. [ABSTRACT FROM AUTHOR]

The current paper deals with the major causes of air pollution in the big cities of Azerbaijan. In this context, industrial gas wastes and transport systems are the most important sources of air pollution. Also, relevant factors following the intensity of air pollution have recently been recognized. The motor transport system and its detrimental effects on human health are tightly connected with air pollution. Therefore, the use of ecological and geographical data could be an efficient approach to preventing air pollution in urban regions. Also, the advantage of nanotechnology in removing air contaminants has been introduced as a promising solution. Nanomaterials can serve as nano adsorbents, nanocatalysts, nano filters/membranes, and nanosensors in air pollution remediation. Moreover, the green synthesis of nanomaterials from plant-based origins is promising in this context. Also, nanotechnology is a robust candidate for the production of green and sustainable energy resources. In a nutshell, recommendations on the prevention and alleviation of air pollution, as well as the methods of refining the urban environment and controlling polluting agents are represented. [ABSTRACT FROM AUTHOR]