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Many cities worldwide are at the frontline of environmental challenges during their future planning. Navi Mumbai presents many opportunities to move a city toward sustainability. The land is scarce for development purposes in Mumbai, which has resulted in the creation of the suburban area of Navi Mumbai. As the latter has numerous water bodies and drainage channels, which lie in the ecosensitive zone, to stop organic development, the environmental suitability index along with the accessibility index needs to be assessed to promote future organized growth. The present study investigated the planning of the new areas and assessed environmental suitability and accessibility for various zones seeking future development. The analytical derivation of the environmental suitability index was based on the parameters, subindex, and attributes of environmental issues in that area. The parameters included (1) soil permeability, (2) water-holding capacity, (3) porosity, (4) properties of groundwater and marine water, (5) properties of air, and (6) noise. The entire area was divided into five zones for the current work. The analytical framework was a developed methodology of indices calculated from five monitoring stations obtained from the City and Industrial Development Corporation of Maharashtra Limited, India (CIDCO). The accessibility parameters of the transportation network were measured using alpha indices (α). Finally, a nondominated sorting algorithm was used to obtain final suitability based on the Pareto optimality condition. The development suitability of an area was obtained by computing the environmental suitability index and accessibility index. This computation decides which of the five stations is best for which type of planning. The study developed a dimension-wise suitability index for Navi Mumbai, aiming to streamline the planning process and integrate policies essential for planning to attain sustainable development for Navi Mumbai. During future planning, policymakers and planners have often ignored environmental aspects in ecofragile areas. Hence, planning for new development in ecofragile areas has often been haphazard, piecemeal, and organic. The framework adopted in this paper, combining the environmental suitability index and the accessibility index, intended to assess the area's health and prescribe future actions to reduce the negative environmental impacts of development and regulate the same. The paper showed how data on soil, water, groundwater, air, and noise can be used in a simple mathematical model to compute the environmental suitability index of an area. Similarly, graph-theoretic measures of the whole transportation network can measure the accessibility index of the area. Finally, the development suitability of the area can be calculated by combining the environmental suitability index and the accessibility index. The level of domination result signifies the most suitable station/node for different types of development. Based on the results, policymakers and planners can determine an ecofragile area's future land use development as required. This model can further be adopted by local governing authorities to plan for zoning, zoning regulations, and approval of a proposed development. [ABSTRACT FROM AUTHOR]

Software quality prediction is the Machine Learning (ML) based technique in which ML models are trained using historical data. Output from these quality models can be used by software experts in the early phase of software development for improving the quality of software by controlling the various quality attributes like maintainability, reliability, security issues of software etc. In this study a systematic review of studies from 2005 to 2021 is performed. Studies that use ML techniques and source code metrics for Software Quality Prediction (SQP) are included for review. Study assesses the commonly used machine learning techniques and source code metric for SQP. Commonly used datasets, feature selection techniques and commonly used performance measures in software quality prediction are also assessed. In this paper 53 primary studies are selected for systematic review. Results of this study prove that Bayesian Learning (BL), Regression, Ensemble Learning (EL), Decision Tree (DT) and Support Vector Machine (SVM) are most commonly ML techniques used for quality prediction which comprises 58%, 52%, 41%, 32%, and 32% of the overall studies respectively. It is also assessed that NASA, PROMISE, Apache, Mozilla Firefox and Eclipse are the most commonly used datasets for training and testing the SQP models. LOC, CC, CBO, RFC, WMC, LCOM, DIT and NOC are among the most commonly used source code metrics in SQP. Based on the results from the selected studies it is concluded that ML techniques and source code metrics have the ability to improve the overall quality of the software. [ABSTRACT FROM AUTHOR]