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The extant literature reveals that scholars and policy makers are highly concerned about exploring the validity of the environmental Kuznets curve (EKC) hypothesis using a different set of variables with the prime objective of exploring environmental degradation issues related to sustainable economic development for different countries. We examine the validity of the EKC hypothesis for the five most influenced economies of the G-20 from 1993 to 2017 using GDP per capita and CO2 emissions, along with some other variables, namely technological development, financial development (FD), energy use, and social globalization to avoid any misspecification in the empirical model. The LM bootstrap approach confirms the co-integration in the series, and the panel Driscoll–Kraay standard error method confirms that veto-power economies have an N-shaped relationship between CO2 emissions and GDP per capita. Furthermore, empirical findings exhibit that technological advancement and energy consumption positively correlate with CO2 emissions, whereas FD and social globalization attenuate environmental degradation. These empirical findings suggest that appropriate policies need to be designed for these sample countries, depending on their GDP per capita and CO2 emissions levels. An environmentally friendly policy may be adopted to achieve sustainable development goals. Policymakers also need to implement a policy that encourages financial development and boosts technologies with fewer polluting characteristics.

Radiography is being widely used as a nondestructive testing technique to investigate the safety and reliability of welded process. In this paper an automated weld defect recognition framework is presented, which employs image processing and pattern recognition methods on weld radiographs. Initially, a pre-processing step is performed on radiographs that suppresses undesired distortions and enhances image features important for further processing. After image pre-processing, a set of features including the geometric and texture features are extracted from each object in a segmented image. Genetic algorithm is applied for selecting the optimal feature sets. The optimal and reduced feature vector is then given as input to SVM and ANN for classification. The last step of the recognition system includes the evaluation of the detected/classified defects in the weld on the basis of acceptance criterion. Experimental results show that an overall improvement in performance and accuracy is achieved using GA based optimal features with SVM as classifier.

In this paper we present a welding defect detection system using radiographic images. Main goal is to craft a dependable system because a human evaluator is not a stable evaluator besides other humanoid constraints. We present a novel technique for the detection and classification of weld defects by means of geometric features. Firstly noise reduction is done as radiographic images contain noise due to several effects. After this we tend to localize defects with maximum interclass variance and minimum intra class variance. Further we move towards extracting features describing the shape of localized objects in segmented images. Using these shape descriptors (geometric features) we classify the defects by Artificial Neural Network.

Intelligent systems based on machine learning techniques, such as classification, clustering, are gaining wide spread popularity in real world applications. This paper presents work on developing a software system for predicting crop yield, for example oil-palm yield, from climate and plantation data. At the core of our system is a method for unsupervised partitioning of data for finding spatio-temporal patterns in climate data using kernel methods which offer strength to deal with complex data. This work gets inspiration from the notion that a non-linear data transformation into some high dimensional feature space increases the possibility of linear separability of the patterns in the transformed space. Therefore, it simplifies exploration of the associated structure in the data. Kernel methods implicitly perform a non-linear mapping of the input data into a high dimensional feature space by replacing the inner products with an appropriate positive definite function. In this paper we present a robust weighted kernel k-means algorithm incorporating spatial constraints for clustering the data. The proposed algorithm can effectively handle noise, outliers and auto-correlation in the spatial data, for effective and efficient data analysis by exploring patterns and structures in the data, and thus can be used for predicting oil-palm yield by analyzing various factors affecting the yield., {"references":["US Department of Agriculture, Production Estimates and Crop\nAssessment Division, 12 November 2004.\nhtttp://www.fas.usda.gov/pecad2/highlights//2004/08/maypalm/","F. Camastra, A. Verri. A Novel Kernel Method for Clustering. IEEE\nTrans. on Pattern Analysis and Machine Intelligence, vol 27, pp 801-805,\nMay 2005.","I.S. Dhillon, Y. Guan, B. Kulis. Kernel kmeans, Spectral Clustering and\nNormalized Cuts. KDD 2004.","C. Ding and X. He. K-means Clustering via Principal Component\nAnalysis. Proc. of Int. Conf. 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This paper presents work on developing a software system for predicting crop yield from climate and plantation data. At the core of this system is a method for unsupervised partitioning of data for finding spatio-temporal patterns in climate data using kernel methods which offer strength to deal with complex data. For this purpose, a robust weighted kernel k-means algorithm incorporating spatial constraints is presented. The algorithm can effectively handle noise, outliers and auto-correlation in the spatial data, for effective and efficient data analysis, and thus can be used for predicting oil-palm yield by analyzing various factors affecting the yield.