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In general, Indian Classical Music (ICM) is classified into two: Carnatic and Hindustani. Even though, both the music formats have a similar foundation, the way of presentation is varied in many manners. The fundamental components of ICM are raga and taala. Taala basically represents the rhythmic patterns or beats (Dandawate et al., 2015; Kirthika and Chattamvelli, 2012). Raga is determined from the flow of swaras (notes), which is denoted as the wider terminology. The raga is defined based on some vital factors such as swaras, aarohana-avarohna and typical phrases. Technically, the fundamental frequency is swara, which is definite through duration. Moreover, there are many other problems for automatic raga recognition model. Thus, in this work, raga is recognized without utilizing explicit note series information and necessary to adopt an efficient classification model.

This paper proposes an efficient raga identification system through which music of Carnatic genre can be effectively recognized. This paper also proposes an adaptive classifier based on NN in which the feature set is used for learning. The adaptive classifier exploits advanced metaheuristic-based learning algorithm to get the knowledge of the extracted feature set. Since the learning algorithm plays a crucial role in defining the precision of the raga recognition, this model prefers to use the GWO.

Through the performance analysis, it is witnessed that the accuracy of proposed model is 16.6% better than NN with LM, NN with GD and NN with FF respectively, 14.7% better than NN with PSO. Specificity measure of the proposed model is 19.6, 24.0, 13.5 and 17.5% superior to NN with LM, NN with GD, NN with FF and NN with PSO, respectively. NPV of the proposed model is 19.6, 24, 13.5 and 17.5% better than NN with LM, NN with GD, NN with FF and NN with PSO, respectively. Thus it has proven that the proposed model has provided the best result than other conventional classification methods.

This paper intends to propose an efficient raga identification system through which music of Carnatic genre can be effectively recognized. This paper also proposes an adaptive classifier based on NN.

In classical perspective, projects under a certain size are not feasible for P3. However, there is an emerging trend on using P3 to deliver projects which are frequently at small- to medium- size to meet ever-increasingly complex social needs, including enhancing lifecycle performance of existing facilities, designing and building for resilience and sustainability, ensuring cost effectiveness of public spending and fostering innovation. In contrast with the increasing implementation, small and medium P3s, especially those in the United States, receive little attention in existing studies. This study aims at answering the question: in the context of US, what features of those small- to medium- sized P3s with success records enable the selection of P3 as delivery method.

By critically reviewing the literature, this study synthesizes and discusses the challenges in classical perspective. The authors use a framework drawn from the transaction cost to propose two types of enabling features that could contribute to the success of small and medium P3s. The proposed enabling features are supported by case study of twelve identified small- to medium- sized P3s which have reached financial closure as of 2018 in the United States.

The results show how the identified enabling opportunities have been used in these cases to enhance the viability of the P3 model in the infrastructure market. The two types of features are high tolerance enabler explained by the expectations on indirect and non-monetary compensations, and cost reduction enablers including: (1) being in the sectors with well-established traditions on using private investments; (2) having developers with expertise on infrastructure finance; (3) being in the jurisdictions with favorable legislative environment and (4) having less-uncertain future project revenue.

This study, for the first time, critically examines the enabling features of the P3 model for delivering small and medium infrastructure projects in the United States. This research sheds light on the credibility and viability of small- to medium- sized P3 and increases the confidence in policy makers to promote this model.

This paper presents a new mixed methods approach which allows researchers to scan industry sectors for institutional change periods and to locate periods of significant institutional change agency.

The approach is grounded on the institutional logics perspective and on institutional entrepreneurship theory and combines an automated quantitative content analysis with a cognitive mapping exercise.

The paper describes the development of this approach and its application to the urban water management sector of Australia. Three periods of significant institutional change agency are identified, described and discussed.

The paper puts forward a new methodological approach that enables a robust and objective identification of actor-driven institutional change periods which can be used as a precursor for more targeted qualitative inquiries into institutional change research.