International Journal of Society Systems Science (5 papers in press)
Modelling Urban Sprawl Using Fuzzy Cellular Automata Model For The City Jaipur
by Pushpendra Sisodia
Abstract: The unorganised, unplanned, uncontrolled, and unauthorised development of urban growth has been termed as urban sprawl and often referring as complex pattern of transportation, social, economic, and land use development. The rapid increase in population and economic development are the primary influences of urban sprawl. Urban sprawl is the major problem of metropolitan areas in all over the world especially developing countries. The impact of urban sprawl can be noted as pollution, traffic, loss of prime agricultural land, deforestation, congestion of places, and water pollution. The proper planning of metropolitan areas is major concern of stockholders, especially for those, who are involved in forecasting, modelling, and policy making related to sustainable development. Urban sprawl is a major obstacle for urban planner to achieve sustainable development. In this paper, we have proposed a Fuzzy-CA-Markov model for modelling urban sprawl. We have chosen the city Jaipur as study area. The factors which affect the urban sprawl is considered as fuzzy parameter like accessibility from local road, accessibility from main road, accessibility from major road, slop, altitude, and density. The Fuzzy-CA-Markov model has also been used to measure the future urban sprawl. The future prediction of urban sprawl was measured through Markovs cellular automata model. The output of Markov model is used by CA model. Suitability maps were prepared by setting fuzzy transition rules from a land use state to another state. The best results have been received by using 14 iteration and 15*15 neighbourhood (Maximum Kappa = 0.78). LULC map of year 2015 is prepared using transformation matrix, and predicted for the same 2015 year for validation. By comparing both images, there is close similarity in both images that is confirmed by kappa coefficient as 0.89. Using the same assessment procedure, and parameter, we have projected the future urban sprawl pattern for the year 2025.
Keywords: Keywords: Remote sensing; GIS; Fuzzy Logic; Image Classification.
Comparing different Classifiers and Feature Selection techniques for Emotion Classification
by Satish M. Srinivasan, Prashanth Ramesh
Abstract: In this study, we have explored the potentiality of six different supervised machine learning approaches and feature selection filters to recognize four basic emotions (anger, happy, sadness and surprise) using three different heterogeneous emotion-annotated dataset which combines sentences from news headlines, fairy tales and blogs. For classification purpose, we have chosen the feature set to include the bag-of-words. Our study reveals the fact that the use of the resampling filter and other various feature selection filters together contribute towards boosting the prediction accuracies of the classifiers. However, the boosting capabilities are more profound in the resampling filter in comparison to the use of the different feature selection filters.
Keywords: Text Mining; Feature selection filters; supervised classifiers; emotion classification; accuracy.
PROTECTING THE RIGHTS OF PEOPLE DISPLACED BY DAMS: AN ASSESSMENT OF THE DEVELOPMENT OF GHANAS BUI DAM
by Mahmud Mukhtar Muhammed
Abstract: For over a half century now, large-scale dams and other large infrastructure projects have been pursued to advance the socioeconomic transformation of various countries and regions. However, adverse effects sometimes arise through the execution of the projects including the violations of the rights of affected communities and persons as a result of development-induced displacement and resettlement activities. One of the theoretical approaches that advocate for processes that lead to positive outcomes in the implementation of such development projects is the rights-based approach. Using primary data from Ghanas Bui Dam Project, the perceptions and views of the displaced people, their leaders, the dam managers and other relevant stakeholders are analysed with the view to unmasking the extent to which the rights of the displaced people were safeguarded and promoted throughout the project cycle. While the paper reveals that some positive outcomes have been attained in the Bui Dam Project, more favourable outcomes would have been realised if international covenants relating to the rights of infrastructure-affected-people were mainstreamed into local legal frameworks instead of heavily relying on best practice standards which are only hortatory and prescriptive.
Keywords: Development-induced displacement and resettlement (DIDR); Rights-Based Approach (RBA); large-scale dams; Bui Dam Project; Free Prior and Informed Consent (FPIC); Ghana.
Use of Metaphors for Acceptance of Cognitive Assistants: A Qualitative Study
by Md Abul Kalam Siddike, Youji Kohda
Abstract: Cognitive assistants (CAs) are new decision tools, able to provide people with high quality recommendations. CAs are very primitive and beginning to appear in the market. As a result, trustworthiness and relative advantages of using CAs are the most influential factors for acceptance of CAs by the people in the society. The prime objective of this paper is to investigate how trustworthiness and relative advantages play the most important role for acceptance of CAs using novel approach of metaphors. Three metaphors namely: pets, alarm clock and vase were used to investigate the acceptance of CAs by the people in the society. To achieve the objective, a qualitative research was undertaken to deeply investigate the issue. A total of 32 interviews was conducted into three steps. The interview data was analyzed using MAXQDA 12 (a qualitative data analysis software) by applying the grounded theory research approach. Results indicate that the metaphor pets (trustworthiness and relative advantages) and alarm clock (only relative advantages of using CAs) influence the peoples acceptance of CAs in the society. A theoretical framework of acceptance of CAs was presented based on the findings and insights from this research. Finally, the paper concludes by suggesting future research directions.
Keywords: Cognitive assistants (CAs); people’s interactions with CAs; relative advantages of using CAs; Trustworthiness.
Customer Retention and Credit Risk Analysis Using ANN, SVM and DNN
by Nagaraj V. Dharwadkar, Priyanka Patil
Abstract: Nowadays, banking sectors are facing various challenges such
as customer retention, fraud detection, risk management and customer
segmentation. It can be possible to find solutions to these problems with the
help of data analytics and Machine Learning (ML). In this paper, we have
proposed a model which provides the solution to problems of banking sector
for customer retention and credit risk analysis. We used supervised learning
techniques namely Artificial Neural Network (ANN), Support Vector Machine
(SVM) and Deep Neural Network (DNN) to analyze bank customer data.
In order to analyse the algorithms we have used German credit dataset to
evaluate the customer retention and credit risk. The experimental result shows
that the using ANN, SVM and DNN algorithms, we could able to to reach
recognition accuracy of 98%, 92% and 97% respectively for bank customer
data and 72%, 72% and 76% for German credit dataset. The proposed method
provides an efficient solution for retention and credit risk analysis of bank
customers, which improves the profit of the banks by retaining the customers.
Keywords: Banking industry; Customer Retention; Credit Risk Analysis; Machine Learning; ML; Classification; Artificial Neural Network; ANN; Support Vector Machine; SVM; Deep Neural Network; DNN.