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This paper explores the different insider employee-led cyber frauds (IECF) based on the recent large-scale fraud events of prominent Indian banking institutions. Examining the different types of fraud and appropriate control measures will protect the banking industry from fraudsters. In this study, we identify and classify Cyber Fraud (CF), map the severity of the fraud on a scale of priority, test the mitigation effectiveness, and propose optimal mitigation measures. The identification and classification of CF losses were based on a literature review and focus group discussions with risk and vigilance officers and cyber cell experts. The CF was analyzed using secondary data. We predicted and prioritized CF based on machine learning-derived Random Forest (RF). An efficient fraud mitigation model was developed based on an offender-victim-centric approach. Mitigation is advised both before and after fraud occurs. Through the findings of this research, banks and fraud investigators can prevent CF by detecting it quickly and controlling it on time. This study proposes a structured, sustainable CF mitigation plan that protects banks, employees, regulators, customers, and the economy, thus saving time, resources, and money. Further, these mitigation measures will improve the reputation of the Indian banking industry and ensure its survival. [ABSTRACT FROM AUTHOR]

Deficits in social verbal communication in individuals with Social Communication Disorder (SCD) is of concern and SCD in the human community is prevalent in large population throughout the globe. Deficits in verbal social communication are prevalent in a large population. This paper aimed to propose internet connected multi-system architecture which is capable to support verbal communication in a social environment for individuals with social communication deficits. Implementation methodology was included with corpus collection for specific communication, deep learning based machine training for intelligent communication, and implementation of the trained algorithm on internet connected electronic multiple social communication devices. The implemented system is smart enough to initiate and maintain two types of communication; the first type includes communication between multiple individuals on the remote location and the second type includes communication with the individual present in the physical listening range. The system was investigated in terms of its algorithmic parameters and found 97% to 100% in terms of training and testing accuracy with negligible mean squared error. Vocal-Friend analysed results based on audio-bot simulative conditions provide more than 91% accuracy, interaction rate and fallback rate. On the basis of the satisfaction analysis, above average results were noticed. In terms of technical implementations and satisfaction analysis, results found acceptable with above average score. Proposed framework is easy to use by caregivers with even having little knowledge. Support individual with deficit to learn social verbal communication skill to survive in society. Aiding parents, caregivers and professionals to understand the communication needs of individuals with communication deficits. Since technology is also grooming in the domain of rehabilitation, so this system could be used in various future applications such as social robots, social virtual assistants etc. [ABSTRACT FROM AUTHOR]

The ability to exploit students' sentiments using different machine learning techniques is considered an important strategy for planning and manoeuvring in a collaborative educational environment. The advancement of machine learning technology is energised by the healthy growth of big data technologies. This helps the applications based on Sentiment Mining (SM) using big data to become a common platform for data mining activities. However, very little has been studied on the sentiment application using a huge amount of available educational data. Therefore, this paper has made an attempt to mine the academic data using different efficient machine learning algorithms. The contribution of this paper is two-fold: (i) studying the sentiment polarity (positive, negative and neutral) from students' data using machine learning techniques, and (ii) modelling and predicting students' emotions (Amused, Anxiety, Bored, Confused, Enthused, Excited, Frustrated, etc.) using the big data frameworks. The developed SM techniques using big data frameworks can be scaled and made adaptable for source variation, velocity and veracity to maximise value mining for the benefit of students, faculties and other stakeholders. [ABSTRACT FROM AUTHOR]

The characterization of ground targets from a remotely sensed Synthetic Aperture Radar (SAR) image is addressed by polarimetric decomposition. The polarimetric SAR (PolSAR) decomposition measures the contribution of total backscatter from different scattering mechanisms using SAR images. The ambiguities present in the retrieval of scattering are the major problems associated with the model-based decomposition which could be reduced with a combination of interferometric coherence and PolSAR backscatter. The objective of this study is to improve the polarimetric decomposition model for identifying the scattering mechanisms based on the Polarimetric SAR Interferometry (PolInSAR) coherence for natural and manmade features. In this paper, we have proposed a model-based polarimetric decomposition using PolInSAR decorrelation. The PolInSAR decorrelation is exploited here to distinguish the time-varying and invariant scatterers present in the ground. The volume scattering power was calculated using the proposed decorrelation parameter which is the combination of PolInSAR coherence and decorrelation. The proposed algorithm has been tested on spaceborne multifrequency SAR datasets consisting of X-band TerraSAR-X and TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X), C-band Radarsat-2, and phased array L-band synthetic aperture radar-2 (PALSAR-2) data of advanced land observing satellite-2 (ALOS-2) PolInSAR data for the Dehradun region, India. The results show that there is a remarkable reduction in the ambiguities present in the identification of the scattering mechanism from the SAR image by using the proposed decorrelation-based decomposition model. Moreover, the algorithm is tested on X-band TerraSAR-X and TanDEM-X data of the Haridwar area and Rudrapur area, Uttarakhand, India to analyse the potential of the proposed decomposition technique in representing different manmade and natural features. [ABSTRACT FROM AUTHOR]

Few studies on single-frame super-resolution (SFSR) exist for real-world hyperspectral images due to fewer training samples and paucity of freely available high-resolution ground truth for validation. Consequently, a user relies on the simulated results from test datasets. This gap is filled by super-resolving real-time Hyperion and PRISMA data of Ahmedabad in India and introducing a structure of comparing and validating super-resolved outputs. Proposing open-source panchromatic and multispectral data as visual examination references, the first-ever SFSR results on PRISMA are presented. Findings suggest reconstruction-based SFSR algorithm outperforms at scale factors (SFs) 2 and 4 for Hyperion and SFs 2, 3 and 4 for PRISMA. The hybrid SFSR algorithm stands out at SF 3 for Hyperion. External dictionaries utilized by learning-based SFSR algorithms experience degradation with increasing spatial resolution. Although image quality declines slightly at higher SFs, out of the chosen algorithms, reconstruction and hybrid-based algorithms outperform learning-based algorithms in producing enhanced spatial and spectral fidelity outputs in the present case. [ABSTRACT FROM AUTHOR]

Optimisation is the process of trying to find out the best possible solution to any problem satisfying constraints. Soft computing is the class of methods which have been inspired by the biological computational methods and nature's problem-solving strategies. Currently, these methods include neural networks, evolutionary computational models such as genetic algorithms, random cost and linguistic models such as fuzzy logic. Ant colony optimisation (ACO) is one such method applied for large engineering combinatorial optimisation problems. A design procedure utilising an ACO technique is developed for discrete optimisation of reticulated steel space trusses. The ACO algorithm is motivated by the analogy with natural phenomena, in particular the ability of a colony of ants to 'optimise' their collective endeavours. In this paper, the computational implementation of ACO is presented in a structural design context. The objective function considered is the total weight/cost of the structure subjected to material and performance constraints in the form of stress and deflection limits. In the case of reticulated space trusses, the design variables are the cross-sectional areas of members belonging to various groups. The objective function and constraints are obtained by using structural analysis package FEAST (Anonymous, 1995. FEAST user manual. Trivandrum, India: SEG, SDS Group, ISRO, VSSC) in case of structures subjected to static loading and SAP90 (Anonymous, 1990. SAP90, ETABS, SAFE - computer software for structural and earthquake engineering. Berkeley, CA: Computers and Structures) for earthquake loading for reticulated steel space trusses. The numerical examples presented demonstrate the computational advantage of the ACO for large-scale optimisation problems. [ABSTRACT FROM AUTHOR]

A key agenda of policy-makers in both the developing and the developed worlds is to have an inclusive growth. Financial services have been recognised as one of the fundamental services that have the potential to achieve this objective and help move towards a more just and equitable society. This paper attempts to assess the impact of three information and communication technology-driven initiatives in the field of financial services by differentiating them on the basis of service design. This study goes beyond the stated objective of these services and uses Sen’s capability perspective to study their impact across three non-economic outcome variables viz. literacy, service-specific well-being and empowerment. The data for the study are collected from the urban poor across different locations in a large metropolitan city in South India where the three different services are being offered. The study follows a quasi-experimental design (field study) and ensures matching of the treatment and control groups through propensity score matching. The matched data are analysed using an independent samplet-test. The result of the analysis shows that service design plays a key role in increasing service literacy, improving service-specific well-being and empowering end users. The study suggests that the true challenge for providers (government and partner agencies) of these services is in designing a service that ensures a balance of structure and flexibility without constraining the choice of end users through innovative service blueprints. [ABSTRACT FROM AUTHOR]

In this paper we present the results of our analyses of multidiurnal low-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) thermal data for coal fire-related thermal anomaly detection. Results are presented for data of the Jharia coal mining region of India. We combine three relatively new approaches: first, we use low-resolution MODIS data for coal fire area analyses, which has only been undertaken by a few authors. Second, we analyse data from four different times of day (morning, afternoon, evening and predawn) and for three different bands (MODIS bands 20, 32 and a ratio thereof); and third, we use an unbiased automated algorithm for thermal anomaly extraction of local thermal anomalies. The MODIS data analysed stem from the years 2001 and 2005. In 2001, MODIS data were only been available from the platform TERRA as morning and evening data (around 1030 and 2200 h). In 2005, MODIS data were available from this platform as well as from the platform AQUA as afternoon and predawn data (around 0130 and 0200 h). Our analyses indicate that MODIS multidiurnal data, and especially bands 20, 32 and ratio bands thereof, have a high potential for the detection of coal fire zones and coal fire hot spot zones, as well as for regular thermal monitoring activities. However, the data are not suitable for a quantitative coal fire analysis concerning fire outline, fire temperature or fire classification into surface and subsurface fires. We used higher-resolution ASTER and LANDSAT data from 2005 and 2002 for general orientation and later comparison of thermal anomaly extraction results. We also used high-resolution Quickbird data for the characterization of individual anomalous thermal clusters. Comparisons demonstrate that even low-resolution thermal sensors such as MODIS can support coal fire detection and zonation into warm and very hot zones. [ABSTRACT FROM AUTHOR]

The aquaculture expansion has made significant contributions to global food security, socio-economic development and, if implemented sustainably, can help preserve stable coastal environments. This study explicitly details the rapid expansion of largescale aquaculture growth across the Kolleru and Upputeru regions of South India. We developed a novel classification method for automated extraction of aquaculture ponds in the Kolleru zone using the Canny Edge-Otsu algorithm to segment and extract the ponds applied to SAR-VV images in Google Earth Engine. This approach enables the area estimation of dense aquaculture ponds are essential for monitoring and management purposes. The results indicated that this method could effectively map the aquaculture ponds and the overall accuracy achieved in 2020 for the Kolleru and Upputeru areas by 90.6% and 95.7%, respectively. The aquaculture maps of this study can help government organizations, resource managers, stakeholders, and decision-makers understand the dynamics and plan sustainable measures in this area. [ABSTRACT FROM AUTHOR]

The article addresses the void in developing analytical methods concerning to design urban configurations that could reduce fire risks, and, thus, could help in achieving sustainable goals. A novel algorithm is developed to generate alternative Urban Built Form (UBF) models that could be less susceptible to fire compared to the existing built-form. Fire susceptibility of a generated UBF is predicted using a developed linear regression model. The algorithm considers existing regulations to derive rules and develop scenarios that might be effective in building fire-resilient cities. The outcomes of the simulations showed a significant decrease in the fire susceptibility of the southern region of Mumbai city. Moreover, for a certain simulated scenario the predicted UBF could accommodate twice the current population while being less susceptible than the existing UBF. The proposed techniques and methods can act as a decision-making tool in taking pre-emptive planning measures to develop fire resilient cities. [ABSTRACT FROM AUTHOR]

A new optimal power flow model for wind, solar, and solar-thermal bundled power scheduling and dispatch is proposed, incorporating the deviation incentive/penalty charges for renewable energy introduced in India. The multiobjective function is solved using the flower pollination algorithm; the scheme is successfully tested on the IEEE 30-bus and Indian utility 30-bus systems. The forecasting error constraints introduced in renewable energy scheduling and dispatch are demonstrated to be beneficial in several aspects. Solar-thermal bundling is shown to create win-win situations for thermal and solar generators. The effectiveness of the flower pollination algorithm in solving optimal power flow models is proved. [ABSTRACT FROM AUTHOR]

This article analyses sudden changes of volatility in the stock markets of the BRIC countries (Brazil, Russia, India and China) using the iterated cumulative sums of squares algorithm for the period 1990 to 2007 and examines their impacts on the persistence of volatility. The results show that when endogenously determined sudden shifts in variance are taken into account in the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model, the estimated persistence in return volatility is reduced significantly in every return series. These results suggest that the findings of previous studies could have overestimated the degree of the persistence of volatility existing in the financial time series. These results have important policy implications for the financial market participants and policy makers. [ABSTRACT FROM AUTHOR]