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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]

Introduction: Data mining in healthcare is a nascent arena of research in healthcare. Heterogeneity of Diabetes Mellitus in terms of clinical presentation calls for newer methods of research to study potential risk factors. Aim: The paper aims to use clustering techniques to identify the relationship between the four variables, namely the pre-prandial and postprandial sugar level, age and sex. Methods: The data was taken from a diagnostic laboratory in Wagholi, Pune. We conducted K-mean algorithm, EM algorithm, model-based clustering and t-mixture model. Results: It is evidenced that the data was best fitted to the t-mixture model. Our 50% samples were people with diabetes, 17% had prediabetes. Trivial correlation existed between age and sugar level. Males and females were equally at risk of having diabetes. Data presented concludes that age and sex have no effect on the risk of having diabetes. Data mining can be used to deduce meaningful clusters to drive plan-based interventions in the population. Conclusion: Methods of data mining can be used to deduce meaningful clusters in a heterogeneous dataset thus providing policymakers and healthcare researchers with novel information that will potentially contribute in formulating evidence-based policies. [ABSTRACT FROM AUTHOR]

In schools, children are taught algorithm for extracting the square and cube roots of a number. However, they are not informed that Aryabhata (b. 476 CE) was the first Indian mathematician to present a systematic algorithm for the extraction of the square and cube root of a number. This algorithm has been succinctly presented by Aryabhata in two successive verses in the Ganitapada of his Aryabhatiyam composed in 499 CE. A careful study of the algorithm given by Aryabhata shows that it can be easily extended to find higher order roots. It also clearly reveals that the decimal place value system should have been in vogue in India for quite a long time before him, because this algorithm is heavily based on a thorough understanding of the decimal place value system. In our paper, we will discuss some of these aspects as well as outline how his algorithm can be extended to find the higher order roots. [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]

Numerous research based on offline Tamil recognition deals only with few Tamil characters since it becomes extremely complicated in distinguishing small variations in large handwritten document. The writer's complexity affects the overall formation of the characters. Such types of complexities are due to discontinuation of structures, unnecessary over loops, variation in shapes as well as irregular curves. This complex issue results in enhanced error value rate. Therefore, to conquer such issues, this paper proposes a novel approach to enhance the offline Tamil handwritten character recognition by utilizing four principal steps: pre-processing, segmentation, feature extraction and classification. For optimal segmentation of Tamil characters, this paper utilizes the Tsallis entropy approach-based atom search (TEAS) optimization algorithm. Then a Newton algorithm based deep convolution extreme learning (DELM) approach is utilized for the extraction and classification of input images. Finally, experiments are carried out for numerous Tamil handwritten recognition-based approaches. The proposed Tamil character recognition utilizes the datasets of isolated Tamil handwritten characters established by HP lab India to evaluate the efficiency of the system. [ABSTRACT FROM AUTHOR]

Mesothelioma is a form of cancer that is aggressive and fatal. It is a thin layer of tissue that covers the majority of the patient's internal organs. The treatments are available; however, a cure is not attainable for the majority of patients. So, a lot of research is being done on detection of mesothelioma cancer using various different approaches; but this paper focuses on optimization techniques for optimizing the biomedical images to detect the cancer. With the restricted number of samples in the medical field, a Relief-PSO head and mesothelioma neck cancer pathological image feature selection approach is proposed. The approach reduces multilevel dimensionality. To begin, the relief technique picks different feature weights depending on the relationship between features and categories. Second, the hybrid binary particle swarm optimization (HBPSO) is suggested to automatically determine the optimum feature subset for candidate feature subsets. The technique outperforms seven other feature selection algorithms in terms of morphological feature screening, dimensionality reduction, and classification performance. [ABSTRACT FROM AUTHOR]

The rapid growth of electric vehicles in India necessitates more power to energize such vehicles. Furthermore, the transport industry emits greenhouse gases, particularly SO2, CO2. The national grid has to supply an enormous amount of power on a daily basis due to the surplus power required to charge these electric vehicles. This paper presents the various hybrid energy system configurations to meet the power requirements of the electric vehicle charging station (EVCS) situated in the northwest region of Delhi, India. The three configurations are: (a) solar photovoltaic/diesel generator/battery-based EVCS, (b) solar photovoltaic/battery-based EVCS, and (c) grid-and-solar photovoltaic-based EVCS. The meta-heuristic techniques are implemented to analyze the technological, financial, and environmental feasibility of the three possible configurations. The optimization algorithm intends to reduce the total net present cost and levelized cost of energy while keeping the value of lack of power supply probability within limits. To confirm the solution quality obtained using modified salp swarm algorithm (MSSA), the popularly used HOMER software, salp swarm algorithm (SSA), and the gray wolf optimization are applied to the same problem, and their outcomes are equated to those attained by the MSSA. MSSA exhibits superior accuracy and robustness based on simulation outcomes. The MSSA performs much better in terms of computation time followed by the SSA and gray wolf optimization. MSSA results in reduced levelized cost of energy values in all three configurations, i.e., USD 0.482/kWh, USD 0.684/kWh, and USD 0.119/kWh in configurations 1, 2, and 3, respectively. Our findings will be useful for researchers in determining the best method for the sizing of energy system components. [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 wireless sensor network is gaining significant attention because of its ubiquitous deployability nature. In general, the role of the wireless sensor network is remarkable in smart city applications for data sensing, collecting, and transmitting. Advanced metering infrastructure is an automatic system for the reading of electricity consumption by individual users. The reading of data from meters normally communicated over a wireless medium. Apart from collecting and sensing, routing is one of the major attributes in the AMI network. Successful communication is possible when the link and node are in the proper state. In this paper, we propose a fault-tolerant routing mechanism named Grid Topological Routing scheme to provide an efficient route in case of encountering a faulty path. The simulation shows better performance against the Routing Protocol for Low-Power and Lossy Networks and Ad-Hoc On-Demand Vector algorithm in terms of power consumption and packet delivery ratio. This model is explicitly designed for the smart grid in India. [ABSTRACT FROM AUTHOR]

Renewable energy presently occupies a prominent position in India's overall energy generation scheme. In the midst of numerous alternative energy resources, solar energy is widely used as it persists in large volumes with varying specification ratings. It is suited for both stand-alone and grid-coupled application. The Maximum Power Tracking (MPPT) scheme has a profound effect on the operating efficiency of a Photovoltaic (PV) power plant. This paper proposes an inventive hybridized Human Psychology Optimization-Perturb and Observation (HPO-PO) MPPT approach for obtaining the optimal duty cycle of the boost converter to harvest global maxima from a grid-connected Total Cost Tied (TCT) configured PV array of 4080 W. The suggested method provides enriched performance both in steady-state, as well as in rapid and randomly changing weather conditions. Comparison studies of various MPPT procedures, including Perturbation and Observation (PO), Artificial Bee Colony (ABC), and Human Psychology Optimization (HPO) in MATLAB environment, illustrate the usefulness of the evoked system in meeting its goals. The suggested MPPT procedure has offered enhanced activities in terms of voltage quality, maximum power tracking capability, and converter efficiency compared to other methods. The recommended hybridized MPPT approach is experimentally validated on a hardware set-up using a 16-bit dsPIC30F2010 Digital Signal Controller in enhancing the behavior of the grid-connected PV system. [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]

NavIC (Navigation with Indian Constellation) constellation consists of four geosynchronous and three geostationary satellites and it is developed by ISRO, India. It provides position, velocity and timing services. Doppler Collision (DC) is a phenomenon where tracking errors are introduced in the measurements due to cross-correlation between the satellites. If relative doppler between satellites is less than the code loop bandwidth, then DC occurs. In this paper, to analyze DC impact on NavIC, the Doppler shift of each GEO satellite is calculated, the most effected GEO satellite pair is identified. In high dynamic applications like missile launches the effect of DC is very significant. In order to investigate DC in high dynamic conditions a simulation of trajectory path of the receiver is considered. For the precise position estimation, the contribution of DC error will be high, so it needs to be minimized. An efficient algorithm is developed to mitigate the DC using narrow correlator design of the receiver. Using the proposed algorithm, the DC duration for 1C-1G satellite pair has been reduced from 50min 21sec to 2sec in static conditions. Whereas in dynamic conditions from 4h20min 28sec to 5sec with DLL bandwidth of 4Hz and 0.1 chip spacing of receiver design. [ABSTRACT FROM AUTHOR]

In this paper, we introduce two types of hybridization. The first contribution is the hybridization between the Viterbi algorithm and Baum Welch in order to predict crime locations. While the second contribution considers the optimization based on decision tree (DT) in combination with the Viterbi algorithm for criminal identification using Iraq and India crime dataset. This work is based on our previous work [1]. The main goal is to enhance the results of the model in both consuming times and to get a more accurate model. The obtained results proved the achievement of both goals in an efficient way. [ABSTRACT FROM AUTHOR]

Eastern Regional (ER) grid of India is one of the five regional grids consisting of voltage level 765 kV, 400 kV, 220 kV and numerous buses at lower voltage range. ER grid is the backbone of the Indian Grid as it is one of the main power generating hubs. Stability of the Indian Grid depends a lot on ER grid as it is the core of the generation and transmission system. For performing stability studies and to determine the weak area of the grid generally L-Index method is used. For detailed analysis like computation of voltage recovery time, maximum frequency deviation and critical clearing time of the buses of the system L-Index method is not suitable. Hence dynamic simulation is performed as it provides options to compute various parameters of ER Grid. The system has been studied using 197 buses of voltage levels 220 kV and above and transmission lines running over a length of 23,693 km. In this paper the power flow modelling, simulation and analysis of the Eastern Grid of India is performed. L-Index algorithm along with dynamic simulation provides the scope of computation of voltage recovery time, maximum frequency deviation and critical clearing time (by introducing an initial transient disturbance) along with the prediction of weak area of the system which gives a complete picture of transient stability of ER Grid. [ABSTRACT FROM AUTHOR]

Wildfires are one of the most destructive disasters on the planet. They also significantly impact the land surface. Satellite data have been widely used to detect the outbreak and monitor the expansion of fire incidents for damage assessment and disaster management. Polar-orbiting satellite data have been used for several decades but data from geostationary satellites, which can provide observations with a high temporal resolution, have received much less attention. This paper utilizes data from FengYun-2G, a Chinese geostationary satellite, to detect wildfires in two selected research regions in January 2016. The detection algorithm systemizes image-based analysis to filter out obvious nonfire pixels and temporal analysis to confirm the true detections. Fire detection is based on comparisons between predicted and observed values. The results show that the proposed method has some advantages compared with the use of polar-orbiting satellite data, including early detection and continuous observation. The validation work is conducted based on the collection 6.1 Global Monthly Fire Location Product generated from fire detections by Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. The average accuracy within the target time is 56%, while the omission error rate is over 78%. In detail, the algorithm has a lower omission error rate in Australia while it fails in detecting most of the fire pixels in India. The dominance of small fire incidents, as well as low spatial resolution greatly limit the detection ability. Many small fires were beyond the ability of Stretched Visible and Infrared Spin Scan Radiometer (S-VISSR) data when no significant fire characteristics could be captured. Future development of the algorithm will focus on improving the results by enhancing the adaption to different regions, as well as, including multisource data sets. [ABSTRACT FROM AUTHOR]

The classification of recurrence and non recurrence of Hepato Cellular carcinoma (HCC) outcome after Radio Frequency Ablation therapy is a critical task. Multiple time series clinical liver cancer dataset is collected from different dataset and time interval. A merging algorithm is used to merge all attributes collected from different sources in multiple time periods. In order to preserve the originality of information, statistical measures of each attribute is calculated and considered them as additional attributes for accurate prediction. However the merged dataset is unbalanced, in which, the number of samples from HCC recurrence class is much smaller than from HCC non recurrence. The feature weighting scheme select optimal features and parameter of classifiers are sequentially obtained from multiple iterations which causes higher computation time. In this paper, an efficient sampling approach is proposed using Inverse Random under Sampling (IRUS) to overcome class imbalance issue. IRUS under sample the majority class which creates a number of distinct partitions with a boundary separated minority and majority class samples. Additionally an optimization approach is proposed using Artificial Plant Optimization (APO) algorithm to select optimal features and parameters of classifiers to improve the effectiveness and efficiency of classification. The optimization approach reduces the number of iteration and computation time for feature selection and parameter selection for classifiers which classify the recurrence and non recurrence of HCC. Classify patients with HCC and without HCC based on optimal features and parameters by Support Vector Machine (SVM) and Random Forest(RF) classifiers. Finally, the experimental results are conducted to prove the effectiveness of the proposed method over existing method in terms of accuracy, specificity, sensitivity and balanced accuracy. [ABSTRACT FROM AUTHOR]

Due to lack of a central bureaucrat in mobile ad hoc networks, the security of the network becomes serious issue. During malicious attacks, according to the motivation of intruder the severity of the threat varies. It may lead to loss of data, energy or throughput. This paper proposes a lightweight Intruder Node Detection and Isolation Action mechanism (INDIA) using feature extraction, feature optimization and classification techniques. The indirect and direct trust features are extracted from each node and the total trust feature is computed by combining them. The trust features are extracted from each node of MANET and these features are optimized using Particle Swarm Optimization (PSO) algorithm as feature optimization technique. These optimized feature sets are then classified using Neural Networks (NN) classifier which identifies the intruder node. The performance of the proposed methodology is studied in terms of various parameters such as success rate in packet delivery, delay in communication and the amount of energy consumption for identifying and isolating the intruder. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to ascertain the awareness and use of cloud computing technologies among the library professionals in the Kerala University Library system, India. A survey was conducted using questionnaire among the 102 library professionals employed in the central and departmental libraries of the University of Kerala. The study revealed that 42.16% of the library professionals did not have much idea about cloud computing technology. Analysis showed that Facebook and Google Apps like Gmail, Google Doc etc are the cloud computing technologies used by majority of the respondents. It is also worth to note that the library professionals in Kerala University Library are using cloud computing technologies without being aware of doing so. Library professionals' awareness of cloud service models is relatively very low. Web OPAC and Journal Discovery Service are the areas known to the respondents in applying cloud computing technologies in libraries. Out of 102 respondents, 14.71% of the library professionals in the University of Kerala have average skill in using these technologies. The findings of the study throw lights into the need of providing training for the library professionals in handling technology enriched library services to the users. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid model to better predict Indian summer monsoon rainfall. The algorithm considers suitable techniques for processing dense datasets. The proposed three-step algorithm comprises closed itemset generation-based association rule mining for feature selection, cluster membership for dimensionality reduction, and simple logistic function for prediction. The application of predicting rainfall into flood, excess, normal, deficit, and drought based on 36 predictors consisting of land and ocean variables is presented. Results show good accuracy in the considered study period of 37years (1969-2005). [ABSTRACT FROM AUTHOR]

In this paper, we examine patterns of self-reported diagnosis of noncommunicable diseases (NCDs) and prevalences of algorithm/measured test-based, undiagnosed, and untreated NCDs in China, Ghana, India, Mexico, Russia, and South Africa. Nationally representative samples of older adults aged ≥50 years were analyzed from wave 1 of the World Health Organization's Study on Global Ageing and Adult Health (2007-2010; n = 34,149). Analyses focused on 6 conditions: angina, arthritis, asthma, chronic lung disease, depression, and hypertension. Outcomes for these NCDs were: 1) self-reported disease, 2) algorithm/measured test-based disease, 3) undiagnosed disease, and 4) untreated disease. Algorithm/measured test-based prevalence of NCDs was much higher than self-reported prevalence in all 6 countries, indicating underestimation of NCD prevalence in low- and middle-income countries. Undiagnosed prevalence of NCDs was highest for hypertension, ranging from 19.7% (95% confidence interval (CI): 18.1, 21.3) in India to 49.6% (95% CI: 46.2, 53.0) in South Africa. The proportion untreated among all diseases was highest for depression, ranging from 69.5% (95% CI: 57.1, 81.9) in South Africa to 93.2% (95% CI: 90.1, 95.7) in India. Higher levels of education and wealth significantly reduced the odds of an undiagnosed condition and untreated morbidity. A high prevalence of undiagnosed NCDs and an even higher proportion of untreated NCDs highlights the inadequacies in diagnosis and management of NCDs in local health-care systems. [ABSTRACT FROM AUTHOR]

Background and Objectives: Current practices and available resources for nutrition therapy in paediatric intensive care units (PICUs) in the Asia Pacific-Middle East region are expected to differ from western countries. Existing guidelines for nutrition management in critically ill children may not be directly applicable in this region. This paper outlines consensus statements developed by the Asia Pacific-Middle East Consensus Working Group on Nutrition Therapy in the Paediatric Critical Care Environment. Challenges and recommendations unique to the region are described.Methods and Study Design: Following a systematic literature search from 2004-2014, consensus statements were developed for key areas of nutrient delivery in the PICU. This review focused on evidence applicable to the Asia Pacific-Middle East region. Quality of evidence and strength of recommendations were rated according to the Grading of Recommendation Assessment, Development and Evaluation approach.Results: Enteral nutrition (EN) is the preferred mode of nutritional support. Feeding algorithms that optimize EN should be encouraged and must include: assessment and monitoring of nutritional status, selection of feeding route, time to initiate and advance EN, management strategies for EN intolerance and indications for using parenteral nutrition (PN). Despite heterogeneity in nutritional status of patients, availability of resources and diversity of cultures, PICUs in the region should consider involvement of dieticians and/or nutritional support teams.Conclusions: Robust evidence for several aspects of optimal nutrition therapy in PICUs is lacking. Nutritional assessment must be implemented to document prevalence and impact of malnutrition. Nutritional support must be given greater priority in PICUs, with particular emphasis in optimizing EN delivery. [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]

The present paper reports a study, where growing hierarchical self-organising map (GHSOM) has been applied to achieve a visual cluster analysis to the Indian rainfall dataset consisting of 142 years of Indian rainfall data so that the yearly rainfall can be segregated into small groups to visualise the pattern of clustering behaviour of yearly rainfall due to changes in monthly rainfall for each year. Also, through support vector machine (SVM), it has been observed that generation of clusters impacts positively on the prediction of the Indian summer monsoon rainfall. Results have been presented through statistical and graphical analyses. [ABSTRACT FROM AUTHOR]

Conventional load flow algorithms are very commonly used in the system planning. In the conventional load flow methods, system electrical parameters at specific weather conditions will be considered. These methods gives results for given loading condition and have no provision to incorporate weather condition of a specific geographical location. If the load flow has the provision to incorporate the dynamic electrical characteristics of the conductors based on the operating weather condition, the results of the methods are going to be more accurate or in other words the results are close to the real values. In view of the above, in this paper a modified load flow algorithm incorporating the thermal modeling of the conductors with ambient temperature, wind velocity, conductor surface absorptivity, global solar radiation, Wind direction factor etc. is proposed. To verify the effectiveness of the proposed algorithm, the thermal mathematical model of transmission lines available in IEEE Std. 738™ -2006, IEEE Std. 738™ -2012 and CIGRE WG 22.12 are considered. In the proposed algorithm, new variables corresponding to Conductor Temperature are incorporated. The proposed algorithm is tested with the real time weather data of India and demonstrated on IEEE 30 and 118 bus systems. • An algorithm for power flow including thermal models of the transmission lines. • Variable weather and ZIP load models are incorporated in the proposed algorithm. • It estimates the transmission line temperature using the field measurements. • Seasonal variations of the critical bus voltages are estimated using proposed method. • Impact of renewable generation on system performance is evaluated. [ABSTRACT FROM AUTHOR]

Two algorithms are outlined, each of which has interesting features for modeling of spatial variability of rock depth. In this paper, reduced level of rock at Bangalore, India, is arrived from the 652 boreholes data in the area covering 220 sq⋅km. Support vector machine (SVM) and relevance vector machine (RVM) have been utilized to predict the reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth. The support vector machine (SVM) that is firmly based on the theory of statistical learning theory uses regression technique by introducing ε-insensitive loss function has been adopted. RVM is a probabilistic model similar to the widespread SVM, but where the training takes place in a Bayesian framework. Prediction results show the ability of learning machine to build accurate models for spatial variability of rock depth with strong predictive capabilities. The paper also highlights the capability of RVM over the SVM model. [ABSTRACT FROM AUTHOR]

• Bringing the asset management of transformer under the purview of building manager from utility. • Derived mathematical model of demand response from transformer thermal model. • Method to estimate transformer temperature time constant from measurements and algorithm for improving the life of transformer by regulating HVAC and AHUs. • Smart grid architecture for demand responsive HVAC. • Real time simulation of impact of demand response using the actual building consumption data from an academic campus. Overloading and ageing effects contribute to 12-15% of distribution transformer failures in India. This research is motivated in extending life of transformer by using demand response as an operational strategy where the demand response events are issued by predicting overheating of transformer before it occurs. In this paper heating ventilation and air conditioning is considered as a flexible load for responding to demand response events. A demo site is created at 11 KV/440 V substation with 1.6 MVA transformer at Indian Institute of Technology Hyderabad (IITH) campus and the proposed architecture and algorithm is implemented. This substation supplies power to chiller plant and three academic buildings with heating ventilation and air conditioning (HVAC). Analysis for improving the transformer lifetime is carried out for 5 consecutive days during the period (25–29)th Feb 2020 using real time field data. It is observed that during this period 11 demand response events occurred with a total demand response of 919 kWH. From the obtained results it is observed that there is a saving of 15 hoursof transformer life within a period of 5 days (120 hours) of transformer operation. The key contribution in this paper is to bring together three concepts transformer lifetime estimation, HVAC flexibility and demand response for improving the transformer lifetime. [ABSTRACT FROM AUTHOR]

This paper describes a comprehensive approach to improved lithofacies characterization using the simultaneous pre-stack inversion, Bayesian classification, and multiple-point geostatistics. This integrated workflow provides an enhanced lithofacies characterization by reproducing the continuity of geological facies in the meandering-fluvial sandstone reservoir from Upper Assam, India. The simultaneous pre-stack inversion provides the rock elastic properties of the reservoir, such as P- impedance, s-impedance, density, and V P /V S ratio. Next, the probabilistic modeling approach of Baye's theorem is used to integrates seismic elastic properties with well log lithofacies identified from the cross-plot analysis for probability volumes of lithofacies. Finally, the multiple-point geostatistics based on the Single Normal equation Simulation algorithm is used to generate the improved lithofacies model by constraining of the lithofacies probability volumes (soft data) and well logging data (hard data). These results were compared with the Bayesian lithofacies classification and proved that the adopted method had successfully improved the continuation of the reservoir lithofacies by reproducing the lithofacies, hence an improvement in the reservoir characterization. • An integrated workflow is used that provides better lithofacies characterization. • Estimation of seismic elastic properties using simultaneous prestack inversion. • Prediction probability volumes of lithofacies by Bayesian classification. • The SNESIM algorithm applied with constrained by probability volumes and well data. • Improved lithofacies volume generated by the multiple-point geostatistics. [ABSTRACT FROM AUTHOR]

This paper present the results of a study made to assess the grades of iron ore deposits in parts of Noamundi area using the hyperspectral image data (EO-1 Hyperion). The study involves hyperspectral image data, preprocessing (removal of atmospheric effects), spectral curve generation from the image, extraction of certain spectral parameters and spectral data analysis. Atmospheric correction was carried out by using algorithms involving atmospheric analysis of spectral hyper-cubes and Quick Atmospheric Correction modules. Spectral curves generated for the pixels that contain iron ores resemble the spectral signature of iron ores, i.e., very strong absorption at 850-900nm region is observed. The strength of the absorption varies spatially at different pixels. Hence, spectral parameters such as radius of curvature of NIR absorption trough (750-1000nm), position of NIR absorption trough and distance from a reference(100% reflectance) line have been derived for all the image-derived spectra and were compared with the concentration of iron in the ore samples of mines. Empirical models were generated by relating the image-derived spectral parameters of iron ore locations and geochemistry of the samples collected from the same location which shows a reasonable match. Strong negative correlation is seen between the radius of curvature of the NIR absorption trough and concentration of iron with a R value of 0.809. The position of the NIR absorption trough is seen to shift towards longer wavelengths with decreasing iron content and the distance from the reference line increases with increasing iron content. Thus, this study indicates that it is feasible to discriminate the grades of iron ores from the spectral parameters derived from Hyperspectral satellite images. [ABSTRACT FROM AUTHOR]

• Studied the effect of catchment size and simulation time steps on parameter sensitivity. • Hydrological model is developed for 10 different watersheds of Narmada River basin. • Two Global Sensitivity Analysis methods used to identify the sensitive parameters. • To avoid over parameterization, most and least sensitive parameters are identified. • Identifying a set of sensitive parameters that covers all the hydrological processes. Hydrological models have many parameters representing various hydrological processes. These parameters are effective at different spatial and temporal resolution. Most of the parameters can be measured such as slope, elevation, area, vegetation type etc., but others cannot be estimated from the available data. Thus, they remain unknown in a hydrological simulation. Local and Global Sensitivity Analyses (LSA and GSA respectively) are therefore used to reduce the number of parameters that need to be equipped with input–output data. GSA also improves the efficiency of the model calibration and validation which in turn increases the reliability of the model. In this paper, we investigate into the effect of size of a catchment (small, medium, and large) time steps of simulation (Daily and Monthly), and the choice of GSA method in performing sensitivity analysis. A Soil and Water Assessment Tool (SWAT) hydrologic model is developed for 10 subbasins of Narmada River basin at daily and monthly temporal simulations from January 1991 to December 2000. The sensitivity of 29 input parameters have been analysed with two GSA methods: the multilinear regression SA used by sequential uncertainty fitting algorithm (SUFI-2) in SWAT – Calibration and Uncertainty procedure (SWAT-CUP), and the first order sensitivity indices driven by Fourier Amplitude Sensitivity Test (FAST). Our results show that a hydrological model set up with only sensitive parameters can also produce similar results as when all the parameters are considered. We concluded that the selection of catchment size and simulation time step strongly influence the parameter sensitivity. Our results show that the sensitivity of some of the parameters like Curve number for moisture condition (CN2.mgt), baseflow alpha factor for bank storage (ALPHA_BNK.rte) and lateral flow travel time (LAT_TTIME.hru) are independent of catchment size and simulation time step. Moreover, baseflow alpha factor for the recession constant (ALPHA_BF.gw), manning's value for overland flow (OV_N.hru) and soil erodibility factor (USLE_K(..).sol) are not sensitive for large and medium range watersheds but those are sensitive for small watershed at a monthly time step. Ground water delay (GW_DELAY.gw), average slope steepness (HRU_SLP.hru) and support practice (p) factor of USLE soil equation (USLE_P.mgt) are not sensitive for large watersheds but sensitive for small and medium range watersheds at a daily time step. Our results strongly recommends that a detailed parameter sensitivity analysis is an important step in setting up any hydrological model to reduce the number of parameters while addressing all relevant hydrological processes. [ABSTRACT FROM AUTHOR]

Abstract: Many technological alternatives for wastewater treatment are available, ranging from advanced technologies to conventional treatment options. It is difficult to select the most appropriate technology from among a set of available alternatives to treat wastewater at a particular location. Many factors, such as capital costs, operation and maintenance costs and land requirement, are involved in the decision-making process. Sustainability criteria must also be incorporated into the decision-making process such that appropriate technologies are selected for developing economies such as that of India. A scenario-based multiple-attribute decision-making (MADM) methodology has been developed and applied to the selection of wastewater treatment alternative. The four most commonly used wastewater treatment technologies for treatment of municipal wastewater in India are ranked for various scenarios. Six scenarios are developed that capture the regional and local societal priorities of urban, suburban and rural areas and translate them into the mathematical algorithm of the MADM methodology. The articulated scenarios depict the most commonly encountered decision-making situations in addressing technology selection for wastewater treatment in India. A widely used compensatory MADM technique, TOPSIS, has been selected to rank the alternatives. Seven criteria with twelve indicators are formulated to evaluate the alternatives. Different weight matrices are used for each scenario, depending on the priorities of the scenario. This study shows that it is difficult to select the most appropriate wastewater treatment alternative under the “no scenario” condition (equal weights given to each attribute), and the decision-making methodology presented in this paper effectively identifies the most appropriate wastewater treatment alternative for each of the scenarios. [Copyright &y& Elsevier]

Abstract: Increasing demand of bandwidth in communication satellites has forced satellite links to be designed in Ku bands and above. But at these frequencies, rain and other tropospheric elements result in large attenuation. To mitigate the tropospheric attenuation of microwave satellite signals above 10GHz using any standard Fade Mitigation Technique (FMT), it is essential to have a priori knowledge about the level of attenuation. Hence, short-term rain attenuation prediction models play a key role in maintaining the link in which necessary compensation can be applied depending on the early information of attenuation. This paper presents a method of attenuation prediction using Adaptive Artificial Neural Network. Here In situ Learning Algorithm (ILA) has been used to enable the system to track the non-stationary nature of the attenuation. To validate this, Ku Band data, collected at three different sites in India have been used for the purpose of prediction. The performance of the algorithm is determined through the estimation of prediction accuracy by comparing the predicted values with the measured data. Results obtained using the mentioned technique shows considerably good accuracy even up to 20s of prediction interval with acceptable ratio between the under and over predictions. The prediction performance is evaluated for different prediction intervals. Furthermore the present model is also compared with the persistence model and the relative performance is quantified. [Copyright &y& Elsevier]

Abstract: This paper proposes a new algorithm for modeling the nonlinear seismic behavior of fractured concrete gravity dams considering dam–reservoir interaction effects. In this algorithm, the cracked concrete gravity dam is modeled by distinct element (DE) method, which has been widely used for the analysis of blocky media. Dynamic response of the reservoir is obtained using boundary element (BE) method. Formulation and various computational aspects of the proposed staggered hybrid approach are thoroughly discussed. To the authors'' knowledge, this is the first study of a hybrid DE–BE approach for seismic analysis of cracked gravity dam–reservoir systems. The validity of the algorithm is discussed by developing a two-dimensional computer code and comparing results obtained from the proposed hybrid DE–BE approach with those reported in the literature. For this purpose, a few problems of seismic excitations in frequency- and time-domains, are presented using the proposed approach. Present results agree well with the results from other numerical methods. Furthermore, the cracked Koyna Dam is analyzed, including dam–reservoir interaction effects with focus on the nonlinear behavior due to its top profile crack. Results of the present study are compared to available results in the literature in which the dam–reservoir interaction were simplified by added masses. It is shown that the nonlinear analysis that includes dam–reservoir interaction gives downstream sliding and rocking response patterns that are somehow different from that of the case when the dam–reservoir interaction is approximated employing added masses. [Copyright &y& Elsevier]

This paper demonstrates the estimation and simulation of discharge and sediment concentration for two river basins in the USA and India. The first-order Sugeno fuzzy inference system was utilised to model the stage, discharge and sediment concentration relationship. A subtractive clustering algorithm, along with a least-squares estimation, was used to generate the fuzzy rules that describe the relationship between input and output data of stage, discharge and sediment concentration, which change over time. The fuzzy rules were tuned by a back-propagation algorithm. The results are illustrated using simulation and virtual reality. A comparison was made between the estimates provided by the neuro-fuzzy model and a multi-linear regression model. Different statistical criteria were used to evaluate the performance of both models in estimating discharge and sediment concentration. Comparison of the results reveals that, in general, the neuro-fuzzy model gives better estimates than the multi-linear regression model in terms of root mean square and sum of squares errors. Furthermore, compared with the multi-linear regression model, the neuro-fuzzy model yields statistical properties of estimates that are closer to actual historical data. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the parameter-wise optimization training process is implemented to achieve an optimal configuration of focused time lagged recurrent neural network (FTLRNN) models by embedding the gamma, laguarre, and multi-channel tapped delay line memory structure. The aim is to examine the prediction ability of the proposed models in order to predict one-day-ahead electric power load simultaneously as usual to oppose 1–24h forecast in sequel with a special emphasis on seasonal changes over a year. An improved delta-bar-delta algorithm is used to accelerate the training of neural networks and to improve the stability of the convergence. Experimental results indicate that the FTLRNN with time delay neural network (TDNN) clearly outperformed the gamma and laguarre based short-term memory structure in various performance metrics such as mean square error (MSE), normalized MSE, correlation coefficient (r) and mean absolute percentage error (MAPE) during evaluation process. Empirical results show that the proposed dynamic NN model consistently performs well on daily, weekly, and monthly average basis in terms of prediction accuracy. It is noticed from the literature review that an optimally configured FTLRNN with multi-channel tapped delay line memory structure is not currently available to solve short-term electrical power load prediction. The proposed method gives acceptable errors in all seasons, months and on daily basis. The average prediction error on three weeks is obtained as low as 1.67%. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the results of the application of model (developed in part I) and simulation algorithm (developed in part II) for determining the techno-economics of battery storage type hybrid energy system intended to supply the load of a rural remote area having a cluster of nine villages (grid isolated). The hour-by-hour simulation model is intended to simulate a typical one month period of system operation. For simulation purpose, hourly solar insolation data and load data have been generated and used as an input data. Demand side management (DSM) is used in this study to smooth out the daily peaks and fill valleys in the load curve to make the most efficient use of energy sources. The economic analysis has resulted in the calculation of optimized hourly, daily, and monthly system unit cost of proposed hybrid energy system. The obtained results represent also a helpful reference for energy planners in Uttarakhand state and justify the consideration of hybrid energy systems more seriously. [ABSTRACT FROM AUTHOR]

general neural network based methodology that has the potential to replace the computationally-intensive site-specific seismic analysis of structures is proposed in this paper. The basic framework of the methodology consists of a feed forward back propagation neural network algorithm with one hidden layer to represent the seismic potential of a region and soil amplification effects. The methodology is implemented and verified with parameters corresponding to Delhi city in India. For this purpose, strong ground motions are generated at bedrock level for a chosen site in Delhi due to earthquakes considered to originate from the central seismic gap of the Himalayan belt using necessary geological as well as geotechnical data. Surface level ground motions and corresponding site-specific response spectra are obtained by using a one-dimensional equivalent linear wave propagation model. Spectral acceleration values are considered as a target parameter to verify the performance of the methodology. Numerical studies carried out to validate the proposed methodology show that the errors in predicted spectral acceleration values are within acceptable limits for design purposes. The methodology is general in the sense that it can be applied to other seismically vulnerable regions and also can be updated by including more parameters depending on the state-of-the-art in the subject. [ABSTRACT FROM AUTHOR]

Abstract: This paper attempts to assess the adequacy of wind power generation systems using the data collected from seven wind farms in Muppandal, Tamilnadu (India) with a total capacity of 37 MW. A Monte Carlo model simulation is incorporated in the algorithm to obtain the hourly power output of wind farms, which also takes into account the unavailability of wind turbines. A typical load demand profile is used to examine the chronological hourly wind power generation for each month. The reliability index of LOLE (loss of load expectation) is used to estimate the reliable contribution of wind farm power generation. [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]

Steganography is the method to hide a message inside another message in such a way that can only be detected by its intended recipient. Text documents have been widely used for a very long time. A number of text steganography algorithms have been introduced. This paper presents a new approach for steganography in Bengali texts. Considering the structure of Bengali alphabet/words, in this approach, we hide the secret message in the text by shifting the specific 'matra' towards left, by shifting the points of some characters and by shifting the character 'ref' respectively. This approach can be categorized under feature coding methods. The approach shows good result on the sample collected from different Bengali newspaper. [ABSTRACT FROM AUTHOR]

Abstract: A broad range of current airborne gamma ray spectrometry (AGRS) applications involve environmental mapping and mineral exploration. One common goal for such applications is the development of an algorithm for reliable on line classification of radio elements. In this paper, we propose the concept of maximization of correlated information as the similarity measure for classification. In order to achieve this similarity measure, we have developed an algorithm using the concept of minimization of mutual information, which is computationally faster, and requiring less memory than the hierarchical agglomerative clustering (HAC) method. The minimization of mutual information is achieved by maximizing the correlated information of the correlation matrix. The correlated information is maximized by the determination of its lower bound using the technique of determinant inequalities developed by us. We demonstrate the robustness of our results using mutual information and its superiority over that of Ward''s method of minimum variance for the aerial survey carried out in central India. [Copyright &y& Elsevier]

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]

This paper proposes an expert system approach to short-term expansion planning (STEP). The rules which drive STEP can be classified into MW, MVAR, and ampacity management rules. MW and ampacity management rules are for alleviating transmission line congestion. Reactive power management is required for voltage control at load busses, conformity to the capacity curve of the generators, and containing the MW losses within acceptable limits. Embedding reactive power management in STEP is a challenging task since ac load flow may not converge in absence of proper reactive power planning and load modeling. Therefore, we also propose enhancements to the fast decoupled load flow algorithm for on-the-fly reactive power management. The enhanced algorithm not only can detect divergent load flow scenarios but also self-correct it by restarting the whole process with greater degree of freedom in reactive power controls. The proposed approach leads to development of an automated tool for STEP which has the capability to work, even with incomplete information. A simple method for evaluating location and requirement of shunt reactor is also proposed. By analysis and comparative evaluation, we show that the proposed system can arrive at a solution which is close to optimal. Results on the Western Regional Grid of India with an approximate load of 28000 MW and 1200 nodes are presented to demonstrate effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The positional accuracy of the Global Positioning System (GPS) is limited due to several error sources. The major error is ionosphere. By augmenting the GPS, the Category I (CAT I) Precision Approach (PA) requirements can be achieved. The Space-Based Augmentation System (SBAS) in India is known as GPS Aided Geo Augmented Navigation (GAGAN). One of the prominent errors in GAGAN that limits the positional accuracy is instrumental biases. Calibration of these biases is particularly important in achieving the CAT I PA landings. In this paper, a new algorithm is proposed to estimate the instrumental biases by modelling the TEC using 4th order polynomial. The algorithm uses values corresponding to a single station for one month period and the results confirm the validity of the algorithm. The experimental results indicate that the estimation precision of the satellite-plus-receiver instrumental bias is of the order of ±0.17 nsec. The observed mean bias error is of the order −3.638 nsec and −4.71 nsec for satellite 1 and 31 respectively. It is found that results are consistent over the period. [ABSTRACT FROM AUTHOR]

This paper reports results on the LAI Retrieval and Validation Experiment (LRVE) that was conducted for two agricultural areas in Central India during the winter season of 2001–2002. The study aimed at relating field measurements of leaf area index (LAI) to spaceborne Indian Remote Sensing Satellite (IRS) Linear Imaging Self Scanning Sensor-III (LISS-III) data, preparation of site-level LAI maps, and validation of Moderate Resolution Imaging Spectroradiometer (MODIS) 1-km LAI global fields. Measurements of field-level LAI, aerosol optical thickness and water vapor were carried out on the day of LISS-III overpasses. Empirical models based on the site-specific LAI-vegetation index relation were developed and used to generate 23-m resolution LAI maps for two sites (Indore and Bhopal) covering 30 km × 30 km. These LAI images were degraded to 1-km spatial resolution and used for validation of the version 3 and 4 MODIS LAI products (MOD15A2). The results indicate a positive correlation (r = 0.78) between LAI derived from LISS-III data and MODIS data. However an overestimate by a factor of 1.6 to 2.5 in the version 3 MODIS product is observed with root mean square error (RMSE) ranging from 0.20 to 1.26. The factor of overestimation reduces significantly by 50% and RMSE by 40% when version 4 MODIS LAI was analyzed. The improvement in accuracy was observed to be associated with the change in algorithm path adopted for retrieving version 3 and 4 MODIS LAI. Analysis of the MODIS land cover product that is an input in the MODIS LAI retrieval algorithm indicated errors in assigning land cover classes for the study sites, which could be one of the sources of error in MODIS LAI product. [ABSTRACT FROM AUTHOR]

Road traffic accidents are a major social concern as well as a crucial issue for the public in recent days due to the risk factors involved. Analysing and identifying the major risk factors of road accident is still a challenging task. In this paper, a fuzzy Context-free Grammar (FCFG)-based association rule mining (ARM) technique is proposed to categorize a heterogeneous road accident dataset into two categories based on the critical factors such as total number of accidents (TA), persons killed (PK) and persons injured (PI). The role of the fuzzy grammar in this paper is to govern the entire algorithm using the prescribed grammar rules to proceed further. The considered road accident dataset does not have class labels; hence there is a need to assign class labels for the available data instance. The accident data with assigned class labels are given as input to K-nearest neighbour (KNN) machine learning algorithm in order to train the classifier for testing purpose. Further, the collected test data from the user are utilized by the KNN classifier for carrying out the performance analysis of the proposed algorithm. The case study is conducted on the National Highway roads, India, to examine the proposed approach. The experimentations are executed for road accident records using MATLAB software and the analysis is made using the following performance measures: accuracy, recall or sensitivity, precision or specificity and F1 score. A comparative study is accomplished with existing algorithms in order to show that the proposed algorithm works with improved accuracy of more than 83%. The results suggested that the road users are responsible for the acceptance or rejection of safe or un-safe roads, respectively. [ABSTRACT FROM AUTHOR]

Accurately forecasting energy demand can better predict future changes in energy demand. This study is aimed to develop more accurate clean energy prediction model from two perspectives of the modeling mechanisms and improvements on the model structure. For this purpose, a novel Riccati–Bernoulli differential equation is established through social practice theory, supply–demand relationship, and the preference of consumption in the energy economy. Considering the limited information in observation data, this differential equation is then transformed into a grey Riccati–Bernoulli model (GRBM(1,1)) according to the differential information principle. With the response function solved on the basis of polynomial equation theory and the power exponent optimized combining the modified flower pollination algorithm, the main process of GRBM(1,1) can be summarized. The four validation examples are provided for confirming the effectiveness and reliability of the new model by comparing with other existing models. Finally, the proposed model is employed to estimate and forecast the clean energy consumption in China and India. The results show that the proposed model demonstrates better estimation in all cases and efficiency in short-term clean energy consumption forecasting. Therefore, by using this optimum model, China's preference coefficient in total energy consumption is 0.7103, lower than that of India (0.8799), and China's preference coefficient in clean energy consumption is 0.9665, higher than that of Indian (0.7155), which means China has less interest to increase total energy consumption but more interest in popularizing the clean energy. • This paper proposed a novel grey Riccati–Bernoulli model for energy consumption prediction. • A specific solution algorithm for this Riccati–Bernoulli equation is discussed in this research. • We compared the proposed novel grey model with other 12 exiting prediction models. • The novel model also reveals China's effort in popularizing the use of clean energy. [ABSTRACT FROM AUTHOR]

• Develop an Optimal Resource Selection Framework on Internet-of-Things (ORSF-IoT). • It supports optimal resource selection through the proposed algorithms, namely, Resource Discovery and Improved Firefly Algorithms. • Propose an Iterative Agglomerative Fuzzy K-Means (IAFKM) Clustering Algorithm for selection of best resources by indexing and ranking them on the basis of matched resources. • The framework provides optimal solution for selecting the parametric value having exceeded limit as the brightest firefly in less time. • It helps in providing timely alarms to the system for corrective measurements and enhances system performancesignificantly. The fundamental requirement for communication and computation across distinct application areas on Internet-of-Things is the resource discovery that demands appropriate reasoning for the optimal selection. With exponential growth of resources and their produced huge amount of heterogeneous data, various activities with respect to foraging and sense-making loops face challenges due to interoperability. Hence, interoperability emerges as a major bottleneck for the requirement. Therefore, to eliminate the challenge, the paper has proposed an "Optimal Resource Selection Framework for Internet-of-Things" that deals with the interoperability and ease the resource discovery and selection. The framework facilitates formation of semantic knowledge base as Shared Virtual Composite Ontology for capturing dynamic IoT heterogeneous data. Moreover, it supports optimal resource selection through the proposed algorithms, namely , Resource discovery Algorithm and Improved Firefly Algorithm. Both algorithms target coordination and optimization with Shared Ontology, respectively. The feasibility of the framework is checked against data collected from Sutlej river, Ludhiana, Punjab, India. The proposed framework is evaluated using benchmark functions with respect to metrics such as mean, standard deviation, processing and execution time. The obtained results are compared with the existing Nature-Inspired algorithms to confirm the efficiency of the proposed framework. [ABSTRACT FROM AUTHOR]