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Sustainable Development Goals (SDG) are at the forefront of government initiatives across the world. The SDGs are primarily concerned with promoting sustainable growth via ensuring wellbeing, economic growth, environmental legislation, and academic advancement. One of the most prominent goals of the SDG is to provide learners with high-quality education (SDG 4). This paper aims to look at the perspectives of the Sustainable Development Goals improvised to provide quality education. We also analyze the existing state of multiple initiatives implemented by the Indian government in the pathway to achieving objectives of quality education (SDG 4). Additionally, a case study is considered for understanding the association among the observed indicators of SDG4. For this purpose, exploratory data analysis, and numerical association rule mining in combination with QuantMiner genetic algorithm approaches have been applied. The outcomes reveal the presence of a significant degree of association among these parameters pointing out the fact that understanding the impact of one (or more) indicator on other related indicators is critical for achieving SDG 4 goals (or factors). These findings will assist governing bodies in taking preventive measures while modifying existing policies and ensuring the effective enactment of SDG 4 goals, which also will subsequently aid in the resolution of issues related to other SDGs.

Snakebite poses a significant health threat in numerous tropical and subtropical nations, with around 5.4 million cases reported annually, which results in 1.8-2.7 million instances of envenomation, underscoring its critical impact on public health. The 'BIG FOUR' group comprises the primary committers responsible for most snake bites in India. Effective management of snakebite victims is essential for prognosis, emphasizing the need for preventive measures to limit snakebite-related deaths. The proposed initiative seeks to develop a transfer learning-based image classification algorithm using DenseNet to identify venomous and non-venomous snakes automatically. The study comprehensively evaluates the image classification results, employing accuracy, F1-score, Recall, and Precision metrics. DenseNet emerges as a potent tool for multiclass snake image classification, achieving a notable accuracy rate of 86%. The proposed algorithm intends to be incorporated into an AI-based snake-trapping device with artificial prey made with tungsten wire and vibration motors to mimic heat and vibration signatures, enhancing its appeal to snakes. The proposed algorithm in this research holds promise as a primary tool for preventing snake bites globally, offering a path toward automated snake capture without human intervention. These findings are significant in preventing snake bites and advancing snakebite mitigation strategies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Determining the causality of Adverse Drug Reactions (ADRs) is essential for management and prevention of future occurrences. The WHO-Uppsala Monitoring Centre (UMC) system is recommended under the Pharmacovigilance Program of India whereas Naranjo's algorithm is commonly utilized by clinicians, but their agreement remains a subject of investigation. This study aims to compare the inter-rater agreement between these two scales for causality assessment of ADRs. In this cross-sectional study, two groups of pharmacovigilance experts were given a set of total 399 anonymized individual case safety reports, collected over six months. The raters were blinded to each other's assessments and applied the WHO-UMC system and Naranjo algorithm to each case independently. Inter-rater agreement was then evaluated utilizing Cohen's kappa. The suspected ADRs were also comprehensively analysed on parameters like age, sex, route of administration, speciality, organ system affected, most common drug categories and individual drugs, outcome of ADRs. Analysis of 399 suspected ADRs revealed that mean age of patients was 36.8 ± 18.0 years, females were more frequently affected, highest proportion of reports were from psychiatry inpatients, seen with antipsychotic drugs, involved the central nervous system, with oral administration, and 91% resolved. On causality assessment by the WHO-UMC system, 53.3% were "Certain" whereas Naranjo's algorithm categorized 96.74% of ADRs as "Probable". Cohen's kappa showed a "Minimal" agreement (0.22) between WHO-UMC and Naranjo system of causality assessment. The considerable lack of agreement between the two commonly employed systems of causality assessment of ADRs warrants further investigation into specific factors influencing the disagreement to improve the accuracy of causality assessments., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

[Purpose] How to prevent fake news without spoiling the freedom of speech is a growing concern among governments across the world. Some countries see legislation as being the best approach to counter fake news. In the legislation proposals, accountability is mostly placed on technology companies, but also individuals seem to have responsibility in the legislation of some countries. Some other governments see non-legislative means to counter fake news. But it's a fact that countering fake news without compromising free speech is a high priority across governments in the world and a challenging task too. This paper investigates the India scenario and tries to list out other than legislation what other measures are required. [Methodology] This paper takes a survey of mitigation efforts in select countries. This survey is used to testify against similar efforts in India, if any and adopts comparative approach to understand where Indian efforts stand at. [Findings] From using fact-checking tools available online, finding the source, locating how many people viewed a particular story to check grammar and spelling, and developing a critical mindset; plenty of things become a critical means in fighting down fake news. Legislation alone is insufficient. Media literacy, public scrutiny, good citizenship, and education along with sensitive civil society require playing its significant part in India to fight fake news. In India, the policy is vague. It gives the government enormous power to surveillance in the name of fake news. [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]

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]

The road is the most commonly used means of transportation and serves as a country's arteries, so it is extremely important to keep the roads in good condition. Potholes that happen to appear in the road must be repaired to keep the road in good condition. Spotting potholes on the road is difficult, especially in a country like India where roads stretch millions of kilometres across the country. Therefore, there is a need to automate the identification of potholes with high speed and real-time precision. YOLOX is an object detection algorithm and our main goal of this article is to train and analyse the YOLOX model for pothole detection. The YOLOX model is trained with a pothole dataset and the results obtained are analysed by calculating the accuracy, recall and size of the model which is then compared to other YOLO algorithms. The experimental results in this article show that the YOLOX-Nano model predicts potholes with higher accuracy compared to other models while having low computational costs. We were able to achieve an Average Precision (AP) value of 85.6% from training the model and the total size of the model is 7.22 MB. The pothole detection capabilities of the newly developed YOLOX algorithm have never been tested before and this paper is one of the first to detect potholes using the YOLOX object detection algorithm. The research conducted in this paper will help reduce costs and increase the speed of pothole identification and will be of great help in road maintenance. [ABSTRACT FROM AUTHOR]

Big data analytics involve a systematic approach to find hidden patterns to help the organization grow from large volume and variety of data. In recent years big data analytics is widely used in the agricultural domain to improve yield. Viticulture (the cultivation of grapes) is one of the most lucrative farming in India. It is a subdivision of horticulture and is the study of wine growing. The demand for Indian Wine is increasing at about 27% each year since the 21st century and thus more and more ways are being developed to improve the quality and quantity of the wine products. In this paper, we focus on a specific agricultural practice as viticulture. Weather forecasting and disease detection are the two main research areas in precision viticulture. Leaf disease detection as a part of plant pathology is the key research area in this paper. It can be applied on vineyards of India where farmers are bereft of the latest technologies. Proposed system architecture comprises four modules: Data collection, data preprocessing, classification and visualization. Database module involves grape leaf dataset, consists of healthy images combined with disease leaves such as Black measles, Black rot, and Leaf blight. Models have been implemented on Apache Hadoop using map reduce programming framework. It applies feature extraction to extract various features of the live images and classification algorithm with reduced computational complexity. Gray Level Co-occurrence Matrix (GLCM) followed by K-Nearest Neighborhood (KNN) algorithm. The system also recommends the necessary steps and remedies that the viticulturists can take to assure that the grapes can be salvaged at the right time and in the right manner based on classification results. The overall system will help Indian viticulturists to improve the harvesting process. Accuracy of the model is 82%, and it can be increased as a future work by including deep learning with time-series grape leaf images. [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]

Neural Style Transfer (NST) has been a widely researched topic as of late enabling new forms of image manipulation. Here we perform an extensive study on NST algorithms and extend the existing methods with custom modifications for application to Indian art styles. In this paper, we aim to provide a comprehensive analysis of various methods ranging from the seminal work of Gatys et al which demonstrated the power of Convolutional Neural Networks (CNNs) in creating artistic imagery by separating and recombining image content and style, to the state of the art image-to-image translation models which use Generative Adversarial Networks (GANs) to learn the mapping between two domain of images. We observe and infer based on the results produced by the models on which one could be a more suitable approach for Indian art styles, especially Tanjore paintings which are unique compared to the Western art styles. We then propose the method which is more suitable for the domain of Indian Art style along with custom architecture which includes an enhancement and evaluation module. We then present evaluation methods, both qualitative and quantitative which includes our proposed metric, to evaluate the results produced by the model.

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]

Chronic wounds are associated with significant clinical, economic and quality‐of‐life burden. Despite the variety of wound imaging systems available in the market for wound assessment and surveillance, few are clinically validated among patients of Asian ethnicity. We aimed to clinically validate the accuracy of a smartphone wound application (Tissue Analytics [TA], Net Health Systems Inc, Florida, USA), versus conventional wound measurements (visual approximation and paper rulers), in patients of Asian ethnicity with venous leg ulcers (VLU). A prospective cohort study of patients presenting with VLU to a specialist wound nurse clinic over a 5‐week duration was conducted. Each patient received seven wound measurements: one by a trained wound nurse clinician, and three separate wound measurements using TA on each of the iOS and Android operating systems. Inter‐rater and intra‐rater reliability between clinical and TA‐based measurements were analysed using intra‐class correlation statistics, with values of <0.5, 0.5 to 0.75, 0.75 to 0.9, and >0.9 indicating poor, moderate, good and excellent reliability, respectively. 82 patients (51% males), with a mean age at 65.8 years, completed the 5‐week study duration. 25 (30%) had underlying diabetes mellitus. Chinese, Malay and Indian ethnicity comprised 68%, 12% and 11%, respectively. The VLU healed in 26 (32%) of patients within the study period. In total, 358 wound episodes with 2334 wound images were analysed. Inter‐rater reliability for length, width and area between wound nurse measurements and TA application measurements was good (range 0.799‐0.919, P < 0.001). Separate measurements of intra‐rater reliability for length, width and area within the iOS or Android systems were excellent (range 0.967‐0.985 and range 0.977‐0.984 respectively, P < 0.001). Inter‐rater reliability between TA used on the iOS and Android systems was also excellent (0.987‐0.989, P < 0.001). Tissue Analytics, a smartphone wound application, is a useful adjunct for wound assessment and surveillance in VLU patients of Asian ethnicity. [ABSTRACT FROM AUTHOR]

This paper proposes a blend of three techniques to select COVID-19 testing centers. The objective of the paper is to identify a suitable location to establish new COVID-19 testing centers. Establishment of the testing center in the needy locations will be beneficial to both public and government officials. Selection of the wrong location may lead to lose both health and wealth. In this paper, location selection is modelled as a decision-making problem. The paper uses fuzzy analytic hierarchy process (AHP) technique to generate the criteria weights, monkey search algorithm to optimize the weights, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to rank the different locations. To illustrate the applicability of the proposed technique, a state named Tamil Nadu, located in India, is taken for a case study. The proposed structured algorithmic steps were applied for the input data obtained from the government of India website, and the results were analyzed and validated using the government of India website. The ranks assigned by the proposed technique to different locations are in aligning with the number of patients and death rate., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this article., (Copyright © 2022 S. Saroja et al.)

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]

Communication establishment is crucial for rescue operations in disaster affected areas. A standard tool for communication is the use of cell phones. However, they can be useless in situations where the cellular network's base stations are damaged in a disaster. A contemporary approach to re-establishing a communication network is by hosting base stations in drones. However, low battery life and difficulty in calculating the number of drones needed in different terrains are limitations of the above approach. This paper introduces a novel terrain-aware algorithm that calculates the minimum number of drones needed to cover an area with no voids in the network coverage. Our method ensures that the drones are deployed at optimal heights to maximize the average leftover energy in the network. We apply the algorithm for an actual location in Pettimudi, India and find the optimal number and positions of the drones to cover the area effectively without voids. In addition, we provide a simulation of the the communication establishment using above drones, and our experiments yield an average network efficiency of 98%, showing the effectiveness of our method. [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 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]

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 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]

Purpose: Any business that opts to adopt a recommender engine (RE) for various potential benefits must choose from the candidate solutions, by matching to the task of interest and domain. The purpose of this paper is to choose RE that fits best from a set of candidate solutions using rule-based automated machine learning (ML) approach. The objective is to draw trustworthy conclusion, which results in brand building, and establishing a reliable relation with customers and undeniably to grow the business. Design/methodology/approach: An experimental quantitative research method was conducted in which the ML model was evaluated with diversified performance metrics and five RE algorithms by combining offline evaluation on historical and simulated movie data set, and the online evaluation on business-alike near-real-time data set to uncover the best-fitting RE. Findings: The rule-based automated evaluation of RE has changed the testing landscape, with the removal of longer duration of manual testing and not being comprehensive. It leads to minimal manual effort with high-quality results and can possibly bring a new revolution in the testing practice to start a service line "Machine Learning Testing as a service" (MLTaaS) and the possibility of integrating with DevOps that can specifically help agile team to ship a fail-safe RE evaluation product targeting SaaS (software as a service) or cloud deployment. Research limitations/implications: A small data set was considered for A/B phase study and was captured for ten movies from three theaters operating in a single location in India, and simulation phase study was captured for two movies from three theaters operating from the same location in India. The research was limited to Bollywood and Ollywood movies for A/B phase, and Ollywood movies for simulation phase. Practical implications: The best-fitting RE facilitates the business to make personalized recommendations, long-term customer loyalty forecasting, predicting the company's future performance, introducing customers to new products/services and shaping customer's future preferences and behaviors. Originality/value: The proposed rule-based ML approach named "2-stage locking evaluation" is self-learned, automated by design and largely produces time-bound conclusive result and improved decision-making process. It is the first of a kind to examine the business domain and task of interest. In each stage of the evaluation, low-performer REs are excluded which leads to time-optimized and cost-optimized solution. Additionally, the combination of offline and online evaluation methods offer benefits, such as improved quality with self-learning algorithm, faster time to decision-making by significantly reducing manual efforts with end-to-end test coverage, cognitive aiding for early feedback and unattended evaluation and traceability by identifying the missing test metrics coverage. [ABSTRACT FROM AUTHOR]

This research work is concerned with the development of a rule-based stemmer for stemming of adjectives in the Punjabi language. Stemming is a method of deriving the root word from the inflected word. The proposed Punjabi Adjective Stemmer (PAS) uses a rule-based approach for converting the inflected Punjabi adjectives to their root forms. A database containing valid root adjectives occurring in the Punjabi language has been created. This database stores 1,762 Punjabi root adjectives. When an adjective word is fed to PAS as an input, first it compares the input word with the root database to determine whether the input adjective is a root adjective or an inflected one. If the input adjective is a root adjective, then no stemming is required and the input adjective is returned as the output. Otherwise, the inflected input adjective is sent to the suffix-stripping algorithm to get the corresponding root adjective. The suffix-stripping algorithm uses a set of predefined rules. India is a linguistically rich country with 22 languages recognized officially. But the computational resources developed for these languages are very scarce. Most of the stemmers developed for Punjabi language so far concentrated on nouns and proper names. PAS is the only stemmer developed so far for specifically addressing the problem of stemming of Punjabi adjectives. PAS has an overall accuracy of 88.76%. [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]

Diabetic retinopathy (DR) is an eye disease, which affects the people who are all having the diabetes for more than 10 years. The ophthalmologist identifies when the dilated eye exam causes severe in any one of the following deviations in the retina: changes in blood vessels, leaking blood vessels, newly grown blood vessels, swelling of the macula, changes in the lens, and damages to the nerve tissue. It can eventually lead to vision loss. The early detection of DR prevents the cause of blindness. In this paper, we propose the retinal image segmentation and extraction of blood vessels by morphological processing, thresholding, edge detection, and adaptive histogram equalization. For the automatic diagnosis of DR from the fundus image, we also developed a network with the convolutional neural network architecture for accurately classifying its severity. By using high-end graphical processor unit (GPU), we trained this network on the publicly available dataset such as DRIVE, DIARETDB0, and DIARETDB1&#95;v1, and the images collected from the Aravind Eye Hospital, Coimbatore, India. Our proposed CNN achieves a sensitivity of 98%, a specificity of 93%, and an accuracy of 96.9% containing a database of 854 images.

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]

Background: India is a large and populous country where reliable data on headache disorders are relatively scarce. This study in northern India (Delhi and National Capital Territory Region [NCR], including surrounding districts in the States of Haryana, Uttar Pradesh and Rajasthan) continues the series of population-based studies within the Global Campaign against Headache and follows an earlier study, using the same protocol and questionnaire, in the southern State of Karnataka. Methods: This cross-sectional study used the Global Campaign's established methodology. Biologically unrelated Indian nationals aged 18–65 years were included through multistage random sampling in both urban and rural areas of NCR. Interviews at unannounced household visits followed the structured Headache-Attributed Restriction, Disability, Social Handicap and Impaired Participation (HARDSHIP) questionnaire in its original English version or in the validated Hindi version. Demographic enquiry was followed by a neutral headache screening question and diagnostic questions based on the International Classification of Headache Disorders edition 3 (ICHD-3), which focused on each respondent's most bothersome headache. Questions about headache yesterday (HY) enabled estimation of 1-day prevalence. A diagnostic algorithm first identified participants reporting headache on ≥ 15 days/month (H15+), diagnosing probable medication-overuse headache (pMOH) in those also reporting acute medication use on ≥ 15 days/month, and "other H15+" in those not. To all others, the algorithm applied ICHD-3 criteria in the order definite migraine, definite tension-type headache (TTH), probable migraine, probable TTH. Definite and probable diagnoses were combined. Results: Adjusted for age, gender and habitation, 1-year prevalences were 26.3% for migraine, 34.1% for TTH, 3.0% for pMOH and 4.5% for other H15+. Female preponderance was seen in all headache types except TTH: migraine 35.7% vs. 15.1% (aOR = 3.3; p < 0.001); pMOH 4.3% vs. 0.7% (aOR = 5.1; p < 0.001); other H15 + 5.9% vs. 2.3% (aOR = 2.5; p = 0.08). One-day prevalence of (any) headache was 12.0%, based on reported HY. One-day prevalence predicted from 1-year prevalence and mean recalled headache frequency over 3 months was slightly lower (10.5%). Conclusions: The prevalences of migraine and TTH in Delhi and NCR substantially exceed global means. They closely match those in the Karnataka study: migraine 25.2%, TTH 35.1%. We argue that these estimates can reasonably be extrapolated to all India. [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]

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]

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]

The increase in the usage of internet and social media has enabled people exchange views, opinions and thoughts as never before. This exchange of data has paved the way for sentiment analysis. The basic task of sentiment analysis is to classify the data into positive, negative and neutral. In this paper an effective MapReduce-Based Hybrid NBC-TFIDF (Naive Bayes Classifier -Term Frequency Inverse Document Frequency) algorithm is proposed to mine people sentiment. A Map Reduce-Based Hybrid NBC is employed to classify the data based on the polarity score of each sentence in social media data. The polarity score is calculated using the emotion corpus and the Diabetic corpus is created using food Glycemic Index and physical activity index. This study analyses the correlation of food habits, physical activity and diabetic risk factors among Indian population using social network data. Around two million data has been identified for the study and the study is restricted to India. The experimental result shows that MapReduce-Based Hybrid NBC–TFIDF performs efficiently in multimode cluster. The results reveal that no individual factor is associated with diabetic risk and also a group of common factors contribute to diabetes mellitus. It is found that 60% of the social media data had positive polarity about the food items that are high in Glycemic Index which is the main root cause for type – 2 Diabetes. This Big-Data analysis reveals that the young generations of India are unaware of risk factors of Diabetes mellitus. [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]

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]

Retinal image registration is a necessary step in diagnosis and monitoring of Diabetes Retinopathy (DR), which is one of the leading causes of blindness. Long term diabetes affects the retinal blood vessels and capillaries eventually causing blindness. This progressive damage to retina and subsequent blindness can be prevented by periodic retinal screening. The extent of damage caused by DR can be assessed by comparing retinal images captured during periodic retinal screenings. During image acquisition at the time of periodic screenings translation, rotation and scale (TRS) are introduced in the retinal images. Therefore retinal image registration is an essential step in automated system for screening, diagnosis, treatment and evaluation of DR. This paper presents an algorithm for registration of retinal images using orthogonal moment invariants as features for determining the correspondence between the dominant points (vessel bifurcations) in the reference and test retinal images. As orthogonal moments are invariant to TRS; moment invariants features around a vessel bifurcation are unaltered due to TRS and can be used to determine the correspondence between reference and test retinal images. The vessel bifurcation points are located in segmented, thinned (mono pixel vessel width) retinal images and labeled in corresponding grayscale retinal images. The correspondence between vessel bifurcations in reference and test retinal image is established based on moment invariants features. Further the TRS in test retinal image with respect to reference retinal image is estimated using similarity transformation. The test retinal image is aligned with reference retinal image using the estimated registration parameters. The accuracy of registration is evaluated in terms of mean error and standard deviation of the labeled vessel bifurcation points in the aligned images. The experimentation is carried out on DRIVE database, STARE database, VARIA database and database provided by local government hospital in Pune, India. The experimental results exhibit effectiveness of the proposed algorithm for registration of retinal images., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

Autoregressive Integrated Moving Average (ARIMA) and seasonal ARIMA (SARIMA) models are common techniques that are widely used in analysing and forecasting stationary and seasonal time series data. The three essential steps involved to construct ARIMA are identification, estimation, and checking the validity of the model. The most critical step followed in constructing the ARIMA model is model identification. However, overcoming the difficult local optima problem for both ARIMA and SARIMA is still challenging as there is no appropriate method to solve it. In this paper, the proposed parallel GA-SARIMA model is used to solve the problem of local optima, where the genetic algorithm (GA) is used at the initial stage to identify the order and estimation of the parameters for SARIMA. The National Climate Data Centre (NCDC) time series dataset is used for testing the efficiency of the final parallel GA-SARIMA model to forecast the mean temperature of India from 2000 to 2017. The GA algorithm is successfully implemented to solve the optimization problems by introducing better solutions suitable for SARIMA models. The results of the study showed that the implementation of the combined approach of parallel GA and SARIMA enhances the prediction accuracy of the model. The parallel GA-SARIMA method is particularly robust, faster and performs better than sequential SARIMA models in terms of running time and cost function values. [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]

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]

The present world data is most important in public because without data cannot live in the world. This data is big data and that data daily increases through Social Media like "Twitter, Facebook, Youtube, WhatsApp, Instagram, LinkedIn", etc.., because this media only share public opinion very fast through the net. From this media, more people used especially Twitter. Thus media are used to analyze the public opinion of the tweets and predict the future through Machine Learning Algorithm. This analyzed data should make it polarity score. This score data has very little security because, this score can change the score and affected the future, so apply the existing security algorithms is Salsa and ChaCha. The Salsa algorithm is diagonal values moved to the first row. The ChaCha algorithm is diagonal values moved to the first column. The existing algorithms do not have good security because they focused only on performance, not security. So, the novel security algorithm is RPBB31. This algorithm has seven stages. The 1st stage is to find the secret key N, n, and p values from the matrix. The 2nd stage is to apply the secret key in PN(n) operation. The 3rd stage operation of PN(n) up to n = 1. The 4th stage is all PN(n) operations make their single line. The 5th stage is to pair the values and swap the values in the matrix. The 6th stage is column operations in the matrix. The 7th stage is again Step 4 values used to swap but "0th" cell value start from reverse in the matrix. In world cricket is the most famous game. In all social media Millions of people are following in different manner, especially in indian cricketer are more famous in India and world. In some point of time it may lead to match fixing, betting. To overcome this kind of issue the four datasets chosen. The proposed algorithm has provide good security and performance while compare to existing algorithms. [ABSTRACT FROM AUTHOR]

Monitoring the Earth's surface and objects is important for many applications, such as managing natural resources, crop yield predictions, and natural hazard analysis. Remote sensing is one of the most efficient and cost-effective solutions for analyzing land-use and land-cover (LULC) changes over the Earth's surface through advanced computer algorithms, such as classification and change detection. In the past literature, various developments were made to change detection algorithms to detect LULC multitemporal changes using optical or microwave imagery. The optical-based hyperspectral highlights the critical information, but sometimes it is difficult to analyze the dataset due to the presence of atmospheric distortion, radiometric errors, and misregistration. In this work, an artificial neural network-based post-classification comparison (ANPC) as change detection has been utilized to detect the muti-temporal LULC changes over a part of Uttar Pradesh, India, using the Hyperion EO-1 dataset. The experimental outcomes confirmed the effectiveness of ANPC (92.6%) as compared to the existing models, such as a spectral angle mapper (SAM) based post-classification comparison (SAMPC) (89.7%) and k-nearest neighbor (KNN) based post-classification comparison (KNNPC) (91.2%). The study will be beneficial in extracting critical information about the Earth's surface, analysis of crop diseases, crop diversity, agriculture, weather forecasting, and forest monitoring. [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]

Thermal Power plant (TPP) is biggest source of power in India. About 65% of electricity is generated by using thermal power plant. The equipment of TPP runs for 24x7 days through the year. The fault of TPP equipment needs to identify at early stage before the breakdown of system or unit. The maintenance functions of TPP should be optimized carefully by selecting suitable maintenance strategy at least cost. Identification and confirmation of fault in TPP equipment is very important step for decision making. It helps to optimize the maintenance activity. This paper proposes an algorithm for use of co-ordinated condition monitoring approach for identification and confirmation of fault. The objective of this study is to improve the availability and heath of critical equipment of Dahanu Thermal Power Station (DTPS) located in western region of India. [ABSTRACT FROM AUTHOR]