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Graph theory plays a crucial role in the era of computer science, medical science and information technology. The fundamental motivation behind this paper is to present some availability ideas in the m polar interval-valued fuzzy graph (m-PIVFG), which are utilized to portray the interval of the uncertainty of items. What's more, the m-PIVFG graphs are utilized to portray the underlying connection between ideas in which the vertices and edges are of multi-poles and in the form of interval values to feature the uncertainty conditions. The dominating set involves a basic situation in graph analysis. This paper essentially adds to expanding the idea of double domination in the fuzzy graph to the m-PIVFG and getting the related extended ideas of m-PIVFG. In the interim, the ways to get the particular double dominating sets are introduced. At long last, a numeral model on ambulance service on some villages information in India is introduced to clarify the necessity of double domination in m-PIVFG in the particular application. [ABSTRACT FROM AUTHOR]

Goal 1 of the 2030 Agenda for Sustainable Development, adopted by all United Nations member States in 2015, is to end poverty in all forms everywhere. The major indicator to monitor the goal is the so-called headcount ratio or poverty rate, i.e., proportion or percentage of people under poverty. In India, where nearly a quarter of population still live below the poverty line, monitoring of poverty needs greater attention, more frequently at shorter intervals (e.g., every year) to evaluate the effectiveness of planning, programs and actions taken by the governments to eradicate poverty. Poverty rate computation for India depends on two basic ingredients – rural and urban poverty lines for different states and union territories and average Monthly Per-capita Consumer Expenditure (MPCE). While MPCE can be obtained every year, usually from the Consumer Expenditure Survey on shorter schedules with a few exceptions where the information is obtained from another survey, determination of poverty lines is a highly complex, costly and time-consuming process. Poverty lines are essentially determined by a panel of experts who draws their conclusions partly based on their subjective opinions and partly based on data from multiple sources. The main data source the panel uses is the Consumer Expenditure Survey data with a detailed schedule, which are usually available every five years or so. In this paper, we undertake a feasibility study to explore if estimates of headcount ratios or Poverty Ratios in intervening years can be provided in absence of poverty lines by relating poverty ratios with average MPCE through a statistical model. Then we can use the fitted model to predict poverty rates for intervening years based on average MPCE. We explore a few in this work models using Bayesian methodology. The reason behind calling this 'synthetic prediction' rests on the synthetic assumption of model invariance over years, often used in the small area literature. While the data-based assessment of our Bayesian synthetic prediction procedure is encouraging, there is a great potential for improvements on the models presented in this paper, e.g., by incorporating more auxiliary data as they become available. In any case, we expect our preliminary work in this important area will encourage researchers to think about statistical modeling as a possible way to at least partially solve a problem for which no objective solution is currently available. [ABSTRACT FROM AUTHOR]

BACKGROUND: The recent COVID-19 pandemic like situation has killed a large numbers of people and caused havoc across the world. With the aim of providing the COVID-19 related updated information about facilities, availability, utilization of COVID-19 related resources like vaccines, hospitals, beds, oxygen to the people and preventing its surge, many websites were developed for health guidance to the general masses. OBJECTIVE: The objective of this paper is to investigate the accessibility and usability of websites providing COVID-19 information in India. METHODS: This study evaluates the 38 websites providing COVID-19 information in India using automated tools. RESULTS: It was found that digital healthcare information is far from accessibility conformance and none of the websites conformed to web content accessibility guidelines (WCAG) 2.0 completely. The website adhered to understandability with average errors of 2.8 while less compliant to perceivability with average errors of 30.5. It was also found that most of the websites were mobile friendly. [ABSTRACT FROM AUTHOR]

In India, most of the Science and Technology resources available are in English. Developing an Automatic Language Translation Engine from English (source language) to Tamil (target language) is very essential for the people who need to get technical resources in their native language. The challenges in designing such engines using Natural Language Processing (NLP) tools include Lexical, Structural, and Syntax level ambiguity. To solve these challenges, the development of a Part- Of-Speech (POS) tagger is essential. The Verb-Framed languages like Tamil, Japanese, and many languages in Romance, Semitic, and Mayan languages families have high morphological richness but lack either a large volume of annotated corpora or manually constructed linguistic resources for building POS tagger. Moreover, the Tamil Language has a low resource, high word sense ambiguity, and word-free order form giving rise to challenges in designing Tamil POS taggers. In this paper, we postulate a Hybrid POS tagger algorithm for Tamil Language using Cross-Lingual Transformation Learning Techniques. It is a novel Mining-based algorithm (MT), which finds equivalent words of Tamil in English on less volume of English- Tamil bilingual unannotated parallel corpus. To enhance the performance of MT, we developed Tamil language-specific auxiliary algorithms such as Keyword-based tagging algorithm (KT) and Verb pattern-based tagging algorithm (VT). We also developed a Unique pair occurrence-tagging algorithm (UT) to find the one-time occurrence of Tamil-English pair words. Our experiments show that by improving Context-based Bilingual Corpus to Bilingual parallel corpus and after leaving onetime occurrence words, the proposed Hybrid POS tagger can predict 81.15% words, with 73.51% accuracy and 90.50% precision. Evaluations prove our algorithms can generate language resources, which can improve the performance of NLP tasks in Tamil. [ABSTRACT FROM AUTHOR]

Curfews and lockdowns around the world in the Covid-19 era have increased the usage of the internet drastically and accordingly the amount of data shared on social media. In addition to using social media for sharing useful information, some miscreants are using the power of social media to spread hate speech and offensive content. Filtering the offensive language content manually is a laborious task due to the huge volume of data. Further, rapid developments in hardware and software technology have provided opportunities for users to post their comments not only in English but also in their native language scripts. However, based on the ease of Roman script usage, social media users specifically in multilingual countries like India, prefer to comment in code-mixed and multi-script texts. The typical systems that are employed to process and analyze monolingual texts are usually not appropriate for these kinds of texts. Further, as these texts do not adhere to the rules and regulations of any language to frame the words and sentences, the complexity of analyzing such texts increases. The novelty of the present study is to address the Offensive Language Identification (OLI) task in code-mixed and multi-script texts, this paper proposes to use relevant syllable and character n-grams features to train Machine Learning (ML) classifiers. The performance of the proposed models is evaluated on three Dravidian language pairs, namely: Malayalam-English, Tamil- English, and Kannada-English. The performances of ML classifiers prove the effectiveness of syllable and character n-grams features for code-mixed and multi-script texts analysis. [ABSTRACT FROM AUTHOR]

These days, the appraisal of the COVID-19 vulnerability has become a difficult errand for the whole world. The COVID-19 administration dynamic issue frequently includes numerous elective arrangements clashing standards. In this paper, we present a multi-criteria decision-making (MCDM) procedure based on the fuzzy VIKOR method to survey the COVID-19 vulnerability in the state of Assam, India. The trapezoidal fuzzy number is utilized to evaluate the rating of the loads for the set-up models. We have observed environment, social, and Medical factors after observing the spread of COVID-19. To study and to have comments, a committee of five experts has been formed from a different region of Assam to observe and comment to identify Coronavirus's weakest factors. For a better survey, we have divided the state into four areas namely Rural Area, Urban Area, Market Area in Rural Area, and Market Area in Urban Area. The current research looked at how the fuzzy VIKOR selects provinces for urgent adaptation needs differently than a traditional MCDM technique. [ABSTRACT FROM AUTHOR]

E-Rickshaw is an E-vehicle that has three wheels, a rechargeable battery driven electric motor as engine. E-rickshaw has become very popular due to low operating cost, low maintenance cost, eco-friendliness and ease of driving. It is perfect for small distance transport. As a last mile connector, it has transformed the public transport system in India. The low cost electric vehicle carries enough people to make a decent income and hence has become a source of livelihood for many. For considering the issues in this paper, detailed attributes of E-rickshaw are studied and Analytical Hierarchy Process (AHP) has been applied to calculate criteria weights for the sorted attributes. Subsequently, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), a Multi Criteria Decision Making (MCDM) technique has been applied for the selection of best E-Rickshaw. In this paper, sensitivity analysis and comparative analysis have been conducted for further insight. [ABSTRACT FROM AUTHOR]

Tyre condition monitoring system (TCMS) is an emerging electronic safety system present in most of the new generation vehicles. It is intended to warn the driver when wheels are unbalanced or inflation pressure in a tyre goes below a certain recommended value. In India only limited vehicles having this critical safety system installed because it is highly expensive. The existing TCMS monitor only the tyre pressure using direct or indirect methods. In recent days the indirect TCMS using vibration signals and machine learning techniques are gaining momentum. This paper reports the effect of wheel balancing on a proposed affordable indirect TCMS. The present study goes one step forward to include the wheel balancing in the TCMS, thus increasing the scope of TCMS. The study was carried out using vibration signal data acquired from a low cost accelerometer placed on the wheel hub. The features required were taken out from the acquired data with the help of statistical feature extraction techniques. Features selection and feature classification were processed with J48 decision tree algorithm. The effect of wheel balancing in classification accuracy is clearly explained. [ABSTRACT FROM AUTHOR]

Indoor air pollution (IAP) has become a serious concern for developing countries around the world. As human beings spend most of their time indoors, pollution exposure causes a significant impact on their health and well-being. Long term exposure to particulate matter (PM) leads to the risk of chronic health issues such as respiratory disease, lung cancer, cardiovascular disease. In India, around 200 million people use fuel for cooking and heating needs; out of which 0.4% use biogas; 0.1% electricity; 1.5% lignite, coal or charcoal; 2.9% kerosene; 8.9% cow dung cake; 28.6% liquified petroleum gas and 49% use firewood. Almost 70% of the Indian population lives in rural areas, and 80% of those households rely on biomass fuels for routine needs. With 1.3 million deaths per year, poor air quality is the second largest killer in India. Forecasting of indoor air quality (IAQ) can guide building occupants to take prompt actions for ventilation and management on useful time. This paper proposes prediction of IAQ using Keras optimizers and compares their prediction performance. The model is trained using real-time data collected from a cafeteria in the Chandigarh city using IoT sensor network. The main contribution of this paper is to provide a comparative study on the implementation of seven Keras Optimizers for IAQ prediction. The results show that SGD optimizer outperforms other optimizers to ensure adequate and reliable predictions with mean square error = 0.19, mean absolute error = 0.34, root mean square error = 0.43, R2 score = 0.999555, mean absolute percentage error = 1.21665%, and accuracy = 98.87%. [ABSTRACT FROM AUTHOR]

Public service delivery is an essential component of e-governance initiatives but the digital divide limits citizens' access to e-government services, especially in rural communities. The Digital India initiative launched Common Service Centers (CSCs) to mitigate the physical-digital divide issue. The privately operated local kiosks are meant to ensure easy access to digital government services in India's remote pockets. Although the literature indicates that females generally have lower perceptions and adoption rates of digital government, existing studies commonly assume women directly interact with government websites. We know little about gender-specific experiences of digital government through the user assisted and privately operated local kiosks. This survey study utilizes the Technology Acceptance Model (TAM) to explore the gender-specific acceptance of public service through local kiosks in rural India. This paper argues that user-assisted kiosks help mitigate the gender digital divide by increasing the usefulness and ease of use of e-government, which improves rural women's acceptance and use of digital government services. This field research brings insights from the developing world while recording women's voices from underserved communities. [ABSTRACT FROM AUTHOR]

According to the National Crime Records Bureau, 63,407 children have gone missing in the year 2016, which makes almost 174 children go missing in India every day, out of which only 50% are ever found again. This brings up a need for an efficient solution to trace missing children. The proposed solution uses machine assistance during these search activities with face recognition technologies and can be used for essential development of applications which use CCTV footage across a camera network to identify the person lost. In our solution we use One Shot learning for face recognition to identify stranded people in places such as mass gatherings. The same technology can be used for identification of criminals across the city. The paper also talks about the tracking of people across a network of multiple non-overlapping cameras, with a feature of shifting the target tovehicle, if the target gets into one. The experimentation is performed using mobile cameras and thus, helps in monitoring actions of criminals and finding their hideout. [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]

In this paper, we present our effort on the development of a Maithili Named Entity Recognition (NER) system. Maithili is one of the official languages of India, with around 50 million native speakers. Although various NER systems have been developed in several Indian languages, we did not find any openly available NER resource or system in Maithili. For the development, we manually annotated a Maithili NER corpus containing around 200K words. We prepared a baseline classifier using Conditional Random Fields (CRF). Then we ran many experiments using various recurrent neural networks (RNN). We collected larger raw corpus to obtain better word embedding and character embedding. In our experiments, we found, neural models are better than CRF; a CRF layer is effective for the prediction of the final output in the RNN models; character embedding is effective in Maithili language. We also investigated the effectiveness of gazetteer lists in neural models. We prepared a few gazetteer lists from various web resources and used those in the neural models. The incorporation of the gazetteer layer caused performance improvement. The final system achieved an f-measure of 91.6% with 94.9% precision and 88.53% recall. [ABSTRACT FROM AUTHOR]

Plastic pollution has become a major environmental concern around the world due to the rapidly increasing production, consumption of single-use plastic products, and our inability to manage it properly. Plastic production increased exponentially, from about 2 million metric tonnes in 1950 to 348 million metric tonnes in 2017, and it is expected to double in capacity yet again by 2040. However, only 9 percent of the 8.3 billion metric tonnes of plastic produced since the early 1950s, has been recycled, and that most plastic ends its life in landfills, dumps and the environment is an increasing cause of concern. Plastic pollution is a major challenge in developing nations like India, where garbage collection systems are often informal with low recycling rates. The COVID 19 pandemic has exacerbated the use of plastic through personal protective equipments (PPEs). Management of this biomedical plastic waste is an addition to this existing challenge. Tackling plastic pollution is high up on UNEP's global agenda to enhance the political visibility of this concern, UNEP along with the Government of India designated it as the theme of World Environment Day 2018. UNEP also provides technical assistance through its partners to support India towards its national and sub-national initiatives. This paper highlights the magnitude of the problem and the role UNEP is playing in addressing some concerns. [ABSTRACT FROM AUTHOR]

BACKGROUND: Web accessibility is one of the most important aspects of building a website. It is important for web developers to ensure that their website is accessible according to WCAG standards for people with different range of abilities. There is plethora of tools for ensuring conformance to WCAG standards but not many studies compared performance of automatic WCAG tools. OBJECTIVE: This paper compares a set of ten WCAG tools and their results in terms of ease of comprehension and interpretation by web developers. We proposed a Common User Profile format to help personalize contents of website making it accessible to people with different range of abilities. METHODS: We selected ten WCAG tools from World Wide Web Consortium (W3C) to evaluate landing pages of two popular websites. For each webpage, we identified accessibility issues and recommended alternate suggestions to help developers improve accessibility. Further, we highlighted accessibility issues that cannot be captured only through conformance to WCAG tools; and proposed additional methods to evaluate accessibility through an Inclusive User Model. We then demonstrated how simulation of user interaction can capture usability and accessibility issues that are not detected through only syntactic analysis of websites' content. Finally, we proposed a Common User Profile format that can be used to compare and contrast accessibility systems and services, and to simulate and personalize interaction for users with different range of abilities. RESULTS AND CONCLUSIONS: After careful evaluation of two websites using the ten tools, we noted that, both websites lacked color contrast between background and foreground; lack of sign language alternatives; opening of pop-ups without proper warnings and so on. Further, results from comparative analysis of selected web accessibility tools noted that, there is no single tool that can be found ideal in all aspects. However, from our study, Utilitia Validator by Utilitia SP. z O.O was considered the most feasible tool. By rectifying and incorporating issues and alternate suggestions by simulation study and Common User Profile format respectively, developers can improve both websites making it accessible to maximum audience. [ABSTRACT FROM AUTHOR]

BACKGROUND: The dominance of a digital wave is being felt in every sphere of the industry, and has been a big boon for online shoppers today. However, companies tend to ignore a section of their customer base, i.e. those who are visually impaired; although there are well-established standards set by WCAG 2.0 (Web Content Accessibility Guidelines). Although most e-commerce companies in India conform to these guidelines, as per the recommendations from the Indian government; however ironically; these are not accessible by the blind. OBJECTIVE: This paper specially focuses on the list of problems that the visually-impaired in India face while trying to access e-commerce sites, as most of the sites haven't conformed to the WCAG 2.0 guidelines. METHOD: Qualitative analysis through the Repertory technique by Kelly in which, the grid developed for different constructs addressing various problems faced by such users in accessing e-commerce websites. FINDINGS: From the findings, it is evident that these visually impaired people are extremely open to gaining access towards the companies' strategies, as they also want to be a part of the main segment of the society, and would in turn be a strong contributor to the profits of the companies, if they're taken seriously. The findings also have a series of managerial as well as social implications. CONCLUSION: Further, the study also looks at lending some suggestions through which, marketing managers would be able to look into this prevailing problem, and thereby address the same, while enhancing their level of engagement with this specially-enabled client base. This would result not only in adding profits to the business at large, but also ensure customer satisfaction and delight, albeit the fact that they're differently abled. [ABSTRACT FROM AUTHOR]

There has been a major and rising interest in India for increasing vaccination rate among peoples to make the nation healthier and safer. In this paper, a new hybrid deep learning model is proposed to predict and target vaccination rates in the less immunized regions. The Rank-Based Multi-Layer Perceptron (R-MLP) hybrid deep learning framework uses the data collected from the recently updated District Level Household Survey-4 (DLHS). R-MLP model predicts and categorizes the percentage of partly immunized vaccination rates as extreme, low and medium ranges. This predicted findings are cross-verified by Deep Soft Cosine Semantic and Ranking SVM based model (DSS-RSM). DSS-RSM model uses the data obtained from the medical practitioners through a location-based social network. The proposed model predicts and extracts patterns with high similarity frequency for identifying vulnerable low immunization regions. It classifies the predicted patterns into two classes such as Class 1 is denoted as high ranked regions and Class 2 is denoted as low ranked regions based on the percentage of pattern matches. Finally, the results from R-MLP and DSS-RSM models are cross-linked together using ensemble model. This model finds the loss values to identify the target regions were health care program need to be conducted for increasing the level of immunization among children's. The proposed hybrid deep learning models trains and validates using python-based Keras and TensorFlow deep learning libraries. The performance of the proposed hybrid deep learning model is compared with other variant machine learning techniques such as Decision Tree C5.0, Naive Bayes and Linear Regression. This comparative results are evaluated using evaluation measures such as Precision, Recall, Accuracy and F1-Measure. Our results show that the hybrid deep learning system is clearly superior to any other alternative approach. [ABSTRACT FROM AUTHOR]

Wetlands are a critical resource, essential not only to water quality and quantity, but also to human sustenance and to the thriving ecosystems. Human and ecosystem dependence on wetlands is not well recognised in governmental oversight and community attitudes. As such, markets undervalue them and businesses and industry consider them disposable. Given that the market economy controls the "new normal", it has not been uncommon for States, when formulating policies to exploit wetland resources for gain, thereby undermining their future and sustainability. India has made some attempts, both nationally and internationally to resolve these matters. This paper is an attempt to showcase one such common property resource, i.e. wetlands in the city of Bangalore, which are under tremendous stress from capitalism. [ABSTRACT FROM AUTHOR]

Human-Elephant Conflict (HEC) and its mitigation have always been a serious conservation issue in India. It occurs mainly due to the encroachment of forests by humans as part of societal development. Consequently, these human settlements are highly affected by the intrusion of wild elephants as they cause extensive crop-raiding, injuries and even death in many cases. HEC is a growing problem in rural areas of India which shares a border with forests and other elephant habitats. Based on the studies, it is very explicit that HEC is an important conservation issue which affects the peaceful co-existence of both humans and elephants near the forest areas. The desirable solution for this problem would be to facilitate co-existence among humans and elephants, but this often fails because of technical difficulties. Hence, this paper presents an end-to-end technological solution to facilitate smoother coexistence of humans and elephants. The proposed work deploys a live video surveillance system along with deep learning strategies to effectively detect the presence of elephants. From the numerical analysis, it is revealed that the post-training accuracy of the deep learning model used in the proposed approach is evaluated at 98.7% and outperforms an an out-of-the-box image detector. The layered approach used in the proposed work improves resource management which is a major bottleneck in real-time deployment scenarios. [ABSTRACT FROM AUTHOR]

Quick UDP Internet (QUIC) protocol is a transport and application layer solution developed by Google, which is the strong competitor to popular and well established Transmission Control Protocol (TCP). The QUIC protocol is being updated faster resulting into many new versions of the protocol. In this paper, the performance of Modified QUIC (ModQUIC) protocol has been tested with respect to congestion control using India's rapidly growing Internet service provider, the Reliance Jio 4G network (JioFi), which has captured 17% of the market share in a short time. This experimental study investigated ModQUIC protocol performance with congestion control mechanisms CUBIC and Bottleneck Bandwidth Round-trip-propagation-time (BBR) in the JioFi network. The experiment is conducted using a testbed, developed with JioFi and RaspberryPi-3 wireless router along with network emulator: Netem. The ModQUIC performance is verified using test video files stored on Google drive. The performance is tested in packet loss and packet reorder situations using metrics, Throughput and Retransmission Ratio (RTR). We observed that overall ModQUIC/BBR performance is better than ModQUIC/CUBIC in the current Internet. We observed that Reliance Jio is an economical solution for highly populated countries like India, but not a contemporary solution to fulfill India's newly launched digitization project. [ABSTRACT FROM AUTHOR]

The timeliness dimension of data quality has not received sufficient scholarly attention even though the publication of official statistics of various countries are often delayed. This paper examines the growing delays in the release of census data in India amidst the technocratisation of policy-making, public professions of faith in evidence based policy-making and growing fascination with big data. We show that the growing delay in the release of census data of India is a fact, rather than a mere allegation, and contextualise the problem by comparing seven decennial censuses conducted between 1951 and 2011. We suggest that delays can be measured vis-à-vis usual, desirable, declared and feasible schedules of publication. Further, delays can be understood from the perspectives of the unwillingness of the government statistical system to face public scrutiny, decline in the quality of public goods, political interference and, in case of data on identity, communalisation of politics. [ABSTRACT FROM AUTHOR]

Intelligent Transportation Systems (ITS) aim at reducing the risks associated with the transportation system as road accidents are becoming one of the primary causes of death in developing countries. Monitoring of driver behavior is one of the key areas of ITS and assists in vehicle safety systems. It has gained importance in order to reduce traffic accidents and ensure the safety of all the road users, from the drivers to the pedestrians. In this work, we present a context-aware system that considers the vehicle, driver and the environment for driver behavior classification as a safe or fatigue or unsafe driver (representing any other unsafe driving behavior like a drunk driver, reckless driver etc.) using a Dynamic Bayesian Network (DBN). We have designed a questionnaire to obtain the influencing factors that decide safe, unsafe and fatigue driving behavior. The collected data has been analyzed using Statistical Package for Social Sciences (SPSS). It has been observed that several techniques in the past have been proposed for driver behavior classification or detection; which either use specialized sensors or hardware devices, inbuilt smartphone sensors (like a gyroscope, accelerometer, magnetometer and GPS etc.), complex sensor fusion algorithms and techniques to detect driver behavior. The novelty of our work lies in designing and developing a context-aware system based on Android smartphone; that considers the complete driving context (driver, vehicle and surrounding environment) and classifies the driver behavior using a DBN. In order to identify driver fatigue, results from the designed questionnaire and previous research studies have been used without the need for special hardware devices. A DBN that combines all the contextual information has been created using GeNIe Modeler. Learning of DBN has been carried out using the Expec-tation–Maximization (EM) algorithm. The real-time data for DBN learning and testing has been collected on Chandigarh-Patiala National Highway, India using an Android smartphone. The proposed system yields an overall classification accuracy of 80–83%.The focus of this paper is to develop a cost-effective context-aware driver behavior classification system, to promote ITS in developing countries. [ABSTRACT FROM AUTHOR]

Housing around 17% of the earth's total population, the Republic of India is a place where 91.6 million of its total population is above 60 years old. Elderly people in India has always been taken care of by their children; however, due to the rampant urbanization and employment constraints, children along with their own families move to the places of their employment leaving old parents alone in their houses, unattended. In this paper, a fuzzy probability transformation based system is proposed to ensure that the elderly are safe in their houses. The scope of this work is limited to learn the bathroom usage pattern of an elderly individual. If an unusual pattern is detected, the system will inform the caregivers immediately. To deal with the ambiguities, the concept of fuzzy numbers is used in the system. The proposed system utilizes the idea of the Internet of Things (IoT) to collect, transmit and present data to the stakeholders. Bayesian reasoning is used as a rider to the proposed methodology for robust decision making in cases of deep uncertainties. An alternative analysis is also performed on the same data using logistic regression and time-series analysis. The results show that the proposed method learns well with time than the other methods and provides superior accuracy. [ABSTRACT FROM AUTHOR]

The effect of climate change on agriculture and food system is well studied. Altered rainfall pattern or temperature significantly influences crop productivity. This results into the socioeconomic vulnerability affecting the food system and security. Even though there are numerous independent studies carried out on the aspects of climate change, food security and nutrition availability, there are few studies interlinking them, particularly in Indian context. There is a dire necessity to understand the inter-linkage among these three owing to the population growth rate in India. Present paper gives an overview on (a) what are the impacts of climate change on agriculture productivity in India; (b) how the altered productivity is going to influence food security and (c) how does the nutritional availability score in Indian context. The discussion paper also focuses on the policy steps taken by Government of India to safeguard the food security of the population. [ABSTRACT FROM AUTHOR]

India has shown rapid economic development and this has forced the country to adopt smart traffic management and surveillance process. The intelligent traffic management and surveillance is the basic need for development of smart cities in India. The Intelligent traffic surveillance includes the detection of moving vehicles, estimation of their speed and extracting its registration number. This paper proposes an efficient and novel approach for the detection of moving vehicles as well as estimation of their speeds for detection of any speed limit violation by using a single camera in daylight or properly illuminated environment. The proposed approach detects and tracks the vehicle passing through the surveillance area and maintains the record of vehicles position. The Region of Interest (ROI) of the approach is the dark shaded area formed under the vehicles road clearance area instead of the vehicles. The average detection accuracy achieved by proposed approach is 93.32%. This is on an average 7.54% higher than other existing approaches. The proposed approach uses cropping operation to minimize the scope of any false positive detection on both sides of road. The average tracking accuracy of the proposed approach is about 92.2%. The tracking accuracy achieved by the proposed approach is on an average 2.2% higher than the other leading approaches. The average speed estimation error in proposed approach is +2.2 km/h which is lower than other existing leading approaches. The proposed approach is also time and space efficient over the existing approaches. [ABSTRACT FROM AUTHOR]

BACKGROUND: Research into education and skill development of drug users to facilitate social and economic inclusion has not previously occurred in India. OBJECTIVE: This paper examines education and skill development in government, NGO and private drug treatment and rehabilitation facilities of India. METHODS: A mapping exercise was undertaken to identify drug treatment and rehabilitation facilities. A total of 119 responses received and one-third of drug users were described as illiterate or had achieved primary school only. RESULTS: The majority of facilities did not provide basic education but virtually all provided HIV and drug education. A majority of facilities did not provide vocational education and livelihood skills and referrals to others for education and skills development were not common. CONCLUSION: To ensure and promote the social and economic re-integration of drug users into the community the provision of vocational education and skill development opportunities compatible with the needs of the society should be encouraged. [ABSTRACT FROM AUTHOR]

India, located in south central Asia, has great economic dependence on agriculture. A likely impact of climate change on agricultural productivity is causing great concern to the scientists and planners in the country. Weather forecasting at different spatial and temporal scales would be significant tool for adaption in agriculture under future climate change scenario. In the present paper, detailed elaboration has been made how the stateof- art technologies of weather forecasting including the climate projections can be taken as a tool to reduce the negative impacts of climate change and climatic variability. Also for combating the climate change issues, the adaptation requires effective and timely delivery and exchange of all supporting mechanism and meteorological information. Information and Communication Technologies (ICT) can also play a great role to disseminate the information. Efforts were also made to show the active role of on-going and proposed ICTs for adaptation in agriculture to climate change. [ABSTRACT FROM AUTHOR]

This paper presents a computationally efficient functional link artificial neural network (CEFLANN) based adaptive model for financial time series prediction of leading Indian stock market indices. Financial time-series data are usually non-stationary and volatile in nature. The proposed adaptive CEFLANN based model employs the least mean square (LMS) algorithm with a new cost function to train the weights of the networks. The mean absolute percentage error (MAPE) with respect to actual stock prices is selected as the performance index to estimate the quality of prediction. The CEFLANN model inputs are chosen from the past stock prices of different market sectors along with technical indicators to determine best stock trend prediction one day ahead in time. Further to improve the performance of the CEFLANN model, weights are optimized using an adaptive differential evolution (DE) algorithm and its overall prediction performance is compared with the improved LMS algorithm showing the effectiveness of the DE in producing more accurate forecast. We have selected different combinations of important technical indicators to have a strong control on changes in stock indices. [ABSTRACT FROM AUTHOR]

BACKGROUND: HMIS will incorporate a paradigm shift in health such as removing manual records and transformation of data through the complex structure of health departments in Tamilnadu. Thus developing a model of technology acceptance in HMIS contest is important and necessary in order to promote usage of the HMIS in rural health care system.OBJECTIVE: The papers purpose is to formulate a model of technology acceptance of Health Management Information System (HMIS) by generating and validating a research model that best describes rural health care workers usage behavior and behavior intention. METHODS: This research proposes a theoretical framework which is comprised of key determinants that influence usage behavior of HMIS together with moderator. It has been tested through different parametric test and confirmatory factor analysis. RESULTS: Data analysis shows that health workers innovativeness and voluntariness have a direct and positive influence on Technology Acceptance level and that the basic TAM hypotheses are fulfilled. HMIS usage behavior and behavior intention can be increased with factors that are effecting the successful implementation of HMIS when it remains high. CONCLUSIONS: This research enables health departments to know which aspects of their HMIS components and variables to improve. This shows that HMIS usage and health workers/staffs acceptance level are key tools in the success of HMIS. This research has seemed to be done at the right time and in the right place to the best of its kind. [ABSTRACT FROM AUTHOR]

This paper presents a Wavelet Fuzzy Neural Network (WFNN) that takes the fuzzified wavelet features as inputs to Fuzzy Neural Network. This network is constructed from the fuzzy rules which are modified form of the fuzzy rules of Takagi-Sugeno fuzzy model. The number of fuzzy rules is found from the fuzzy curve approach. As the output of the model is the forecasted demand, we need a fuzzy curve corresponding to each input-output. The model can forecast hourly load with a lead time of one hour as this work is concerned with short-term electric load forecasting. Electric Load demand data and weather variables are procured from Northern Region Load Dispatch Centre and Mausam Bhawan, Delhi (India) respectively. The results of the network are compared with ANFIS and other conventional methods. The performance of the proposed Wavelet Fuzzy Neural Network is found to be superior to all others compared. [ABSTRACT FROM AUTHOR]

The paper aims to bring out the problems and prospects of the professional development opportunities of academic library professionals in the Universities in Kerala. The study is a part of research undertaken to survey the professional development activities and educational needs of library professionals in the major Universities of Kerala in the context of developments in information communication technology. The study recommends methods to improve the knowledge and skills of library professionals. The aim of the study is to evaluate the professional development activities of Library professionals and their attitude towards continuing education programmes. In order to achieve the objectives of the study a survey was conducted with the help of structured questionnaires distributed to 203 library professionals in seven major universities in Kerala, (South India) of which 185 questionnaires were returned. Results of the analysis show that majority of the professionals have pursued higher degrees in library science or IT allied courses after entering the profession, and that they have a positive attitude towards participation in training programmes and workshops. The results show that developments in ICT have a positive influence on majority of library professionals' attitude towards continuing education programmes. [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]