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Indigenous communities and their way of life are facing significant challenges due to globalisation, industrialisation and urbanisation, which have forced them to gradually adopt the architecture, lifestyle, and culture of mainstream society. This paper proposes a Smart Vernacular Architecture (SVA) framework and applies it to the indigenous dwellings of the Todas, a tribal community living in India's Nilgiri mountains. The architecture and indoor space quality of these dwellings are investigated in this paper based on specific thermal comfort parameters like the interior temperature and airflow. The indoor temperatures of Toda dwellings are simulated for the predicted climate of 2100 to evaluate their climate resilience. Finally, the paper illustrates the usage of virtual reality (VR) to explore and visualise the Toda settlement and the interior environment parameters of a Toda dwelling to effectively convey and preserve the knowledge of vernacular architecture. In a nutshell, this paper elucidates the advantages of vernacular architecture and construction methods to arouse the interest of academics, policymakers, students, and professionals in preserving architecture, culture, and indigenous knowledge. [ABSTRACT FROM AUTHOR]

Municipal solid waste generation in India is increasing as a result of rapid urbanisation and a growing population. Due to gaps in the existing waste collection, treatment and recovery methods, large-scale unscientific disposal and lesser waste recovery have been observed. Insufficient source segregation, unorganised efforts, less awareness and indifference towards malpractice, and a lack of strict implementation were identified as the causes through literature review, surveys and interviews, Therefore, there is a need for a system of interventions, which organises efforts toward waste management and reduces the tolerance for source segregation errors. This paper aims to provide a solution that increases community participation and prevents the generation of mixed waste to ensure waste recovery. The proposed framework integrates municipal solid waste, human elements, and technology into an intelligent app-based system. This includes a smart bin that segregates waste at the source through image classification and an application for communication and data collection. [ABSTRACT FROM AUTHOR]

At the heart of India's livelihood lies agriculture, a foundational and ever-evolving sector. The realm of agriculture faces modern challenges, encompassing unpredictable climate fluctuations, insufficient irrigation infrastructure, and erratic weather conditions. Amidst these challenges, machine learning has emerged as a valuable tool for evaluating soil fertility and Crop assessment in agriculture. Ensemble machine learning methodologies have garnered attention for their potential to enhance predictive capabilities. These techniques involve constructing meta-classifiers that collectively contribute to improved predictive accuracy. This paper centers on the analysis of soil data acquired from testing laboratories, to predict fertility based on a comprehensive dataset. The study employs prominent ensemble machine learning algorithms, specifically focusing on boosting techniques, to elevate predictive accuracy and ensure heightened consistency. The assessment of soil fertility categories involved the examination of 12 carefully chosen attributes. Diverse soil parameters were measured to facilitate the prediction of soil fertility levels. The outcomes of the experimentation revealed that the application of the boosting technique using the Xgboost algorithm yielded superior accuracy compared to alternative ensemble classifiers, achieving a remarkable 96% accuracy rate. [ABSTRACT FROM AUTHOR]

Recognizing specific characters from a large set of handwritten characters is called off-line character recognition. Devanagari, the reading and writing script, is widely used in a broad region of India. This paper proposes using the modified LeNet-5 architecture for recognizing Devanagari off-line handwritten characters using the Devanagari Handwritten Character Dataset (DHCD). The dataset comprises 46 distinct character classes, each with 2000 images. The proposed modified Lenet-5 Base Architecture (MLCNN8) uses more filters and kernels of small sizes with batch normalization. The proposed modified Lenet-5 Base Architecture (MLCNN8) outperformed the Lenet-5 Base Architecture (LCNN) with an accuracy of 99.21% in 53 epochs, whereas LCNN achieved an accuracy of 94.50% in 48 epochs. Hence, the proposed modified Lenet-5 architecture outperforms the Lenet-5 Base Architecture. The proposed model's results were compared with other state-of-the-art and found that it also has an edge for recognizing the Devanagari handwritten characters. [ABSTRACT FROM AUTHOR]

Mushrooms are fungi that play significant roles in ecosystems, food, and medicine. However, distinguishing toxic mushrooms from non-toxic ones is challenging, as their visual characteristics can be misleading. Deep learning, a subset of artificial intelligence, offers a promising approach to detecting and classifying mushrooms based on their toxicity. By training deep neural networks on datasets of labelled mushroom images, it becomes possible to create models capable of accurately differentiating between toxic and non-toxic mushrooms. Mushrooms are fungi that play significant roles in ecosystems, food, and medicine. However, manually distinguishing toxic mushrooms from non-toxic ones is a challenging and time-consuming task. This research work is to help the local farmers and exporters to identify the difference between the toxic and the edible mushroom which will eventually boost production and will directly have an impact on the economic growth in India. This paper compared different deep learning algorithms such as Support Vector Machine (SVM), Resnet50, YOLO V5 and Alexnet, which have been found to be the most useful in classifying the mushrooms effectively as toxic and non-toxic respectively. The patterns in the images of the mushrooms have been predicted through the above classifiers. The validation techniques show SVM to be the most effective with an accuracy of 83%. [ABSTRACT FROM AUTHOR]

Due to the cultural and traditional diversity of the Indian environment, the successful implementation of self-automated and smart architectures is very challenging. As the IoT expands the network of devices, it also increases the hazards with increasing demands. This paper aims to propose an efficient model with the combination of interdisciplinary aspects of Indian building automation and numerous factors of Indian cultural differences. The proposed fuzzy-colored Petri-nets-based ubiquitous framework utilizes the capabilities of ubiquity for the diversified culture of Indian Building Automation systems. The proposed algorithms establish the Wi-Fi component connectivity and the working of the LED pin. Further, the modules of the Wi-Fi component connectivity, set up function and get values function to illustrate the working principle of the proposed model. Lastly, the integrated system using the FCPN tools. The significance of developing the proposed model is in its usefulness, usage of maximum ideas of different modeling techniques, cultural support, and its conversion into a formal qualitative and quantitative model. [ABSTRACT FROM AUTHOR]

Challenged by complex problems and the speed of technological, social, and environmental change, the joint approach of Lean Management and the Agile Mindset has been explored in business as a management model. This paper aimed to carry out a bibliometric analysis of Lean Agile Management, pointing out trends in scientific research. To this end, an exploratory and descriptive study was developed, with a quantitative approach and based on secondary data from the Web of Science and Scopus, between 1994 and 2022. Bibliometric indicators pointed to 1808 different papers in 897 sources, with 18 citations per paper, and higher scientific productivity of U.S. (276), India (221), and UK (191), with production peaks in 2018 and 2021, suggesting the diffusion and contemporaneity of the research topic. The laws of bibliometrics were not met for this research subject, and dense networks of collaboration and co-citation among researchers were identified, mainly in four application domains, including supply chain, manufacturing, sustainability, and software development as corroborated by the word network. [ABSTRACT FROM AUTHOR]

Agriculture and its allied sector provide the livelihoods of vast rural areas in India. It also plays a vital role in the growth of developing countries like India. Rice, one of the primary components of Indian agriculture plays a significant role in the food security of the country and the world. However, it is stricken by many diseases that affect the quality and quantity of rice yield. This paper proposes a custom CNN architecture for detecting and classifying common diseases found in rice plants by reducing the number of parameters associated with the network. The proposed CNN architecture has been trained using a dataset of four types of common rice plant diseases. In addition, 1400 on-field images of a healthy rice leaf image dataset have been introduced in the paper for the detection of disease-free plants as well. Independent experiments were carried out with and without the inclusion of the healthy leaf image dataset. The performance of the proposed model is evaluated with the use of Stochastic Gradient Descent with Momentum (SGDM) and Adaptive Moment Estimation (Adam) optimization techniques using several performance matrices. Experimental results from the dataset for the classification of four rice plant diseases show that the model using SGDM optimization gives maximum accuracy of 99.66% and the model using the Adam optimization gives maximum accuracy of 99.83% on the test set in the 7th epoch respectively. However, the model with Adam optimizer performed better than the model with SGDM optimizer when the healthy leaf image dataset is included giving a maximum accuracy of 99.66% and 97.61% in the 7th epoch respectively. [ABSTRACT FROM AUTHOR]

For the very first time, on 22-March-2020 the Indian government forced the only known method at that time to prevent the outburst of the COVID-19 pandemic which was restricting the social movements, and this led to imposing lockdown for a few days which was further extended for a few months. As the impact of lockdown, the major causes of air pollution were ceased which resulted in cleaner blue skies and hence improving the air quality standards. This paper presents an analysis of air quality particulate matter (PM)2.5, PM10, Nitrogen Dioxide (NO2), and Air quality index (AQI). The analysis indicates that the PM10 AQI value drops impulsively from (40–45%), compared before the lockdown period, followed by NO2 (27–35%), Sulphur Dioxide (SO2) (2–10%), PM2.5 (35-40%), but the Ozone (O3) rises (12–25%). To regulate air quality, many steps were taken at national and regional levels, but no effective outcome was received yet. Such short-duration lockdowns are against economic growth but led to some curative effects on AQI. So, this paper concludes that even a short period lockdown can result in significant improvement in Air quality. [ABSTRACT FROM AUTHOR]

An increasing number of organizations are encouraging and accepting the research information management system in the institute level to assess the research progress of the organization and bring out a report on scholarly communication activities. Open and commercial product on the Current Research Information System (CRIS) is emerging in the last decade. The Directory of Current Research Information system listed 25 CRIS system across European countries. The European Current Research Information System (euroCRIS) support the CRIS thought the European countries through the conference, working group and pioneer in the advocacy since 2002. This paper considers the recent development in the CRIS related activities around the world and recent progress in India. This paper describes the various challenges to setting up the RIM for the higher education institutions including identification of nodal officers, collecting faculty profile information, retrieving scholarly communication activities from internal and external sources, etc. Also, discuss the training programme to the nodal officer level and institute level to promote the academic identities and their importance. The paper also discusses the impact of the IRINS in the higher education system in India and its use case with different stakeholders such as students, scholars, faculty members, scientists, research administrators, funding agencies and policymakers, etc. [ABSTRACT FROM AUTHOR]

Municipal solid waste generation in India is increasing as a result of rapid urbanisation and a growing population. Due to gaps in the existing waste collection, treatment and recovery methods, large-scale unscientific disposal and lesser waste recovery have been observed. Insufficient source segregation, unorganised efforts, less awareness and indifference towards malpractice, and a lack of strict implementation were identified as the causes through literature review, surveys and interviews, Therefore, there is a need for a system of interventions, which organises efforts toward waste management and reduces the tolerance for source segregation errors. This paper aims to provide a solution that increases community participation and prevents the generation of mixed waste to ensure waste recovery. The proposed framework integrates municipal solid waste, human elements, and technology into an intelligent app-based system. This includes a smart bin that segregates waste at the source through image classification and an application for communication and data collection. [ABSTRACT FROM AUTHOR]

The paper revisits the Porter-Wagner dilemma about the association between environmental management (EM) and firm performance (FP). The context of informal Micro, Small and Medium Enterprises (MSMEs) provides an interesting case since they are away from regulatory scrutiny or corporate/social obligations. The study resorts to labor productivity as the measure of FP and EM is represented by the various solid waste management practices (WMPs) adopted by the informal enterprises. Using the latest available cross-sectional data on un-incorporated firms by National Sample Survey Office (NSSO) 73rd round 2015-16, we found the positive impact of WMPs was higher in the case of more productive firms in the quantile, indicating the relevance of the Porter-Wagner dilemma. The direction of causality was further validated using the propensity score matching technique by decomposing the average treatment effect. [ABSTRACT FROM AUTHOR]

It is known that social media is one of the largest sources of unstructured data. Analyzing that data and harvesting meaning out of that is a tedious job. Recently opinion mining has become an emerging topic due to the vast amount of opinioned data available on the various social networking sites. Microblogging has appeared relatively recently, and twitter is the most popular microblogging sites used by the people. It is one of the biggest free, open data-source. In the world today, twitter often witnesses a lot of opinions. Opinion mining and sentiment analysis help researchers to gain insight into public emotions. In this paper, Twitter is used as a source of opinioned data. Twitter APIs are used for the collection of tweets. In this paper, R is used for the acquisition, pre- processing, analyzing the tweets, then sentiment analysis is performed based on the different approaches. In this paper, Tweets were collected from the period of Jan 2019 to March 2019. Using that tweets, sentiment analysis was performed to gain the opinion polarity of the folks concerning general elections held in India. Two candidates were considered for this study: Candidate-1 and Candidate-2. It was concluded that Candidate-1 is more liked and is famous as compared to Candidate-2. The result obtained in our paper was in full compliance with the actual election results obtained in May 2019. [ABSTRACT FROM AUTHOR]

Background, Motivation, and Objective – Inculcating critical thinking, teamwork, and lifelong learning are the basic tenets of Outcome Based Engineering Education (OBEE). Collaborative learning is one such pedagogy of engagement to achieve the same. In addition, the implementation of OBEE demands the adoption of corporate best practices in deploying new initiatives, which streamlines the process, removes waste in the process, and ensures sustainment of the improved process. Lean Thinking is one such approach adopted by the industry to ensure productivity, performance, and sustainment of new initiatives. The objective of this paper is to study the scope of collaborative learning in OBEE based on the Lean Thinking approach, and to explore its successful deployment in Indian higher education. Statement of Contribution/Methods – The research reported in this paper is based on Action Research methodology carried out using the Lean Thinking PDCA (Plan-Do-Check-Act) approach and its application in improving OBEE through collaborative learning in India. STAD (Student-Teams-Achievement-Division) collaborative learning was deployed through heterogeneous group formation for enhancing the teaching-learning process. The Indo-Universal Collaboration on Engineering Education (IUCEE) consortium knowledge base and assistance has been effectively used to chalk out the strategy for effective deployment. Basic statistics and outlier analysis is used to draw the inferences. Results, Discussion, and Conclusions – The study describes the effective deployment of collaborative learning to enhance the effectiveness of the OBEE ecosystem through students' performance by utilizing the power of Lean Thinking. The study shows that not only Turn-Around-Time (TAT) of the deployment process is enhanced, but also the attainment of the students' performance. Moreover, it improves team spirit and team performance during the process. The paper is based on a single case study executed in an engineering college and hence, there is limitation in generalizing the specific results from the study. But the approach adopted and the learning from this study can be generalized. The study is of use to academicians, researchers, practitioners, and policymakers seeking to implement collaborative learning for an OBEE set- up with optimal utilization of the available resources based on the Lean Thinking platform in their organizations. This approach ensures value-added activity throughout the process and effective utilization of industrial best practices to higher education. Although collaborative learning and Lean Thinking have been extremely successful in the education sector and the corporate sector, respectively, the application of both to the engineering education has been limited. In addition, it describes the end-to-end implementation strategy of OBEE starting from planning, execution, meticulous assessment, and sustainment of the improved process in a step-by-step approach. This study illustrates how its application can improve the performance of the process and thus, productivity in the course. [ABSTRACT FROM AUTHOR]

In India, around 1.5 million engineers graduate every year from various colleges and universities but 80 percent of them do not have the skills required to get employed. If they do, only 3% of them have the skills. Some of the reasons for this are student's attitude towards learning and teacher's attitude towards teaching. This leads to problems such as lack of student's attendance, lack of student's attention in the classroom, high failure rates, marks conflict between teacher and students, students unable to excel in the placement interviews, etc. However, it is the responsibility of the colleges/institutions to provide necessary tools/trainings to the teacher and the students so that the above problems could be solved without compromising the quality of the education. Therefore, the main objective of this paper is to develop a teaching-learning framework which addresses the above problems. In the teaching-learning framework, several teaching pedagogies are introduced. In this paper, 16 different teaching pedagogies are used in various classrooms of undergraduate and graduate programs. The teacher should use different teaching-learning methodology (not same methodology) in a classroom to enable the maximum learning. Based on the past seven years (2011-2018) of handling undergraduate (2135 students) and graduate (340 students) in a total of 60 courses data, five teaching-learning methodologies are proposed. Some of the outcomes of this study are: (a) improved the classroom attendance by 14%; (b) reduced the course failure rate to 2.02%; (c) improved the industry-academia relationship by getting more student projects (90%); (d) twelve journal papers published along with students, etc. [ABSTRACT FROM AUTHOR]

In the past few decades, many urban areas around the world have suffered from severe air pollution and the health hazards that come with it, making gathering real-time air quality and air quality forecasting very important to take preventive and corrective measures. This paper proposes a scalable architecture to monitor and gather real-time air pollutant concentration data from various places and to use this data to forecast future air pollutant concentrations. Two sources are used to collect air quality data. The first being a wireless sensor network that gathers and sends pollutant concentrations to a server, with its sensor nodes deployed in various locations in Bengaluru city in South India. The second source is the real-time air quality data gathered and made available by the Government of India as a part of its Open Data initiative. Both sources provide average concentrations of various air pollutants on an hourly basis. Due to its proven track record of success with time-series data, a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) model was chosen to perform the task of air quality forecasting. This paper critically analyses the performance of the model in two regions that exhibit a significant difference in temporal variations in air quality. As these variations increase, the model suffers performance degradation necessitating adaptive modelling. [ABSTRACT FROM AUTHOR]

Agriculture is the major occupation and the main origin of income in India, and most of the population depends on agriculture for their living. The smart farming system's foremost goal is to monitor farmlands by automatically rinsing the lands with appropriate water and a pest detection framework. The conventional agricultural methods lack higher crop productivity with increased human efforts. Hence, the paper proposes an IoT-based solution for smart farming. The proposed system focuses on automatic irrigation of water and plant disease detection. It uses machine learning algorithms to accurately predict adequate water required by the fields and automatic pest identification based on the requirements of the farmland. K-Nearest Neighbour and support vector machine learning algorithms in the pest detection module are used to precisely predict plant diseases. The convenient features from the leaves of the plants were obtained. Then the obtained features are used for classification purposes. Extracting the suitable features and classifying them helps detect whether the plant is pest infected or not. The proposed system monitors, analyses, assesses and controls agricultural fields for the automatic irrigation of the water and disease identification of the plants. The numerical analysis of the machine learning algorithms and the significance of the classification with accuracy are investigated. From the numerical results, it is demonstrated that an accuracy of 84 % is achieved. [ABSTRACT FROM AUTHOR]

Crop disease in the plant is a significant issue in the agriculture sector, and it is currently very difficult to detect these illnesses in crop leaves. The foundation of the global economy is agriculture. India ranks second in the production of tomatoes worldwide. The tomato crop is affected by various diseases which lead to a reduction in product quality and quantity. The advancement in computer vision and deep learning opens up the door for predicting diseases that appear in the crops. The aim of this paper is classification among 10 different categories of tomato plant leaves using the proposed novel TomConv model which deploys an improved Convolutional Neural Network (CNN). For this purpose, the publicly available dataset called PlantVillage comprising of more than 16000 images of tomato leaves, both diseased and healthy was used for the experimentation purpose. The proposed model is the simplest model among all the available state-of-the-art models. The tomato leaf images were preprocessed for reducing the size in 150 × 150 dimension. The model constitutes four layered CNN followed by a max pooling layer. The model splits the corpus into training and validation datasets in 80:20 ratio, is trained under 105 epochs for tomato leaf images, and achieved an accuracy of 98.19%. The proposed model is compared with existing models under different parameters such as no. of classes, no. of layers, and accuracy. The results are promising as they outperform all the available state-of-the-art models. [ABSTRACT FROM AUTHOR]

Agricultural sector is one of the main sectors in India. Productive growth and high yield production of fruits is important and required for the agricultural industry. Application of computer vision and image processing has aid agriculture to boost yield rating, disease spotting, irrigation, fruit classification and maturity grading. Computer vision and image processing approach can be used to lower the time utilization and has made it economical. A convolutional neural network (CNN) is a type of artificial neural network used in image identification and processing that is mainly planned to process pixel data. This paper utilized a convolutional neural network (CNN) to classify bananas into raw or ripe without requiring labour. Bananas were classified by their colour appearance in the CNN classification and not only this it can be also used to classify many other types of fruits and vegetables. [ABSTRACT FROM AUTHOR]

Agriculture is a significant contributor to India's economic growth. The rising population of country and constantly changing climatic conditions have an impact on crop production and food security. A variety of factors influence crop selection, including market price, production rate, soil type, rainfall, temperature, government policies, etc. Many changes are required in the agricultural sector in order to enhance the Indian economy. In this research work authors have implemented various machine learning techniques to estimate the crop yield in Rajasthan state of India on five identified crops. The results indicate that among all the applied algorithms; Random Forest, SVM, Gradient Descent, long short-term memory, and Lasso regression techniques; the random forest performed better than others with 0.963 R2, 0.035 RMSE, and 0.0251 MAE. The results were validated using R2, root mean squared error, and the mean absolute error to cross-validation techniques. This paper intends to put the crop selection method into practice to help farmers solve crop yield problems. [ABSTRACT FROM AUTHOR]

Good health and well being is one of the most essential targets of the Sustainable Development Goals (SDGs). This paper primarily focuses on Preventive and Diagnostic care of Women Health because even today, women are disadvantaged by discrimination in many societies especially in rural sectors. Two main health issues, fetal abnormality in pregnant women and cervical cancer in women are analyzed so that the doctors and patients can be given early signals to take proactive measures. As per National Health Portal of India, around 1.7 million birth defects occur in India every year. So Antenatal Care(ANC) should be given utmost importance during Pregnancy in a woman's life. Cervical cancer is another issue prevalent amongst women, especialy over the age of 30. It's critical to catch it early and eliminate any risks that come with it. Here arises the need to develop a system that can analyze and predict the aforementioned anomalies at an early stage. This work proposes approximate query processing using different dynamic machine learning algorithms to analyze and predict the abnormalities. Further, a web application is built to facilitate the stakeholders, especially doctors, to interact with the system and iteratively query the system to understand the relationships amongst the various data variables and get appropriate predictions about fetal anomaly from CTG scans as well as presence of cervical cancer from various demographic information, habits, and historic medical records. Approximate Query Processing is accomplished using Correlation analysis, linear, and logistic regression algorithms in a dynamic, interactive, and iterative manner. [ABSTRACT FROM AUTHOR]

China and India are two major contributors to the growth of global energy demand. As energy is one central part of the geopolitics, the energy security for both countries get complicated by the political environment. The paper investigates the positions of China and India in the trading relationships with different regions of the world. The paper also examines recent events that affect the energy security of China and India. The conclusion is that during the past decade, China and India have diversified sources of energy, especially from the Middle East, though China demonstrates a higher level of diversification and occupies a less passive position in its trading relationships. Shale revolution, the US energy sanctions on Iran and Venezuela and the US-Chinese trade conflicts, can bring China, India and Russia closer to each other due to the shrinking market influence of Russia in Europe and the needs of India and China to further diversify import sources so that energy supplies will not be disrupted by political chaos. [ABSTRACT FROM AUTHOR]

This paper focuses on the behaviour of pedestrians when they cross at signalized intersection under mixed traffic situations. Based on a field study at a typical signalized intersection in the commercial hub in the city of Kolkata, the paper describes various characteristics of pedestrian movements. Several attributes like width of the road, age and gender of the pedestrian and if they are carrying any baggage were considered at the time of performing such analysis. The age and gender of pedestrians were found to have an impact on their speeds; however, children were observed to walk faster since they are accompanied by their parents in most occasions. The speed of panic-stricken pedestrians who prefer to run on the zebra marking while crossing the road at intersection was observed to be quite high. Interestingly, the effect of carrying baggage on walking speeds was found to be insignificant at the study site. Accordingly, the current study made an attempt to further investigate the fact by conducting an informal opinion poll. The poll of about 50 road users revealed that since most of them walk towards their office or business centre, they usually carry lighter baggage and quite often they are compelled to walk fast. Further, at crosswalk, speed does not vary to a considerable extent with the increase of flow; this attributes to the absence of confined flow. The observed flow parameters were plotted and the scattered diagrams indicate widespread data points that largely follow the Greenberg’s logarithmic model. [ABSTRACT FROM AUTHOR]

This paper presents a detailed approach for an efficient blood bank database management system. The database is the single most useful setting for caching data, and it is also an ideal tool for contriving, managing, updating, and modifying data from different angles. The benefits of a well-structured blood bank database are limitless and yield the benefits of improving efficiency and saving time. Here, our motive is centred on this area. India faces a shortage when it comes to the amount of blood donated. The gap in demand and supply in widened due to mismanagement and inefficient databases. We have modelled a well-organized database to try and reduce this gap. Alongside, we have developed an application that reminds donors when they become eligible again, gives locations of nearby blood donation camps, makes requesting blood easier for blood recipients etc. as well as promoting a healthy community. IOT is used for interlinking the application to the server as well as for inter-application communication. With the help of IOT this collection and exchange of data becomes more efficient. [ABSTRACT FROM AUTHOR]

It has been observed that there is asymmetry in the approach of Indian banks when it comes to reacting to interest rate signals given by the Central Bank. While they are quick to react to interest rate increases, they drag their feet when it comes to reacting to interest rate cuts. More often banks pass only a small part of the interest rate cuts. This paper attempts to understand the reasons behind such an asymmetric behaviour. The paper identifies two reasons in the case of Indian commercial banks which are leading to such behaviour. The first reason identified is the oligarchic structure of the Indian banking system where there is a dominance of the public sector banks. The second reason identified in this paper is the pro cyclical behaviour of the Indian public sector banks which has given them an asset-liability profile where their flexibility to react to interest rate signals got severely compromised. [ABSTRACT FROM AUTHOR]

In rapidly growing urban slums in India, with little structured urban planning and limited infrastructural facilities, the number of people living without access to basic water and sanitation services is increasing. Tragically in India, a growing slum population and lack of adequate sanitation makes over 50 million men and women to defecate in the open every day. This research paper sought to measure attitudes and behaviour towards toilet construction and its utilization by the poor people in urban areas in India. The survey collected primary data through questionnaire and analysed by cross tabulation, decision tree using chi-Square automatic interaction detector, multiple classification analysis, and logistic regression. Findings guided the design of a mass media communication campaign to impact toilet construction, its increased uptake, universal and consistent use of safe sanitation among poor urban communities, to reduce open defecation in urban areas. Results of the study demonstrate, principally, that while both women, and men, use toilets more during the rainy season, the drop-outs among men in the summer is greater than for women. The paper also documents new evidence on the pathways to decision-making regarding toilet use providing key insights for Behaviour Change Communication (BCC) campaign architects. It also presents evidence on how non-users behave with regard to their usage of community toilets and construction of toilets. [ABSTRACT FROM AUTHOR]

Indian banking is witnessing a dynamic phase due to the continuous change in polices of RBI and the government of India. In 2015, RBI issued licenses for the first time to 11 entities to start payment banksin order to improve banking penetration. Of these 11, three entities have started their payment banks and are trying to woo customers through technology driven services. Riding on technology to achieve competitive advantage, these banks will aid to make banking more convenient for the customers. The paper focuses on assessing customers’ preference in using services of payment banks over universal banks by using bivariate correlation analysis. The paper talks about the services that the customers prefer from the payment banks. It also captures how convenience/ease is helping payment banks to expand customer base. The role of educational background, occupation and age group as deciding factors for customers in choosing payment banks is studied in this paper. The paper further studies the effect of reference groups on the customer’s decision to choose a bank. [ABSTRACT FROM AUTHOR]

Social media plays a major role in the propagation of information during disasters. This paper mainly contains a study regarding how people of Chennai used social media especially twitter, in response to the country’s worst flood that had occurred recently. The tweets collected were analysed using machine learning algorithms such as Random Forests, Naive Bayes and Decision Tree. By comparing the performances of all the three it was found that Random Forests is the best algorithm that can be relied on, during a disaster. This paper also targeted the sources of the Twitter messages to explore the most influential users of Chennai flood. [ABSTRACT FROM AUTHOR]

Background, Motivation and Objective With regard to engineering, there have been serious concerns about the employability of Indian graduates. There are alarming statistics in a NASSCOM report, which estimates that, of the 3 million joining the IT workforce, only twenty five percent of graduates with engineering background are employable. The figures are grave in the context of graduates from sciences and humanities, which is less than fifteen percent. Aspiring Minds has been administering a computer-based test called AMCAT to lakhs of students in 650+ engineering institutions measure employability of technical graduates. This considers parameters like Business Communication & English, Logical & Numerical skills, analytical & problem-resolution skills and coding. The results are a revelation, 47% of graduates cannot be employed in any domain or sector of the knowledge economy. 17.91%, 3.67% and 40.57% are the employability figures for software services, software products and BPO. Only 3.84% graduates are start-up ready and 6.56% are design job ready and the same trend for other core engineering jobs. There is an urgent need to improve employability of our engineering graduates. This calls for lateral thinking and out-of-the-box initiatives such as experiential learning programs and competitive programming; implementations of which we explore in a top-ranking private university as a case study. Statement of Contribution/Methods The case study of implementation of initiatives in experiential learning programs and competitive programming in a private university is highlighted. An Experiential learning program titled Live-in-Labs as part of the curriculum is explored. This program is student-centric, learner-centric, participatory and hands-on and they provide students an avenue to apply their acquired engineering knowledge, concepts and skills and deploy on a real-time basis in India's villages. It's Course Outcomes (CO) such as human-centered design concepts to document observations and user experiences, user-needs assessment and prioritization are enlisted. A very strong mapping to several Program Outcomes (PO) is observed unlike various regular courses in curriculum. Structured competitive programming initiative in which students compete with others in a contest environment in parameters such as program correctness, execution time, and development time is yet another effort towards student-centered learning. Platforms such as CodeChef, HackerRank and contests such International Collegiate Programming Contest (ICPC), which is considered as the Olympics of Collegiate Programming with annual participation of 50,000 students in 2000+ universities in 100+ countries are efforts in this direction. Competitive Programming initiative's learning objectives also spans several POs. Results, Discussion and Conclusions. Strong mapping of COs to POs inherent in the experiential learning programs improves the employability as also the progression and prospects of the students. Live-in-Labs® program exposes students to pressing issues confronted by village communities in India, through experiential learning opportunities, in order to apply theoretical concepts into application & deployment, by the devising of innovative technology remedies, and facilitation of crucial and collaborative problem-resolution capabilities of the students participating in the program. The participation of several students from foreign universities also enriches learning, collaboration and diversity. Every Live-in-Labs project results in a student paper published in reputed journals and conferences. It also improves student progression in terms of higher studies and high-paying jobs. Competitive programming dramatically improves student skills and capabilities in problem solving, coding, team work, innovation and creativity. It is also observed that code geeks from competitive programming initiatives are invariably the ones to secure the highest paying jobs in dream companies like Google, Amazon and Facebook. [ABSTRACT FROM AUTHOR]

Outcome Based Education is one of the most significant developments in Indian engineering education in recent years. Outcome based approach is a shift of teaching learning paradigm from teacher centric to student centric. Outcome based education has been practiced worldwide since nineteen nineties, In India adoption of OBE was a result of signing Washington Accord. The National Board of Accreditation (NBA), apex body for monitoring quality of engineering education in India has made it mandatory as a part of accreditation. The positive results of implementing OBE in engineering education are inevitable but whether the faculty members are prepared to implement the strategy is the question to be answered. This paper discusses the impact assessment OBE in the Indian engineering education. The study is carried out through a survey. The observations are encouraging, the concept of OBE in terms of curriculum design, delivery, assessment, evaluation and reflection is penetrating in the Engineering education in India but still trainings in certain aspects are required. [ABSTRACT FROM AUTHOR]

The primary purpose of this work is to study the impact of motivational factors on the engineering educational result of students. Much of the current findings indicate that the major reason for poor time management is due to the lack of interest in the topic of study which in turn leads to procrastination. Following the several exemplary works of education specialists outside the Indian subcontinent, this paper attempts to study the motivational factors such as rewards, acknowledgment and supplemental resources against procrastination of engineering students in India. A hypothesis was constructed and tested in a reputed engineering university in South India. A survey was conducted at four notable engineering institutions in South India and the obtained result shows that 22% required a practical study approach for motivation, 18% wanted collaborative work and approximately 49% suggested the need for recognition in order to overcome procrastination. In conclusion, providing sufficient recognition was found to be one of the biggest motivational factors to reduce procrastination among engineering students and increase their interest in routine and technical studies. [ABSTRACT FROM AUTHOR]

Since last five years the demand for post graduate (PG) programs in Indian scenario are decreasing continuously year by year due to many reasons. The percentage of students opting for higher education, especially in Engineering domain hasn't increased from 2015-16 onwards. The expectations from today's graduates in engineering disciplines from the post graduate programs are high and much different from those of a decade ago. They expect a quality education from the institutes offering PG program with equal opportunities to interact with industries during their two years journey. Many PG programs in India have become obsolete in offering and delivering the post graduate program. These institutes have to reject the traditional approach of engineering education and have a paradigm shift to transform them in terms of delivering and improving the quality of teaching-learning process through adapting industry based curriculum design by fostering industry-institute collaborations. An attempt has been made to build a specific industry based curriculum model by introducing PLM and ERP (industry based) verticals in PG program by proper industry-institute collaborations at School of Mechanical Engineering. Two year PG program curriculum has been developed in consultation with leading PLM (Product Life Cycle Management) and ERP (Enterprise Resource Planning) companies. As an example for industry based curriculum design, the PLM course design has been considered in the paper. The program outcomes 3 and 5 (as per post graduate NBA norms) are attained through the design, development, delivery and assessment of PLM and related courses. The assessments in both theory and laboratory course are jointly done by teaching faculty and industry professional through appropriate assessment rubrics. Overall, adapting industry based curriculum model and frequent industry institute interactions are found to be beneficiary for students, faculties and industry professional. [ABSTRACT FROM AUTHOR]

The developments at various fields of technologies are due to the contribution of both industry and academia of the country and India is playing a major role in the rapid change of technology. The current problems of the society can be meet by the industry and institution by working together for the new creations. Producing skilled, globally competent professionals through quality technical education and to prepare them for immediate employment should be goal of any technical institute. To produce capable graduates ready for the industry, it is the need to know the requirements of the industries through a sustainable model industry-institute interaction and engraving best practices to arrive with desired outcome. Hence, a better and vibrant mechanism with strategic action to bridge gap is absolute need. To build good bond between both of them, to maintain trust between two pivots can be defined through a formal and most common process of entering to MoU between institutes with the industries is adopted by most of the organizations across the globe. This research paper describes the several types of activities in context to interactions between an industry and institute maintained in BVM addressing to present global perspective. The various Ingenuities& Approaches, types of exchanges of academic institute for industry are discussed in details. Impact of III has resulted in more innovation and more placement. Industry and Institute together playing a vital role in the development of the nation. [ABSTRACT FROM AUTHOR]

Telemedicine in India is expanding and thus it is essential for application to reduce storage and transmission bandwidth. The prime objective in this paper is to demonstrate an investigative protocol for medical image compression in telemedicine. The method is based on Integer Wavelet Transform (IWT) and Huffman's Coding for image compression with an added novelty of machine learning terminology for extracting the region of image based on pixel density and optimized layering for neighboring pixel ratio analysis. The experimental results are evaluated and demonstrated on open medical datasets and has acquired minimal predictive error, improvised Signal to Noise Ratio (SNR) with respect to Compression Ratio (CR) ratio and optimized bandwidth under transmission. [ABSTRACT FROM AUTHOR]

Corn is one of the most popular food grains in the India and crop loss due to diseases substantially affects the Indian economy and threatens the food availability. Recent access of smart devices can be utilized to provide automatic diagnosis of corn diseases and prevent severe crop losses. This paper presents a real time method based on deep convolutional neural network for corn leaf disease recognition. Deep neural network performance is improved by tuning the hyper-parameters and adjusting the pooling combinations on a system with GPU. Further, the number of parameters of the developed model is optimized to make it suitable for real time inference. The pre-trained deep CNN model was deployed onto raspberry pi 3 using Intel Movidius Neural Compute Stick consisting dedicated CNN hardware blocks. During the recognition of corn leaf diseases, the deep learning model achieves an accuracy of 88.46% demonstrating the feasibility of this method. The presented corn plant disease recognition model is capable of running on standalone smart devices like raspberry-pi or smart-phone and drones. [ABSTRACT FROM AUTHOR]

Drastic increases in oil prices and need to reduce global warming are issues that have raised huge public awareness, which in turn encouraged many developed and developing countries to adopt new energy policies. Renewable energy resources are gaining fast attention in solving power supply problems in almost every area to shield the environment. In solving power supply problem standalone microgrid systems including renewable energy sources plays a key role. For a stand-alone renewable power delivery system, battery is the crucial component which allows storing excess renewable energy and further using the energy as and when needed by the load. In order to achieve the best performance and utilization the state of charge of the batteries should be assessed. This paper presents heuristic approaches to evaluate the state of charge (SOC) of lead–acid battery through optimal power generation in a standalone hybrid wind-solar renewable power delivery system. The mathematical analysis is made by the application of modified heuristic techniques, i.e. tuned genetic algorithm (TGA) and improved colliding body optimization(ICBO), for a specific loading condition in India. More optimized power is generated by the renewable distributed energy sources when evaluated by ICBO than that achieved by TGA, for both winter and summer load demands. These optimized value of power generated by the application of ICBO, resulted in low average value of SOC. The low values of SOC further prevent the battery from malfunctioning for a longer period of time. In order, to explore more optimal generation pertaining to good value of SOC parametric studies of modified algorithms, i.e. TGA and ICBO have been carried out. A set of critical operational parameters have also been obtained through parametric study of TGO and ICBO for a specific real time load profiles. Thus the reliable and optimal values lead to a better prospect in utilizing standalone renewable power delivery system for Indian future power scenario. Moreover, a comparative study has also been performed in analysing the performances of conventional and non-conventional methods in this regard. It has been found that the heuristic techniques, viz ICBO and TGA delivered 13% and 17% better result in comparison to that obtained by linear programming method respectively. Thus it can be surmise that the heuristic techniques perform well in considering the uncertainties and approximations of real world problem of generation renewable power, pertaining to better values of SOC. [ABSTRACT FROM AUTHOR]

The aim of this paper is to develop an intelligent cyber-physical system for the waste management in a locality which will not only keep records of the use of the trash bins in the but also ensure they have an additional blockchain based verification system which will ensure the proper working even in the presence of an adversary. The proposed system is an ensemble of the database server, blockchain server, embedded system based clients and an android app. This is inspired by the recent global trend in smartphone popularity which has piqued interest in the above mentioned division. The advent of smartphone popularity helps us to create an ideal system for a plethora of reasons such as processing capability and native storage etc. Furthermore, blockchain technology, introduced by Satoshi Nakamoto in 2009 ensures decentralization and security of transactions due to its use of cryptographic hash functions and public key cryptographic system. To further excite the situation, India recently revised its solid waste management policy in 2016 after 16 long years. All of the above factors together pointed to creation of a smart waste management system to prevent further exacerbation of India's existing waste management crisis. [ABSTRACT FROM AUTHOR]

The increasing coverage of Internet has created opportunities and advantages for different aspects of society. However, there come new threats and challenges to information security. One of the typical types of attacks that has increasingly occurred is the APT attack (Advanced Persistent Threat). APT is dangerous with clear purposes. APT attacks employ different sophisticated methods and techniques attacking targets in order to steal confidential and sensitive information. In the past, hackers attacked information systems with personal and financial motives. However, there are nowadays other motives such as political ones and they are potentially backed by governments or nations. Nations that own advanced technologies such as United States, India, Russia, UK are also suffering from special purpose attacks. APT is an advanced type of attacks that consists of many stages and concrete strategies. Besides, techniques and technologies employed in APT attack are usually new and developed by hackers in order to break through the monitoring of security software. However, APT is normally implemented through concrete steps and stages. If one of the steps or stages fails, the entire APT attack will fail. This paper presents a method of detecting APT attacks based on monitoring accesses to unknown domains. This detection method results into high effectiveness in the initial stage of APT attacks. [ABSTRACT FROM AUTHOR]

In the present era, there are too many developments in precision agriculture for increasing the crop productivity. Especially, in the developing countries like India, over 70% of the rural people depends upon the agriculture fields. The agriculture fields faces dramatic losses due to the diseases. These diseases came from the pests and insets, which reduces the productivity of the crops. Pesticides and fertilizers are used to kill the insects and pests in order to enhance the crop quality. The WHO (World Health Organization) estimated as one million cases of ill effected, when spraying the pesticides in the crop filed manually. The Unmanned aerial vehicle (UAV) – aircrafts are used to spray the pesticides to avoid the health problems of humans when they spray manually. UAVs can be used easily, where the equipment and labors difficulty to operate. This paper reviews briefly the implementation of UAVs for crop monitoring and pesticide spraying. [ABSTRACT FROM AUTHOR]

The rapid industrial and economic development in India leads to high level of pollution in environment. Due to increased level of aerosols oceans are warming. This paper highlights on aerosols effect on sea surface wind and sea surface temperature by using remote sensing data. The windplays important role in Aerosol Optical Depth (AOD) and radiative forcing and is analyzed using National Centers for Environmental Prediction (NCEP) monthly wind data. The radiative forcing observed is much higher and up to 60% of total AOD during summer monsoon. SeaSurface Temperature (SST) is another important parameter in ocean atmosphere system and a key variable in coupling the atmosphere and ocean. The SST has changed during the change of atmospheric pattern and it plays an important role in aerosols mechanism. The study observed that during summer monsoon SST over the South Eastern Tropical Indian Ocean (SETIO) much higher than Western Tropical Indian Ocean (WTIO) and it plays important role in aerosols mechanism. [ABSTRACT FROM AUTHOR]

The enormity of air pollution has always been a matter of concern due to rapid development and urbanization over a long period. The increasing level of pollutants in ambient air in 2016-2017 has deteriorated the air quality of Delhi at an alarming rate. This brought us to focus our study on air quality in Delhi region. The prediction of future air quality has been carried out by analyzing the pollutants using data analysis techniques. In our previous study, we had analyzed the data from 2011-2015. Detailed analysis from 2009-2017 of air pollutants has been proposed in this extended paper along with the critical observation of 2016-2017 air pollutants trend in Delhi. Descriptive analysis and predictive analysis have been used to study the trends of various air pollutants like sulphur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), suspended particulate matter (PM), ozone (O 3 ) carbon monoxide (CO), benzene, and forecast the future trend. We have observed through data analytics techniques that SO 2 is likely to increase by 1.24ug/m 3 , NO 2 is likely to increase by 16.77ug/m 3 , O3 is likely to increase by 6.11 mg/m 3 , benzene is likely to reduce by 1.33 mg/m 3 and NO 2 is predicted to reduce by 0.169 mg/m 3 in the coming years. [ABSTRACT FROM AUTHOR]

This paper highlights the role of social media in the tourism industry. The conventional models used to reach potential tourists are having geographical and content related constraints. Social media provides various platforms to different stakeholders to communicate and exchange information among them. Due to its dynamics and viral capability, tourism businesses need to formulate some guideline or framework before start using Social Media. This study includes two-way methodological approach; primary and secondary as well. The primary study involves the in-depth interviews with the key stakeholders, and secondary part is based on the existing literature and portal of Department of Tourism (DoT), Rajasthan. Findings facilitate various challenges of social media use such as legal and administrative compliance faced by usual providers of tourist-related information. It further provides a better overview of tourist’s use of social media and their characteristics, in order to acclimatize online marketing strategies by Rajasthan tourism. [ABSTRACT FROM AUTHOR]

India is an agriculture based economy and sugarcane is one of the major crops produced in northern India. Productivity of sugarcane decreases due to inappropriate soil conditions and infections caused by various types of diseases; timely and accurate disease diagnosis, plays an important role towards optimizing crop yield. This paper presents a system model for monitoring of sugarcane crop, the proposed model continuously monitors parameters (temperature, humidity and moisture) responsible for healthy growth of the crop in addition KNN clustering along with SVM classifier is utilized for infection identification if any through images obtained at regular intervals. The data has been transmitted through a wireless network from the site to the control unit. Model achieves an accuracy of 96%. on a sample of 200 images; the model was tested at Lolai, near Malhaur, Gomti Nagar Extension. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to identify and prioritize the factors which influence the whistle blowing intentions of teachers working in private higher education sector in India. The prioritization of the factors is done by using Analytic Hierarchy Process (AHP) to determine their relative importance. 13 influencing factors of whistle blowing are considered in the study, which are categorized under 4 main factors viz. whistleblower’s demographic characteristics; whistleblower’s personality variables; contextual variables and wrongdoing characteristics. The findings of the study suggest that wrongdoing characteristics and contextual variables are the most important factors which may influence the whistle blowing intentions of teachers working in private higher education sector in India. [ABSTRACT FROM AUTHOR]

This paper examines the reasons for farmer suicides in India. Inability to get the right price, crop failures, and insurmountable debt are the factors that may drive the farmers to take this extreme step. A key factor for farmers being unable to get market prices is inefficient agriculture supply chain management. We find that the reasons for inefficient supply chain management include lack of reforms in the Agricultural Produce Market Committee (APMC) Act, low bargaining power due to small farm size, and lack of warehousing facilities. Crop failures happen because of poor irrigation facilities. Considering agricultural output and rainfall data from four different states in India we find evidence in favor of association between the cyclical component of agricultural output and rainfall data. Understanding this linkage is important from the perspective of formulating demand management policies (read, intervention by the government and central bank). [ABSTRACT FROM AUTHOR]

Finance has always been considered as one of the important parameter of the growth for any economic activity. The economic growth of a country can be enlarged by using financial inclusion as a bridge that would facilitate fuller participation by weaker sections of the country. Financial inclusion is a system through which a developing country like India can attain inclusive growth by connecting the contribution of weaker or rural population of the country with the main stream. If there is an easy access of weaker section to the financial system of the country, country can move towards higher economic growth. In the available literature, it has been found that one or the other indicator is absent for different reasons. However, since each of the indicator is important for calculating the more comprehensive financial inclusion index. The main objective of this paper is to introduce an index that allows for a general overview of India in terms of financial inclusion. The index is calculated using three dimensions related to the measurement of the levels of access and usage of financial services. [ABSTRACT FROM AUTHOR]

Malnutrition is the leading causes of infant mortality among the developing countries including India. This study designs a prediction model for malnutrition based on machine learning approach, using the available features in the Indian Demographic and Health Survey (IDHS) dataset and comparing that with the literature identified features. Our findings suggest that machine learning approach identifies some important features not identified in extant literature. Subsequently, logistic regression was carried out to identify the probabilities of these features in explaining malnutrition. The paper contributes in exploring the possibilities of using artificial intelligence in identifying probable correlates of malnutrition. [ABSTRACT FROM AUTHOR]

The rise of autonomous vehicles (AV) will revolutionize the transportation industry and it will transform the society itself, much like the nineteenth century shift from horse-carriages to automobiles. The automobile industry as well as governments/authorities all around the world are trying to create vehicles and road traffic infrastructures to support this futuristic public/private transportation technology. In our paper we are putting forward a feasible method of implementing the autonomous vehicular technology in a developing country like India as a public transport system in itself or as a feeder system to public transport systems like metro by using the concepts of safe state and kill switch. [ABSTRACT FROM AUTHOR]

How are citizens experiencing the increase in Air pollution? How can the Social media data be explored and used to develop deeper insights to study citizen’s observations, opinions and experiences? In current research paper, by utilizing micro-blog social media application – Twitter data, these questions are examined. A suitable framework, He a l th P ractice - “ HELP ”, is emerged based on computational analysis and visualizations of the content of the Tweets. [ABSTRACT FROM AUTHOR]

Agriculture sector plays a key role in the economic development of India. The task of fruit grading is vital in the agricultural industry because there is a great demand for high quality fruits in the market. However, fruit grading by human is inefficient, labor intensive and prone to error. The automated grading system not only speeds up the time of processing, but also minimizes error. There is a great demand for tomatoes in both local and foreign markets. The tomato fruit is very delicate and hence careful handling of this fruit is required during grading. Thus, this paper proposes an automatic and effective tomato fruit grading system based on computer vision techniques. The proposed quality evaluation method consists of two phases: development of hardware and software. The hardware is developed to capture the image of the tomato and move the fruit to the appropriate bins without manual intervention. The software is developed using image processing techniques to analyze the fruit for defects and ripeness. Experiments were carried out on several images of the tomato fruit. It was observed that the proposed method was successful with 96.47% accuracy in evaluating the quality of the tomato. [ABSTRACT FROM AUTHOR]