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Educational system is significantly changing in today's world. Recently, the New Education Policy (NEP)-2020 isstarted implementing in India. Students can have various options for getting education according to their choices and requirements. NEP-2020 is more student-centric rather than making them compulsory to get the degree with prescribed syllabus. AI has a major role in NEP-2020. Data mining technology plays a vital role in this new higher education system. As the Higher Education Institutions are growing rapidly, it is necessary for them to impart quality education for enrollment of students. Institutions can maintain educational quality by improving their results. This can be achieved by predicting student academic performance with the help of data mining algorithms. Classification, clustering, regression and association rule mining are the data mining techniques which can be implemented on student dataset to predict the final grade. This study focuses on prediction of student performance using classification and regression data mining techniques. The aim of this literature review is to study various data mining tools, algorithms and the important attributes that affect the student academic performance. [ABSTRACT FROM AUTHOR]

An integrated approach combining water quality indices (WQIs), multivariate data mining, and geographic information system (GIS) was employed to examine the water quality of Bheemasandra Lake, located adjacent to a sewage treatment plant (STP) in Tumakuru city, India. The analysis of 22 lake water samples, examined before and after the monsoons, revealed that the physicochemical parameters namely — electrical conductivity, biochemical oxygen demand, turbidity, total dissolved solids, ammoniacal nitrogen, nitrates, phosphates, magnesium, total hardness, total alkalinity, and calcium — exceeded the acceptable limits stipulated by national and international standards. The Canadian Council of Ministers of the Environment WQI (pre-monsoon: 25.3; post-monsoon: 33.9) and weighted arithmetic WQI (pre-monsoon: 3398; post-monsoon: 2093) designated the water as unsafe for drinking. Irrigation WQIs (sodium adsorption ratio, sodium percentage, residual sodium carbonate, magnesium hazard, permeability index, and potential salinity) implied water's suitability for irrigation. However, electrical conductivity indicated otherwise. Industrial WQIs (Larson–Skold Index, Langelier Index, Aggressive Index, and Puckorius Scaling Index) illustrated scaling propensity and the chloride sulfate mass ratio alluded galvanic corrosion potential. Hierarchical cluster analysis gathered 22 sampling points into two clusters (cluster 1: relatively lower polluted regions; cluster 2: highly polluted regions) for each season based on similarities in water features. Principal component analysis extracted four (79.07% cumulative variance) and six (87.14% cumulative variance) principal components before and after the monsoons, respectively. These components identified the primary pollution sources as urban sewage and natural lithological processes. WQI maps, created using the inverse distance weighted interpolation technique, enhanced the visualization of spatial–temporal variations. This study highlights the dire consequences of urbanization, STP pollution, and sewage management failures, necessitating that concerned authorities should implement policies and measures to curb the negative impacts on the environment and public health. [ABSTRACT FROM AUTHOR]

Indian cuisines are known for its exquisite delicacy and there are over 5000 such traditional preparations practiced in the country. Some of these cuisines are identified with cuisine families such as Mughlai, Awadhi, Udupi, Chettinad, Hyderabadi, Punjabi and so on. These cuisine groups might have emerged owing to various historical and geographical factors viz., food availability, climatic conditions, cooking traditions and cultural choices over a period. However, the divergence or convergence among the selected cuisine families other than referring to the attributes in the subjective manner, are rarely studied. This study focuses on data mining coupled with visual analytic methods for comparison of Mughlai and Awadhi cuisines. [ABSTRACT FROM AUTHOR]

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

Early diagnosis and prognosis of deadly illnesses have been made possible by advancements in machine learning algorithms. In order to analyze patient pathology reports and make informed decisions regarding medicine supply and marketing strategies, pharmaceutical companies employ advanced data mining tools to generate statistical reports and extract valuable information. Our proposed system fulfils the requirement of patients as well as pharmaceutical clients that are concerned with the two diseases: diabetes mellitus and hypothyroidism. Generating statistical reports from the relevant data and providing an aerial view of the occurrence and spread of a disease in India. The diabetes mellitus and hypothyroidism prediction is carried out using three models: Support Vector Machine (SVM), Naive Bayes, and Random Forest (RF). The Random Forest model is the most appropriate for predicting diabetes mellitus with an accuracy of 90% and 98.05% for predicting hypothyroidism. Diabetes increases a patient's risk of heart disease, stroke and vision problems. Hence our findings help patients to take proactive care. [ABSTRACT FROM AUTHOR]

The two-phase Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRIS-NG) campaign over India has generated datasets of numerous natural and artificial surroundings with high signal-to-noise ratios and higher spatial and spectral consistencies. However, the first phase AVIRIS-NG datasets of urban areas have 8.1 m spatial resolution, which is insufficient for producing level-4 urban material and natural cover maps required to model Earth's processes at the micro-scale or constantly monitor urban processes. The current work addresses this drawback by demonstrating a part of the proposed proof-of-concept approach on the AVIRIS-NG dataset of Ahmedabad, India, wherein super-resolution (SR) is proposed to enhance the hyperspectral image's spatial resolution and preserve its rich spectral content at the targeted spatial scale before utilizing it for producing detailed urban land cover maps. A step-wise account of the SR process has been provided herein to aid the reader in reproducing these experiments. Super-resolved products generated using four SR algorithms have been compared and validated using a composite framework based on processing time, qualitative visual investigation of selected image patches and spectral profiles, re-interpretation of Wald's reduced resolution protocol and full-reference image quality metrics. Results suggest the AVIRIS-NG dataset's successful spectral and spatial fidelity at a larger spatial scale, albeit varying in each super-resolved output. Sparse regression and natural prior (SRP), and anchored neighbourhood regression (ANR) produce the best and poorest super-resolved outputs among the tested SR algorithms. Although gradient profile prior (GPP) generated super-resolved product is the second best, it is also the most computationally expensive algorithm. An additional yardstick in the form of a spectral similarity analysis of randomly selected unknown spectral signatures on super-resolved output against a reference spectral library has been proposed to ascertain spectral content preservation further before utilizing the super-resolved product for urban information extraction. This exercise's findings reveal the presence of the target urban land cover feature at the unknown pixels with matching scores of greater than 0.5 in both best and worst super-resolved products, thereby advocating the extension of the proposed proof-of-concept to recently launched spaceborne hyperspectral missions' datasets. [ABSTRACT FROM AUTHOR]

The extraction of drainage network and watershed information is prerequisite for the study of watershed characteristics like morphometric analysis, which provides a basis for hydrological planning and modeling. The advanced tools of algorithms, Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM) data and Geographical Information System (GIS) software are used to extract drainage networks and their watershed boundaries. These tools are complicated to use or produce more errors in the extraction of elevation and drainage networks when applied to flat areas. For removal of errors and to improve the accuracy in preparation of DEM and extraction of drainage network, Burada Kalava River Basin, Andhra Pradesh, India has been taken for application of accuracy improvement algorithms. An automatic generation of drainage network and watershed using digital elevation model results in positional errors due to variations in slope and topography. This study aimed to generate a catchment area and stream network that closely represent the natural stream network and the streams' real positions. The step-by-step methodology using GRASS-interfaced Quantum GIS algorithms are given for pre-processing of DEM data to improve the positional accuracy before automatic extraction of the stream network and catchment area to resemble the real situation of the watershed. Secondly, efforts are made to analyze the DEM during automatic generation of the stream network and catchment area by assigning various area threshold values, including the application of pour point coordinates in improving the stream network and watershed characteristics. The results are verified and validated with the field information in order to improve the accuracy levels of DEM quality in generation of drainage network and catchment area. [ABSTRACT FROM AUTHOR]

This study aims to discover the impact of social networking sites on applicant quality, target group orientation and diversity of applicants. The study was conducted in Jaipur, Rajasthan, India and involved 387 respondents from different companies working at different levels in the recruitment department; mainly researcher has taken major responses form the BPO industry. Data were collected through an online survey and both descriptive and inferential statistics were used for data analysis. The results indicated that social networking sites had a significant impact on applicant quality, target group orientation and diversity of applicants. The study findings provide insights into the role of social networking sites in the recruitment process and have implications for HR practitioners and organizations. However, the study's conducted in a specific region of India and mainly in the BPO industry. Future research should expand the study's scope to different regions and industries to increase its generalizability.. [ABSTRACT FROM AUTHOR]

This study aims to analyze India's publications on organic chemistry and related fields at the micro, meso, and macro levels. The study attempts to map and visualize the publications in organic chemistry from 2016 to 2020 to identify the country co-authorship, author co-authorship, bibliographic coupling of authors, keyword co-occurrence, etc. Performance analysis techniques incorporating publication-related metrics, citation-related metrics, and citation and publication-related metrics (number of cited publications, citations per publication, h-index) were utilized to analyze the publications. Further, science mapping techniques are used to investigate citation analysis, bibliographic coupling, and co-authorship analysis. Moreover, enriched bibliometric techniques meant for computational evaluation like network analysis, including cluster analysis and visualization, were used in later phases using the software VOSviewer and Biblioshiny. VOSviewer, a bibliometric mapping tool, is extensively used in scientometric studies. For analysis of performance and visualization of research hotspots in Organic Chemistry, Biblioshiny web interface through R-studio is used, which is performing intensive data mining. A total of 1804 journals are encountered in the period taken for study which obeys a non-exponential growth with the highest number of papers (370) in the year 2017 and the least (344) in the year 2020 and as per the forecasting, the highest number of publications are expected in the year 2021 (349). Analysis of prolific authors reveals that A Kumar is the most productive author in the period with 47 publications and an h-index of 12 and KR Prabhu of IISc. Bangalore is the most impactful author with 387 citations and 27.64 CPP. Journal of Organic Chemistry is the most productive journal. Trending topics of research like azo dyes and metal-organic frameworks indicate the industrial and commercial importance of organic compounds. No similar studies have been found with matching objectives with this study. [ABSTRACT FROM AUTHOR]

Summary: Agriculture is the major backbone of India. Therefore, the crop recommendation system is important for the farmers and the country as it reflects the country's economic growth. The crop yield rate is affected due to various parameters such as climatic changes, soil properties, temperature, humidity and so forth. An effective prediction system is required to monitor the field and suggest suitable crops that can provide a maximum yield rate. Therefore, a prediction system is developed in the proposed framework for crop recommendation using the sensor information collected from Maharashtra, India. The dataset has been built with the information collected by 250 sensors located in different Maharashtra places. Initially, the gathered dataset is subjected to preprocessing steps like data cleaning, removing duplicate values, and filling up the missing values. Then, robust and flexible machine learning models like decision tree, random forest, and support vector machine are used, which analyze the preprocessed data and predict the suitable crops that show high yield for a particular area. The implementation of the proposed framework is done using Python. The RF classifier achieved the highest accuracy rate of 95% among the three classifiers. [ABSTRACT FROM AUTHOR]

For decades, changes in fish catch composition and the marine environment have been monitored worldwide and recorded in databases like FAO FishStatJ and the European Union Copernicus Marine Service. However, the complexity and high variability in the dataset makes it challenging to find meaningful information through conventional data analytical methods. Therefore, in this pilot data mining study, we employed association rule mining algorithms (Apriori, ECLAT, and FP-Growth) to find frequently occurring itemsets in the fish-catch composition and marine environment data of the west and east coasts of India during the past decade (2011-2020). Firstly, the inherent spatial and temporal variations in fish-catch composition and marine environment (sea surface temperature and chlorophyll) on the west and east coasts of India were statistically analyzed and described. Then, the data were preprocessed, selected, and transformed into categorical attributes. By applying the association rule mining algorithms written in the Python language in the Google Colab workspace, we obtained frequent itemsets of fish catch and marine environment with different levels of minimum support and confidence. The preliminary results showed linear and inverse associations between changes in the sea surface temperature, chlorophyll concentration, and major catch groups, such as anchovies, Indian oil sardine, Indian mackerel, hairtails, butterfish-pomfrets, Bombay duck, flatfish, tunas, giant tiger prawn, crabs, lobsters, and cephalopods. Among the tested data mining algorithms, FP-Growth was found to be more efficient and reliable in finding associations between the spatiotemporal dynamics of the marine environment and fish distribution and abundance. Therefore, it can be potentially used to support marine fisheries' resource assessment and management strategies after refinement. [ABSTRACT FROM AUTHOR]

Agriculture is the economic backbone and the main means of livelihood in numerous developing countries. Numerous challenges related to farming and agriculturists exist. Cultivators face crop loss due to inappropriate selection of crops, inappropriate use of fertilizers, alterations in soil, ambiguous conditions in climate, and so on. The type of soil forms a crucial element in agriculture.The class of soil plays an important role in identifying what kind of crop should be planted along with the manure type to be applied. Classification of soil is essential to make effective use of the resources of soil. The texture of the soil has a major impact on crop growth. The role played by soil texture in determining the type of crop to be grown is significant. It is also employed in soil labs for determining the categories of soil. Soil texture plays a major role in determining the suitability of crops and handling famines. Soil chemical properties include "Electrical Conductivity" ( E C ), "Organic Carbon" ( O C ), "Phosphorous" (P), "Potassium" (K), "Power of Hydrogen" ( P H ), "Zinc" ( Z n ), "Boron" (B), and "Sulphur" (S). The crop growth is heavily influenced by the soil's chemical composition. Keeping these considerations in mind, this work develops a customised decision tree ( C DT ) that serves as a soil classifier (SC). A predictive framework is then devised that utilises the C DT to perform the soil classification based on the texture of the soil and its chemical properties. Extensive experiments on several real-world soil datasets from Karnataka, India; and benchmark agricultural datasets such as seeds, Urban Land Cover (ULC), Satellite Image of Land Data (LS), and Forest Cover Type (FCT) were conducted. The results demonstrated that the designed C DT classifier outperformed existing classifiers such as k-Nearest Neighbor ( K NN ), Logistic Regression ( L R ), Artificial Neural Network ( A NN ), Classification and Regression Trees ( C ART ), C 4.5 ), traditional SVM ( S VM ), and Random Forest ( R F ) in terms of Accuracy ( A cc ), Sensitivity ( S ens ), Specificity ( S pec ), Precision ( P rec ), and F-Score ( F S ) on these datasets. The devised SC was deployed on the Heroku (Hk) cloud for effective access. Effective access in terms of end-user availability at all times was provided. An expert system for soil classification was built to provide information about soil classification round the clock using an internet-enabled device to the stakeholders of agriculture, such as cultivators and agricultural organizations. The agricultural raw data was stored in the form of blob objects on Amazon S3 (AS3). [ABSTRACT FROM AUTHOR]

In this paper, we discuss an approach that predicts the quantitative value of rainfall. The proposed algorithm uses a combination of data mining and neuro-fuzzy inference system for prediction. The model is demonstrated on north interior Karnataka (a state in India) rainfall data as a case study. This model is applicable to any geographical area provided apt predictors are included. For north interior Karnataka rainfall prediction predictors are derived from local and global climate conditions. The local condition variables are derived from the mean sea level pressure, temperature, and wind speed in south India. The global variables affecting the north interior Karnataka rainfall include, Darwin sea level pressure, the ENSO indices and southern oscillation. The data mining technique, association rule mining, is used to study the correlation among the predictors; clustering is used for predictor selection as well as membership function creation for fuzzyfication. Neuro-fuzzy inference system is further used for fine tuning the "If-then" rules and crisp value prediction of the rainfall. The prediction accuracy is observed to be good considering Tropical Meteorological Department data. [ABSTRACT FROM AUTHOR]

Maternal and child healthcare (MCH) has always been regarded as one of the important issues globally. The aim of this article is to find out all frequently occurring healthcare service elements along with their availability conditions and their interactions with other health system (HS) elements, when coverage of priority MCH intervention was either poor or moderate or good in any region. Association rule mining technique has been used to understand the probability of occurrences of different healthcare service elements among regions. Along with the proposed analytical framework, an interactive decision support system (DSS) has also been developed on a web platform, which would help healthcare policymakers to integrate the analytical framework easily in their processes of decision-making. The system has been developed by using Shiny package on R software. Data for all variables are collected for 584 Indian districts from the third phase of district-level household and facility survey. From results, it is observed that community healthcare services, sub-centre (SC)-level healthcare services and PHC-level healthcare services of a region are very much interlinked with each other, and their relationships define the healthcare condition of that region. [ABSTRACT FROM AUTHOR]

Summary: Over the past decade, enterprises have broadly adopted data warehousing in various activities. Today, abundant information is available on websites in the form of tables or spreadsheets. This huge amount of data cannot be processed directly because of its complexity, heterogeneity, and gap between user requirements. In this work, an automatic approach is proposed to build the multi‐dimensional structure (MDS) of heterogeneous tabular data format for intelligent decision‐making. The proposed MDS is generated by identifying components such as dimensions and hierarchies. It automatically extracts measures based on the spatial characteristics of data dimensions like region, time as well as their hierarchies. This proposed approach automatically generates a multi‐dimensional model for BI tools without complicated ETL (Extraction, Transformation and Loading) process and helps to solve several business queries, like "Top 5 states in India based on Irrigated area in 2009". Moreover, the proposed method reduces the time and cost of building multi‐dimensional models to a very large extent. The correctness of proposed method is tested with the synthetic and economic datasets of Government websites where information is stored in tabular formats and various heterogeneous setups where the proposed method saved approximately 4000 to 5000 computing hours of the ETL process. [ABSTRACT FROM AUTHOR]

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Background: Mental illness in parents impairs their parenting capability, which has a lifelong detrimental impact on their children's physical and psychological health. In the current Indian context, due to weak social security nets, family is the only plausible intervention to ensure adequate substitute child care. Therefore, this study explores various risk factors and barriers to providing substitute family care. Methods: We used a mixed-method approach to gather information from 94 substitute family caregivers. Quantitative screening data were collected from four hospitals using a clinical data mining tool and an interview guide to gather caregiver perspectives on economic, familial, and social risks and barriers associated with caring. We used thematic analysis to consolidate the qualitative findings. Results: Most of the substitute caregivers were females from low-income households. The study identified 11 sub-themes and 23 specific themes associated with risks and barriers to substitute care. These themes fell into four broad areas: economic, familial, school-related risks, and specific cultural and service access barriers. Focus on economic interventions is likely to result in strengthening the substitute family caregiver. Conclusion: The paradigmatic shift of treatment focus from the patient to the entire household would benefit the children just as it does the patient. [ABSTRACT FROM AUTHOR]

China and India have unique traditional medicine systems with vast territory and rich medical resources. Traditional medicines in China include traditional Chinese medicine, Tibetan medicine, Mongolian medicine, Uyghur medicine, Dai medicine, etc. In the third national survey of Chinese medicine resources, 12694 medicinal materials were identified. Traditional medicines in India include Ayurveda, Unani, Siddha, Homoeopathy, etc. There are 7263 medicinal materials in India. To reveal the characteristics of medicinal materials between China and India respectively, and to compare the similarities and differences in terms of properties, tastes, medicinal parts and therapeutic uses and to promote the exchange of traditional medicine between China and India and the international trade of traditional medicine industry. The information of medicinal materials between China and India was extracted from The Chinese Traditional Medicine Resource Records and Pharmacopoeia of the People's Republic of China , as well as from 71 Indian herbal monographs. The information of each medicinal material, such as types, families, genera, properties, distribution, medicinal parts, efficacy, therapeutic uses, dosage form and dosage, was recorded in Excel for statistical analysis and visual comparison. A total of 12694 medicinal materials in China and 5362 medicinal materials in India were identified. The medicinal materials were mostly distributed in Southwest China and northern India. Plants were the main sources of medicinal materials. The common medicinal parts in China were whole medicinal materials, roots and rhizomes, and India used more renewable fruits, seeds and leaves. They are commonly used in the treatment of digestive system diseases. There were 1048 medicinal materials used by both China and India, which were distributed in 188 families and 685 genera. The Chinese and Indian pharmacopoeias had a total of 80 species of medicinal materials used by both China and India. The characteristics of medicinal materials between China and India were somewhat different, which was conducive to provide a reference basis for traditional medicine in China or India to increase the medicinal parts and indications when using a certain medicinal material, as well as to expand the source of medicine and introduce new resources. However, there were certain similarities and shared medicinal materials, which can tap the potential of bilateral trade of medicinal materials between China and India, so as to promote the medical cultural exchange and economic and trade cooperation between the two countries. [Display omitted] [ABSTRACT FROM AUTHOR]

• Here, POS tagger and Chunker developed for Maithili, Magahi and Bhojpuri languages. • Non-continuous long dependencies among words captured by self attention. • Robust features extracted by SAHBiLC and Fine-tuned SAHBiLC architectures. • Linguistic Error analyses of proposed architectures are described. Sequential labelling plays a vital role in solving numerous Natural Language Processing (NLP) applications such as Machine Translation and Information Extraction etc. One of these is Part-of-Speech (POS) tagging, which assigns a sequence of grammatical categories to the given sentence, and Chunking which groups them into 'chunks' or what can be called minimal phrases. Bhojpuri, Maithili and Magahi are low resource languages and widely spoken in central north-eastern India, belonging to the Indo-Aryan language family. The creation of an annotated corpus for POS tagging and Chunking, and then building an initial automatic tool for these problems is the first attempt towards building language technology tools for these languages. The annotated corpus used to develop POS Taggers and Chunkers, based on various machine learning algorithms (TnT, CRF, MEMM and Structured SVM) and state-of-the-art LSTM-CNN-CRF model, and then these compared with the obtained results on two new proposed deep learning-based models, Self-Attention Hierarchical Bi-LSTM CRF (SAHBiLC) and a fine-tuned version of it, Fine-SAHBiLC. The SAHBiLC and Fine-SAHBiLC models outperform on Bhojpuri (Accuracy for POS and Chunking is 0.86% and 0.94%, respectively) and Maithili (Accuracy for POS and Chunking is 0.86% and 0.95%, respectively) and Magahi (Accuracy for POS is 0.86%). [ABSTRACT FROM AUTHOR]

The human-computer interaction has become inevitable in digital world. HCI helps humans to incorporate technology to resolve even their day-to-day problems. The main objective of the paper is to utilize HCI in Intelligent Transportation Systems. In India, the most common and convenient mode of transportation is the buses. Every state government provides the bus transportation facility to all routes at an affordable cost. The main difficulty faced by the passengers (humans) is lack of information about bus numbers available for the particular route and Estimated Time of Arrival (ETA) of the buses. There may be different reasons for the bus delay. These include heavy traffic, breakdowns, and bad weather conditions. The passengers waiting in the bus stops are neither aware of the delay nor the bus arrival time. These issues can be resolved by providing an HCI-based web/mobile application for the passengers to track their bus locations in real time. They can also check the Estimated Time of Arrival (ETA) of a particular bus, calculated using machine learning techniques by considering the impacts of environmental dynamics, and other factors like traffic density and weather conditions and track their bus locations in real time. This can be achieved by developing a real-time bus management system for the benefit of passengers, bus drivers, and bus managers. This system can effectively address the problems related to bus timing transparency and arrival time forecasting. The buses are equipped with real-time vehicle tracking module containing Raspberry Pi, GPS, and GSM. The traffic density in the current location of the bus and weather data are some of the factors used for the ETA prediction using the Support Vector Regression algorithm. The model showed RMSE of 27 seconds when tested. The model is performing well when compared with other models. [ABSTRACT FROM AUTHOR]

There is an increasing demand for electricity on a global level. Thus, the utility companies are looking for the effective implementation of demand response management (DRM). For this, utility companies should know the energy demand and optimal household consumer classification (OHCC) of the end users. In this regard, data mining (DM) techniques can give better insights and support. This work proposes a DM-technique-based novel methodology for OHCC in the Indian context. This work uses the household electricity consumption (HEC) of 225 houses from three districts of Maharashtra, India. The data sets used are namely questionnaire survey (QS), monthly energy consumption (MEC), and tariff orders. This work addresses the challenges for OHCC in energy meter data sets of the conventional grid and smart grid (SG). This work uses expert classification and clustering-based classification methods for OHCC. The expert classification method provides four new classes for OHCC. The clustering method is employed to develop eight different classification models. The two-stage clustering model, using K-means (KM) and the self-organizing map (SOM), is the best fit among the eight models. The result shows that the two-stage clustering of the SOM with the KM model provides 88% of overlap-free samples and 0.532 of the silhouette score (SS) mean compared to the expert classification method. This study can be beneficial to the electricity distribution companies for OHCC and can offer better services to consumers. [ABSTRACT FROM AUTHOR]

Background: Type 2 Diabetes Mellitus (T2DM) has emerged as a major threat to global health that fosters life-threatening clinical complications, taking a huge toll on our society. More than 65 million Indians suffer from T2DM, making it one of the leading causes of death. T2DM and associated complications have to be constantly monitored and managed which reduces the overall quality of life and increases socioeconomic burden. Therefore, it is crucial to develop specific treatment and management strategies. In order to achieve this, it is essential to understand the underlying genetic causes and molecular mechanisms. Methods: Integrated gene network and ontology analyses facilitate prioritization of plausible candidate genes for T2DM and also aid in understanding their mechanistic pathways. In this study, T2DM-associated genes were subjected to sequential interaction network and gene set enrichment analysis. High ranking network clusters were derived and their interrelation with pathways was assessed. Results: About 23 significant candidate genes were prioritized from 615 T2DM-associated genes which were overrepresented in pathways related to insulin resistance, type 2 diabetes, signaling cascades such as insulin receptor signaling pathway, PI3K signaling, IGFR signaling pathway, ERBB signaling pathway, MAPK signaling pathway and their regulatory mechanisms. Conclusion: Of these, two tyrosine kinase receptor genes- EGFR and IGF1R were identified as common nodes and can be considered to be significant candidate genes in T2DM. [ABSTRACT FROM AUTHOR]

Coronavirus presenting an unforeseeable chain of events has exaggerated misery for students in India as they attracted the most detrimental experiences associated with lockdown restrictions leading to a shutdown of colleges as a preventive measure. The research endeavors to furnish a review of the overall hardships and psychological state of mind of college students and improvement in the implementation of policy decisions. Researchers conceptualize the newly discovered phenomenon by adopting grounded theory. Data from 256 newspaper articles, online articles and magazines have been gathered and converted into 256 separate files. To broaden the justification of research, social media analysis employing tweets, Facebook posts and Whatsapp messages are considered adding to the contributory prospects of the study. Compiled data is then refined through data mining technique. Triangulation approach amalgamating content analysis and thematic analysis has been deployed, thereby exploring the qualitative aspect of data gathering. Reviews from 31 students through telephonic conversation and 8 academic experts extended more accuracy to the research process. Findings administered academic disruptions with career concern, emotional suffering, financial concern, online learning, overseas injustice and psychological effects as the final themes representing various concerns experienced by college students. Hence, this work concludes with some constructive suggestions to deteriorate the amplified concerns. [ABSTRACT FROM AUTHOR]

Purpose: Our objective is to identify the predictive factors and predict hospital length of stay (LOS) in dengue patients, for efficient utilization of hospital resources. Methods: We collected 1360 medical patient records of confirmed dengue infection from 2012 to 2017 at Max group of hospitals in India. We applied two different data mining algorithms, logistic regression (LR) with elastic-net, and random forest to extract predictive factors and predict the LOS. We used an area under the curve (AUC), sensitivity, and specificity to evaluate the performance of the classifiers. Results: The classifiers performed well, with logistic regression (LR) with elastic-net providing an AUC score of 0.75 and random forest providing a score of 0.72. Out of 1148 patients, 364 (32%) patients had prolonged length of stay (LOS) (> 5 days) and overall hospitalization mean was 4.03 ± 2.44 days (median ± IQR). The highest number of dengue cases belonged to the age group of 10-20 years (21.1%) with a male predominance. Moreover, the study showed that blood transfusion, emergency admission, assisted ventilation, low haemoglobin, high total leucocyte count (TLC), low or high haematocrit, and low lymphocytes have a significant correlation with prolonged LOS. Conclusion: Our findings demonstrated that the logistic regression with elastic-net was the best fit with an AUC of 0.75 and there is a significant association between LOS greater than five days and identified patient-specific variables. This method can identify the patients at highest risks and help focus time and resources. [ABSTRACT FROM AUTHOR]

The processes related to solid waste management (SWM) are being revised as new technologies emerge and are applied in the area to achieve greater environmental, social and economic sustainability for society. To achieve our goal, two robust review protocols (Population, Intervention, Comparison, Outcome, and Context (PICOC) and Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA)) were used to systematically analyze 62 documents extracted from the Web of Science database to identify the main techniques and tools for Knowledge Discovery in Databases (KDD) and Data Mining (DM) as applied to SWM and explore the technological potential to optimize the stages of collecting and transporting waste. Moreover, it was possible to analyze the main challenges and opportunities of KDD and DM for SWM. The results show that the most used tools for SWM are MATLAB (29.7%) and GIS (13.5%), whereas the most used techniques are Artificial Neural Networks (35.8%), Linear Regression (16.0%) and Support Vector Machine (12.3%). In addition, 15.3% of the studies were conducted with data from China, 11.1% from India and 9.7% of the studies analyzed and compared data from several other countries. Furthermore, the research showed that the main challenges in the field of study are related to the collection and treatment of data, whereas the opportunities appear to be linked mainly to the impact on the pillars of sustainable development. Thus, this study portrays important issues associated with the use of KDD and DM for optimal SWM and has the potential to assist and direct researchers and field professionals in future studies. [ABSTRACT FROM AUTHOR]

The high rate of urbanisation has increased the need for state-of-art health models that can meet the growing needs of society during any pandemic. Information-theoretic algorithms based on decision tree can mine the data to establish standards for the final decision by classifying the related data. Classification is an effective tool to analyse the existing health system in India’s states and union territories. For this purpose, the data is categorised and then treated with the enhanced Shannon Entropy-based C4.5 decision tree algorithm to set some rules. These rules are capable of finding the major gaps in the health care systems after the analysis. Supposedly, these gaps are taken care of properly in the affected regions. In that case, the health care models will accomplish the endeavouring Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

The Coronavirus pandemic has induced a huge economic crisis. The norms of social distancing and consequent lockdown to flatten the curve of this infection has brought economic activity across the globe to a standstill. A mass exodus of workers from major urban centres of India to their native villages started. Mental, financial and emotional agony inflicted due to job-loss, lack of job and livelihood opportunities led to this. A massive macroeconomic crisis for the country with serious ramifications has consequently exploded. The present study explores and captures the diffusion and discovery of information about the various facets of reverse migration in India using Twitter mining. Tweets provide extensive opportunities to extract social perceptions and insights relevant to migration of workers. The massive Twitter data were analysed by applying text mining technique and sentiment analysis. The results of the analysis highlight five major themes. The sentiment analysis confirms the confidence and trust in the minds of masses about tiding through this crisis with government support. The study brings out the major macroeconomic ramifications of this reverse migration. The study's findings indicate that a concentrated joint intervention by the State and Central Governments is critical for successfully tiding through this crisis and restoring normalcy. The subsequent policy measures announced by the government are being critically gauged. In addition, the authors have proposed measures to ameliorate this damage on the formal and informal sectors. [ABSTRACT FROM AUTHOR]

Background: Crop yield prediction is an important issue for the proper selection of crop for sowing. Earlier prediction of crop is done by the farmer’s experience on a particular type of field and crop. Predicting the crop is done by the farmer’s experience based on the factors like soil types, climatic condition, seasons and weather, rainfall and irrigation facilities. Methods: Data mining techniques is the better choice for predicting the crop. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year’s crop production. This research proposes and implements a system to predict crop yield from soil data. This is achieved by applying Decision Tree Algorithm on agricultural data. The main aim of this research is to pinpoint the accuracy of Decision Tree Algorithm and C 5.0 algorithm which is used to predict the crop yield. Result: This paper presents a brief analysis of Crop yield prediction using data mining technique based decision tree algorithm and C5.0 algorithm for the selected region (Krishnagiri) district of Tamil Nadu in India. The experimental result shows that the proposed work efficiently to determine the accuracy of decision tree algorithm and also to predict the crop yield production using R- Tool. [ABSTRACT FROM AUTHOR]

In order to explore the possibility of crop estimation, data mining approach being multidisciplinary was followed. The district of Ranga Reddy, Telangana State, India has been chosen for the study and its year wise average yield data of rice and daily weather over a period of 31 years i.e. from 1988-2019 (30th to 47th Standard Meteorological Weeks). Data mining tool WEKA (V3.8.1). Min-Max Normalization technique followed by Feature Selection algorithm, 'cfsSubsetEval' was also adopted to improve quality and accuracy of data mining algorithms. Thus, after cleaning and sorting of data, five classifiers viz., Logistic, MLP (Multi Layer Perceptron), J48 Classifier, LMT (Logistic Model Trees) and PART Classifier were employed over the trained data. The results indicated that the function based and tree based models have better performance over rule based model. In case of function based two models examined, viz., Logistic and MLP, the later performed better over Logistic model. Between tree based two models, LMT performed better over J48. Thus, MLP classifier model found to be the best fit model in predicting rice yields as it recorded an accuracy of 74.19 %, sensitivity of 0.742 and precision of 0.743 as compared with other models. The MLP has also achieved the highest F1 score of (0.742) and MCC (0.581). [ABSTRACT FROM AUTHOR]

Agriculture and allied industries play an important role in the development of our nation. In India more than 55% of people make a living from farming. Crop yields are an essential aspect of every farmerâs day. It depends on many factors like soil quality, seeds, planting practices, humidity, fertilizers and pesticides. Besides all factors, diagnosing soil quality is a fundamental and essential task in farming, as it provides background knowledge of the soil and its physical, chemical and biological prominence. Hence, soil analytics is inevitable that gives information about the present nutrient availability or the need of the nutrients for effective cultivation. It helps to interpret the physico-chemical properties of soil nutrients and to classify the nutrient content as very low, low, medium, high, or very high based on pH values. Thus, predictive analytics based on the soil parameters offer precise and sensible solutions for soil fertility problems and enable suitable decisions on crop cultivation. This study attempts to exploit the benchmark classification algorithms from data mining to classify soil samples of Tiruppur district using pH levels. The prediction of pH levels is important to know the nutrients availability in the soil. Classification algorithms like Logistic Regression (LR), Bernoulli Naive Bayes (BNB), Decision Tree (DT), Extra Tree (ET), Random Forest (RF) and K-Nearest Neighbor (KNN) are used to evaluate and predict the pH values. After the comprehensive evaluation, this study determined that the performance of the DT and RF model for pH prediction is high compared to the other algorithms in terms of accuracy. Further, the classifiers performance has improved by possessing feature scaling techniques like normalization and standardization. Results showed that the prediction accuracy of KNN and BNB with feature scaling outperforms the other algorithms. [ABSTRACT FROM AUTHOR]

Visceral leishmaniasis or Kala-azar (KA) is a Vector-Borne Disease (VBD) that remains the second-largest parasitic killer across the globe (mortality rate: 75–95%). More than 60% of KA cases originate in South Asia, wherein India accounts for 2/3rd of the cases, and Bihar, a state in India, alone accounts for more than 50% of the Indian cases. Past studies suspected climate change vulnerabilities as a driving cause of KA outbreaks. The VBDs-based epidemic prediction systems have been developed to mitigate recurrent outbreaks; however, Machine Learning (ML) based approaches still need to be explored for modeling changing climate impacts on KA cases. This study, for the first time, develops a Radial Basis Function (RBF) kernel-based Support Vector Regression (SVR), hereinafter RBF-kernel-based-SVR model for the most-affected endemic districts of Bihar (northern-India), using the data from 2016 and 2021. Forward selection, backward elimination, and stepwise regression procedures were adopted while selecting influential climatic variables, followed by the k -fold cross-validation technique and, then, the RBF-kernel-based-SVR algorithm for classification. Results suggested that temperature, wind speed, rainfall, and population density significantly contributed to the KA outbreaks. This study also developed Multiple Linear Regression (MLR) and Multilayer Perceptron (MLP) models to compare SVR with other classification models. Findings indicated that the proposed RBF-kernel-based-SVR model [Correlation Coefficient (CC) = 0.82, Root-Mean-Square Error (RMSE) = 12.20, and Nash–Sutcliffe Efficiency (NSE) = 0.66] outperformed MLR (0.81, 14.20, 0.48) and MLP (0.81, 12.95, 0.61). Study recommends using the RBF-kernel-based-SVR model as a quick and efficient model capable of detecting KA cases with high predictability even under limited data availability. Such models can assist public health authorities, given monitoring KA spread, learning the climate impacts of outbreaks, and ensuring timelier health services. [ABSTRACT FROM AUTHOR]

Hyperspectral data present better opportunities to exploit the treasure of spectral and spatial content that lies within their spectral bands. Hyperspectral data are increasingly being considered for exploring levels of urbanization, due to their capability to capture the spectral variability that a modern urban landscape offers. Data and algorithms are two sides of a coin: while the data capture the variations, the algorithms provide suitable methods to extract relevant information. The literature reports a variety of algorithms for extraction of urban information from any given data, with varying accuracies. This article aims to explore the binary-classifier approach to target detection to extract certain features. Roads and roofs are the most common features present in any urban scene. These experiments were conducted on a subset of AVIRIS-NG hyperspectral data from the Udaipur region of India, with roads and roofs as targets. Four categories of target-detection algorithms are identified from a literature survey and our previous experience--distance measures, angle-based measures, information measures, and machinelearning measures--followed by performance evaluation. The article also presents a brief taxonomy of algorithms; explores methods such as the Mahalanobis angle, which has been reported to be effective for extraction of urban targets; and explores newer machine-learning algorithms to increase accuracy. This work is likely to aid in city planning, sustainable development, and various other governmental and nongovernmental efforts related to urbanization. [ABSTRACT FROM AUTHOR]

Data Mining plays a decisive role especially in medical domain. Decision trees are predominant model in machine learning. Decision trees are simple and very effective classification approach. The decision tree identifies the utmost prime features of a given problem. One of the most common disease in India is Liver Cirrhosis. It is distinctly difficult to uncover Liver Cirrhosis in its initial stage. However early diagnosis of Liver Cirrhosis is highly important. The liver disease data set has a collection of distinguishing features that affect the healthy state of a patient. Machine Learning methods enable knowledge acquisition in early stages and use of this acquired knowledge plays an important role in solving problems like suppose if we want to predict whether the patient with the Liver Cirrhosis has also been suffering from Hepatitis C or not. In order to easily arrive at this knowledge certainly there is a need for fully integrated system. In this paper the collected Liver disease data set is analyzed and prognosticated whether the patient is suffering from liver cirrhosis or not. [ABSTRACT FROM AUTHOR]

Oral cancer is highly prevalent in India and is the most frequent cancer type among Indian males. It is also very common in southeast Asia. India has participated in the International Cancer Genome Consortium (ICGC) and some national initiatives to generate large-scale genomic data on oral cancer patients and analyze to identify associations and systematically catalog the associated variants. We have now created an open, web-accessible database of these variants found significantly associated with Indian oral cancer patients, with a user-friendly interface to enable easy mining. We have value added to this database by including relevant data collated from various sources on other global populations, thereby providing opportunities of comparative geographical and/or ethnic analyses. Currently, no other database of similar nature is available on oral cancer. We have developed Database of GENomic Variants of Oral Cancer, a browsable online database framework for storage, retrieval and analysis of large-scale data on genomic variants and make it freely accessible to the scientific community. Presently, the web-accessible database allows potential users to mine data on ∼24 million clinically relevant somatic and germline variants derived from exomes (n  = 100) and whole genomes (n  = 5) of Indian oral cancer patients; all generated by us. Variant data from The Cancer Genome Atlas and data manually curated from peer-reviewed publications were also incorporated into the database for comparative analyses. It allows users to query the database by a single gene, multiple genes, multiple variant sites, genomic region, patient ID and pathway identities. Database URL : http://research.nibmg.ac.in/dbcares/dbgenvoc/ [ABSTRACT FROM AUTHOR]

Coronary illness is one of the most basic human infections on the planet and influences human life seriously. Heart-related maladies or Cardiovascular Diseases (CVDs) are the primary explanation behind countless passings on the planet in the course of the most recent couple of decades and has risen as the most perilous infection, in India as well as in the entire world. In coronary illness, the heart cannot push the necessary measure of blood to different pieces of the body. Exact and on-time analysis of coronary illness is significant for cardiovascular breakdown anticipation and treatment. The analysis of coronary illness through customary clinical history has been considered as not solid in numerous angles. Along these lines, there is a need for a solid, exact and practical framework to analyze such illnesses in an ideal opportunity for appropriate treatment. The proposed Naïve Bayes grouping framework can without much of a stretch distinguish and arrange individuals with coronary illness from solid individuals. The proposed Naïve Bayes order-based choice emotionally supportive network will help the specialists to conclusion heart patients productively. A significant test in Data Mining is to manufacture exact and computationally productive classifiers for clinical application. In this paper we considered order rule digging for information disclosure and produced the guidelines by applying our created approach on Heart expire databases [1, 2, 3]. [ABSTRACT FROM AUTHOR]

With its high importance in the Indian economy, roadways are one of the major forms of transportation in India that cannot be avoided. There is a public safety problem with roadways and a significant number of injuries in our minds. Due to lack of proper real-time monitoring of the accident details that could be transferred to the nearest police station, hospitals, the percentage of incidents in the country is rising to such a high level that leads to loss of human life. Paper helps to provide a solution to this issue by providing information on the location of accident and the severity of the victim during the accident, which will be instantly shared with the police station, hospitals with details such as images, location, video and impact of the accident for quicker decision making, will improve the chances of saving the lives of the sufferers. It is a system which is fully automated. Therefore, protecting the lives of individuals will be a huge contribution to humanity. [ABSTRACT FROM AUTHOR]

This study analyses the Indian Scientists contributions of research papers related to the topic in Data Mining was undertaken from Web of Science Databases has been used to retrieve the data for 22 years (1999-2020) by the searching the keyword "Data Mining". The study reveals that, most of the researchers preferred to publish their research results in journals; as such 88.59% of articles were published in journals. More numbers of articles were published in the year 2019. The authorship trend shows that, out of total 1096 literature published, 95.53 % of the publication published under the joint author. It is observed that author productivity is not in agreement with Lotka's law, but productivity distribution data partially fits the law when the value of Chi-square to 25212.62. Further this study also identified to analyses source wise. Degree of collaboration, Areas of research concentration, word frequency, Geographical distribution of the literature and citation analysis is also noted. [ABSTRACT FROM AUTHOR]

Data analysis plays an important role in the health sector of any government in predicting the health conditions of their people and take measures to improve the health of their people. In this article, we present the problems faced by the department of health and family welfare in collecting and analyzing data from various directorates of the government of Tamilnadu in India. We then propose a framework that can collect data from various directorates of the government and analyze the data collected to predict the resources needed by the hospital to better serve their patients. Logistic regression and decision trees were used to analyze the data collected from various directorates. Further, the data are researched to identify potholes in the healthcare system to improve the existing system. In addition, we used five key performance indicator measures to evaluate the outcomes. [ABSTRACT FROM AUTHOR]

The topic of mate selection in contemporary India provides an opportunity to illustrate the value of using scientific theory to guide family research. This modified transcript from a keynote address first describes the benefit to theory guided family science research and then provides a few select examples of the way theory informs a better understanding of patterns and trends in contemporary Indian mate selection. The availability of robust data, powerful computing and advanced methodologies has made data mining, or the unguided exploration of the data, more attractive to researchers. When data analysis is not guided by theoretical principals, generalizable advances in research is compromised. This paper focuses on a quantitative, deductive approach to knowledge building yet understands that qualitative and inductive research is also important in the scientific process and theory building. Contemporary Indian mate selection continues to adapt to a 21st century, globally influenced, socio-cultural landscape. The Indian population is large and diverse. The author recognizes that heterogeneity while also connecting current Indian mate selection patterns to select well established research in the field. [ABSTRACT FROM AUTHOR]

The fragmented nature of arable landscapes and diverse cropping patterns often thwart the precise mapping of crop types. Recent advances in remote-sensing technologies and data mining approaches offer a viable solution to this mapping problem. We demonstrated the potential of using hyperspectral imaging and an ensemble classification approach that combines five machine-learning classifiers to map crop types in the Anand District of Gujarat, India. We derived a set of narrow/broad-band indices from the Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) imagery to represent spectral variations and identify target classes and their distribution patterns. The results showed that Maximum Entropy (MaxEnt) and Generalised Linear Model (GLM) had strong discriminatory image classification abilities with Area Under the Curve (AUC) values ranging between 0.75 and 0.93 for MaxEnt and between 0.73 and 0.92 for GLM. The ensemble model resulted in improved accuracy scores compared to individual models. We found the Photochemical Reflectance Index (PRI) and Moment Distance Ratio Right/Left (MDRRL) to be important predictors for target classes such as wheat, legumes, and eggplant. Results from the study revealed the potential of using one-class ensemble modelling approach and hyperspectral images with limited field dataset to map agricultural systems that are fragmented in nature. [ABSTRACT FROM AUTHOR]

This paper presents a prototype to classify stroke that combines text mining tools and machine learning algorithms. Machine learning can be portrayed as a significant tracker in areas like surveillance, medicine, data management with the aid of suitably trained machine learning algorithms. Data mining techniques applied in this work give an overall review about the tracking of information with respect to semantic as well as syntactic perspectives. The proposed idea is to mine patients' symptoms from the case sheets and train the system with the acquired data. In the data collection phase, the case sheets of 507 patients were collected from Sugam Multispecialty Hospital, Kumbakonam, Tamil Nadu, India. Next, the case sheets were mined using tagging and maximum entropy methodologies, and the proposed stemmer extracts the common and unique set of attributes to classify the strokes. Then, the processed data were fed into various machine learning algorithms such as artificial neural networks, support vector machine, boosting and bagging and random forests. Among these algorithms, artificial neural networks trained with a stochastic gradient descent algorithm outperformed the other algorithms with a higher classification accuracy of 95% and a smaller standard deviation of 14.69. [ABSTRACT FROM AUTHOR]