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The emergent technology has been increasingly incorporated with the tourism industry. In the digital world, social media plays an important role in identifying the most visited tourist places. More than 80% of people commonly used the social media platform called 'Twitter'. By using tweet reviews, tourists around the world know about the feelings, suggestions and opinions of most visited tourist places in the selected region. This paper recommends an approach for the development of trip planning in South India based on tourist's preferences. For the research, we use Twitter Application Programming Interface to collect information about the most visited tourist places in South India by using tweets reviews and store it as South India Tourism Tweet reviews database. This paper mainly focuses on analyzing Twitter reviews, which are very helpful to locate the most visited tourist place. In the tweets where reviews are mostly unstructured and heterogeneous, which are then classified into positive tweets, negative tweets and neutral tweets by using the preprocessing technique and machine learning algorithm called support vector machine. Performance measures are calculated by using the classification results. Finally, the results are plotted by using the classification results and performance measures as a wordcloud. This can be very useful for tourists to select the most visited tourist locations. The proposed system can satisfy individual tourist needs. [ABSTRACT FROM AUTHOR]

Crime prediction is an effort to cut down the crime rate and criminal activities in the society. This paper concentrates on monitoring the crime trends in India using data from 2001 to 2019. The work analyzes the crime condition in India by following a top-to bottom approach and implementing visualization and machine learning algorithms. Heat map visualization of India is done to focus on specific crime types for all states. Furthermore, various demography of human population like age-groups, sex, caste, etc., are considered to have a larger perspective of the issue. The model also suggest proactive measures based on these findings. [ABSTRACT FROM AUTHOR]

Aim/Purpose This paper aims to analyze the availability and pricing of perishable farm produce before and during the lockdown restrictions imposed due to Covid-19. This paper also proposes machine learning and deep learning models to help the farmers decide on an appropriate market to sell their farm produce and get a fair price for their product. Background Developing countries like India have regulated agricultural markets governed by country-specific protective laws like the Essential Commodities Act and the Agricultural Produce Market Committee (APMC) Act. These regulations restrict the sale of agricultural produce to a predefined set of local markets. Covid-19 pandemic led to a lockdown during the first half of 2020 which resulted in supply disruption and demand-supply mismatch of agricultural commodities at these local markets. These demand-supply dynamics led to disruptions in the pricing of the farm produce leading to a lower price realization for farmers. Hence it is essential to analyze the impact of this disruption on the pricing of farm produce at a granular level. Moreover, the farmers need a tool that guides them with the most suitable market/city/town to sell their farm produce to get a fair price. Methodology One hundred and fifty thousand samples from the agricultural dataset, released by the Government of India, were used to perform statistical analysis and identify the supply disruptions as well as price disruptions of perishable agricultural produce. In addition, more than seventeen thousand samples were used to implement and train machine learning and deep learning models that can predict and guide the farmers about the appropriate market to sell their farm produce. In essence, the paper uses descriptive analytics to analyze the impact of COVID-19 on agricultural produce pricing. The paper explores the usage of prescriptive analytics to recommend an appropriate market to sell agricultural produce. Contribution Five machine learning models based on Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Random Forest, and Gradient Boosting, and three deep learning models based on Artificial Neural Networks were implemented. The performance of these models was compared using metrics like Precision, Recall, Accuracy, and F1-Score. Findings Among the five classification models, the Gradient Boosting classifier was the optimal classifier that achieved precision, recall, accuracy, and F1 score of 99%. Out of the three deep learning models, the Adam optimizer-based deep neural network achieved precision, recall, accuracy, and F1 score of 99%. Recommendations for Practitioners Gradient boosting technique and Adam-based deep learning model should be the preferred choice for analyzing agricultural pricing-related problems. Recommendations for Researchers Ensemble learning techniques like Random Forest and Gradient boosting perform better than non-Ensemble classification techniques. Hyperparameter tuning is an essential step in developing these models and it improves the performance of the model. Impact on Society Statistical analysis of the data revealed the true nature of demand and supply and price disruption. This analysis helps to assess the revenue impact borne by the farmers due to Covid-19. The machine learning and deep learning models help the farmers to get a better price for their crops. Though the dataset used in this paper is related to India, the outcome of this research work applies to many developing countries that have similar regulated markets. Hence farmers from developing countries across the world can benefit from the outcome of this research work. Future Research The machine learning and deep learning models were implemented and tested for markets in and around Bangalore. The model can be expanded to cover other markets within India. [ABSTRACT FROM AUTHOR]

Identification of the native language from speech segment of a second language utterance, that is manifested as a distinct pattern of articulatory or prosodic behavior, is a challenging task. A method of classification of speakers, based on the regional English accent, is proposed in this paper. A database of English speech, spoken by the native speakers of three closely related Dravidian languages, was collected from a non-overlapping set of speakers, along with the native language speech data. Native speech samples from speakers of the regional languages of India, namely Kannada, Tamil, and Telugu are used for the training set. The testing set contains utterances of non-native English speakers of compatriots of the above three groups. Automatic identification of native language is proposed by using the spectral features of the non-native speech, that are classified using the classifiers such as Gaussian Mixture Models (GMM), GMM-Universal Background Model (GMM-UBM), and i-vector. Identification accuracy of 87.9 % was obtained using the GMM classifier, which was increased to 90.9 % by using the GMM-UBM method. But the i-vector-based approach gave a better accuracy of 93.9 % , along with EER of 6.1 % . The results obtained are encouraging, especially viewing the current state-of-the-art accuracies around 85 % . It is observed that the identification rate of nativity, while speaking English, is relatively higher at 95.2 % for the speakers of Kannada language, as compared to that for the speakers of Tamil or Telugu as their native language. [ABSTRACT FROM AUTHOR]

Thyroid hormones are essential for all the metabolic and reproductive activities with significance to growth, and neuron development in the human body. The thyroid hormone dysfunction has many ill consequences, affecting the human population; thereby being a global epidemic. It is noticed that every one in 10 persons suffer from different thyroid disorders in India. In recent years, many researchers have implemented various disease predictive models based on Information and Communications Technology (ICT). Increasing the accuracy of disease classification is a critical and challenging task. To increase the accuracy of classification, in this paper, we propose a hybrid optimization algorithm‐based feature selection design for thyroid disease classifier with rough type‐2 fuzzy support vector machine. This work uses the hybrid optimization algorithm, which combines the firefly algorithm (FA) and butterfly optimization algorithm (BOA) to select the top‐n features. The proposed hybrid firefly butterfly optimization‐rough type‐2 fuzzy support vector machine (HFBO‐RT2FSVM) is evaluated with several key metrics such as specificity, accuracy, and sensitivity. We compare our approach with well‐known benchmark methods such as improved grey wolf optimization linear support vector machine (IGWO Linear SVM) and mixed‐kernel support vector machine (MKSVM) methods. From the experimental evaluations, we justify that our technique improves the accuracy by large thereby precise in identifying the thyroid disease. HFBO‐RT2FSVM model attained an accuracy of 99.28%, having specificity and sensitivity of 98 and 99.2%, respectively. [ABSTRACT FROM AUTHOR]

India, being an abundant source of minerals, Mineral exploration, on a large scale, is promising to provide good impact for the future of the country. India has a rich source of coal, bauxite, limestone etc. With the advent of remote sensing technologies capturing broader area, exploration of minerals has now become an appreciable problem. Satellite Cluster Images spanning over wider areas can effectively serve as a tool for mapping minerals, qualitatively and quantitatively. In this paper, an algorithm, based on decision tree classification, is developed to map minerals, specifically, coal and limestone for a specific region. Multi-temporal cluster images are employed to map dynamic change detection resulting in greater accuracy. In this paper, multi-temporal cluster images (Landsat 8 OLI/TIRS) are analyzed to map coal, limestone and 'no-mineral' regions with the help of the algorithm developed using decision tree classification. Classification results are compared with ground truth data for assessing its accuracy. [ABSTRACT FROM AUTHOR]

Summary: The substantial increase in new integration of wind power has required changes in grid codes to ensure grid security. Providing low voltage ride through (LVRT) protection helps to sustain the transient conditions without tripping and supports its recovery post‐fault. The grid codes for LVRT capability in countries with substantial wind capacity are surveyed in this paper. The challenges that arise, endangering the grid security due to the non‐implementation of these grid codes in the wind turbines (WT) which were pre‐installed before the mandatory grid code was established is rarely a topic focused in literature. The LVRT capability requirement as per the wind turbine type classification is elucidated. The challenges due to enhancement and upgradation of technology include financial constraints for additional LVRT fittings, planning and renovation. Discussion and prognosis on the challenges provide direction for investigation and research. [ABSTRACT FROM AUTHOR]

This paper aims to improve the site classification scheme and amplification factors for the Indian Seismic Code. The present code IS 1893 (1):2016 ignores the amplification of effective peak ground acceleration due to the existence of soft soil layers. This results in lower design spectral accelerations on soil sites compared to those adopted by codes in other nations. 124 well documented sites from India are considered for ground response analysis using the equivalent linear procedure of DEEPSOIL. Time histories recorded from India and abroad are made spectrum compatible with the Type I spectra of Indian code, corresponding to all the seismic intensities with Effective Peak Ground Acceleration of 0.1 g, 0.16 g, 0.24 g and 0.36 g. Statistical methods are used to construct mean, mean plus standard deviation and mean minus standard deviation response spectra and amplification factors for the sites considered. Before the statistical analysis, each amplification factor curve is normalized with the effective site period (Tg) which exhibits a "characteristic peak at values of T/Tg not far from 1". Normalized acceleration response spectra as well as period dependent soil amplification factors and equations are proposed for the new classification system. It is observed that the stratigraphy, impedance contrast, sediment thickness and site period play an important role in determining the soil amplification factors and therefore, should be considered in the future site classification schemes of the Indian Seismic Code. [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]

In the age of social media, every second thousands of messages are exchanged. Analyzing those unstructured data to find out specific emotions is a challenging task. Analysis of emotions involves evaluation and classification of text into emotion classes such as Happy, Sad, Anger, Disgust, Fear, Surprise, as defined by emotion dimensional models which are described in the theory of psychology (www 1; Russell, 2005). The main goal of this paper is to cover the COVID-19 pandemic situation in India and its impact on human emotions. As people very often express their state of the mind through social media, analyzing and tracking their emotions can be very effective for government and local authorities to take required measures. We have analyzed different machine learning classification models, such as Naïve Bayes, Support Vector Machine, Random Forest Classifier, Decision Tree and Logistic Regression with 10-fold cross validation to find out top ML models for emotion classification. After tuning the Hyperparameter, we got Logistic regression as the best suited model with accuracy 77% with the given datasets. We worked on algorithm based supervised ML technique to get the expected result. Although multiple studies were conducted earlier along the same lines, none of them performed comparative study among different ML techniques or hyperparameter tuning to optimize the results. Besides, this study has been done on the dataset of the most recent COVID-19 pandemic situation, which is itself unique. We captured Twitter data for a duration of 45 days with hashtag #COVID19India OR #COVID19 and analyzed the data using Logistic Regression to find out how the emotion changed over time based on certain social factors. [ABSTRACT FROM AUTHOR]

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

Urban development is an overall marvel yet the rate of urbanization is quick in creating nation like India. It is chiefly determined by sloppy development, expanded movement and quickly expanding populace. In this unique situation, land use/ land cover change are viewed as one of the focal parts in current techniques for overseeing normal assets and observing ecological changes. In India, urban development has brought genuine misfortunes of agrarian land and water bodies. Urban development is in charge of an assortment of urban ecological issues like diminished air quality, expanded overflow and ensuing flooding, expanded nearby temperature, decay of water quality, and so on. India had various quickly developing urban areas. Jaipur urban areas in Rajasthan state are extending quickly with differing development rates and patterns. In this specific situation, geospatial innovations and remote detecting technique give basic devices which can be connected in the investigation of land utilize change recognition. This paper is an endeavor to survey the land utilize change identification by utilizing GIS in Jaipur urban built-up area from 1989 to 2013. Change identification examination demonstrates that developed territory has been expanded from 10 sq. km in 1989 to 225 sq. km in 2013. [ABSTRACT FROM AUTHOR]

This paper proposes a Probabilistic Neural Network (PNN) based framework for classification of lithology from a number of seismic attributes. The PNN has been the natural choice for classification in several research areas due to its insensitivities towards outliers and higher computational speed compared to multilayer perceptron (MLP) networks. Initially, the lithology is labelled into four classes such as sand, shaly sand, sandy shale, and shale through thorough analysis of multiple well logs by a proficient geologist. The seismic attributes and well logs pertaining to twelve closely spaced boreholes from a western onshore hydrocarbon field in India are used in this study. The performance of the designed framework consisting of pre-processing, classification, and lithological maps generation stages is compared with existing supervised classifiers in terms of classification accuracy, sensitivity, and specificity and the results are reported. The selection of appropriate parameters associated with individual classifier and importance of individual seismic predictors are also investigated. Finally, lithology maps indicating the different classes are produced using the tuned parameters of PNN over the study area. This framework would be of immense help to geologists along with other geological measures to estimate the probability of the presence of hydrocarbon in a large study area. • A framework having pre-processing, classification and lithological maps generation. • Generation of lithology maps over the study area. • Comparison with supervised classifiers using accuracy, sensitivity, and specificity. • Included results of hyperparameter tuning for different classifiers. • Investigation on the relative importance of the predictor variables. [ABSTRACT FROM AUTHOR]

This paper analyses the literature of classification published during 2000 to 2009 and finds that there is sustainability in the growth of literature on classification in the first decade of the 21st century. It traces the pattern in scattering of literature on classification in library and information science (LIS) journals and concludes that the literature adheres to the Bradford's law of scattering. It produces rank list of journals publishing the literature on classification and identifies authorship patterns and the prominent writers in classification. The research finds that the Indian LIS writers have shown sustained interest in classification domain. [ABSTRACT FROM AUTHOR]

Abstract: Mine fires due to spontaneous combustion in coal mines is a global concern. This leads to serious environmental and safety hazards and considerable economic losses. Therefore it is essential to assess and classify the coal seams with respect to their proneness to spontaneous combustion to plan the production, storage and transportation capabilities in mines. This paper presents the formulation of clustering problem into a linear assignment model and the application of a discrete particle swarm optimization approach for the classification of coal seams based on their proneness to spontaneous combustion. In this research work, twenty nine coal samples of varying ranks belonging to both high and low susceptibilities to spontaneous combustion have been collected from all the major coalfields of India. Using moisture, volatile matter, and ash content and crossing point temperature of the coal samples as the parameters, the proposed algorithm has been applied to classify the coal seams into three different categories. This classification will be useful for the planners and field engineers for taking ameliorative measures in advance for preventing the occurrence of mine fires. [ABSTRACT FROM AUTHOR]

Abstract: Mine fires due to spontaneous heating are an inherent but serious problem in the coal mines across the globe. Past research studies have proposed many classification schemes for categorization of coal seams with respect to their spontaneous heating susceptibility. This paper presents application of an empirical approach for classification of coal seams based on their proneness to spontaneous heating. In this research work, twenty nine coal samples of varying ranks belonging to both high and low susceptibility to spontaneous heating have been collected from all the major coalfields of India. Using moisture, volatile matter, ash and crossing point temperature of the coal samples as the parameters, principal component analysis has been applied to classify the coal seams into three different categories. This classification will be useful for the planners and field engineers for taking ameliorative measures in advance for preventing the occurrence of mine fires. [Copyright &y& Elsevier]

This paper briefly traces the history of library education in India and examines the status of bibliographic organization and control in the country as a major factor influencing the nature and content of courses in knowledge organization. The two basic documents from the University Grants Commission-the first one issued in 1965 and the recent one in 2001-that have influenced university-level course contents are examined. Finally, the nature of changes that are being brought about in recent years in some universities are highlighted. [ABSTRACT FROM AUTHOR]

This paper presents the seasonal mean, rain integral parameters, and gamma parameters at the surface associated with the different precipitating systems over a coastal station Thumba (8.53°N, 76.87°E). The primary datasets consist of Joss-Waldvogel disdrometer (JWD) observations collected during 2007–2015 and 2018 over Thumba. The precipitating cloud systems observed over three seasons (pre-monsoon: March-May, Monsoon: June-September and post-monsoon: October and November) are classified into convective, transition and stratiform rain based on the ratio of mass-weighted mean diameter (D m) and rain rate (R). The results show a distinct difference in raindrop size distribution (DSD) patterns in the convective, transition and stratiform rain types. In general, the mean DSD spectra for convective precipitation is found to be broad compared to that of the transition and stratiform rain types. The DSD spectra in the convective precipitation have relatively higher (smaller) number concentration of large (small) raindrops as compared to other precipitation types, resulting in larger D m values than the other two rain regimes. These differences in DSD for the convective, transition and stratiform resulted in a systematic variation in Z-R relationships. A distinct inverse relationship is found between A and b for convective and stratiform rain types irrespective of seasons. The coefficient A (exponent b) is found to be larger (smaller) for stratiform (convective) rain regime. Out of about 1826 h of total precipitation, stratiform rain is observed for 63% of the time, while convective and transition rain is observed for about 8% and 29% respectively. However, the contribution of stratiform rain is 11% of total rainfall, while that of convective and transition rain are about 55% and 35% respectively, consistent with the earlier studies. • Precipitating clouds are classified into convective, transition and stratiform. • Distinct raindrop size distribution (DSD) spectras observed for three rain types. • Mean DSD spectra for convective rain is broader compared to other two rain types. • Inverse relation found between A and b for convective and stratiform rain types. [ABSTRACT FROM AUTHOR]

The ability to create and sustain educational infrastructure is a major challenge to nations across the world. Today, information technology is increasingly being used to alleviate this problem by bridging the gap between learners and the textual materials by automating the process of teaching and learning. Due to this, there has been a steep rise in the information need for pedagogical content in recent years. Although there is increasing interest in building question-answering systems, there is a scarcity of intelligent tutoring systems, particularly, in physics education that can aid both students and teachers in secondary education. In this paper, we introduce a novel method for multi-label classification of paragraphs, where the paragraphs are chosen from physics subject of 6 t h to 1 2 t h grades from the curriculum of Central Board of Secondary Education (CBSE), India. This curriculum is common across India. For this purpose, we have constructed an attention-based recurrent interleaved multi-task learning (MTL) network, namely InPHYNet that can be used for any general purpose multi-label classification task related to the educational domain. The proposed solution is contextual and scalable. Although related to physics education, it is generalizable as an approach for other subjects. We perform experiments (i) to verify and validate the labels of data collected, and (ii) to conduct robust analysis of the proposed InPHYNet network. It is observed to yield significant accuracy on the dataset and can be used for any education-based text classification/annotation or as a module within the educational question-answering systems to enhance its quality. • An attention based multi-task recurrent network is proposed for multilabel classification of school physics text data of 6th to 12th grade. • The proposed solution is contextual and scalable. • Although related to physics education, the proposed model is generalizable as an approach to other subjects. • Importantly, it is observed that there is a correlation between information holding capacity and grade. • The proposed work can contribute to building an intelligent tutoring system for science education. [ABSTRACT FROM AUTHOR]

In India, which has numerous officially recognized scripts, there is a primary need for categorizing the documents on the basis of the scripts used therein. Identification of script used in a document is essential for its effective handling both manually and digitally. Identification of script in a document image is an important research problem in the pattern recognition field, which, at times, suffers from the issue of growing dimensionality of the feature vector and requires an efficient feature selection technique. Keeping this fact in mind, in this paper, we propose a clustering‐based filter feature selection framework in order to extract an optimal and effective feature subset from the original feature vector. The present feature selection methodology is evaluated on a script classification problem involving handwritten documents in 12 major Indic scripts. Experiments are done at word‐level, text‐line‐level, and block‐level. Experiments demonstrate that a reasonable increment in classification accuracy has been realized using comparatively lesser number of features. The proposed framework for feature selection is computationally inexpensive and can be applied to other pattern recognition problems as well. [ABSTRACT FROM AUTHOR]