Soumya Sen

Bio: Soumya Sen is an academic researcher from University of Calcutta. The author has contributed to research in topics: Data warehouse & Materialized view. The author has an hindex of 15, co-authored 98 publications receiving 737 citations. Previous affiliations of Soumya Sen include Information Technology University.

Evaluation of chemical constituents and free-radical scavenging activity of Swarnabhasma (gold ash), an ayurvedic drug.

Abstract: From ancient times, Swarnabhasma (gold ash) has been used in several clinical manifestations including loss of memory, defective eyesight, infertility, overall body weakness and incidence of early aging. Swarnabhasma has been used by Ayurvedic physicians to treat different diseases like bronchial asthma, rheumatoid arthritis, diabetes mellitus, nervous disorders, etc. In the present investigation, Swarnabhasma was prepared after proper purification and calcination as per Ayurvedic pharmacy which consisted of Realger (As(2)S(2)), Lead oxide (Pb(3)O(4)), Pure gold (Au) and Latex of Calotropis gigantea. Qualitative analyses indicated that Swarnabhasma contained not only gold but also several microelements (Fe, Al, Cu, Zn, Co, Mg, Ca, As, Pb, etc.). Infrared spectroscopy showed that the material was free from any organic compound. The metal content in the bhasma was determined by atomic absorption spectrometry. Acute oral administration of Swarnabhasma showed no mortality in mice (up to 1 ml /20 g b.w. of Swarnabhasma suspension containing 1mg of drug). Chronic administration of Swarnabhasma also showed no toxicity as judged by SGPT, SGOT, serum creatinine and serum urea level and histological studies. In an experimental animal model, chronic Swarnabhasma-treated animals showed significantly increased superoxide dismutase and catalase activity, two enzymes that reduce free radical concentrations in the body.

Chemical and Pharmacological Evaluation of Different Ayurvedic Preparations of Iron

Abstract: Ayurvedic preparations of metallic iron commonly categorised as different 'putas' of 'Louha Bhasma' was chemically analysed and pharmacologically investigated in iron deficiency anemia. Atomic absorption spectral (AAS) study of different putas of Louha Bhasma revealed the presence of various proportions of important metals along with varied concentration of iron in it. The effect of a representative puta viz. 50 puta of Louha Bhasma in the management of agar gel diet and phlebotomy induced iron deficiency anemia in animal model was found to be statistically highly significant (P < 0.001) in comparison to the control and standard drug Fefol treated groups.

Clinical application of modified bag-of-features coupled with hybrid neural-based classifier in dengue fever classification using gene expression data

Abstract: Dengue fever detection and classification have a vital role due to the recent outbreaks of different kinds of dengue fever. Recently, the advancement in the microarray technology can be employed for such classification process. Several studies have established that the gene selection phase takes a significant role in the classifier performance. Subsequently, the current study focused on detecting two different variations, namely, dengue fever (DF) and dengue hemorrhagic fever (DHF). A modified bag-of-features method has been proposed to select the most promising genes in the classification process. Afterward, a modified cuckoo search optimization algorithm has been engaged to support the artificial neural (ANN-MCS) to classify the unknown subjects into three different classes namely, DF, DHF, and another class containing convalescent and normal cases. The proposed method has been compared with other three well-known classifiers, namely, multilayer perceptron feed-forward network (MLP-FFN), artificial neural network (ANN) trained with cuckoo search (ANN-CS), and ANN trained with PSO (ANN-PSO). Experiments have been carried out with different number of clusters for the initial bag-of-features-based feature selection phase. After obtaining the reduced dataset, the hybrid ANN-MCS model has been employed for the classification process. The results have been compared in terms of the confusion matrix-based performance measuring metrics. The experimental results indicated a highly statistically significant improvement with the proposed classifier over the traditional ANN-CS model.

A systematic survey on influential spreaders identification in complex networks with a focus on K-shell based techniques

Abstract: Almost all the complex interactions between humans, animals, biological cells, neurons, or any other objects are now modeled as a graph with the nodes as the objects of interest and interactions as the edges. The identification of the most central or influential node in such a complex network has many practical applications in diverse domains such as viral marketing, infectious disease spreading, rumor spreading in a social network, virus/worm spreading in computer networks, etc. Many centrality measures using the position/location of a node and network structure have been proposed in the literature. The node degree, shortest paths(closeness), and betweenness are used since long with degree capturing local effect and others global effect. The k-shell considers the coreness of the nodes by dividing the network into layers or shells. Many variations of k-shell proposed in recent years, as well as many researchers, use k-shell as a building block in their heuristic technique to alleviate the problems of classical k-shell and to identify influential spreaders more elegantly. The main objective of this paper is to analyze and compare the major variations of the k-shell based methods along with representative network topology based hybrid techniques by considering a toy network with detailed computations. A discussion on different performance evaluation metrics and, simulation models such as the SIR epidemic model, has been undertaken with a comparative analysis between different state-of-the-art on a few standard real networks.

Stock Price Prediction Using LSTM on Indian Share Market

Abstract: Predicting stock market is one of the most difficult tasks in the field of computation. There are many factors involved in the prediction – physical factors vs. physiological, rational and irrational behavior, investor sentiment, market rumors,etc. All these aspects combine to make stock prices volatile and very difficult to predict with a high degree of accuracy. We investigate data analysis as a game changer in this domain.As per efficient market theory when all information related to a company and stock market events are instantly available to all stakeholders/market investors, then the effects of those events already embed themselves in the stock price. So, it is said that only the historical spot price carries the impact of all other market events and can be employed to predict its future movement. Hence, considering the past stock price as the final manifestation of all impacting factors we employ Machine Learning (ML) techniques on historical stock price data to infer future trend. ML techniques have the potential to unearth patterns and insights we didn’t see before, and these can be used to make unerringly accurate predictions. We propose a framework using LSTM (Long ShortTerm Memory) model and companies’ net growth calculation algorithm to analyze as well as prediction of future growth of a company.

The DBLP Computer Science bibliography: Evolution, research issues, perspectives

Abstract: Publications are essential for scientific communication. Access to publications is provided by conventional libraries, digital libraries operated by learned societies or commercial publishers, and a huge number of web sites maintained by the scientists themselves or their institutions. Comprehensive meta-indices for this increasing number of information sources are missing for most areas of science. The DBLP Computer Science Bibliography of the University of Trier has grown from a very specialized small collection of bibliographic information to a major part of the infrastructure used by thousands of computer scientists. This short paper first reports the history of DBLP and sketches the very simple software behind the service. The most time-consuming task for the maintainers of DBLP may be viewed as a special instance of the authority control problem; how to normalize different spellings of person names. The third section of the paper discusses some details of this problem which might be an interesting research issue for the information retrieval community.

A survey on river water quality modelling using artificial intelligence models: 2000–2020

Abstract: There has been an unsettling rise in the river contamination due to the climate change and anthropogenic activities. Last decades’ research has immensely focussed on river basin water quality (WQ) prediction, risk assessment and pollutant classification techniques to design more potent management policies and advanced early warning system. The next challenge is dealing with water-related data as they are problematic to handle owing to their nonlinearity, nonstationary feature and vague properties due to the unpredictable natural changes, interdependent relationship, human interference and complexity. Artificial intelligence (AI) models have shown remarkable success and superiority to handle such data owing to their higher accuracy to deal with non-linear data, robustness, reliability, cost-effectiveness, problem-solving capability, decision-making capability, efficiency and effectiveness. AI models are the perfect tools for river WQ monitoring, management, sustainability and policymaking. This research reports the state of the art of various AI models implemented for river WQ simulation over the past two decades (2000–2020). Correspondingly, over 200 research articles are reviewed from the Web of Science journals. The survey covers the model structure, input variability, performance metrics, regional generalisation investigation and comprehensive assessments of AI models progress in river water quality research. The increasing contaminants, the lack of funding and the deficiency in data, numerous variables and unique data time series pattern based on the geological area have increased the need for river WQ monitoring and control even more. Hence, this is highly emphasising the involvement of AI models development which can deal with missing data, able to integrate the features of a black-box model and white-box models, benchmarked model and automated early warning system are few of many points need more research. Despite extensive research on WQ simulation using AI models, shortcomings remain according to the current survey, and several possible future research directions are proposed. Overall, this survey provides a new milestone in water resource engineering on the AI model implementation, innovation and transformation in surface WQ modelling with many formidable problems in different blossoming area and objectives to be achieved in the future.

Mineral arsenicals in traditional medicines: orpiment, realgar, and arsenolite

Abstract: Mineral arsenicals have long been used in traditional medicines for various diseases, yet arsenic can be highly toxic and carcinogenic. Arsenic in traditional medicines typically comes from deliberate addition for therapeutic purposes, mainly in the form of mineral arsenicals, including orpiment (As2S3), realgar (As4S4), and arsenolite (contains arsenic trioxide, As2O3). Inorganic arsenic is now accepted in Western medicine as a first line chemotherapeutic agent against certain hematopoietic cancers. This perspective analyzes the pharmacology and toxicology of these arsenicals used in traditional medicines. Orpiment and realgar are less soluble and poorly absorbed from the gastrointestinal tract, whereas the bioavailability of arsenic trioxide is similar to inorganic arsenic salts such as sodium arsenite. Pharmacological studies show that arsenic trioxide and realgar are effective against certain malignancies. Orpiment and realgar are used externally for various skin diseases. Realgar is frequently included as an ingredient in oral traditional remedies for its antipyretic, anti-inflammatory, antiulcer, anti-convulsive, and anti-schistosomiasis actions, but the pharmacological basis for this inclusion still remains to be fully justified. Toxicological studies show that cardiovascular toxicity is the major concern for arsenic trioxide and that the gastrointestinal and dermal adverse effects may occur after prolonged use of mineral arsenicals. Little is known regarding the possible secondary cancers resulting from the long-term use of any of these arsenicals. Similar to the safety evaluation of seafood arsenicals, total arsenic content alone appears to be insufficient for mineral arsenical safety evaluation. Arsenic speciation, bioavailability, and toxicity/benefit should be considered in evaluation of mineral arsenical-containing traditional medicines.

Forecasting yield by integrating agrarian factors and machine learning models: A survey

Abstract: The advancement in science and technology has led to a substantial amount of data from various fields of agriculture to be incremented in the public domain. Hence a desideratum arises from the investigation of the available data and integrating them with a process like a crop improvement, yield prediction, crop disease analysis, identifying water stress, and so on. Computing techniques like Machine learning is a new advent for the analysis and resoluteness of these intricate issues. Various analytical models like Decision Trees, Random Forests, Support Vector Machines, Bayesian Networks, and Artificial Neural Networks, and so on, have been utilized for engendering the models and analyze the results. These methods enable to analyze soil, climate, and water regime which are significantly involved in crop growth and precision farming. This survey incorporates an overview of some of the existing supervised and unsupervised machine learning models associated with the crop yield in literature. Moreover, this survey compares one approach with other using various error measures like Root Mean Square Error (RMSE), Relative Root Mean Square Error (RRMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2).

Socio-geography of Human Mobility: A study using longitudinal mobile phone data

Abstract: A relationship between people’s mobility and their social networks is presented based on an analysis of calling and mobility traces for one year of anonymized call detail records of over one million mobile phone users in Portugal. We find that about 80% of places visited are within just 20 km of their nearest (geographical) social ties’ locations. This figure rises to 90% at a ‘geo-social radius’ of 45 km. In terms of their travel scope, people are geographically closer to their weak ties than strong ties. Specifically, they are 15% more likely to be at some distance away from their weak ties than strong ties. The likelihood of being at some distance from social ties increases with the population density, and the rates of increase are higher for shorter geo-social radii. In addition, we find that area population density is indicative of geo-social radius where denser areas imply shorter radii. For example, in urban areas such as Lisbon and Porto, the geo-social radius is approximately 7 km and this increases to approximately 15 km for less densely populated areas such as Parades and Santa Maria da Feira.