This paper comprehensively analyzes freshwater withdrawal patterns in six UN member countries—China, France, India, Russia, United Kingdom, and the United States. By analyzing historical data, this study explores the time-related trends in the use of water, identifies the factors that drive these patterns, and forecasts future water demands through the application of sophisticated time series modeling techniques, specifically the ARIMA model. Upon analysis of the results, striking differences are observed among the countries in respect to the withdrawal patterns of water and the main drivers behind these; including agricultural practices, industrial activities, demographic growth, governmental policies, among others. The ARIMA model represents the customized specific water usage of each country and provides reliable forecasts that reveal both challenges and opportunities for water resource management in the near future. This research emphasizes the need for proactive policy interventions to promote sustainable water use amid increasing demand and environmental variability.

Data catalogs play an essential role in supporting efficient data discovery, integration, and reuse. Unfortunately, many of these real-world data catalogs are often sparse and lack sufficient or consistent metadata, reducing their overall utility. Existing approaches for the automatic generation of metadata, such as rule-based systems, large language models (LLMs), retrieval-augmented generation (RAG) frameworks, and knowledge graph techniques, have their own set of limitations. These models often struggle with incomplete or diverse catalogs, can produce errors or even hallucinations, and typically depend on structured input or significant human intervention to function properly. However, their performance may be further constrained by challenges such as limited coverage, scalability, and integration complexity. An Enhanced Knowledge-Augmented Metadata Generation (EKAMG) framework has been proposed to address this issue. It uses machine learning, semantic enrichment, and external knowledge bases to automatically produce high-quality metadata from sparse data sets. The suggested approach is assessed using benchmark and real-world catalog datasets and compared with several current cutting-edge metadata generation models. The EKAMG framework achieves an improvement of 25–30% in metadata completeness, 20% increase in semantic alignment accuracy, and reduces hallucination errors by almost 10% compared to existing methods.

The paper proposes a hybrid algorithm for forecasting multiple correlated time-series data, which consists of two main steps. First, it employs a multivariate Bayesian structural time series (MBSTS) approach as a base step. This method allows for the incorporation of potentially high-dimensional regression components, and it utilizes spike and slab priors to identify a parsimonious model. Second, the algorithm includes a post-model fitting diagnostic step where the residuals from the MBSTS step are processed through a multi-input/output temporal convolutional network (M-TCN) with multiple time scale feature learning. This step serves as an alternative to traditional subjective residual-based diagnostic procedures in time-series analysis, with the aim of improving forecasting accuracy. The key advantage of the M-TCN is its ability to capture sequential information efficiently. The M-TCN expands the field of convolution kernel without increasing the number of parameters, thus enhancing the capacity of model to capture complex sequential patterns. The paper presents two applications showcasing the effectiveness of the proposed hybrid algorithm. First, it utilizes pre-lockdown data from eleven Nifty stock sectoral indices to predict stock price movements, including the initial post-lockdown upturn. In the second application, it focuses on stock market data from pharmaceutical companies involved in manufacturing COVID-19 vaccines. In both cases, sentiment data sourced from newspapers and social media serve as the regression component. Through rigorous analysis, the paper demonstrates that the hybrid model outperforms various benchmark models, including LSTM, Bidirectional Encoder Representations from Transformers (BERT)-based LSTM, Deep Transformer Model, and GRU, among others, in terms of forecasting accuracy. This underscores the utility of the hybrid algorithm, particularly in predicting stock market trends during the COVID-19 pandemic period and its associated market dynamics.

Social networks have changed the communication patterns significantly. Information available from different social networking sites can be well utilized for the analysis of users opinion. Hence, the organizations would benefit through the development of a platform, which can analyze public sentiments in the social media about their products and services to provide a value addition in their business process. Over the last few years, deep learning is very popular in the areas of image classification, speech recognition, etc. However, research on the use of deep learning method in sentiment analysis is limited. It has been observed that in some cases the existing machine learning methods for sentiment analysis fail to extract some implicit aspects and might not be very useful. Therefore, we propose a deep learning approach for aspect extraction from text and analysis of users sentiment corresponding to the aspect. A seven layer deep convolutional neural network (CNN) is used to tag each aspect in the opinionated sentences. We have combined deep learning approach with a set of rule-based approach to improve the performance of aspect extraction method as well as sentiment scoring method. We have also tried to improve the existing rule-based approach of aspect extraction by aspect categorization with a predefined set of aspect categories using clustering method and compared our proposed method with some of the state-of-the-art methods. It has been observed that the overall accuracy of our proposed method is 0.87 while that of the other state-of-the-art methods like modified rule-based method and CNN are 0.75 and 0.80 respectively. The overall accuracy of our proposed method shows an increment of 7–12% from that of the state-of-the-art methods.

In the financial sector, the stock market and its trends are highly volatile in nature. Recent studies have shown that news articles and social media analysis can have an immense impact on investors' opinion toward financial markets. Thus, the purpose of this study is to explore the relationship between news sentiment and stock market movement using information from different news agencies, business magazines, and financial portals. This study offers an application of the Bayesian structural time (BST) series model that is more transparent and facilitates better handling of uncertainty than the autoregressive integrated moving average (ARIMA) model and the vector autoregression (VAR) method by using prior information about the structure of the model. One of the main pitfalls of this model is the presumption of linearity. The long short-term memory (LSTM) model is a nonlinear model that can capture various nonlinear structures present in the data set. We propose a hybrid model, which combines the LSTM model with the BST model along with the regression component that captures information from different news sources to identify market predictors. The proposed model detects unusual behavior or anomalous pattern of the stock price movement, which makes our model superior compared to the traditional methods. Our new hybrid model accumulates error with lower rates (3.5%) and shows a remarkable performance over some of the other existing hybrid models, such as AR-MLP, ARIMA-LSTM, and VAR-LSTM model.

Sentiment analysis has become a very useful tool in recent times for studying people’s opinions, sentiments and subjective evaluation of any event of social and economic relevance, and in particular, policy decisions. The present paper proposes a framework for sentiment analysis using twitter data for the ’demonetization’ effort of the Government of India. The paper employs twitter data using Twitter API. The methodology of the paper involves collection of data from twitter from different cities of India using geolocation and preprocessing followed by a lexicon-based approach to analyse users’ sentiments over a period of five weeks preceding the policy announcement. In addition to this, the paper also attempts to analyse the sentiments of specific groups of people representing diverse interest groups.

In recent times, people share their opinions, ideas through social networking site, electronic media etc. Different organizations always want to find public opinions about their products and services. Individual consumers also want to know the opinions from existing users before purchasing product. Sentiment analysis is the computational treatment of user's opinions, sentiments and subjectivity of text. In this paper we propose a framework for sentiment analysis using R software which can analyze sentiment of users on Twitter data using Twitter API. Our methodology involves collection of data from Twitter, its pre-processing and followed by a lexicon based approach to analyze user's sentiment.