Water quality is essential to the population’s well-being, water resources management, and environmental development strategies. In this article, we propose a framework based on machine learning (ML) techniques for enhancing the assessment of water quality based on water quality indices (WQIs). It consists of three algorithms that could serve as a foundation for automating the evaluation of any resource based on indices and can operate locally or globally. Local-level algorithms assist in selecting suitable WQIs tailored to specific water sources and quality requirements, while global-level algorithm evaluates WQI robustness across diverse water sources. We also provide a warning system to mitigate differences in water quality evaluation using WQIs and a valuable tool (based on the features’ importance) for selecting ML models that prioritize the water parameters’ significance. The framework’s design draws upon conclusions from a case study involving the forecast and comparison of two WQIs for the Brahmaputra River. Any other data series, WQIs, and water parameters can be employed.

In the digital era, the need for precise and efficient search operations is paramount as users increasingly rely on online resources to access specific information. However, search accuracy is often hindered by errors in user queries, such as incomplete or degraded input. Errors in search queries can reduce both the precision and speed of search results, making error correction a key factor in enhancing the user experience. This paper addresses the challenge of improving search performance through query error correction. We propose a novel methodology and architecture aimed at optimizing search results across thematic websites, such as those for universities, hospitals, or tourism agencies. The proposed solution leverages an intelligent model based on Gated Recurrent Units (GRUs) and Bahdanau Attention mechanisms to reconstruct erroneous or incomplete text in search queries. To validate our approach, we embedded the model in a prototype website consolidating data from multiple universities, demonstrating significant improvements in search accuracy and efficiency.

This article proposes a novel solution aimed at streamlining the student feedback, enabling them to assess various aspects and activities within their institution. Different from the classical solution of collecting students feedback using a set of questions grouped in one or many forms, we designed a solution capable to empower students of all age groups to enhance their higher education experiences by openly expressing opinions about their enrolled faculties, providing valuable guidance to prospective college students. Our proposal introduces a generic application architecture designed to integrate the presentation of the main opportunities and offers from many universities with the student reviews on all aspects of these universities. Thus, we avoid a number of factors that reduce the quantity and objectivity of student feedback. Important elements in our architecture refer to reviewing and rating system as well as the search system which facilitate the use of the existing evaluations on different aspects of an university. We devised a generic architecture for a client-server application, specifying a minimum set of required functionalities. For validation, we designed and implemented a client-server application based on the generic architecture we outlined. The application offers several key advantages, including robust information security measures, and diverse communication channels for authenticated users within the platform. The application’s architecture includes an encryption system based on multiple encryption techniques. Additionally, an algorithm for search optimization within the platform have been developed, further enhancing its utility.