The voice identifies each person and allows them to relate to others. Mobile applications and AI tools make your study easy. In this case, it is proposed to evaluate the parameters of the voice using a mobile application in students of the subject Phonetic Grammar and Linguistics of the Bachelor of Fonoaudiology of a Faculty of Medical Sciences of the city of Mar del Plata, Argentina, identifying also tools of Artificial Intelligence Generative for their study.

A forest is a sizable tract of agricultural land that has been left untouched for several decades and is covered with trees and plants. Water hyacinth and harboring river were collected from new Calabar River in River state Nigeria. Microbiological analyses of the samples were determined using standard techniques. Bacteria population of the soil and water hyacinth compost ranged from 100.66 ± 2.07x 109 CFU/g to 56.00 ± 8.88x 108 CFU/g. The hydrocarbon utilizing bacterial (HUB) counts range from 55.33± 5.50x 108 to 31.0± 3.11x 108 CFU/g respectively. Klebsiella sp., Escherichia sp., Staphylococcus sp., Bacillus sp., Pseudomonas sp and Serratia sp. with Bacillus sp. and Pseudomonas sp. as the predominate genera. This study finds that soil from forests and water hyacinth is a good source of microbes that contain organic soil amendment of various groups of nitrogen-fixing and phosphate-solubilizing bacteria. These bacteria can be harvested as biomass or prepared into microbial suspension. To assess their effectiveness in enhancing soil quality and integrity for productive agriculture, this study suggests using the isolates from water hyacinth (Eichhornia crassipes) compost and forest soil either separately or in combination as soil amendment.

Technological advances and the popularity of cryptocurrencies have encouraged many people to seek in-depth understanding through educational platforms such as Crypto Academy which is the largest cryptocurrency community in Indonesia. This research aims to analyze public sentiment towards Crypto Academy classes using the Convolutional Neural Network method. The dataset contains 3467 comments collected through scrapping from Youtube. The data goes through a pre-processing stage which includes cleansing, case folding, normalizing, tokenizing, stopwords, stemming and also TF-IDF weighting before being used in the CNN model. This research evaluates the performance of the model on two data sharing ratios of 80:20 and 70:30 and evaluates hyperparameters such as learning rate and optimizer. The results of the CNN model with optimal hyperparameters produced the best accuracy of 83.75% at a ratio of 70:30 with a learning rate of 0.0005 and an ADAM optimizer. Based on the results of sentiment analysis, it can be seen that sentiments tend to convey more positive sentiments with a total of 1580 positive comments compared to neutral sentiments totaling 1298 and negative sentiments totaling 548. This indicates that the majority of people's views on the Crypto Academy class are very good or positive.

Fuzzy soft sets is an extension of traditional soft sets that incorporates fuzzy logic, which provides a robust framework to handle imprecision and uncertainty in decision-making systems. Within the framework, several algorithms have been developed to handle parameter reduction and decision-making challenges, with each providing unique methodologies and applications. Despite this prominence, systematic review which covers the different aspects of studies on parameter reduction and decision-making algorithms is largely lacking. Hence, this study presents a comprehensive analysis of existing algorithms for parameter reduction and decision-making in fuzzy soft sets. The paper classifies, discuss, and evaluates key methods in terms of their computational efficiency, applicability, and effectiveness in decision support. Our findings underscore the strengths and limitations of current approaches and suggest avenues for future research aimed at enhancing the efficiency and applicability of fuzzy soft set algorithms in complex decision-making scenarios.

The role of information technologies in the healthcare sector has been expanding significantly, transforming the way medical services are delivered, managed, and optimized. The integration of digital systems into healthcare institutions has enabled the efficient storage, retrieval, and sharing of patient-related data, thus enhancing the quality of medical care and operational efficiency. Through the development of electronic health records (EHRs), clinical decision support systems (CDSS), artificial intelligence (AI)-based diagnostics, and hospital management information systems (HMIS), healthcare organizations can ensure better coordination among medical professionals and improve patient outcomes. Furthermore, the use of data analytics allows healthcare administrators to monitor hospital bed capacity, emergency room efficiency, and resource allocation, contributing to more effective decision-making processes. Advanced technologies such as artificial intelligence, big data analytics, and expert systems further strengthen the predictive and analytical capabilities of healthcare institutions, enabling personalized treatment plans and improving public health surveillance. The study highlights the critical role of these technologies in ensuring patient safety, reducing medical errors, and streamlining hospital workflows. Additionally, challenges such as data security, interoperability, and ethical considerations are discussed to provide a comprehensive perspective on the implementation of IT in healthcare services. The research emphasizes the necessity of adopting cutting-edge digital solutions to achieve a more efficient, sustainable, and patient-centered healthcare system.