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this article examines the impact, potentials, challenges, and future prospects of using Machine Learning (ML) algorithms in clinical psychology, with a focus on basic research and clinical application. ML is reshaping both research and clinical practice by analyzing complex data and uncovering patterns for understanding mental disorders and predicting diagnoses, among other noteworthy aspects. despite the challenges of interpretation, privacy, and bias, the transformative potential of ML algorithms in clinical psychology is acknowledged. interdisciplinary collaboration is essential to maximize these benefits. emphasizing the importance of addressing ethical and practical considerations, this paper envisions a future where ML algorithms become integral tools in the research, diagnosis, and treatment of mental disorders, contributing to an overall enhancement in people's quality of life. [ABSTRACT FROM AUTHOR]

The water scarcity is a concern of the agricultural industry as it uses four fifths of the of the total fresh water consumed for irrigation and two thirds of the total used for human consumption. For this reason, the development of systems that optimize the use of water in irrigation is essential. In the greenhouses of the UEB "Valle del Yabú" of the Santa Clara municipality, irrigation is carried out using a drip-based system that requires the presence of an operator for decisionmaking who does not have information about some of the hydrometeorological variables that govern the crop. This paper focused on to design a support system for decision-making in irrigation based on machine learning. As an important parameter of the system, the evapotranspiration coefficient of the crop is calculated using the Turc formula. The collected environmental data is conditioned and linear regression, regressive random forests, and gradient-boosted trees regression models are trained with them to determine future evapotranspiration values using the Apache Spark framework. The model that obtained the best results was the regressive random forest with a coefficient of determination (r2) of 0,79 and with it the volume of water lost by the crop is calculated. Finally, the system was able to provide the estimates of both variables, which favour decision-making by specialists. [ABSTRACT FROM AUTHOR]

Performance tests are crucial to measure the quality of software developments, since they allow identifying aspects to be improved in order to achieve customer satisfaction. The objective of this research was to identify the optimal Machine Learning technique to predict whether or not a software development meets the customer's acceptance criteria. A dataset with information obtained from web services performance tests and the F1-score quality metric were used. This paper concludes that, although the Random Forest technique obtained the best score, it is not correct to state that it is the best Machine Learning technique; the quantity and quality of the data used in the training play a very important role, as well as an adequate processing of the information. [ABSTRACT FROM AUTHOR]

Traffic control in data networks has recently become very important due to the massive use of computer networks in different areas of society. Different techniques are usually used to carry out effective traffic control, allowing, among other things, to classify, predict and monitor network traffic. These techniques have evolved and are currently supported by artificial intelligence tools, which have made it possible to improve the results obtained with conventional techniques. This paper collects the different contributions made by the field of artificial intelligence to the improvement of these techniques and network management in general. The article describes the contributions made in aspects such as security, prediction, and classification of data traffic, as well as the optimization of routing in a computer network. [ABSTRACT FROM AUTHOR]

This paper describes a process of semi-automatic systematic reviews in scientific research. On the one hand, it is presented a software prototype based on artificial intelligence, semi-automatic systematic reviews tool. On the other hand, a study case of a phd thesis project shows the maps and information with aggregate value. In addition, the software prototype were evaluated by 40 students of master and Ph.D projects, they report the utility of the software tool by a survey. This work presents the modules and functionalities, software architecture and software deployment. Furthermore, a comparative study of a set of similar software tools has been conducted according to the following criteria: input files, analysis and mapping, and software license. Finally, it is discussed the results of the comparative study, the implementation of artificial intelligence techniques, the software scalability and the easiness of include new analysis and maps. [ABSTRACT FROM AUTHOR]

This paper offers a comparative analysis of the effectiveness of eight popular forecasting methods: univariate, linear, discriminate and logit regression; recursivepartitioning, rough sets, artificial neural networks, and DEA. Our goals are: clarify the complexity-effectiveness balance of each methodology; identify a reduced set of independent variables that are significant predictors whatever the methodology is; and discuss and relate these findings to the financial theory, to help consolidate the foundations of a theory of financial failure. Our results indicate that, whatever the methodology is, reliable predictions can be made using four variables; these ratios convey informatión about profitability, financial structure, rotation, and operating cash flows. [ABSTRACT FROM AUTHOR]

This paper proposes a comprehensive evaluation of supervised classification algorithms without semantic information for opinion mining on Twitter. The evaluation will assess the potential of this approach to tackle the given problem. Also, as result of evaluating, the impact of different variants of the text preprocessing is assessed, as well as different alternatives of dimension reduction techniques in the problem solution. Finally, the results obtained in the experiments offer evidence to direct the use of supervised classification to classify opinions and its valence in a domain of short messages such as Twitter. [ABSTRACT FROM AUTHOR]

One of the most fundamental problems formulated to machine learning is the development of habits and abilities in complex systems modeling. At the present paper a system named Generator of Problems Hypermedia is described and developed. This software system allows automating the process of optimization problem construction and automatic generation of problem statements for complex problem situations using the multimedia technology tools and intelligent interfaces. The work possesses significant importance in the introduction of computers in Education and in the development of Mathematical Modeling. Their successful application increases considerably the efficiency of the teaching and learning process. By means of the rational introduction of the Multimedia Technology tools students have exercised the construction and representation process of is used with the technology .net for Windows, as welloptimization problem situations. The system C as diverse multimedia technologies. [ABSTRACT FROM AUTHOR]