Critical ML or CML is a critical approach development of the standard ML (SML) procedure. Conventional ML (ML) is being used in radiology departments where complex neuroimages are discriminated using ML technology. Radiologists and researchers found that sole decision by the ML algorithms is not accurate enough to implement the treatment procedure. Thus, an intelligent decision is required further by the radiologists after evaluating the ML outcomes. The current research is based on the critical ML, where radiologists’ critical thinking ability, IQ (intelligence quotient), and experience in radiology have been examined to understand how these factors affect the accuracy of neuroimaging discrimination. A primary quantitative survey has been carried out, and the data were analysed in IBM SPSS. The results showed that experience in works has a positive impact on neuroimaging discrimination accuracy. IQ and trained ML are also responsible for improving the accuracy as well. Thus, radiologists with more experience in that field are able to improve the discriminative and diagnostic capability of CML.

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In the digital technology environment, business enterprises are focusing in enhancing the precision on marketing efforts so as to remain more competitive and enhance profit margins. The application of Machine Learning, Deep Learning, Data analytics in supply chain management (SCM) is getting more popular due to the growing consumer demand and organisation are identifying various ways in order to lower the cost of transportation of goods from one location to another. Through the enhancement in theology across SCM process, the data is highly critical for analysing the location and movement of the networks so as to reduce the overall cost involvement in the goods and services. The supply chain process is highly interconnected through physical flow of goods from raw materials to finished goods, hence there are more volume of data and financial flow across the supply chain. Therefore, it is highly important in analysing the increasing complexity in supply chain and also to understand the implementation of ML in enhancing the SCM process for sustainable development and growth among the various companies. The study is an empirical investigation on the key factors influencing the design and implementation of ML in the SCM process by major companies located in India for achieving sustainable development. A total of 132 respondents were chosen and closed ended questionnaire were distributed to them, based on the data collected the researchers performed detailed statistical analysis like Correlation analysis, Multiple regression analysis using SPSS package.

Current educational gamifiers are based on the application of semi-formal and non-formal platforms to lead to more flexible learning in the current pandemic context. Given this, we propose the objective of investigating teaching and stimulating the learning of mathematical competencies in two Latin American contexts. We included in the first study 142 professors from the São Paulo Government of Brazil to study the use of the game through an evaluation by Kahoot! In another phase, we included 257 Basic Education teachers and 1,456 students from Peru to develop an experiment with non-serious video games. The conclusions allow us to argue that a large part of the teachers accept the Kahoot! Platform as an evaluating and gamifying means with equal effects to the use of traditional games in current education. Similarly, video games develop numerical thinking and mathematical reasoning in students who receive a virtual education in pandemic contexts. The contributions of the study also invite to introduce gamifiers as attitudinal increasing elements of complex learning in the classroom.

In a general parlance, wireless communication tends to be investigated based on the available methods that support enhancing the optimized data link, especially the software-based methods. AI is mainly used to create and design efficient communication network systems and variable node locations. The major factors impacting wireless communications in the current context are enhanced channel frequency, efficiency of using the bandwidth, and modulation type. The software-defined ratio enables collecting the information and analyzing the overall signal-related components and processing them in real-time situations. This will support in detecting unnecessary information and identifying latency at each stage of communication. The study is intended to measure the influence of critical factors in enhancing the overall management of wireless communication systems through the application of AI technologies. The researchers used the questionnaire method in order to collect the data from the respondents and enable them to analyze the data using the SPSS data package.