Abstract Each language is a system of understanding and skills that allow language users to work together, hypothesize, express thoughts, opinions; wishes need to be articulated. Linguistics is the research of these structures in all respects: the composition, usage, and sociology of language, in particular, are the core of linguistics. Machine learning is the research area that allows machines to learn without being specifically scheduled. In linguistics, the design of writing is understood to be a foundation for many distinct company apps and probably the most useful if incorporated with machine learning methods. Research shows that besides text tagging and algorithm training, there are major problems in the field of big data. This article provides a collaborative effort (transfer learning integrated into recurrent neural network) to analyze the distinct kinds of writing between the language's linear and noncomputational sides and to enhance granularity. In addition to this, this article creates a recurrent neural network model for learning and processing text data automatically. RNN based linguistic process is fast and cost‐effective. It analyses the text data automatically and iteratively. This is the main reason for using RNN for the linguistic process. The outcome demonstrates stronger incorporation of granularity into the language from both sides. Comparative results of machine learning algorithms are used to determine the best way to analyze and interpret the structure of the language.

Abstract The applications of IoT have been employed in diverse domains like industries, clinical care, and farming, and so forth. Nowadays, the constitution of this technology is more prevalent in clinical observation, where the wearable devices have stimulated the development of the Internet of Medical Things (IoMT). In the process of reducing the death rate, it is necessary to detect the disease at an earlier stage. The cardiac disease prediction is a major defect in the examination of the dataset in clinics. The research proposed aims to recognize the important cardiac complaint prediction characteristics by utilizing machine‐learning methodologies. Numerous projects have been established regarding the diagnosis of cardiac complaints, which results in low accuracy rate. Thus, for improving the accuracy of prediction and for cardiac complaint investigation this article utilized a fuzzy c‐means neural network (FNN) and a deep convolution neural network for feature extraction. From the clinical dataset, data were obtained for the risk prediction of cardiac complaints that includes blood pressure (BP), age, sex, chest pain, cholesterol, blood sugar, and so forth. The hearts condition is recognized by categorizing the sensor data received by FNN. The evaluation performances were carried out and the results revealed that FNN is good in predicting the cardiac complaints. In addition to this, the proposed model achieves better accuracy than the other approaches through the demonstration of simulation results. The proposed approach attains the accuracy rate of 86.4% and F1‐score of 97%, precision 76.2%, and 64.6% of FPR.

In an interference channel, IA (interference alignment) yields exquisite channel state data and uncorrelated channel components and gains high DoF (degrees of freedom). This paper proposes the clustering predicated interference alignment with the neural network. Here Adam Optimizer utilized for signal optimization and K-means clustering in which it is utilized in clustering the minuscule cells with the base station and utilizer in the heterogeneous network based on MIMO mmWave. The neural network used here is a convolutional neural network (CNN) which is integrated with the Adam optimizer. The experimental results consider the parameters particularly DoF, spectral efficiency, energy efficiency, signal to interference noise ratio (SINR), and computational complexity. While considering energy efficiency, spectral efficiency, and maximum DoF, simulation results betoken proposed method procures better performance when compared to classical methodology.