With the increasing demand for smarter antenna design in advanced technology applications, well-designed antennas have been an important factor in enhancing system performance. Most traditional antenna design requires multiple iterations and extensive testing to produce a final product. Machine learning (ML) algorithms have been used as an alternative to predict the optimal design parameters, but the outcome depends highly on the ML model efficiency. With recent development in machine learning algorithms and the availability of data for antenna design, we investigated different machine learning algorithms for optimizing the output strength of three basic antennae by analyzing the signal strength of the antenna for various antenna parameters. Different regression-based ML models were used to learn the behaviors and efficiency of three different antennas and to predict the output strength (S11) for different ranges of frequencies. The experiment compared and analyzed these ML regression algorithms for three different antennas: shot antenna, patch antenna, and bowtie antenna. In addition, the paper also provides comparison of ensemble ML models for performance analysis using the best three ML algorithms from the preliminary study. This study optimizes antenna parameters and quicker and smarter antenna design procedure using ML algorithms as compared to traditional design methods.

The integration of technology and the Internet of things (IoT) in industries, especially in the manufacturing industry, has been increased massively in recent times. Some of the main objectives to implement the Industrial Internet of things (IIoT) has been but is not limited to, increase productivity, security, and effectiveness of the company. With recent development in fields like comprehensive networking and 5G technology, IIoT has been an effective tool for industrial factories to increase production, lower operational costs, regularize distribution systems, and optimize the process through remote monitoring and smart automation. With the adoption of this heterogeneous and complex technology in the industrial field, it has cope with many challenges in this sector such as interoperability, security, privacy, scalability, heterogeneity, reliability, and resource management. These challenges have been hard to overcome with an increase in real-time load in the system due to different factors and doing so has been the most essential goal and the requirement for the IIoT to be more reliable and work on its full potential.

Diabetes is a chronic condition that affects how the body processes blood sugar. Early diagnosis and management of diabetes are essential for preventing these complications. Machine Learning (ML) techniques offer an effective means to accurately diagnose diabetes by identifying key risk factors and developing predictive models. In this study, we assess the performance of 11 ML algorithms on four diabetes prediction datasets, considering the top 2, top 3, and all attributes. Through k-fold cross-validation, we ensure robust and generalizable results. We use a set of standard evaluation metrics such as accuracy, precision, recall, f1-score, and Receiver Operating Characteristic curve (ROC_AUC). Our analysis aims to determine the optimal number of features and assess how performance changes with feature additions. Notably, some ML classifiers achieve satisfactory classification and predictive abilities using only the top 2 or 3 features. Furthermore, varying dataset performances across algorithms highlight the need for assessing multiple models to identify the most suitable one. These findings enable the creation of dependable models that enhance patient outcomes by leveraging effective algorithms and pertinent features.

In this paper, we introduce an experiment evaluating performance of football players in countermovement jumps (CMJs). Three methods including time domain, frequency domain, and machine learning algorithms are proposed for performance evaluation. Correlation coefficients and p-values are given for time domain and frequency domain methods, and prediction errors are given for different machine learning algorithms.