Advances in wireless systems have encouraged the growth and improvement of tiny, low-cost, efficient, and multi-tasking smart sensors. Wireless sensor networks are employed for sensing, collecting data and information, analysing it, and sending it to the main center to be considered quickly. During the performance, a wide range of issues appear, and the existence of some restrictions on the movement of sensors, reliable data collection mechanisms and transferring them properly and securely to the main center, and issues in the sensor network topology. In fact, all traditional methods need more ability to deal with and solve these situations. In this regard, the authors decided to highlight the importance and role of artificial intelligence technologies in designing wireless sensor networks by proposing a hybrid model that helps to construct a more practical sensor network and solve all the situations it faces. These techniques significantly improve network performance, extend battery life, develop smart nodes, and improve network functionality.

Knowledge of the pathogen-host interactions between the species is essentialin order to develop a solution strategy against infectious diseases. In vitro methods take extended periods of time to detect interactions and provide very few of the possible interaction pairs. Hence, modelling interactions between proteins has necessitated the development of computational methods. The main scope of this paper is integrating the known protein interactions between thehost and pathogen organisms to improve the prediction success rate of unknown pathogen-host interactions. Thus, the truepositive rate of the predictions was expected to increase.In order to perform this study extensively, encoding methods and learning algorithms of several proteins were tested. Along with human as the host organism, two different pathogen organisms were used in the experiments. For each combination of protein-encoding and prediction method, both the original prediction algorithms were tested using only pathogen-host interactions and the same methodwas testedagain after integrating the known protein interactions within each organism. The effect of merging the networks of pathogen-host interactions of different species on the prediction performance of state-of-the-art methods was also observed. Successwas measured in terms of Matthews correlation coefficient, precision, recall, F1 score, and accuracy metrics. Empirical results showed that integrating the host and pathogen interactions yields better performance consistently in almost all experiments.

Objective: In response to the nuanced glycemic challenges faced by women with iron deficiency anemia (IDA) associated with diabetes, this study uses advanced machine learning algorithms to redefine hemoglobin (Hb)A1c measurement values. We aimed to improve the accuracy of glycemic interpretation by recognizing the critical interaction between erythrocytes, iron, and glycemic levels in this specific demographic group.Methods: This retrospective observational study included 17,526 adult women with HbA1c levels recorded from 2017 to 2022. Samples were classified as diabetic, prediabetic, or non‐diabetic based on HbA1c and fasting blood glucose (FBG) levels for distribution analysis without impacting model training. Support Vector Machines, Linear Regression, Random Forest, and K‐Nearest Neighbor algorithms as machine learning (ML) methods were used to predict HbA1c levels. Following the training of the model, HbA1c values were predicted for the IDA samples using the trained model.Results: According to our results, there has been a 0.1 unit change in HbA1c values, which has resulted in a clinical decision change in some patients.Discussion: Using ML to analyze HbA1c results in women with IDA may unveil distinctions among patients whose HbA1c values hover near critical medical decision thresholds. This intersection of technology and laboratory science holds promise for enhancing precision in medical decision‐making processes.

POMA test, is a type of test used to evaluate an individual's ability to perform functional movements and activities and is commonly used in the field of rehabilitation and physical therapy to assess an individual's mobility and identify areas for improvement. POMA evaluation is typically conducted by experts who are trained in using this tool to assess mobility and balance in individuals. However, it is possible that the POMA score may vary depending on the expert who conducts the evaluation because different experts may have different approaches to evaluation, and an individual expert's evaluation may also vary over time. These differences in the evaluation may lead to variations in the POMA score and may impact its reliability. Gait analysis, on the other hand, is objective in nature, providing a more reliable way of assessing the mobility of individuals. This study aimed to predict the measurements obtained in the gait portion of the POMA test (POMA-G) based on objectively obtained spatiotemporal gait parameters. To do this, a dataset consisting of gait parameters from 44 older adults was used. The POMA-G scores were rated by two physiotherapists, one of whom was an expert and the other who was familiar with the test but less experienced. The study also included an analysis of the reliability of physiotherapists’ assessment and the proposed prediction models.