The Internet of Medical Things (IoMT) has transformed healthcare by connecting medical devices, sensors, and patients, significantly improving patient care. However, the sensitive data exchanged through IoMT is vulnerable to security attacks, raising serious privacy concerns. Traditional key sharing mechanisms are susceptible to compromise, posing risks to data integrity. This paper proposes a Timestamp-based Secret Key Generation (T-SKG) scheme for resource-constrained devices, generating a secret key at the patient's device and regenerating it at the doctor's device, thus eliminating direct key sharing and minimizing key compromise risks. Simulation results using MATLAB and Java demonstrate the T-SKG scheme's resilience against guessing, birthday, and brute force attacks. Specifically, there is only a 9 % chance of key compromise in a guessing attack if the attacker knows the key sequence pattern, while the scheme remains secure against brute force and birthday attacks within a specified timeframe. The T-SKG scheme is integrated into a healthcare framework to securely transmit health vitals collected using the MySignals sensor kit. For confidentiality, the Data Encryption Standard (DES) with various Cipher Block modes (ECB, CBC, CTR) is employed.

The article analytically summarizes the idea of applying Shannon’s principle of entropy maximization to sets that represent the results of observations of the “input” and “output” entities of the stochastic model for evaluating variable small data. To formalize this idea, a sequential transition from the likelihood function to the likelihood functional and the Shannon entropy functional is analytically described. Shannon’s entropy characterizes the uncertainty caused not only by the probabilistic nature of the parameters of the stochastic data evaluation model but also by interferences that distort the results of the measurements of the values of these parameters. Accordingly, based on the Shannon entropy, it is possible to determine the best estimates of the values of these parameters for maximally uncertain (per entropy unit) distortions that cause measurement variability. This postulate is organically transferred to the statement that the estimates of the density of the probability distribution of the parameters of the stochastic model of small data obtained as a result of Shannon entropy maximization will also take into account the fact of the variability of the process of their measurements. In the article, this principle is developed into the information technology of the parametric and non-parametric evaluation on the basis of Shannon entropy of small data measured under the influence of interferences. The article analytically formalizes three key elements: -instances of the class of parameterized stochastic models for evaluating variable small data; -methods of estimating the probability density function of their parameters, represented by normalized or interval probabilities; -approaches to generating an ensemble of random vectors of initial parameters.

The production of biocompatible medical implants is accompanied by technological and time costs. As a result, to be used in the human body, such a product must be of the highest quality. Assessing the quality of biomedical implants made of titanium alloys is relevant given their impact on the health and life of their wearer. In the case of the production of such implants by additive technologies, an important task is to evaluate the properties of the alloys from which it is made. The modern development of Artificial Intelligence allows replacing traditional assessment methods with machine learning methods for such assessment. Existing machine learning methods demonstrate very low classification accuracy, and existing hybrid systems, although increasing classification accuracy, are not sufficient to apply such schemes in practice. The authors improved the hybrid PNN-SVM system to solve this problem in this paper. It is based on the combining use of PNN, Ito Decomposition, and SVM. The PNN’s summation layer outputs were used as additional attributes to an initial dataset. Ito decomposition was used to nonlinearly model relationships between features of an extended dataset. Further classification is carried out using SVM with a linear kernel. The proposed approach’s modeling is performed based on a real-world dataset using the smart web service designed by the authors. Experimentally found an increase in the classification accuracy by 6% of the proposed system compared to existing ones. It makes it possible to use it in practice. Designed smart web service, in which the authors implemented both improved and existing hybrid classification schemes allows to quickly, easily, and without high qualification of the user to implement and explore in more detail chosen classification scheme when classification tasks in various fields of industry.