Biometric security is a major emerging concern in the field of data security. In recent years, research initiatives in the field of biometrics have grown at an exponential rate. The multimodal biometric technique with enhanced accuracy and recognition rate for smart cities is still a challenging issue. This paper proposes an enhanced multimodal biometric technique for a smart city that is based on score-level fusion. Specifically, the proposed approach provides a solution to the existing challenges by providing a multimodal fusion technique with an optimized fuzzy genetic algorithm providing enhanced performance. Experiments with different biometric environments reveal significant improvements over existing strategies. The result analysis shows that the proposed approach provides better performance in terms of the false acceptance rate, false rejection rate, equal error rate, precision, recall, and accuracy. The proposed scheme provides a higher accuracy rate of 99.88% and a lower equal error rate of 0.18%. The vital part of this approach is the inclusion of a fuzzy strategy with soft computing techniques known as an optimized fuzzy genetic algorithm.

This article presents a novel data encryption technique suitable for Internet of Things (IoT) applications. The cryptosystem is based on the application of a Catalan object (as a cryptographic key) that provides encryption based on combinatorial structures with noncrossing or nonnested matching. The experimental part of this article includes a comparative analysis of the proposed encryption method with the Catalan numbers and data encryption standard (DES) algorithm, which is performed with machine learning-based identification of the encryption method using ciphertext only. These tests showed that it is much more difficult to recognize ciphertext generated with the Catalan method than one made with the DES algorithm. System reliability depends on the quality of the key, therefore, statistical testing proposed by National Institute of Standards and Technology was also performed. Twelve standard tests, the approximate entropy measurement, and random digression complexity analysis are applied in order to evaluate the quality of the generated Catalan key. A proposal for applying this method in e-Health IoT is also given. Possibilities of applying this method in the IoT applications for smart cities data storage and processing are provided.

This research presents the epileptic focus region localization during epileptic seizures by applying different signal processing and ensemble machine learning techniques in intracranial recordings of electroencephalogram (EEG). Multi-scale Principal Component Analysis (MSPCA) is used for denoising EEG signals and the autoregressive (AR) algorithm will extract useful features from the EEG signal. The performances of the ensemble machine learning methods are measured with accuracy, F-measure, and the area under the receiver operating characteristic (ROC) curve (AUC). EEG-based focus area localization with the proposed methods reaches 98.9% accuracy using the Rotation Forest classifier. Therefore, our results suggest that ensemble machine learning methods can be applied to differentiate the EEG signals from epileptogenic brain areas and signals recorded from non-epileptogenic brain regions with high accuracy.

This study presents a secret key sharing protocol that establishes cryptographically secured communication between two entities. A new symmetric key exchange scenario for smart city applications is presented in this research. The protocol is based on the specific properties of the Fuss‐Catalan numbers and the Lattice Path combinatorics. The proposed scenario consists of three phases: generating a Fuss‐Catalan object based on the grid dimension, defining the movement in the Lattice Path Grid and defining the key equalisation rules. In the experimental part, the authors present the security analysis of the protocol as well as its test. Also, they examine the equivalence of the proposed with Maurer's satellite scenario and suggest a new scenario that implements an information‐theoretical protocol for the public key distribution. Additionally, a comparison with related studies and methods is provided, as well as a comparison with satellite scenario, which proves the advantages of solution presented by the authors. Finally, they propose further research directions regarding key management in smart city applications.

The environment in which the decision-making process takes place is often characterized by uncertainty and vagueness and, because of that, sometimes it is very hard to express the criteria weights with crisp numbers. Therefore, the application of the Grey System Theory, i.e., grey numbers, in this case, is very convenient when it comes to determination of the criteria weights with partially known information. Besides, the criteria weights have a significant role in the multiple criteria decision-making process. Many ordinary multiple criteria decision-making methods are adapted for using grey numbers, and this is the case in this article as well. A new grey extension of the certain multiple criteria decision-making methods for the determination of the criteria weights is proposed. Therefore, the article aims to propose a new extension of the Step-wise Weight Assessment Ratio Analysis (SWARA) and PIvot Pairwise Relative Criteria Importance Assessment (PIPRECIA) methods adapted for group decision-making. In the proposed approach, attitudes of decision-makers are transformed into grey group attitudes, which allows taking advantage of the benefit that grey numbers provide over crisp numbers. The main advantage of the proposed approach in relation to the use of crisp numbers is the ability to conduct different analyses, i.e., considering different scenarios, such as pessimistic, optimistic, and so on. By varying the value of the whitening coefficient, different weights of the criteria can be obtained, and it should be emphasized that this approach gives the same weights as in the case of crisp numbers when the whitening coefficient has a value of 0.5. In addition, in this approach, the grey number was formed based on the median value of collected responses because it better maintains the deviation from the normal distribution of the collected responses. The application of the proposed approach was considered through two numerical illustrations, based on which appropriate conclusions were drawn.