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The rapid growth of internet technologies has provided various functions that allow easy remote user or customer access to its many systems. However, the responsibility of the system does not end after providing access to such remote users. The framework must consummate a precise level of security, because otherwise, an adversary may be able to disrupt the system or use its resources without authorization. This affects the system and its clients directly or obliquely, and to restrict access, an authentication scheme must be implemented. Recently, Nikooghadam et al. proposed an authentication and key agreement protocol and illustrated how it would be secure against various types of security attacks. In this study, we show that their proposed system is susceptible to insider, replay, and password-guessing attacks. We propose a more secure authentication system that can withstand discrete attacks, and measure the total execution time to determine what realistic implementation is needed to carry out an entire scheme. [ABSTRACT FROM AUTHOR]

Abstract: Three series of novel glitazones were designed and prepared by using appropriate synthetic schemes to incorporate glycine, aromatic and alicyclic amines via two carbon linker. Compounds were synthesized both under conventional and microwave methods. Nineteen out of twenty four synthesized compounds were evaluated for their in vitro glucose uptake activity using isolated rat hemi-diaphragm. Compounds, 6, 9a, 13a, 13b, 13c, 13f and 13h exhibited significant glucose uptake activity. Illustration about their synthesis and in vitro glucose uptake activity is described along with the structure–activity relationships. [Copyright &y& Elsevier]

The structure and function of dietary proteins, as well as their subcellular prediction, are critical for designing and developing new drug compositions and understanding the pathophysiology of certain diseases. As a remedy, we provide a subcellular localization method based on feature fusion and clustering for dietary proteins. Additionally, an enhanced PseAAC (Pseudo-amino acid composition) method is suggested, which builds upon the conventional PseAAC. The study initially builds a novel model of representing the food protein sequence by integrating autocorrelation, chi density, and improved PseAAC to better convey information about the food protein sequence. After that, the dimensionality of the fused feature vectors is reduced by using principal component analysis. With prediction accuracies of 99.24% in the Gram-positive dataset and 95.33% in the Gram-negative dataset, respectively, the experimental findings demonstrate the practicability and efficacy of the proposed approach. This paper is basically exploring pseudo-amino acid composition of not any clinical aspect but exploring a pharmaceutical aspect for drug repositioning. • Accurately predicting dietary protein subcellular composition using machine learning methods remains a challenging scientific inquiry. • This study provides a feature-fusion and clustering-based subcellular localization method for dietary proteins. • The construction of food protein feature expression models involves the use of autocorrelation and chi-density approaches. • An enhanced PseAAC (Pseudo-amino Acid Composition) method is suggested, which builds upon the conventional PseAAC. • Prediction accuracies of 99.24% on the Gram-positive dataset and 95.33% on the Gram-negative dataset, respectively, is observed. [ABSTRACT FROM AUTHOR]

• A novel Multi-objective Seagull Optimization Algorithm is proposed. • The algorithm is tested on 24 real challenging benchmark test function. • The results show the superior convergence behaviour of proposed algorithm. • The results on engineering design problems prove its efficiency and applicability. This study introduces the extension of currently developed Seagull Optimization Algorithm (SOA) in terms of multi-objective problems, which is entitled as Multi-objective Seagull Optimization Algorithm (MOSOA). In this algorithm, a concept of dynamic archive is introduced, which has the feature to cache the non-dominated Pareto optimal solutions. The roulette wheel selection approach is utilized to choose the effective archived solutions by simulating the migration and attacking behaviors of seagulls. The proposed algorithm is approved by testing it with twenty-four benchmark test functions, and its performance is compared with existing metaheuristic algorithms. The developed algorithm is analyzed on six constrained problems of engineering design to assess its appropriateness for finding the solutions of real-world problems. The outcomes from the empirical analyzes depict that the proposed algorithm is better than other existing algorithms. The proposed algorithm also considers those Pareto optimal solutions, which demonstrate high convergence. [ABSTRACT FROM AUTHOR]

The challenges of human behavior recognition based on sensor data often require addressing the needs of various new users in real-world situations, which leads to difficulties in personalizing user data and resolving concerns related to resolution. This relates to the fact that persons with different characteristics who engage in the same behavior inevitably provide varying data discrepancies. Models that are faced with unfamiliar users encounter difficulties in accurately recognizing expected behavior, and it is not practicable to gather a large amount of training data every time the model needs to be reconfigured for new users. This study introduces a novel approach known as the FSLBR model, which combines the domains of few-shot learning and behavior recognition algorithms. Initially, a meta-learning technique focused on optimization is employed to categories datasets according to user categories. The paradigm of few-shot learning has shown significant effectiveness by leveraging a limited amount of data for new tasks. The FSLBR method and an attention-centered Memory module are then smoothly integrated into the FSLBR model. In the field of behavior recognition, it is observed that a small selection of data is sufficient for good classification when dealing with new users. The capacity of the model network to extract and extrapolate data features is improved. The empirical experiments conducted on the MEx dataset support the assertion that the proposed FSLBR model demonstrates improved performance under the few-shot learning framework compared to standard deep learning methods. • Applying the ideas and methods of MetaLearning to field of behavior recognition. • MetaLearning method employed to train multiple tasks, and summarizing inductions. • Highly generalized initialization parameters, allowing model update high accuracy. • Building upon MAML-GM framework, enhancing the model's classification capabilities. [ABSTRACT FROM AUTHOR]

Devices connected to the Internet of Things (IoT) are growing in popularity across multiple industries. IoT links intelligent homes, buildings, businesses, and communities, and also links computers and mobile devices. Cybersecurity in IoT networks remains challenging, although many intrusion detection services for assault avoidance systems have been developed. Because of enormous growth in computing resources, attack surfaces, communications infrastructure, and attack rates, IoT services encounter substantial security issues. Innovative intrusion-detection systems ensuring data security, availability, and integrity have been developed to mitigate security problems; these systems are recommended for preventing common internal and external assaults and managing IoT environments. To clearly understand any impending attacks, the data are first preprocessed. Missing values are categorized with the concordance connection coefficient, and preprocessing is applied before handling of the missing values. The unevenly distributed and overlapping data are then handled through maximum likelihood estimation-based robust scalar (MLE-RS) scaling followed by random oversampling-based cluster-initialized divisive clustering (ROS-CInitDC). This study conceptualizes a Squirrel search optimization-based Bengio Nesterov momentum convolutional neural network (SSO-BNMCNN)-based approach, which indicates the relationships among information by considering causation. It keeps an optimal processing time and correctness to get data understanding. The selected features are then tested against the categorization of a He initialization-based Wasserstein gradient penalty loss generative adversarial network (HeInit-WGAN). The HeInit-WGAN technique identifies attacks more reliably and accurately than existing methods, reducing false alarms. The proposed strategy had an F-measure of 0.91, and scores of 0.96 for precision, 0.91 for awareness, and 0.98 for explicitness. Moreover, it decreased the false acceptance rate, achieving a 0.02 false positive rate and 0.09 false negative rate. • Highlights the necessity for cutting-edge intrusion detection systems by recognising the expanding attack surface, expanded communication infrastructure, and greater assault rates in IoT contexts. • Analyse and comprehend the connections between data points in order to improve intrusion detection's precision and effectiveness. • Demonstrated improved GAN methodology outperforms conventional ones, cutting down on false alarms and boosting the accuracy of intrusion detection. • Apply methods like resilient scalar scaling based on maximum likelihood estimate and classification of missing data based on concordance connection coefficients. [ABSTRACT FROM AUTHOR]

Computer-generated imagery has been made more lifelike and misleading by new Generative Adversarial Network (GAN) models, such as StyleGAN, posing severe risks to people's safety, social order, and privacy. Deceptive content creation, including Deepfakes, image tampering, and information hiding, can be facilitated through the misuse of GANs. To tackle these challenges, a detection model is proposed in this research, employing a spatial-frequency joint dual-stream convolutional neural network. Learnable frequency-domain filtering kernels and frequency-domain networks are leveraged to thoroughly learn and extract frequency-domain features, considering the discernible artifacts left by GAN images in the frequency spectrum due to the upsampling process during production. Lastly, the two sets of traits are combined to identify GAN-created faces. The proposed model outperforms state-of-the-art methods, as evidenced by experimental findings on various datasets, both in terms of detection accuracy on high-quality created datasets and generalization across datasets. [Display omitted] [ABSTRACT FROM AUTHOR]

• The rule conflicts are subdivided into seven categories. • Different rule conflicts are formally expressed so that the detection of rows can be automated to improve the accuracy. • Designs and implementation of prototype system for rule conflict detection. • F1 value of proposed rule conflict detection in the IIOT system is better. Improving Industrial Internet of Things (IIOT), device flexibility and lowering maintenance costs are significant problems. However, as the scale of the Intelligent Industrial Internet of Things (IIIOTs) system expands, the interactions between the rules get more sophisticated, potentially leading to rule discrepancies. The algorithm for Formal Rule Conflict Detection (FRCD) is created, and a thorough explanation of the procedure is given in this paper. Two IIIOT systems were used in experiments and the results were compared with three existing standard IIIOT rule conflict detection techniques. They include policy conflict detection systems based on Web semantics (Semantic Web-Based Policy Interaction Detection with Rules, (SPIDER)), conflict detection methods based on Users, Triggers, Environment entities, and Actuators (UTEA), and semiformal conflict detection methods (Identifying Requirements Interactions using Semiformal (IRIS)). The experimental results show that the FRCD rule conflict detection method is superior. [Display omitted] [ABSTRACT FROM AUTHOR]