International Journal of Intelligent Systems

In 2019, a deadly coronaviral infection (COVID-19) that infected millions of people globally was detected in China. This fatal virus affects the respiratory system and currently spreads to more than 200 nations worldwide. COVID-19 may be found using a chest X-ray scan, a reliable imaging method. Although an expert may examine an X-ray scan manually, this process takes a lot of time. Therefore, deep convolutional neural networks (CNNs) may be utilized to automate this procedure. In this work, at the first step, a novel isolated 19-layer CNN model is developed from scratch to detect chest infections using X-rays. Then, the developed model is reutilized to distinguish the type of chest infection, such as COVID-19, fibrosis, pneumonia, and tuberculosis, using the transfer learning approach. Stochastic gradient descent with momentum is utilized to optimize the model. The proposed multistage framework shows 98.85% and 97% classification accuracies for chest infection detection (binary classification between normal and patient) and four-class subclassification (COVID-19, fibrosis, pneumonia, and tuberculosis) for an online chest X-ray dataset. The reliability of the proposed multistage CNN model was further validated through a new dataset, showing an accuracy of 98.5%. The proposed multistage methodology took minimal training time compared to publically available pretrained models. Therefore, the presented multistage deep learning framework can help doctors in clinical practices.

Multiattribute decision-making (MADM) approach is an effective method for handling ambiguous information in a practical situation. The process of the MADM technique has drawn a lot of interest from various academic and selection processes of extensive analysis. The aggregation operators (AOs) are the best mathematical tools and received a lot of attention from researchers. This article explored the theory of intuitionistic fuzzy IF sets (IFSs) and their certain fundamental operations. The theory of triangular norms also explores Aczel Alsina operations (AAOs) in advanced mathematical tools. The concepts of Heronian mean (HM) and geometric HM (GHM) operators are presented to define interrelationships among different opinions. We developed a list of certain AOs by utilizing AAOs under the system IF information, namely, IF Aczel Alsina HM (IFAAHM), IF Aczel Alsina weighted HM (IFAAWHM), IF Aczel Alsina GHM (IFAAGHM), and IF Aczel Alsina weighted GHM (IFAAWGHM) operators. Some particular characteristics of our invented methodologies are also presented. Solar energy is an effective, efficient resource to enhance electricity production and the country’s economic growth. Therefore, we studied an application of solar panel systems to solve real-life problems under a robust technique of the MADM approach by utilizing our invented approaches of IFAAWHM and IFAAWGHM operators. A numerical example was also given to select more suitable solar panels under our proposed methodologies. To find the competitiveness and feasibility of discussed methodologies, we make an inclusive comparative study in which we contrast the results of existing AOs with the consequences of current approaches.

Multiview learning (MVL) frequently uses support vector machine- (SVM-) based models, but it can be difficult to select appropriate kernel functions and corresponding parameters. Then, by introducing kernel-free tricks, two multiview classifiers are proposed, called -multiview kernel-free nonlinear support vector machine (-MKNSVM) and its -version, namely, -MKNSVM. They try to find a quadratic hypersurface under each view to classify the sample points and employ a consistency constraint to fuse the sample points from two views. Both the primal and dual problems of -MKNSVM and -MKNSVM do not involve kernel functions; thus, they are allowed to be solved directly. In addition, the relationship of solutions between the primal and dual problems is discussed in each classifier. For the -version and -version of MKNSVM, the meanings of their parameters and the relationship between them are analyzed in detail. The experimental results of artificial and benchmark datasets show that our methods are superior to some traditional MVL classifiers like SVM-2K, PSVM-2V, and MvTSVM, especially -MKNSVM.

With the rapid development of Internet of Things (IoT), designing a secure two-factor authentication scheme for IoT is becoming increasingly demanding. Two-factor protocols are deployed to achieve a higher security level than single-factor protocols. Given the resource constraints of IoT devices, other factors such as biometrics are ruled out as additional authentication factors due to their large overhead. Smart cards are also prone to side-channel attacks. Therefore, historical big data have gained interest recently as a novel authentication factor in IoT. In this paper, we show that existing big data-based schemes fail to achieve their claimed security properties such as perfect forward secrecy (PFS), key compromise impersonation (KCI) resilience, and server compromise impersonation (SCI) resilience. Assuming a real strong attacker rather than a weak one, we show that previous schemes not only fail to provide KCI and SCI but also do not provide real two-factor security and revocability and suffer inside attack. Then, we propose our novel scheme which can indeed provide real two-factor security, PFS, KCI, and inside attack resilience and revocability of the client. Furthermore, our performance analysis shows that our scheme has reduced modular exponentiation operation and multiplication for both the client and the server compared to Liu et al.’s scheme which reduces the execution time by one third for security levels of . Moreover, in order to cope with the potential threat of quantum computers, we suggest using lightweight XMSS signature schemes which provide the desired security properties with bit postquantum security. Finally, we prove the security of our proposed scheme formally using both the real-or-random model and the ProVerif analysis tool.

Recognizing the users of devices (or clusters of devices) who use IP addresses as unique identities on the Internet can easily enable numerous security applications. Fast and accurate user recognition is critical for supervisors to find influenced organizations connected to their networks in light of new security threats. Many users’ information scatters in the multisource data of IP addresses. Up until now, user recognition of devices has had two main problems. On the one hand, existing methods could not fully use multisource data of the IP addresses and wastes the valuable information of labels. On the other hand, only a tiny portion of devices can be tagged with highly confident known users manually, making it an urgent need to infer unknown users of devices. So, the problem of user recognition on devices is to guess the unknown user with multisource data and existing devices with known users. Therefore, this paper proposes a multiview fusion method to deal with multisource data from devices with a small number of manually labelled samples. The paper uses GraphSAGE to obtain an exemplary representation of IP addresses and designs a label encoder to fully use a small number of devices with known users. Then, the paper builds a specific unified transformer to achieve high performance to determine whether two devices have the same user. At the same time, the paper conducts real-world experiments and finds that the proposed method can achieve 0.9158 accuracy and 0.6131 F1 to find devices with the same users on the constructed dataset in the real world.

Object detection has become a crucial technology in intelligent vision systems, enabling automatic detection of target objects. While most detectors perform well on open datasets, they often struggle with small-scale objects. This is due to the traditional top-down feature fusion methods that weaken the semantic and location information of small objects, leading to poor classification performance. To address this issue, we propose a novel feature pyramid network, the adaptive learnable feature pyramid network (ALFPN). Our approach features an adaptive feature inspection that incorporates learnable fusion coefficients in the fusion of different levels of feature layers, aiding the network in learning features with less noise. In addition, we construct a context-aligned supervisor that adjusts the feature maps fused at different levels to avoid scaling-related offset effects. Our experiments demonstrate that our method achieves state-of-the-art results and is highly robust for the small object detection on the TT-100K, PASCAL VOC, and COCO datasets. These findings indicate that a model’s ability to extract discriminant features is positively correlated with its performance in detecting small objects.