Similarity join is a fundamental operation for managing semantic trajectory data. In massive data scenarios, distributed paradigms can be utilized to process similarity joins for huge amounts of semantic trajectory data, but they face the challenge of locally aware partitioning of the data. To address this problem, we propose a distributed similarity join framework based on trajectory segments. The semantic trajectory data are partitioned by segments, and semantically similar trajectories are stored in the same partition to improve the local similarity of the partitions. We design global and local indexes to efficiently manage partitioned data. We develop a filtering validation framework to enhance similarity query performance by pruning irrelevant trajectories based on the temporal, spatial, and semantic distances of trajectory segments. Extensive experiments on three real-world datasets demonstrate that our method achieves superior scalability and query efficiency, compared to other methods.

Digital Twin (DT) is a high-fidelity simulation technique that converts a real-world workplace into a virtual environment. Information technology and the industrial industry are now deeply integrated and developing as a result of the advent of DT technology. The application of this technology to telecommunications has come to the forefront. In this study, artificial intelligence methods were applied for failure root cause analysis in transport systems adapted to digital twin technology used in telecommunication systems. Three different classification methods were used to analyze the obtained data. The Synthetic Minority Oversampling Technique (SMOTE) model was used to stabilize the data. The study includes the analysis of a data pool created by correlating the data received from the devices belonging to the fiber optic cable (Fiber), Dense Wavelength Division Multiplexing (DWDM) systems, and Internet Protocol Multi-Protocol Label Switching (IP-MPLS) layer, which constitute the transport layer of telecommunication systems. The use of DT terminology in telecommunication technologies is directly related to determining which use case to address, which domains, and which parameters to take in the existing network. For this purpose, the DT structure created by topologically associating DWDM technology and IP-MPLS technology, and then matching them at fault and alarm level, is used in root cause analysis prediction using artificial intelligence algorithms. As the output of the data pool; 3 basic error conditions “MPLS Flap”, “Short Term Interruption”, and “Long Term Interruption”, which cause interruptions on the user side, are discussed. Random Forest (RF), Naive Bayes (NB), and K-Nearest Neighbors (KNN) methods were used as artificial intelligence classification methods. When the results are analyzed, it is seen that the results of the Random Forest (RF) method are the most accurate prediction.

Malicious firmware upgrading represents a critical security vulnerability in Internet of Things (IoT) devices. This study introduces HyCNNAt, a novel hybrid deep learning network for IoT malware detection that synergistically combines Convolutional Neural Networks (CNNs) with transformer attention mechanisms. HyCNNAt’s architecture vertically and horizontally stacks convolution and attention layers, enhancing the network’s generalization capabilities, capacity, and overall effectiveness. We evaluated HyCNNAt using a publicly available IoT firmware dataset, where it demonstrated superior performance with the highest accuracy ([Formula: see text]), F1-score ([Formula: see text]), and recall ([Formula: see text]), highlighting its robust classification capabilities, although its precision ([Formula: see text]) exhibited variability compared to state-of-the-art models such as CoAtNet, MobileViT, MobileNet, and MobileNet variants using transfer learning. These results underscore HyCNNAt’s potential as a robust solution for addressing the pressing challenge of IoT malware detection.

All three contrast-enhanced (CE) phases (e.g., Arterial, Portal Venous, and Delay) are crucial for diagnosing liver tumors. However, acquiring all three phases is constrained due to contrast agents (CAs) risks, long imaging time, and strict imaging criteria. In this paper, we propose a novel Common-Unique Decomposition Driven Diffusion Model (CUDD-DM), capable of converting any two input phases in three phases into the remaining one, thereby reducing patient wait time, conserving medical resources, and reducing the use of CAs. 1) The Common-Unique Feature Decomposition Module, by utilizing spectral decomposition to capture both common and unique features among different inputs, not only learns correlations in highly similar areas between two input phases but also learns differences in different areas, thereby laying a foundation for the synthesis of remaining phase. 2) The Multi-scale Temporal Reset Gates Module, by bidirectional comparing lesions in current and multiple historical slices, maximizes reliance on previous slices when no lesions and minimizes this reliance when lesions are present, thereby preventing interference between consecutive slices. 3) The Diffusion Model-Driven Lesion Detail Synthesis Module, by employing a continuous and progressive generation process, accurately captures detailed features between data distributions, thereby avoiding the loss of detail caused by traditional methods (e.g., GAN) that overfocus on global distributions. Extensive experiments on a generalized CE liver tumor dataset have demonstrated that our CUDD-DM achieves state-of-the-art performance (improved the SSIM by at least 2.2% (lesions area 5.3%) comparing the seven leading methods). These results demonstrate that CUDD-DM advances CE liver tumor imaging technology.

Abstract The visual quality of images captured under sub-optimal lighting conditions, such as over and underexposure may benefit from improvement using fusion-based techniques. This paper presents the Caputo Differential Operator-based image fusion technique for image enhancement. To effect this enhancement, the proposed algorithm first decomposes the overexposed and underexposed images into horizontal and vertical sub-bands using Discrete Wavelet Transform (DWT). The horizontal and vertical sub-bands are then enhanced using Caputo Differential Operator (CDO) and fused by taking the average of the transformed horizontal and vertical fractional derivatives. This work introduces a fractional derivative-based edge and feature enhancement to be used in conjuction with DWT and inverse DWT (IDWT) operations. The proposed algorithm combines the salient features of overexposed and underexposed images and enhances the fused image effectively. We use the fractional derivative-based method because it restores the edge and texture information more efficiently than existing method. In addition, we have introduced a resolution enhancement operator to correct and balance the overexposed and underexposed images, together with the Caputo enhanced fused image we obtain an image with significantly deepened resolution. Finally, we introduce a novel texture enhancing and smoothing operation to yield the final image. We apply subjective and objective evaluations of the proposed algorithm in direct comparison with other existing image fusion methods. Our approach results in aesthetically subjective image enhancement, and objectively measured improvement metrics.

Atrial fibrillation (AF) is one of the clinic's most common arrhythmias with high morbidity and mortality. Developing an intelligent auxiliary diagnostic model of AF based on a body surface electrocardiogram (ECG) is necessary. Convolutional neural network (CNN) is one of the most commonly used models for AF recognition. However, typical CNN is not compatible with variable-duration ECG, so it is hard to demonstrate its universality and generalization in practical applications. Hence, this paper proposes a novel Time-adaptive densely network named MP-DLNet-F. The MP-DLNet module solves the problem of incompatibility between variable-duration ECG and 1D-CNN. In addition, the feature enhancement module and data imbalance processing module are respectively used to enhance the perception of temporal-quality information and decrease the sensitivity to data imbalance. The experimental results indicate that the proposed MP-DLNet-F achieved 87.98% classification accuracy, and F1-score of 0.847 on the CinC2017 database for 10-second cropped/padded single-lead ECG fragments. Furthermore, we deploy transfer learning techniques to test heterogeneous datasets, and in the CPSC2018 12-lead dataset, the method improved the average accuracy and F1-score by 21.81% and 16.14%, respectively. Experimental results indicate that our method can update the constructed model's parameters and precisely forecast AF with different duration distributions and lead distributions. Combining these advantages, MP-DLNet-F can exemplify all kinds of varied-duration or imbalance medical signal processing problems such as Electroencephalogram (EEG) and Photoplethysmography (PPG).

Abstract The COVID‐19 pandemic has a significant impact on human health globally. The illness is due to the presence of a virus manifesting itself in a widespread disease resulting in a high mortality rate in the whole world. According to the study, infected patients have distinct radiographic visual characteristics as well as dry cough, breathlessness, fever, and other symptoms. Although, the reverse transcription polymerase‐chain reaction (RT‐PCR) test has been used for COVID‐19 testing its reliability is very low. Therefore, computed tomography and X‐ray images have been widely used. Artificial intelligence coupled with X‐ray technologies has recently shown to be more effective in the diagnosis of this disease. With this motivation, a comparative analysis of fine‐tuned deep learning architectures has been made to speed up the detection and classification of COVID‐19 patients from other pneumonia groups. The models used for this analysis are MobileNetV2, ResNet50, InceptionV3, NASNetMobile, VGG16, Xception, InceptionResNetV2 DenseNet121, which have been fine‐tuned using a new set of layers replaced with the head of the network. This research work has carried out an analysis on two datasets. Dataset‐1 includes the images of three classes: Normal, COVID, and Pneumonia. Dataset‐2, in contrast, contains the same classes with more focus on two prominent pneumonia categories: bacterial pneumonia and viral pneumonia. The research was conducted on 959 X‐ray images (250 of Bacterial Pneumonia, 250 of Viral Pneumonia, 209 of COVID, and 250 of Normal cases). Using the confusion matrix, the required results of different models have been computed. For the first dataset, DenseNet121 has obtained a 97% accuracy, while for the second dataset, MobileNetV2 has performed best with an accuracy of 81%.

Filtration to optimal exactness is mandatory since the options inundate the online world. Knowledge graph embedding is extraordinarily contributing to the recommendations, but the existing knowledge graph (KG)-based recommendation methods only exploit the correlations among the preferences and stand-alone entities, without bonding the cocurricular features and tendencies of the context. Additionally, the integration of the location-based current data of coronavirus disease 2019 (COVID-19) into the KG is necessary for the recommendation of region-aware precautionary alerts to the concerned people—an essential application of the current and future Internet of Medical Things. Therefore, in this article, we propose a novel deep collaborative alert recommendation (DCA) approach to cope with the situation. Particularly, DCA collects current online data about COVID-19, purifies, and transforms them to the KG. Furthermore, it independently encapsulates the cocurricular features and tendencies of the context in the embedding space and encodes them to the independent hidden factors via a graph neural network. The bi-end hidden factors are computed via matrix factorization to infer the potential connections. Moreover, a relevance estimator and a cross transistor are configured to enhance the generalization capability of the model. Experiments on two real-world datasets are performed to evaluate the effectiveness of DCA. Results and analysis show that the proposed approach has outperformed the baseline methods with fine improvements in providing the required recommendations.

Abstract:The aim of this research was to compare classifier algorithms including the C4.5 decision tree classifier, the least squares support vector machine (LS‐SVM) and the artificial immune recognition system (AIRS) for diagnosing macular and optic nerve diseases from pattern electroretinography signals. The pattern electroretinography signals were obtained by electrophysiological testing devices from 106 subjects who were optic nerve and macular disease subjects. In order to show the test performance of the classifier algorithms, the classification accuracy, receiver operating characteristic curves, sensitivity and specificity values, confusion matrix and 10‐fold cross‐validation have been used. The classification results obtained are 85.9%, 100% and 81.82% for the C4.5 decision tree classifier, the LS‐SVM classifier and the AIRS classifier respectively using 10‐fold cross‐validation. It is shown that the LS‐SVM classifier is a robust and effective classifier system for the determination of macular and optic nerve diseases.

Abstract:The artificial immune recognition system (AIRS) has been shown to be an efficient approach to tackling a variety of problems such as machine learning benchmark problems and medical classification problems. In this study, the resource allocation mechanism of AIRS was replaced with a new one based on fuzzy logic. The new system, named Fuzzy‐AIRS, was used as a classifier in the classification of three well‐known medical data sets, the Wisconsin breast cancer data set (WBCD), the Pima Indians diabetes data set and the ECG arrhythmia data set. The performance of the Fuzzy‐AIRS algorithm was tested for classification accuracy, sensitivity and specificity values, confusion matrix, computation time and receiver operating characteristic curves. Also, the AIRS and Fuzzy‐AIRS algorithms were compared with respect to the amount of resources required in the execution of the algorithm. The highest classification accuracy obtained from applying the AIRS and Fuzzy‐AIRS algorithms using 10‐fold cross‐validation was, respectively, 98.53% and 99.00% for classification of WBCD; 79.22% and 84.42% for classification of the Pima Indians diabetes data set; and 100% and 92.86% for classification of the ECG arrhythmia data set. Hence, these results show that Fuzzy‐AIRS can be used as an effective classifier for medical problems.