Session-based recommendation is a popular research topic that aims to predict users’next possible interactive item by exploiting anonymous *** existing studies mainly focus on making predictions by considering users...
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Session-based recommendation is a popular research topic that aims to predict users’next possible interactive item by exploiting anonymous *** existing studies mainly focus on making predictions by considering users’single interactive *** recent efforts have been made to exploit multiple interactive behaviors,but they generally ignore the influences of different interactive behaviors and the noise in interactive *** address these problems,we propose a behavior-aware graph neural network for session-based ***,different interactive sequences are modeled as directed ***,the item representations are learned via graph neural ***,a sparse self-attention module is designed to remove the noise in behavior ***,the representations of different behavior sequences are aggregated with the gating mechanism to obtain the session *** results on two public datasets show that our proposed method outperforms all competitive *** source code is available at the website of GitHub.
Infectious lung diseases, such as pneumonia and COVID-19, pose significant threats to global health, with high mortality rates and substantial burdens on healthcare systems. Accurate and timely diagnosis is crucial fo...
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Infectious lung diseases, such as pneumonia and COVID-19, pose significant threats to global health, with high mortality rates and substantial burdens on healthcare systems. Accurate and timely diagnosis is crucial for effective management and treatment. This study addresses the limitations of existing diagnostic methods by proposing advanced techniques based on computer-aided diagnosis systems and enhanced machine-learning algorithms. The methodology involves the development of novel algorithms for image enhancement, segmentation, feature selection, and classification. A kurtosis-based multi-thresholding grasshopper optimization algorithm is proposed for image segmentation, reducing complexity and enhancing the accuracy of lesion identification. An improved rider optimization algorithm is also introduced for feature selection, aiming to prioritize relevant features and reduce dimensionality effectively. Furthermore, an enhanced support vector machine (SVM) algorithm for lesion classification is presented, utilizing linear mapping to generate feature scores for regions of interest. This facilitates the evaluation of the loss function and improves classification results. The approach’s effectiveness is demonstrated using datasets comprising chest X-ray and CT scan images from the LIDC-IDRI and Montgomery datasets. The improved optimization algorithms were trained and tested over the chest X-ray and CT scan image datasets. An improved SVM classified the lesions with an accuracy of 99.9% for chest X-ray images and 99.8% for CT scan images. The results proved that the improved SVM adequately classifies lung diseases from the chest X-ray and CT scan images. The findings suggest that the proposed methodologies significantly enhance the accuracy and efficiency of diagnosing pneumonia and COVID-19 from medical images. By addressing the limitations of existing diagnostic techniques, this research contributes to improving healthcare practices and ultimately reducing the burde
In the realm of computer systems, efficient utilization of the CPU (Central Processing Unit) has always been a paramount concern. Researchers and engineers have long sought ways to optimize process execution on the CP...
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With the unprecedented prevalence of Industrial Internet of Things(IIoT)and 5G technology,various applications supported by industrial communication systems have generated exponentially increased processing tasks,whic...
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With the unprecedented prevalence of Industrial Internet of Things(IIoT)and 5G technology,various applications supported by industrial communication systems have generated exponentially increased processing tasks,which makes task assignment inefficient due to insufficient *** this paper,an Intelligent and Trustworthy task assignment method based on Trust and Social relations(ITTS)is proposed for scenarios with many tasks and few ***,ITTS first makes initial assignments based on trust and social influences,thereby transforming the complex large-scale industrial task assignment of the platform into the small-scale task assignment for each ***,an intelligent Q-decision mechanism based on workers'social relation is proposed,which adopts the first-exploration-then-utilization principle to allocate *** when a worker cannot cope with the assigned tasks,it initiates dynamic worker recruitment,thus effectively solving the worker shortage problem as well as the cold start *** importantly,we consider trust and security issues,and evaluate the trust and social circles of workers by accumulating task feedback,to provide the platform a reference for worker recruitment,thereby creating a high-quality worker ***,extensive simulations demonstrate ITTS outperforms two benchmark methods by increasing task completion rates by 56.49%-61.53%and profit by 42.34%-47.19%.
With the recent demonstration of quantum computers,interests in the field of reversible logic synthesis and optimization have taken a different *** every quantum operation is inherently reversible,there is an immense ...
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With the recent demonstration of quantum computers,interests in the field of reversible logic synthesis and optimization have taken a different *** every quantum operation is inherently reversible,there is an immense motivation for exploring reversible circuit design and *** it comes to faults in circuits,the parity-preserving feature donates to the detection of permanent and temporary *** the context of reversible circuits,the parity-preserving property ensures that the input and output parities are *** this paper we suggest six parity-preserving reversible blocks(ZFATSL)with improved quantum *** reversible blocks are synthesized using an existing synthesis method that generates a netlist of multiple-control Toffoli(MCT)*** optimization rules are applied at the reversible circuit level,followed by transformation into a netlist of elementary quantum gates from the NCV *** designs of full-adder and unsigned and signed multipliers are proposed using the functional blocks that possess parity-preserving *** proposed designs are compared with state-of-the-art methods and found to be better in terms of cost of *** savings of 25.04%,20.89%,21.17%,and 51.03%,and 18.59%,13.82%,13.82%,and 27.65% respectively,are observed for 4-bit unsigned and 5-bit signed multipliers in terms of quantum cost,garbage output,constant input,and gate count as compared to recent works.
Knowledge distillation has demonstrated significant potential in addressing the challenge of unsupervised anomaly detection (AD). The representation discrepancy of anomalies in the teacher–student (T-S) model pr...
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The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic *** this article,a hybrid approach in terms of datasets as well as the methodology by utilizing a previously unexplored d...
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The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic *** this article,a hybrid approach in terms of datasets as well as the methodology by utilizing a previously unexplored dataset obtained from a private hospital for detecting COVID-19,pneumonia,and normal conditions in chest X-ray images(CXIs)is proposed coupled with Explainable Artificial Intelligence(XAI).Our study leverages less preprocessing with pre-trained cutting-edge models like InceptionV3,VGG16,and VGG19 that excel in the task of feature *** methodology is further enhanced by the inclusion of the t-SNE(t-Distributed Stochastic Neighbor Embedding)technique for visualizing the extracted image features and Contrast Limited Adaptive Histogram Equalization(CLAHE)to improve images before extraction of ***,an AttentionMechanism is utilized,which helps clarify how the modelmakes decisions,which builds trust in artificial intelligence(AI)*** evaluate the effectiveness of the proposed approach,both benchmark datasets and a private dataset obtained with permissions from Jinnah PostgraduateMedical Center(JPMC)in Karachi,Pakistan,are *** 12 experiments,VGG19 showcased remarkable performance in the hybrid dataset approach,achieving 100%accuracy in COVID-19 *** classification and 97%in distinguishing normal ***,across all classes,the approach achieved 98%accuracy,demonstrating its efficiency in detecting COVID-19 and differentiating it fromother chest disorders(Pneumonia and healthy)while also providing insights into the decision-making process of the models.
Owing to the extensive applications in many areas such as networked systems,formation flying of unmanned air vehicles,and coordinated manipulation of multiple robots,the distributed containment control for nonlinear m...
Owing to the extensive applications in many areas such as networked systems,formation flying of unmanned air vehicles,and coordinated manipulation of multiple robots,the distributed containment control for nonlinear multiagent systems (MASs) has received considerable attention,for example [1,2].Although the valued studies in [1,2] investigate containment control problems for MASs subject to nonlinearities,the proposed distributed nonlinear protocols only achieve the asymptotic *** a crucial performance indicator for distributed containment control of MASs,the fast convergence is conducive to achieving better control accuracy [3].The work in [4] first addresses the backstepping-based adaptive fuzzy fixed-time containment tracking problem for nonlinear high-order MASs with unknown external ***,the designed fixedtime control protocol [4] cannot escape the singularity problem in the backstepping-based adaptive control *** is well known,the singularity problem has become an inherent problem in the adaptive fixed-time control design,which may cause the unbounded control inputs and even the instability of controlled ***,how to solve the nonsingular fixed-time containment control problem for nonlinear MASs is still open and awaits breakthrough to the best of our knowledge.
Permissioned blockchain is a promising methodology to build zero-trust storage foundation with trusted data storage and sharing for the zero-trust network. However, the inherent full-backup feature of the permissioned...
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This research Pteropus Unicinctus optimisation (PUO) is proposed which is mathematically derived from the echolocation character of Pteropusand and the eye power searching ability of Unicinctus. The integration of con...
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