Paper deals with the predictive apriori algorithm for a disease related to hemoglobin blood test data. The dataset has been collected from public dataset. The dataset was pre-processed to remove missing and unwanted d...
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Session-based recommendation(SBR)and multibehavior recommendation(MBR)are both important problems and have attracted the attention of many researchers and *** from SBR that solely uses one single type of behavior sequ...
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Session-based recommendation(SBR)and multibehavior recommendation(MBR)are both important problems and have attracted the attention of many researchers and *** from SBR that solely uses one single type of behavior sequences and MBR that neglects sequential dynamics,heterogeneous SBR(HSBR)that exploits different types of behavioral information(e.g.,examinations like clicks or browses,purchases,adds-to-carts and adds-to-favorites)in sequences is more consistent with real-world recommendation scenarios,but it is rarely *** efforts towards HSBR focus on distinguishing different types of behaviors or exploiting homogeneous behavior transitions in a sequence with the same type of ***,all the existing solutions for HSBR do not exploit the rich heterogeneous behavior transitions in an explicit way and thus may fail to capture the semantic relations between different types of ***,all the existing solutions for HSBR do not model the rich heterogeneous behavior transitions in the form of graphs and thus may fail to capture the semantic relations between different types of *** limitation hinders the development of HSBR and results in unsatisfactory *** a response,we propose a novel behavior-aware graph neural network(BGNN)for *** BGNN adopts a dual-channel learning strategy for differentiated modeling of two different types of behavior sequences in a ***,our BGNN integrates the information of both homogeneous behavior transitions and heterogeneous behavior transitions in a unified *** then conduct extensive empirical studies on three real-world datasets,and find that our BGNN outperforms the best baseline by 21.87%,18.49%,and 37.16%on average correspondingly.A series of further experiments and visualization studies demonstrate the rationality and effectiveness of our *** exploratory study on extending our BGNN to handle more than two types of behaviors show that our BGNN can e
The fashion industry is on the verge of an unprecedented change. Fashion applications are benefiting greatly from the development of machine learning, computer vision, and artificial intelligence. In this article, we ...
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Software-defined networking (SDN) uses a controller to manage the network. Applying SDN to resist distributed denial-of-service flood (DDoS-F) attacks receives attention. A controller identifies attack flows and gives...
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Software-defined networking (SDN) uses a controller to manage the network. Applying SDN to resist distributed denial-of-service flood (DDoS-F) attacks receives attention. A controller identifies attack flows and gives rules to switches to discard attack packets. Doing so may cause the controller to be busy and impact SDN performance. P4 switches, on the other hand, can recognize DDoS-F attacks without controller involvement. However, some non-DDoS attacks like keylogging and data theft cannot be well identified by P4 switches due to their local views. Thus, the paper makes the controller and P4 switches cooperate to defend against hybrid network attacks that include both DDoS-F attacks and non-DDoS attacks. To this end, we propose a collaborative defense by control and data planes (CD2P) framework. P4 switches (i.e., data plane) find DDoS-F packets by using an entropy-aware detection scheme that can adjust thresholds based on the network status. They also report flow information (excluding DDoS-F flows) to the controller. With the deep learning technique, the controller (i.e., control plane) analyzes these reports to discover non-DDoS attacks. Hence, the controller can focus on detecting these attacks without the disturbance of many DDoS-F packets. Experimental results reveal that CD2P can quickly block DDoS-F attacks and better identify keylogging and data theft. Our contribution is to propose a novel framework for the controller and P4 switches to collaborate to defend against hybrid network attacks efficiently. IEEE
This study investigates the effectiveness of two fairness-based distribution approaches—type I and type II—in enhancing the resilience of supply chain networks (SCNs) during disruptions. The research contributes to ...
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Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization *** approach aims to leverage the strengths of mult...
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Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization *** approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization *** this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)*** proposed hybrid algorithm will be referred to as *** this fusion,the BHJO algorithm aims to leverage the strengths of each *** this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and *** meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization *** addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid ***,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem ***,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation *** rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedd
Sentiment Analysis is widely used in the process of mining the data, to predict emotion of a sentence through Natural Language Processing (NLP). The main aim is to find the accurate polarity of a sentence. Therefore, ...
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This paper proposes a strategic model for recommending recipes called SARDG. The model focuses on dynamically generating knowledge tags from the current World Wide Web structure. It uses a dynamic learning approach wi...
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With the continuous rise in computer tech, programmers and hacking occurrences are expanding and require additional security requests. Malware has been an extraordinary torment for computer clients around the world. B...
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Binary classification tasks often face challenges due to imbalanced datasets. The Weighted Binary Extreme Learning Machine (WB-ELM) has emerged as a potential solution, but its performance relies heavily on tuning fun...
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