This paper proposes an unsupervised deep-learning (DL) approach by integrating Transformer and Kolmogorov-Arnold networks (KAN) termed KANsformer to realize scalable beamforming for mobile communication systems. Speci...
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In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of th...
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In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire ***,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack ***,recently a Distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been ***,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure ***,recently the high frequencies of DDoS attacks have increased *** this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has *** proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS *** obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.
With the wide application of IoT and industrial IoT technologies, the network structure is becoming more and more complex, and the traffic scale is growing rapidly, which makes the traditional security protection mech...
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Liver segmentation is one of the crucially important components in clinical-decision support system. Improving the tasks of early diagnosis of the critical liver infections and diseases such as jaundice, hepatitis inf...
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ISBN:
(数字)9798331502768
ISBN:
(纸本)9798331502775
Liver segmentation is one of the crucially important components in clinical-decision support system. Improving the tasks of early diagnosis of the critical liver infections and diseases such as jaundice, hepatitis infections and carcinoma can result in effectual outcomes and higher rates of mortality. Infection segmentation from liver is one of the challenging tasks to due to their varying structures, sizes and the location of infections. These are also affected by the low ranges of contrast and to their blurred boundaries. To resolve these pitfalls encountered by the existing approaches, compiled with less accuracy rates, higher model complexity and with higher time for both detection and segmentation. Thus, the proposed method using the U-Net with the Extended Echo Skip based ResoReplicate Layer for segmenting the infectious regions from the livers, using the image dataset from two various sources comprising various CT scan images of livers and images containing hepatic carcinomas of both the gender, 2 anonymous CT-scans and DEPOLL dataset. Thus, the segmentation aids in the appropriate and earlier diagnosis of the infections and for a delivery of precise treatment in aspects of avoiding further complications. These modifications in U-net results in replicating retrieving only the important and the precise features for the detection and are replicated in the following layers for a precise segmentation of infectious areas from liver. The overall segmentation ability of the proposed model will be validated using the probabilistic metrics comprising the Dice Coefficient rates and the Intersection over Union (IOU) score.
In an effort to support data privacy, safeguarding privacy guidelines for association mining techniques have recently been presented. Nevertheless, the algorithms are unable to conceal data frequency and have an extra...
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ISBN:
(数字)9798331518592
ISBN:
(纸本)9798331518608
In an effort to support data privacy, safeguarding privacy guidelines for association mining techniques have recently been presented. Nevertheless, the algorithms are unable to conceal data frequency and have an extra expense when inserting fictitious objects or transactions. In this research, we present a connection constraint mining technique for cloud computing encrypted data that protects confidentiality. In order to accomplish the data mining operations using the two cloud servers, the strategy first uses propagated encryption, which impairs the cloud infrastructure’ encryption capabilities and shields the server from active attacks. Second, our approach uses combinations, coincidence masking, and cybernetic transactions in tandem to conceal the information while mining in order to safeguard the privacy of the association rule utilized in the extraction procedure. We use the FP growth algorithm for rule-mining for associations avoiding the need for extra fraudulent transactions. Therefore, the suggested technique can hide data frequency while ensuring query and data privacy. We show that, with regard to the time requirements of the Mining Association, the recommended strategy operates roughly three to five times better than the current algorithm.
Classroom scheduling is vital but difficult for educational institutions, especially those with large student populations and diverse needs. Traditional methods using outdated software or manual processes often cause ...
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ISBN:
(数字)9798331528140
ISBN:
(纸本)9798331528157
Classroom scheduling is vital but difficult for educational institutions, especially those with large student populations and diverse needs. Traditional methods using outdated software or manual processes often cause inefficiencies, disagreements, and administrative workload. In low-resource regions without access to cutting-edge technology and infrastructure, these issues are severe. This paper introduces Android-based SmartClass Mobile to transform classroom scheduling. The program's SOA-based functionality offers flexibility, adaptability, and integration with institution-specific procedures. The Heuristic algorithm and Solution Space Navigation (SSN) tackle scheduling problems such as room availability, multimedia needs, and seating capacities. SSN dynamically resolves conflicts by exploring alternate configurations, while the Heuristic Algorithm assigns time slots and classrooms depending on availability and priority. SmartClass Mobile analysis indicates benefits. The solution reduces conflicts, increases user satisfaction, and decreases scheduling time by 50% compared to manual methods with 85% test user approval. Due to its mobile-first approach, the initiative is open to urban and rural institutions. Dependence on internet access for some functions is a downside, especially in resource-constrained contexts. The findings suggest that SmartClass Mobile provides a scalable, effective, and simple classroom scheduling solution. Mobile technology, advanced algorithms, and SOA concepts make it valuable for school administrators. Additions like AI and machine learning will boost its potential.
INTRODUCTION: The human blood as a collection of tissues containing Red Blood Cells (RBCs), circular in shape and acting as an oxygen carrier, are frequently deformed by multiple blood diseases inherited from parents....
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INTRODUCTION: The human blood as a collection of tissues containing Red Blood Cells (RBCs), circular in shape and acting as an oxygen carrier, are frequently deformed by multiple blood diseases inherited from parents. These hereditary diseases of blood involve abnormal haemoglobin (Hb) or anemia which are major public health issues. Sickle Cell Disease (SCD) is one of the common non-communicable disease and genetic disorder due to changes in hematological conditions of the RBCs which often causes the inheritance of mutant Hb genes by the patient.. OBJECTIVES: The process of manual valuation, predictions and diagnosis of SCD necessitate for a passionate time spending and if not done properly can lead to wrong predictions and diagnosis. Machine Learning (ML), a branch of AI which emphases on building systems that improve performance based on the data they consume is appropriate. Despite previous research efforts in predicting with single ML algorithm, the existing systems still suffer from high false and wrong predictions. METHODS: Thus, this paper aimed at performing comparative analysis of individual ML algorithms and their ensemble models for effective predictions of SCD (elongated shapes) in erythrocytes blood cells. Three ML algorithms were selected, and ensemble models were developed to perform the predictions and metrics were used to evaluate the performance of the model using accuracy, sensitivity, Receiver Operating Characteristics-Area under Curve (ROC-AUC) and F1 score metrics. The results were compared with existing literature for model(s) with the best prediction metrics performance.. RESULTS: The analysis was carried out using Python programming language. Individual ML algorithms reveals that their accuracies show MLR=87%, XGBoost=90%, and RF=93%, while hybridized RF-MLR=92% and RF-XGBoost=99%. The accuracy of RF-XGBoost of 99% outperformed other individual ML algorithms and Hybrid models. CONCLUSION: Thus, the study concluded that involving hybridized M
Vehicular metaverses, blending traditional vehicular networks with metaverse technology, are expected to revolutionize fields such as autonomous driving. As virtual intelligent assistants in vehicular metaverses, Arti...
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Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models ...
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In this paper, we study a facility location problem within a competitive market context, where customer demand is predicted by a random utility choice model. Unlike prior research, which primarily focuses on simple co...
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