Power harvesting efficiency remains a key limiting factor for passive radio frequency identification (RFID) tags. This study aims to improve the power harvesting efficiency of next-generation RFID tags by implementing...
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Although deep neural networks have demonstrated exceptional performance in various fields, especially in image processing, they still face some significant unresolved challenges. Catastrophic forgetting is one of the ...
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With the rising prominence of gold as a lucrative investment avenue in Iran, this research delves into predicting the future price of 18-carat gold. In pursuit of this objective, a comprehensive comparison is conducte...
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In an era characterized by the overflow of textual information, the demand for effective text summarization techniques has become increasingly evident. This research study presents a novel solution to address this dem...
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Federated learning (FL) trains a model collaboratively but is susceptible to backdoor attacks for its privacy-preserving nature. Existing defenses against backdoor attacks in FL always make specific assumptions on dat...
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Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient *** different types of brain tumors,including gliomas,meningiomas,pituitary tumors...
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Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient *** different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI *** approaches predominantly rely on traditional machine learning and basic deep learning methods for image *** methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI *** the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor *** approach highlights a major advancement in employing sophisticated machine learning techniques within computer Science and engineering,showcasing a highly accurate framework with significant potential for healthcare *** model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification *** successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current *** integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider *** research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.
Ubiquitous intelligence empowered internet of vehicles (UIIoV) is an emerging paradigm where network entities such as mobile vehicles, edge/cloud servers, and intermediate nodes interact to achieve effective data sens...
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Lightweight video representation techniques have advanced significantly for simple activity recognition, but they still encounter several issues when applied to complex activity recognition: (i) The presence of numero...
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A mobile ad hoc network (MANET) is an independent wireless temporary network established by employing a set of mobile nodes (i.e. laptops, smartphones, iPods, etc.) appropriate for the environment in which the network...
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A mobile ad hoc network (MANET) is an independent wireless temporary network established by employing a set of mobile nodes (i.e. laptops, smartphones, iPods, etc.) appropriate for the environment in which the network infrastructures are not fixed. The most common problems faced by MANET are energy efficiency, high energy consumption, low network lifetime as well as high traffic overhead which create an impact on overall network topology. Hence, it is necessary to provide an energy-effective CH election to take steps against such issues. Therefore, this paper proposes a novel model to enhance the network lifetime and energy efficiency by performing a routing strategy in MANET. In this paper, an optimal CH is selected by proposing a novel Fuzzy Marine White Shark optimization (FMWSO) algorithm which is obtained by integrating fuzzy operation with two optimization algorithms namely the marine predator algorithm and white shark optimizer. The proposed approach comprises three diverse stages namely Generation of data, Cluster Generation and CH selection. A novel FMWSO algorithm is proposed in such a way to determine the CH selection in MANET thereby enhancing the network topology, network lifetime and minimizing the overhead rate, and energy consumption. Finally, the performance of the proposed FMWSO approach is compared with various other existing techniques to determine the effectiveness of the system. The proposed FMWSO approach consumes minimum energy of 0.62 mJ which is lower than other approaches.
Temporal networks are an effective way to encode temporal information into graph data *** the bursting cohesive subgraph(BCS),which accumulates its cohesiveness at the fastest rate,is an important problem in temporal ...
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Temporal networks are an effective way to encode temporal information into graph data *** the bursting cohesive subgraph(BCS),which accumulates its cohesiveness at the fastest rate,is an important problem in temporal *** BCS has a large number of applications,such as representing emergency events in social media,traffic congestion in road networks and epidemic outbreak in ***,existing methods demand the BCS lasting for a time interval,which neglects the timeliness of the *** this paper,we design an early bursting cohesive subgraph(EBCS)model based on the k-core to enable identifying the burstiness as soon as *** find the EBCS,we first construct a time weight graph(TWG)to measure the bursting level by integrating the topological and temporal ***,we propose a global search algorithm,called GS-EBCS,which can find the exact EBCS by iteratively removing nodes from the ***,we propose a local search algorithm,named LS-EBCS,to find the EBCS by first expanding from a seed node until obtaining a candidate k-core and then refining the k-core to the result subgraph in an optimal time ***,considering the situation that the massive temporal networks cannot be completely put into the memory,we first design an I/O method to build the TWG and then develop I/O efficient global search and local search algorithms,namely I/O-GS and I/O-LS respectively,to find the EBCS under the semi-external *** experiments,conducted on four real temporal networks,demonstrate the efficiency and effectiveness of our proposed *** example,on the DBLP dataset,I/O-LS and LS-EBCS have comparable running time,while the maximum memory usage of I/O-LS is only 6.5 MB,which is much smaller than that of LS-EBCS taking 308.7 MB.
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