In the era of COVID-19, past face recognition algorithms' performance is downgraded due to the partial occlusion of face mask. A new Indian face image dataset has been proposed titled, Handcrafted Indian Face (HIF...
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One of the successful practical applications of chaos theory and nonlinear dynamics is chaos-based cryptology studies. In this study, a new chaotic system is proposed. The proposed chaotic system generator model has a...
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In recent years, transformer-based photo captioning frameworks plays a crucial role in improving individuals’ overall well-being, self-reliance, and inclusivity by giving them access to visual content via written and...
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It is a significant and challenging task to detect the informative features to carry out explainable analysis for high dimensional data,especially for those with very small number of *** selection especially the unsup...
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It is a significant and challenging task to detect the informative features to carry out explainable analysis for high dimensional data,especially for those with very small number of *** selection especially the unsupervised ones are the right way to deal with this challenge and realize the ***,two unsupervised spectral feature selection algorithms are proposed in this *** group features using advanced Self-Tuning spectral clustering algorithm based on local standard deviation,so as to detect the global optimal feature clusters as far as *** two feature ranking techniques,including cosine-similarity-based feature ranking and entropy-based feature ranking,are proposed,so that the representative feature of each cluster can be detected to comprise the feature subset on which the explainable classification system will be *** effectiveness of the proposed algorithms is tested on high dimensional benchmark omics datasets and compared to peer methods,and the statistical test are conducted to determine whether or not the proposed spectral feature selection algorithms are significantly different from those of the peer *** extensive experiments demonstrate the proposed unsupervised spectral feature selection algorithms outperform the peer ones in comparison,especially the one based on cosine similarity feature ranking *** statistical test results show that the entropy feature ranking based spectral feature selection algorithm performs *** detected features demonstrate strong discriminative capabilities in downstream classifiers for omics data,such that the AI system built on them would be reliable and *** is especially significant in building transparent and trustworthy medical diagnostic systems from an interpretable AI perspective.
Deep neural networks have shown promising results in the classification of skin lesion images, particularly when they focus on the most significant regions of an image. However, the identification of melanoma continue...
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Flood disasters pose significant threats to human lives and infrastructure, necessitating advanced methods for the timely and accurate monitoring of water levels in rivers. This study introduces an innovative approach...
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This research aims to propose a practical framework designed for the automatic analysis of a product’s comprehensive functionality and security vulnerabilities,generating applicable guidelines based on real-world ***...
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This research aims to propose a practical framework designed for the automatic analysis of a product’s comprehensive functionality and security vulnerabilities,generating applicable guidelines based on real-world *** existing analysis of software security vulnerabilities often focuses on specific features or *** partial and arbitrary analysis of the security vulnerabilities makes it challenging to comprehend the overall security vulnerabilities of the *** key novelty lies in overcoming the constraints of partial *** proposed framework utilizes data from various sources to create a comprehensive functionality profile,facilitating the derivation of real-world security *** guidelines are dynamically generated by associating functional security vulnerabilities with the latest Common Vulnerabilities and Exposure(CVE)and Common Vulnerability Scoring System(CVSS)scores,resulting in automated guidelines tailored to each *** guidelines are not only practical but also applicable in real-world software,allowing for prioritized security *** proposed framework is applied to virtual private network(VPN)software,wherein a validated Level 2 data flow diagram is generated using the Spoofing,Tampering,Repudiation,Information Disclosure,Denial of Service,and Elevation of privilege(STRIDE)technique with references to various papers and examples from related *** analysis resulted in the identification of a total of 121 *** successful implementation and validation demonstrate the framework’s efficacy in generating customized guidelines for entire systems,subsystems,and selected modules.
As many countries face the challenges of an aging population and declining birth rates, the demand for labor, particularly for assisting the elderly, is increasing. Traditional robots, being standardized products, req...
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Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the ***,many existing data aggregation techniq...
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Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the ***,many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater ***,conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink,commonly known as the energy hole ***,cluster-based aggregation methods face significant challenges such as cluster head(CH)failures and collisions within clusters that degrade overall network *** address these limitations,this paper introduces an innovative framework,the Cluster-based Data Aggregation using Fuzzy Decision Model(CDAFDM),tailored for mobile *** proposed method has four main phases:clustering,CH selection,data aggregation,and *** CH selection,a fuzzy decision model is utilized to ensure efficient cluster head selection based on parameters such as residual energy,distance to the sink,and data delivery likelihood,enhancing network stability and energy *** the aggregation phase,CHs transmit a single,consolidated set of non-redundant data to the base station(BS),thereby reducing data duplication and saving *** adapt to the changing network topology,the re-clustering phase periodically updates cluster formations and reselects *** results show that CDAFDM outperforms current protocols such as CAPTAIN(Collection Algorithm for underwater oPTical-AcoustIc sensor Networks),EDDG(Event-Driven Data Gathering),and DCBMEC(Data Collection Based on Mobile Edge Computing)with a packet delivery ratio increase of up to 4%,an energy consumption reduction of 18%,and a data collection latency reduction of 52%.These findings highlight the framework’s potential for reliable and energy-efficient data aggregation mobile UWSNs.
Accurate significant wave height(SWH)prediction is essential for the development and utilization of wave *** learning methods such as recurrent and convolutional neural networks have achieved good results in SWH ***,t...
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Accurate significant wave height(SWH)prediction is essential for the development and utilization of wave *** learning methods such as recurrent and convolutional neural networks have achieved good results in SWH ***,these methods do not adapt well to dynamic seasonal variations in wave *** this study,we propose a novel method—the spatiotemporal dynamic graph(STDG)neural *** method predicts the SWH of multiple nodes based on dynamic graph modeling and multi-characteristic ***,considering the dynamic seasonal variations in the wave direction over time,the network models wave dynamic spatial dependencies from long-and short-term pattern ***,to correlate multiple characteristics with SWH,the network introduces a cross-characteristic transformer to effectively fuse multiple ***,we conducted experiments on two datasets from the South China Sea and East China Sea to validate the proposed method and compared it with five prediction methods in the three *** experimental results show that the proposed method achieves the best performance at all predictive scales and has greater advantages for extreme value ***,an analysis of the dynamic graph shows that the proposed method captures the seasonal variation mechanism of the waves.
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