Dynamic Attribute Research Articles

Post-Natural Disasters Emergency Response Scheme Selection: An Integrated Application of Probabilistic T-Spherical Hesitant Fuzzy Set, Penalty-Incentive Dynamic Attribute Weights, and Non-Compensation Approach

This paper presents an innovative methodology for the dynamic emergency response scheme selection (ERSS) problem in post-major natural disasters. It employs a combination of subjective and objective composite weights and the integrated ELECTRE-score approach. The study aims to provide a practical approach for continuously determining optimal decision schemes at various time points during the decision period in the aftermath of significant natural disasters while accommodating evolving real-world scenarios. Firstly, the probabilistic T-spherical hesitant fuzzy set (Pt-SHFS) captures decision-makers’ ambivalence and hesitation regarding diverse evaluation attributes of different schemes. Subsequently, Pt-SHFS is integrated with the best–worst method (BWM) to determine subjective weights, followed by the structured CRITIC method to amalgamate subjective weights and derive the final combination weights of criteria. Additionally, this paper proposes applying a penalty-incentive mechanism to establish dynamic attribute weights during scenario evolution. Furthermore, the ELECTRE-score method, which may fully exploit the advantages of non-compensation situations, is adopted to obtain more reliable dynamic optimal decision outcomes. Consequently, based on these foundations, an integrated dynamic ERSS approach is formulated to determine optimal dynamic emergency response schemes. Finally, a case study on the Gansu Jishishan earthquake, sensitivity analysis, comparative analysis, and continuous analysis are conducted to verify the practicality, stability, and effectiveness of the proposed approach. The result shows that the proposed comprehensive approach can depict variances among experts’ information, dynamically adjust attribute weights in response to evolving scenarios, and assign a score range and a representative score to each scheme at each decision state. Sensitivity and comparative analyses show this model has strong stability and dynamics. Furthermore, the proposed approach can effectively deal with the complex dynamic situation in the earthquake rescue process, such as the secondary collapse of buildings after the earthquake, the damage of materials caused by heavy rain, and the occurrence of aftershocks. The model can continuously optimize decision-making and provide scientific and reliable support for emergency decision-making.

Dynamic Attribute-guided Few-shot Open-set Network for medical image diagnosis

The scarcity of data on rare diseases poses a significant challenge to the development of diagnostic systems. While few-shot learning (FSL) offers promise in low-data regimes, it often struggles in open-set scenarios, failing to identify unknown diseases. In this paper, we introduce a Dynamic Attribute-guided Few-shot Open-set Network (DAFON), representing the first effort to simultaneously address closed-set classification and open-set recognition in rare disease diagnosis. To alleviate incomprehensive category knowledge stemming from data scarcity, we propose a Global Attribute Generator to create attributes and produce image attribute activations for closed-set data as auxiliary information. An attribute space is then constructed and employed to generate the pseudo open-set attribute activations using a designed Open-set Data Sampler. By incorporating closed-set and open-set attribute activations as conditions, we propose a Dynamic Attribute Guided Alignment module to align feature space with attribute space, so as to derive feature space with intra-class compactness for closed-set and open-set classes. Our DAFON achieves state-of-the-art performance on two public medical image datasets, demonstrating its effectiveness for FSOSR in medical image diagnosis.

Coarse-to-fine Image Aesthetics Assessment With Dynamic Attribute Selection

Coarse-to-fine Image Aesthetics Assessment With Dynamic Attribute Selection

Dynamic Attribute Tree for the Data Encryption and Third Party Auditing for Cloud Storage

Background: To reduce the burden of the users to perform cloud audits. The tree based key handling method provides the structure format of data and increases performance in the search process. Methods: The Dynamic Attribute Tree method is proposed for Third-Party auditing to encrypt the data based on its attribute. The attributes are stored in tree structure along with data and key that helps to effectively improve the dynamic update of the data in the cloud. Findings: The proposed Dynamic Attribute Tree method has 6.7milliseconds computation time for 100 blocks of data and the existing Merkel Tree method has 7.5milliseconds computation time. Novelty: The Novelty of the project is the Dynamic Attribute Tree method uses bilinear mapping to verify the integrity of the data without retrieving the actual data from the cloud. Keywords: Bilinear Mapping; Cloud audit; Cloud storage services; Dynamic Attribute Tree; and Third Party Auditing

RSA CP-ABE with Access Tree Structure for Secure Revocable Scheme for Building Trust Model

Attribute-Based Encryption (ABE) can provide a technique of fine-grained control. The suggested Enhanced CP-ABE technique includes the use of a proxy to securely communicate the key to users. The Rivest Shamir Adleman (RSA) Algorithm secures outsourced big data in the cloud by allowing public key encryption to safeguard data transported across an unsecured network like the internet. During decryption, the proposed RSA CP-ABE with Access tree structure for Secure Revocable scheme for Building Trust model successfully identifies the users who decrypt the cipher messages. For Third Party auditing, the Dynamic Attribute Tree approach is proposed to encrypt data based on its attribute. The attributes, together with data and keys, are stored in a tree structure, which aids in improving the dynamic update of data in the cloud. Bilinear mapping is used by the Dynamic Attribute Tree approach to validate the integrity of the data without having to retrieve it from the cloud. The experimental results reveal that for a 256-bit key length, the proposed Scheme achieves encryption and decryption times of 1638 ms and 1102 ms, respectively.

Dynamic multi-attribute priority based face attribute detection for robust face image retrieval system

The increase in the popularity of social websites and smartphones has helped people to easily click photos and upload them on the internet. Almost 60% of the photos are human faces. There might be an increase in face search in future because human faces are closely associated with social media where people show special interest on specific personalities. The main issue raised in the face retrieval system is the various intra-class variances like expression, pose and illumination. Most of the conventional approaches lack the accuracy to meet the human intuition for retrieving images. The proposed approach develops a Dynamic Multi-Attribute Priority-based Face Attribute Detection (DMAP-FAD) method based on contextual information of the face (Race and Gender) by detecting the attributes dynamically. This approach can provide better discrimination of the face image retrieval system by detecting the attributes dynamically. The digital binning technique is applied for improving the lighting differences for illumination. The proposed method effectively minimized the semantic gap and achieved high accuracy by focusing on the variations involved in the pose, illumination, and expression with dynamically detected an optimal set of attributes from the original set of attributes by using contextual relationship. In the experimental results, the proposed method has been tested efficiently by the metrics such as F-Score, Precision and Recall with help of Pubfig and LFW datasets and it is observed that the proposed method obtained an overall accuracy of 95.63% for higher discrimination of face image retrieval system. The results are comparable with those of the state-of-the-art methods.

An Improved and Adaptive Attribute Selection Technique to Optimize Dengue Fever Prediction

Clinical information mining is rapidly gaining popularity. Restorative information are high dimensional in nature which contains unessential elements that diminish prediction capability. Hence Attribute Optimization is required to retain only the essential features while eradicating irrelevant features. Dengue is one of the major worldwide medical related disease. It has affected millions of people throughout world while a majority of them being women. With constant upgradation of information technology and its application in healthcare domain, several cases relating to diabetes along with its symptoms are properly documented. Our study is centered on developing and implementing a new Adaptive and Dynamic Attribute Optimization algorithm to determine whether patients suffer from Dengue. Our algorithm is evaluated against some vital performance metrics and compared with other sub-modules of the proposed algorithm and traditional Genetic Algorithm. The results indicate our algorithm is more efficient and accurate in determining presence of Dengue disease. This may assist the medical experts in effective diagnosis of patients suffering from Dengue.

Data Privacy Protection Based on Micro Aggregation with Dynamic Sensitive Attribute Updating.

With the rapid development of information technology, large-scale personal data, including those collected by sensors or IoT devices, is stored in the cloud or data centers. In some cases, the owners of the cloud or data centers need to publish the data. Therefore, how to make the best use of the data in the risk of personal information leakage has become a popular research topic. The most common method of data privacy protection is the data anonymization, which has two main problems: (1) The availability of information after clustering will be reduced, and it cannot be flexibly adjusted. (2) Most methods are static. When the data is released multiple times, it will cause personal privacy leakage. To solve the problems, this article has two contributions. The first one is to propose a new method based on micro-aggregation to complete the process of clustering. In this way, the data availability and the privacy protection can be adjusted flexibly by considering the concepts of distance and information entropy. The second contribution of this article is to propose a dynamic update mechanism that guarantees that the individual privacy is not compromised after the data has been subjected to multiple releases, and minimizes the loss of information. At the end of the article, the algorithm is simulated with real data sets. The availability and advantages of the method are demonstrated by calculating the time, the average information loss and the number of forged data.

Dynamic attribute based vehicle authentication

Modern vehicles are proficient in establishing a spontaneous connection over a wireless radio channel, synchronizing actions and information. Security infrastructure is most important in such a sensitive scope of vehicle communication for coordinating actions and avoiding accidents on the road. One of the first security issues that need to be established is authentication via IEEE 1609.2 security infrastructure. According to our preliminary work, vehicle owners are bound to preprocess a certificate from the certificate authority. The certificate carries vehicle static attributes (e.g., licence number, brand and color) certified together with the vehicle public key in a monolithic manner. Nevertheless, a malicious vehicle might clone the static attributes to impersonate a specific vehicle. Therefore, in this paper we consider a resource expensive attack scenario involving multiple malicious vehicles with identical visual static attributes. Apparently, dynamic attributes (e.g., location and direction) can uniquely define a vehicle and can be utilized to resolve the true identity of the vehicle. However, unlike static attributes, dynamic attributes cannot be signed by a trusted authority beforehand. We propose an approach to verify the coupling between non-certified dynamic attributes and certified static attributes on an auxiliary communication channel, for example, a modulated laser beam. Furthermore, we illustrate that the proposed approach can be used to facilitate the usage of existing authentication protocols such as NAXOS, in the new scope of ad-hoc vehicle networks. We use BAN logic to verify the security claims of the protocol against the passive and active interception.

Dynamic MAPT Approach for Vertical Handover Optimization in Heterogeneous Network for CBR and VBR QoS Guarantees

Efficient handover optimization and intelligent mobility management mechanism are the key requirements for designing next generation wireless network. Therefore, a dynamic Multiple Attribute decision based on Priority of Traffic classes (MAPT) for Constant Bit rate (CBR) and Variable Bit Rate (VBR) are proposed in this paper to optimize the handover operations. The IEEE 802.21 Media Independent Handover (MIH) standard provides an associated service to MAPT for achieving seamless handover. Simulation results prove that the proposed work resourcefully minimizes the vertical handover delay, packet loss, jitter and thus improves the throughput for both real time and non- real time applications.

Some Weighted Dominant Ranking Functions on Intuitionistic Fuzzy Sets with Double Parameters and Their Applications to Dynamic Multiple Attribute Decision Making

Some Weighted Dominant Ranking Functions on Intuitionistic Fuzzy Sets with Double Parameters and Their Applications to Dynamic Multiple Attribute Decision Making

Method for Dynamic Multiple Attribute Decision Making under Interval Uncertainty and its Application to Supplier Selection

Method for Dynamic Multiple Attribute Decision Making under Interval Uncertainty and its Application to Supplier Selection

A Dynamic Vague Multiple Attribute Decision-Making Method for Emergency Capability Assessment

In order to assess the emergency capability of emergency departments dynamically, this paper introduces a dynamic vague multiple attribute decision-making (DV-MADM) method where all assessment values are represented by vague values. Firstly, the method utilizes the dynamic vague weighted averaging (DVWA) operator to aggregate the original assessment information collected at different periods into the complex assessment information. Secondly, the VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) is modified and developed to solve the complex assessment information. Finally, the emergency departments are ranked according to the ascending orders of S, R and Q values and the best department is selected. A numerical example is used to determine the effectiveness and feasibility of the proposed method.

A Quadtree-Based Dynamic Attribute Indexing Method

Dynamic attributes are attributes that change continuously over time making it impractical to issue explicit updates for every change. In this paper, we adapt a variant of the quadtree structure to solve the problem of indexing dynamic attributes. The approach is based on the key idea of using a linear function of time for each dynamic attribute that allows us to predict its value in the future. We contribute an algorithm for regenerating the quadtree-based index periodically that minimizes CPU and disk access cost. We also provide an experimental study of performance focusing on query processing and index update overheads.

Association of graphic images and dynamic attributes

Various approaches to explicit and implicit control of time and motion in procedural real-time graphic packages and languages are discussed and illustrated. The particular approach of independentty defining static objects and dynamic attribute is introduced and developed. The procedures which alter and manipulate basic objects at interactive run time, teamed "dynamic attributes," may be defined independently of objects as a function of time, input, and other variables. Attributes, once defined, may be associated with and dissociated from objects dynamically. Several objects may simultaneously have the identical attribute. A particular & association exists within its own zero-origin local time frame. Basic objects are static two-or three-dimensional entities in some space. Dynamic objects termed "images" may be recursively defined in teams oa basic objects and attributes. Objects and attributes are the basic building blocks enabling modular program construction. The notions presented here can be used as the basis for design of interactive graphic languages or oda the implementation of graphics packages. An experimental FORTRAN-callable package has been developed to test these notions.