In the area of decision-making, where complexities often exceed experts’ analytical capabilities, this study addresses a critical gap by introducing a novel methodology for sensitivity analysis within the Multi-Crite...
详细信息
In the area of decision-making, where complexities often exceed experts’ analytical capabilities, this study addresses a critical gap by introducing a novel methodology for sensitivity analysis within the Multi-Criteria Decision Analysis (MCDA) problems. Current analytical frameworks struggle to comprehensively consider potential modifications to values within decision matrices, making it challenging to understand their impact in detail. To address this challenge, decision-makers need enhanced tools, and MCDA methods combined with systematic changes in selected elements of the decision matrix stand out as a promising approach. However, existing studies primarily focus on different criteria-weight scenarios, leaving an unexplored gap in the simultaneous modification of multiple values within the decision matrix. This paper integrates sensitivity analysis within the MCDA methods, extending its role in the comprehensive assessment of decision problems. Sensitivity analysis becomes crucial in offering decision-makers a broader perspective, aiding them in navigating the complexities of decision-making in dynamic environments. Recognizing the unexplored potential in sensitivity analysis, the study proposed a novel approach of simultaneous modification of multiple values in a decision matrix, offering an extension of conventional one-at-a-time modifications. As a Proof of Concept (PoC) research work, the study investigated whether the determined approach provides divergent preference scores compared to the traditional single-modification method across ten different MCDA techniques. The results showed that modifying multiple values simultaneously produced different preference scores of alternatives than in the case of a conventional single change, showing that additional insight knowledge could be extracted from this type of sensitivity analysis.
Traditional portfolio management methods can incorporate specific investor preferences but rely on accurate forecasts of asset returns and covariances. Reinforcement learning (RL) methods do not rely on these explicit...
详细信息
This paper develops two parameter-free methods for solving convex and strongly convex hybrid composite optimization problems, namely, a composite subgradient type method and a proximal bundle type method. Both functio...
详细信息
Complex IoT ecosystems often require the usage of Digital Twins (DTs) of their physical assets in order to perform predictive analytics and simulate what-if scenarios. DTs are able to replicate IoT devices and adapt o...
详细信息
The paper describes the process of the information objects classification in e-learning systems using the mathematical background of fuzzy logic. This approach can be used to improve the process of searching and organ...
详细信息
Foam bed flotation processes are common in the poly metallic, potash and food industries. A worker who visually assesses the condition of the foam bed controls the process. This reduces process control and product qua...
详细信息
Discrete dynamical models of walking droplets ("walkers") have allowed swift numerical experiments revealing heretofore unobserved quantum statistics and related behaviors in a classical hydrodynamic system....
详细信息
Recent transformer-based methods for estimating 3D human pose have gained widespread attention, achieving state-of-the-art results. Previous methods have primarily focused on capturing motion patterns of the human bod...
详细信息
ISBN:
(数字)9798350385724
ISBN:
(纸本)9798350385731
Recent transformer-based methods for estimating 3D human pose have gained widespread attention, achieving state-of-the-art results. Previous methods have primarily focused on capturing motion patterns of the human body at a single scale or cascading multiple scales, such as joints, bones, and body-parts. However, they are difficult to simultaneously capture spatial-temporal motion patterns of the human body at different scales due to the complex motion patterns. To address this issue, we propose Dual-scale Spatial and Temporal transFormer (DSTFormer), which can concurrently explore the spatial dependencies and temporal motion patterns of human joints and bones. Additionally, we introduce a Gcn-Spatial Transformer Block (GSTB), which introduces Graph Convolutional Networks (GCN) into transformer to enhance the exploitation of local relationships and global information between adjacent joints or bones. Extensive experiments are conducted on the Human3.6M benchmark dataset, and superior results are reported when comparing to other state-of-the-art methods. More remarkably, our model achieves to-date the best published performance, with P1 errors of 37.9 mm and 15.6 mm, respectively.
Secret Image Sharing (SIS) transfers an image to mutually suspicious receivers as $n$ meaningless shares, where $k$ or more shares must be present to recover the secret. This paper proposes a $(k, n)$ -SIS system...
详细信息
ISBN:
(数字)9798350385427
ISBN:
(纸本)9798350385434
Secret Image Sharing (SIS) transfers an image to mutually suspicious receivers as
$n$
meaningless shares, where
$k$
or more shares must be present to recover the secret. This paper proposes a
$(k, n)$
-SIS system for any image type using polynomial interpolation based on Lagrange polynomials, where the generated shares are of size
$1/k$
of the secret image size. A full encryption system, consisting of substitution and permutation stages, is employed by using the generalized Tent map as a source of randomness. In addition to using a long and sensitive system key, steganography using the Least Significant Bits (LSBs) embedding technique is utilized to improve security. Detailed experimental analysis of the security, robustness and performance of the proposed system is provided, which is more comprehensive than the analyses given in other related works. Security is demonstrated using statistical tests, and robustness against noise and cron attacks is validated.
暂无评论