Achieving Efficient Decision Making Through Hybrid Reduction in Soft Set Theory

The main intention of proposing an alternative technique is to ensure consistency is been upheld besides successfully reducing the file. Of all the reduction techniques available currently, only normal parameter reduction has managed to address the issue of consistency at optimal and suboptimal level. In this paper, we initiated another form of reduction known as hybrid reduction by complementing the normal parameter reduction with object reduction. It has already demonstrated that the proposed hybrid reduction has successfully reduced data by 55% with the sample used, thus proving that it as a good alternative for the process of decision making using less amount of data

A review on soft set-based parameter reduction and decision making

Many real world decision making problems often involve uncertainty data, which mainly originating from incomplete data and imprecise decision. The soft set theory as a mathematical tool that deals with uncertainty, imprecise, and vagueness is often employed in solving decision making problem. It has been widely used to identify irrelevant parameters and make reduction set of parameters for decision making in order to bring out the optimal choices. In this paper, we present a review on different parameter reduction and decision making techniques for soft set and hybrid soft sets under unpleasant set of hypothesis environment as well as performance analysis of the their derived algorithms. The review has summarized this paper in those areas of research, pointed out the limitations of previous works and areas that require further research works. Researchers can use our review to quickly identify areas that received diminutive or no attention from researchers so as to propose novel methods and applications

Intertemporal Choice of Fuzzy Soft Sets

This paper first merges two noteworthy aspects of choice. On the one hand, soft sets and fuzzy soft sets are popular models that have been largely applied to decision making problems, such as real estate valuation, medical diagnosis (glaucoma, prostate cancer, etc.), data mining, or international trade. They provide crisp or fuzzy parameterized descriptions of the universe of alternatives. On the other hand, in many decisions, costs and benefits occur at different points in time. This brings about intertemporal choices, which may involve an indefinitely large number of periods. However, the literature does not provide a model, let alone a solution, to the intertemporal problem when the alternatives are described by (fuzzy) parameterizations. In this paper, we propose a novel soft set inspired model that applies to the intertemporal framework, hence it fills an important gap in the development of fuzzy soft set theory. An algorithm allows the selection of the optimal option in intertemporal choice problems with an infinite time horizon. We illustrate its application with a numerical example involving alternative portfolios of projects that a public administration may undertake. This allows us to establish a pioneering intertemporal model of choice in the framework of extended fuzzy set theorie

SkipNet: Learning Dynamic Routing in Convolutional Networks

While deeper convolutional networks are needed to achieve maximum accuracy in visual perception tasks, for many inputs shallower networks are sufficient. We exploit this observation by learning to skip convolutional layers on a per-input basis. We introduce SkipNet, a modified residual network, that uses a gating network to selectively skip convolutional blocks based on the activations of the previous layer. We formulate the dynamic skipping problem in the context of sequential decision making and propose a hybrid learning algorithm that combines supervised learning and reinforcement learning to address the challenges of non-differentiable skipping decisions. We show SkipNet reduces computation by 30-90% while preserving the accuracy of the original model on four benchmark datasets and outperforms the state-of-the-art dynamic networks and static compression methods. We also qualitatively evaluate the gating policy to reveal a relationship between image scale and saliency and the number of layers skipped.Comment: ECCV 2018 Camera ready version. Code is available at https://github.com/ucbdrive/skipne

An SMP Soft Classification Algorithm for Remote Sensing

This work introduces a symmetric multiprocessing (SMP) version of the continuous iterative guided spectral class rejection (CIGSCR) algorithm, a semiautomated classification algorithm for remote sensing (multispectral) images. The algorithm uses soft data clusters to produce a soft classification containing inherently more information than a comparable hard classification at an increased computational cost. Previous work suggests that similar algorithms achieve good parallel scalability, motivating the parallel algorithm development work here. Experimental results of applying parallel CIGSCR to an image with approximately 10^8 pixels and six bands demonstrate superlinear speedup. A soft two class classification is generated in just over four minutes using 32 processors

Hybridization of multi-objective deterministic particle swarm with derivative-free local searches

The paper presents a multi-objective derivative-free and deterministic global/local hybrid algorithm for the efficient and effective solution of simulation-based design optimization (SBDO) problems. The objective is to show how the hybridization of two multi-objective derivative-free global and local algorithms achieves better performance than the separate use of the two algorithms in solving specific SBDO problems for hull-form design. The proposed method belongs to the class of memetic algorithms, where the global exploration capability of multi-objective deterministic particle swarm optimization is enriched by exploiting the local search accuracy of a derivative-free multi-objective line-search method. To the authors best knowledge, studies are still limited on memetic, multi-objective, deterministic, derivative-free, and evolutionary algorithms for an effective and efficient solution of SBDO for hull-form design. The proposed formulation manages global and local searches based on the hypervolume metric. The hybridization scheme uses two parameters to control the local search activation and the number of function calls used by the local algorithm. The most promising values of these parameters were identified using forty analytical tests representative of the SBDO problem of interest. The resulting hybrid algorithm was finally applied to two SBDO problems for hull-form design. For both analytical tests and SBDO problems, the hybrid method achieves better performance than its global and local counterparts

Operationalizing the circular city model for naples' city-port: A hybrid development strategy

The city-port context involves a decisive reality for the economic development of territories and nations, capable of significantly influencing the conditions of well-being and quality of life, and of making the Circular City Model (CCM) operational, preserving and enhancing seas and marine resources in a sustainable way. This can be achieved through the construction of appropriate production and consumption models, with attention to relations with the urban and territorial system. This paper presents an adaptive decision-making process for Naples (Italy) commercial port's development strategies, aimed at re-establishing a sustainable city-port relationship and making Circular Economy (CE) principles operative. The approach has aimed at implementing a CCM by operationalizing European recommendations provided within both the Sustainable Development Goals (SDGs) framework-specifically focusing on goals 9, 11 and 12-and the Maritime Spatial Planning European Directive 2014/89, to face conflicts about the overlapping areas of the city-port through multidimensional evaluations' principles and tools. In this perspective, a four-step methodological framework has been structured applying a place-based approach with mixed evaluation methods, eliciting soft and hard knowledge domains, which have been expressed and assessed by a core set of Sustainability Indicators (SI), linked to SDGs. The contribution outcomes have been centred on the assessment of three design alternatives for the East Naples port and the development of a hybrid regeneration scenario consistent with CE and sustainability principles. The structured decision-making process has allowed us to test how an adaptive approach can expand the knowledge base underpinning policy design and decisions to achieve better outcomes and cultivate a broad civic and technical engagement, that can enhance the legitimacy and transparency of policies

A Concurrent Fuzzy-Neural Network Approach for Decision Support Systems

Decision-making is a process of choosing among alternative courses of action for solving complicated problems where multi-criteria objectives are involved. The past few years have witnessed a growing recognition of Soft Computing technologies that underlie the conception, design and utilization of intelligent systems. Several works have been done where engineers and scientists have applied intelligent techniques and heuristics to obtain optimal decisions from imprecise information. In this paper, we present a concurrent fuzzy-neural network approach combining unsupervised and supervised learning techniques to develop the Tactical Air Combat Decision Support System (TACDSS). Experiment results clearly demonstrate the efficiency of the proposed technique

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging