The alarming rates of biodiversity loss has draw attention of the scientific community in developing quantitative characterization methods of the state of life variety, ecosystem and habitats. One way to address such aspects involves acoustically ...

• Heterogeneous acoustical features yield effective classification of bird species.
• Candidate features for high-performance birdsongs classification are identified.
• High classification accuracy and recall rate achieved with 19 ...

This study addresses the challenge of multi-objective optimization of a composite shell structure while adhering to constraints on the number of calls to a pseudo-experimental model, simulating real experiments. Two considered objective functions ...

• Multi-Fidelity Modeling: Demonstrated the effectiveness of a multi-fidelity approach.
• Deep Network Surrogate Models: Showed the ability to capture complex data structures.
• Multi-Objective Optimization: Determined dynamic properties ...

Representation of compound information in a truthful, coarse way forms the layout of the granular computing paradigm. In granular computing, the continuous variables are mapped into intervals to be utilized in the extraction of fuzzy graphs from ...$\mathrm{}$$\mathrm{}$$\mathrm{}$$\mathrm{}$

This work relies on DESERTICAS software, which is designed to model desertification dynamics. Spatiotemporal land changes are represented by cellular automata with continuous states. Their transition function incorporates various factors from the ...

The theory of cognitive diagnosis is deeply rooted in psychological and educational perspectives on human learning behavior. Student grouping is a fundamental part of collaborative learning, and complementary knowledge of students within a group can ...

• A handheld STN detector based on phase-locked amplification technology.
• A novel ring-shaped optical path structure is designed to improve accuracy.
• Eight characteristic wavelengths for STN detection is efficiently extracted.

Soil total nitrogen (STN) serves as a critical indicator for agricultural productivity. Rapid STN detection is crucial for nutrient monitoring in farmland and for guiding precision fertilization. Traditional near-infrared spectroscopy-based STN ...

A multi-fidelity optimisation strategy has been developed in the present work, and its performance is illustrated through a series of test cases. The strategy is based on hybrid methods such that two genetic optimisation algorithms are employed, ...

Energy communities serve as vital stakeholders within contemporary power grids. Nevertheless, managing these communities presents formidable challenges, owing to the intricate nature of the task, the presence of uncertainties, and competing ...

Procedural Content Generation (PCG) aims to automatically generate the content of games using algorithmic approaches, as this can reduce the cost of game design and development. PCG algorithms can be applied to all elements of a game, including terrain, ...

We consider a two-agent scheduling problem with interfering job sets. Agent A—which can be considered as the resource manager—is associated with the whole set of jobs, and agent B—which can be considered as an application master—is associated with ...$$

• Explore complexities in process selection in additive and conventional manufacturing.
• Assess current role of neural networks, fuzzy logic, and genetic algorithms in process selection.
• Evaluate limitations of neural networks, fuzzy ...

Technological advances, dynamic customer needs, growing uncertainty, and the imperative for sustainable development pressure manufacturing entities to enhance productivity and competitiveness. In this challenging landscape, decision-making in ...

This study proposes the combined use of a phenomenological model and neural networks trained by experimental data to predict Carbon dioxide adsorption on Zeolitic imidazolate framework – 8 (ZIF-8) considering different temperature and surface ...

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• Combination of phenomenological and neural networks modeling.
• A novel approach to model CO2 adsorption on ZIF-8.
• The model includes a wide range of temperature and surface area.
• Especially at high pressures, surface area had a ...

The main problem of deploying service function chains in virtualized 5G networks is dealt with in wireless 5G communications and effective ubiquitous learning models. The aim is to ensure differentiated network performance for a wide range of ...

• Main problem of deploying service function chains in virtualized 5G networks is dealt with in wireless 5G communications.
• The aim of this research is to ensure differentiated network performance for a wide range of services.
• A ...

The recent widespread of AI-powered real-time applications necessitates the use of edge computing for inference task offloading. Power constrained edge devices are required to balance between processing inference tasks locally or offload to edge ...

Software design optimization (SDO) demands advanced abstract reasoning to define optimal design components’ structure and interactions. Modeling tools such as UML and MERISE, and to a degree, programming languages, are chiefly developed for lucid ...

The Boolean satisfiability problem holds a significant place in computer science, finding applications across various domains. This problem consists of looking for a truth assignment to a given Boolean formula that either validates it or proves ...

In this study, we redefine FCM algorithm by integrating fuzzy set theory, fuzzy metrics, and Sugeno negation principles. This innovative approach overcomes the limitations inherent in conventional machine learning models, especially in situations ...

High-order mesh optimization has many goals, such as improving smoothness, reducing approximation error, and improving mesh quality. The previous methods do not optimize these objectives together, resulting in suboptimal results. To this end, we ...

• We propose a multi-objective optimization method for high-order tetrahedral meshes.
• Employ the multi-objective genetic algorithm to relocate control points to achieve a favorable trade-off among objectives.
• Select the best ...

• An hybrid approach for the batch assignment problem is proposed.
• A machine learning model is used to predict the picking time of future orders.
• A genetic algorithm is adopted to optimize learning and fatigue of pickers.
• The ...

Modeling human behaviors has become increasingly relevant to improving the performance of manual order-picking systems. However, although a vast corpus of literature has recently started to consider the human factors in these systems, several ...

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• Digital system for optimizing dynamic container loading is developed.
• Multiple objectives and constrains are considered for container loading optimization.
• Placement heuristic, hybrid genetic algorithm (HGA), and deep learning ...

Global logistics are increasingly important owing to shifting paradigm by strategically focusing on core competences, outsourcing manufacturing to pursue higher value proposition in the supply chain. Container loading has become a bottleneck in ...