Evolutionary algorithms (EAs) have achieved great performance in solving multi-objective optimization problems (MOPs). However, there are more complex features in real-world MOPs. For example, the neural network train...
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Unmanned aerial vehicles (UAVs) are used as supportive edge computing for sparsely located user equipment on a large scale. In this work, we propose and address a collaborative edge computing system involving multiple...
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Mealtime is one of the pleasures of daily life and an important factor in ensuring the quality of life. Aging and brain and neurological diseases often complicate eating. In particular, the ability to swallow food dir...
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multi-objective optimization algorithms are essential for addressing real-world challenges characterized by conflicting objectives. Although conventional algorithms are effective in exploring solution spaces and gener...
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In multi-label classification, machine learning encounters the challenge of domain generalization when handling tasks with distributions differing from the training data. Existing approaches primarily focus on vision ...
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The efficiency of multi-objective evolutionary algorithms (MOEAs) in tackling issues with multiple objectives is examined. However, it is noted that current MOEA-based feature selection techniques often converge towar...
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The efficiency of multi-objective evolutionary algorithms (MOEAs) in tackling issues with multiple objectives is examined. However, it is noted that current MOEA-based feature selection techniques often converge towards the center of the Pareto front due to inadequate selection forces. The study proposes the utilization of a novel approach known as MOEA/D, which partitions complex multi-objective problems into smaller, more feasible single-objective sub-problems. Each sub-problem may then be addressed using an equal amount of computational resources. The predetermined size of the neighborhood used by MOEA/D may lead to a delay in the algorithm's merging and reduce the effectiveness of the failure. The paper proposes the Adaptive Neighbourhood Adjustment Strategy (ANAS) as a novel approach to improve the efficiency of multi-objective optimisation algorithms in order to tackle this issue. The ANAS algorithm allows for adaptive adjustment of the subproblem neighborhood size, hence enhancing the trade-off between merging and variety. In the following section of the study, a novel feature selection technique called MOGHHNS3/D-ANA is introduced. This technique utilizes ANAS to expand the potential solutions for a particular subproblem. The approach evaluates the chosen features using the Regulated Extreme Learning Machine (RELM) classifier on sixteen benchmark datasets. The experimental results demonstrate that MOGHHNS3/D-ANA outperforms four commonly employed multi-objective techniques in terms of accuracy, precision, recall, F1 score, coverage, hamming loss, ranking loss, and training time, error. The APBI approach in decomposition-based multi-objective optimization focuses on handling constraints by adjusting penalty parameters to guide the search towards feasible solutions. On the other hand, the ANA approach focuses on dynamically adjusting the neighborhood size or search direction based on the proximity of solutions in the detached space to adapt the search process.
The fast adoption of collaborative software development by the industry allied with the demand for a short time to market has led to a dramatic change in IT roles. New practices, tools, and environments are available ...
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ISBN:
(纸本)9798400704987
The fast adoption of collaborative software development by the industry allied with the demand for a short time to market has led to a dramatic change in IT roles. New practices, tools, and environments are available to support professionals in their day-to-day activities. In this context, the demand for software engineers with these skills continues to increase, specifically those related to Extreme Programming, Agile frameworks, CI/CD, and DevOps. To match computerscience undergraduate students' skills with existing job offers, some universities have begun to include DevOps topics in their curriculums. However, due to the wide range of courses covered in computerscience majors, it is particularly challenging to introduce DevOps within the context of Software engineering fundamentals, i.e., connect abstract concepts to skills needed for software engineers in the industry. This paper investigates ways of introducing computerscience students to industry-relevant practices and technologies early from two Software engineering fundamentals courses. Student outcomes were extremely positive, providing insights into ways to introduce students to DevOps-related practices and technologies and bridge the gap between academia and industry.
This research paper focuses on the application of Convolutional Neural Networks (CNNs) for the multi-class classification of kidney conditions, including Cyst, Normal, Stone, and Tumor. Chronic kidney diseases pose a ...
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Purpose-The Internet of Things(IoT)cloud platforms provide end-to-end solutions that integrate various capabilities such as application development,device and connectivity management,data storage,data analysis and dat...
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Purpose-The Internet of Things(IoT)cloud platforms provide end-to-end solutions that integrate various capabilities such as application development,device and connectivity management,data storage,data analysis and data *** high use of these platforms results in their huge availability provided by different ***,choosing the optimal IoT cloud platform to develop IoT applications successfully has become *** key purpose of the present study is to implement a hybrid multi-attribute decision-making approach(MADM)to evaluate and select IoT cloud ***/methodology/approach-The optimal selection of the IoT cloud platforms seems to be dependent on multiple ***,the optimal selection of IoT cloud platforms problem is modeled as a MADM problem,and a hybrid approach named neutrosophic fuzzy set-Euclidean taxicab distance-based approach(NFS-ETDBA)is implemented to solve the ***-ETDBA works on the calculation of assessment score for each alternative,*** cloud platforms,by combining two different measures:Euclidean and taxicab ***-A case study to illustrate the working of the proposed NFS-ETDBA for optimal selection of IoT cloud platforms is *** results obtained on the basis of calculated assessment scores depict that“Azure IoT suite”is the most preferable IoT cloud platform,whereas“Salesman IoT cloud”is the least ***/value-The proposed NFS-ETDBA methodology for the IoT cloud platform selection is implemented for the first time in this *** is highly capable of handling the large number of alternatives and the selection attributes involved in any decision-making ***,the use of fuzzy set theory(FST)makes it very easy to handle the impreciseness that may occur during the data collection through a questionnaire from a group of experts.
We reviewed the application of modern technology for rapid and accurate multi-person real-time pose detection in the hazardous field of electrical engineering. We focused on two leading pose detection technologies: YO...
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