Towards on Develop a Framework for the Evaluation and Benchmarking of Skin Detectors Based on Artificial Intelligent Models Using Multi-Criteria Decision-Making Techniques

Yas, Qahtan M.; A.A.Zadain,; Zaidan, B. B.; Lakulu, Modi; Rahmatullah, Bahbibi

doi:10.1142/s0218001417590029

Cited by 52 publications

(13 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To our knowledge, none of these methods have been used to benchmark the approaches of digital watermarking. According to literature , the benefits, drawbacks, and recommendations for popular methods of MCDM techniques can be summarized as follows. Weighted sum model and hierarchical adaptive weighting are easy to use and understand, but the weights of the attributes are arbitrarily assigned; both techniques are difficult to use with increasing number of criteria.…”

Section: Methodology Designmentioning

confidence: 99%

A new digital watermarking evaluation and benchmarking methodology using an external group of evaluators and multi‐criteria analysis based on ‘large‐scale data’

Zaidan

Karim

et al. 2016

Softw Pract Exp

Self Cite

View full text Add to dashboard Cite

Summary Digital watermarking evaluation and benchmarking are challenging tasks because of multiple evaluation and conflicting criteria. A few approaches have been presented to implement digital watermarking evaluation and benchmarking frameworks. However, these approaches still possess a number of limitations, such as fixing several attributes on the account of other attributes. Well‐known benchmarking approaches are limited to robust watermarking. Therefore, this paper presents a new methodology for digital watermarking evaluation and benchmarking based on large‐scale data by using external evaluators and a group decision making context. Two experiments are performed. In the first experiment, a noise gate‐based digital watermarking approach is developed, and the scheme for the noise gate digital watermarking approach is enhanced. Sixty audio samples from different audio styles are tested with two algorithms. A total of 120 samples were evaluated according to three different metrics, namely, quality, payload, and complexity, to generate a set of digital watermarking samples. In the second experiment, the situation in which digital watermarking evaluators have different preferences is discussed. Weight measurement with a decision making solution is required to solve this issue. The analytic hierarchy process is used to measure evaluator preference. In the decision making solution, the technique for order of preference by similarity to the ideal solution with different contexts (e.g., individual and group) is utilized. Therefore, selecting the proper context with different aggregation operators to benchmark the results of experiment 1 (i.e., digital watermarking approaches) is recommended. The findings of this research are as follows: (1) group and individual decision making provide the same result in this case study. However, in the case of selection where the priority weights are generated from the evaluators, group decision making is the recommended solution to solve the trade‐off reflected in the benchmarking process for digital watermarking approaches. (2) Internal and external aggregations show that the enhanced watermarking approach demonstrates better performance than the original watermarking approach. © 2016 The Authors. Software: Practice and Experience published by John Wiley & Sons Ltd.

show abstract

Section: Methodology Designmentioning

confidence: 99%

A new digital watermarking evaluation and benchmarking methodology using an external group of evaluators and multi‐criteria analysis based on ‘large‐scale data’

Zaidan

Karim

et al. 2016

Softw Pract Exp

Self Cite

View full text Add to dashboard Cite

show abstract

“…Firstly, the prioritisation is dependant on important attributes; thus, patient and CP selection based on multiple criteria is a multi-attribute decision matrix [57][58][59][60][61]. Secondly, different values of importance are often given for each attribute, which further increases the complexity of the task [63][64][65][66][67]. Finally, a prioritisation process requires synchronised consideration of the inverse relationship amongst the mentioned criteria; thus, a trade-off is created [ 4 9 , 6 8 ], [122].…”

Section: Intelligent Computing Issuesmentioning

confidence: 99%

Helping doctors hasten COVID-19 treatment: Towards a rescue framework for the transfusion of best convalescent plasma to the most critical patients based on biological requirements via ml and novel MCDM methods

Albahri

Al‐Obaidi

Zaidan

et al. 2020

Computer Methods and Programs in Biomedicine

View full text Add to dashboard Cite

Context: People who have recently recovered from the threat of deteriorating coronavirus disease-2019 (COVID-19) have antibodies to the coronavirus circulating in their blood. Thus, the transfusion of these antibodies to deteriorating patients could theoretically help boost their immune system. Biologically, two challenges need to be surmounted to allow convalescent plasma (CP) transfusion to rescue the most severe COVID-19 patients. First, convalescent subjects must meet donor selection plasma criteria and comply with national health requirements and known standard routine procedures. Second, multi-criteria decision-making (MCDM) problems should be considered in the selection of the most suitable CP and the prioritisation of patients with COVID-19. Objective: This paper presents a rescue framework for the transfusion of the best CP to the most critical patients with COVID-19 on the basis of biological requirements by using machine learning and novel MCDM methods.

show abstract

“…MCDM involves structuring, planning and solving decision problems by utilising numerous criteria [222,226,227]. The purpose of MCDM is to support decision-makers to solve problems with numerous criteria and alternatives [228][229][230]. It uses a group of qualitative and quantitative methods to overcome complex decision cases, considering multiple criteria, which might be conflicting [231,232].…”

Section: Mcdm: Definition and Importancementioning

confidence: 99%

“…The advantages and disadvantage of the popular MCDM methods are shown in Table 2 [22,229,235,252,[256][257][258][259][260][261][262].…”

Section: Critical Analysis For Mcdm Techniquesmentioning

confidence: 99%

Real-Time Remote-Health Monitoring Systems: a Review on Patients Prioritisation for Multiple-Chronic Diseases, Taxonomy Analysis, Concerns and Solution Procedure

et al. 2019

Self Cite

View full text Add to dashboard Cite

Remotely monitoring a patient's condition is a serious issue and must be addressed. Remote health monitoring systems (RHMS) in telemedicine refers to resources, strategies, methods and installations that enable doctors or other medical professionals to work remotely to consult, diagnose and treat patients. The goal of RHMS is to provide timely medical services at remote areas through telecommunication technologies. Through major advancements in technology, particularly in wireless networking, cloud computing and data storage, RHMS is becoming a feasible aspect of modern medicine. RHMS for the prioritisation of patients with multiple chronic diseases (MCDs) plays an important role in sustainably providing high-quality healthcare services. Further investigations are required to highlight the limitations of the prioritisation of patients with MCDs over a telemedicine environment. This study introduces a comprehensive and inclusive review on the prioritisation of patients with MCDs in telemedicine applications. Furthermore, it presents the challenges and open issues regarding patient prioritisation in telemedicine. The findings of this study are as follows: (1) The limitations and problems of existing patients' prioritisation with MCDs are presented and emphasised. (2) Based on the analysis of the academic literature, an accurate solution for remote prioritisation in a large scale of patients with MCDs was not presented. (3) There is an essential need to produce a new multiple-criteria decisionmaking theory to address the current problems in the prioritisation of patients with MCDs.

show abstract

Towards on Develop a Framework for the Evaluation and Benchmarking of Skin Detectors Based on Artificial Intelligent Models Using Multi-Criteria Decision-Making Techniques

Cited by 52 publications

References 32 publications

A new digital watermarking evaluation and benchmarking methodology using an external group of evaluators and multi‐criteria analysis based on ‘large‐scale data’

A new digital watermarking evaluation and benchmarking methodology using an external group of evaluators and multi‐criteria analysis based on ‘large‐scale data’

Helping doctors hasten COVID-19 treatment: Towards a rescue framework for the transfusion of best convalescent plasma to the most critical patients based on biological requirements via ml and novel MCDM methods

Real-Time Remote-Health Monitoring Systems: a Review on Patients Prioritisation for Multiple-Chronic Diseases, Taxonomy Analysis, Concerns and Solution Procedure

Contact Info

Product

Resources

About