Cloud-based technology for small and medium scale enterprises: a decision-making paradigm using IPA, AHP and fuzzy-AHP techniques

Anbuudayasankar, S. P.; Srikanthan, Ramesh; Karthik, M.; Nair, Prashant; Sivakarthik, Nagarajan; Indukumar, Packirisamy

doi:10.1504/ijism.2020.110732

Cited by 22 publications

(6 citation statements)

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“…In this work, we adopt the latter context and approaches with their steps presented below. Popular methods in the latter context include AHP (Anbuudayasankar et al 2020 ) and entropy (Kumar et al 2021 ; Namdari et al 2019 ) that cannot effectively capture the hesitation of experts and also the variability in the distributions. Furthermore, a method such as AHP faces the overhead of consistency, thereby increasing complexity.…”

Section: Methodsmentioning

confidence: 99%

The q-rung fuzzy LOPCOW-VIKOR model to assess the role of unmanned aerial vehicles for precision agriculture realization in the Agri-Food 4.0 era

et al. 2023

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Smart agriculture is gaining a lot of attention recently, owing to technological advancement and promotion of sustainable habits. Unmanned aerial vehicles (UAVs) play a crucial role in smart agriculture by aiding in different phases of agriculture. The contribution of UAVs to sustainable and precision agriculture is a critical and challenging issue to be taken into account, particularly for smallholder farmers in order to save time and money, and improve their agricultural skills. Thence, this study targets to propose an integrated group decision-making framework to determine the best agricultural UAV. Previous studies on UAV evaluation, (i) could not model uncertainty effectively, (ii) weights of experts are not methodically determined; (iii) importance of experts and criteria types are not considered during criteria weight calculation, and (iv) personalized ranking of UAVs is lacking along with consideration to dual weight entities. Herein, nine critical selection criteria are identified, drawing upon the relevant literature and experts’ opinions, and five extant UAVs are considered for evaluation. To circumvent the gaps, in this work, a new integrated framework is developed considering q-rung orthopair fuzzy numbers (q-ROFNs) for apt UAV selection. Specifically, methodical estimation of experts’ weights is achieved by presenting the regret measure. Further, weighted logarithmic percentage change-driven objective weighting (LOPCOW) technique is formulated for criteria weight calculation, and an algorithm for personalized ranking of UAVs is presented with visekriterijumska optimizacija i kompromisno resenje (VIKOR) approach combined with Copeland strategy. The findings show that the foremost criteria in agricultural UAV selection are “ camera ,” “ power system ,” and “ radar system ,” respectively. Further, it is inferred that the most promising UAV is the DJ AGRAS T30. Since the applicability of UAV in agriculture will get inevitable, the developed framework can be an effective decision support system for farmers, managers, policymakers, and other stakeholders.

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Section: Methodsmentioning

confidence: 99%

The q-rung fuzzy LOPCOW-VIKOR model to assess the role of unmanned aerial vehicles for precision agriculture realization in the Agri-Food 4.0 era

et al. 2023

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“…Optimization models (Krishankumar et al, 2021 ; Sivagami et al, 2021 ) are put forward in the former category and the system incurs overhead for obtaining partial information on each criterion. When experts are unable/unwilling to express their views on the importance of criterion, the methods from the latter category are put forward, such as entropy (Zheng et al, 2020 ; Mahmood and Ali, 2020), analytical hierarchy process (Anbuudayasankar et al, 2020 ), best–worst approach (Rezai, 2015; Pamucar et al, 2020 ), and so on. These methods determine weights methodically, but are complex and are unable to capture the hesitation of experts during choice sharing.…”

Section: Methodsmentioning

confidence: 99%

Prioritization of healthcare systems during pandemics using Cronbach’s measure based fuzzy WASPAS approach

et al. 2022

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Pandemics are well-known as epidemics that spread globally and cause many illnesses and mortality. Because of globalization, the accelerated occurrence and circulation of new microbes, the infection has emerged and the incidence and movement of new microbes have sped up. Using technological devices to minimize the visit durations, specifying days for handling chronic diseases, subsidy for the staff are the alternatives that can help prevent healthcare systems from collapsing during pandemics. The study aims to define the efficient usage of optimization tools during pandemics to prevent healthcare systems from collapsing. In this study, a new integrated framework with fuzzy information is developed, which attempts to prioritize these alternatives for policymakers. First, rating data are assigned respective fuzzy values using the standard singleton grades. Later, criteria weights are determined by extending Cronbach´s measure to fuzzy context. The measure not only understands data consistency comprehensively, but also takes into consideration the attitudinal characteristics of experts. By this approach, a rational weight vector is obtained for decision-making. Further, an improved Weighted Aggregated Sum Product Assessment (WASPAS) algorithm is put forward for ranking alternatives, which is flexibly considering criteria along with personalized ordering and holistic ordering alternatives. The usefulness of the developed framework is tested with the help of a real case study. Rank values of alternatives when unbiased weights are used is given by 0.741, 0.582, 0.640 with ordering as $$R_{1} \succ R_{3} \succ R_{2}$$ R 1 ≻ R 3 ≻ R 2 . The sensitivity/comparative analysis reveals the impact of the proposed model as useful in selecting the best alternative for the healthcare systems during pandemics.

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“…Asmita Varma et al, used IoT to sense the seepage in the gas cylinder and give information to the user via call, text message and email [3]- [5].In their paper, Lei Lei Hnin et al suggested using fuzzy logic to regulate the temperature in an induction furnace by taking user feedback (amount of meat, amount of water, and cooking time) and S. Jayapoorani et al, devised a plan to completely automate the wet grinder for grinding rice. The design of the system and timer is done [6]- [9].M. Gopi Perumal et al, used a sound sensor to detect the number of whistles from a pressure cooker and notify the user to turn it off.…”

Section: Inroductionmentioning

confidence: 99%

IoT Based Smart Kitchen Using Fuzzy Intelligence and Signal Processing

Janarthanan N., Chandra Dev K. A., Dasi Raghuram

2024

jes

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Kitchen management remains a challenging task, despite many technological advancements. As a result, it's clearly time for us to get the kitchen work done with ease. The ingenious Kitchen uses the Internet of Things for design and features a mobile application as a user interface. Smart Kitchen includes a grocery management system, a fuzzy system for adaptive cooking and signal processing for identifying the cooker whistle. In the grocery management system, an ultra-sonic sensor measures the inventory level and using a Wi-Fi module, data updates the value of a particular key in the cloud. Users can see the amount of item left, be notified of shortage, and get the shopping list using the mobile application.

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Cloud-based technology for small and medium scale enterprises: a decision-making paradigm using IPA, AHP and fuzzy-AHP techniques

Cited by 22 publications

References 0 publications

The q-rung fuzzy LOPCOW-VIKOR model to assess the role of unmanned aerial vehicles for precision agriculture realization in the Agri-Food 4.0 era

The q-rung fuzzy LOPCOW-VIKOR model to assess the role of unmanned aerial vehicles for precision agriculture realization in the Agri-Food 4.0 era

Prioritization of healthcare systems during pandemics using Cronbach’s measure based fuzzy WASPAS approach

IoT Based Smart Kitchen Using Fuzzy Intelligence and Signal Processing

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