User-centric framework to facilitate trust worthy cloud service provider selection based on fuzzy inference system

2021 ◽  
pp. 1-9
Author(s):  
M. Sujatha ◽  
K. Geetha ◽  
P. Balakrishnan
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Service Providers ◽  
Selection Process ◽  
Cost Model ◽  
Service Selection ◽  
Cloud Service ◽  
Second Phase ◽  
Inference System ◽  
The Cost

The widespread adoption of cloud computing by several companies across diverse verticals of different sizes has led to an exponential growth of Cloud Service Providers (CSP). Multiple CSPs offer homogeneous services with a vast array of options and different pricing policies, making the suitable service selection process complex. Our proposed model simplifies the IaaS selection process that can be used by all users including clients from the non-IT background. In the first phase, requirements are gathered using a simple questionnaire and are mapped with the compute services among different alternatives.In the second phase, we have implemented the Sugeno Fuzzy inference system to rank the service providers based on the QoS attributes to ascertain the appropriate selection. In the third phase, we have applied the cost model to identify the optimal CSP. This framework is validated by applying it for a gaming application use case and it has outperformed the online tools thus making it an exemplary model.

IMPLEMENTASI PENENTUAN ARSITEKTUR MASJID MODERN DI ACEH MENGGUNAKAN PEMODELAN SISTEM CERDAS

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Soraya Masthura Hasan ◽  
T Iqbal Faridiansyah
Keyword(s):  
Fuzzy Inference System ◽  
Architectural Design ◽  
Fuzzy Inference ◽  
Intelligent System ◽  
Selection Process ◽  
System Modeling ◽  
Inference System ◽  
Normal Limits ◽  
Roof Structure ◽  
Selection Of

Mosque architectural design is based on Islamic culture as an approach to objects and products from the Islamic community by looking at their suitability and values and basic principles of Islam that explore more creative and innovative ideas. The purpose of this system is to help the team and the community in seeing the best mosque in the top order so that the system can be used as a reference for the team and the community. The variables used in the selection of modern mosques include facilities and infrastructure, building structure, roof structure, mosque area, level of security and facilities. The system model used is a fuzzy promethee model that is used for the modern mosque selection process. Fuzzy inference assessment is used to determine the value of each variable so that the value remains at normal limits. Fuzzy values will then be included in promethee assessment aspects. The highest promethee ranking results will be made a priority for the best mosque ranking. This fuzzy inference system and promethee system can help the management team and the community in determining the selection of modern mosques in aceh in accordance with modern mosque architecture. Intelligent System Modeling System In Determining Modern Mosque Architecture in the City of Aceh, this building will be web based so that all elements of society can see the best mosque in Aceh by being assessed by all elements of modern mosque architecture.Keywords: Fuzzy inference system, Promethe, Option of  Masjid

Modeling Trust Based Risk Management in Cloud Adoption using Fuzzy Inference System

2017 ◽  
Vol 8 (1) ◽  
pp. 52-60
Author(s):  
Shikha Gupta ◽  
Anil K. Saini
Keyword(s):  
Risk Management ◽  
Service Provider ◽  
Fuzzy Inference ◽  
Service Providers ◽  
Cloud Service ◽  
It Infrastructure ◽  
Cloud Service Provider ◽  
Inference System ◽  
Cloud Adoption ◽  
Negative Role

Cloud has revolutionized the entire landscape of IT usage, storage and maintenance. It has shifted the focus from owning physical IT infrastructure, facility and storage to the use of same in an abstract form with pay per use facility. This has led to economizing the entire IT infrastructure. Cloud's various characteristics including on demand pay per use, scalability and flexibility of computing highly depends on cloud service provider which can often lead to low results and serious issues. These issues may include concerns about losses due to non-compliance with the promises made. Hence selection of cloud service provider can play a positive or negative role in establishing an initial trust between the cloud service client and provider. The authors propose a model of Trust based Risk management of cloud adoption which can be used by cloud users. The model provides the functioning of comparing service providers using calculated trust ratings.

scholarly journals Mobile App for Grading of Peanuts using Fuzzy Inference System

2019 ◽  
Vol 9 (1) ◽  
pp. 1262-1269
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Major Axis ◽  
Input Image ◽  
Mobile App ◽  
Second Phase ◽  
Shape Features ◽  
Inference System ◽  
Pie Chart

The peanut a globally adopted vegetable protein with appreciable nutritive contents. The grading is one of the significant way to ensure that quality of the peanuts regulate the appropriate price in market. The proposed work is carried out in two phases. As part of first phase segmentation is done using canny edge detection followed by extraction of four Shape Features (SF): Area (Ar), Major Axis (MajAxis), Minor Axis (MinAxis), Perimeter (PeriMt). As part of second phase, Fuzzy Inference System (FIS) has been adopted for grading of four categories of peanuts. .The interval scaled shape features obtained in phase one are converted to Linguistic Terms (LingTerms). The fuzzy data base rules are attained using Association Rules with the constraints that the consequent can have only class label. The proposed work is developed as mobile app which takes peanut image as input and processes the image using FIS and provides the output in terms of pie chart. The pie chart provides the percentage of different qualities of peanuts (low/medium/good/fine) in the input image. The proposed work resulted in an accuracy of 95% when compared with the ground truth. The work can be applied in automation process for grading of food grains using mobile apps, which helps the lay customer to know about the quality of peanuts. The work can also be used for auto separation of peanuts for packing based on grade of the peanuts.

scholarly journals Sustainable Supplier Selection in Construction Industry through Hybrid Fuzzy-Based Approaches

2021 ◽  
Vol 13 (3) ◽  
pp. 1413
Author(s):  
Seyed Amirali Hoseini ◽  
Alireza Fallahpour ◽  
Kuan Yew Wong ◽  
Amir Mahdiyar ◽  
Morteza Saberi ◽  
...  
Keyword(s):  
Decision Making ◽  
Construction Industry ◽  
Fuzzy Inference System ◽  
Supplier Selection ◽  
Fuzzy Inference ◽  
Social Issues ◽  
Second Phase ◽  
Inference System ◽  
Sustainable Supplier Selection ◽  
Best Worst Method

Due to increase in the public and stakeholders’ awareness regarding economic, environmental, and social issues, the construction industry tends to follow the sustainability policies and practices in supply chain management. Hence, one of the most crucial aspects for a construction company in this regard is sustainable supplier selection, and, to this end, an accurate and reliable model is required. In this paper a hybrid fuzzy best-worst method and fuzzy inference system model is developed for sustainable supplier selection. In the first phase of this study, after determining 19 criteria in three main aspects, the final weight of each aspect and criterion is obtained using fuzzy best-worst method approach. In the second phase, the most sustainable supplier is selected by running the weighted fuzzy inference system both in aspect and criterion level, providing more accurate results compared to the use of other available models. Finally, two different tests are employed to validate the results and evaluate the robustness of the proposed model. The novel developed model enables the decision-maker to simulate the decision-making process, reduce the calculations loads, consider a large number of criteria in decision making, and resolve the inherited uncertainties in experts’ responses.

The Technical Debt in Cloud Software Engineering

2015 ◽  
pp. 24-42 ◽  
Author(s):  
Georgios Skourletopoulos ◽  
Rami Bahsoon ◽  
Constandinos X. Mavromoustakis ◽  
George Mastorakis
Keyword(s):  
Software Engineering ◽  
Cost Estimation ◽  
Cost Model ◽  
Service Selection ◽  
Cloud Service ◽  
Cloud Services ◽  
Software As A Service ◽  
Technical Debt ◽  
Selection Decisions ◽  
The Cost

Predicting and quantifying promptly the Technical Debt has turned into an issue of significant importance over recent years. In the cloud marketplace, where cloud services can be leased, the difficulty to identify the Technical Debt effectively can have a significant impact. In this chapter, the probability of introducing the Technical Debt due to budget and cloud service selection decisions is investigated. A cost estimation approach for implementing Software as a Service (SaaS) in the cloud is examined, indicating three scenarios for predicting the incurrence of Technical Debt in the future. The Constructive Cost Model (COCOMO) is used in order to estimate the cost of the implementation and define a range of secureness by adopting a tolerance value for prediction. Furthermore, a Technical Debt quantification approach is researched for leasing a cloud Software as a Service (SaaS) in order to provide insights about the most appropriate cloud service to be selected.

A Fuzzy Based Trust Evaluation Model for Service Selection in Cloud Environment

2019 ◽  
Vol 11 (4) ◽  
pp. 13-27
Author(s):  
Priya G. ◽  
Jaisankar N.
Keyword(s):  
Fuzzy Inference ◽  
Service Providers ◽  
Evaluation Model ◽  
Service Selection ◽  
Cloud Service ◽  
Fuzzy Evaluation ◽  
Trust Evaluation ◽  
Trust Value ◽  
Computing Paradigm ◽  
Fuzzy Input

Cloud computing is a popular computing paradigm among several computing environments, but a deficit in trust among the users and the service providers prevents the large adoption of the cloud in most of the businesses. Cloud service providers should give assurances for providing the reliable services to the cloud consumers. The proposed work explains about the architecture of the trust evaluation model and considered four service measurement indexes (SMI) namely: availability, success rate, turnaround efficiency and feedback about a resource. The trust value for each resource is estimated by the fuzzy evaluation engine in which a fuzzy input set is derived from the SMI parameters. By applying a fuzzy inference rule on fuzzy input sets will yield a fuzzy output set and finally, the most trusted resource value is calculated by defuzzification process called center of gravity. The proposed work is done the implementation by using cloudsim with jfuzzycloud.

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

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