scholarly journals Integrating machine learning with QFD for selecting functional requirements in construction automation

2020 ◽  
Vol 1 (1) ◽  
pp. 76-88
Author(s):  
Edgar Tamayo ◽  
Yasir Khan ◽  
Mohamed Al-Hussein ◽  
Ahmed Qureshi
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Conceptual Design ◽  
Quality Function Deployment ◽  
Functional Requirements ◽  
Customer Requirements ◽  
Customer Requirement ◽  
Quality Function ◽  
Requirement Definition ◽  
Feature Selection Techniques

An important aspect of the conceptual design is at the customer requirement definition stage, where an optimal number of functional requirements are specified with the application of quality function deployment. To facilitate a systematic specification of functional requirements, state-of-the-art unsupervised machine learning techniques will be introduced in the feature selection of functional requirements. However, the scarcity of references on unsupervised feature selection in the literature reflects the difficulty associated with this topic. At the customer requirement definition phase, three techniques will be proposed for selecting functional requirements, namely: (a) principal component analysis, (b) forward orthogonal search, and (c) Kohonen self-organizing map neural network. These machine learning feature selection techniques address the limitations of current approaches in systematically determining the minimum functional requirements from the mapping of customer requirements in quality function deployment. When applied to the conceptual design of the transportable automated wood wall framing machine that is under development at the University of Alberta, the proposed feature selection techniques have been observed to be: (i) fast, (ii) amenable to small quality function deployment dataset, and (iii) adequate in realizing design objectives. The results presented in this paper can be easily extended to online determination of customer requirements and functional requirements, project management, contract management, and marketing.

scholarly journals Analisis Manajemen Kualitas Pelayanan dengan Metode Quality Function Deployment

JURNAL UNITEK ◽  
2017 ◽  
Vol 10 (1) ◽  
pp. 46-59
Author(s):  
John Suarlin
Keyword(s):  
Quality Function Deployment ◽  
Technical Difficulty ◽  
Technical Requirement ◽  
House Of Quality ◽  
Customer Requirements ◽  
Customer Requirement ◽  
Quality Function ◽  
Technical Requirements ◽  
Technical Measurement

Philip Kotler menyatakan keberhasilan dari suatu industri jasa sangat tergantung daripenilaian konsumen, maka merupakan hal yang sangat penting untuk memperhatikankepuasan dari konsumen(2010). Oleh karenanya analisis maupun perbaikan kualitas produkjasa menjadi sangat penting dilakukan jika perusahaan ingin tetap eksis dimatapelanggannya.Penelitian ini melakukan analisis manajemen kualitas di RS Permata Hatiuntuk mengidentifikasi consumer requirements dan technical requirements mengetahui atributapa saja yang masih memerlukan fokus perhatian yang besarperlu diperbaiki danditingkatkan agar bisa mencapai tingkat pelayanan yang optimal pada jasa pelayanankesehatan RS Permata Hati. Analisis manajemen kualitas pelayanan ini dilakukan denganmetode quality function deployment.dengan menggunakan house of quality.Untuk dapatmenyusun House of Quality, daridata primer maupun data sekunder yang terkumpul diolahmelalui tahap-tahap: 1) analisis customer requirement, 2) analisis tingkat kepentingan, 3)analisis tingkat perbaikan, 4) titik penjualan, 5) analisis customer requiremen score, 6) analisistechnical requirement,7) analisis hubungan customer requirement dan technical requirement, 7)analisis technical measurement, 8) analisis relative technical difficulty. Hasil penelitianmenunjukkan terdapat 36 atribut customer requirements yang menjadi faktor penilaian pasienterhadap kualitas jasa pelayanan kesehatan RS Permata Hati Duri. Ke 36 atribut tersbutdikelompokkan menjadi 5 kelompok dimensi penilaian, yaitu: tangible, realibility,responsiveness, assurance, danemphaty.

Development of Data Mining Driven Software Tool to Forecast the Customer Requirement for Quality Function Deployment

2017 ◽  
Vol 4 (1) ◽  
pp. 56-86 ◽  
Author(s):  
Shivani K. Purohit ◽  
Ashish K. Sharma
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Comparative Analysis ◽  
Quality Function Deployment ◽  
Software Tool ◽  
Customer Requirements ◽  
Customer Requirement ◽  
Quality Function ◽  
Analysis Techniques ◽  
Using Data

Quality Function Deployment (QFD) is widely used customer driven process for product development. Thus, Customer Requirements (CRs) play a key role in QFD process. However, the diversification in marketplace makes these CRs more dynamic and changing, giving rise the need to forecast CRs to improve competitiveness and increase customer satisfaction. The purpose can be served by using Data Mining techniques of forecasting. With the pool of forecasting techniques available, it is important to evaluate a suitable one for more effective results. To this end, the paper presents a novel software tool to efficiently forecast CRs in QFD. The tool allows for forecasting using various data mining based time series analysis techniques that strongly assists in doing comparative analysis and evaluating out the most apt technique for forecasting of CRs. The tool is developed using VB.Net and MS-Access. Finally, an example is presented to demonstrate the practicability of proposed software tool.

Effective combining of feature selection techniques for machine learning-enabled IoT intrusion detection

2021 ◽  
Author(s):  
Md Arafatur Rahman ◽  
A. Taufiq Asyhari ◽  
Ong Wei Wen ◽  
Husnul Ajra ◽  
Yussuf Ahmed ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Feature Selection Techniques

scholarly journals Feature-Selection and Mutual-Clustering Approaches to Improve DoS Detection and Maintain WSNs’ Lifetime

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4821
Author(s):  
Rami Ahmad ◽  
Raniyah Wazirali ◽  
Qusay Bsoul ◽  
Tarik Abu-Ain ◽  
Waleed Abu-Ain
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Open Field ◽  
Network Lifetime ◽  
Detection Efficiency ◽  
Denial Of Service ◽  
Harmony Search ◽  
Machine Learning Algorithms ◽  
Transport Layer ◽  
Feature Selection Techniques

Wireless Sensor Networks (WSNs) continue to face two major challenges: energy and security. As a consequence, one of the WSN-related security tasks is to protect them from Denial of Service (DoS) and Distributed DoS (DDoS) attacks. Machine learning-based systems are the only viable option for these types of attacks, as traditional packet deep scan systems depend on open field inspection in transport layer security packets and the open field encryption trend. Moreover, network data traffic will become more complex due to increases in the amount of data transmitted between WSN nodes as a result of increasing usage in the future. Therefore, there is a need to use feature selection techniques with machine learning in order to determine which data in the DoS detection process are most important. This paper examined techniques for improving DoS anomalies detection along with power reservation in WSNs to balance them. A new clustering technique was introduced, called the CH_Rotations algorithm, to improve anomaly detection efficiency over a WSN’s lifetime. Furthermore, the use of feature selection techniques with machine learning algorithms in examining WSN node traffic and the effect of these techniques on the lifetime of WSNs was evaluated. The evaluation results showed that the Water Cycle (WC) feature selection displayed the best average performance accuracy of 2%, 5%, 3%, and 3% greater than Particle Swarm Optimization (PSO), Simulated Annealing (SA), Harmony Search (HS), and Genetic Algorithm (GA), respectively. Moreover, the WC with Decision Tree (DT) classifier showed 100% accuracy with only one feature. In addition, the CH_Rotations algorithm improved network lifetime by 30% compared to the standard LEACH protocol. Network lifetime using the WC + DT technique was reduced by 5% compared to other WC + DT-free scenarios.

scholarly journals Automated Feature Selection and Classification for High-Dimensional Biomedical Data

2021 ◽  
Author(s):  
Tammo P.A. Beishuizen ◽  
Joaquin Vanschoren ◽  
Peter A.J. Hilbers ◽  
Dragan Bošnački
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Automated System ◽  
Complex Data ◽  
Biomedical Data ◽  
Selection Methods ◽  
Model Predictions ◽  
Automated Machine Learning ◽  
Feature Selection Techniques ◽  
Best Fit

Abstract Background: Automated machine learning aims to automate the building of accurate predictive models, including the creation of complex data preprocessing pipelines. Although successful in many fields, they struggle to produce good results on biomedical datasets, especially given the high dimensionality of the data. Result: In this paper, we explore the automation of feature selection in these scenarios. We analyze which feature selection techniques are ideally included in an automated system, determine how to efficiently find the ones that best fit a given dataset, integrate this into an existing AutoML tool (TPOT), and evaluate it on four very different yet representative types of biomedical data: microarray, mass spectrometry, clinical and survey datasets. We focus on feature selection rather than latent feature generation since we often want to explain the model predictions in terms of the intrinsic features of the data. Conclusion: Our experiments show that for none of these datasets we need more than 200 features to accurately explain the output. Additional features did not increase the quality significantly. We also find that the automated machine learning results are significantly improved after adding additional feature selection methods and prior knowledge on how to select and tune them.

Mapping the customer centric weather index insurance service design using quality function deployment

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pankaj Singh ◽  
Gaurav Agrawal
Keyword(s):  
Quality Function Deployment ◽  
Service Design ◽  
Index Insurance ◽  
Importance Rating ◽  
Customer Requirements ◽  
Weather Index ◽  
Quality Function ◽  
Content Type ◽  
Weather Index Insurance ◽  
Design Requirements

PurposeThe present paper aims to propose a framework on weather index insurance (WII) service design by using quality function deployment (QFD).Design/methodology/approachThis study utilizes QFD technique to propose a customer oriented framework on WII service design. In initial phase, customer and design requirements were gathered to derive the relationship between customers' and managers' voice for construct the house of quality (HOQ). Later on, prioritized customer and design requirements as QFD outcome were utilized to develop the action plan matrix in order to suggest the future action plans.FindingsThis study proposed a customer centric framework on WII service design to address the customer requirements. Findings show that adequate claim payments, hassle free prompt claim payment and transparency in losses computation are prioritized customer requirements with highest importance rating, whereas, accurate claim estimation, claim management system and advancement of technology are prioritized service design necessities with highest importance rating.Research limitations/implicationsThe proposed WII service design can enhance the quality of WII service by attain the higher standards of WII service in order to completely satisfy the customers.Practical implicationsThe proposed WII service design can provide a solution to the problems faced by WII industry by improve the customer's service experience and satisfaction.Originality/valueBased on best of author's knowledge, this paper first proposed a framework on WII service design by integrating customer and design requirements by using QFD.

Application of QFD in the Design of Fire Main Fight Vehicles

2013 ◽  
Vol 694-697 ◽  
pp. 2729-2732
Author(s):  
Wu Ba Zhu
Keyword(s):  
Correlation Matrix ◽  
Quality Function Deployment ◽  
Customer Requirements ◽  
Ahp Method ◽  
Quality Function

Quality function deployment (QFD) is a powerful tool of the customer requirements into technical characteristics. With QFD, the design of fire main fight vehicles can be expressed more credibility via the correlation matrix. After the client important degree is calculated through AHP method, requirement is deployed and correlation matrix is built, the requirement important degree is decided. And an example is presented to express and verify the method. It shows that the method is simple and reflects the requirement of users.

Fuzzy Quantitative Approach to Prioritize Green Factors in Supply Chain

2014 ◽  
Vol 592-594 ◽  
pp. 2645-2653
Author(s):  
K.R. Anand ◽  
Ramalingaiah ◽  
P. Parthiban
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Automotive Industry ◽  
Quality Function Deployment ◽  
Quantitative Approach ◽  
Green Supply Chain Management ◽  
Green Supply Chain ◽  
Customer Requirements ◽  
Chain Management ◽  
Quality Function

Eco Design integrates environmental thinking into product design and packaging including in its production, consumption and disposal of the product life cycle in the supply chain. In today’s scenario eco design is very important for saving our environment. This papers aims to investigate the technology, organization and environment factors of the eco design that influence the adoption of Green Supply Chain Management using Fuzzy Quality Function Deployment (FQFD). Quality function deployment (QFD) is a planning and problem-solving methodology used to translate customer requirements (CRs) into technical requirements (TRs) in the Course of new product development (NPD). In the proposed model, fifteen fundamental requirements of customers are identified and eight main factors of eco design are derived to satisfy the overall requirements as detailed. The importance of the customer requirements and relationship strength were identified as linguistic data. We have collected data for the criteria from the decision makers of the automotive industry. Under different situation the values of subjective data are often inaccurate so we have applied Fuzzy Quantitative Approach to overcome this deficiency of high subjectivity and low reliability. This study shows the fuzzy logic using Quality Function Deployment for easy decision making. So this proposed method shows the final ranking of the important eco design factors that influences the adoption of Green Supply Chain Management in the automotive industry. The final result of paper gives Stakeholder Cooperation is the most important factors of eco design.

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