scholarly journals Early Cost Estimation in Customized Furniture Manufacturing Using Machine Learning

2021 ◽  
Vol 11 (1) ◽  
pp. 28-33
Author(s):  
O. Kurasova ◽  
◽  
V. Marcinkevičius ◽  
V. Medvedev ◽  
B. Mikulskienė
Keyword(s):  
Machine Learning ◽  
Cost Estimation ◽  
Early Stage ◽  
Machine Learning Techniques ◽  
Design Stage ◽  
Early Design Stage ◽  
Furniture Manufacturing ◽  
Learning Techniques ◽  
Key Factor ◽  
The Cost

Accurate cost estimation at the early stage of a construction project is a key factor in the success of most projects. Many difficulties arise when estimating the cost during the early design stage in customized furniture manufacturing. It is important to estimate the product cost in the earlier manufacturing phase. The cost estimation is related to the prediction of the cost, which commonly includes calculation of the materials, labor, sales, overhead, and other costs. Historical data of the previously manufactured products can be used in the cost estimation process of the new products. In this paper, we propose an early cost estimation approach, which is based on machine learning techniques. The experimental investigation based on the real customized furniture manufacturing data is performed, results are presented, and insights are given.

scholarly journals A Data Science Approach to Cost Estimation Decision Making - Big Data and Machine Learning

2022 ◽  
Vol 25 (1) ◽  
pp. 45-57
Author(s):  
Luis Fernández-Revuelta Pérez ◽  
Álvaro Romero Blasco
Keyword(s):  
Machine Learning ◽  
Cost Estimation ◽  
Data Science ◽  
Management Accounting ◽  
Machine Learning Techniques ◽  
Estimation Task ◽  
Learning Techniques ◽  
Potential Applications ◽  
The Cost ◽  
Science To Management

Cost estimation may become increasingly difficult, slow, and resource-consuming when it cannot be performed analytically. If traditional cost estimation techniques are usable at all under those circumstances, they have important limitations. This article analyses the potential applications of data science to management accounting, through the case of a cost estimation task posted on Kaggle, a Google data science and machine learning website. When extensive data exist, machine learning techniques can overcome some of those limitations. Applying machine learning to the data reveals non-obvious patterns and relationships that can be used to predict costs of new assemblies with acceptable accuracy. This article discusses the advantages and limitations of this approach and its potential to transform cost estimation, and more widely management accounting. The multinational company Caterpillar posted a contest on Kaggle to estimate the price that a supplier would quote for manufacturing a number of industrial assemblies, given historical quotes for similar assemblies. Hitherto, this problem would have required reverse-engineering the supplier’s accounting structure to establish the cost structure of each assembly, identifying non-obvious relationships among variables. This complex and tedious task is usually performed by human experts, adding subjectivity to the process. La estimación de costes puede resultar cada vez más difícil, lenta y consumidora de recursos cuando no puede realizarse de forma analítica. Cuando las técnicas tradicionales de estimación de costes son utilizadas en esas circunstancias se presentan importantes limitaciones. Este artículo analiza las posibles aplicaciones de la ciencia de datos a la contabilidad de gestión, a través del caso de una tarea de estimación de costes publicada en Kaggle, un sitio web de ciencia de datos y aprendizaje automático de Google. Cuando existen muchos datos, las técnicas de aprendizaje automático pueden superar algunas de esas limitaciones. La aplicación del aprendizaje automático a los datos revela patrones y relaciones no evidentes que pueden utilizarse para predecir los costes de nuevos montajes con una precisión aceptable. En nuestra investigación se analizan las ventajas y limitaciones de este enfoque y su potencial para transformar la estimación de costes y, más ampliamente, la contabilidad de gestión. La multinacional Caterpillar publicó un concurso en Kaggle para estimar el precio que un proveedor ofrecería por la fabricación de una serie de conjuntos industriales, dados los presupuestos históricos de conjuntos similares. Hasta ahora, este problema habría requerido una ingeniería inversa de la estructura contable del proveedor para establecer la estructura de costes de cada ensamblaje, identificando relaciones no obvias entre las variables. Esta compleja y tediosa tarea suele ser realizada por expertos humanos, lo que añade subjetividad al proceso.

scholarly journals Statistical and Machine Learning-Driven Optimization of Mechanical Properties in Designing Durable HDPE Nanobiocomposites

Polymers ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 3100
Author(s):  
Anusha Mairpady ◽  
Abdel-Hamid I. Mourad ◽  
Mohammad Sayem Mozumder
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Genetic Algorithm ◽  
Machine Learning Techniques ◽  
Hybrid Technique ◽  
Learning Techniques ◽  
Artificial Neural Network Ann ◽  
Major Factors ◽  
The Cost ◽  
Design Factors

The selection of nanofillers and compatibilizing agents, and their size and concentration, are always considered to be crucial in the design of durable nanobiocomposites with maximized mechanical properties (i.e., fracture strength (FS), yield strength (YS), Young’s modulus (YM), etc). Therefore, the statistical optimization of the key design factors has become extremely important to minimize the experimental runs and the cost involved. In this study, both statistical (i.e., analysis of variance (ANOVA) and response surface methodology (RSM)) and machine learning techniques (i.e., artificial intelligence-based techniques (i.e., artificial neural network (ANN) and genetic algorithm (GA)) were used to optimize the concentrations of nanofillers and compatibilizing agents of the injection-molded HDPE nanocomposites. Initially, through ANOVA, the concentrations of TiO2 and cellulose nanocrystals (CNCs) and their combinations were found to be the major factors in improving the durability of the HDPE nanocomposites. Further, the data were modeled and predicted using RSM, ANN, and their combination with a genetic algorithm (i.e., RSM-GA and ANN-GA). Later, to minimize the risk of local optimization, an ANN-GA hybrid technique was implemented in this study to optimize multiple responses, to develop the nonlinear relationship between the factors (i.e., the concentration of TiO2 and CNCs) and responses (i.e., FS, YS, and YM), with minimum error and with regression values above 95%.

Early Stage Detection of Heart Failure Using Machine Learning Techniques

2021 ◽  
pp. 75-88
Author(s):  
Zulfikar Alom ◽  
Mohammad Abdul Azim ◽  
Zeyar Aung ◽  
Matloob Khushi ◽  
Josip Car ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Early Stage ◽  
Machine Learning Techniques ◽  
Learning Techniques

Predictive Analysis of Diabetes Mellitus Using Machine Learning Techniques

2020 ◽  
Vol 17 (8) ◽  
pp. 3449-3452
Author(s):  
M. S. Roobini ◽  
Y. Sai Satwick ◽  
A. Anil Kumar Reddy ◽  
M. Lakshmi ◽  
D. Deepa ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Early Stage ◽  
The Body ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Pima Indians ◽  
Classification Techniques ◽  
Learning Techniques ◽  
Prediction Of Diabetes

In today’s world diabetes is the major health challenges in India. It is a group of a syndrome that results in too much sugar in the blood. It is a protracted condition that affects the way the body mechanizes the blood sugar. Prevention and prediction of diabetes mellitus is increasingly gaining interest in medical sciences. The aim is how to predict at an early stage of diabetes using different machine learning techniques. In this paper basically, we use well-known classification that are Decision tree, K-Nearest Neighbors, Support Vector Machine, and Random forest. These classification techniques used with Pima Indians diabetes dataset. Therefore, we predict diabetes at different stage and analyze the performance of different classification techniques. We Also proposed a conceptual model for the prediction of diabetes mellitus using different machine learning techniques. In this paper we also compare the accuracy of the different machine learning techniques to finding the diabetes mellitus at early stage.

Cataloguing of Attention Deficit Hyperactivity Disorder (ADHD) among Children

2021 ◽  
Author(s):  
Sumathi M ◽  
Dr. G S Mamatha ◽  
Dr. Ramaa A
Keyword(s):  
Machine Learning ◽  
Early Stage ◽  
Machine Learning Techniques ◽  
Medical Field ◽  
Hyperactivity Disorder ◽  
Learning Techniques ◽  
Chronic Disorder ◽  
Adhd Children ◽  
Anxiety Depression ◽  
Treatment Rules

<p>Children are the dream of parents. Children ADHD is a bygone and chronic disorder which leads to problems in children. If not solved in childhood stages will continue in future till adolescents. The disorder consequences are difficulty to study the tasks which are related to anxiety, depression and other psychological problems. Hence the disorder must be resolved in the early stage to control any type of consequences in future for our children. The medical field is an eminent area in today’s world such as signal processing, Imaging, MRI, EEG etc. to diagnose and offer treatment. Even technology field too contributing to ADHD children by providing different techniques in different areas such as IoT, mobile, Robot, Application, virtual reality, augmented reality, machine learning techniques etc. to give diagnosis and treatment methods. The paper reviews and summarizes the set of features, diagnosis methods, treatment rules for ADHD children.</p>

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