scholarly journals Clustering Analysis using an Unsupervised Machine Learning Method

2021 ◽  
pp. 602-609
Author(s):  
Tashfin Ansari ◽  
Dr. Almas Siddiqui ◽  
Awasthi G. K
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computational Complexity ◽  
Clustering Analysis ◽  
Clustering Algorithm ◽  
Experimental Result ◽  
Machine Learning Method ◽  
Learning Method ◽  
Science Study ◽  
Data Object

Artificial Intelligence (AI) and Machine Learning (ML), which are becoming a part of interest rapidly for various researchers. ML is the field of Computer Science study, which gives capability to learn without being absolutely programmed. This work focuses on the standard k-means clustering algorithm and analysis the shortcomings of the standard k-means algorithm. The k-means clustering algorithm calculates the distance between each data object and not all cluster centres in every iteration, which makes the efficiency of clustering is high. In this work, we have to try to improve the k-means algorithm to solve simple data to store some information in every iteration, which is to be used in the next interaction. This method avoids computing distance of data object to the cluster centre repeatedly, saving the running time. An experimental result shows the enhanced speed of clustering, accuracy, reducing the computational complexity of the k-means. In this, we have work on iris dataset extracted from Kaggle.

scholarly journals Application of Sensors in Intelligent Clothing Design

2018 ◽  
Vol 14 (06) ◽  
pp. 4
Author(s):  
Shali Jiang ◽  
Qiong Ren
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Experimental Results ◽  
Machine Learning Method ◽  
Learning Method ◽  
Clothing Design ◽  
Artificial Intelligence Technology ◽  
Contact Sensor ◽  
Intelligent Clothing ◽  
Contact Sensors

<p class="0abstract"><span lang="EN-US">In order to study the application of sensors in intelligent clothing design, the artificially intelligent cutting-edge technology -machine learning method was proposed to combine a variety of signals of non-contact sensors in several different positions. Higher accuracy was achieved, while maintaining the comfort brought by a non-contact sensor. The experimental results showed that the proposed strategy focused on the combination of clothing design technology and artificial intelligence technology. As a result, without changing the sensor materials, it enhances the comfort and precision of clothing, eliminates the comfort reduced by sensor close to the skin, and transforms inaccurate measurement into accurate measurement. </span></p>

scholarly journals Artificial Intelligence for Modeling Real Estate Price Using Call Detail Records and Hybrid Machine Learning Approach

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1421
Author(s):  
Gergo Pinter ◽  
Amir Mosavi ◽  
Imre Felde
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Estate ◽  
Model Performance ◽  
Machine Learning Method ◽  
Learning Method ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Price ◽  
Call Detail Records

Advancement of accurate models for predicting real estate price is of utmost importance for urban development and several critical economic functions. Due to the significant uncertainties and dynamic variables, modeling real estate has been studied as complex systems. In this study, a novel machine learning method is proposed to tackle real estate modeling complexity. Call detail records (CDR) provides excellent opportunities for in-depth investigation of the mobility characterization. This study explores the CDR potential for predicting the real estate price with the aid of artificial intelligence (AI). Several essential mobility entropy factors, including dweller entropy, dweller gyration, workers’ entropy, worker gyration, dwellers’ work distance, and workers’ home distance, are used as input variables. The prediction model is developed using the machine learning method of multi-layered perceptron (MLP) trained with the evolutionary algorithm of particle swarm optimization (PSO). Model performance is evaluated using mean square error (MSE), sustainability index (SI), and Willmott’s index (WI). The proposed model showed promising results revealing that the workers’ entropy and the dwellers’ work distances directly influence the real estate price. However, the dweller gyration, dweller entropy, workers’ gyration, and the workers’ home had a minimum effect on the price. Furthermore, it is shown that the flow of activities and entropy of mobility are often associated with the regions with lower real estate prices.

An Integrative Machine Learning Method to Improve Fault Detection and Productivity Performance in a Cyber-Physical System

2020 ◽  
Vol 20 (2) ◽  
Author(s):  
Ming-Chuan Chiu ◽  
Chien-De Tsai ◽  
Tung-Lung Li
Keyword(s):  
Machine Learning ◽  
Computational Complexity ◽  
Fault Detection ◽  
Real Time ◽  
Physical System ◽  
Shop Floor ◽  
Cyber Physical System ◽  
Machine Learning Method ◽  
Learning Method ◽  
The Impact

Abstract A cyber-physical system (CPS) is one of the key technologies of industry 4.0. It is an integrated system that merges computing, sensors, and actuators, controlled by computer-based algorithms that integrate people and cyberspace. However, CPS performance is limited by its computational complexity. Finding a way to implement CPS with reduced complexity while incorporating more efficient diagnostics, forecasting, and equipment health management in a real-time performance remains a challenge. Therefore, the study proposes an integrative machine-learning method to reduce the computational complexity and to improve the applicability as a virtual subsystem in the CPS environment. This study utilizes random forest (RF) and a time-series deep-learning model based on the long short-term memory (LSTM) networking to achieve real-time monitoring and to enable the faster corrective adjustment of machines. We propose a method in which a fault detection alarm is triggered well before a machine fails, enabling shop-floor engineers to adjust its parameters or perform maintenance to mitigate the impact of its shutdown. As demonstrated in two empirical studies, the proposed method outperforms other times-series techniques. Accuracy reaches 80% or higher 3 h prior to real-time shutdown in the first case, and a significant improvement in the life of the product (281%) during a particular process appears in the second case. The proposed method can be applied to other complex systems to boost the efficiency of machine utilization and productivity.

scholarly journals Simulation of liquid flow with a combination artificial intelligence flow field and Adams–Bashforth method

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Meisam Babanezhad ◽  
Iman Behroyan ◽  
Ali Taghvaie Nakhjiri ◽  
Azam Marjani ◽  
Saeed Shirazian
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Fluid Flow ◽  
Flow Field ◽  
Computational Time ◽  
Machine Learning Method ◽  
Learning Method ◽  
Particle Movement ◽  
Lagrangian Framework ◽  
Cfd Method

Abstract Direct numerical simulation (DNS) of particle hydrodynamics in the multiphase industrial process enables us to fully learn the process and optimize it on the industrial scale. However, using high-resolution computational calculations for particle movement and the interaction between the solid phase and other phases in fine timestep is limited to excellent computational resources. Solving the Eulerian flow field as a source of solid particle movement can be very time-consuming. However, by the revolution of the fast and accurate learning process, the Eulerian domain can be computed by smart modeling in a very short computational time. In this work, using the machine learning method, the flow field in the square shape cavity is trained, and then the Eulerian framework is replaced with a machine learning method to generate the artificial intelligence (AI) flow field. Then the Lagrangian framework is coupled with this AI flow field, and we simulate particle motion through the fully AI framework. The Adams–Bashforth finite element method is used as a conventional CFD method (Eulerian framework) to simulate the flow field in the cavity. After simulating fluid flow, the ANFIS method is used as an AI model to train the Eulerian data-set and represents AI fluid flow (framework). The Lagrangian framework is coupled with the AI method, and the particle freely migrates through this artificial framework. The results reveal that there is a great agreement between Euler-Lagrangian and AI- Lagrangian in the cavity. We also found that there is an excellent agreement between AI overview with the Adams–Bashforth approach, and the new combination of machine learning and CFD method can accelerate the calculation of the flow field in the square-shaped cavity. AI model can mimic the vortex structure in the cavity, where there is a zero-velocity structure in the center of the domain and maximum velocity near the moving walls.

scholarly journals Modeling Drugs-PLGA Nanoparticles Interactions Using Gaussian Processes: Pharmaceutics Informatics Approach

2021 ◽  
Author(s):  
Rania M. Hathout ◽  
Orchid A Mahmoud ◽  
Dalia S Ali ◽  
Marina Mamdouh ◽  
Abdelkader A Metwally
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Gaussian Processes ◽  
Plga Nanoparticles ◽  
Binding Energies ◽  
Machine Learning Method ◽  
Learning Method ◽  
Physico Chemical ◽  
Chemical Descriptors ◽  
Delta G

The objective of this study was to correlate the binding of drugs on a very popular nanoparticulate polymeric matrix; PLGA nanoparticles with their main constitutional, electronic and physico-chemical descriptors. Gaussian Processes (GPs) was the artificial intelligence machine learning method that was utilized to fulfil this task. The method could successfully model the results where optimum values of the investigated descriptors of the loaded drugs were deduced. A percentage bias of 12.68 % &plusmn; 2.1 was obtained in predicting the binding energies of a group of test drugs. As a conclusion, GPs could successfully model the drugs-PLGA interactions associated with a good predicting power. The GPs-predicted binding energies (&Delta;G) can easily be projected to the drugs loading as was previously proven. Adopting the &ldquo;Pharmaceutics Informatics&rdquo; approach can save the pharmaceutical industry and the drug delivery scientists a lot of exerted resources, efforts and time.

scholarly journals Maternal dietary patterns during early pregnancy and birth weight in Japan

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
T Yamashita ◽  
Y Yonezawa ◽  
T Obara ◽  
M Ishikuro ◽  
T Usuzaki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Birth Weight ◽  
Early Pregnancy ◽  
Dietary Patterns ◽  
Clustering Algorithm ◽  
Reference Group ◽  
Black Tea ◽  
Machine Learning Method ◽  
Learning Method ◽  
High Consumption

Abstract Background The average birth weight in Japan has decreased by 200 g in the last 40 years. Only three studies were reported for the association between maternal dietary patterns and birth weight in East Asia, whose results were inconsistent. We examined what maternal dietary patterns were associated with the birth weight in Japan. Methods Totally 22,493 pregnant women were recruited between July 2013 and September 2016 into the Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study. We included 17,287 women who had a full-term single healthy baby into the analysis. Consumption of food and beverage items was evaluated based on food frequency questionnaire at the first-trimester. Dietary patterns were analyzed using a machine learning method of k-means clustering algorithm. Birth weight was obtained from the medical record. The association between dietary patterns and birth weight was analyzed using multiple liner regression model adjusted for potential confounders with multiple imputation method for missing values. Results Dietary patterns were classified into seven groups by cluster analysis: “high in rice (reference) (n = 8046)”, “middle in vegetables, beans, mushrooms, seaweeds and miso-soup (n = 4842)”, “high in fruits (n = 1176)”, “high in bread, dairy and alcohol (n = 1091)”, “high in meat and fish (n = 1049)”, “high in coffee, black tea, soft drinks and confections (high in coffee) (n = 585)”, and “high in vegetables, beans, mushrooms, seaweeds and miso-soup (n = 498)” groups. In multiple liner regression models, birth weight was 22.6 g (95%CI: 0.1 to 45.2 g) heavier in “high in fruits” group than in reference group and 39.4 g (95%CI: 8.6 to 70.3 g) lighter in “high in coffee” group than in reference group. The other groups were not statistically significant. Conclusions High consumption of fruits and high consumption of coffee, black tea, soft drinks and confections during early pregnancy were associated with increased and decreased birth weight, respectively. Key messages We found maternal dietary patterns during early pregnancy using a machine learning method of k-means clustering algorithm. We found maternal dietary patterns which associated with the birth weight in Japan.

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