Data mining in manufacturing: a review based on the kind of knowledge

In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, including product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection etc. Data mining has emerged as an important tool for knowledge acquisition from the manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with a special emphasis on the type of functions to be performed on the data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been used on the abstracts and keywords of 150 papers to identify the research gaps and find the linkages between knowledge area, knowledge type and the applied data mining tools and techniques

Random forest age estimation model based on length of left hand bone for Asian population

In forensic anthropology, age estimation is used to ease the process of identifying the age of a living being or the body of a deceased person. Nonetheless, the specialty of the estimation models is solely suitable to a specific people. Commonly, the models are inter and intra-observer variability as the qualitative set of data is being used which results the estimation of age to rely on forensic experts. This study proposes an age estimation model by using length of bone in left hand of Asian subjects range from newborn up to 18-year-old. One soft computing model, which is Random Forest (RF) is used to develop the estimation model and the results are compared with Artificial Neural Network (ANN) and Support Vector Machine (SVM), developed in the previous case studies. The performance measurement used in this study and the previous case study are R-square and Mean Square Error (MSE) value. Based on the results produced, the RF model shows comparable results with the ANN and SVM model. For male subjects, the performance of the RF model is better than ANN, however less ideal than SVM model. As for female subjects, the RF model overperfoms both ANN and SVM model. Overall, the RF model is the most suitable model in estimating age for female subjects compared to ANN and SVM model, however for male subjects, RF model is the second best model compared to the both models. Yet, the application of this model is restricted only to experimental purpose or forensic practice

Flight Delay Prediction Using a Hybrid Deep Learning Method

The operational effectiveness of airports and airlines greatly relies on punctuality. Many conventional machine learning and deep learning algorithms are applied in the analysis of air traffic data. However, the hybrid deep learning (HDL) model demonstrates great success with superior results in many complex problems, e.g. image classification and behaviour detection based on video data. Interestingly, no previous attempts have been made to apply the concept of HDL in analysing structured air traffic data before. Hence, this research investigates the effectiveness of the HDL in the departure delays severity prediction (i.e. on-time, delay and extremely delay) for 10 major airports in the U.S. that experience high ground and air congestion. The proposed HDL model is a combination of a feed-forward artificial neural network model with three hidden layers and a conventional gradient boosted tree model (XGBoost). Utilising the passenger flight on-time performance data from the U.S. Department of Transportation, the proposed HDL model achieves a sharp rise of 22.95% in accuracy when compared to a pure neural network model. However, with current data used in this research, a pure machine learning model achieves the best prediction accuracy

Enhancing Security in Internet of Healthcare Application using Secure Convolutional Neural Network

The ubiquity of Internet of Things (IoT) devices has completely changed the healthcare industry by presenting previously unheard-of potential for remote patient monitoring and individualized care. In this regard, we suggest a unique method that makes use of Secure Convolutional Neural Networks (SCNNs) to improve security in Internet-of-Healthcare (IoH) applications. IoT-enabled healthcare has advanced as a result of the integration of IoT technologies, giving it impressive data processing powers and large data storage capacity. This synergy has led to the development of an intelligent healthcare system that is intended to remotely monitor a patient's medical well-being via a wearable device as a result of the ongoing advancement of the Industrial Internet of Things (IIoT). This paper focuses on safeguarding user privacy and easing data analysis. Sensitive data is carefully separated from user-generated data before being gathered. Convolutional neural network (CNN) technology is used to analyse health-related data thoroughly in the cloud while scrupulously protecting the privacy of the consumers.The paper provide a secure access control module that functions using user attributes within the IoT-Healthcare system to strengthen security. This module strengthens the system's overall security and privacy by ensuring that only authorised personnel may access and interact with the sensitive health data. The IoT-enabled healthcare system gets the capacity to offer seamless remote monitoring while ensuring the confidentiality and integrity of user information thanks to this integrated architecture

COMPARISON OF SELECTED CLASSIFICATION METHODS BASED ON MACHINE LEARNING AS A DIAGNOSTIC TOOL FOR KNEE JOINT CARTILAGE DAMAGE BASED ON GENERATED VIBROACOUSTIC PROCESSES

Osteoarthritis is one of the most common cause of disability among elderly. It can affect every joint in human body, however, it is most prevalent in hip, knee, and hand joints. Early diagnosis of cartilage lesions is essential for fast and accurate treatment, which can prolong joint function. Available diagnostic methods include conventional X-ray, ultrasound and magnetic resonance imaging. However, those diagnostic modalities are not suitable for screening purposes. Vibroarthrography is proposed in literature as a screening method for cartilage lesions. However, exact method of signal acquisition as well as classification method is still not well established in literature. In this study, 84 patients were assessed, of whom 40 were in the control group and 44 in the study group. Cartilage status in the study group was evaluated during surgical treatment. Multilayer perceptron - MLP, radial basis function - RBF, support vector method - SVM and naive classifier – NBC were introduced in this study as classification protocols. Highest accuracy (0.893) was found when MLP was introduced, also RBF classification showed high sensitivity (0.822) and specificity (0.821). On the other hand, NBC showed lowest diagnostic accuracy reaching 0.702. In conclusion vibroarthrography presents a promising diagnostic modality for cartilage evaluation in clinical setting with the use of MLP and RBF classification methods

Predictive Modeling of Fuel Efficiency of Trucks

This research studied the behavior of several controllable variables that affect the fuel efficiency of trucks. Re-routing is the process of modifying the parameters of the routes for a set of trips to optimize fuel consumption and also to increase customer satisfaction through efficient deliveries. This is an important process undertaken by a food distribution company to modify the trips to adapt to the immediate necessities. A predictive model was developed to calculate the change in Miles per Gallon (MPG) whenever a re-route is performed on a region of a particular distribution area. The data that was used, was from the Dallas center which is one of the distribution centers owned by the company. A consistent model that could provide relatively accurate predictions across five distribution centers had to be developed. It was found that the model built using the data from the Corporate center was the most consistent one. The timeline of the data used to build the model was from May 2013 through December 2013. The predictive model provided predictions of which about 88% of the data that was used, was within the 0-10% error group. This was an improvement on the lesser 43% obtained for the linear regression and K-means clustering models. The model was also validated on the data for January 2014 through the first two weeks of March 2014 and it provided predictions of which about 81% of the data was within the 0-10 % error group. The average overall error was around 10%, which was the least for the approaches explored in this research. Weight, stop count and stop time were identified as the most significant factors which influence the fuel efficiency of the trucks. Further, neural network architecture was built to improve the predictions of the MPG. The model can be used to predict the average change in MPG for a set of trips whenever a re-route is performed. Since the aim of re-routing is to reduce the miles and trips; extra load will be added to the remaining trips. Although, the MPG would decrease because of this extra load, it would be offset by the savings due to the drop in miles and trips. The net savings in the fuel can now be translated into the amount of money saved

Machine Learning in Dentistry: A Scoping Review

Machine learning (ML) is being increasingly employed in dental research and application. We aimed to systematically compile studies using ML in dentistry and assess their methodological quality, including the risk of bias and reporting standards. We evaluated studies employing ML in dentistry published from 1 January 2015 to 31 May 2021 on MEDLINE, IEEE Xplore, and arXiv. We assessed publication trends and the distribution of ML tasks (classification, object detection, semantic segmentation, instance segmentation, and generation) in different clinical fields. We appraised the risk of bias and adherence to reporting standards, using the QUADAS-2 and TRIPOD checklists, respectively. Out of 183 identified studies, 168 were included, focusing on various ML tasks and employing a broad range of ML models, input data, data sources, strategies to generate reference tests, and performance metrics. Classification tasks were most common. Forty-two different metrics were used to evaluate model performances, with accuracy, sensitivity, precision, and intersection-over-union being the most common. We observed considerable risk of bias and moderate adherence to reporting standards which hampers replication of results. A minimum (core) set of outcome and outcome metrics is necessary to facilitate comparisons across studies

Estudio de métodos de construcción de ensembles de clasificadores y aplicaciones

La inteligencia artificial se dedica a la creación de sistemas informáticos con un comportamiento inteligente. Dentro de este área el aprendizaje computacional estudia la creación de sistemas que aprenden por sí mismos. Un tipo de aprendizaje computacional es el aprendizaje supervisado, en el cual, se le proporcionan al sistema tanto las entradas como la salida esperada y el sistema aprende a partir de estos datos. Un sistema de este tipo se denomina clasificador. En ocasiones ocurre, que en el conjunto de ejemplos que utiliza el sistema para aprender, el número de ejemplos de un tipo es mucho mayor que el número de ejemplos de otro tipo. Cuando esto ocurre se habla de conjuntos desequilibrados. La combinación de varios clasificadores es lo que se denomina "ensemble", y a menudo ofrece mejores resultados que cualquiera de los miembros que lo forman. Una de las claves para el buen funcionamiento de los ensembles es la diversidad. Esta tesis, se centra en el desarrollo de nuevos algoritmos de construcción de ensembles, centrados en técnicas de incremento de la diversidad y en los problemas desequilibrados. Adicionalmente, se aplican estas técnicas a la solución de varias problemas industriales.Ministerio de Economía y Competitividad, proyecto TIN-2011-2404