scholarly journals Image-Based Artificial Intelligence Methods for Product Control of Tablet Coating Quality

Pharmaceutics ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 877
Author(s):  
Cosima Hirschberg ◽  
Magnus Edinger ◽  
Else Holmfred ◽  
Jukka Rantanen ◽  
Johan Boetker
Keyword(s):  
Classification Model ◽  
Computational Time ◽  
Support Vector ◽  
Coating Quality ◽  
Coating Solution ◽  
Human Decision ◽  
Control Situations ◽  
The Individual ◽  
Product Control

Mimicking the human decision-making process is challenging. Especially, many process control situations during the manufacturing of pharmaceuticals are based on visual observations and related experience-based actions. The aim of the present work was to investigate the use of image analysis to classify the quality of coated tablets. Tablets with an increasing amount of coating solution were imaged by fast scanning using a conventional office scanner. A segmentation routine was implemented to the images, allowing the extraction of numeric image-based information from individual tablets. The image preprocessing was performed prior to utilization of four different classification techniques for the individual tablet images. The support vector machine (SVM) technique performed superior compared to a convolutional neural network (CNN) in relation to computational time, and this approach was also slightly better at classifying the tablets correctly. The fastest multivariate method was partial least squares (PLS) regression, but this method was hampered by the inferior classification accuracy of the tablets. Finally, it was possible to create a numerical threshold classification model with an accuracy comparable to the SVM approach, so it is evident that there exist multiple valid options for classifying coated tablets.

scholarly journals Nonlinear Inertia Classification Model and Application

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Mei Wang ◽  
Pai Wang ◽  
Jzau-Sheng Lin ◽  
Xiaowei Li ◽  
Xuebin Qin
Keyword(s):  
Search Algorithm ◽  
Great Influence ◽  
Nonlinear Function ◽  
Classification Model ◽  
Support Vector ◽  
Power Cable ◽  
Optimal Position ◽  
Kernel Parameter ◽  
Svm Model

Classification model of support vector machine (SVM) overcomes the problem of a big number of samples. But the kernel parameter and the punishment factor have great influence on the quality of SVM model. Particle swarm optimization (PSO) is an evolutionary search algorithm based on the swarm intelligence, which is suitable for parameter optimization. Accordingly, a nonlinear inertia convergence classification model (NICCM) is proposed after the nonlinear inertia convergence (NICPSO) is developed in this paper. The velocity of NICPSO is firstly defined as the weighted velocity of the inertia PSO, and the inertia factor is selected to be a nonlinear function. NICPSO is used to optimize the kernel parameter and a punishment factor of SVM. Then, NICCM classifier is trained by using the optical punishment factor and the optical kernel parameter that comes from the optimal particle. Finally, NICCM is applied to the classification of the normal state and fault states of online power cable. It is experimentally proved that the iteration number for the proposed NICPSO to reach the optimal position decreases from 15 to 5 compared with PSO; the training duration is decreased by 0.0052 s and the recognition precision is increased by 4.12% compared with SVM.

scholarly journals Classifying a Lending Portfolio of Loans with Dynamic Updates via a Machine Learning Technique

Mathematics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 17
Author(s):  
Fazlollah Soleymani ◽  
Houman Masnavi ◽  
Stanford Shateyi
Keyword(s):  
Machine Learning ◽  
Mathematical Finance ◽  
Bankruptcy Prediction ◽  
Computational Time ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Machine Learning Approach ◽  
Learning Technique ◽  
Dynamic Updates

Bankruptcy prediction has been broadly investigated using financial ratios methodologies. One involved factor is the quality of the portfolio of loans which is given. Hence, having a model to classify/predict position of each loan candidate based on several features is important. In this work, an application of machine learning approach in mathematical finance and banking is discussed. It is shown how we can classify some lending portfolios of banks under several features such as rating categories and various maturities. Dynamic updates of the portfolio are also given along with the top probabilities showing how the financial data of this type can be classified. The discussions and results reveal that a good algorithm for doing such a classification on large economic data of such type is the k-nearest neighbors (KNN) with k=1 along with parallelization even over the support vector machine, random forest, and artificial neural network techniques to save as much as possible on computational time.

Multi-layer Hybrid Classification Model of COVID-19 Chest X-ray Images v1

2021 ◽  
Author(s):  
Thanakorn Poomkur ◽  
Thakerng Wongsirichot
Keyword(s):  
Machine Learning ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Computational Time ◽  
Support Vector ◽  
X Ray ◽  
Learning Techniques ◽  
Hybrid Classification ◽  
Chest X Ray ◽  
Segmentation Image

The coronavirus disease of 2019 (COVID-19) has been declared a pandemic and has raised worldwide concern. Lung inflammation and respiratory failure are commonly observed in moderate-to-severe cases. Chest X-ray imaging is compulsory for diagnosis, and interpretation is commonly performed by skilled medical specialists. Many studies have been conducted using machine learning approaches such as Deep Learning (DL) with acceptable accuracy. However, other dimensions such as computational time were less discussed. Thus, our work is motivated to design anew computer-aided diagnosis (CADx) tool for identifying chest X-ray images of COVID-19 infection using machine learning techniques including Decision Tree (DT), Support Vector Machine (SVM), and Neural Networks (NNs). Our work is designed with the concept of multi-layer classification architecture and performs with minimal computational time and acceptable classification results. First, image segmentation, image enhancement and feature extraction techniques are performed. Second, machine learning techniques are selected based on classification performance. Finally, selected machine learning techniques are assembled into a multi-layer hybrid classification model for COVID-19 (MLHC-COVID-19). Specifically, the MLHC-COVID-19 consists of two layers, Layer I: Healthy and Unhealthy; Layer II: COVID-19 and non-COVID-19.

scholarly journals Individual Driver Crash Risk Classification Based on IoV Data and Offline Consumer Behavior Data

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xuemei Zhao ◽  
Ting Lu ◽  
Yonghui Dai
Keyword(s):  
Consumer Behavior ◽  
Information Integration ◽  
Spare Part ◽  
Risk Classification ◽  
Classification Model ◽  
Insurance Companies ◽  
Crash Risk ◽  
Support Vector ◽  
Big Data Technologies ◽  
The Individual

With the development of big data technologies, usage-based insurance (UBI) has received considerable attention from insurance companies. UBI products focus on identifying the relationship between the individual driver’s risk and online channel behavior variables from Internet of Vehicles (IoV) data. Although omnichannel information integration has promoted the development of many industries, it has not been used to improve the accuracy of driver risk classification models in insurance industries. This paper investigates the role of combining different channel variables in improving the classification of driver’s risk. Specifically, several models, including logistic regression and three different data mining techniques (neural networks, random forests, and support vector machines), augmented with driving behavior data based on the IoV and offline consumer behavior data collected from 4S (Sale, Spare part, Service, Survey) dealers, are applied to the classification model of risk. The empirical results show that the inclusion of online and offline channel data improves the different risk assessments; results also demonstrate the importance of offline consumer behavior variables in different models. These insights have important implications for insurance companies on UBI pricing strategy and cost management.

scholarly journals Microgrid Fault Detection and Classification: Machine Learning Based Approach, Comparison, and Reviews

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3460 ◽  
Author(s):  
Shahriar Rahman Fahim ◽  
Subrata K. Sarker ◽  
S. M. Muyeen ◽  
Md. Rafiqul Islam Sheikh ◽  
Sajal K. Das
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Classification Model ◽  
Superior Performance ◽  
Fault Classification ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Fault Detection And Classification ◽  
Proposed Model ◽  
The Individual

Accurate fault classification and detection for the microgrid (MG) becomes a concern among the researchers from the state-of-art of fault diagnosis as it increases the chance to increase the transient response. The MG frequently experiences a number of shunt faults during the distribution of power from the generation end to user premises, which affects the system reliability, damages the load, and increases the fault line restoration cost. Therefore, a noise-immune and precise fault diagnosis model is required to perform the fast recovery of the unhealthy phases. This paper presents a review on the MG fault diagnosis techniques with their limitations and proposes a novel discrete-wavelet transform (DWT) based probabilistic generative model to explore the precise solution for fault diagnosis of MG. The proposed model is made of multiple layers with a restricted Boltzmann machine (RBM), which enables the model to make the probability reconstruction over its inputs. The individual RBM layer is trained with an unsupervised learning approach where an artificial neural network (ANN) algorithm tunes the model for minimizing the error between the true and predicted class. The effectiveness of the proposed model is studied by varying the input signal and sampling frequencies. A level of considered noise is added with the sample data to test the robustness of the studied model. Results prove that the proposed fault detection and classification model has the ability to perform the precise diagnosis of MG faults. A comparative study among the proposed, kernel extreme learning machine (KELM), multi KELM, and support vector machine (SVM) approaches is studied to confirm the robust superior performance of the proposed model.

scholarly journals Morphological Segmentation Analysis and Texture-based Support Vector Machines Classification on Mice Liver Fibrosis Microscopic Images

2019 ◽  
Vol 14 (4) ◽  
pp. 282-294 ◽  
Author(s):  
Yu Wang ◽  
Fuqian Shi ◽  
Luying Cao ◽  
Nilanjan Dey ◽  
Qun Wu ◽  
...  
Keyword(s):  
Image Classification ◽  
Training Model ◽  
Quantitative Information ◽  
Classification Model ◽  
Computational Time ◽  
Support Vector ◽  
Research Approach ◽  
Morphological Segmentation ◽  
Mice Liver ◽  
Occurrence Matrix

Background: To reduce the intensity of the work of doctors, pre-classification work needs to be issued. In this paper, a novel and related liver microscopic image classification analysis method is proposed. Objective: For quantitative analysis, segmentation is carried out to extract the quantitative information of special organisms in the image for further diagnosis, lesion localization, learning and treating anatomical abnormalities and computer-guided surgery. </P><P> Methods: In the current work, entropy-based features of microscopic fibrosis mice’ liver images were analyzed using fuzzy c-cluster, k-means and watershed algorithms based on distance transformations and gradient. A morphological segmentation based on a local threshold was deployed to determine the fibrosis areas of images. Results: The segmented target region using the proposed method achieved high effective microscopy fibrosis images segmenting of mice liver in terms of the running time, dice ratio and precision. The image classification experiments were conducted using Gray Level Co-occurrence Matrix (GLCM). The best classification model derived from the established characteristics was GLCM which performed the highest accuracy of classification using a developed Support Vector Machine (SVM). The training model using 11 features was found to be accurate when only trained by 8 GLCMs. Conclusion: The research illustrated that the proposed method is a new feasible research approach for microscopy mice liver image segmentation and classification using intelligent image analysis techniques. It is also reported that the average computational time of the proposed approach was only 2.335 seconds, which outperformed other segmentation algorithms with 0.8125 dice ratio and 0.5253 precision.</P>

Semi-automated methods for 3-D reconstruction of macromolecular complexes

1995 ◽  
Vol 53 ◽  
pp. 42-43
Author(s):  
B. Carragher ◽  
M. Whittaker
Keyword(s):  
Three Dimensional ◽  
Time Saving ◽  
Macromolecular Complexes ◽  
Symmetry Properties ◽  
Dimensional Reconstruction ◽  
Experienced Operator ◽  
Projection Images ◽  
The Individual ◽  
Natural Symmetry

Techniques for three-dimensional reconstruction of macromolecular complexes from electron micrographs have been successfully used for many years. These include methods which take advantage of the natural symmetry properties of the structure (for example helical or icosahedral) as well as those that use single axis or other tilting geometries to reconstruct from a set of projection images. These techniques have traditionally relied on a very experienced operator to manually perform the often numerous and time consuming steps required to obtain the final reconstruction. While the guidance and oversight of an experienced and critical operator will always be an essential component of these techniques, recent advances in computer technology, microprocessor controlled microscopes and the availability of high quality CCD cameras have provided the means to automate many of the individual steps.During the acquisition of data automation provides benefits not only in terms of convenience and time saving but also in circumstances where manual procedures limit the quality of the final reconstruction.

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

Manageable Self in the Early Hellenistic Era

2010 ◽  
Vol 39 (2) ◽  
pp. 34-36
Author(s):  
Vaia Touna
Keyword(s):  
Social Economic ◽  
Ancient Greece ◽  
The Self ◽  
Hellenistic Period ◽  
Political Conditions ◽  
The Individual

This paper argues that the rise of what is commonly termed "personal religion" during the Classic-Hellenistic period is not the result of an inner need or even quality of the self, as often argued by those who see in ancient Greece foreshadowing of Christianity, but rather was the result of social, economic, and political conditions that made it possible for Hellenistic Greeks to redefine the perception of the individual and its relationship to others.

Reflecting the Sky in Water

2017 ◽  
Vol 3 (1) ◽  
pp. 112-126 ◽  
Author(s):  
Ilaria Cristofaro
Keyword(s):  
The Unconscious ◽  
Related Case ◽  
Quality Of Water ◽  
Fluid Properties ◽  
Phenomenological Perspective ◽  
Auto Ethnography ◽  
The Individual ◽  
Artificial Water ◽  
Land Water

From a phenomenological perspective, the reflective quality of water has a visually dramatic impact, especially when combined with the light of celestial phenomena. However, the possible presence of water as a means for reflecting the sky is often undervalued when interpreting archaeoastronomical sites. From artificial water spaces, such as ditches, huacas and wells to natural ones such as rivers, lakes and puddles, water spaces add a layer of interacting reflections to landscapes. In the cosmological understanding of skyscapes and waterscapes, a cross-cultural metaphorical association between water spaces and the underworld is often revealed. In this research, water-skyscapes are explored through the practice of auto-ethnography and reflexive phenomenology. The mirroring of the sky in water opens up themes such as the continuity, delimitation and manipulation of sky phenomena on land: water spaces act as a continuation of the sky on earth; depending on water spaces’ spatial extension, selected celestial phenomena can be periodically reflected within architectures, so as to make the heavenly dimension easily accessible and a possible object of manipulation. Water-skyscapes appear as specular worlds, where water spaces are assumed to be doorways to the inner reality of the unconscious. The fluid properties of water have the visual effect of dissipating borders, of merging shapes, and, therefore, of dissolving identities; in the inner landscape, this process may represent symbolic death experiences and rituals of initiation, where the annihilation of the individual allows the creative process of a new life cycle. These contextually generalisable results aim to inspire new perspectives on sky-and-water related case studies and give value to the practice of reflexive phenomenology as crucial method of research.

Sign in / Sign up

Export Citation Format

Share Document