Machine Learning Workbench Research Articles

Trace and ultratrace elements in spinel subgroup minerals of ultramafic rocks from the Voltri Massif (NW Italy): the influence of microstructure and texture

Abstract. An innovative multi-analytical approach comprising mineralogical, minero-chemical, and microstructural analyses as well as an indirect machine learning-based statistical method was applied to investigate the mineralogy and the mineral chemistry of spinel subgroup minerals (SSMs) of different ultramafic rocks from the high-pressure metaophiolites of the Voltri Massif (Central Liguria, NW Italy). The study was focused on the correlation between the compositional variations of SSMs and their texture, microstructure, and the degree of serpentinization of the host rock. The SSM occurs with three main textures and microstructures linked to the progressive serpentinization and deformation of ultramafic rocks during the Alpine orogenic events: (i) Cr-spinel porphyroclasts with various degrees of recrystallization (up to magnetite porphyroblasts) within partially serpentinized peridotite and massive serpentinite; (ii) magnetite crystals associated with pseudomorphic and non-pseudomorphic serpentine textures (e.g., mesh, hourglass, ribbon, and interpenetrating textures) in partially serpentinized peridotite and massive serpentinites; and (iii) magnetite crystals re-oriented along the foliations developed in serpentine schist. The chemical composition of SSMs varies systematically within the textures and microstructures. These processes also affected the chemical composition of SSMs, the availability of Mn, Zn, Ni, and Co in solution, and their consequent incorporation in the lattice of chromian spinel due to olivine breakdown, the major repository of these elements in ultramafic rocks. At a general scale, the trace and ultratrace variability is primarily related to the petrologic and tectonic evolution but, at a local scale, also the mineralogical, lithological, structural, and textural features correlated to the degree of serpentinization and/or deformation. These significantly influence the distribution and concentration of trace and ultratrace elements in SSMs. The results of the present work were also confirmed by an innovative indirect statistical method performed through the Weka Machine Learning Workbench.

LE2ML: a microservices-based machine learning workbench as part of an agnostic, reliable and scalable architecture for smart homes

LE2ML: a microservices-based machine learning workbench as part of an agnostic, reliable and scalable architecture for smart homes

The assignment success for 22 horse breeds registered in the Czech Republic: The machine learning perspective

The paper demonstrates the dependability of assignment testing in the identification of an appropriate breed to monitor comprehensive genetic information from molecular markers to analyse the collection of real population data covering 22 horse breeds registered in the Czech Republic, including native breeds and genetic resources. If 17 microsatellites are used, the mean number of alleles per locus corresponds to 10.4. The count of alleles at the individual loci ranges between five (HTG07) and 17 (ASB17). The loci ASB02, ASB23, HMS03, HTG10, and VHL20 exhibit the highest gene diversity and observed heterozygosity (both above 80%), with the mean value of 0.77 and 0.73, respectively. The moderate total inbreeding coefficient (5.2%) is estimated across all the loci and breeds. The levels of apparent breed differentiation span from zero between the Czech Warmblood and Slovak Warmblood to 0.15 between the Shetland Pony and Standardbred. The phylogenetic breed relationships are revealed via the NeighbourNet dendrogram constructed from Reynolds’ genetic distances, which clearly separate the Coldblood draught, Hot/Warmblood, and Pony horses. Our results reveal that the Bayesian approach (the Rannala and Mountain technique) provides the most intensive prediction power (83.6%) out of the GeneClass tools and that the Bayes Net algorithm exhibits the best efficiency (78.4%) from the WEKA machine learning workbench options, considering the use of the five-fold cross validation technique. The algorithms could be trained on large real reference data sets, and thus there appears another viable perspective for machine learning in horse ancestry testing. In this context, it is also important to stress the fact that innovated computational tools will potentially lead towards structuring a novel web server to allow the identification of horse breeds.

KLOSURE: Closing in on open\u2013ended patient questionnaires with text mining

BackgroundKnee injury and Osteoarthritis Outcome Score (KOOS) is an instrument used to quantify patients’ perceptions about their knee condition and associated problems. It is administered as a 42-item closed-ended questionnaire in which patients are asked to self-assess five outcomes: pain, other symptoms, activities of daily living, sport and recreation activities, and quality of life. We developed KLOG as a 10-item open-ended version of the KOOS questionnaire in an attempt to obtain deeper insight into patients’ opinions including their unmet needs. However, the open–ended nature of the questionnaire incurs analytical overhead associated with the interpretation of responses. The goal of this study was to automate such analysis. We implemented KLOSURE as a system for mining free–text responses to the KLOG questionnaire. It consists of two subsystems, one concerned with feature extraction and the other one concerned with classification of feature vectors. Feature extraction is performed by a set of four modules whose main functionalities are linguistic pre-processing, sentiment analysis, named entity recognition and lexicon lookup respectively. Outputs produced by each module are combined into feature vectors. The structure of feature vectors will vary across the KLOG questions. Finally, Weka, a machine learning workbench, was used for classification of feature vectors.ResultsThe precision of the system varied between 62.8 and 95.3%, whereas the recall varied from 58.3 to 87.6% across the 10 questions. The overall performance in terms of F–measure varied between 59.0 and 91.3% with an average of 74.4% and a standard deviation of 8.8.ConclusionsWe demonstrated the feasibility of mining open-ended patient questionnaires. By automatically mapping free text answers onto a Likert scale, we can effectively measure the progress of rehabilitation over time. In comparison to traditional closed-ended questionnaires, our approach offers much richer information that can be utilised to support clinical decision making. In conclusion, we demonstrated how text mining can be used to combine the benefits of qualitative and quantitative analysis of patient experiences.

Comparing assignment-based approaches to breed identification within a large set of horses.

Considering the extensive data sets and statistical techniques, animal breeding embodies a branch of machine learning that has a constantly increasing impact on breeding. In our study, information regarding the potential of machine learning and data mining within a large set of horses and breeds is presented. The individual assignment methods and factors influencing the success rate of the procedure are compared at the Czech population scale. The fixation index values ranged from 0.057 (HMS1) to 0.144 (HTG6), and the overall genetic differentiation amounted to 8.9% among the breeds. The highest genetic divergence (FST = 0.378) was established between the Friesian and Equus przewalskii; the highest degree of gene migration was obtained between the Czech and Bavarian Warmblood (Nm = 14,302); and the overall global heterozygote deficit across the populations was 10.4%. The eight standard methods (Bayesian, frequency, and distance) using GeneClass software and almost all mainstream classification algorithms (Bayes Net, Naive Bayes, IB1, IB5, KStar, JRip, J48, Random Forest, Random Tree, PART, MLP, and SVM) from the WEKA machine learning workbench were compared by utilizing 314,874 real allelic data sets. The Bayesian method (GeneClass, 89.9%) and Bayesian network algorithm (WEKA, 84.8%) outperformed the other techniques. The breed genomic prediction accuracy reached the highest value in the cold-blooded horses. The overall proportion of individuals correctly assigned to a population depended mainly on the breed number and genetic divergence. These statistical tools could be used to assess breed traceability systems, and they exhibit the potential to assist managers in decision-making as regards breeding and registration.

Predicting Safety Solutions via an Artificial Neural Network

Considering the extensive data sets and statistical techniques, Industry 4.0 embodies a branch of machine learning that has a constantly increasing impact on machine safety. We propose an preliminary study based on application of multi-layer feed-forward neural networks in machine safety solutions; the approach is expected to simplify the user choice of suitable measures and safety functions. The prediction method and factors influencing the success rate of the procedure are indicated in a safety parameter scale reflecting industrial experience with classic methods. The multilayer perceptron, a mainstream classification algorithm from the WEKA machine learning workbench, was employed in our primary dataset as a class of the feed-forward artificial neural network. Our initial experimental data were collected from various experts within the industry. The overall proportion of individual safety solutions was correctly assigned by using the training-evaluated test mode, and its prediction accuracy was 100%; further, when assessing the 5-fold cross-validation test mode, we obtained the success rate of 40%. These statistical tools could be used to assess safety PLC traceability systems, and they exhibit the potential to assist managers in decision-making as safety devices. We demonstrate that machine learning is widely usable by the expert community and might bring multiple advantages, such as reduction of the safety solution design time, major cost cutback, and engineering tool availability.

A Data Mining Approach for Cardiovascular Diagnosis

Abstract The large amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analysed by traditional methods. Data mining can improve decision-making by discovering patterns and trends in large amounts of complex data. In the healthcare industry specifically, data mining can be used to decrease costs by increasing efficiency, improve patient quality of life, and perhaps most importantly, save the lives of more patients. The main goal of this project is to apply data mining techniques in order to make possible the prediction of the degree of disability that patients will present when they leave hospitalization. The clinical data that will compose the data set was obtained from one single hospital and contains information about patients who were hospitalized in Cardio Vascular Disease’s (CVD) unit in 2016 for having suffered a cardiovascular accident. To develop this project, it will be used the Waikato Environment for Knowledge Analysis (WEKA) machine learning Workbench since this one allows users to quickly try out and compare different machine learning methods on new data sets

Comparative Study of Classification Techniques For Large Scale Data - Case Study

The existence of Massive datasets that are generated in many applications provides various opportunities and challenges. Especially, scalable mining of such large-scale datasets is a challenging issue that attracted some recent research. In the present study, the main focus is to analyse the classification techniques using WEKA machine learning workbench. Moreover, a large-scale dataset was used. This dataset comes from the protein structure prediction field. It has already been partitioned into training and test sets using the ten-fold cross-validation methodology. In this experiment, nine different methods have been tested. As a result, it became obvious that it is not applicable to test more than one classifier from the (tree) family in the same experiment. On the other hand, using (NaiveBayes) Classifier with the default properties of the attribute selection filter has a great time consuming. Finally, varying the parameters of the attribute selections should be prioritized for more accurate results.

An evolving spatio-temporal approach for gender and age group classification with Spiking Neural Networks

This research study proposes a novel method of inter-related problems in face recognition using the NeuCube neuromorphic computational platform. We investigated age classification and gender recognition. The well-known FG-NET and MORPH Album 2 image gallery were used and anthropometric features were extracted from landmark points on the face. The landmarks were pre-processed with the procrustes algorithm before feature extraction was performed. The Weka machine learning workbench was used to compare the performance of traditional techniques such as the K nearest neighbor (Knn) and Multi-LayerPerceptron (MLP) with NeuCube. Our empirical results show that NeuCube performed consistently better across both problem types that we investigated.

Intelligent Video QoE Prediction Model for Errorprone Networks

Objective: Quantifying the quality of video transmitted over diversified networks is becoming critical in the frontiers of digital video Communication. The exponential growth of video-driven applications in wireless domain have created plethora of research issues to be explored in real time. Estimation of video quality in wireless environment requires the conception of better framework and methodologies to improve users’ Quality of Experience (QoE).This paper depicts a novel QoE prediction model that uses machine learning algorithms in predicting video quality over an error prone environment with better prediction accuracy. Methods: The work deploys Pseudo Subjective Quality Assessment (PSQA) method that involves a hybrid technique in assessing the multimedia quality using WEKA machine learning workbench. Findings: The proposed model provides a comparative study of well-known Artificial Intelligence (AI) techniques in predicting the perceived quality of multimedia and the efficiency is analyzed using performance indicators such as Root Mean Squared Error, Correlation Coefficient and Mean Absolute Error. The results of the proposed method underline the effective and inherent advantage in using machine learning methods for video quality prediction. Application: The evaluation results exemplify the importance of applying machine learning paradigms in broad areas of visual quality assessment. The output of various error measures and error variation analysis of the model shows the superiority of Multilayer perceptron based AI technique over other methods.

ASSESSMENT OF PERFORMANCES OF VARIOUS MACHINE LEARNING ALGORITHMS DURING AUTOMATED EVALUATION OF DESCRIPTIVE ANSWERS

Automation of descriptive answers evaluation is the need of the hour because of the huge increase in the number of students enrolling each year in educational institutions and the limited staff available to spare their time for evaluations. In this paper, we use a machine learning workbench called LightSIDE to accomplish auto evaluation and scoring of descriptive answers. We attempted to identify the best supervised machine learning algorithm given a limited training set sample size scenario. We evaluated performances of Bayes, SVM, Logistic Regression, Random forests, Decision stump and Decision trees algorithms. We confirmed SVM as best performing algorithm based on quantitative measurements across accuracy, kappa, training speed and prediction accuracy with supplied test set.

Setting water quality objectives for the health of freshwater fish

An important responsibility of the Environment Protection Authority, Victoria, is to set objectives for levels of environmental contaminants. To support the development of environmental objectives for water quality, a need has been identified to understand the dual impacts of concentration and duration of a contaminant on biota in freshwater streams. For suspended solids contamination, information reported by Newcombe and Jensen [North American Journal of Fisheries Management, 16(4):693--727, 1996] study of freshwater fish and the daily suspended solids data from the United States Geological Survey stream monitoring network is utilised. The study group was requested to examine both the utility of the Newcombe and Jensen and the USA data, as well as the formulation of a procedure for use by the Environment Protection Authority Victoria that takes concentration and duration of harmful episodes into account when assessing water quality. The extent to which the impact of a toxic event on fish health could be modelled deterministically was also considered. It was found that concentration and exposure duration were the main compounding factors on the severity of effects of suspended solids on freshwater fish. A protocol for assessing the cumulative effect on fish health and a simple deterministic model, based on the biology of gill harm and recovery, was proposed. References D. W. T. Au, C. A. Pollino, R. S. S Wu, P. K. S. Shin, S. T. F. Lau, and J. Y. M. Tang. Chronic effects of suspended solids on gill structure, osmoregulation, growth, and triiodothyronine in juvenile green grouper epinephelus coioides. Marine Ecology Press Series, 266:255--264, 2004. J.C. Bezdek, S.K. Chuah, and D. Leep. Generalized k-nearest neighbor rules. Fuzzy Sets and Systems, 18:237--26, 1986. E. T. Champagne, K. L. Bett-Garber, A. M. McClung, and C. Bergman. {Sensory characteristics of diverse rice cultivars as influenced by genetic and environmental factors}. Cereal Chem., {81}:{237--243}, {2004}. S. G. Cheung and P. K. S. Shin. Size effects of suspended particles on gill damage in green-lipped mussel perna viridis. Marine Pollution Bulletin, 51(8--12):801--810, 2005. D. H. Evans. The fish gill: site of action and model for toxic effects of environmental pollutants. Environmental Health Perspectives, 71:44--58, 1987. G. C. Grigg. The failure of oxygen transport in a fish at low levels of ambient oxygen. Comp. Biochem. Physiol., 29:1253--1257, 1969. G. Holmes, A. Donkin, and I.H. Witten. {Weka: A machine learning workbench}. In Proceedings of the Second Australia and New Zealand Conference on Intelligent Information Systems, volume {24}, pages {357--361}, {Brisbane, Australia}, {1994}. {IEEE Computer Society}. D. D. Macdonald and C. P. Newcombe. Utility of the stress index for predicting suspended sediment effects: response to comments. North American Journal of Fisheries Management, 13:873--876, 1993. C. P. Newcombe. Suspended sediment in aquatic ecosystems: ill effects as a function of concentration and duration of exposure. Technical report, British Columbia Ministry of Environment, Lands and Parks, Habitat Protection branch, Victoria, 1994. C. P. Newcombe and J. O. T. Jensen. Channel suspended sediment and fisheries: A synthesis for quantitative assessment of risk and impact. North American Journal of Fisheries Management, 16(4):693--727, 1996. C. P. Newcombe and D. D. Macdonald. Effects of suspended sediments on aquatic ecosystems. North American Journal of Fisheries Management, 11(1):72--82, 1991. K. Schmidt-Nielsen. Scaling. Why is animal size so important? Cambridge University Press, NY, 1984. J. S. Schwartz, A. Simon, and L. Klimetz. Use of fish functional traits to associate in-stream suspended sediment transport metrics with biological impairment. Environmental Monitoring and Assessment, 179(1--4):347--369, 2011. E. Al Shaw and J. S. Richardson. Direct and indirect effects of sediment pulse duration on stream invertebrate assemblages and rainbow trout (Oncorhynchus mykiss) growth and survival. Canadian Journal of Fish and Aquatic Science, 58:2213--2221, 2001. P. Tiwari and H. Hasegawa. {Demand for housing in Tokyo: A discrete choice analysis}. Regional Studies, {38}:{27--42}, {2004}. Y. Tramblay, A. Saint-Hilaire, T. B. M. J. Ouarda, F. Moatar, and B Hecht. Estimation of local extreme suspended sediment concentrations in california rivers. Science of the Total Environment, 408:4221--

Statistical Analysis and Learning Method on Users' Feedbacks

Problem statement: The purpose of this study was constructing an effective algorithm in order to learn the users’ feedbacks from their displayed visualization. This is due to existing visualization tools typically involve presenting network data regardless of considering level of network data knowledge among different levels of computer users. The machine learning algorithm has been applied in order to find the most effective statistical analysis and learning algorithm in learning users’ feedbacks. Approach: The objectives of this study were to conduct statistical analysis and learning algorithm model for different levels of computer users’ feedbacks and procedure to test the classifier. Results: WEKA the machine learning workbench that supports many activities of machine learning practitioners will be used to implement the proposed algorithm. The implemented program will work as training testing model. Conclusion: We can produce an adaptive visualization to the different levels of computer users as we have learnt their feedbacks (behavior) and update the classifier model.

WEKA---Experiences with a Java Open-Source Project

WEKA is a popular machine learning workbench with a development life of nearly two decades. This article provides an overview of the factors that we believe to be important to its success. Rather than focussing on the software's functionality, we review aspects of project management and historical development decisions that likely had an impact on the uptake of the project.

Boosting the prediction and understanding of DNA-binding domains from sequence

DNA-binding proteins perform vital functions related to transcription, repair and replication. We have developed a new sequence-based machine learning protocol to identify DNA-binding proteins. We compare our method with an extensive benchmark of previously published structure-based machine learning methods as well as a standard sequence alignment technique, BLAST. Furthermore, we elucidate important feature interactions found in a learned model and analyze how specific rules capture general mechanisms that extend across DNA-binding motifs. This analysis is carried out using the malibu machine learning workbench available at http://proteomics.bioengr.uic.edu/malibu and the corresponding data sets and features are available at http://proteomics.bioengr.uic.edu/dna.

Data mining in bioinformatics using Weka.

The Weka machine learning workbench provides a general-purpose environment for automatic classification, regression, clustering and feature selection-common data mining problems in bioinformatics research. It contains an extensive collection of machine learning algorithms and data pre-processing methods complemented by graphical user interfaces for data exploration and the experimental comparison of different machine learning techniques on the same problem. Weka can process data given in the form of a single relational table. Its main objectives are to (a) assist users in extracting useful information from data and (b) enable them to easily identify a suitable algorithm for generating an accurate predictive model from it. http://www.cs.waikato.ac.nz/ml/weka.

User satisfaction with machine learning as a data analysis method in agricultural research

Machine learning has potential use in the understanding of information hidden in large datasets, but little is known about user perceptions of the use of the technology. In this study, datasets were solicited from agricultural researchers and processed using a machine learning workbench. The results were reported to the researchers, and then interviews were conducted with some of them to determine their perceptions about the use of machine learning as an additional analysis technique to traditional statistical analysis. A number of themes about their satisfaction with this technique were constructed from the interview transcripts, which generally indicate that machine learning may be able to contribute to analysis and understanding of these kinds of datasets.