Protein analysis has a major part in the domain of biological research. So before analyzing, identification ofprotein is mostly needed. As a result different proteinsequenceclassification techniques are invented, this has enhance the research scope in Biological Domain. Choosing the perfect techniques regarding this classification is also a most important part of the research. Recent trend analysis shows that it is very difficult to classify large amount of biological data like protein sequences using traditional database system. Datamining technique are appropriate to handle the large data sources. A number of different techniques are prevalent for classifying protein sequences. This dissertation includes a detail review of ongoing research work involving three different techniques to classify the protein sequences. A comparative study is done to understand the basic difference between these models. The accuracy level of each proposed model has been studied. Those brief review and comparative study help to understand the insufficiency of the previous classification techniques with respect to the accuracy level and computational time.
An algorithm is presented that returns the optimal pairwise gapped alignment of two sets of signed numerical sequence values. One distinguishing feature of this algorithm is a flexible comparison engine (based on both relative shape and absolute similarity measures) that does not rely on explicit gap penalties. Additionally, an empirical probability model is developed to estimate the significance of the returned alignment with respect to randomized data. The algorithm’s utility for biological hypothesis formulation is demonstrated with test cases including database search and pairwise alignment ofprotein hydropathy. However, the algorithm and probability model could possibly be extended to accommodate other diverse types ofprotein or nucleic acid data, including positional thermodynamic stability and mRNA translation efficiency. The algorithm requires only numerical values as input and will readily compare data other than protein hydropathy. The tool is therefore expected to complement, rather than replace, existing sequence and structure based tools and may inform medical discovery, as exemplified by proposed similarity between a chlamydial ORFan protein and bacterial colicin pore- forming domain. The source code, documentation, and a basic web-server application are available.
(greater than 0.7) with the exception for classification trees (median AUC of 0.6). No statistically significant differences were found in the total accuracy of 8 of the 10 evaluated classifiers (Medians between 0.63 and 0.73), but RF (Me = 0.74) and SVM (Me = 0.76) obtained statistically significant higher classification accuracy. Median specificity ranged from a minimum of 0.64 (CART and LDA) to a maximum of 1 (SVM). With the exception of LDA, CART and QUEST, all the other classifiers were quite efficient in predicting group mem- bership in the group with larger number of elements (the MCI group corresponding to 69% of the sample) (Median specificity larger than 0.6). Judging from total accuracy, SVM and RF rank highest amongst the classifiers tested as has been suggested elsewhere [47,48,57,58]. However, a quite different picture emerges from the analysis of the sensitivity of the classifiers. Prediction for the group with lower frequency (the Dementia group, 31% of the sample) was quite poor for several of the tested classifiers, includ- ing the ones with some of the highest specificity values. Minimum median sensitivity was 0.30 (SVM) and maxi- mum median sensitivity was 0.66 (QUEST, followed by 0.64 for LDA and RF). Only six of the ten classifiers tested showed median sensitivity larger than 0.5 (and only five had 1 st quartile sensitivity larger than 0.5). Con- sidering that conversion into dementia is the key predic- tion in this biomedical application and thus higher sensitivity of classifiers is required, classifiers like Logistic Regression, Neural Networks, Support Vector Machines and CHAID trees are inappropriate for this type of binary
Random Forest, composed of many decision trees. Where each tree predicts an outcome, affecting the final answer of the forest. It is an ensemble learning method for classification, regression and other tasks. By using Random Forest I avoided the overfitting of decision trees, which is possible by having a random element that enables that all trees in the forest are not identical. It lacks explainability but generally provides good results. It can deal with the multivariate data and by averaging across the booking probabilities, should help prevent over-fitting by individual trees (Niklas Donges, 2018). XGBoost (eXtreme Gradient Boosting method), an advanced gradient tree boosting algorithm, which uses parallel processing, regularization (that helps prevent overfitting) and early stopping, makes it a fast, scalable and accurate algorithm. Being an ensemble learning method it combines the predictive power of multiple learners. The result is a single model which gives the aggregated output from several models. The models forming the ensemble can be either from the same or different learning algorithms. Still, they have been mostly used with decision trees. All the additive learners in boosting are modelled after the residual errors at each step. The boosting learners make use of the patterns in residual errors. At the stage where maximum accuracy is reached by boosting, the residuals are randomly distributed without any pattern (Ramya Bhaskar Sundaram, 2018).
Applicationof a pattern classifier first requires selection of features that must be tailored separately for each problem domain. Features should contain information required to distinguish between classes, be insensitive to irrelevant variability in the input and also be limited in number to permit efficient computation of discriminant functions and to limit the amount of training data required. Good classification performance requires selection of effective features and also selection of a classifier that can make good use of those features with limited training data, memory and computing power. Following feature selection, classifier development requires collection of training and test data and separate training and test or use phases. During the training phase, a limited amount of training data and a priori knowledge concerning the problem domain is used to adjust parameters and/or learn the structure of the classifier. During the test phase, the classifier designed from the training phase is evaluated on new test data by providing a classification decision for each input pattern. Classifier parameters and/or structure may then be adapted to take advantage of new training data or to compensate for nonstationary inputs, variation in internal components, or internal faults. Further evaluations require new test data.
Conventional datamining is thought to be as containing a large repository, and then mine knowledge. But there is an eminent need for mining knowledge from distributed resources. Typical algorithms which are available to us are based on assumption that the data is memory resident, which makes them unable to cope with the increasing complexity of distributed algorithms . Similar issues also rise while miningdatain sensor network, and grid datamining. We need distribution classification algorithms. A technique called partition tree construction approach  can be used for parallel decision tree construction. We also need distributed algorithms for association analysis. Distributed ARM algorithms needs to be developed as the sequential algorithms like Apriori, DIC, DHP and FP Growth , , , ,  do not scale well in distributed environment. In his research paper the author presents a Distributed Apriori algorithm . The FMGFI algorithm  presents a distributed FP Growth algorithm
Sequences are an important type ofdata which occur frequently in many scientific, medical, security, business and other applications. For example, DNA sequences encode the genetic makeup of humans and all other species; and protein sequences describe the amino acid composition of proteins and encode the structure and function of proteins. Moreover, sequences can be used to capture how individual humans behave through various temporal activity histories such as weblogs histories and customer purchase ones. In general there are various methods to extract information and patterns from data bases, such as Time series, association rule mining and datamining.
Abstract – The objective of this work was to use the accumulated reflectance technique and dataminingapplication, followed by object-oriented classification, in images of Operational Land Imager (OLI) sensor, Landsat 8, for the classificationof native vegetation and agricultural coverage of Cerrado. Four reflectance images were used for the discrimination of six classes – agriculture, livestock, wetland, savannah, forest, and grassland –, for classificationof Parque Nacional das Emas and surrounding areas in the state of Goiás, Brazil. The images were segmented for the extraction of sample spectral attributes and applicationof attribute combinations (mean + mode, all attributes) on datamining. The Weka software was used to construct the decision trees. This methodology indicated that the differentiation among targets increased from the temporal accumulation of the reflectance in all bands and classes, and that the optimal image was that of the sum of the four dates. The classification based on the attribute associations mean + mode showed no restraints in the decision rules processing, unlike the association of all attributes. The mean + mode classification showed a satisfactory accuracy (global accuracy, 69%; Kappa, 58%; and TAU, 63%). The integration of these techniques shows potential to differentiate native and anthropogenic vegetation in the Cerrado.
Advances in information technologies have led to the storage of large amounts ofdata by organizations. An analysis of this data through datamining techniques is important support for decision-making. This article aims to apply techniques for the classificationof the beneficiaries of an operator of health insurance in Brazil, according to their financial sustainability, via their sociodemographic characteristics and their healthcare cost history. Beneficiaries with a loss ratio greater than 0.75 are considered unsustainable. The sample consists of 38875 beneficiaries, active between the years 2011 and 2013. The techniques used were logistic regression and classification trees. The performance of the models was compared to accuracy rates and receiver operating Characteristic curves (ROC curves), by determining the area under the curves (AUC). The results showed that most of the sample is composed of sustainable beneficiaries. The logistic regression model had a 68.43% accuracy rate with AUC of 0.7501, and the classification tree obtained 67.76% accuracy and an AUC of 0.6855. Age and the type of plan were the most important variables related to the profile of the beneficiaries in the classification. The highlights with regard to healthcare costs were annual spending on consultation and on dental insurance.
voice and handwriting motor abilities probably due to the fact that they are in the early stages of disease duration (3-4 years). As for C2 Patients, they were clustered together in three local prototypes (voice, trace L and phrase). These local prototypes interpretations indicate that C2 patients have low voice quality with respect to the extracted acoustic features. On the other hand they showed weak ability to control axiomatic traces (Trace L). Also they negatively responded to hand-motor physiotherapy (Trace Phrase). Hence, C2 patients weren’t able to control their handwriting or acoustic abilities. This is maybe linked to their belated disease duration (11-15 years). Finally, the common feature characteristics of C3 patients are moderate kinematic features during hand-motor physiotherapy (trace phrase) and moderate acoustic features acquired from the sustained vowel. In addition we noticed that C3 patients have disease duration (2-6 years). No further interpretation for C3 patients was required. We suppose that this cluster should be divided into sub-clusters for labeling intentions.
Considerăm că firmele de pe piaţa românească, mai ales cele autohtone, trebuie să se adapteze rapid tendinţei globale de focalizare a strategiilor de afaceri pe managementul rela ţiilor cu clienţii, bazat pe utilizarea tehnicilor datamining, pentru a face faţă competiţiei firmelor multinaţionale, acestea aplicând cu succes principiile acestei abordări. În numeroase firme din România se creeaz ă confuzii privind acest concept. Pentru unele, managementul relaţiilor cu clienţii înseamnă doar implementarea unei programe de loialitate, pentru altele crearea unei baze de date cu informa ţii despre clienţi cu ajutorul cărora se poate realiza o segmentare mai fină a pieţei, însă puţine au implementat sisteme de relaţii cu clienţii integrate şi au o idee clară despre cum trebuie tehnologii informa ţionale în managementul relaţiilor cu clienţii.
Abstract - Datamining techniques are the result of a long process of research and product development. Large amount ofdata are searched by the practice ofDataMining to find out the trends and patterns that go beyond simple analysis. For segmentation ofdata and also to evaluate the possibility of future events, complex mathematical algorithms are used here. Specific algorithm produces each DataMining model. More than one algorithms are used to solve in best way by some DataMining problems. DataMining technologies can be used through Oracle. Generalized Linear Models (GLM) Algorithm is used in Regression and Classification Oracle DataMining functions. For linear modelling, GLM is one the popular statistical techniques. For regression and binary classification, GLM is implemented by Oracle DataMining. Row diagnostics as well as model statistics and extensive co- efficient statistics are provided by GLM. It also supports confidence bounds.. This paper outlines and produces analysis of GLM algorithm, which will guide to understand the tuning, diagnostics & data preparation process and the importance of Regression & Classification supervised Oracle DataMining functions and it is utilized in marketing, time series prediction, financial forecasting, overall business planning, trend analysis, environmental modelling, biomedical and drug response modelling, etc.
NaiveBayes is widely used for the classification due to its simplicity, elegance, and robustness. NavieBayes can be characterized as Navie and Bayes. Navie stands for independence i.e. true to multiply probabilities when the events are independent and Bayes is used for the bayes rule. This technique assumes that attributes of a class are independent in real life. The performance of the NavieBayes is better when the data set is actual. Kernel density estimators can be used to measure the probability in NavieBayes that improve the performance of the model. A large number of modifications have been introduced, by the statistical, datamining, machine learning, and pattern recognition communities, in an attempt to make it more flexible, but one has to recognize that such modifications are necessarily complications, which detract from its basic simplicity.
a number of (possibly) correlated variables into a smaller number of uncorrelated variables called prin- cipal components; (ii) - Chi-squared test which evalu- ated the dependence between the attribute and its clas- sifier (the class attribute); (iii) - Wrapper, that evalu- ates the attribute cluster in a machine learning process and verifies the classifying accuracy of crossing vali- dation; (iv) - Correlation Feature Selection (CFS) that searches the cluster of correlated attributes avoiding re-use of the same information; (v) - InfoGain, that evaluates the gain in information in relation to the clas- sifier; and (vi) - GainRatio that analyzes the informa- tion gain rate related to the specific class correcting impaired measurements. Alternatively, a new feature selection approach was used considering the knowl- edge of the domain experts who selected the main at- tributes based on their expertise.
FL requires some numerical parameters in order to operate such as what is considered significant error and significant rate-of-change-of-error, but exact values of these numbers are usually not critical unless very responsive performance is required in which case empirical tuning would determine them. There are several unique features that make FL a particularly good choice for many control problems. It is inherently robust since it does not require precise, noise-free inputs and can be programmed to fail safely if a feedback sensor quits or is destroyed. The output control is a smooth control function despite a wide range of input variations. Since the FL controller processes user-defined rules governing the target control system, it can be modified and tweaked easily to improve or drastically alter system performance. New sensors can easily be incorporated into the system simply by generating appropriate governing rules. Also, any sensor data that provides some indication of a system's actions and reactions is sufficient. This allows the sensors to be inexpensive and imprecise thus keeping the overall system cost and complexity low.
Abstract. Recently, at the 119th European Study Group with Indus- try, the Energy Solutions Operator EDP proposed a challenge concern- ing electricity prices simulation, not only for risk measures purposes but also for scenario analysis in terms of pricing and strategy. The main pur- pose was short-term Electricity Price Forecasting (EPF). This analysis is contextualized in the study of time series behavior, in particular multi- variate time series, which is considered one of the current challenges indatamining. In this work a short-term EPF analysis making use of vec- tor autoregressive models (VAR) with exogenous variables is proposed. The results show that the multivariate approach using VAR, with the season of the year and the type of day as exogenous variables, yield a model that explains the intra-day and intra-hour dynamics of the hourly prices.
uncertainty of hydrological predictions in ungauged sites; we strongly encourage to perform PCA, and in particular CCA, on the available set of catchment descriptors before apply- ing SOM; (iii) catchment classification provides a great deal of information for enhancing hydrological predictions in un- gauged basins, yet the applicationof objective but merely statistical criteria and algorithms (PCA and CCA with SOM) revealed some limitations that may be significantly reduced by switching from data-driven to data- and process-driven catchment classification. Designing a theoretical framework for combining these two different perspectives is an excit- ing open problem for future analyses. Our study focuses on a multipurpose catchment classification, future analyses will also consider hydrological classifications that are identified by focusing on a more specific water-problem, e.g., predic- tion of low-lows, flood flows, or surface water availability, to assess whether or not the same conclusions still hold. Acknowledgements. The study has been partially supported by the Italian Government through its national grants to the programmes on “Advanced techniques for estimating the magnitude and fore- casting extreme hydrological events, with uncertainty analysis” and “Relations between hydrological processes, climate, and physical attributes of the landscape at the regional and basin scales”. Two anonymous reviewers and the handling editor, Peter Troch, are thankfully acknowledged for their very useful comments on a previous version of this paper.
II. J YAGUCHI C LOUD S YSTEM AND I TS O VERVIEW In order to perform our experiment, we utilize the Jyaguchi cloud ,, system. The term Jyaguchi was introduced by the author  and was derived from the Japanese language, in which the term means “an outlet portion of a tube or tap, which has opening and closing valves to regulate the rate of water flow.” Accordingly, such a behavior of regulating resources is incorporated in the field of service usage, which was introduced in the Jyaguchi architecture. Jyaguchi proposed a hybrid architectural model because no single architectural model sufficiently provides a solution that is capable of regulating services on a pay per use basis, thereby providing features of SaaS. Furthermore, Jyaguchi is an architectural model for the development of distributed applications that can be extended to an architecture for cloud services and demonstrates how this style can be used to enhance the architectural design of a next-generation service cloud . Fig. 1 below portrays the interaction between service provider and client.
Abstract: Due to the large number of influencing factors, it is difficult to predict the earthquake which is a natural disaster. Researchers are working intensively on earthquake prediction. Loss of life and property can be minimized with earthquake prediction. In this study, a system is proposed for earthquake prediction with datamining techniques. In the study in which Cross Industry Standard Process for DataMining (CRISP-DM) approach has been used as datamining methodology, seismic bumps data obtained from mines has been analyzed. Extreme learning machine (ELM) which is an effective and rapid classification algorithm has been used in the modeling phase. In the evaluation stage, different performance evaluation criteria such as classification accuracy, sensitivity, specificity and kappa value have been used. The results are promising for earthquake prediction.
The effects of two common filtering parameters (deltCN and high mass accuracy) on MS/MS peptide assignment were examined by determining the quantity of MS/MS spectra not assigned to the same peptide in multiple database searches (Text S1). These results (Figure S1) suggest that filtering on high mass accuracy rather than deltCN can decrease ambiguous peptide-spectrum matches and provide more consistent and reproducible MS/MS identifications. In order to maintain high specificity and accuracy with increasing metagenomic sequencedata, a FDR was estimated at the peptide level using an established method of reverse database searching [34,35] for each metagenomic processing method for a total of 6 target-decoy databases (RM, RFM, CAFM, KG, NM_KG, RMPS- 6b). Because we are using methods that directly measure peptides, not proteins, the FDR was estimated at the peptide level. In addition, we are primarily comparing the performance of all databases by peptide-spectrum matches, not proteins, given the nature of the metagenomic processing methods and their corresponding databases (i.e., not all databases contain assembled contigs, but only reads). It has previously been noted  that false discovery rates can be difficult to accurately determine with metaproteome datasets due to problems associated with massive peptide degeneracy. We concur with this difficulty in accurately quantifying FDRs for metaproteomes and thus have carefully evaluated how we might handle this issue, as defined in the following discussion. In this study, for example, of all the identified peptides for 6a (Run 2), only 7–30% were unique peptides from each database. Consequently, if only unique peptides are used, the false discovery rate would be overestimated; on the contrary, if all peptides are used the false discovery rate could be underestimated . Therefore, to set a static FDR threshold and filter multiple