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Naive Bayes (NB) is one of the top ten machine learning algorithms whereas its attribute independence assumption rarely holds in practice. A feasible and efficient approach to improving NB is relaxing the assumption by adding augmented edges to the restricted topology of NB. In this paper we prove theoretically that the generalized topology may be a suboptimal solution to model multivariate probability distributions if its fitness to data cannot be measured. Thus we propose to apply log-likelihood function as the scoring function, then introduce an efficient heuristic search strategy to explore high-dependence relationships, and for each iteration the learned topology will be improved to fit data better. The proposed algorithm, called log-likelihood Bayesian classifier (LLBC), can respectively learn two submodels from labeled training set and individual unlabeled testing instance, and then make them work jointly for classification in the framework of ensemble learning. Our extensive experimental evaluations on 36 benchmark datasets from the University of California at Irvine (UCI) machine learning repository reveal that, LLBC demonstrates excellent classification performance and provides a competitive approach to learn from labeled and unlabeled data. [ABSTRACT FROM AUTHOR]

Feature selection is an optional preprocessing procedure and is frequently used to improve the classification accuracy of a machine learning algorithm by removing irrelevant and/or redundant features. However, in many real-world applications, the test cost is also required for making optimal decisions, in addition to the classification accuracy. To the best of our knowledge, thus far, few studies have been conducted on test-cost-sensitive feature selection (TCSFS). In TCSFS, the objectives are twofold: 1) to improve the classification accuracy and 2) to decrease the test cost. Therefore, in fact, it constitutes a multiobjective optimization problem. In this paper, we transformed this multiobjective optimization problem into a single-objective optimization problem by utilizing a new evaluation function and in this paper, we propose a new general wrapper framework for TCSFS. Specifically, in our proposed framework, we add a new term to the evaluation function of a wrapper feature selection method so that the test cost of measuring features is taken into account. We experimentally tested our proposed framework, using 36 classification problems from the University of California at Irvine (UCI) repository, and compared it to some other state-of-the-art feature selection frameworks. The experimental results showed that our framework allows users to select an optimal feature subset with the minimal test cost, while simultaneously maintaining a high classification accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a workbench to get simple neural classification models based on product evolutionary networks via a prior data preparation at attribute level by means of filter-based feature selection. Therefore, the computation to build the classifier is shorter, compared to a full model without data pre-processing, which is of utmost importance since the evolutionary neural models are stochastic and different classifiers with different seeds are required to get reliable results. Feature selection is one of the most common techniques for pre-processing the data within any kind of learning task. Six filters have been tested to assess the proposal. Fourteen (binary and multi-class) difficult classification data sets from the University of California repository at Irvine have been established as the test bed. An empirical study between the evolutionary neural network models obtained with and without feature selection has been included. The results have been contrasted with nonparametric statistical tests and show that the current proposal improves the test accuracy of the previous models significantly. Moreover, the current proposal is much more efficient than the previous methodology; the time reduction percentage is above 40%, on average. Our approach has also been compared with several classifiers both with and without feature selection in order to illustrate the performance of the different filters considered. Lastly, a statistical analysis for each feature selector has been performed providing a pairwise comparison between machine learning algorithms. [ABSTRACT FROM AUTHOR]

Purpose: The tracheoesophageal puncture for the voice prosthesis (VP) placement is the recognized gold standard in post-laryngectomy voice rehabilitation. Despite the development of specific intraoperative techniques, a subset of patients will suffer from poor functional outcomes due to pharyngoesophageal spasms (PES). This paper evaluates the functional outcomes after transcutaneous botulinum toxin type A (BTX-A) infiltration for PES with a videofluoroscopy-guided technique. Methods: Since 2022, eight consecutive patients with VP and affected by PES were treated with BTX-A injection by a standard videofluoroscopic guided technique at the European Institute of Oncology, IRCCS (IEO) in Milan. A lidocaine test was performed pre-operatively to evaluate the potential effect of chemical neurectomy. All patients with positive lidocaine tests were injected with 50 IU of BTX-A (Allergan, Irvine, CA) according to the sites marked during the videofluoroscopy. Reported symptoms (VHI, SECEL), perceptual (INFVo), aerodynamic (MPT) and manometric parameters were collected before and after treatment. Results: In all cases, BTX-A was performed as an outpatient procedure without complications. For seven patients, only one BTX-A injection was needed, while one patient required a re-injection. Subjective and perceptive improvement after BTX-A was significant for VHI, SECEL and INFVo. MPT showed significant improvement after a chemical neurectomy. After a mean follow-up of 6 months, all patients maintained a good TES quality. Conclusion: The videofluoroscopic guided BTX-A injection of the pharyngoesophageal tract showed to be a feasible and reproducible technique in all cases. The pharyngoesophageal videofluoroscopy allows defining of patients' anatomical landmarks that help the surgeon to perform a homogeneous injection, empowered by post-injection massage. [ABSTRACT FROM AUTHOR]

The rapid development of aquaculture makes it desirable to use environment-friendly methods of purifying the wastewater effluent. Seaweed is often cultured for the dual purpose of useful algal biomass production and its bioremediation potential. This paper offers a survey on growth characters and ecological functions of the macroalgae Gracilaria gracilis (Steentoft, L.M. Irvine & Farnham, 1995), collected from Ganzirri lagoon (Messina, Italy). The results obtained provided promising data in terms of growth rates, biomass and biofiltering potential of G. gracilis. A daily growth rate (DGR) of 0.70%/g and biomass 36,05 g/m3 was obtained using tanks with lower lighting. Its biofiltration efficiency was demonstrated by the strong correlation between the algal growth parameters (Y and DGR) and the concentration decrease of dissolved nutrients. Results provided in this study indicated that G. gracilis can find application in aquaculture systems as a biofilter and that biomass produced offers a valid resource for further biotechnological applications. [ABSTRACT FROM AUTHOR]

Averaged one-dependence estimators (AODE) is a state-of-the-art machine learning tool for classification due to its simplicity, high computational efficiency, and excellent classification accuracy. Weighting provides an effective mechanism to ensemble superparent one-dependence estimators (SPODEs) in AODE by linearly aggregating their weighted probability estimates. Supervised weighting and unsupervised weighting are proposed to learn weights from labeled or unlabeled data, whereas their interoperability has not previously been investigated. In this paper, we propose a novel weighting paradigm in the framework of semi-supervised learning, called semi-supervised weighting (SSW). Two different versions of weighted AODEs, supervised weighted AODE (SWAODE) which performs weighting at training time and unsupervised weighted AODE (UWAODE) which performs weighting at classification time, are built severally. Log likelihood function is introduced to linearly aggregate the outcomes of these two weighted AODEs. The proposed algorithm, called SSWAODE, is validated on 38 benchmark datasets from the University of California at Irvine (UCI) machine learning repository and the experimental results prove the effectiveness and robustness of SSW for weighting AODE in terms of zero-one loss, bias, variance and etc. SSWAODE well achieves the balance between the ground-truth dependencies approximation and the effectiveness of probability estimation. [ABSTRACT FROM AUTHOR]

As a widely known disease diabetes mellitus makes the human body produce quite less hormone and also tend to cause increased glucose that results in abnormal metabolism of varied organs in the body like eyes, kidneys, etc. Diabetic analysis has attracted the research community to treat some missing values and class imbalance issues. The performance of diabetes mellitus classification by the usage of machine learning techniques is comparatively low. We suggest this paper on imbalanced dataset with missing values, an efficient prediction algorithm for diabetes mellitus classification using Deep 1D-Convolutional Neural Network values. The outlier detection is used for removing missing values first. Then, oversampling method (SMOTE) is used to reduce the influence of imbalance class on prediction performance. Finally, predictions are produced using a DCNN classifier and are evaluated using a selective set of evaluation indicators. Experiments on the Pima Indian diabetes dataset (PIDD) from UCI Repository (University of California at Irvine) have yielded positive results. Our proposed DCNN algorithm has been shown to be successful and superior. [ABSTRACT FROM AUTHOR]

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Multigranulation rough set (MGRS) theory provides an effective manner for the problem solving by making use of multiple equivalence relations. As the information systems always dynamically change over time due to the addition or deletion of multiple objects, how to efficiently update the approximations in multigranulation spaces by making fully utilize the previous results becomes a crucial challenge. Incremental learning provides an efficient manner because of the incorporation of both the current information and previously obtained knowledge. In spite of the success of incremental learning, well-studied findings performed to update approximations in multigranulation spaces have relatively been scarce. To address this issue, in this paper, we propose matrix-based incremental approaches for updating approximations from the perspective of multigranulation when multiple objects vary over time. Based on the matrix characterization of multigranulation approximations, the incremental mechanisms for relevant matrices are systematically investigated while adding or deleting multiple objects. Subsequently, in accordance with the incremental mechanisms, the corresponding incremental algorithms for maintaining multigranulation approximations are developed to reduce the redundant computations. Finally, extensive experiments on eight datasets available from the University of California at Irvine (UCI) are conducted to verify the effectiveness and efficiency of the proposed incremental algorithms in comparison with the existing non-incremental algorithm. [ABSTRACT FROM AUTHOR]

• A general model of sagged-cable-crosstie is established by introducing spatial and temporal nondimensionalization. • The general expression of the characteristic equation's 2N(M+1)-order coefficient matrix is given for the first time. • A minimal set of fundamental key dimensionless parameters that govern the system's modal characteristics are intuitively found. • The general model is degenerated into three models and is high-precisely verified by literature and FEM. Though crosstie has become a promising approach for vibration mitigation of long cables, its mechanism is yet to be fully understood, restricting the set of engineering design theory. Scholars worldwide have continuously proposed various mechanical models to investigate modal characteristics of cable-crosstie structures. However, first, dynamic equations were dimensional or not fully dimensionalized, hindering the definition of essential independent key parameters that govern the modal characteristics of the system; second, no general expression of the coefficient matrix in characteristic equations was given. This paper proposes a general sagged-cable-crosstie model applicable to structures with a generic number of cables and crossties. A universal dimensionless dynamic equation is derived by thorough dimensionless treatment, and the universal expression of a 2 N (M + 1) -order coefficient matrix is obtained by introducing boundary, equilibrium, and continuity conditions. A minimal set of dimensionless parameters that govern modal characteristics of any sagged-cable-crosstie system is found, i.e., crosstie positions ε j , p , Irvine parameters of cables λ j 2 , dimensionless wave speed parameters α j , and dimensionless crosstie stiffnesses k j , p. Subsequently, the general model degenerates into three representative models: double-cable-single-crosstie, three-cable-single-crosstie, and double-cable-double crosstie. Frequencies of each degenerated model are compared with that of literature and FEM, and good consistency is obtained. [ABSTRACT FROM AUTHOR]

• The simultaneous existence of cross-over and veering that commonly wouldnt co-existence in single classical cables is found. • The setting of crosstie(s) can only significantly increase the out-of-phase modal frequency of specific orders. • The increment limits of dimensionless frequencies for systems with identical sagged cables are found. • Wave speed difference of sagged cables can enhance the effect of crosstie(s) to further increase the high-order modal frequencies. Analysis of key parameters is an effective means to understanding modal characteristics of a system. However, because of the large number of components, there are various geometric, material, and physical parameters in a sagged-cable-crosstie structure, which inevitably hinders the reasonable selection of key parameters. Based on the companion (Part 1) paper, the effect of four fundamental key parameters, i.e., the Irvine parameter, position and stiffness of crosstie(s), and wave speed ratio, on the modal characteristics of three representative models: double-cable-single-crosstie, three-cable-single-crosstie, and double-cable-double-crosstie, are investigated by mechanical modeling-based parametric analysis. The simultaneous existence of cross-over and veering phenomena that commonly wouldn't co-exist in single classical cables is found in sagged-cable-crosstie structures. Generally, the frequency curves of all the in-phase modes, out-of-phase modes with symmetrically arranged crossties, and specific out-of-phase modes with crossties just at modal nodes show cross-over phenomena, while that of out-of-phase modes with non-symmetrically arranged crossties show veering phenomena. Setting one or two crossties can only significantly increase the out-of-phase modal frequencies of specific orders, and the increment limits of dimensionless frequency for systems with identical sagged cables are found to be 1 and 2 respectively, no matter how the crosstie stiffness and position are adjusted and how many cables are connected. However, the wave speed difference between sagged cables can enhance the effect of crosstie(s) to further increase modal frequencies, especially for high-order ones, and hence break through the above increment limits. Moreover, the more cables with wave speed differences connected by crossties and the greater the difference in wave speed, the more the system frequency increases. [ABSTRACT FROM AUTHOR]

This paper explores three controversial and large-scale attempts by architects to build more attractive suburban areas: Cumbernauld in Scotland (key designs constructed in the 1950s and 1960s), Irvine in California (key designs from the 1960s and 1970s onward), and Poundbury in England (key designs created in the 1980s and built from the 1990s on). They represent major approaches to the issue of aesthetics and place—modernism, humanistic imageability and legibility, and new urbanism or the Urban Villages Movement. The paper distinguishes between several terms relevant in assessing visual character: objective aesthetics, style, place and satisfaction. It is argued that all three developments conform to some principles of the visual and psychological aspects of good design; but these principles differ, resulting in criticisms from those promoting different styles. [ABSTRACT FROM AUTHOR]

Scores of machine learning algorithms depend on a good distance measure to achieve high performance. The inverted specific-class distance measure, simply ISCDM, is proposed to find reasonable distance measure between each pair of instances with nominal attributes only. ISCDM does not depend on the attribute value of the training instance, which makes it less sensitive to missing values in the training set and more robust to non-class attribute noise. However, in ISCDM, all attributes are assumed to be fully independent. It is obvious that the attribute independence assumption in ISCDM is rarely true in reality, which would harm its performance in the applications with complex attribute dependencies. In this paper, we single out an improved inverted specific-class distance measure by relaxing its unrealistic attribute independence assumption. We call it averaged one-dependence inverted specific-class distance measure, simply AODISCDM. We experimentally tested it on 29 classification problems from the University of California at Irvine (UCI) repository and found that it significantly outperforms ISCDM in terms of the negative conditional log likelihood (-CLL) and the root relative squared error (RRSE). Besides, the proposed AODISCDM maintains the computational simplicity (no search involved) and robustness that characterise ISCDM. [ABSTRACT FROM AUTHOR]

• We have developed a clustering algorithm for an unknown number of clusters by MOGA. • It works with continuous and categorical featured data sets. • It can work with data sets having missing values. • The final solution is selected by majority vote by all non-dominated solutions. • Context-sensitive and cluster-orient genetic operators are designed. Many clustering algorithms categorized as K -clustering algorithm require the user to predict the number of clusters (K) to do clustering. Due to lack of domain knowledge an accurate value of K is difficult to predict. The problem becomes critical when the dimensionality of data points is large; clusters differ widely in shape, size, and density; and when clusters are overlapping in nature. Determining the suitable K is an optimization problem. Automatic clustering algorithms can discover the optimal K. This paper presents an automatic clustering algorithm which is superior to K -clustering algorithm as it can discover an optimal value of K. Iterative hill-climbing algorithms like K -Means work on a single solution and converge to a local optimum solution. Here, Genetic Algorithms (GA s) find out near global optimum solutions, i.e. optimal K as well as the optimal cluster centroids. Single-objective clustering algorithms are adequate for efficiently grouping linearly separable clusters. For non-linearly separable clusters they are not so good. So for grouping non-linearly separable clusters, we apply Multi-Objective Genetic Algorithm (MOGA) by minimizing the intra-cluster distance and maximizing inter-cluster distance. Many existing MOGA based clustering algorithms are suitable for either numeric or categorical features. This paper pioneered employing MOGA for automatic clustering with mixed types of features. Statistical testing on experimental results on real-life benchmark data sets from the University of California at Irvine (UCI) machine learning repository proves the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• Subset-based feature selection + multi-layer perceptron. • Subset-based feature selection + radial basis function neural networks. • Semi-wrapper feature subset selection approach based on Naïve Bayes. • Extensive experiments on 34 data sets (22 from University of California at Irvine –UCI- and 12 synthetic data sets). Data preparation via feature selection + neural networks. • Feature selection: 7 approaches, which are 5 filters and 2 semi-wrappers (based on Correlation, consistency and Naïve Bayes; other with SOAP or SU), have been tested on difficult classification problems. This paper explores widely the data preparation stage within the process of knowledge discovery and data mining via feature subset selection in the context of two very well-known neural models: radial basis function neural networks and multi-layer perceptron. It is known the best performance of wrapper attribute selection methods based on the evaluation measure provided by a classifier, although the temporal complexity of learning neural networks practically precludes the use of wrapper techniques, especially in complex databases with high dimensionality and a large number of labels. In this paper, we propose the use of the Naïve Bayes classifier as a fitness function within a semi-wrapper feature selection approach. The Naïve Bayes classifier is a good fast approach to a neural network and utilising it as a measure of goodness in a backward search on a ranking provides a specific attribute selection method for neural networks in complex data. The test-bed consists of 34 binary and multi-class classification problems and 7 feature selectors. Of these, there are 6 data sets with upwards of 5 classes. According to the reported accuracy results that have been supported by non-parametric statistical tests in different scenarios, our method has been shown to be very suitable for both kinds of neural networks. Moreover, the reduced feature-space is around 20% of the full attribute space. The speedup with the aforementioned semi-wrapper is very outstanding and its value fluctuates, on average, from about 1.5 with radial basis function neural networks to around 30 with multi-layer perceptron. [ABSTRACT FROM AUTHOR]

In this paper, a correlation index-based clustering algorithm (COIN) is proposed for clustering the categorical data. The proposed algorithm was tested on nine datasets gathered from the University of California at Irvine (UCI) repository. The experiments were made in two ways, one by specifying the number of clusters and another without specifying the number of clusters. The proposed COIN algorithm is compared with five existing categorical clustering algorithms such as Mean Gain Ratio (MGR), Min–Min-Roughness (MMR), COOLCAT, K-ANMI, and G-ANMI. The result analysis clearly reports that COIN outperforms other algorithms. It produced better accuracies for eight datasets (88.89%) and slightly lower accuracy for one dataset (11%) when compared individually with MMR, K-ANMI, and MGR algorithms. It produced better accuracies for all nine datasets (100%) when it is compared with G-ANMI and COOLCAT algorithms. When COIN was executed without specifying the number of clusters, it outperformed MGR for 88.89% of the test instances and produced lower accuracy for 11% of the test instances. [ABSTRACT FROM AUTHOR]

After years of minimal electronic reserve use at the University of California, Irvine Libraries, a combination of opportunity, hard work and luck has made electronic reserves the central part of reserves as a whole. Reviewing the time from January 2003 to June 2004, this paper discusses the many different factors and circumstances that resulted in this change, including advantages resulting from software updates and resultant reorganization of workflow, as well as reflecting on where the Libraries would like to go next. [ABSTRACT FROM AUTHOR]

Background: Pulmonary embolism (PE) is a common disease with an annual incidence of about 1/1000 persons. About every sixth patient dies within the first 30 days after diagnosis. The electrocardiogram (ECG) is one of the first diagnostic tests performed, and is able to confirm the suspicion of PE with typical electrocardiographic signs. Some ECG signs and their regression are also prognostically relevant. Endovascular mechanical thrombectomy is one option for PE treatment, and aims to relieve right heart strain immediately. The first studies on endovascular mechanical thrombectomy using a dedicated device (FlowTriever System, Inari Medical, Irvine, CA, USA) yielded promising results. Methods: In the following, we report the case of a 66-year-old male patient who presented with New York Heart Association III dyspnea in our emergency department. Among typical clinical and laboratory results, he displayed very impressive electrocardiographic and radiological findings at the time of PE diagnosis. Results: After endovascular mechanical thrombectomy, the patient's complaints and pulmonary hemodynamics improved remarkably. In contrast, the ECG worsened paradoxically 18 h after intervention. Nevertheless, control echocardiography 4 days after the intervention no longer showed any signs of right heart strain, and dyspnea had disappeared completely. At a 4-month follow-up visit, the patient presented as completely symptom-free with a high quality of life. His ECG and echocardiography were normal and excluded recurrent right heart strain. Conclusions: Overall, the patient benefitted remarkably from endovascular mechanical thrombectomy, resulting in an almost complete resolution of electrocardiographic PE signs at the 4-month follow-up after exhibiting multiple typical electrocardiographic PE signs at time of diagnosis and initial electrocardiographic worsening 18 h post successful intervention. [ABSTRACT FROM AUTHOR]

After the presentation of 60 papers at the conference “Advancing Aerosol Dosimetry Research” (October 24–25, 2014 in Irvine, CA, USA), attendees submitted written descriptions of needed research. About 40 research needs were submitted. The suggestions fell into six broad categories: 1) Access to detailed anatomic data; 2) Access to subject-specific aerosol deposition datasets; 3) Improving current inhaled aerosol deposition models; 4) Some current experimental data needs and hot topics; 5) Linking exposure and deposition modeling to health endpoints; and 6) Developing guidelines for appropriate validation of dosimetry and risk assessment models. Summaries of suggestions are provided here as an update on research needs related to inhaled aerosol dosimetry modeling. Taken together, the recommendations support the overarching need for increased collaborations between dose modelers and those that use the models for risk assessments, aerosol medicine applications, design of toxicology experiments, and extrapolation across species. This paper is only a snapshot in time of perceived research needs from the conference attendees; it does not carry the approval of any agency or other group that plans research priorities or that funds research. [ABSTRACT FROM AUTHOR]

There are many useful data mining methods for diagnosis of diseases and cancers. However, early diagnosis of a disease or cancer could significantly affect the chance of patient survival in some cases. The objective of this study is to develop a method for helping accurate diagnosis of different diseases based on various classification methods. Knowledge collection from domain experts is challenging, inaccessible and time-consuming; so we design a multi-classifier using a dynamic classifier and clustering selection approach to takes advantages of these methods based on data. We combine Forward-backward and Principal Component Analysis for feature reduction. The multi-classifier evaluates three clustering methods and ascertains the best classification methods in each cluster based on some training data. In this study, we use ten datasets taken from Machine Learning Repository datasets of the University of California at Irvine (UCI). The proposed multi-classifier improves both computation time and accuracy as compared with all other classification methods. It achieves maximum accuracy with minimum standard deviation over the sampled datasets. [ABSTRACT FROM AUTHOR]

Data splitting is an important step in artificial neural network (ANN) models, which is found in the form of training and testing subsets. In general, a random data splitting method is favored to divide a pool of samples into subsets, without considering the quality of data for the training step of a neural network. The drawback of poor data splitting methods is that they poses ill effects to the performance of the neural network when the data involves complex matrices or multivariate modeling. In order to overcome this drawback, the current paper presents our proposed M-SPXY method. It is based on a modified version of Sample Set Partitioning, which relies on a joint X-y distances (SPXY) method. The proposed method has resulted in better performance, compared to the modified Kennard-Stone (KS) method, using Mahalanobis distances (MDKS). In our experiments, the proposed approach was employed to compare various data splitting methods using data sets from the repository of the University of California in Irvine (UCI), processed through an Extreme Learning Machine (ELM) neural network. Performance was measured in terms of classification accuracy. The results indicate that the classification accuracy of the proposed M-SPXY process is superior to that of the MDKS data splitting method. [ABSTRACT FROM AUTHOR]

STERIX is a new data-intensive storage and computing platform project spanning UC Irvine, UC Riverside, and UC San Diego. In this paper we provide an overview of the ASTERIX project, starting with its main goal-the storage and analysis of data pertaining to evolving-world models. We describe the requirements and associated challenges, and explain how the project is addressing them. We provide a technical overview of ASTERIX, covering its architecture, its user model for data and queries, and its approach to scalable query processing and data management. ASTERIX utilizes a new scalable runtime computational platform called Hyracks that is also discussed at an overview level; we have recently made Hyracks available in open source for use by other interested parties. We also relate our work on ASTERIX to the current state of the art and describe the research challenges that we are currently tackling as well as those that lie ahead. [ABSTRACT FROM AUTHOR]

This paper presents a new learning algorithm for multitask pattern recognition (MTPR) problems. We consider learning multiple multiclass classification tasks online where no information is ever provided about the task category of a training example. The algorithm thus needs an automated task recognition capability to properly learn the different classification tasks. The learning mode is "online" where training examples for different tasks are mixed in a random fashion and given sequentially one after another. We assume that the classification tasks are related to each other and that both the tasks and their training examples appear in random during "online training." Thus, the learning algorithm has to continually switch from learning one task to another whenever the training examples change to a different task. This also implies that the learning algorithm has to detect task changes automatically and utilize knowledge of previous tasks for learning new tasks fast. The performance of the algorithm is evaluated for ten MTPR problems using five University of California at Irvine (UCI) data sets. The experiments verify that the proposed algorithm can indeed acquire and accumulate task knowledge and that the transfer of knowledge from tasks already learned enhances the speed of knowledge acquisition on new tasks and the final classification accuracy. In addition, the task categorization accuracy is greatly improved for all MTPR problems by introducing the reorganization process even if the presentation order of class training examples is fairly biased. [ABSTRACT FROM AUTHOR]

The Irvine Ranch in Southern California is the largest privately master-planned 'new community' or satellite new town ever to be built in the US. The development was led by the private sector but government played an important role. This paper traces how the federal government shaped the Irvine development through a range of policies and programmes from defence to habitat protection. In turn, the developers of Irvine were cast in a role of co-ordinating parts of government. This analysis highlights some of the problems likely to be faced as governments promote similarly phased, co-ordinated and mixed-use proposals under the rubric of smart growth or sustainable development. [ABSTRACT FROM AUTHOR]

The research in this paper presents the detailed development and on-line evaluation of a microscopic traffic flow model for the city of Irvine in Southern California. This effort is the first stage of evaluating micro-simulators in terms of their ability to model and analyze Intelligent Transportation Systems (ITS) under faster-than-real-time conditions. We utilize "Paramics," a particularly promising ITS-capable advanced traffic flow simulator and visualization tool, as one of an array of newly emerging ITS-capable simulation tools and we apply it to the Irvine network as part of a staged effort to model the much larger Southern California network. The driver and vehicle models and parameters were developed to reflect U.K. driver and vehicle characteristics. In this effort we explain our procedure used the calibrate these parameters to reproduce local U.S. traffic behavior. We built a model of a conventional U.S. freeway/arterial network in Southern California and calibrated its parameters using on-line field data. The calibrated models are validated, both at the section and network levels, and evaluated relative to their potential application in Advanced Traffic Management and Information Systems (ATMIS). Based on obtained results, the calibrated model performed well during validation on a freeway link. On the full network, the vehicle release mechanism showed some time-lag in releasing demand onto the network. This is potentially due to stacking of vehicles in memory before adequate headways are found on the road to release the vehicles. Although the problem itself is simple, its effects on the results were notable. [ABSTRACT FROM AUTHOR]

The study's main goal is to identify if a person has chronic renal illness or not. The medical condition known as chronic kidney failure damages overall kidneys' ability to collect harmful substances from your blood and keep good health in general. Some causes for chronic kidney disease contain lower blood pressure, anemia, weaker bones, poor diet, and trauma. Some causes for chronic kidney disease contain lower blood pressure, anemia, weaker bones, poor diet, and trauma. The value was estimated by the classification method using machine learning technologies. The patient's status of non-chronic kidney failure & chronic kidney failure will be estimated using classification models constructed using various classification techniques. These models were put to the test using a collection of data on chronic kidney illness from the University of California at Irvine, this had four hundred entries of data with twenty-five attributes. The outcomes of various models are examined. The model built with Decision tree method performed the best in terms of correctness using 14 attributes for the small dataset, based on the comparison. The final phase of this study will examine how effectively the machine learning system predicts chronic kidney failure with respect to precision, recall, accuracy, & F1-Score. [ABSTRACT FROM AUTHOR]

This study focuses on legislative bans imposed on short‐term rentals (STRs) and evaluates their effects on long‐term rentals in Irvine, CA. We find that contract rental prices in the long‐term rental market decrease by 3.0% within approximately 2 years after the enforcement of STR ordinance. The results are primarily driven by the supply side for long‐term rentals. The decline in rents is more pronounced: (1) for long‐term rental units that have similar property characteristics as those listed through Airbnb and (2) for those located in geographic areas with greater Airbnb exposure before the ban was enforced. [ABSTRACT FROM AUTHOR]

Predictive emission monitoring systems (PEMS) are software solutions for the validation and supplementation of costly continuous emission monitoring systems for natural gas electrical generation turbines. The basis of PEMS is that of predictive models trained on past data to estimate emission components. The gas turbine process dataset from the University of California at Irvine open data repository has initiated a challenge of sorts to investigate the quality of models of various machine learning methods to build a model for predicting CO and NOx emissions depending on ambient variables and the parameters of the technological process. The novelty and features of this paper are: (i) a contribution to the study of the features of the open dataset on CO and NOx emissions for gas turbines, which will enable one to more objectively compare different machine learning methods for further research; (ii) for the first time for the CO and NOx emissions, a model based on symbolic regression and a genetic algorithm is presented—the advantage of this being the transparency of the influence of factors and the interpretability of the model; (iii) a new classification model based on the symbolic regression model and fuzzy inference system is proposed. The coefficients of determination of the developed models are: R 2 = 0.83 for NOx emissions, R 2 = 0.89 for CO emissions. [ABSTRACT FROM AUTHOR]

In the design of classification models, irrelevant or noisy features are often generated. In some cases, there may even be negative interactions among features. These weaknesses can degrade the performance of the models. Feature selection is a task that searches for a small subset of relevant features from the original set that generate the most efficient models possible. In addition to improving the efficiency of the models, feature selection confers other advantages, such as greater ease in the generation of the necessary data as well as clearer and more interpretable models. In the case of medical applications, feature selection may help to distinguish which characteristics, habits, and factors have the greatest impact on the onset of diseases. However, feature selection is a complex task due to the large number of possible solutions. In the last few years, methods based on different metaheuristic strategies, mainly evolutionary algorithms, have been proposed. The motivation of this work is to develop a method that outperforms previous methods, with the benefits that this implies especially in the medical field. More precisely, the present study proposes a simple method based on tabu search and multistart techniques. The proposed method was analyzed and compared to other methods by testing their performance on several medical databases. Specifically, eight databases belong to the well-known repository of the University of California in Irvine and one of our own design were used. In these computational tests, the proposed method outperformed other recent methods as gauged by various metrics and classifiers. The analyses were accompanied by statistical tests, the results of which showed that the superiority of our method is significant and therefore strengthened these conclusions. In short, the contribution of this work is the development of a method that, on the one hand, is based on different strategies than those used in recent methods, and on the other hand, improves the performance of these methods. [ABSTRACT FROM AUTHOR]

Inhaled aerosols whether beneficial or harmful express their effects depending on the amount initially deposited in the body (i.e. the deposition dose), with clearance mechanisms including dissolution, mucociliary transport and translocation then determining retained dose in the respiratory tract. The many regions of the respiratory tract differ in their shapes, cellular makeups, defenses, and pathologies. As a result, predicting, measuring, and simulating initial deposition doses and subsequent events for deposited particles, vapors and gases presents formidable challenges. Interest in supplementing and in some cases replacing laboratory animals in inhalation studies with in vitro cellular exposures, challenges researchers with both designing exposure systems and defining the internal doses to exposed cells. Addressing these challenges requires the cooperation and collaboration of many specialists that do not normally meet at discipline-specific conferences. To address that historical limitation, research related to the dosimetry of inhaled aerosols has been addressed at a series of three issue oriented international conferences. This paper addresses the research-related needs and recommendations from the third, October 2019 conference held in Irvine, CA, USA. Conference participants were invited to submit suggestions related to research. This paper includes those suggestions plus some offered by e-mail after the conference. Over 40 submitted suggestions are organized into several categories. Sample suggestions include; 1. research-related activities should address an identified health related problem; 2. in vitro aerosol systems require development in order to have better-defined doses to cells and to represent actual in vivo exposures; 3. additional knowledge on respiratory tract anatomy is needed for several species and for the variations within humans and other species; 4. dosimetric models should be expanded in scope, and should improve their usability by non-modelers; and 5. there is value in introducing newcomers to the field of inhaled aerosols. Collaborations among several disciplines are emphasized in order to advance inhaled dosimetry models and their applications to laboratory and real-life inhalation exposures. The suggestions reported here are a snapshot in time from experts from a variety of disciplines. The categories represent the major topic areas discussed at the meeting, but within each area the recommendations are not prioritized. As such, they do not carry the approval of any research institutions, research funding agencies, private entities, or regulatory agencies. The authors are solely responsible for the contents of this paper. • Presents several suggestions for improving inhaled aerosol dosimetry modeling. • Considers computational fluid dynamic and traditional mechanistic model approaches. • Includes both in vivo and in vitro aerosol exposure scenarios. • Includes applications of aerosol models for risk assessments. • Stresses the importance of collaborative multi-disciplinary efforts. [ABSTRACT FROM AUTHOR]

The exponential growth in the use of smartphones means that users must constantly be concerned about the security and privacy of mobile data because the loss of a mobile device could compromise personal information. To address this issue, continuous authentication systems have been proposed, in which users are monitored transparently after initial access to the smartphone. In this study, the authors address the problem of user authentication by considering human activities as behavioural biometric information. The authors convert the behavioural biometric data (considered as time series) into a 2D colour image. This transformation process keeps all the characteristics of the behavioural signal. Time series does not receive any filtering operation with this transformation, and the method is reversible. This signal‐to‐image transformation allows us to use the 2D convolutional networks to build efficient deep feature vectors. This allows them to compare these feature vectors to the reference template vectors to compute the performance metric. The authors evaluate the performance of the authentication system in terms of Equal Error Rate on a benchmark University of Californy, Irvine Human Activity Recognition dataset, and they show the efficiency of the approach. [ABSTRACT FROM AUTHOR]

Hepatitis C is a liver infection caused by a virus, which results in mild to severe inflammation of the liver. Over many years, hepatitis C gradually damages the liver, often leading to permanent scarring, known as cirrhosis. Patients sometimes have moderate or no symptoms of liver illness for decades before developing cirrhosis. Cirrhosis typically worsens to the point of liver failure. Patients with cirrhosis may also experience brain and nerve system damage, as well as gastrointestinal hemorrhage. Treatment for cirrhosis focuses on preventing further progression of the disease. Detecting cirrhosis earlier is therefore crucial for avoiding complications. Machine learning (ML) has been shown to be effective at providing precise and accurate information for use in diagnosing several diseases. Despite this, no studies have so far used ML to detect cirrhosis in patients with hepatitis C. This study obtained a dataset consisting of 28 attributes of 2038 Egyptian patients from the ML Repository of the University of California at Irvine. Four ML algorithms were trained on the dataset to diagnose cirrhosis in hepatitis C patients: a Random Forest, a Gradient Boosting Machine, an Extreme Gradient Boosting, and an Extra Trees model. The Extra Trees model outperformed the other models achieving an accuracy of 96.92%, a recall of 94.00%, a precision of 99.81%, and an area under the receiver operating characteristic curve of 96% using only 16 of the 28 features. [ABSTRACT FROM AUTHOR]

The Irvine School refers to an intellectual perspective on information technology in complex organizational settings that emerged at the University of California in Irvine, California (UCI), over the last three decades of the 20th century. In many ways, the rise of the Irvine School was synonymous with the rise of researcher Rob Kling's influence on the international community of scholars who would eventually form what Kling called social informatics. This article reflects on Kling's role in the creation of the Irvine School. Kenneth Kraemer, an architect and city planner by training, had joined the UCI Graduate School of Administration in 1967. He was one of the first scholars to begin careful empirical study of the effect of computerization in government agencies, and soon started a research program on information technology and local government at UCI's Public Policy Research Organization. In the early 1970s, researchers Jim Danziger and Rob Kling joined him in this endeavor. Danziger had been trained in political science and public administration. Kling had been trained in electrical engineering and computer science, and had done important work at the intersection of fuzzy logic and planning systems in the area of symbolic artificial intelligence.

Many investigations on the response phase of cables mainly focus on the phase shift value in the linear solution, while the effect of the higher-order approximate terms (HOAT) is often omitted. To ascertain the effect of the HOAT on response phases, instantaneous phase-frequency characteristics of a classical externally- and parametrically-excited suspended cable are investigated. The Galerkin method is used to discretize the motion equations into ordinary differential equations, and the Multiple Scales Method (MSM) is used to solve these equations. Afterward, cable responses under these two types of excitation with different Irvine parameters λ 2 and excitation frequency Ω are numerically solved, and instantaneous phase differences between the responses and excitations are obtained by using the Hilbert transform. Then, variation characteristics of the instantaneous phase differences and corresponding amplitudes are analyzed in the ( λ 2 , Ω ) plane. It is shown that if the HOAT are not considered, the phase shifts of cable response would be constants. On the contrary, if they are included, the drift term (DT) and the doubling-frequency term (DFT) in the HOAT would vary periodically with time. Due to the difference in the frequency-response equation's right-hand terms between these two excitations, response amplitudes are different, affecting the phase-frequency characteristics through the DT and the DFT. The response-excitation instantaneous phase difference amplitude p max under external and parametric excitation are both suddenly increase in the local region centered on λ 2 ≈ 3.0 and Ω ≈ 1.125 and present a near-antisymmetrical distribution. However, the sudden-change-region of the former is a long and narrow band along the axis of λ 2 in the ( λ 2 , Ω ) plane, while that of the latter is a point field. Besides, values of the former are significantly larger than of the latter. [ABSTRACT FROM AUTHOR]

The modern industrial and consumer applications in accordance with the concepts of Industry 4.0 and the Internet of Things are characterized by autonomy and self-sufficiency. This has led to an increase in the interest for the so-called smart materials, capable of combining the functionalities of sensors, actuators and, in some applications, control systems. An important group of smart materials are shape-memory alloys, among which nickel–titanium (NiTi) alloys are the most known. In this article, the influence of phase transformation on the mechanical properties of thin NiTi alloy wires was investigated. During the test, the influence of the heating currents on the displacement and the force generated by the thin NiTi wires were analyzed. The temperature of the wires during heating was measured by a thermographic camera. This study proved the maximum value of the wires' displacement was related to the value of the heating current. During the research, the dependence of the transformation dynamics on the value of the heating currents was also proved. In addition, the influence of the surface inhomogeneity of the thin NiTi alloy wires on the accuracy of the thermographic measurements was analyzed. For the experimental research described in this article, we used the NiTi alloy whose trade name is Flexinol, produced by DYNALLOY (Inc. 2801 McGaw Ave. Irvine, CA, USA). [ABSTRACT FROM AUTHOR]

Summary: Scientific and technological advancements lead to the continuous generation of a large amount of data. These datasets are analyzed computationally to reveal patterns and trends. While the presence of noisy and irrelevant features or attributes in these datasets is unavoidable, they negatively impact the performance of classification techniques. Feature selection is a method to pre‐process these datasets by selecting the most informative features while concurrently improving the classification accuracy. Recently, several metaheuristic algorithms were employed in this feature selection process, including particle swarm optimization (PSO). PSO is prominent in the field of feature selection due to its simplicity and global search abilities. However, it may get stuck in local optima. To solve this problem, a new update mechanism in PSO is proposed and the PSO is hybridized with a local search method. To evaluate the performance of the proposed algorithm, benchmark datasets from the University of California in Irvine (UCI) repository were utilized, the k‐nearest neighbor as the classifier. Results show that the proposed feature selection technique outperforms other optimization algorithms on these feature selection problems. [ABSTRACT FROM AUTHOR]

This paper introduces a novel classification rule mining model based on Pareto-based Multiobjective Optimization called CRM-PM. The process of rule extraction is a challenging classification task in data mining since it has several constraints and conflicting objectives such as accuracy and comprehensibility. In this study, this task is accepted as a multi-objective optimization problem. Classification accuracy and misclassification ratio are assigned as evaluation criteria. The candidate solutions are generated in the direction of a proposed strategy to determine optimal ranges of the attributes that form the rules. The proposed approach is applied on eight benchmark datasets (Iris Plants, Wine Quality, Glass Identification, Stat log (Heart), Haberman's Survival, E-coli, Wisconsin Breast Cancer, and Pima Indians Diabetes) included in the University of California at Irvine machine learning repository. Furthermore, CRM-PM is run in three different validation modes: cross-validation, training without test data, and training with random splitting. Regarding experimental results, it can be said that the presented method has a promising capability for classification, and it achieves comparative or superior results. [Display omitted] • A novel CRM model based on multiobjective optimization, run fast, and has a high accuracy rate. • A stepwise rule extraction process. • A new flexible structure to determine the optimum ranges of each attribute in multi-class data sets. • A new running model depends on a removing strategy that evolves the classification accuracy. • A fair comparison with 3 different validations: cross-validation, training without test, and random split. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents experimental and numerical results of the first phase of a multi-phase comprehensive joint research program between University of Salerno, Italy, and the University of California, Irvine, USA, on investigating one of the major structural issues that defines the strength limit-state of pultruded fiber-reinforced polymer (PFRP) profiles. Specifically, the strength and stiffness of the web–flange junction (WFJ) of the majority of commercially-produced pultruded composite profiles. A summary of experimental results for 28 full-scale pull-out tests are presented and typical modes failure are identified. Moreover, the influence of the pull-out load distance (d) from the edge of the specimens on the failure strength of the web–flange junction has been investigated and a new definition for an “influence zone” is proposed that is found to be dependent on the loaded length, with a maximum value equal to approximately the PFRP member’s depth. This proposed zone was observed in all laboratory tests and its existence was confirmed by the results of FEM numerical analysis. 3-D finite-element models were also developed to predict the behavior of these specimens. The results from the numerical models were compared to those obtained from the experimental program and found to be satisfactory. [Copyright &y& Elsevier]

Information about several topics discussed at the 5th annual Coloquios Internationales sobre Cerebro y Agresión and Society for Terrorism Research (CICA-STR) international conference in Irvine, California on September 7-9, 2011. Topics include aggression, violence and terrorism. The symposium featured several speakers including House of Representatives member Loretta Sanchez, Orange County Sheriff-Coroner Sandra Hutchens and Ambassador Crescencio Arcos.