Search

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]

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]

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]

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]

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]

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 article focuses on the significance of microfluidic centers in the U.S. It cites the Micro/nano Fluidics Fundamentals Focus (MF3) Center in Irvine, California, founded by microfluidic expert Abraham Lee. According to the article, MF3 Center partners with several commercial developers in the country to speed the transition from design stage to commercialization. It aims to develop microfluidic modules for various tasks such as cell sorting or fluid transport.

Swarm intelligence techniques have a vast range of real world applications.Some applications are in the domain of medical data mining where, main attention is on structure models for the classification and expectation of numerous diseases. These biomedical applications have grabbed the interest of numerous researchers because these are most serious and prevalent causes of death among the human whole world out of which breast cancer is the most serious issue. Mammography is the initial screening assessment of breast cancer. In this study, an enhanced version of Harris Hawks Optimization (HHO) approach has been developed for biomedical databases, known as DLHO. This approach has been introduced by integrating the merits of dimension learning-based hunting (DLH) search strategy with HHO. The main objective of this study is to alleviate the lack of crowd diversity, premature convergence of the HHO and the imbalance amid the exploration and exploitation. DLH search strategy utilizes a dissimilar method to paradigm a neighborhood for each search member in which the neighboring information can be shared amid search agents. This strategy helps in maintaining the diversity and the balance amid global and local search. To evaluate the DLHO lot of experiments have been taken such as (i) the performance of optimizers have analysed by using 29-CEC -2017 test suites, (ii) to demonstrate the effectiveness of the DLHO it has been tested on different biomedical databases out of which we have used two different databases for Breast i.e. MIAS and second database has been taken from the University of California at Irvine (UCI) Machine Learning Repository.Also to test the robustness of the proposed method its been tested on two other databases of such as Balloon and Heart taken from the UCI Machine Learning Repository. All the results are in the favour of the proposed technique. [ABSTRACT FROM AUTHOR]

The article reports on the claims of PrimeGen, a biotechnological company based in Irvine, California, that it has used carbon nanotubes to reprogram adult human cells to an embryonic-like state, a breakthrough that removes the elevated risk of cancer that blights other techniques. PrimeGene researchers used the nanotube delivery system to introduce genes into human testicular and retinal cells, and PrimeGene has reported that they were quickly taken up by an impressive 80% of the cells. It is reported that PrimeGene announced an alliance with Unidym, based in Menlo Park, California, which makes the nanotubes.

Background: This study aims to determine if the intravitreal dexamethasone implant (DEX implant, Ozurdex; Allergan, Inc., Irvine, California) is effective for treating intermediate, posterior, and panuveitis as a monotherapy or adjunctive treatment to systemic immunomodulatory therapies. Methods: A systematic review using MEDLINE, EMBASE, and PubMed database searches was conducted with the Oxford Centre for Evidence-based Medicine Levels of Evidence criteria to select publications. Available background information and patient data from each study was tabulated. Outcomes studied were central retinal thickness (CRT), best corrected visual acuity, intraocular inflammation (anterior chamber cells, vitreous haze), number of patients with prior and concomitant immunomodulatory treatments, intraocular pressure (IOP) elevation (≥ 25 mmHg), and other adverse effects associated with the implant. Results: One hundred ninety-five (61.51%) patients had previous immunomodulatory treatment while 232 (64.8%) were treated with concomitant immunomodulatory therapy with the DEX implant. CRT decreased by an average of 198.65 μm (42.74%). Visual acuity improved to an average of 0.451 (logMAR) or 20/57 (Snellen) which is a 43.11% improvement from baseline. One hundred seventy-three (59%) of eyes were quiescent at the end of the trials, of which 40 (13.7%) previously inflamed eyes became quiescent. Elevated IOP occurred in 91 (20.6%). The most common adverse events were cataract/posterior subcapsular opacities in 47 (11.03%) patients and conjunctival hemorrhage in 24 (5.44%) patients. Conclusions: The DEX implant is an effective medication for the treatment of posterior segment uveitis, uveitic macular edema, and results in improved visual acuity. Development of elevated IOP and cataract should be closely monitored as they are tangible risks associated with the DEX implant. This study was not able to determine whether the DEX implant was more effective as a monotherapy or as an adjunctive therapy to systemic immunomodulatory treatment. [ABSTRACT FROM AUTHOR]

We present the long-term clinical and angiographic follow-up results of 100 consecutive intracranial aneurysms treated with Onyx liquid embolic system (MTI, Irvine, Calif.), either alone or combined with an adjunctive stent, in a single center. A total of 100 aneurysms in 94 patients were treated with endosaccular Onyx packing. Intracranial stenting was used adjunctively in 25 aneurysms including 19 during initial treatment and 6 during retreatment. All aneurysms except two were located in the internal carotid artery. Of the 100 aneurysms, 35 were giant or large/wide-necked, and 65 were small. Follow-up angiography was performed in all 91 surviving patients (96 aneurysms) at 3 and/or 6 months. Follow-up angiography was performed at 1, 2, 3, 4 and 5 years in 90, 41, 26, 6 and 2 patients, respectively. Overall, aneurysm recanalization was observed in 12 of 96 aneurysms with follow-up angiography (12.5%). All 12 were large or giant aneurysms, resulting in a 36% recanalization rate in the large and giant aneurysm group. One aneurysm out of 25 treated with the combination of a stent and Onyx showed recanalization. There was also no recanalization in the follow-up of small internal carotid artery aneurysms treated with balloon assistance only. At final follow-up, procedure- or device-related permanent neurological morbidity was present in eight patients (8.3%). There were two procedure-related and one disease-related (subarachnoid hemorrhage) deaths (mortality 3.2%). Delayed spontaneous asymptomatic occlusion of the parent vessel occurred in two patients, detected on routine follow-up. Onyx provides durable aneurysm occlusion with parent artery reconstruction resulting in perfectly stable 1-year to 5-year follow-up angiography both in small aneurysms treated with balloon assistance only (0% recanalization rate) and large or giant aneurysms treated with stent and Onyx combination (4% recanalization rate). Endosaccular Onyx packing with balloon assistance may not be adequate for stable long-term results in those with a large or giant aneurysm. However, the recanalization rate of 36% in these aneurysms is better than the reported results with other techniques, i.e., coils with or without adjunctive bare stents. [ABSTRACT FROM AUTHOR]

The article offers reports on the creation of a synthetic polymer urged by the protein titin, which also presents combination of mechanical properties, by Professor Zhibi Guan and colleagues at the University of California, Irvine, California. Guan and his co-workers projected a synthetic modular polymer that comprises of domains made of a dimeric supramolecular motif restrained within a macrocycle. The leading synthetic polymer indicates very interesting mechanical properties, comparatively strong and undergoes large plastic distortions.

The article reports on the result of a research study about the condition of human embryonic stem cells transplanted into the brain of an irradiated rat, conducted by researcher Charles Limoli and colleagues from the University of California in Irvine, California. The study revealed that the human embryonic stem cells have survived in the brain of the rats. It showed that the animals who receive the cells performed much better in a task of recognizing place.