Search results for: Naïve Bayesian

Authors: Nadjat Zerf Boudjettou, Fahima Nader, Rachid Chalal

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Knowledge management is to acquire and represent knowledge relevant to a domain, a task or a specific organization in order to facilitate access, reuse and evolution. This usually means building, maintaining and evolving an explicit representation of knowledge. The next step is to provide access to that knowledge, that is to say, the spread in order to enable effective use. Knowledge management in the medical field aims to improve the performance of the medical organization by allowing individuals in the care facility (doctors, nurses, paramedics, etc.) to capture, share and apply collective knowledge in order to make optimal decisions in real time. In this paper, we propose a knowledge management approach based on integration technique of heterogeneous data in the medical field by creating a data warehouse, a technique of extracting knowledge from medical data by choosing a technique of data mining, and finally an exploitation technique of that knowledge in a case-based reasoning system.

Keywords: data warehouse, data mining, knowledge discovery in database, KDD, medical knowledge management, Bayesian networks

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Authors: Yalong Jiang, Zheru Chi

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In this paper, we study the factors which determine the capacity of a Convolutional Neural Network (CNN) model and propose the ways to evaluate and adjust the capacity of a CNN model for best matching to a specific pattern recognition task. Firstly, a scheme is proposed to adjust the number of independent functional units within a CNN model to make it be better fitted to a task. Secondly, the number of independent functional units in the capsule network is adjusted to fit it to the training dataset. Thirdly, a method based on Bayesian GAN is proposed to enrich the variances in the current dataset to increase its complexity. Experimental results on the PASCAL VOC 2010 Person Part dataset and the MNIST dataset show that, in both conventional CNN models and capsule networks, the number of independent functional units is an important factor that determines the capacity of a network model. By adjusting the number of functional units, the capacity of a model can better match the complexity of a dataset.

Keywords: CNN, convolutional neural network, capsule network, capacity optimization, character recognition, data augmentation, semantic segmentation

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Authors: Sunghoon Park, Hank Kim, Jinwon An, Sungzoon Cho

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Internet of Things allows one to collect data from facilities which are then used to monitor them and even predict malfunctions in advance. Conventional quality control methods focus on setting a normal range on a sensor value defined between a lower control limit and an upper control limit, and declaring as an anomaly anything falling outside it. However, interactions among sensor values are ignored, thus leading to suboptimal performance. We propose a multivariate approach which takes into account many sensor values at the same time. In particular Gaussian Mixture Model is used which is trained to maximize likelihood value using Expectation-Maximization algorithm. The number of Gaussian component distributions is determined by Bayesian Information Criterion. The negative Log likelihood value is used as an anomaly score. The actual usage scenario goes like a following. For each instance of sensor values from a facility, an anomaly score is computed. If it is larger than a threshold, an alarm will go off and a human expert intervenes and checks the system. A real world data from Building energy system was used to test the model.

Keywords: facility anomaly detection, gaussian mixture model, anomaly score, expectation maximization algorithm

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Authors: Leila Jafari, Viliam Makis, G. B. Akram Khaleghei

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In this paper, we present a maintenance model of a two-unit series system with economic dependence. Unit#1, which is considered to be more expensive and more important, is subject to condition monitoring (CM) at equidistant, discrete time epochs and unit#2, which is not subject to CM, has a general lifetime distribution. The multivariate observation vectors obtained through condition monitoring carry partial information about the hidden state of unit#1, which can be in a healthy or a warning state while operating. Only the failure state is assumed to be observable for both units. The objective is to find an optimal opportunistic maintenance policy minimizing the long-run expected average cost per unit time. The problem is formulated and solved in the partially observable semi-Markov decision process framework. An effective computational algorithm for finding the optimal policy and the minimum average cost is developed and illustrated by a numerical example.

Keywords: condition-based maintenance, semi-Markov decision process, multivariate Bayesian control chart, partially observable system, two-unit system

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Authors: Yavuz Unal, Kemal Polat, H. Erdinc Kocer

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In this study, a decision support system comprising three stages has been proposed to detect the disc abnormalities of the lumbar region. In the first stage named the feature extraction, T2-weighted sagittal and axial Magnetic Resonance Images (MRI) were taken from 55 people and then 27 appearance and shape features were acquired from both sagittal and transverse images. In the second stage named the feature weighting process, k-means clustering based feature weighting (KMCBFW) proposed by Gunes et al. Finally, in the third stage named the classification process, the classifier algorithms including multi-layer perceptron (MLP- neural network), support vector machine (SVM), Naïve Bayes, and decision tree have been used to classify whether the subject has lumbar disc or not. In order to test the performance of the proposed method, the classification accuracy (%), sensitivity, specificity, precision, recall, f-measure, kappa value, and computation times have been used. The best hybrid model is the combination of k-means clustering based feature weighting and decision tree in the detecting of lumbar disc disease based on both sagittal and axial MR images.

Keywords: lumbar disc abnormality, lumbar MRI, lumbar spine, hybrid models, hybrid features, k-means clustering based feature weighting

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Authors: Queen Suraajini Rajendran, Sai Hung Cheung

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Statistical down scaling models are required to connect the global climate model outputs and the local weather variables for climate change impact prediction. For reliable climate change impact studies, the uncertainty associated with the model including natural variability, uncertainty in the climate model(s), down scaling model, model inadequacy and in the predicted results should be quantified appropriately. In this work, a new approach is developed by the authors for statistical classification, statistical down scaling and uncertainty assessment and is applied to Singapore rainfall. It is a robust Bayesian uncertainty analysis methodology and tools based on coupling dependent modeling error with classification and statistical down scaling models in a way that the dependency among modeling errors will impact the results of both classification and statistical down scaling model calibration and uncertainty analysis for future prediction. Singapore data are considered here and the uncertainty and prediction results are obtained. From the results obtained, directions of research for improvement are briefly presented.

Keywords: statistical downscaling, global climate model, climate change, uncertainty

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Authors: Kang-Mo Jung

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The use of regularization for statistical methods has become popular. The least absolute shrinkage and selection operator (LASSO) framework has become the standard tool for sparse regression. However, it is well known that the LASSO is sensitive to outliers or leverage points. We consider a new robust estimation which is composed of the weighted loss function of the pairwise difference of residuals and the adaptive penalty function regulating the tuning parameter for each variable. Rank regression is resistant to regression outliers, but not to leverage points. By adopting a weighted loss function, the proposed method is robust to leverage points of the predictor variable. Furthermore, the adaptive penalty function gives us good statistical properties in variable selection such as oracle property and consistency. We develop an efficient algorithm to compute the proposed estimator using basic functions in program R. We used an optimal tuning parameter based on the Bayesian information criterion (BIC). Numerical simulation shows that the proposed estimator is effective for analyzing real data set and contaminated data.

Keywords: adaptive penalty function, robust penalized regression, variable selection, weighted rank regression

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Authors: S. I. Nwadioha, M. S. Odimayo, G. T. A. Jombo, E. O. P. Nwokedi

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Aims and Objectives: To determine the baseline CD4 positive T lymphocytes count of HIV/AIDS treatment naïve adults clients presenting for the first time treatment in Benue State University Teaching Hospital. Subjects and Methods: A total of 700 subjects age between 18 years to 70 years, were recruited for the study, comprising 600 HIV sero-positive patients and 100 healthy controls in Benue State University Teaching Hospital, Makurdi from 2013 to 2014. The CD4 counts of the subjects were evaluated using a Partec flow cytometer. Results: CD4 count of 200-299 cells/μl peaked with 25% (n=150/600)[control; 0%( n= 0/100)]. The study also showed that 44% (266/600) of HIV subjects had acquired immunodeficiency syndrome as defined by low CD4 counts below 200 cells/μl. Seventy-five per cent (n=451/600)of our patients would require to be placed on antiretroviral therapy with CD4 count of less than 350 cells/μl. At CD4 350 baseline criterion, age group 20-29 years had the highest demand 35%(160/451) for ARV followed by age groups 30-39 and 40-49 years with 28%(128/451) and 22%(98/451) respectively. Conclusion: There is a high prevalence of acquired immunodeficiency syndrome as defined by CD4 counts below 200 cells/μl, among the young active productive age group. The strict adopting of the ART WHO 2010 scale- up criteria doubles the number of the HIV clients that would qualify for ART with its attendant health benefits on the long run.

Keywords: CD4 counts, HIV patients, young age group, Nigeria

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Authors: Farhad Asadi, Mohammad Javad Mollakazemi

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In this paper, Bayesian online inference in models of data series are constructed by change-points algorithm, which separated the observed time series into independent series and study the change and variation of the regime of the data with related statistical characteristics. variation of statistical characteristics of time series data often represent separated phenomena in the some dynamical system, like a change in state of brain dynamical reflected in EEG signal data measurement or a change in important regime of data in many dynamical system. In this paper, prediction algorithm for studying change point location in some time series data is simulated. It is verified that pattern of proposed distribution of data has important factor on simpler and smother fluctuation of hazard rate parameter and also for better identification of change point locations. Finally, the conditions of how the time series distribution effect on factors in this approach are explained and validated with different time series databases for some dynamical system.

Keywords: time series, fluctuation in statistical characteristics, optimal learning, change-point algorithm

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Authors: Rikson Gultom

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Hate Speech and Abusive language on social media is difficult to detect, usually, it is detected after it becomes viral in cyberspace, of course, it is too late for prevention. An early detection system that has a fairly good accuracy is needed so that it can reduce conflicts that occur in society caused by postings on social media that attack individuals, groups, and governments in Indonesia. The purpose of this study is to find an early detection model on Twitter social media using machine learning that has high accuracy from several machine learning methods studied. In this study, the support vector machine (SVM), Naïve Bayes (NB), and Random Forest Decision Tree (RFDT) methods were compared with the Support Vector machine with genetic algorithm (SVM-GA), Nave Bayes with genetic algorithm (NB-GA), and Random Forest Decision Tree with Genetic Algorithm (RFDT-GA). The study produced a comparison table for the accuracy of the hate speech and abusive language detection model, and presented it in the form of a graph of the accuracy of the six algorithms developed based on the Indonesian-language Twitter dataset, and concluded the best model with the highest accuracy.

Keywords: abusive language, hate speech, machine learning, optimization, social media

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Authors: Altaf H. Khan, Frank Stenger, Mohammed A. Hussein, Reaz A. Chaudhuri, Sameera Asif

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This paper discusses a novel approach to approximate quadrature integrals that arise in the estimation of likelihood parameters for the generalized linear mixed models (GLMM) as well as Bayesian methodology also requires computation of multidimensional integrals with respect to the posterior distributions in which computation are not only tedious and cumbersome rather in some situations impossible to find solutions because of singularities, irregular domains, etc. An attempt has been made in this work to apply Sinc function based quadrature rules to approximate intractable integrals, as there are several advantages of using Sinc based methods, for example: order of convergence is exponential, works very well in the neighborhood of singularities, in general quite stable and provide high accurate and double precisions estimates. The Sinc function based approach seems to be utilized first time in statistical domain to our knowledge, and it's viability and future scopes have been discussed to apply in the estimation of parameters for GLMM models as well as some other statistical areas.

Keywords: generalized linear mixed model, likelihood parameters, qudarature, Sinc function

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Authors: Katharina Fellnhofer

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The more information and knowledge that technology provides, the more important are profoundly human skills like intuition, the skill of using nonconscious information. As our world becomes more complex, shaken by crises, and characterized by uncertainty, time pressure, ambiguity, and rapidly changing conditions, intuition is increasingly recognized as a key human asset. However, due to methodological limitations of sample size or time frame or a lack of real-world or cross-cultural scope, precisely how to measure intuition when facing risks on a nonconscious level remains unclear. In light of the measurement challenge related to intuition’s nonconscious nature, a technique is introduced to measure intuition via hidden images as nonconscious additional information to trigger intuition. This technique has been tested in a within-subject fully online design with 62,721 real-world investment decisions made by 657 subjects in Europe and the United States. Bayesian models highlight the technique’s potential to measure skill at using nonconscious information for conscious decision making. Over the long term, solving the mysteries of intuition and mastering its use could be of immense value in personal and organizational decision-making contexts.

Keywords: cognition, intuition, investment decisions, methodology

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Authors: Tilouche Rahma, Sayeb Soumaya, Ben Hassen Mohamed

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The wettability of materials is a very important aspect of surface science, it presents a key factor providing the best characteristic of product, especially in hygienic field. Hydrophobic polypropylene is used as nonwoven topsheet in disposable diaper, for its interesting properties (toughness, lightness...) by comparing with traditional product previously used. SURFACTANTs are widely used to reduce contact angle (water contact angles larger than 90° on smooth surfaces) and to change wetting properties. In the present work, we study ways to obtain hydrophilic polypropylene surface, by the deposition of a variety of surfactant on surfaces of varying morphology. We used two different methods for surface wetting: Spraying method and the coating method. The concentration of the wetting agent, the type of non-woven fabric and the parameters in the method for controlling, hugely affect the quality of treatment. Therefore need that the treatment is effective in terms of contact angle without affecting the mechanical properties of the nonwoven. For the assessment of the quality of treatment, two methods are used: The measurement of the contact angle and the strike trough time. Also, with subjective evaluation by Hedonic test (which involves the consumer preference (naive panel: group of moms). Finally, we selected the better treated topsheet referring to the assessment results.

Keywords: SURFACTANT, topsheet polypropylene, hydrophilic, hydrophobic

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Authors: Emmanuel Osaze Oshoiribhor, Adetokunbo MacGregor John-Otumu

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There has been a need in recent years to predict student academic achievement prior to graduation. This is to assist them in improving their grades, especially for those who have struggled in the past. The purpose of this research is to use supervised learning techniques to create a model that predicts student academic progress. Many scholars have developed models that predict student academic achievement based on characteristics including smoking, demography, culture, social media, parent educational background, parent finances, and family background, to mention a few. This element, as well as the model used, could have misclassified the kids in terms of their academic achievement. As a prerequisite to predicting if the student will perform well in the future on related courses, this model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester. With a 96.7 percent accuracy, the model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost. This model is offered as a desktop application with user-friendly interfaces for forecasting student academic progress for both teachers and students. As a result, both students and professors are encouraged to use this technique to predict outcomes better.

Keywords: artificial intelligence, ML, logistic regression, performance, prediction

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Authors: Aref Aasi, Sahar Ebrahimi Bajgani, Erfan Aasi

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Breast cancer (BC) continues to be the most frequent cancer in females and causes the highest number of cancer-related deaths in women worldwide. Inspired by recent advances in studying the relationship between different patient attributes and features and the disease, in this paper, we have tried to investigate the different classification methods for better diagnosis of BC in the early stages. In this regard, datasets from the University Hospital Centre of Coimbra were chosen, and different machine learning (ML)-based and neural network (NN) classifiers have been studied. For this purpose, we have selected favorable features among the nine provided attributes from the clinical dataset by using a random forest algorithm. This dataset consists of both healthy controls and BC patients, and it was noted that glucose, BMI, resistin, and age have the most importance, respectively. Moreover, we have analyzed these features with various ML-based classifier methods, including Decision Tree (DT), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) along with NN-based Multi-Layer Perceptron (MLP) classifier. The results revealed that among different techniques, the SVM and MLP classifiers have the most accuracy, with amounts of 96% and 92%, respectively. These results divulged that the adopted procedure could be used effectively for the classification of cancer cells, and also it encourages further experimental investigations with more collected data for other types of cancers.

Keywords: breast cancer, diagnosis, machine learning, biomarker classification, neural network

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Authors: Sang-Sun Yoon, Yea-Hyun Leem, Sangmee Ahn Jo

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The N-acetylated-alpha-linked-acidic (NAAG) peptidase inhibitor 2-phosphonomethyl pentanedioic acid (2-PMPA) has demonstrated to be neuroprotective against glutamate-mediated neuron degeneration and neurological disorders such as ischemia. Several studies have demonstrated impaired psychiatric function by altered glutamate carboxypeptidase II expression, although 2-PMPA has not yet been studied. Thus, we investigated effect of 2-PMPA on depressive-like phenotype using C57BL/6 mice. Treatment of 2-PMPA (10 mg/kg for 6 days/daily, ip injection) on C57BL/6 naïve mice showed depressive-like symptoms such as decreased social preference, but did not affect the immobility measured by tail suspension test. Reduction of phosphorylated cAMP-responsive element binding (p-CREB) known as a representative marker of depressive-like behavior was observed in layer 1 and piriform cortex subregions of the prefrontal cortex of 2-PMPA-treated mice. The immunoreactivity of metabotropic glutamate receptors 5 (mGluR5) that mediate phosphorylation of CREB was also decreased in layer 1 and piriform cortex subregions of the prefrontal cortex of 2-PMPA injected mice. Thus, our results suggest that dysregulation of the GCPII or NAAG by 2-PMPA treatment is likely to be associated with pathogenesis of depression and further studies are needed to understand whether the reduced NAAG level or enhanced glutamate level in the brain is involved in this response.

Keywords: depression, GCPII, 2-PMPA, p-CREB, mGluR5

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Authors: Tianyi Wang, Hieyong Jeong, An Guo, Yuko Ohno

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Healthcare robot for assisting sit-to-stand (STS) performance had aroused numerous research interests. To author’s best knowledge, knowledge about how evaluating healthcare robot is still unknown. Robot should be labeled as fault if users feel demanding during STS when they are assisted by robot. In this research, we aim to propose a method to evaluate sit-to-stand assist robot through center of pressure (CoP), then classify different STS performance. Experiments were executed five times with ten healthy subjects under four conditions: two self-performed STSs with chair heights of 62 cm and 43 cm, and two robot-assisted STSs with chair heights of 43 cm and robot end-effect speed of 2 s and 5 s. CoP was measured using a Wii Balance Board (WBB). Bayesian classification was utilized to classify STS performance. The results showed that faults occurred when decreased the chair height and slowed robot assist speed. Proposed method for fault classification showed high probability of classifying fault classes form others. It was concluded that faults for STS assist robot could be detected by inspecting center of pressure and be classified through proposed classification algorithm.

Keywords: center of pressure, fault classification, healthcare robot, sit-to-stand movement

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Authors: Yu-Ling Tsai, Chih-Jung Chang, Chia-Chen Lu, Eric Wu, Chuan-Sheng Lin, Tzu-Lung Lin, Hsin-Chih Lai

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It is urgent that novel anti-obesity measures that are safe, effective and widely available are developed for counteracting the rapidly growing obesity epidemics. In the present study, we show that a probiotic bacterium Parabacteroides goldsteinii screened through culture under the high molecular weight polysaccharides prepared from two iconic medicinal fungi, the Ganoderma lucidum and the Hirsutella sinensis, reduced body weight by ca. 20% in high-fat diet (HFD)-fed mice. The bacterium also decreased intestinal permeability, metabolic endotoxemia, inflammation and insulin resistance. Notably, oral administration of live, but not high temperature-killed, P. goldsteinii to HFD fed mice considerably reduces weight gain and obesity-associated metabolic disorders. A three months feeding of the mice with P. goldsteinii did not show any aberrant side effects, indicating the safety of this bacterium. Transcriptome analysis indicated that P. goldsteinii enhances immunity in resting dendritic cells, but reduces inflammation in lipopolysaccharide (LPS)-induced dendritic cells. On top, Naïve T-cells were skewed towards regulatory T-cells after encountering with dendritic cells (DCs) pretreated with P. goldsteinii. These results indicated P. goldsteinii showed anti-inflammatory effects and can work as a potential probiotic ameliorating obesogenicity and related metabolic syndromes.

Keywords: Parabacteroides goldsteinii, gut microbiome, obesity, immune modulation

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Authors: Emmanuel Osaze Oshoiribhor, Adetokunbo MacGregor John-Otumu

Abstract:

In recent years, there has been a desire to forecast student academic achievement prior to graduation. This is to help them improve their grades, particularly for individuals with poor performance. The goal of this study is to employ supervised learning techniques to construct a predictive model for student academic achievement. Many academics have already constructed models that predict student academic achievement based on factors such as smoking, demography, culture, social media, parent educational background, parent finances, and family background, to name a few. This feature and the model employed may not have correctly classified the students in terms of their academic performance. This model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester as a prerequisite to predict if the student will perform well in future on related courses. The model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost, returning a 96.7% accuracy. This model is available as a desktop application, allowing both instructors and students to benefit from user-friendly interfaces for predicting student academic achievement. As a result, it is recommended that both students and professors use this tool to better forecast outcomes.

Keywords: artificial intelligence, ML, logistic regression, performance, prediction

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Authors: Raymond Xu, Cindy Jingru Wang

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Social media has become a globally connected and influencing platform. Social media data, such as tweets, can help predict the spread of pandemics and provide individuals and healthcare providers early warnings. Public psychological reactions and opinions can be efficiently monitored by AI models on the progression of dominant topics on Twitter. However, statistics show that as the coronavirus spreads, so does an infodemic of misinformation due to pandemic-related factors such as unemployment and lockdowns. Social media algorithms are often biased toward outrage by promoting content that people have an emotional reaction to and are likely to engage with. This can influence users’ attitudes and cause confusion. Therefore, social media is a double-edged sword. Combating fake news and biased content has become one of the essential tasks. This research analyzes the variety of methods used for fake news detection covering random forest, logistic regression, support vector machines, decision tree, naive Bayes, BoW, TF-IDF, LDA, CNN, RNN, LSTM, DeepFake, and hierarchical attention network. The performance of each method is analyzed. Based on these models’ achievements and limitations, a multi-dimensional AI framework is proposed to achieve higher accuracy in infodemic detection, especially pandemic-related news. The model is trained on contextual content, images, and news metadata.

Keywords: artificial intelligence, fake news detection, infodemic detection, image recognition, sentiment analysis

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Authors: Raouf Mbarki, Fadi Al Khatib, Malek Adouni

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Knee collateral ligaments play a significant role in restraining excessive frontal motion (varus/valgus rotations). In this investigation, a multiscale frame was developed based on structural hierarchies of the collateral ligaments starting from the bottom (tropocollagen molecule) to up where the fibred reinforced structure established. Experimental data of failure tensile test were considered as the principal driver of the developed model. This model was calibrated statistically using Bayesian calibration due to the high number of unknown parameters. Then the model is scaled up to fit the real structure of the collateral ligaments and simulated under realistic boundary conditions. Predications have been successful in describing the observed transient response of the collateral ligaments during tensile test under pre- and post-damage loading conditions. Collateral ligaments maximum stresses and strengths were observed near to the femoral insertions, a results that is in good agreement with experimental investigations. Also for the first time, damage initiation and propagation were documented with this model as a function of the cross-link density between tropocollagen molecules.

Keywords: multiscale model, tropocollagen, fibrils, ligaments commas

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Authors: Deepshikha Shukla, C. H. Solanki, Mayank K. Desai

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Amongst all the natural hazards, earthquakes have the potential for causing the greatest damages. Since the earthquake forces are random in nature and unpredictable, the quantification of the hazards becomes important in order to assess the hazards. The time and place of a future earthquake are both uncertain. Since earthquakes can neither be prevented nor be predicted, engineers have to design and construct in such a way, that the damage to life and property are minimized. Seismic hazard analysis plays an important role in earthquake design structures by providing a rational value of input parameter. In this paper, both mathematical, as well as computational methods adopted by researchers globally in the past five years, will be discussed. Some mathematical approaches involving the concepts of Poisson’s ratio, Convex Set Theory, Empirical Green’s Function, Bayesian probability estimation applied for seismic hazard and FOSM (first-order second-moment) algorithm methods will be discussed. Computational approaches and numerical model SSIFiBo developed in MATLAB to study dynamic soil-structure interaction problem is discussed in this paper. The GIS-based tool will also be discussed which is predominantly used in the assessment of seismic hazards.

Keywords: computational methods, MATLAB, seismic hazard, seismic measurements

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Authors: Kaleem Kashif, Tayyaba Anam, Yizhi Wu

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This paper introduces a methodology for advancing Italian speech vowels landmark detection within the distinctive feature-based speech recognition domain. Leveraging the legacy tool 'xkl' by integrating combined convolutional neural networks (CNNs) and recurrent neural networks (RNNs), the study presents a comprehensive enhancement to the 'xkl' legacy software. This integration incorporates re-assigned spectrogram methodologies, enabling meticulous acoustic analysis. Simultaneously, our proposed model, integrating combined CNNs and RNNs, demonstrates unprecedented precision and robustness in landmark detection. The augmentation of re-assigned spectrogram fusion within the 'xkl' software signifies a meticulous advancement, particularly enhancing precision related to vowel formant estimation. This augmentation catalyzes unparalleled accuracy in landmark detection, resulting in a substantial performance leap compared to conventional methods. The proposed model emerges as a state-of-the-art solution in the distinctive feature-based speech recognition systems domain. In the realm of deep learning, a synergistic integration of combined CNNs and RNNs is introduced, endowed with specialized temporal embeddings, harnessing self-attention mechanisms, and positional embeddings. The proposed model allows it to excel in capturing intricate dependencies within Italian speech vowels, rendering it highly adaptable and sophisticated in the distinctive feature domain. Furthermore, our advanced temporal modeling approach employs Bayesian temporal encoding, refining the measurement of inter-landmark intervals. Comparative analysis against state-of-the-art models reveals a substantial improvement in accuracy, highlighting the robustness and efficacy of the proposed methodology. Upon rigorous testing on a database (LaMIT) speech recorded in a silent room by four Italian native speakers, the landmark detector demonstrates exceptional performance, achieving a 95% true detection rate and a 10% false detection rate. A majority of missed landmarks were observed in proximity to reduced vowels. These promising results underscore the robust identifiability of landmarks within the speech waveform, establishing the feasibility of employing a landmark detector as a front end in a speech recognition system. The synergistic integration of re-assigned spectrogram fusion, CNNs, RNNs, and Bayesian temporal encoding not only signifies a significant advancement in Italian speech vowels landmark detection but also positions the proposed model as a leader in the field. The model offers distinct advantages, including unparalleled accuracy, adaptability, and sophistication, marking a milestone in the intersection of deep learning and distinctive feature-based speech recognition. This work contributes to the broader scientific community by presenting a methodologically rigorous framework for enhancing landmark detection accuracy in Italian speech vowels. The integration of cutting-edge techniques establishes a foundation for future advancements in speech signal processing, emphasizing the potential of the proposed model in practical applications across various domains requiring robust speech recognition systems.

Keywords: landmark detection, acoustic analysis, convolutional neural network, recurrent neural network

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Authors: Derssie Mebratu

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As a large variety of micro services, web services, social graphic applications, and media applications are continuously developed, it is substantially vital to design and build a reliable, efficient, and faster memory tiering system. Despite limited design, implementation, and deployment in the last few years, several techniques are currently developed to improve a memory tiering system in a cloud. Some of these techniques are to develop an optimal scanning frequency; improve and track pages movement; identify pages that recently accessed; store pages across each tiering, and then identify pages as a hot, warm, and cold so that hot pages can store in the first tiering Dynamic Random Access Memory (DRAM) and warm pages store in the second tiering Compute Express Link(CXL) and cold pages store in the third tiering Non-Volatile Memory (NVM). Apart from the current proposal and implementation, we also develop a new technique based on a machine learning algorithm in that the throughput produced 25% improved performance compared to the performance produced by the baseline as well as the latency produced 95% improved performance compared to the performance produced by the baseline.

Keywords: machine learning, bayesian optimization, memory tiering, CXL, DRAM

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Authors: Petcharat Lovichakorntikul, John Walsh

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Meditation is in the process of becoming a globalized practice and its benefits have been widely acknowledged. The first wave of internationalized meditation techniques and practices was represented by Chan and Zen Buddhism and a new wave of practice has arisen in Thailand as part of the Phra Dhammakaya temple movement. This form of meditation is intended to be simple and straightforward so that it can easily be taught to people unfamiliar with the basic procedures and philosophy. This has made Phra Dhammakaya an important means of outreach to the international community. One notable aspect is to encourage adults to become like children to perform it – that is, to return to a naïve state prior to the adoption of ideology as a means of understanding the world. It is said that the Lord Buddha achieved the point of awakening at the age of seven and Phra Dhammakaya has a program to teach meditation to both children and adults. This brings about the research question of how practitioners respond to the practice of meditation and how should they be taught? If a careful understanding of how children behave can be achieved, then it will help in teaching adults how to become like children (albeit idealized children) in their approach to meditation. This paper reports on action research in this regard. Personal interviews and focus groups are held with a view to understanding self-learning methods with respect to Buddhist meditation and understanding and appreciation of the practices involved. The findings are considered in the context of existing knowledge about different learning techniques among people of different ages. The implications for pedagogical practice are discussed and learning methods are outlined.

Keywords: Buddhist meditation, Dhammakaya, meditation technique, pedagogy, self-learning

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Authors: Ailing Yang, Penghan Song, Aihua Cai

Abstract:

The nonlinear maneuvering target tracking problem is mainly a state estimation problem when the target motion model is uncertain. Traditional solutions include Kalman filtering based on Bayesian filtering framework and extended Kalman filtering. However, these methods need prior knowledge such as kinematics model and state system distribution, and their performance is poor in state estimation of nonprior complex dynamic systems. Therefore, in view of the problems existing in traditional algorithms, a convolution LSTM target state estimation (SAConvLSTM-SE) algorithm based on Self-Attention memory (SAM) is proposed to learn the historical motion state of the target and the error distribution information measured at the current time. The measured track point data of airborne radar are processed into data sets. After supervised training, the data-driven deep neural network based on SAConvLSTM can directly obtain the target state at the next moment. Through experiments on two different maneuvering targets, we find that the network has stronger robustness and better tracking accuracy than the existing tracking methods.

Keywords: maneuvering target, state estimation, Kalman filter, LSTM, self-attention

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Authors: S. Abera, T. Gidey, W. Terefe

Abstract:

Introduction: Testing for HIV is the key entry point to HIV prevention, treatment, and care and support services. Hence, predictive data mining techniques can greatly benefit to analyze and discover new patterns from huge datasets like that of EDHS 2011 data. Objectives: The objective of this study is to build a predictive modeling for HIV testing and identify determinants of HIV testing for adults with age above fourteen years using data mining techniques. Methods: Cross-Industry Standard Process for Data Mining (CRISP-DM) was used to predict the model for HIV testing and explore association rules between HIV testing and the selected attributes among adult Ethiopians. Decision tree, Naïve-Bayes, logistic regression and artificial neural networks of data mining techniques were used to build the predictive models. Results: The target dataset contained 30,625 study participants; of which 16, 515 (53.9%) were women. Nearly two-fifth; 17,719 (58%), have never been tested for HIV while the rest 12,906 (42%) had been tested. Ethiopians with higher wealth index, higher educational level, belonging 20 to 29 years old, having no stigmatizing attitude towards HIV positive person, urban residents, having HIV related knowledge, information about family planning on mass media and knowing a place where to get testing for HIV showed an increased patterns with respect to HIV testing. Conclusion and Recommendation: Public health interventions should consider the identified determinants to promote people to get testing for HIV.

Keywords: data mining, HIV, testing, ethiopia

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Authors: Sushma Ghogale

Abstract:

With a surge in user-generated content or feedback or reviews on the internet, it has become possible and important to know consumers' opinions about products and services. This data is important for both potential customers and businesses providing the services. Data from social media is attracting significant attention and has become the most prominent channel of expressing an unregulated opinion. Prospective customers look for reviews from experienced customers before deciding to buy a product or service. Several websites provide a platform for users to post their feedback for the provider and potential customers. However, the biggest challenge in analyzing such data is in extracting latent features and providing term-level analysis of the data. This paper proposes an approach to use topic modeling to classify the reviews into topics and conduct sentiment analysis to mine the opinions. This approach can analyse and classify latent topics mentioned by reviewers on business sites or review sites, or social media using topic modeling to identify the importance of each topic. It is followed by sentiment analysis to assess the satisfaction level of each topic. This approach provides a classification of hotel reviews using multiple machine learning techniques and comparing different classifiers to mine the opinions of user reviews through sentiment analysis. This experiment concludes that Multinomial Naïve Bayes classifier produces higher accuracy than other classifiers.

Keywords: latent Dirichlet allocation, topic modeling, text classification, sentiment analysis

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Authors: Benmansour Nassima, Benmansour Souheyla

Abstract:

The appropriate integration of reward processing and decision making provided by the environment is vital for behavioural success and individuals’ well being in everyday life. Functional neurological investigation has already provided an inclusive image on affective and emotional (motivational) processing in the healthy human brain and has recently focused its interest also on the assessment of brain function in anxious and depressed individuals. This article offers an overview on the theoretical approaches that relate emotion and decision-making, and spotlights investigation with anxious or depressed individuals to reveal how emotions can interfere with decision-making. This research aims at incorporating the emotional structure based on response and stimulation with a Bayesian approach to decision-making in terms of probability and value processing. It seeks to show how studies of individuals with emotional dysfunctions bear out that alterations of decision-making can be considered in terms of altered probability and value subtraction. The utmost objective is to critically determine if the probabilistic representation of belief affords could be a critical approach to scrutinize alterations in probability and value representation in subjective with anxiety and depression, and draw round the general implications of this approach.

Keywords: decision-making, motivation, alteration, reward processing, response selection

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Authors: Xia Huo

Abstract:

The composition and metabolites of the gut microbiota can be altered by environmental pollutants. However, the effect of co-exposure to multiple pollutants on the human gut microbiota has not been sufficiently studied. In this study, gut microorganisms and their metabolites were compared between 33 children from Guiyu and 34 children from Haojiang. The exposure level was assessed by estimating the daily intake (EDI) of polybrominated diphenyl ethers (PBDEs), polychlorinated biphenyls (PCBs), 6PPD-quinone (6PPDQ), and metal(loid)s in dust. Significant correlations were found between the EDIs of 6PPDQ, BDE28, PCB52, Ni, Cu, and both the alpha diversity index and specific metabolites in single-element models. The study found that the Bayesian kernel machine regression (BKMR) model showed a negative correlation between the EDIs of five pollutants (6PPDQ, BDE28, PCB52, Ni, and Cu) and the Chao 1 index, particularly beyond the 55th percentile. Furthermore, the EDIs of these five pollutants were positively correlated with the levels of the metabolite 2,4-diaminobutyric acid while negatively correlated with the levels of d-erythro-sphingosine and d-threitol. Our research suggests that exposure to 6PPDQ, BDE28, PCB52, Ni, and Cu in kindergarten dust is associated with alterations in the gut microbiota and its metabolites. These alterations may be associated with neurodevelopmental abnormalities in children.

Keywords: gut microbiota, 6PPDQ, PBDEs, PCBs, metal(loid)s, BKMR

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