Academic literature on the topic 'Bayes point machine'

Aiming at the defects that failure samples of five-axis NC machine tools is small, traditional reliability analysis is not accurate, this paper presents reliability analysis mode based on improved Bayesian method for AMSAA model. Firstly, we obtain the failure model of NC machine tools meets the AMSAA model according to goodness-of-fit test, and in order to meet the requirements of simplifying engineering calculations, this paper adpots a method of Coefficient equivalent which converts failure Data into index-life data; then using Bayesian methods to estimate reliability parameters for the Index-life data; for the last we proceed point estimation and interval estimation for the MTBF of the machine. Take High-speed five-axis NC machine tools t of VMC650m for example, the result proved that the method can take advantage of a small sample of the equipment to proceed point estimation and interval estimation for MTBF failure data, and provide a reference for the optimization of maintenance strategies and Diagnostic work of the NC machine tools.

<p><span lang="EN-US">Information mining is a capable idea with incredible potential to anticipate future patterns and conduct. It alludes to the extraction of concealed information from vast data sets by utilizing procedures like factual examination, machine learning, grouping, neural systems and genetic algorithms. In naive baye’s, there exists a problem of zero likelihood. This paper proposed RB-Bayes method based on baye’s theorem for prediction to remove problem of zero likelihood. We also compare our method with few existing methods i.e. naive baye’s and SVM. We demonstrate that this technique is better than some current techniques and specifically can analyze data sets in better way. At the point when the proposed approach is tried on genuine data-sets, the outcomes got improved accuracy in most cases. RB-Bayes calculation having precision 83.333.</span></p>

<p><span lang="EN-US">Information mining is a capable idea with incredible potential to anticipate future patterns and conduct. It alludes to the extraction of concealed information from vast data sets by utilizing procedures like factual examination, machine learning, grouping, neural systems and genetic algorithms. In naive baye’s, there exists a problem of zero likelihood. This paper proposed RB-Bayes method based on baye’s theorem for prediction to remove problem of zero likelihood. We also compare our method with few existing methods i.e. naive baye’s and SVM. We demonstrate that this technique is better than some current techniques and specifically can analyze data sets in better way. At the point when the proposed approach is tried on genuine data-sets, the outcomes got improved accuracy in most cases. RB-Bayes calculation having precision 83.333.</span></p>

Donor-Recipient (D-R) matching is one of the main challenges to be fulfilled nowadays. Due to the increasing number of recipients and the small amount of donors in liver transplantation, the allocation method is crucial. In this paper, to establish a fair comparison, the United Network for Organ Sharing database was used with 4 different end-points (3 months, and 1, 2 and 5 years), with a total of 39, 189 D-R pairs and 28 donor and recipient variables. Modelling techniques were divided into two groups: 1) classical statistical methods, including Logistic Regression (LR) and Naïve Bayes (NB), and 2) standard machine learning techniques, including Multilayer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB) or Support Vector Machines (SVM), among others. The methods were compared with standard scores, MELD, SOFT and BAR. For the 5-years end-point, LR (AUC = 0.654) outperformed several machine learning techniques, such as MLP (AUC = 0.599), GB (AUC = 0.600), SVM (AUC = 0.624) or RF (AUC = 0.644), among others. Moreover, LR also outperformed standard scores. The same pattern was reproduced for the others 3 end-points. Complex machine learning methods were not able to improve the performance of liver allocation, probably due to the implicit limitations associated to the collection process of the database.

Emotion recognition is one of the trending research fields. It is involved in several applications. Its most interesting applications include robotic vision and interactive robotic communication. Human emotions can be detected using both speech and visual modalities. Facial expressions can be considered as ideal means for detecting the persons' emotions. This paper presents a real-time approach for implementing emotion detection and deploying it in the robotic vision applications. The proposed approach consists of four phases: preprocessing, key point generation, key point selection and angular encoding, and classification. The main idea is to generate key points using MediaPipe face mesh algorithm, which is based on real-time deep learning. In addition, the generated key points are encoded using a sequence of carefully designed mesh generator and angular encoding modules. Furthermore, feature decomposition is performed using Principal Component Analysis (PCA). This phase is deployed to enhance the accuracy of emotion detection. Finally, the decomposed features are enrolled into a Machine Learning (ML) technique that depends on a Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Naïve Bayes (NB), Logistic Regression (LR), or Random Forest (RF) classifier. Moreover, we deploy a Multilayer Perceptron (MLP) as an efficient deep neural network technique. The presented techniques are evaluated on different datasets with different evaluation metrics. The simulation results reveal that they achieve a superior performance with a human emotion detection accuracy of 97%, which ensures superiority among the efforts in this field.

There are a lot of research studies that look at "fake news" from an Arabic online source, but they don't look at what makes those fake news spread. The threat grows, and at some point, it gets out of hand. That's why this paper is trying to figure out how to predict the features that make Arabic online fake news spread. It's using Naive Bayes, Logistic Regression, and Random forest of Machine Learning to do this. Online news stories that were made up were used. They are found by using Term Frequency-Inverse Document Frequency (TF-IDF). The best partition for testing and validating the prediction was chosen at random and used in the analysis. So, all three machine learning classifications for predicting fake news in Arabic online were done. The results of the experiment show that Random Forest Classifier outperformed the other two algorithms. It had the best TF-IDF with an accuracy of 86 percent. Naive Bayes had an accuracy rate of 84%, and Logistic Regression had an accuracy rate of 85%, so they all did well. As such, the model shows that the features in TF-IDF are the most essential point about the content of an online Arabic fake news.

Background: Gastric cancer is one of the deadliest malignant diseases, and the non-invasive screening and diagnostics options for it are limited. In this article, we present a multi-modular device for breath analysis coupled with a machine learning approach for the detection of cancer-specific breath from the shapes of sensor response curves (taxonomies of clusters). Methods: We analyzed the breaths of 54 gastric cancer patients and 85 control group participants. The analysis was carried out using a breath analyzer with gold nanoparticle and metal oxide sensors. The response of the sensors was analyzed on the basis of the curve shapes and other features commonly used for comparison. These features were then used to train machine learning models using Naïve Bayes classifiers, Support Vector Machines and Random Forests. Results: The accuracy of the trained models reached 77.8% (sensitivity: up to 66.54%; specificity: up to 92.39%). The use of the proposed shape-based features improved the accuracy in most cases, especially the overall accuracy and sensitivity. Conclusions: The results show that this point-of-care breath analyzer and data analysis approach constitute a promising combination for the detection of gastric cancer-specific breath. The cluster taxonomy-based sensor reaction curve representation improved the results, and could be used in other similar applications.

Penelitian ini membahas tentang estimasi posisi (localization) objek dalam gedung menggunakan jaringan wireless atau IEEE 802.11g dengan pendekatan Machine Learning. Metode pada pengukuran RSS menggunakan RSS-based fingerprint. Algoritma Machine Learning yang digunakan dalam memperkirakan lokasi dari pengukuran RSS-based menggunakan Naive Bayes. Localization dilakukan pada lantai 3 gedung Jurusan Teknik Elektro dan Teknologi Informasi (JTETI) dengan luas 1969,68 m2 dan memiliki 5 buah titik penempatan access point (AP). Untuk membentuk peta fingerprint digunakan dimensi 1 m x 1 m sehingga terbentuk grid sebanyak 1893 buah. Dengan menggunakan software Net Surveyor terkumpul data kekuatan sinyal yang diterima (RSS) dari jaringan wireless ke perangkat penerima (laptop) sebanyak 86.980 record. Hasil nilai rata-rata error jarak estimasi untuk localization seluruh ruangan di lantai 3 dengan menggunakan algoritma Naive Bayes pada fase offline tahap learning adalah 6,29 meter. Untuk fase online dan tahap post learning diperoleh rata-rata error jarak estimasi sebesar 7,82 meter.

Abstract. Automated analysis of three-dimensional (3D) point clouds has become a boon in Photogrammetry, Remote Sensing, Computer Vision, and Robotics. The aim of this paper is to compare classifying algorithms tested on an urban area point cloud acquired by a Mobile Terrestrial Laser Scanning (MTLS) system. The algorithms were tested based on local geometrical and radiometric descriptors. In this study, local descriptors such as linearity, planarity, intensity, etc. are initially extracted for each point by observing their neighbor points. These features are then imported to a classification algorithm to automatically label each point. Here, five powerful classification algorithms including k-Nearest Neighbors (k-NN), Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Multilayer Perceptron (MLP) Neural Network, and Random Forest (RF) are tested. Eight semantic classes are considered for each method in an equal condition. The best overall accuracy of 90% was achieved with the RF algorithm. The results proved the reliability of the applied descriptors and RF classifier for MTLS point cloud classification.

Abstrak - Pemanfaatan teknologi untuk mengembangkan suatu kesenian budaya saat ini dirasa sangat tepat karena begitu pesatnya teknologi berkembang, salah satunya di bidang game. Di era modern seperti saat ini game bukan hanya digunakan sebagai media hiburan tetapi juga dapat dimanfaatkan sebagai media pengenalan suatu budaya. Reog Ponorogo merupakan kesenian budaya khas Jawa Timur yang berasal dari Ponorogo, kesenian ini biasa ditampilkan oleh sekelompok orang yang menari dengan memerankan beberapa tokoh. Klana Sewandono adalah salah satu tokoh yang terkenal dalam kesenian reog ponorogo karakternya digambarkan sebagai seorang raja memakai topeng bermahkota, berwajah merah dan membawa pecut yang dikenal dengan pecut Samandiman. Terinspirasi dari karakter tersebut terciptalah sebuah game survival berjudul “Lord of Sewandono”, game android dengan misi Prabu Sewandono mengalahkan Raja Singabarong untuk menyelamatkan Dwi Sanggalangit yang dibuat menggunakan gabungan antara metode finite state machine dan algoritma naïve bayes. Gabungan tersebut berfungsi untuk menentukan perilaku dari karakter NPC(Non Player Character) atau musuh utama yaitu Raja Singabarong yang terbagi menjadi 4 yaitu maju, mundur, serang, dan bertahan. Variable yang digunakan adalah AP(Attack Power), HP(Health Point), dan Jarak. Dari pengujian naïve bayes sebanyak 25 kali dengan confusion matrix, telah didapatkan hasil persentase tingkat akurasi pada confusion matrix sebesar 88% valid atau sesuai dengan harapan dan 12% invalid atau belum sesuai dengan harapan (error). Hasil pengujian beta rata-rata penilaian yang diperoleh sebesar 71,67% berdasarkan hasil dari responden terhadap tiap butir pertanyaan kuisioner. Kata Kunci - reog ponorogo, klana sewandono, game, metode finite state machine, algoritma naïve bayes.

Online learning algorithms have several key advantages compared to their batch learning algorithm counterparts: they are generally more memory efficient, and computationally mor efficient; they are simpler to implement; and they are able to adapt to changes where the learning model is time varying. Online algorithms because of their simplicity are very appealing to practitioners. his thesis investigates several online learning algorithms and their application. The thesis has an underlying theme of the idea of combining several simple algorithms to give better performance. In this thesis we investigate: combining weights, combining hypothesis, and (sort of) hierarchical combining.¶ Firstly, we propose a new online variant of the Bayes point machine (BPM), called the online Bayes point machine (OBPM). We study the theoretical and empirical performance of the OBPm algorithm. We show that the empirical performance of the OBPM algorithm is comparable with other large margin classifier methods such as the approximately large margin algorithm (ALMA) and methods which maximise the margin explicitly, like the support vector machine (SVM). The OBPM algorithm when used with a parallel architecture offers potential computational savings compared to ALMA. We compare the test error performance of the OBPM algorithm with other online algorithms: the Perceptron, the voted-Perceptron, and Bagging. We demonstrate that the combinationof the voted-Perceptron algorithm and the OBPM algorithm, called voted-OBPM algorithm has better test error performance than the voted-Perceptron and Bagging algorithms. We investigate the use of various online voting methods against the problem of ranking, and the problem of collaborative filtering of instances. We look at the application of online Bagging and OBPM algorithms to the telecommunications problem of channel equalization. We show that both online methods were successful at reducing the effect on the test error of label flipping and additive noise.¶ Secondly, we introduce a new mixture of experts algorithm, the fixed-share hierarchy (FSH) algorithm. The FSH algorithm is able to track the mixture of experts when the switching rate between the best experts may not be constant. We study the theoretical aspects of the FSH and the practical application of it to adaptive equalization. Using simulations we show that the FSH algorithm is able to track the best expert, or mixture of experts, in both the case where the switching rate is constant and the case where the switching rate is time varying.

Introducción El acelerado ritmo al que se genera y crece la información en la sociedad actual y la posible llegada de la tecnología de transistor a sus límites de tamaño exige la puesta en marcha de soluciones para el procesado eficiente de datos en campos específicos de aplicación. Contenido Esta tesis doctoral de carácter transdisciplinar a medio camino entre la ingeniería electrónica y la química computacional presenta soluciones optimizadas en hardware y en software para la construcción y el procesado eficiente de bases de datos moleculares. En primer lugar se propone y se estudia el funcionamiento de bloques digitales que implementan funciones en lógica pulsante estocástica orientadas a tareas de reconocimiento de objetos. Especialmente se proponen y analizan diseños digitales para la construcción de generadores de números aleatorios (RNG) como base de estos sistemas que han sido implementados en dispositivos Field Programable Gate Array (FPGA). En segundo lugar se propone y se evalúa un conjunto reducido de descriptores moleculares para la caracterización de compuestos orgánicos y la generación de bases de datos moleculares. Estos descriptores recogen información sobre la distribución de la carga molecular en el espacio y la energía electrostática. Las bases de datos generadas con estos descriptores se han procesado utilizando sistemas de computación convencionales en software y mediante sistemas de computación estocástica implementados en hardware mediante el uso de circuitería digital programable. Finalmente se proponen optimizaciones para la estimación del potencial electrostático molecular (MEP) y para el cálculo de los puntos de interacción molecular derivados (SSIP). Conclusiones Por una parte, los resultados obtenidos ponen de manifiesto la importancia de la uniformidad de los RNG en el período de evaluación para poder implementar sistemas de computación estocástica de alta fiabilidad. Además, los RNG propuestos tienen una naturaleza aperiódica que minimiza las posibles correlaciones entre señales, haciendo que sean adecuados para la implementación de sistemas de computación estocástica. Por otra parte, el conjunto de descriptores moleculares propuestos PED han demostrado obtener muy buenos resultados en comparación con otros métodos presentes en la literatura. Este hecho se ha discutido mediante los parámetros Area Under The Curve (AUC) y Enrichment Factor (EF) obtenidos de las curvas promedio Receiving Operating Characteristic (ROC). Además, se ha mostrado como la eficacia de los descriptores aumenta cuando se implementan en sistemas de clasificación con aprendizaje supervisado, haciéndolos adecuados para la construcción de un sistema de predicción de dianas terapéuticas eficiente. En esta tesis, además, se ha determinado que los MEP calculados utilizando la teoría DFT y el conjunto de bases B3LYP/6-31*G en la superficie con densidad electrónica 0,01 au correlacionan bien con datos experimentales debido presumiblemente a la mayor contribución de las propiedades electrostáticas locales reflejadas en el MEP. Las parametrizaciones propuestas en función del tipo de hibridación atómica pueden haber contribuido también a esta mejora. Los cálculos realizados en dichas superficies suponen mejoras en un factor cinco en la velocidad de procesamiento del MEP. Dado el aceptable ajuste a datos experimentales del método propuesto para el cálculo del MEP aproximado y de los SSIP, éste se puede utilizar con el fin de obtener los SSIP para bases de datos moleculares extensas o en macromoléculas como proteínas de manera muy rápida (ya que la velocidad de procesamiento obtenida puede alcanzar del orden de cinco mil átomos procesados por segundo utilizando un solo procesador). Estas técnicas resultan de especial interés dadas las numerosas aplicaciones de los SSIP como por ejemplo el cribado virtual de cocristales o la predicción de energías libres en disolución.
Introducció El creixement accelerat de les dades en la societat actual i l'arribada de la tecnologia del transistor als límits físics exigeix la proposta de metodologies per al processament eficient de dades. Contingut Aquesta tesi doctoral, de caràcter transdisciplinària i a mig camí entre els camps de l'enginyeria electrònica i la química computacional presenta solucions optimitzades en maquinari i en programari per tal d’accelerar el processament de bases de dades moleculars. En primer lloc es proposa i s'estudia el funcionament de blocs digitals que implementen funcions de lògica polsant estocàstica aplicades a tasques de reconeixement d'objectes. En concret es proposen i analitzen dissenys específics per a la construcció de generadors de nombres aleatoris (RNG) com a sistemes bàsics per al funcionament dels sistemes de computació estocàstics implementats en dispositius programables com les Field Programable Gate Array (FPGA). En segon lloc es proposen i avaluen un conjunt reduït de descriptors moleculars especialment orientats a la caracterització de compostos orgànics. Aquests descriptors reuneixen la informació sobre la distribució de càrrega molecular i les energies electroestàtiques. Les bases de dades generades amb aquests descriptors s’han processat emprant sistemes de computació convencionals en programari i mitjançant sistemes basats en computació estocàstica implementats en maquinari programable. Finalment es proposen optimitzacions per al càlcul del potencial electroestàtic molecular (MEP) calculat mitjançant la teoria del funcional de la densitat (DFT) i dels punts d’interacció que se’n deriven (SSIP). Conclusions Per una banda, els resultats obtinguts posen de manifest la importància de la uniformitat del RNG en el període d’avaluació per a poder implementar sistemes de computació estocàstics d’alta fiabilitat. A més, els RNG proposats presenten una font d’aleatorietat aperiòdica que minimitza les correlacions entre senyals, fent-los adequats per a la implementació de sistemes de computació estocàstica. Per una altra banda, el conjunt de descriptors moleculars proposats PED, han demostrat obtenir molts bons resultats en comparació amb els mètodes presents a la literatura. Aquest fet ha estat discutit mitjançant l’anàlisi dels paràmetres Area Under The Curve (AUC) i Enrichment Factor (EF) de les curves Receiving Operating Characteristic (ROC) analitzades. A més, s’ha mostrat com l’eficàcia dels descriptors augmenta de manera significativa quan s’implementen en sistemes de classificació amb aprenentatge supervisat com les finestres de Parzen, fent-los adequats per a la construcció d’un sistema de predicció de dianes terapèutiques eficient. En aquesta tesi doctoral, a més, s’ha trobat que els MEP calculats mitjançant la teoria DFT i el conjunt de bases B3LYP/6-31*G en la superfície amb densitat electrònica 0,01 au correlacionen bé amb dades experimentals possiblement a causa de la contribució més gran de les propietats electroestàtiques locals reflectides en el MEP. Les parametritzacions proposades en funció del tipus d’hibridació atòmica han contribuït també a la millora dels resultats. Els càlculs realitzats en aquestes superfícies suposen un guany en un factor cinc en la velocitat de processament del MEP. Donat l’acceptable ajust a les dades experimentals del mètode proposat per al càlcul del MEP aproximat i dels SSIP que se’n deriven, aquest procediment es pot emprar per obtenir els SSIP en bases de dades moleculars extenses i en macromolècules (com ara proteïnes) d’una manera molt ràpida (ja que la velocitat de processament obtinguda arriba fins als cinc mil àtoms per segon amb un sol processador). Les tècniques proposades en aquesta tesi doctoral resulten d’interès donades les nombroses aplicacions que tenen els SSIP com per exemple, en el cribratge virtual de cocristalls o en la predicció d’energies lliures en dissolució.
Introduction Because of the generalized data growth in the nowadays digital era and due to the fact that we are possibly living on the last days of the Moore’s law, there exists a good reason for being focused on the development of technical solutions for efficient data processing. Contents In this transdisciplinary thesis between electronic engineering and computational chemistry, it's shown optimal solutions in hardware and software for molecular database processing. On the first hand, there's proposed and studied a set of stochastic computing systems in order to implement ultrafast pattern recognition applications. Specially, it’s proposed and analyzed specific digital designs in order to create digital Random Number Generators (RNG) as a base for stochastic functions. The digital platform used to generate the results is a Field Programmable Gate Array (FPGA). On the second hand, there's proposed and evaluated a set of molecular descriptors in order to create a compact molecular database. The proposed descriptors gather charge and molecular geometry information and they have been used as a database both in software conventional computing and in hardware stochastic computing. Finally, there's a proposed a set of optimizations for Molecular Electrostatic Potential (MEP) and Surface Site Interaction Points (SSIP). Conclusions Firstly, the results show the relevance of the uniformity of the RNG within the evaluation period in order to implement high precision stochastic computing systems. In addition, the proposed RNG have an aperiodic behavior which avoid some potential correlations between stochastic signals. This property makes the proposed RNG suitable for implementation of stochastic computing systems. Secondly, the proposed molecular descriptors PED have demonstrated to provide good results in comparison with other methods that are present in the literature. This has been discussed by the use of Area Under the Curve (AUC) and Enrichment Factor (EF) of averaged Receiving Operating Characteristic (ROC) curves. Furthermore, the performance of the proposed descriptors gets increased when they are implemented in supervised machine learning algorithms making them appropriate for therapeutic target predictions. Thirdly, the efficient molecular database characterization and the usage of stochastic computing circuitry can be used together in order to implement ultrafast information processing systems. On the other hand, in this thesis, it has been found that the MEP calculated by using DFT and B3LYP/6-31*G basis at 0.01 au density surface level has good correlation with experimental data. This fact may be due to the important contribution of local electrostatics and the refinement performed by the parameterization of the MEP as a function of the orbital atom type. Additionally, the proposed calculation over 0.01 au is five times faster than the calculation over 0.002 au. Finally, due to acceptable agreement between experimental data and theoretical results obtained by using the proposed calculation for MEP and SSIP, the proposed method is suitable for being applied in order to quickly process big molecular databases and macromolecules (the processing speed can achieve five thousand molecules per second using a single processor). The proposed techniques have special interest with the purpose of finding the SSIP because the big number of applications they have as for instance in virtual cocrystal screening and calculation of free energies in solution.

Online learning algorithms have several key advantages compared to their batch learning algorithm counterparts. This thesis investigates several online learning algorithms and their application. The thesis has an underlying theme of the idea of combining several simple algorithms to give better performance. In this thesis we investigate: combining weights, combining hypothesis, and (sort of) hierarchical combining.¶ ...

In this chapter, local conditional probabilities of a query point are used in classification rather than consulting a generalized framework containing a conditional probability. In the proposed locally adaptive naïve Bayes (LANB) learning style, a certain amount of local instances, which are close the test point, construct an adaptive probability estimation. In the empirical studies of over the 53 benchmark UCI datasets, more accurate classification performance has been obtained. A total 8.2% increase in classification accuracy has been gained with LANB when compared to the conventional naïve Bayes model. The presented LANB method has outperformed according to the statistical paired t-test comparisons: 31 wins, 14 ties, and 8 losses of all UCI sets.

This chapter examines one of the most active areas where feature vectors play a central role: machine learning. The Bayes classifier is used as an entry point into the field, showing how a simple statistical technique introduced in the early 1960s is surprisingly instructive for understanding how machine learning operates more broadly. The goal is to shed light on the core principles at work and to explain how they are tweaked, adapted, and developed further into different directions. This chapter also develops the idea that contemporary information ordering represents an epistemological practice that can be described and analyzed as ‘interested reading of reality’, a particular kind of inductive empiricism.

Sentimental Analysis or Opinion Mining plays a vital role in the experimentation field that determines the user’s opinions, emotions and sentiments concealing a text. News on the Internet is becoming vast, and it is drawing attention and has reached the point of adequately affecting political and social realities. The popular way of checking online content, i.e. manual knowledge-based on the facts, is practically impossible because of the enormous amount of data that has now generated online. The issue can address by using Machine Learning Algorithms and Artificial Intelligence. One of the Machine Learning techniques used in this is Naive Bayes classifier. In this paper, the polarity of the news article determined whether the given news article is a positive, negative or neutral Naive Bayes Classifier, which works well with NLP (Natural Language problems) used for many purposes. It is a family of probabilistic algorithms that used to identify a word from a given text. In this, we calculate the probability of each word in a given text. Using Bayes theorem, they are getting the probabilities based on the given conditions. Topic Modeling is analytical modelling for finding the abstract of topics from a cluster of documents. Latent Dirichlet Allocation (LDA) is a topic model is used to classify the text in a given document to a specified topic. The news article is classified as positive or negative or neutral using Naive Bayes classifier by calculating the probabilities of each word from a given news article. By using topic modelling (LDA), topics of articles are detected and record data separately. The calculation of the overall sentiment of a chosen topic from different newspapers from previously recorded data done.

E-commerce has become a daily activity in human life. In it, the opinion and past experience related to particular product of others is playing a prominent role in selecting the product from the online market. In this chapter, the authors consider Tweets as a point of source to express users' emotions on particular subjects. This is scored with different sentiment scoring techniques. Since the patterns used in social media are relatively short, exact matches are uncommon, and taking advantage of partial matches allows one to significantly improve the accuracy of analysis on sentiments. The authors also focus on applying artificial neural fuzzy inference system (ANFIS) to train the model for better opinion mining. The scored sentiments are then classified using machine learning algorithms like support vector machine (SVM), decision tree, and naive Bayes.

Self-assessment is one of the crucial activities within e-learning environments that provide learners with feedback regarding their level of accumulated knowledge. From this point of view, the authors think that guidance of learners in self-assessment activity must be an important goal of e-learning environment developers. The scope of the chapter is to present a recommender software system that runs along the e-learning platform. The recommender software system improves the effectiveness of self-assessment activities. The activities performed by learners represent the input data and the machine learning algorithms are used within the business logic of the recommender software system that runs along the e-learning platform. The output of the recommender software system is represented by advice given to learners in order to improve the effectiveness of self-assessment process. The methodology for obtaining improvement of self-assessment is based on embedding knowledge management into the business logic of the e-learning platform. Naive Bayes Classifier is used as machine learning algorithm for obtaining the resources (e.g., questions, chapters, and concepts) that need to be further accessed by learners. The analysis is accomplished for disciplines that are well structured according to a concept map. The input data set for the recommender software system is represented by student activities that are monitored within Tesys e-learning platform. This platform has been designed and implemented within Multimedia Applications Development Research Center at Software Engineering Department, University of Craiova. Monitoring student activities is accomplished through various techniques like creating log files or adding records into a table from a database. The logging facilities are embedded in the business logic of the e-learning platform. The e-learning platform is based on a software development framework that uses only open source software. The software architecture of the e-learning platform is based on MVC (model-view-controller) model that ensures the independence between the model (represented by MySQL database), the controller (represented by the business logic of the platform implemented in Java) and the view (represented by WebMacro which is a 100% Java open-source template language).

In this paper, we build a new dataset UIT-ViON (Vietnamese Online Newspaper) collected from well-known online newspapers in Vietnamese. We collect, process, and create the dataset, then experiment with different machine learning models. In particular, we propose an open-domain, large-scale, and high-quality dataset consisting of 260,000 textual data points annotated with multiple labels for evaluating Vietnamese short text classification. In addition, we present the proposed approach using transformer-based learning (PhoBERT) for Vietnamese short text classification on the dataset, which outperforms traditional machine learning (Naive Bayes and Logistic Regression) and deep learning (Text-CNN and LSTM). As a result, the proposed approach achieves the F1-score of 80.62%. This is a positive result and a premise for developing an automatic news classification system. The study is proposed to significantly save time, costs, and human resources and make it easier for readers to find news related to their interesting topics. In future, we will propose solutions to improve the quality of the dataset and improve the performance of classification models.

Diabetes mellitus diagnosis is an experience-based problem. Case-Based Reasoning (CBR) is the first choice for these problems. CBR depends on the quality of its case-base structure and contents; however, building a case-base is a challenge. Electronic Health Record (EHR) data can be used as a starting point for building case-bases, but it needs a set of preparation steps. This chapter proposes an EHR-based case-base preparation framework. It has three phases: data-preparation, coding, and fuzzification. The first two phases will be discussed in this chapter using a diabetes diagnosis dataset collected from EHRs of 60 patients. The result is the case-base knowledge. The first phase uses some machine-learning algorithms for case-base data preparation. For encoding phase, we propose and apply an encoding methodology based on SNOMED-CT. We will build an OWL2 ontology from collected SNOMED-CT concepts. A CBR prototype has been designed, and results show enhancements to the diagnosis accuracy.

Food insecurity is a global challenge affecting millions of people especially those from least developed regions. Famine predictions are being carried out to estimate when shortage of food is most likely to happen. The traditional data sets such as house hold information, price trends, crop production trends and biophysical data used for predicting food insecurity are both labor intensive and expensive to acquire. Current trends are towards harnessing big data to study various phenomena such sentiment analysis and stock markets. Big data is said to be easier to obtain than traditional datasets. This study shows that phone messages archives and telephone conversations as big datasets are potential for predicting food crisis. This is timely with the current situation of massive penetration of mobile technology and the necessary data can be gathered to foster studies such as this. Computation techniques such as Naïve Bayes, Artificial Networks and Support Vector Machines are prospective candidates in this strategy. If the strategy is to work in a nation like Uganda, areas of concern have been highlighted. Future work points at exploring this approach experimentally.

Abstract The Large Eddy Simulations (LES) modeling of turbulence effects is computationally expensive even when not all scales are resolved, especially in the presence of deep turbulence effects in the atmosphere. Machine learning techniques provide a novel way to propagate the effects from inner- to outer-scale in atmospheric turbulence spectrum and to accelerate its characterization on long-distance laser propagation. We simulated the turbulent flow of atmospheric air in an idealized box with a temperature difference between the lower and upper surfaces of about 27 degrees Celsius with the LES method. The volume was voxelized, and several quantities, such as the velocity, temperature, and the pressure were obtained at regularly spaced grid points. These values were binned and converted into symbols that were concatenated along the length of the box to create a ‘text’ that was used to train a long short-term memory (LSTM) neural network and propose a way to use a naive Bayes model. LSTMs are used in speech recognition, and handwriting recognition tasks and naïve Bayes is used extensively in text categorization. The trained LSTM and the naïve Bayes models were used to generate instances of turbulent-like flows. Errors are quantified, and portrait as a difference that enables our studies to track error quantities passed through stochastic generative machine learning models — considering that our LES studies have a high state of the art high-fidelity approximation solutions of the Navier-Stokes. In the present work, LES solutions are imitated and compare against generative machine learning models.

Abstract This article presents a characterisation technique of in-motion machine dynamics based on the principles of numerical continuation. A linear two degree of freedom mechanical model is considered, representing e.g. a flexible moving column of a machine tool, and is subjected to a non-smooth friction and a delayed feedback drive control force, resulting in a model governed by a system of piecewise-smooth delay differential equations. By applying harmonic forcing to the system, periodic solutions can be found, through the continuation of which, an accurate vibratory characterisation of in-motion machine dynamics can be acquired. In the continuation routine, spectral collocation techniques were employed to formulate the discretized boundary value problem of piecewise-smooth periodic orbits, and the pseudo-arclength method was implemented. Special care was attributed to the detection of grazing and sliding bifurcations, and the continuation routine was also extended to allow continuation of these critical points.

EXPERIMENTAL STUDY OF MICROWAVE SLOW WAVE COMB AND CERAMIC APPLICATORS FOR SOIL TREATMENT AT FREQUENCY 2.45 GHZ. G. Brodie and G. Torgovnikov University of Melbourne, 4 Water St, Creswick, Victoria 3363, Australia; e-mail: grigori@unimelb.edu.au Keywords: ceramic applicator, comb applicator, microwave, slow wave, soil microwave treatment In many cases in industry it is required to heat or treat surface layers of different material (soil, timber, concrete, plastics and so on) with microwaves (MW). Traditional MW irradiators (antennas) cannot provide heating only in the surface areas and energy penetrates deep into the material, where it decays exponentially due to normal attenuation. Therefore, energy losses, if a heating depth of 20 - 40 mm (for example to heat soil for killing weed seeds) is all that is required, are very significant. Therefore, it is required to develop special MW applicators for surface treatment to increase process efficiency. To address this problem, a slow wave (which is sometimes called a "surface wave" applicator) comb and ceramic structures, was studied. The main property of slow waves is that the energy concentration is very near impedance electrode – comb or ceramic plate surface. Previously, slow wave structures were used mostly as delay lines and as interaction circuits in MW vacuum devices, and their properties were explored only for these specific applications. The work objectives of this study were: design slow wave, ceramic and comb structure applicators for soil treatment at frequency 2.45 GHz;experimentally study the energy distribution from slow wave applicators in the soil;study of opportunities to use slow wave structures for surface soil layer heating; andrecommendations for practical use of new slow wave applicators. Comb and ceramic slab applicators for frequency 2.45 GHz operation were designed for the soil treatment on the bases of theoretical studies and computer modelling. The comb applicator was made from aluminium and the ceramic slab applicator was made from alumina (DC=9.8, loss tangent=0.0002). A 30 kW (2.45 GHz) microwave generator was used for experiments. Containers with soil were placed on the applicator surface. An auto tuner was used in MW system to provided good impedance matching of the generator and applicators (with soil on top). This resulted in practically no power reflection. The soil “Potting Mix Hortico”, with moisture content range 32-174% and density range 590-1070 kg/m3, was used for the experiments. Energy distribution in the soil was determined by temperature measuring in the soil using thermocouples, after MW heating. Distribution of temperature measuring points covered the whole volume of the soil along and across the applicator. Results of the experiments showed that the comb applicator provides maximum energy release in soil in the central vertical plane. The ceramic alumina applicator forms two temperature maximums in two vertical planes at a distance of about 40 mm from the central applicator plane and a minimum in the applicator central plane. The ceramic applicator provides better uniformity of energy distribution across the width of the applicator due to the two temperature maximums. It reduces overheating of the soil surface and energy losses. The depth of energy penetration provided by ceramic applicator is lower compared with the comb applicator. It means that the ceramic applicator provides better energy localization and more energy absorption in the soil surface layers compared with the comb applicator. To provide better uniformity of energy distribution across the ceramic applicator it is recommended to use ceramics with higher dielectric constants, such as in the range of 15-25, which will allow more energy to be released closer to the applicator surface. It will increase efficiency of MW energy use. The ceramic applicator is more effective for MW treatment of the soil surface areas and is recommended for practical use in machines for thermal treatment and sterilization of surface layers of the soil and other materials.