Parkinson's disease is a type of caused by the loss dopamine-producing cells in brain. As amount dopamine decreases, symptoms emerge. slow-developing disease, and such as hands, arms, legs, chin face tremors are increasing over time. progresses, people may have difficulty walking speaking. There no definitive treatment for disease; however, with help some drugs, can be reduced. Although there patient continue his normal life controlling problems disease. At this point, it important to...

Latent Dirichlet allocation (LDA) is one of the probabilistic topic models; it discovers latent structure in a document collection. The basic assumption under LDA that documents are viewed as mixture topics; has probability distribution over words and each modelled on basis bag-of-words model. models such sufficient learning hidden topics but they do not take into account deeper semantic knowledge document. In this article, we propose novel method based modelling to determine aspects online...

In the world we live in, people from different professions are at increased risk for depressive symptoms and posttraumatic stress disorder (PTSD) due to hard working or extreme environmental conditions. Accurate diagnosis determining causes very important solve these kinds of psychological problems. Machine learning (ML) techniques gaining popularity in neuroscience their high diagnostic capability effective classification ability. this paper, alternative hybrid systems which allowed us...

Text categorization has become more and popular important problem day by because of the large proliferation documents in many fields. To come up with this problem, several machine learning techniques are used for such as naîve Bayes, support vector machines, artificial neural networks, etc. In study, we concentrate on ensemble multiple classifiers instead using only a single one. We perform comparative analysis impact text domain. carry out this, same type base but diversified training sets...

Topic models, such as latent Dirichlet allocation (LDA), allow us to categorize each document based on the topics. It builds a mixture of topics and topic is modeled probability distribution over words. However, key drawback traditional model that it cannot handle semantic knowledge hidden in documents. Therefore, semantically related, coherent meaningful be obtained. inference plays significant role modeling well other text mining tasks. In this paper, order tackle problem, novel NET-LDA...

Today, people first make their complaints and compliments on internet about a product which they use or company are customer of. Therefore, when going to buy new product, analyze the made by other users of product. These play an important role in helping decision purchasing not It is impossible online manually due huge data size. However, companies still losing lot time analyzing reading thousands one one. In this article, text based analyzed with Latent Dirichlet Allocation (LDA), GenSim...

Session-based recommendation uses past clicks and interaction sequences from anonymous users to predict the next item most likely be clicked. Predicting user’s subsequent behavior in online transactions becomes a problem mainly due lack of user information limited behavioral information. Existing methods, such as recurrent neural network (RNN)-based models that model graph (GNN)-based capture potential relationships between items, miss different time intervals sequence can only certain types...

The K-means algorithm is quite sensitive to the cluster centers selected initially and can perform different clusterings depending on these initialization conditions.Within scope of this study, a new method based Fuzzy ART which called Improved (IFART) used in determination initial centers.By using IFART, better quality clusters are achieved than do also IFART as good about capable fast clustering capability large scaled data clustering.Consequently, it observed that, with proposed method,...

Sentiment classification has become very popular to analyze opinions about events, products, and so on, especially for social networks such as Twitter. Due the size limitation of expressing ideas on networks, performance needs be boosted by proposing various techniques. In this work, enhancement feature space with word embedding based features is proposed deal issues success sentiment analysis improved employing classifier ensembles. The contributions paper are fivefold. First,...

With the introduction of Industry 4.0 into our lives and creation smart factories, predictive maintenance has become even more important. Predictive systems are often used in manufacturing industry. On other hand, text analysis Natural Language Processing (NLP) techniques gaining a lot attention by both research industry due to their ability combine natural languages industrial solutions. There is great increase number studies on NLP literature. Even though there field systems, no were found...

With the rapid advances in Web technologies, people have changed their way to obtain others' opinion via online review websites. Due increase user generated content on these mediums an important source for sentiment analysis is provided. For more detailed analysis, aspect based getting increasingly important. And complete this implicit extraction should be carried out. In paper we suggested a graph Laplace smoothing method extracting aspects from hotel reviews Turkish. The proposed evaluated...

Sharing content easily on social media has become an important communication choice in the world we live.However, addition to conveniences it provides, some problems have been emerged because sharing is not bounded by predefined rules.Consequently, offensive language a big problem for both and its users.In this article, aimed detect short text messages Twitter.Since texts do contain sufficient statistical information, they drawbacks.To cope with these drawbacks of texts, semantic word...

Today Wikipedia provides a very large and reliable domain-independent encyclopedic repository.With this study mobile system which summarizes Turkish text is presented.The presented selects the sentences due to structural features of language semantic sentences.The performance evaluation made based on judgments human experts.The results are tested precision recall values ranked sentence list it concluded that, summarization promising.

The basic idea behind the classifier ensembles is to use more than one by expecting improve overall accuracy. It known that boost classification performance depending on two factors namely, individual success of base learners and diversity. One way providing diversity same or different type learners. When used, realized using training data subsets for each classifiers. classifiers used achieve diversity, then ensemble system called heterogeneous. In this paper, we focus heterogonous types An...