Demand forecasting is one of the main issues supply chains. It aimed to optimize stocks, reduce costs, and increase sales, profit, customer loyalty. For this purpose, historical data can be analyzed improve demand by using various methods like machine learning techniques, time series analysis, deep models. In work, an intelligent system developed. This improved model based on analysis interpretation different which include support vector regression algorithm, To best our knowledge, first...

Anticancer peptides (ACPs) are a group of that exhibit antineoplastic properties. The utilization ACPs in cancer prevention can present viable substitute for conventional therapeutics, as they possess higher degree selectivity and safety. Recent scientific advancements generate an interest peptide-based therapies which offer the advantage efficiently treating intended cells without negatively impacting normal cells. However, number peptide sequences continues to increase rapidly, developing...

The use of ensemble learning, deep and effective document representation methods is currently some the most common trends to improve overall accuracy a text classification/categorization system. Ensemble learning an approach raise classification system by utilizing multiple classifiers. Deep learning‐based provide better results in many applications when compared with other conventional machine algorithms. Word embeddings enable words learned from corpus as vectors that mapping similar...

Human activity recognition is a challenging problem with many applications including visual surveillance, human-computer interactions, autonomous driving and entertainment. In this study, we propose hybrid deep model to understand interpret videos focusing on human recognition. The proposed architecture constructed combining dense optical flow approach auxiliary movement information in video datasets using learning methodologies. To the best of our knowledge, first study based novel...

Sentiment analysis is a considerable research field to investigate enormous quantity of knowledge and specify user opinions on many subjects resumed as the extraction ideas from textual data.Like sentiment analysis, Bitcoin which digital cryptocurrency also attracts researchers considerably in domain cryptography, computer science, economics.The objective this work forecast direction price by analyzing social media such Twitter.To our knowledge, very first attempt estimates fluctuations...

Electromagnetic resonance is the most important distinguishing property of metamaterials to examine many unusual phenomena. The resonant response can depend parameters such as geometry, incident wave polarization. estimation and design unit cells be challenging for required application. research on behavior yield promising applications. We investigate frequency chiral resonator a metamaterial employing both traditional machine learning algorithms convolutional deep neural networks. To our...

To forecast the movement directions of stocks, exchange rates, and stock markets are significant an active research area for investors, analysts, researchers. In this paper, word embedding deep learning-based direction prediction Istanbul Stock Exchange (BIST 100) is proposed by analyzing nine banking stocks with high volume in BIST 100. Though English news articles have been employed forecasting market previously, to best our knowledge, Turkish user comments from social media different...

Forest fire detection is a very challenging problem in the field of object detection. Fire detection-based image analysis have advantages such as usage on wide open areas, possibility for operator to visually confirm presence, intensity and size hazards, lower cost installation further exploitation. To overcome outdoors, deep learning conventional machine based computer vision techniques are employed determine when indoor systems not capable. In this work, we propose comprehensive forest...

The use of word embedding models and deep learning algorithms are currently the most common popular trends to enhance overall performance a text classification/categorization system. Word vectors that provide mapping words with similar meaning own representation which is learned from corpus. Deep successful produce more results in many areas their applications when they compared conventional machine algorithms. In this study, three different Word2Vec, Glove, FastText employed for...

Exchange rate forecasting has been an important topic for investors, researchers, and analysts. In this study, financial sentiment analysis (FSA) time series (TSA) are proposed to form a predicting model US Dollar/Turkish Lira exchange rate. For purpose, the hybrid is constructed in three stages: obtaining modeling text data FSA, numerical TSA, blending two models like symmetry. To our knowledge, first study literature that uses social media platforms as source FSA blends them with TSA...

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...

Naïve Bayes is a commonly used algorithm in text categorization because of its easy implementation and low complexity. has mainly two event models for which are multivariate Bernoulli multinomial models. A very large number studies choose model Laplace smoothing just based on the assumption that it performs better than under almost any conditions. This study aims to shed some light into this widely adopted by analyzing methods from different perspective. To clarify difference between events...

Parkinson's disease is a common neurodegenerative neurological disorder, which affects the patient's quality of life, has significant social and economic effects, difficult to diagnose early due gradual appearance symptoms. Examining discussion Parkinson’s in media platforms such as Twitter provides platform where patients communicate each other both diagnosis treatment stage disease. The purpose this work evaluate compare sentiment analysis people about by...

It has been shown that Latent Semantic Indexing (LSI) takes advantage of implicit higher-order (or latent) structure in the association terms and documents. Higher order relations LSI capture "latent semantics". Inspired by this, a novel Bayesian framework for classification named Order Naïve Bayes (HONB), which can explicitly make use these relations, introduced previously. We present semantic smoothing method Smoothing (HOS) Naive algorithm. HOS is built on similar graph based data...

The tourism industry is experiencing rapid growth and becoming one of the fastest-expanding sectors. A considerable number travelers now make hotel bookings share their experiences on travel e-commerce platforms. Enhancing quality products services within this can be accomplished by scrutinizing customer feedback. This research employs advanced deep learning techniques to discern subtle sentiments glean insights from an extensive collection reviews. Deep neural networks, such as...

Borsa tahmini, hisse senedi fiyatlarının ya da yönlerinin tahmin edilmesinde analistler ve yatırımcılar için önemli aktif araştırma konusu olmuştur. Bu çalışmada, finansal duygu analizi yapılarak İstanbul 100 endeksinin yönünün tahminlenmesi amaçlanmıştır. Bildiğimiz kadarıyla bu çalışma, borsa yönü tahminlemesinde hem haber kaynağı olarak Twitter ortamını kullanması de bunun derin topluluk modelleriyle yapılması açısından literatürdeki ilk çalışmadır. Ancak, gibi kullanıcı fikirlerini ifade...

Customer churn is one of the most important problems for many industries, including banking, telecommunications, and gaming. In gaming market, it observed that demand on game applications rises with usage mobile devices such as smartphones. Because this, to predict when players tend leave a game. Studies so far focus prediction in or online games by analyzing demographic, economic, behavioral data about their customers. this work, we introduce sentiment analysis-based model using word...

Majority of the existing text classification algorithms are based on "bag words" (BOW) approach, in which documents represented as weighted occurrence frequencies individual terms. However, semantic relations between terms ignored this representation. There several studies address problem by integrating background knowledge such WordNet, ODP or Wikipedia a source. vast majority these applied to English texts and date there no similar Turkish documents. We empirically analyze effect using...

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,...

In this paper, we mainly study on n-gram models text classification domain. order to measure impact of the performance, carry out Naïve Bayes classifier with various smoothing methods. has generally used two main event for which are Bernoulli and multinomial models. Researchers usually address model Laplace similar domains. The objective is demonstrate performance by analyzing both different methods using from a perspective. find patterns between models, experiments large Turkish dataset....