An accurate exchange rate forecasting and its decision-making to buy or sell are critical issues in the Forex market. Short-term currency is a challenging task due inherent characteristics, which include high volatility, trend, noise, market shocks. We propose novel deep learning architecture consisting of an adaptive activation function selection mechanism achieve higher predictive accuracy. The proposed composed seven neural networks that have different functions as well softmax layer...

A multivariate time series forecasting is critical in many applications, such as signal processing, finance, air quality forecasting, and pattern recognition. In particular, determining the most relevant variables proper lag length from challenging. This paper proposes an end-to-end recurrent neural network framework equipped with adaptive input selection mechanism to improve prediction performance for forecasting. The proposed model, named AIS-RNN, consists of two main components: first...

Hematopoietic cancer is a malignant transformation in immune system cells. characterized by the cells that are expressed, so it usually difficult to distinguish its heterogeneities hematopoiesis process. Traditional approaches for subtyping use statistical techniques. Furthermore, due overfitting problem of small samples, case minor cancer, does not have enough sample material building classification model. Therefore, we propose only build model five major subtypes using two kinds losses,...

Healthcare systems are evolving from simple medical devices to ubiquitous healthcare working at anytime and anywhere. In particular, acquisition transmission of vital signs wearable with biosensors will be soon realized in our daily lives. Interestingly, when such as Electrocardiogram (ECG), respiration the motion data collected accumulated, they become a kind big data, which eventually crucial clue for monitoring health everyday life preventing diseases. This paper proposes framework...

Coronary heart disease (CHD) is one of the leading causes death worldwide; if suffering from CHD and being in its end-stage, most advanced treatments are required, such as surgery transplant. Moreover, it not easy to diagnose at earlier stage; hospitals based on various types medical tests. Thus, by predicting high-risk people who suffer CHD, significant reduce risks developing CHD. In recent years, some research works have been done using data mining predict risk diseases this study, we...

Background: With advances in next-generation sequencing technologies, the bisulfite conversion of genomic DNA followed by has become predominant technique for quantifying genome-wide methylation at single-base resolution. A large number computational approaches are available literature identifying differentially methylated regions data, and more being developed continuously. Results: Here, we focused on a comprehensive evaluation commonly used differential analysis methods describe potential...

With the rapid increase of publishable research articles and manuscripts, pressure to find reviewers often overwhelms journal editors. This paper incorporates major entity level metrics found in heterogeneous publication networks into a pattern mining process order recommend academic potential collaborators. In essence, integrates authors' h-index papers' citation count proposes quantification account for author diversity one formula duped impact measure real influence scientific paper....

Proper demand forecasting for postal delivery service can be used optimal logistic management, staff scheduling and topology planning. More especially, during special holidays, such as the Lunar New Year Chuseok (Mid-autumn day), increases sharply in South Korea. It makes a challenge to forecast provide normal schedule Korean mail center. To address this problem, we propose novel deep learning model equipped with selection update layers (MLP-SUL) achieve high predictive performance. The...

Smoking is one of the significant avoidable risk factors for premature death. Most smokers make multiple quit attempts during their lifetime but smoking dependence not easy and many people eventually failed in quit. Therefore, predicting likelihood success cessation intervention necessary public health.In this paper, we analyzed dataset conducted from Korea National Health Nutrition Examination Survey (KNHANES) 2009 to 2017. Accordingly, chi-square test used filter relevant features, thus...

Developing lifelong learning algorithms are mandatory for computational systems biology.Recently, many studies have shown how to extract biologically relevant information from high-dimensional data understand the complexity of cancer by taking benefit deep (DL).Unfortunately, new growing up into hundred types that make difficult classify them efficiently.In contrast, current state-of-the-art continual (CL) methods not designed dynamic characteristics data.And security and privacy some main...

Recent years, the diabetes mellitus is an important public health problem and has been top 10 leading causes of death in lower-middle-income countries upper-middle-income world. In this study, we tried to use a hybrid feature selection approach find proper optimal subsets classify predict patients Korea based on data from National Health Nutrient Examination Survey (KNHANES). We used information gain as filter phase support vector machine with sequential search method wrapper phase. To...

ABSTRACT Smoking is one of the significant avoidable risk factors for premature death. Most smokers make multiple quit attempts during their lifetime but smoking dependence not easy and many people eventually failed attempts. Predicting likelihood success in cessation program necessary public health. In recent years, a few numbers decision support systems have been developed dealing with based on machine learning techniques. However, class imbalance problem increasingly recognized as...

Kidney plays an important role in human bodies. It maintains homeostasis and removes some harmful substance by making ejecting urine. Renal cell carcinoma, especially clear renal carcinoma (ccRCC), is the most common type of kidney disease that accounts for 2~3% malignancies. Early diagnosis accurate classification ccRCC factor to decrease motility rate. In this paper, methods combined with feature selection have been examined on clinical dataset. The data were obtained from Cancer Genome...

Lung adenocarcinoma is the leading cause of death among men and women with cancer worldwide. Here, we performed an analysis Illumina HumanMethylation450K data from TCGA to identify DNA methylation markers for lung diagnosis. We examined landscape investigated relationship between clinical features. then extracted differentially methylated cytosines in CpG island promoter regions, adopted machine learning techniques determine final markers. As a result, identified three subtypes...

Depression, a pervasive mental health ailment impacting millions globally, necessitates precise and intelligible predictive methodologies. Physicians encounter formidable hurdles when screening large populations. Despite plethora of proposed Artificial Intelligence (AI) techniques for depression prediction, creating transparent models remains challenge. In this investigation, we confront challenge by constructing prediction grounded in diverse machine-learning algorithms employing the Hybrid...

Non-Fungible Tokens (NFTs) are digital assets based on a blockchain and those characterized as unique cryptographic tokens non-interchangeable. To date, research into the NFT marketplace has been relatively limited. As it is an emerging platform with many elements, The market impacted due to recent fluctuations in crypto-asset markets more broadly. This current bear cycle shed light concerns around value of NFTs, profit-based motivation, environmental sustainability. However, periods...