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Kotenseki is a collection of classical and ancient Japanese literature. It is comprised of image books that express Japanese stories by using comic drawings of different characters, such as humans, nature, and animals. To effectively store them for posterity, a search system is important. We propose an efficient CBIR system to assist the users in easily accessing the information and have an enjoyable...
Facial micro-expression refers to split-second muscle changes in the face, indicating that a person is either consciously or unconsciously suppressing their true emotions and even mental health. Therefore, micro-expression recognition attracts increasing research efforts in both fields of psychology and computer vision. Existing research on micro-expression recognition has mainly used hand-crafted...
Drug-target interaction identification is of highly importance in drug research and development. The traditional experimental paradigm is costly, while the previous in silico prediction paradigm remains a challenge because of diversified data production platforms and data scarcity. In this paper, we modeled drug-target interaction prediction as a binary classification task based on transcriptome data...
Classification models have proven useful for predicting clinical interventions and patient outcomes. One of the key issues that affect the predictive ability of supervised learning frameworks in the healthcare scenario is imbalance in data sets. In addition, non-uniform data collection processes in clinical scenarios lead to poor quality data sets. We designed a novel approach to predict Intensive...
As the diagnosis of lung cancer, lung mass for the diagnosis of the disease is meaningful, chest radiography has low price, low radiation, popularity and other characteristics, it is a significant attempt for the location of chest masses on chest radiography using deep learning method. In this paper we have established a labeled lung mass database, and presented a state of the art deep learning methodology...
Fingerprinting Localization Solutions (FPSs) enjoy huge popularity due to their good performance and minimal environment information requirement. Considered as a data-driven approach, many modern data analytics can be used to improve its performance. In this paper, we propose tow learning algorithms, namely a deep learning architecture for regression and Support Vector Machine (SVM) for classification,...
Modern drug discovery organizations generate large volumes of SAR data. A promising methodology that can be used to mine this chemical data to identify novel structure-activity relationships is the matched molecular pair (MMP) methodology. However, before the full potential of the MMP methodology can be utilized, a MMP identification method that is capable of identifying all MMPs in large chemical...
Gender is one of the most useful facial attributes which are detected from human face images. In this work, we introduce a new gender classification system based on features extracted by Local Phase Quantization (LPQ) operators from intensity and Monogenic images. More detailed, the LPQ features are obtained from the input image (the intensity one) and from three other Monogenic components in the...
Distance or similarity measures are essence components used by distance-based recognition techniques. Since the Euclidean distance function is the most widely used distance metric in PCA and LDA recognition systems , no empirical study examines the recognition performance based on these two methods by using different distance functions, especially for biometric authentication domain problems. The...
Post-database searching is a key procedure for peptide spectrum matches (PSMs) in protein identification with mass spectrometry-based strategies. Although many machine learning-based approaches have been developed to improve the accuracy of peptide identification, the challenge remains for improvement due to the poor quality of data samples. CRanker has shown its effectiveness and efficiency in terms...
Affective computing research traditionally focused on labeling a person's emotion as one of a discrete number of classes e.g. happy or sad. In recent times, more attention has been given to continuous affect prediction across dimensions in the emotional space, e.g. arousal and valence. Continuous affect prediction is the task of predicting a numerical value for different emotion dimensions. The application...
The process of identifying food items from an image is quite an interesting field with various applications. Since food monitoring plays a leading role in health-related problems, it is becoming more essential in our day-to-day lives. In this paper, an approach has been presented to classify images of food using convolutional neural networks. Unlike the traditional artificial neural networks, convolutional...
Identification of the correct medicinal plants that goes in to the preparation of a medicine is very important in ayurvedic medicinal industry. The main features required to identify a medicinal plant is its leaf shape, colour and texture. Colour and texture from both sides of the leaf contain deterministic parameters to identify the species. This paper explores feature vectors from both the front...
Malawi Children's Village (MCV) operates a secondary school of approximately 560 students in Mangochi, Malawi, and its strategic goal is to add computer studies into its curriculum. The first step to achieving this goal is for MCV to establish and sustain a teachers-only computer lab. This case study examines the process by which a team of volunteers identified this as the first step, the team's recommendations...
Image geolocalization, inferring the geographic location of an image, is a challenging computer vision problem with many potential applications. The recent state-of-the-art approach to this problem is a deep image classification approach in which the world is spatially divided into cells and a deep network is trained to predict the correct cell for a given image. We propose to combine this approach...
A considerable number of indoor positioning systems have been proposed for large-scale environments that extend over several meters. However, there has been less focus on designing an indoor localization system for confined environments where the requirements of reliability and precision are high. The approach discussed in this paper employs a hybrid technique where Received Signal Strength Indicator...
According to the needs of users, Home Service Robots gradually work outside. As a result, new requirements for the detection and recognition performance of Home Service Robots are put forward. Compared with indoor environment, outdoor environment is more complex, which brings difficulties to detect objects. But extracting features by Histogram of Oriented Gradient (HOG) method can not work well in...
Facial expression recognition is a very important research field to understand human emotions. Many facial expression recognition systems have been proposed in the literature over the years. Some of these methods use neural network approaches with deep architectures to address the problem. Although it seems that the facial expression recognition problem has been solved, there is a large difference...
This paper proposes a hybrid speaker diarization system. The main body is a variational Bayes — hidden Markov model (VB-HMM) speaker diarization system. The VB-HMM speaker diarization system avoids making premature hard decision and takes advantages of soft speaker information in an iterative way. Thus, it outperforms most of mainstream speaker diarization systems. Unfortunately, this system is sensitive...
Automatic License Plate Recognition (ALPR) is an important task with many applications in Intelligent Transportation and Surveillance systems. As in other computer vision tasks, Deep Learning (DL) methods have been recently applied in the context of ALPR, focusing on country-specific plates, such as American or European, Chinese, Indian and Korean. However, either they are not a complete DL-ALPR pipeline,...
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