Chest-X ray (CXR) radiography can be used as a first-line triage process for non-COVID-19 patients with pneumonia. However, the similarity between features of CXR images of COVID-19 and pneumonia caused by other infections makes the differential diagnosis by radiologists challenging. We hypothesized that machine learning-based classifiers can reliably distinguish the CXR images of COVID-19 patients from other forms of pneumonia. We used a dimensionality reduction method to generate a set of optimal features of CXR images to build an efficient machine learning classifier that can distinguish COVID-19 cases from non-COVID-19 cases with high accuracy and sensitivity. By using global features of the whole CXR images, we successfully implemented our classifier using a relatively small dataset of CXR images. We propose that our COVID-Classifier can be used in conjunction with other tests for optimal allocation of hospital resources by rapid triage of non-COVID-19 cases.

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COVID-Classifier: an automated machine learning model to assist in the diagnosis of COVID-19 infection in chest X-ray images.

Chest-X ray (CXR) radiography can be used as a first-line triage process for non-COVID-19 patients with pneumonia. However, the similarity between features of CXR images of COVID-19 and pneumonia caused by other infections make the differential diagnosis by radiologists challenging. We hypothesized that machine learning-based classifiers can reliably distinguish the CXR images of COVID-19 patients from other forms of pneumonia. We used a dimensionality reduction method to generate a set of optimal features of CXR images to build an efficient machine learning classifier that can distinguish COVID-19 cases from non-COVID-19 cases with high accuracy and sensitivity. By using global features of the whole CXR images, we were able to successfully implement our classifier using a relatively small dataset of CXR images. We propose that our COVID-Classifier can be used in conjunction with other tests for optimal allocation of hospital resources by rapid triage of non-COVID-19 cases.

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COVID-Classifier: An automated machine learning model to assist in the diagnosis of COVID-19 infection in chest x-ray images.

Knowledge graphs (KGs) have been widely used in the field of artificial intelligence, such as in information retrieval, natural language processing, recommendation systems, etc. However, the open nature of KGs often implies that they are incomplete, having self-defects. This creates the need to build a more complete knowledge graph for enhancing the practical utilization of KGs. Link prediction is a fundamental task in knowledge graph completion that utilizes existing relations to infer new relations so as to build a more complete knowledge graph. Numerous methods have been proposed to perform the link-prediction task based on various representation techniques. Among them, KG-embedding models have significantly advanced the state of the art in the past few years. In this paper, we provide a comprehensive survey on KG-embedding models for link prediction in knowledge graphs. We first provide a theoretical analysis and comparison of existing methods proposed to date for generating KG embedding. Then, we investigate several representative models that are classified into five categories. Finally, we conducted experiments on two benchmark datasets to report comprehensive findings and provide some new insights into the strengths and weaknesses of existing models.

A Survey on Knowledge Graph Embeddings for Link Prediction

Stock market forecasting has attracted significant attention mainly due to the potential monetary benefits. Predicting these markets is a challenging task due to numerous interrelated factors, and needs a complete and efficient feature selection process to identify the most informative factors. As a time series problem, stock price movements are also dependent on movements on its previous trading days. Feature selection techniques have been widely applied in stock forecasting, but existing approaches usually use a single feature selection technique, which may overlook some important assumptions about the underlying regression function linking the input and output variables. In this study, we combine features selected by multiple feature selection techniques to generate an optimal feature subset and then use a deep generative model to predict future price movements. First, we compute an extended set of forty-four technical indicators from daily stock data of eighty-eight stocks and then compute their importance by independently training logistic regression model, support vector machine and random forests. Based on a prespecified threshold, the lowest ranked features are dropped and the rest are grouped into clusters. The variable importance measure is reused to select the most important feature from each cluster to generate the final subset. The input is then fed to a deep generative model comprising of a market signal extractor and an attention mechanism. The market signal extractor recurrently decodes market movement from the latent variables to deal with stochastic nature of the stock data and the attention mechanism discriminates between predictive dependencies of different temporal auxiliary outputs. The results demonstrate that combining features selected by multiple feature selection approaches and using them as input into a deep generative model outperforms state-of-the-art approaches.

Forecasting daily stock trend using multi-filter feature selection and deep learning

This paper proposes models and algorithms for the pickup and delivery vehicle routing problem with time windows and multiple stacks. Each stack is rear-loaded and is operated in a last-in-first-out (LIFO) fashion, meaning that when an item is picked up, it is positioned at the rear of a stack. An item can only be delivered if it is in that position. This problem arises in the transportation of heavy or dangerous material where unnecessary handling should be avoided, such as in the transportation of cars between car dealers and the transportation of livestock from farms to slaughterhouses. To solve this problem, we propose two different branch-price-and-cut algorithms. The first solves the shortest path pricing problem with the multi-stack policy, while the second incorporates this policy partly in the shortest path pricing problem and generates additional inequalities to the master problem when infeasible multi-stack routes are encountered. Computational results obtained on instances derived from benchmark instances for the pickup and delivery traveling salesman problem with multiple stacks are reported, and reveal the advantage of incorporating the multi-stack policy in the pricing problem. Instances with up to 75 requests and with one, two and three stacks can be solved optimally within 2 hours of computational time.

Branch-price-and-cut algorithms for the pickup and delivery problem with time windows and multiple stacks

Feature selection aims to eliminate redundant or irrelevant variables from input data to reduce computational cost, provide a better understanding of data and improve prediction accuracy. Majority of the existing filter methods utilize a single feature-ranking technique, which may overlook some important assumptions about the underlying regression function linking input variables with the output. In this paper, we propose a novel feature selection framework that combines clustering of variables with multiple feature-ranking techniques for selecting an optimal feature subset. Different feature-ranking methods typically result in selecting different subsets, as each method has its own assumption about the regression function linking input variables with the output. Therefore, we employ multiple feature-ranking methods having disjoint assumption about the regression function. The proposed approach has a feature ranking module to identify relevant features and a clustering module to eliminate redundant features. First, input variables are ranked using regression coefficients obtained by training $L1$ regularized Logistic Regression, Support Vector Machine and Random Forests models. Those features which are ranked lower than a certain threshold are filtered-out. The remaining features are grouped into clusters using an exemplar-based clustering algorithm, which identifies data-points that exemplify the data better, and associates each data-point with an exemplar. We use both linear correlation coefficients and information gain for measuring the association between a data-point and its corresponding exemplar. From each cluster the highest ranked feature is selected as a delegate, and all delegates from the three ranked lists are combined into the final feature set using union operation. Empirical results over a number of real-world data sets confirm the hypothesis that combining features selected using multiple heterogeneous methods results in a more robust feature set and improves prediction accuracy. As compared to other feature selection approaches evaluated, features selected using linear correlation-based multi-filter feature selection achieved the best classification accuracy with 98.7%, 100%, 92.3% and 100% for Ionosphere, Wisconsin Breast Cancer, Sonar and Wine data sets respectively.

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Combining Multiple Feature-Ranking Techniques and Clustering of Variables for Feature Selection

Handwritten character recognition has been profoundly studied for many years in the field of pattern recognition. Due to its vast practical applications and financial implications, handwritten character recognition is still an important research area. In this research, the Handwritten Ethiopian Character Recognition (HECR) dataset has been prepared to train the model. The images in the HECR dataset were organized with more than one color pen RGB main spaces that have been size normalized to 28 × 28 pixels. The dataset is a combination of scripts (Fidel in Ethiopia), numerical representations, punctuations, tonal symbols, combining symbols, and special characters. These scripts have been used to write ancient histories, science, and arts of Ethiopia and Eritrea. In this study, a hybrid model of two super classifiers: Convolutional Neural Network (CNN) and eXtreme Gradient Boosting (XGBoost) is proposed for classification. In this integrated model, CNN works as a trainable automatic feature extractor from the raw images and XGBoost takes the extracted features as an input for recognition and classification. The output error rates of the hybrid model and CNN with a fully connected layer are compared. A 0.4630 and 0.1612 error rates are achieved in classifying the handwritten testing dataset images, respectively. Thus XGBoost as a classifier performs a better result than the traditional fully connected layer.

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A New Hybrid Convolutional Neural Network and eXtreme Gradient Boosting Classifier for Recognizing Handwritten Ethiopian Characters

Knowledge graphs (KGs) have recently become increasingly popular due to the broad range of essential applications in various downstream tasks including intelligent search, personalized recommendations, intelligent financial data analytics, etc. During an automated construction of a KG, the knowledge facts from multiple knowledge sources are automatically extracted in the form of triples, and these observed triples are used to derive new unobserved triples for KG completion (also known as link prediction). State-of-the-art link prediction methods are known to be primarily KG embedding models, among which tensor factorization models have recently drawn much attention due to their scalability and expressive feature embeddings, and hence, perform well for link prediction. However, these embedding models consider each KG triple individually and fail to capture the useful information present in the neighborhood of a node. To this end, we propose a novel end-to-end KG embedding learning framework that consists of an encoder of a dual weighted graph convolutional network, and a decoder of a novel fully expressive tensor factorization model. The proposed encoder extends weighted graph convolutional network to generate two rich and high quality embedding vectors for each node by aggregating information from the neighboring nodes. The proposed decoder has a flexible and powerful tensor representation form of the Tensor Train decomposition that takes benefit of the two representations of each node in its embedding space to accurately model the KG triples. We also derive a bound on the size of the embeddings for full expressivity and show that our proposed tensor factorization model is fully expressive. Additionally, we show the relationship of our tensor factorization model to previous tensor factorization models. The experimental results show the effectiveness of the proposed framework that consistently marks performance gains over several previous models on recent standard link prediction datasets.

KGEL: A novel end-to-end embedding learning framework for knowledge graph completion

Personalized recipe recommender systems help users mine certain dishes they want to find and even really desire, which play a significant role in matching dishes, balancing nutrients, and preventing non-communicable diseases. Generally, customer’s preferences or needs vary from person to person, and people are often reluctant to accept recommended food without reasonable explanation, especially when their demands are not explicitly addressed. In this paper, we are devoted to providing recipe suggestions accompanied by rational interpretations generated from images or videos. First, we construct a recipe knowledge graph (RcpKG) through the use of multi-modality and hierarchical thought, which focuses on the underlying demands of users and the consideration of multiple fine-grained factors. On this basis, a novel multi-modal recipe recommendation method via the knowledge graph (RcpMKR) is proposed, which represents nodes in multiple aspects and performs multi-relational graph structure extraction of the RcpKG. It not only takes into account local associations within the graph but also global information, and incorporates user concerns at different levels. Then, we adopt BERT-based multi-modal models and generative adversarial networks to generate interpretations. Additionally, dynamic convolution and random synthetic attention are utilized in our work to discriminate among features. Experimental results show that the proposed method and BERT-based fusion models improve recipe recommendation performance and explanation generation. Specifically, the precision of the RcpMKR method through RcpKG, user concerns and graph convolutional network improves by 7.82%, and the viExpCBTBERT method via 2D&3D convolutional neural networks for developing text interpretations enhances the F1-score by 10% compared with the baseline.

Anwar Ul Haq

Papers

Forecasting daily stock trend using multi-filter feature selection and deep learning

Combining Multiple Feature-Ranking Techniques and Clustering of Variables for Feature Selection

A New Hybrid Convolutional Neural Network and eXtreme Gradient Boosting Classifier for Recognizing Handwritten Ethiopian Characters

KGEL: A novel end-to-end embedding learning framework for knowledge graph completion

Is the suggested food your desired?: Multi-modal recipe recommendation with demand-based knowledge graph