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This article explores the application of image processing techniques in recyclable waste paper sorting. In recycling, waste papers are segregated into various grades as they are subjected to different recycling processes. Highly sorted paper streams facilitate high quality end products and save processing chemicals and energy. From 1932 to 2009, different mechanical and optical paper sorting methods have been developed to fill the paper sorting demand. Still, in many countries including Malaysia, waste papers are sorted into different grades using a manual sorting system. Because of inadequate throughput and some major drawbacks of mechanical paper sorting systems, the popularity of optical paper sorting systems has increased. Automated paper sorting systems offer significant advantages over human inspection in terms of worker fatigue, throughput, speed, and accuracy. This research attempts to develop a smart vision sensing system that is able to separate the different grades of paper using first-order features. To construct a template database, a statistical approach with intra-class and inter-class variation techniques are applied to the feature selection process. Finally, the K-nearest neighbor (KNN) algorithm is applied for paper object grade identification. The remarkable achievement obtained with the method is the accurate identification and dynamic sorting of all grades of papers using simple image processing techniques.

Feature selection determines relevant features in the data. It is often applied in pattern classification, data mining, as well as machine learning. A special concern for feature selection nowadays is that the size of a database is normally very large, both vertically and horizontally. In addition, feature sets may grow as the data collection process continues. Effective solutions are needed to accommodate the practical demands. This paper concentrates on three issues: large number of features, large data size, and expanding feature set. For the first issue, we suggest a probabilistic algorithm to select features. For the second issue, we present a scalable probabilistic algorithm that expedites feature selection further and can scale up without sacrificing the quality of selected features. For the third issue, we propose an incremental algorithm that adapts to the newly extended feature set and captures `concept drifts' by removing features from previously selected and newly added ones. We expect that research on scalable feature selection will be extended to distributed and parallel computing and have impact on applications of data mining and machine learning.

Knowledge-based systems and, particularly, expert systems, are bringing new thinking to how we view knowledge. Increasingly, we see knowledge as a commodity or an object, something generated, discovered, restructured, repackaged and deliverrd Powerful tools have been developed for expediting our work in manipulating knowledge. Knowedge-based systems provide the structure, the process and multidisciplinary integration tools to better understand and further develop projects in areas such as reengineering the organization, concurrent engineering, quality management and other critical management efforts in business and government. Curriculum development in education also benefits greatly from using the integrative powers of the KBS disciplines Examples taken from education focus on knowledge-based systems applied to the areas of K-12 course design, bachelor's degree curriculum design in integrated science, and graduate studies serving “professional transitioning.” Concepts of “idea fields” in organizational behavior are also discussed.

Acquiring knowledge for conversation modeling is an important task in the process of building a Conversational Agent (Chatbot). However, it is a quite difficult process that requires too much time and efforts. To overcome these difficulties, in this paper, we demonstrate a novel methodology for the automatic conversational knowledge extraction from an existing Chatbot. Extracted knowledge will be used as training dataset for building a Neural Network Conversational Agent. The experiments in the paper show that our proposed approach can be used for the automatic knowledge extraction from any type of Chatbot on the Internet. The experiment that is presented in this paper has two phases. In the first phase, we present a methodology for the conversational knowledge extraction. In the second phase of the experiment, we introduce a methodology for building a new Neural Conversational Agent using a deep learning Neural Network framework. The key novelty of our proposed approach is the automated machine-machine conversational knowledge sharing and reuse. This is an important step towards building the new conversational agents skipping the difficult and time-consuming procedure of knowledge acquisition.

In this paper, we address two research topics in Recommender Systems (RSs) which have been developed in parallel without a deeper integration: Cross-Domain RS (CDRS) and Context-Aware RS (CARS). CDRS have emerged to enhance the quality of recommendations in a target domain by leveraging sources of information in different domains. CDRS are especially useful to address cold-start, sparsity and diversity problems in target domains with scarce information. CARS, on its turn, have been proposed to consider contextual information for recommendations. Such systems are suitable when the users’ interests change according to factors like time, location, among others. By combining these two approaches, better RSs can be developed, considering both the availability of useful data from multiple domains and the use of contextual information. In this paper, we formalize the combination of CDRS and CARS, which represents a more systematic integration of these approaches compared to previous work. Based on this formulation, we developed novel RSs techniques, named CD-CARS. To evaluate the developed CD-CARS techniques, we performed extensive experimentation through real datasets taking into account several scenarios. The recommendations were evaluated in terms of predictive and ranking performance, respectively achieving up to 62.6% and 45%, depending on the scenario, in comparison to traditional cross-domain collaborative filtering techniques. Therefore, the experimental results have shown that the integration of techniques developed in isolation can be useful in a variety of situations, in which recommendations can be improved by information gathered from different sources and can be refined by considering specific contextual information.

Text summarization has become an important research area, especially in the biomedical domain, where information overload is a major problem. In this paper, we propose a novel biomedical text summarization system that combines two popular data mining techniques: clustering and frequent itemset mining. Biomedical paper is expressed as a set of biomedical concepts using the UMLS metathesaurus. The K-means algorithm is used to cluster similar sentences. Then, the Apriori algorithm is applied to discover the frequent itemsets among the clustered sentences. Finally, the salient sentences from each cluster are selected to build the summary using the discovered frequent itemsets. For the evaluation step, we selected randomly 100 biomedical papers from the BioMed Central database full-text, and we evaluated the performances of our system by comparing the resulting summaries with the abstracts of these papers using the ROUGE metrics in term of recall, precision, and F-measure. We also compared the obtained summaries with those achieved by five well-known summarizers: TextRank, TextTeaser, SweSum, ItemSet Based Summarizer, Microsoft AutoSummarize, and two baselines: summarization using only the frequent itemsets mining (FRQ-CL), and summarization using only the clustering (CL-FRQ). The results demonstrate that this combination can successfully enhance the summarization performances, and the proposed system outperforms other tested summarizers.

This paper proposes a method of recognizing the intention of human activity based on percept sequences that represent the activities of daily living (ADL) in a residential space. Based on the activity intention ontology representing actions, poses, and nearby objects related to human activity intentions, the proposed method identifies a human activity intention by using the event calculus when a percept sequence is entered. It is very difficult to recognize ADL occurring in various places in a residence by using regular percept sequences without error. Furthermore, a human activity can have complex intentions. To solve these problems, this paper proposes an activity intention recognition process consisting of three steps. First, the activity intention inference step recognizes the intention of a given percept sequence based on the activity intention ontology. Second, the complex intention-identifying step determines whether to terminate the previously maintained activity intention or continue maintaining it in a complex manner based on the newly inferred activity intention. Third, the uncertainty handling step corrects an inaccurate activity intention caused by an error in the percept sequence. To evaluate the proposed method presented in this paper, activity intention recognition experiments were conducted based on the ADL data that were collected for six households of elderly persons older than 70 years. The results showed that the proposed method has a precision of 99.62% and recall of 92.83%.

Software clone detection is an emerging research area in the field of software engineering. Software systems are subjected to continuous modifications in source code to improve the performance of the software, which may lead to code redundancy. Duplicate code/code clone is a piece of code reworked several times in software programs due to copy paste activity or reusability of existing software. Code clone is a prime subject in software evolution. Detection of software clones at the time of software evolution may improve the performance of software and reduce the maintenance cost and effort. This paper proposes metric based methods to detect code clones, as software clone is a universal problem in large scale programming environment. This paper introduces two metric based approaches to detect code clones by comparing (i) Control Statement Features (ii) Program Features like different types of statements, operators and operands. In order to demonstrate the effectiveness of the proposed approaches, extensive experiments are conducted on two datasets, C projects of Bellon's benchmark dataset and student lab programs (SLP).The methods efficiently identify similar functional clones. Proposed models only find similarity of whole programs but intelligent enough to highlight similar code segments across program files.

Classification of text based on its substance is an essential part of analysis to organize enormously large text data and to mine the salient information contained in it. It is gaining greater attention with the surge in the volume of on-line data available. Classical algorithms like k-NN (k-nearest neighbor), SVM (Support Vector Machine) and their variations have been observed to yield only reasonable results in addressing the problem, leaving enough room for further improvement. A class of algorithms commonly referred to as Sparse Methods has been emerged recently from compressive sensing and found numerous effective applications in many areas of data analysis and image processing. Sparse Methods as a tool for text analysis is an alley that is largely unexplored rigorously. This paper presents exploration of sparse representation-based methods for text classification. Based on the success of sparse representation based methods in different areas of data analysis, we intuitively hypothesized that it should work well on text classification problems as well. This paper empirically reinforces the hypothesis by testing the method on Reuters and WebKB data sets. The empirical results on Reuters and WebKB benchmark data show that it can outperform classical classification algorithms like SVM and k-NN. It has been observed that obtaining the basis of representation and sparse codes are computationally costly operations affecting the performance of the system. We also propose a class-wise dictionary refinement algorithm and dynamic dictionary selection algorithm to make sparse coding faster. The addition of dictionary refinement to the classification system not only reduces the time taken for sparse coding but also gives improved classification accuracy. The outcomes of the study are empirical verification of sparse representation classifier as a text classification tool and a computationally efficient solution for the bottleneck operation of sparse coding.

Predictive analytics is an essential capability in business process management to forecast future status and performance of business processes. In this paper, we focus on one particular predictive monitoring task that is solved using classification techniques, i.e. predicting the next event in a case. Several different classifiers have been recently employed in the literature in this task. However, a quantitative benchmark of different classifiers is currently lacking. In this paper, we build such a benchmark by taking into account 20 classifiers from five families, i.e. trees, Bayesian, rule-based, neural and meta classifiers. We employ six real-world process event logs and consider two different sampling approaches, i.e. case and event-based sampling, and three different validation methods in order to acquire a comprehensive evaluation about the classifiers’ performance. According to our benchmark, the classifier most likely to be the overall superior performer is the credal decision tree (C-DT), followed by the other top-4 performers, i.e. random forest, decision tree, dagging ensemble, and nested dichotomies ensemble. We also provide a qualitative discussion of how features of an event log can affect the choice of best classifier.

Every music composition has a composer at the core of its building block, molding it into a style of their own. The creative compositional style of a composer varies dynamically with every composer which is perishable and inimitable but cannot be preserved. This paper proposes a hybrid style transfer model which is an end-to-end approach to transfer style, compose as well as evaluate the style transfer accuracy in the domain of the target music composer. The paper focuses on 3 composer maestros Liszt, Chopin and Schubert taken from the Maestro dataset. The main step of music composer style transfer is accomplished in 3 systematic steps, training composer classifiers according to feature sets, generating a model of a composition in a particular composer’s style from noise, and finally style transfer and its evaluation. The 3 steps are interconnected in a way that each step is the building block for the next step. The GAN architecture of the style transfer step is built out of the GAN architecture of the second step. The compositions generated from each architecture is finally evaluated by the common pre-trained classifier of the first step. The highest accuracy obtained is 80% for composer classification using the maestro dataset, 77.27% for the classification of the generated style transferred version of a composition into the target composer class using the pre-trained classifiers.

This paper presents the Storybox Methodology which combines a novel framework for structuring knowledge and conversations around a story (D-PAF), with a live chatroom-based training approach that builds the conversation knowledge base via live chatroom interactions. Chatbots have achieved success as intelligent interfaces in education, health, sales and support, but their move towards mainstream adoption has been hindered by the large amount of development resources required, in terms of data collection, preparation, user testing and technical knowledge. The complexity of the development task often necessitates both a system author and a domain expert working effectively together, adding further complexity and risk. Overcoming these barriers could increase feasibility of chatbots in a range of expert contexts. In education, there are groups of learners who do not enjoy reading and writing. Storytelling chatbots might be able to introduce these groups to enjoyable new ways to read and write, having a beneficial impact on their education and future prospects. This paper proposes the Storybox Methodology for the rapid development of storytelling chatbots. Storybox is evaluated by creating, training and testing ‘The Ghost’, a chatbot enacting Hamlet's Ghost character from William Shakespeare's dramatic tragedy. The results showed that after a period of live chatbot training of only 25 training conversations, The Ghost was able to conduct convincing conversations with participants.

Lithology identification is an important part of petroleum drilling engineering. Accurate identification of lithology is the foundation to ensure the smooth operation of drilling engineering. Conventional lithology recognition mainly relies on human experience. The recognition accuracy depends on the level of technical personnel and the recognition response time is lagging. It is difficult to meet the demand. How to achieve rapid and intelligent recognition of lithology is one of the core technical problems faced by oil drilling. In order to solve this problem, this paper uses the mechanical specific energy and other ground parameters to establish a rapid and intelligent recognition method of lithology based on the simulated annealing optimization support vector machine model. In order to improve the accuracy of the model recognition, a large number of methods have been developed from two classes and multiple classes, simulation analysis results show that the lithology recognition model based on the principle of mechanical specific energy and the simulated annealing optimization support vector machine algorithm can predict a priori unknown data with a prediction accuracy of over 90%. Compared with the support vector machine model and the K-means model, simulated annealing optimization support vector machine is used for comparative analysis, the algorithm to establish a lithology recognition model has better performance and higher accuracy. The intelligent identification model of lithology based on the principle of mechanical specific energy and simulated annealing optimization support vector machine algorithm established in this paper can quickly and accurately identify lithology, and provide new technical support for oil drilling formation analysis.

Summarization, is to reduce the size of the document while preserving the meaning, is one of the most researched areas among the Natural Language Processing (NLP) community. Summarization techniques, on the basis of whether the exact sentences are considered as they appear in the original text or new sentences are generated using natural language processing techniques, are categorized into extractive and abstractive techniques. Extractive summarization has been a very extensively researched topic and has reached to its maturity stage. Now the research has shifted towards the abstractive summarization. The complexities underlying with the natural language text makes abstractive summarization a difficult and a challenging task. This paper presents a comprehensive review of the various works performed in abstractive summarization field. For this purpose, we have selected the recent papers on this topic from Elsevier, ACM, IEEE, Springer, ACL Anthology, Cornell University Library and Google Scholar. The papers are categorized according to the type of abstractive technique used. The paper lists down the various challenges and discusses the future direction for research in this field. Along with these, we have identified the advantages and disadvantages of various methods used for abstractive summarization. We have also listed down the various tools which have been used or developed by researchers for abstractive summarization. The paper also discusses the evaluation techniques being used for assessing the abstractive summaries.

People with type 1 diabetes must control their blood glucose level through insulin infusion either with several daily injections or with an insulin pump. However, estimating the required insulin dose is not easy. Recommender systems, mainly based on Case-Based Reasoning (CBR), are being developed to provide recommendations to users. These systems are designed to keep the experiences or cases of the user in a case-base, which requires maintenance to keep system's response accurate and efficient. This paper proposes a case-base maintenance methodology that combines case-base redundancy reduction and attribute weight learning. Contrary to previous approaches designed for classification problems, the maintenance methodology presented in this paper deals with numerical recommendations. It can manage a potentially huge case-base due to the combinatorial derived from the number of attributes used to represent a case. The proposed approach has been tested using the UVA/PADOVA type 1 diabetes simulator and the results demonstrate that it can accomplish better levels of accuracy than other insulin recommender systems mentioned in the literature, when a large number of attributes is considered This project has received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No. 689810, www.pepper.eu.com/, PEPPER, and the grant of the University of Girona 20162018 (MPCUdG2016). The work has been developed with the support of the research group SITES awarded with distinction by the Generalitat de Catalunya (SGR 20142016)

Cross purchase modeling (cross model) is a method to simultaneously analyze multiple product categories for their interrelated influences on consumers’ decisions to purchase each category. Traditional econometric modeling methods are hard to compute and complex in model specifications. This paper examines an artificial neural network (ANN) approach in this context. To understand ANN models’ performance in increasing data scale, this paper uses four datasets of 4, 8, 16 and 32 categories to train ANN models and make predictions. Results show that an ANN model of 12 hidden nodes can be trained in about 3 h and make 88% prediction hit rate for the 16-category dataset. In contrast, a traditional econometric model requires more than 300 h to finish computing for the 16-category dataset. A typical critique of ANN technique is that it hides learned knowledge inside its model structure. This is a major obstacle preventing ANN from being more frequently applied in business research and practices. The current study takes two approaches to address this issue, i.e., (1) customizing ANN components to allow prior knowledge plugging in, and (2) using the generalized weight technique to reveal knowledge that is embedded in ANN training results. Findings of this paper help managers understand applicability of ANN technique in cross purchase model. By demonstrating plugging in prior knowledge to ANN model training and extracting knowledge from ANN training results, this paper shows approaches for understanding ANN embedded knowledge.

This paper presents a novel indoor tracking system built with common data mining techniques on radio frequency identification (RFID) tags readings. The system allows tracking of several objects in real-time in a smart home context and is a building block toward the deployment of an expert system to enable aging in place through technology. The indoor localization is modelled as a classification problem, instead of a regression problem as commonly seen in the literature. The paper is divided in two parts. The first one focuses on the ground truth collection that led to the model construction. The second part focuses on the filters that were designed to enable this model to be used in real-time in the smart home as a tracking software. Results from the first part show that most classifiers perform well on the static positioning of RFID tags task, with a random forest of 100 trees performing best at 97% accuracy and 0.9740974 F-Measure. However, collecting data to train the classifier is a long and tedious process. Results from the second part indicate that the accuracy of the random forest drops significantly when confronted with human interference. With the help of some filters, the tracking accuracy of objects can still be as high as 75%. Those results confirm that using passive RFID tags for an indoor tracking system is viable. Our system is easy to deploy and more flexible than trilateration or fingerprinting systems.

To assess the current mental state of an individual, several monitoring systems have been developed. In this paper, we explore the possibility to exploit information recorded noninvasively from the human cortex to develop a brain-computer interface (BCI) able to estimate brain workload and the mental efforts during a cognitive task. The EEG-based workload classifier presented in this paper combines a power spectral density (PSD) analysis and a statistical criterion. The proposed classifier is applied in the context of distance education and online course platform through two experimental protocols. The first one proposes solving a set of matrices products using pen and paper, while the second one proposes answering problems of logic mathematics on a computer-based learning environment. Experimental results show that the averaged accuracy of distinguishing changes in the theta [4–7 Hz] (θ) band is 79%. For the alpha band [8–11 Hz] (α) the averaged accuracy reached 78%. Based on this classifier, we demonstrate that θ and α powers in central, and posterior sites decrease with the increase in difficulty level of the cognitive task. The accuracy of correct decisions obtained from our results are significantly enhanced while comparing our investigation to some similar works from literature. In the realm of intelligent expert systems, our work results represent a first step to an implementation of an intelligent system for the evaluation of reeducative therapies to be used in physiotherapy centers for children with cognitive disorders as it is the case for Cerebral Palsy.

This study evaluates the performance of different data clustering approaches for searching the profitable consumer segments in the UK hospitality industry. The paper focuses on three aspects of datasets including the ordinal nature of data, high dimensionality and outliers. Data collected from 513 sample points are analysed in this paper using four clustering approaches: Hierarchical clustering, K-Medoids, fuzzy clustering, and Self-Organising Maps (SOM). The findings suggest that Fuzzy and SOM based clustering techniques are comparatively more efficient than traditional approaches in revealing the hidden structure in the data set. The segments derived from SOM has more capability to provide interesting insights for data-driven decision making in practice. This study makes a significant contribution to literature by comparing different clustering approaches and addressing misconceptions of using these for market segmentation to support data-driven decision making in business practices.

In recent years, the growing availability of huge amounts of information, generated in every sector at high speed and in a wide variety of forms and formats, is unprecedented. The ability to harness big data is an opportunity to obtain more accurate analyses and to improve decision-making in industry, government and many other organizations. However, handling big data may be challenging and proper data integration is a key dimension in achieving high information quality. In this paper, we propose a novel approach to data integration that calibrates online generated big data with interview based customer survey data. A common issue of customer surveys is that responses are often overly positive, making it difficult to identify areas of weaknesses in organizations. On the other hand, online reviews are often overly negative, hampering an accurate evaluation of areas of excellence. The proposed methodology calibrates the levels of unbalanced responses in different data sources via resampling and performs data integration using Bayesian Networks to propagate the new re-balanced information. In this paper we show, with a case study example, how the novel data integration approach allows businesses and organizations to get a bias corrected appraisal of the level of satisfaction of their customers. The application is based on the integration of online data of review blogs and customer satisfaction surveys from the San Francisco airport. We illustrate how this integration enhances the information quality of the data analytic work in four of InfoQ dimensions, namely, Data Structure, Data Integration, Temporal Relevance and Chronology of Data and Goal.

Sentiment analysis helps evaluating the performance of products or services from user generated contents. Lexicon based sentiment analysis approaches are preferred over learning based ones when training data is not adequate. Existing lexicons contain only unigrams along with their sentiment scores. It is observed that sentiment n-grams formed by combining unigrams with intensifiers or negations show improved results. Such sentiment n-gram lexicons are not publicly available. This paper presents a methodology to create such a lexicon called Senti-N-Gram. Proposed rule-based approach extracts the n-grams sentiment scores from a random corpus containing product reviews and corresponding numeric rating in five-point scale. The scores from this automated procedure are compared with that of the human annotators using t-test and found to be statistically equivalent. The paper also proposes a sentiment classification methodology by using a ratio based approach based on counts of positive and negative sentences of a document. When used Senti-N-Gram lexicon, proposed method outperforms well-known unigram-lexicon based approach using VADER and an n-gram sentiment analysis approach SO-CAL.

Online social networks (OSNs) are receiving great attention from the research community for different purposes, such as event detection, crisis management, and forecasting, among others. The increasing amount of research conducted with social networks opens the need for a classification methodology regarding trends in the field. This work does not cover all types of social networks; it focuses on the analysis of microblogs as a data source in the context of recommender systems (RSs). The main goal of this work is to provide authors with insights on the trends of academic literature reviews in the proposed context and to provide a comparison of different research approaches. The authors searched for up-to-date research papers related to RS methods using microblogs within a time period of five years, from 2012 to January 2018. Starting from 2012, a significant amount of research related to the subject field of RSs was conducted and identified by the authors of this work. After the filtering process, 39 papers were finally selected from journals and conferences in four different databases related to Internet technologies (i.e., IEEE, ACM, Science Direct, and Springer). A general classification presented in this work is then adopted and used to describe state-of-the-art social network recommendation approaches for microblogging. This work can be extended in the future to include novel methodologies and trends of RSs for microblogs.

In statistics, machine learning, and related fields, feature selection is the process of choosing a smaller subset of features to work with. This is an important topic since selecting a subset of features can help analysts to interpret models and data, and to decrease computational runtimes. While many techniques are purely automatic, the data visualization community has produced a number of interactive approaches where users can make decisions taking into account their domain knowledge. In this paper we propose a new visualization technique based on radial axes that allows analysts to perform feature selection effectively, in contrast to previous radial axes methods. This is achieved by employing alternative scaled axes that provide insight regarding the features that have a smaller contribution to the visualizations. Therefore, analysts can use the technique to carry out interactive backwards feature elimination, by discarding the least relevant features according to the information on the plots and their expertise. Our approach can be coupled with any linear dimensionality reduction method, and can be used when performing analyses of cluster structure, correlations, class separability, etc. Specifically, in this paper we focus on combining the proposed technique with methods designed for classification. Lastly, we illustrate the effectiveness of our proposal through a case study analyzing high-dimensional medical chronic conditions data. In particular, clinicians have used the technique for determining the most important features that discriminate between patients with diabetes and high blood pressure.

The research and practice of information systems have progressed by leaps and bounds due to the emergence of big data analytics and machine learning. The paper undertakes a bibliometric study to analyze the contributions of major authors, universities/organizations, and countries in terms of productivity, citations, and bibliographic coupling. A sample of 2160 articles from the Scopus for the period 2006–2020 is the basis of the study. The publications are grouped into five clusters, of which Cluster 1 is consistently dominant in the information systems publication landscape. Cluster 2 includes published studies on the Internet of Things, security, and cloud computing, which have also been widely researched. Cluster 3, the third-largest cluster, has attempted to investigate social media analytics. Cluster 4 aims to look into the impact of classification and predictive, which is found to have sustained research interest. Topics with scant coverage in terms of papers are primarily in Cluster 5, indicating saturation in the area and the need for conducting inter-disciplinary studies. The results of our study provide valuable insights for potential contributors and global audiences in terms of emerging topics for research.

Due to speckle noise, changes in kidney shape and size between patients, and similar regions, segmenting kidneys in ultrasound images is challenging. To alleviate this challenge, we proposed a novel CNN model, namely multi-scale fusion network of structural features and detailed features (SDFNet), to segment kidneys accurately and robustly. Specifically, the architecture includes structure feature extraction network (S-Net), detail information extraction network (D-Net) and multi-scale fusion block (MCBlock), which are in charge of extracting structural features, capturing texture details and merging features, respectively. In S-Net, we designed a boundary detection (BD) module to obtain more complete kidney structural features. In addition, this paper also designed a step-by-step training mechanism to improve the generalization ability of the SDFNet. We validated the proposed method and compared the same kidney ultrasound dataset with several state-of-the-art methods using six quantitative indicators. The results demonstrate that the proposed approach achieves the best overall performance and outperforms all other approaches on kidney ultrasound image segmentation. It is worth noting that this paper quantitatively analyzes the loss function of segment kidney ultrasound images. This work is a good reference for future research.

This short communication provides a discourse emerged after reading “Stacking ensemble based deep neural networks modeling for effective epileptic seizure detection, Expert Systems with Applications, 148, 113239, 2020.” The discussed paper proposes a novel application of stacking-based ensemble for seizure detection, where several deep neural networks were used as base classifiers. The ensemble design and experimental results presented by the author show some weaknesses, which is indicated by, one of which, an inability of the proposed model to outperform previous studies. In this note, controversy of the discussed paper is explained and an improved version of stacking-based deep neural network is also further introduced and detailed to prevent it in the future.

This paper addresses the problem of how to represent education system measurement definitions and the data used to derive them. ISO 37120 is standard for measuring city performance. It defines 100 indicators of which seven focus on Education. As cities adopt the standard and begin publishing their indicator values, citizens, city bureaucrats and politicians will be able to see how well their education systems perform relative to other cities. Hence, education systems will be subject to greater scrutiny and will have to provide evidence-based explanations for their performance. In order to enable the automated analysis of city performance, four problems must be solved: 1) how are indicator definitions represented? 2) how is the data used to derive an indicator value represented? 3) how is educational system specific knowledge represented, and 4) how is city specific educational system knowledge represented? In this paper we present an Education Ontology designed to represent ISO 37120 education indicators and the information delineated above. The ontology is designed to enable an intelligent agent to perform consistency and root cause analysis of city indicators. It can also be used by cities to publish their education indicators and data, using Semantic Web standards, on their Open Data portals.

Music radio data is currently underutilised in radio program management. Software tools that listen to and analyse music airplay are in many markets nonexistent, limited, or unaffordable. In this paper we present a novel knowledge discovery and visualisation framework for broadcast radio, ZeitMetric. The ZeitMetric framework uses machine learning and music information retrieval techniques to label radio audio automatically for knowledge discovery. The framework incorporates a novel music dataset collection technique (MusiGrab) to leverage online music services for ground-truth data, as well as a novel knowledge visualisation and presentation technique based on self-organizing maps (ZeitViz). The framework is compared to what little literature relating to this topic exists, and a set of requirements for a high-quality broadcast radio knowledge discovery is developed. MusiGrab specifically is compared to an existing static music information retrieval dataset and shown to offer superior results in this context. Future research directions using and extending the framework are also discussed. On acceptance of the paper, code for a use-case of the MusiGrab dataset collection technique will be released on GitHub.

The variety and plethora of research papers available on the Web motivated researchers to propose models that could assist users with personalized citation recommendations. In recent years, citation recommendation models using Network Representation Learning (NRL) methods have shown promising results. Nevertheless, existing NRL-based models are limited in terms of exploiting semantic relations and contextual information between the objects of bibliographic papers’ networks. Additionally, these models cannot adequately explore the structure of heterogeneous information networks, topical relevance, and relevant semantics. Consequently, they suffer from network sparsity and inadequate personalization problems. To overcome these shortcomings, we present a network embedding model termed as Global Citation Recommendation employing Generative Adversarial Network (GCR-GAN). The proposed model exploits the Heterogeneous Bibliographic Network (HBN) to generate personalized citation recommendations. In particular, the proposed model utilizes semantic relations corresponding to the objects of the heterogeneous bibliographic network and captures network structure proximity employing the Scientific Paper Embeddings using Citation-informed Transformers (SPECTER) and Denoising Auto-encoder networks to learn semantic-preserving graph representations. Compared to baseline models, the recommendations generated by our model over the DBLP and ACM datasets prove that it outperforms baseline methods by gaining almost 11% and 12% improvement in terms of Mean Average Precision (MAP) and Normalized Discounted Cumulative Gain (nDCG) metrics, respectively. Furthermore, we analyzed the effectiveness of the proposed model considering network sparsity issue, where our model gains almost 7% better recall@100 score against the second-best counterpart.

In the Authorship Attribution (AA) task, the most likely author of textual documents, such as books, papers, news, and text messages and posts are identified using statistical and computational methods. In this paper, a new computational approach is presented for identifying the most likely author of text documents. The proposed solution emphasizes lazy profile-based classification and, by using the Term Frequency-Inverse Document Frequency (TF_IDF) scheme, introduces a new measure for identifying important terms of documents. The importance of the terms is then used to calculate the similarity between an anonymous document and known documents. The proposed solution works with raw text documents and does not require any NLP tools for preprocessing, which makes it language-independent. The efficiency of the proposed solution has been evaluated by conducting several experiments on two English and Persian datasets, each of which contains six corpora with different number of authors. The obtained results demonstrate that the proposed solution outperforms state-of-the-art stylometric features, employed by seven well-known classifiers, by obtaining 0.902 accuracy for the English dataset and 0.931 accuracy for the Persian dataset. In addition, supplementary experiments have been conducted to evaluate the effects of documents’ length on the accuracy of the proposed solution, to examine the computation time of the proposed solution and competitive classifiers, and to identify the most effective stylometric features and classifiers.

Crowdsourcing as an important computing resource for the Crowd-based Cooperative, crowdsourcing workers, due to the complexity of their personnel composition and the ambiguity of group characteristics, make it difficult to accurately recommend suitable worker groups for the task. Recommending crowdsourcing tasks to suitable workers can greatly improve the efficiency and quality of the execution of crowdsourcing tasks. The current crowdsourcing recommendation algorithm is based on the single characteristics of the workers, ignoring the multi-dimensional characteristics of the workers and the community characteristics of the crowd. This paper proposes a worker group recommendation method based on multi-community collaboration (GRMBC) by utilizing the worker characteristics information extracted from crowdsourcing platform. Based on the characteristics of workers'reputation, preference and activity, the method divides the worker group into several characteristics communities with similar behavior to discover the potential multi-community structure in the crowdsourcing worker group, and then selects Top-N worker group for recommendation through the interaction among multi-communities. At the same time, we also proposed two mitigation strategies for the cold start problem of data in crowdsourcing. This paper uses the public data collected by AMT to do the experiments, and compares the aggregation results of the recommendations generated by different algorithms. The results show the recommendations generated by the GRBMC algorithm proposed in this paper performs the best comprehensively. When the GRBMC algorithm that considers both worker attributes and communi-ty characteristics has an accuracy improvement of 0.03 ~ 0.04 compared with not using the recommendation algorithm on each index, the accuracy of 0.01 ~ 0.02 is improved com-pared with the method considering single characteristic. Moreover, compared with the individual recommendation method of the workers, the recommendation method of the workers' group is more in line with the platform requirements, and can well reflect the wisdom of crowd wisdom.

Seam-carving is used for adjusting the size of an image as well as removing objects from images. When seam-carving is applied to delete the object in the image, it may lead to the distortion of the information transmitted by the image, misleading the public, and endangering human health. Therefore, detecting the location of objects removed by seam-carving techniques is a scientific challenge to the image forensics. According to the position movement law of the related pixel in the seam-carving process, a method of the detecting and locating the objects deleted based on the block artifact grid (BAG) mispairing characteristic is proposed in this paper. Firstly, the BAG is extracted from the JPEG image; secondly, 10-dimensional features of the BAG chart is constructed; thirdly, clustering technique is applied on the extracted features of the BAG to obtain the classification results; finally, the location of the removed object is computed based on the previous classification results. Experiment result shows that the proposed method can efficiently detect and locate the object removed from an image by seam-carving. The proposed method in this paper opens up a new research direction for the location and detection of tampering, and has important application value for the intelligent forensics system based on seam carving.

Over the last few years, we have been witnessing a dramatic progress of deep learning in many real world applications. Deep learning concepts have been originated in the area of neural network and show a quantum leap in effective feature learning techniques such as auto-encoders, convolutional neural networks, recurrent neural networks etc. In the case of kernel machines, there are several attempts to model learning machines that mimic deep neural networks. In this direction, Multilayer Kernel Machines (MKMs) was an attempt to build a kernel machine architecture with multiple layers of feature extraction. It composed of many layers of kernel PCA based feature extraction units with support vector machine having arc-cosine kernel as the final classifier. The other approaches like Multiple Kernel Learning (MKL) and deep core vector machines solve the fixed kernel computation problem and scalability aspects of MKMs respectively. In addition to this, there are lot of avenues where the use of unsupervised MKL with both single and multilayer kernels in the multilayer feature extraction framework have to be evaluated. In this context, this paper attempts to build a scalable deep kernel machines with multiple layers of feature extraction. Each kernel PCA based feature extraction layer in this framework is modeled by the combination of both single and multilayer kernels in an unsupervised manner. Core vector machine with arc-cosine kernel is used as the final layer classifier which ensure the scalability in this model. The major contribution of this paper is a novel effort to build a deep structured kernel machine architecture similar to deep neural network architecture for classification. It opens up an extendable research avenue for researchers in deep learning based intelligent system leveraging the principles of kernel theory. Experiments show that the proposed method consistently improves the generalization performances of existing deep core vector machine.

DBN- based classification model is proposed in this paper for web spam detection.SMOTE and DAE algorithms is applied in DBN to improve the classification performanceThe content and link feature is combined.The results obtained in this paper is better than the existing systems. With the development of the Internet, the number of web spam increases gradually, which has seriously affected the user experience of search engines. To improve the classification performance of web spam, the deep belief networks (DBN) is used for the first time, and it is effectively combined with the Synthetic Minority Over-Sampling Technique (SMOTE) and De-Noising Auto-Encoder (DAE) algorithm after the multi-aspect research and consideration. After multiple sets of experiments on WEBSPAM-UK2007 dataset, the results show that the classification method proposed in this paper improves the classification performance to a certain extent, which provides a good direction for the future classification of web spam.

Proposed a new sentiment analysis method, based on text-based hidden Markov models, that uses word orders without the need of sentiment lexicons.Proposed an ensemble of text-based hidden Markov models using boosting and clusters of words produced by latent semantic analysis.Showed the method has potential to classify implicit opinions by the proposed ensemble method.Showed better performance in comparison to several previous algorithms in several datasets.Applied it to a real-life dataset to classify paper titles. With the rapid growth of social media, text mining is extensively utilized in practical fields, and opinion mining, also known as sentiment analysis, plays an important role in analyzing opinion and sentiment in texts. Methods in opinion mining generally depend on a sentiment lexicon, which is a set of predefined key words that express sentiment. Opinion mining requires proper sentiment words to be extracted in advance and has difficulty classifying sentences that imply an opinion without using any sentiment key words. This paper presents a new sentiment analysis method, based on text-based hidden Markov models (TextHMMs), for text classification that uses a sequence of words in training texts instead of a predefined sentiment lexicon. We sought to learn text patterns representing sentiment through ensemble TextHMMs. Our method defines hidden variables in TextHMMs by semantic cluster information in consideration of the co-occurrence of words, and thus calculates the sentiment orientation of sentences by fitted TextHMMs. To reflect diverse patterns, we applied an ensemble of TextHMM-based classifiers. In the experiments with a benchmark data set, we show that this method is superior to some existing methods and particularly has potential to classify implicit opinions. We also demonstrate the practicality of the proposed method in a real-life data set of online market reviews.

This paper presents a complete review of computer vision algorithms and vision-based intelligent applications, that are developed in the field of the Unmanned Aerial Vehicles (UAVs) in the latest decade. During this time, the evolution of relevant technologies for UAVs; such as component miniaturization, the increase of computational capabilities, and the evolution of computer vision techniques have allowed an important advance in the development of UAVs technologies and applications. Particularly, computer vision technologies integrated in UAVs allow to develop cutting-edge technologies to cope with aerial perception difficulties; such as visual navigation algorithms, obstacle detection and avoidance and aerial decision-making. All these expert technologies have developed a wide spectrum of application for UAVs, beyond the classic military and defense purposes. Unmanned Aerial Vehicles and Computer Vision are common topics in expert systems, so thanks to the recent advances in perception technologies, modern intelligent applications are developed to enhance autonomous UAV positioning, or automatic algorithms to avoid aerial collisions, among others. Then, the presented survey is based on artificial perception applications that represent important advances in the latest years in the expert system field related to the Unmanned Aerial Vehicles. In this paper, the most significant advances in this field are presented, able to solve fundamental technical limitations; such as visual odometry, obstacle detection, mapping and localization, et cetera. Besides, they have been analyzed based on their capabilities and potential utility. Moreover, the applications and UAVs are divided and categorized according to different criteria. This research is supported by the Spanish Government through the CICYT projects (TRA2015-63708-R and TRA2013-48314-C3-1-R).

Digital pre-assembly process can evaluate flaws in design prior to manufacturing.As these reports are written in free text form, they are not fully utilized.This paper proposes a method of applying text mining techniques to extract insights.The proposed method successfully extracts useful information from the text database. After digitally designing components of vehicles, a design team creates a virtual manufacturing environment that resembles actual manufacturing facilities. During this digital pre-assembly process, a review team examines each component, and records its problems and requirements in part verification reports. Once these reports are delivered to specific design team responsible for each part, the design team can make appropriate adjustments to their designs. This digital pre-assembly process can evaluate and prevent flaws in design prior to actual manufacturing, improving production quality and reducing manufacturing cost. As these reports are written in free text form, they, however, are not fully utilized for understanding problems arising from the design process. This paper proposes a method of applying text mining techniques on verification reports to extract insights for quality improvement. In this paper, following three text mining approaches are proposed: (1) Extracting n-grams for text preprocessing and constructing domain ontology; (2) Extracting meaningful insights from text preprocessing; (3) Creating intuitive visual tools to understand the extracted insights. The proposed method is applied on approximately 140,000 reports, and is validated through the quality of the answers obtained for the questions posed by the domain experts. The proposed method successfully extracts useful information from the text database, and provides intuitive graphical interface, thereby satisfying the need of the domain experts. This paper proposes a systematic framework of transforming huge amount of raw text data into intuitive visualization. Through this framework, meaningful knowledge can be extracted, analyzed and shared to improve the quality of the products. Main contribution of our paper is that it proposes a framework for knowledge extraction from pre-assembly process. Not only does it systematically arrange the data, but it also combines various data sources and creates a knowledge system to improve efficiency of the design process.

Customer purchase prediction aims to predict customers' future purchases, and the prediction results are of great importance for conducting future commercial activities. To obtain accurate predictions of customer purchases, this paper develops a machine learning framework based on historical behavioural data. First, considering the sparsity of behavioural data, this paper proposes a feature combination method based on the improved factorization machine algorithm. Second, due to the imbalance of customer purchase data, this paper proposes an imbalanced prediction method based on the maximized marginal category and cost-sensitive ensemble learning. Finally, a real-word travel service purchase dataset is adopted to test the feasibility of the proposed prediction framework. The experimental results and comparative analysis verify the validity of the proposed model.

Traditional social community discovery methods concentrate mainly on static social networks, but the analysis of dynamic networks is a prerequisite for real-time and personalized social services. Through the study of community changes, the community structure in a dynamic network can be tracked over time, which helps in the mining of dynamic network information. In this paper, we propose a method of tracking dynamic community evolution that is based on resistance distance. Specifically, we model the time-varying features of dynamic networks using the convergence of a resistance-based distance. In our model, the heterogeneity of neighboring nodes can be obtained in the local topology of nodes by analyzing the resistance distance between nodes. We design a community discovery algorithm that essentially discovers community structures on dynamic networks by identifying the so-called core node. During the process of community evolution analysis, both the dynamic contribution of ordinary nodes and core nodes in each community are considered. In addition, to avoid the inclusion of spurious communities in the community structure, we define the notion of noise community and account for it in our algorithm. Experimental results show that the method proposed in this paper can yield better accuracy than other existing methods.

This paper introduces a three-step framework for classifying multiclass radiography images. The first step utilizes a de-noising technique based on wavelet transform (WT) and the statistical Kolmogorov Smirnov (KS) test to remove noise and insignificant features of the images. An unsupervised deep belief network (DBN) is designed for learning the unlabelled features in the second step. Although small-scale DBNs have demonstrated significant potential, the computational cost of training the restricted Boltzmann machine is a major issue when scaling to large networks. Moreover, noise in radiography images can cause a significant corruption of information that hinders the performance of DBNs. The combination of WT and KS test in the first step helps improve performance of DBNs. Discriminative feature subsets obtained in the first two steps serve as inputs into classifiers in the third step for evaluations. Five frequently used classifiers including naive Bayes, radial basis function network, random forest, sequential minimal optimization, and support vector machine and four different case studies are implemented for experiments using the Image Retrieval in Medical Application data set. The experimental results show that the three-step framework has significantly reduced computational cost and yielded a great performance for multiclass radiography image classification. Along with effective applications in image processing in other fields published in the literature, deep learning network in this paper has again demonstrated its robustness in handling a complex set of medical images. This implies that the proposed approach can be implemented in real practice for analysing noisy radiography images, which have many useful medical applications such as diagnosis of diseases related to lung, breast, musculoskeletal or pediatric studies.

We propose estimating the quality of a contrast pattern using unseen objects.We evaluate quality measures by correlating their values and the estimated quality.We describe the influence of database characteristics in correlations.We perform a study to find how to synergistically combine quality measures. Contrast patterns, which lie in the core of most understandable classifiers, are frequently evaluated by quality measures. Since many different quality measures are available, they should be compared to select the most appropriate for each applications. This paper introduces a method to compare quality measures, using a set of mined patterns and a collection of objects not used for mining. The comparison is performed by correlating quality values with a quality estimation of the patterns. Additionally, a meta-learning study is performed to show that combining quality measures could be better than using the best single measures in isolation. The results of this paper can help researchers to create new quality measures or to find new combinations of quality measures to create better understandable classification systems.

Three meta-heuristic algorithms are adapted to solve the feature selection problem.Feature selection methods are established based on a novel weighting scheme.Dimension reduction technique is proposed to reduce the number of features.K-mean clustering algorithm is used based on the features obtained.The proposed methods outperform the comparative methods. This paper proposes three feature selection algorithms with feature weight scheme and dynamic dimension reduction for the text document clustering problem. Text document clustering is a new trend in text mining; in this process, text documents are separated into several coherent clusters according to carefully selected informative features by using proper evaluation function, which usually depends on term frequency. Informative features in each document are selected using feature selection methods. Genetic algorithm (GA), harmony search (HS) algorithm, and particle swarm optimization (PSO) algorithm are the most successful feature selection methods established using a novel weighting scheme, namely, length feature weight (LFW), which depends on term frequency and appearance of features in other documents. A new dynamic dimension reduction (DDR) method is also provided to reduce the number of features used in clustering and thus improve the performance of the algorithms. Finally, k-mean, which is a popular clustering method, is used to cluster the set of text documents based on the terms (or features) obtained by dynamic reduction. Seven text mining benchmark text datasets of different sizes and complexities are evaluated. Analysis with k-mean shows that particle swarm optimization with length feature weight and dynamic reduction produces the optimal outcomes for almost all datasets tested. This paper provides new alternatives for text mining community to cluster text documents by using cohesive and informative features.

L1 or L2 regularization based representation is not antinoise enough.An antinoise sparse representation via joint L1 and L2 is proposed.The rationale of the objective function for fusion is analyzed.Recognition of noisy samples is evaluated as true positive.The Anti-L1L2 outperforms some state-of-art sparse algorithms. Sparse representation methods based on l1 and/or l2 regularization have shown promising performance in different applications. Previous studies show that the l1 regularization based representation has more sparse property, while the l2 regularization based representation is much simpler and faster. However, when dealing with noisy data, both naive l1 and l2 regularization suffer from the issue of unsatisfactory robustness. In this paper, we explore the method to implement an antinoise sparse representation method for robust face recognition based on a joint version of l1 and l2 regularization. The contributions of this paper are mainly shown in the following aspects. First, a novel objective function combining both l1 and l2 regularization is proposed to implement an antinoise sparse representation. An iterative fitting operation via l1 regularization is integrated with l2 norm minimization, to obtain an antinoise classification. Second, the rationale how the proposed method produces promising discriminative and antinoise performance for face recognition is analyzed. The l2 regularization enhances robustness and runs fast, and l1 regularization helps cope with the noisy data. Third, the classification robustness of the proposed method is demonstrated by extensive experiments on several benchmark facial datasets. The method can be considered as an option for the expert systems for biometrics and other recognition problems facing unstable and noisy data.

Traditionally, the probably approximately correct (PAC) learning refers the single concept class. We discuss the PAC framework of the multiple tasks in the joint inference model. And we extend PAC learning to multi-concept classes.We present an ensemble learning approach to joint inference on the three NLP sub-tasks. We explain how to combine those weak learners to a strong ability and present the dynamic weighted combination method in the ensemble joint inference model.Our Ensemble Markov Logic Networks (EMLNs) address the problem of the Markov Logic Networks intractable dealing with the large scale data. Experiments show that this approach leads to a higher precision and recall than that of pipeline approaches. Joint inference is a fundamental issue in the field of artificial intelligence. The greatest advantage of the joint inference is demonstrated by its capability of avoiding errors from cascading and accumulating on a pipeline of multiple chained sub-tasks. Markov Logic Network(MLN) is the most common joint inference model that provides a flexible representation and handles uncertainty. It has been applied successfully to joint inference on many natural language processing tasks to avoid error propagation. However, due to the great expressiveness of first-order logic, the representation for it in MLN generates rather complicated graph structures, which makes the learning and inference on large scale data intractably. In this paper, we present an ensemble learning approach to deal with the challenges in MLNs. Firstly, we give a proof within the probably approximately correct (PAC) framework. The proof points out what conditions are necessary for successful applying the ensemble learning approach to MLN. Secondly, the paper explains how to combine the learners. Finally, in order to illustrate the working mechanism of the ensemble joint inference model, we present an Ensemble Markov Logic Networks (EMLNs) method and use it to extract knowledge from a large scale corpus published by Google.11code.google.com/p/relation-extraction-corpus/. Experiments suggest that significant speedup can be gained by the EMLNs. Meanwhile, it show that this approach leads to a higher precision and recall than that of those pipeline approaches.

A novel and efficient fingerprint indexing based on minutiae triplets, is proposed.The proposed fingerprint features are invariant to rotation and translation.Our fingerprint representation is very robust to distortions and noise.The experimental results demonstrate the validity of the proposed algorithm. Fingerprint indexing plays a key role in the automatic fingerprint identification systems (AFISs) which allows us to speed up the search in large databases without missing accuracy. In this paper, we propose a fingerprint indexing algorithm based on novel features of minutiae triplets to improve the performance of fingerprint indexing. The minutiae triplet based feature vectors, which are generated by ellipse properties and their relation with the triangles formed by the proposed expanded Delaunay triangulation, are used to generate indices and a recovery method based on k-means clustering algorithm is employed for fast and accurate retrieval. The proposed expanded Delaunay triangulation algorithm is based on the quality of fingerprint images and combines two robust Delaunay triangulation algorithms. This paper also employs an improved k-means clustering algorithm which can be applied over large databases, without reducing the accuracy. Finally, a candidate list reduction criteria is employed to reduce the candidate list and to generate the final candidate list for matching stage. Experimental results over some of the fingerprint verification competition (FVC) and national institute of standards and technology (NIST) databases show superiority of the proposed approach in comparison with state-of-the-art indexing algorithms. Our indexing proposal is very promising for the improvement of real-time AFISs efficiency and accuracy in the near future.

From unsupervised to supervised learning a fault detection model (for robots).Insights to why and when it becomes more accurate.Theoretical analysis and a prediction tool.Empirical results on 3 real-world domains that back these insights. The use of robots in our daily lives is increasing. As we rely more on robots, thus it becomes more important for us that the robots will continue on with their mission successfully. Unfortunately, these sophisticated, and sometimes very expensive, machines are susceptible to different kinds of faults. It becomes important to apply a Fault Detection (FD) mechanism which is suitable for the domain of robots. Two important requirements of such a mechanism are: high accuracy and low computational-load during operation (online). Supervised learning can potentially produce very accurate FD models, and if the learning takes place offline then the online computational-load can be reduced. Yet, the domain of robots is characterized with the absence of labeled data (e.g., faulty, normal) required by supervised approaches, and consequently, unsupervised approaches are being used. In this paper we propose a hybrid approach - an unsupervised approach can label a data set, with a low degree of inaccuracy, and then the labeled data set is used offline by a supervised approach to produce an online FD model. Now, we are faced with a choice should we use the unsupervised or the hybrid fault detector? Seemingly, there is no way to validate the choice due to the absence of (a priori) labeled data. In this paper we give an insight to why, and a tool to predict when, the hybrid approach is more accurate. In particular, the main impacts of our work are (1) we theoretically analyze the conditions under which the hybrid approach is expected to be more accurate. (2) Our theoretical findings are backed with empirical analysis. We use data sets of three different robotic domains: a high fidelity flight simulator, a laboratory robot, and a commercial Unmanned Arial Vehicle (UAV). (3) We analyze how different unsupervised FD approaches are improved by the hybrid technique and (4) how well this improvement fits our prediction tool. The significance of the hybrid approach and the prediction tool is the potential benefit to expert and intelligent systems in which labeled data is absent or expensive to create.

Solving a real-world problem with an uncommon dataset.Highly time-efficient and accurate for classification of high resolution X-Ray images.Emphasizing on keypoints of the image instead of considering all parts of the image.Considering the dependency between the visual words in the bag of visual words.Adopting Tree-Augmented Bayes in the task of image classification. Smuggling has long played an important role in the inefficiency of economies. To secure the borders against this illegal act, X-Ray Inspection Systems are often deployed at the borders and customs. In this paper, we present a new method for classification of shipping containers X-Ray images, produced in the inspection lines. The aim is to improve the matching accuracy of the presented manifest, which lists the claimed contents of the shipping containers, with the real contents of the container. The proposed method is based on utilizing Scale Invariant Feature Transforms (SIFT) feature vectors, Bag of visual words (BOVW) and tree augmented naive Bayes (TAN) approach for classifying containers X-Ray images. The prior research on classification of X-Ray images of shipping containers has focused mostly on working with greedy algorithms such as sliding windows for task of classification. More recent studies introduced filter banks and visual words for extraction of features. The proposed method for the first time considers the salient points and keypoints for the task of feature extraction. In addition, this paper presents a framework using the tree augmented naive Bayes based on the theory of learning Bayesian networks, which is proved to have a significant improvements upon the prior designed systems by considering the correlations among the extracted features. For experimental evaluations, our method is compared with two recently proposed methods on containers X-Ray images categorization. The results show that the proposed method is more accurate and time-efficient in categorization of X-Ray images.

A taxonomy that classifies ensemble models in the literature is presented.Surface and deep features integration is explored to improve classification.Several ensembles of classifiers and features are proposed and evaluated.Performance of the proposed models is evaluated on several sentiment datasets. Deep learning techniques for Sentiment Analysis have become very popular. They provide automatic feature extraction and both richer representation capabilities and better performance than traditional feature based techniques (i.e., surface methods). Traditional surface approaches are based on complex manually extracted features, and this extraction process is a fundamental question in feature driven methods. These long-established approaches can yield strong baselines, and their predictive capabilities can be used in conjunction with the arising deep learning methods. In this paper we seek to improve the performance of deep learning techniques integrating them with traditional surface approaches based on manually extracted features. The contributions of this paper are sixfold. First, we develop a deep learning based sentiment classifier using a word embeddings model and a linear machine learning algorithm. This classifier serves as a baseline to compare to subsequent results. Second, we propose two ensemble techniques which aggregate our baseline classifier with other surface classifiers widely used in Sentiment Analysis. Third, we also propose two models for combining both surface and deep features to merge information from several sources. Fourth, we introduce a taxonomy for classifying the different models found in the literature, as well as the ones we propose. Fifth, we conduct several experiments to compare the performance of these models with the deep learning baseline. For this, we use seven public datasets that were extracted from the microblogging and movie reviews domain. Finally, as a result, a statistical study confirms that the performance of these proposed models surpasses that of our original baseline on F1-Score.

A new initialization technique is proposed to improve the performance of K-means.A data transformation approach is proposed to solve empty cluster problem.An efficient method is proposed to estimate the optimal number of clusters.Proposed clustering method provides more accurate clustering results. Clustering provides a knowledge acquisition method for intelligent systems. This paper proposes a novel data-clustering algorithm, by combining a new initialization technique, K-means algorithm and a new gradual data transformation approach to provide more accurate clustering results than the K-means algorithm and its variants by increasing the clusters coherence. The proposed data transformation approach solves the problem of generating empty clusters, which frequently occurs for other clustering algorithms. An efficient method based on the principal component transformation and a modified silhouette algorithm is also proposed in this paper to determine the number of clusters. Several different data sets are used to evaluate the efficacy of the proposed method to deal with the empty cluster generation problem and its accuracy and computational performance in comparison with other K-means based initialization techniques and clustering methods. The developed estimation method for determining the number of clusters is also evaluated and compared with other estimation algorithms. Significances of the proposed method include addressing the limitations of the K-means based clustering and improving the accuracy of clustering as an important method in the field of data mining and expert systems. Application of the proposed method for the knowledge acquisition in time series data such as wind, solar, electric load and stock market provides a pre-processing tool to select the most appropriate data to feed in neural networks or other estimators in use for forecasting such time series. In addition, utilization of the knowledge discovered by the proposed K-means clustering to develop rule based expert systems is one of the main impacts of the proposed method.

The identification and prediction of situations in smart houses.An approach developed with the principles of reactive and proactive behavior.A case study presented with sensors and appliances.The proactivity and reasoning about uncertainty supported by probabilistic ontology. Considering that the world population is aging, health-support issues are in evidence, and many dangerous situations concerning users in their living environment may arise. This paper presents an approach to allow Ambient Assisted Living (AAL) systems to identify and predict situations that may endanger users in their living environment. Elderly people generally have difficulties to perform daily activities as using some electronic device, cooking, dressing among others. This paper offers an alternative to acquire comprehension of a former person's behavioral providing this knowledge when cognitive impairments will occur. This is a significant improvement to traditional expert systems applied to AAL as the handling of the knowledge base is adapted to the failing users behavior. We consider the user profile dynamics where new and different situations not initially planned in the development of the systems may occur. This approach was developed considering the principles of reactive and proactive behavior and the extensibility capacity of the system. Systems developed following the above-described principles can react to the current situations and anticipate proactively a dangerous situation eliminating it or controlling its impact. The approach is evaluated and verified by a case study where unwanted, dangerous situations were created and subsequently detected. The case study is based on a simulated smart house fulfilled with sensors and appliances. An application was build based on the developed approach, and it was capable of choosing the most appropriate actions to solve the unwanted situations. The main contributions of the present work are the identification and prediction of situations in smart houses, an approach developed with the principles of reactive and proactive behavior validated with a case study and supported by a probabilistic ontology.