A residual network-based framework for COVID-19 detection from CXR images

Neural Computing and Applications - In late 2019, a new Coronavirus disease (COVID-19) appeared in Wuhan, Hubei Province, China. The virus began to spread throughout many countries, affecting a...     

A novel ensemble scheme for the multi-focus image fusion using support vector machine

In the multi-focus image fusion problem, the source images are obtained from the same scene. They are fused to get an image that contains all well-focussed objects. Previously, individual machine-learning models are proposed for image fusion. The performance of individual models is limited to fuse the useful information extracted from the blurred images. To address this problem, we developed a novel ensemble scheme for multi-focus image fusion using support vector machines (SVMs). In the proposed scheme, first, SVM models are constructed using different kernel functions of linear, polynomial, radial basis, and sigmoid. The predictions of individual SVM models are then combined using majority voting. In this way, the combined decision space becomes more informative and discriminant. A comparative analysis of the proposed scheme is carried out with previous techniques. It is found that our scheme is more accurate for synthesized-blurred and real defocussed images.     

A novel optimization framework for salient object detection

Visual saliency aims to locate the noticeable regions or objects in an image. In this paper, a coarse-to-fine measure is developed to model visual saliency. In the proposed approach, we firstly use the contrast and center bias to generate an initial prior map. Then, we weight the initial prior map with boundary contrast to obtain the coarse saliency map. Finally, a novel optimization framework that combines the coarse saliency map, the boundary contrast and the smoothness prior is introduced with the intention of refining the map. Experiments on three public datasets demonstrate the effectiveness of the proposed method.     

A novel intrusion detection framework for wireless sensor networks

Vehicle cloud is a new idea that uses the benefits of wireless sensor networks (WSNs) and the concept of cloud computing to provide better services to the community. It is important to secure a sensor network to achieve better performance of the vehicle cloud. Wireless sensor networks are a soft target for intruders or adversaries to launch lethal attacks in its present configuration. In this paper, a novel intrusion detection framework is proposed for securing wireless sensor networks from routing attacks. The proposed system works in a distributed environment to detect intrusions by collaborating with the neighboring nodes. It works in two modes: online prevention allows safeguarding from those abnormal nodes that are already declared as malicious while offline detection finds those nodes that are being compromised by an adversary during the next epoch of time. Simulation results show that the proposed specification-based detection scheme performs extremely well and achieves high intrusion detection rate and low false positive rate.     

A novel framework for making dominant point detection methods non-parametric

Most dominant point detection methods require heuristically chosen control parameters. One of the commonly used control parameter is maximum deviation. This paper uses a theoretical bound of the maximum deviation of pixels obtained by digitization of a line segment for constructing a general framework to make most dominant point detection methods non-parametric. The derived analytical bound of the maximum deviation can be used as a natural bench mark for the line fitting algorithms and thus dominant point detection methods can be made parameter-independent and non-heuristic. Most methods can easily incorporate the bound. This is demonstrated using three categorically different dominant point detection methods. Such non-parametric approach retains the characteristics of the digital curve while providing good fitting performance and compression ratio for all the three methods using a variety of digital, non-digital, and noisy curves.     

A novel deep learning framework for copy-moveforgery detection in images

Multimedia Tools and Applications - In this era of technology, digital images turn out to be ubiquitous in a contemporary society and they can be generated and manipulated by a wide variety of...     

A novel fuzzy clustering algorithm for the analysis of axillary lymph node tissue sections

Recently Fourier Transform Infrared (FTIR) spectroscopic imaging has been used as a tool to detect the changes in cellular composition that may reflect the onset of a disease. This approach has been investigated as a mean of monitoring the change of the biochemical composition of cells and providing a diagnostic tool for various human cancers and other diseases. The discrimination between different types of tissue based upon spectroscopic data is often achieved using various multivariate clustering techniques. However, the number of clusters is a common unknown feature for the clustering methods, such as hierarchical cluster analysis, k-means and fuzzy c-means. In this study, we apply a FCM based clustering algorithm to obtain the best number of clusters as given by the minimum validity index value. This often results in an excessive number of clusters being created due to the complexity of this biochemical system. A novel method to automatically merge clusters was developed to try to address this problem. Three lymph node tissue sections were examined to evaluate our new method. These results showed that this approach can merge the clusters which have similar biochemistry. Consequently, the overall algorithm automatically identifies clusters that accurately match the main tissue types that are independently determined by the clinician.     

A novel spatiotemporal attention enhanced discriminative network for video salient object detection

Applied Intelligence - In contrast to image salient object detection, on which many achievements have been made, video salient object detection remains a considerable challenge. Not all features...     

A logical framework for commonsense predictions of solid object behaviour

Predicting the behaviour of a qualitatively described system of solid objects requires a combination of geometrical, temporal, and physical reasoning. Methods based upon formulating and solving differential equations are not adequate for robust prediction, since the behaviour of a system over extended time may be much simpler than its behaviour over local time. This paper presents a first-order logic in which one can state simple physical problems and derive their solution deductively, without recourse to solving differential equations. This logic is substantially more expressive and powerful than any previous AI representational system in this domain.     

A novel ensemble decision tree based on under-sampling and clonal selection for web spam detection

Currently, web spamming is a serious problem for search engines. It not only degrades the quality of search results by intentionally boosting undesirable web pages to users, but also causes the search engine to waste a significant amount of computational and storage resources in manipulating useless information. In this paper, we present a novel ensemble classifier for web spam detection which combines the clonal selection algorithm for feature selection and under-sampling for data balancing. This web spam detection system is called USCS. The USCS ensemble classifiers can automatically sample and select sub-classifiers. First, the system will convert the imbalanced training dataset into several balanced datasets using the under-sampling method. Second, the system will automatically select several optimal feature subsets for each sub-classifier using a customized clonal selection algorithm. Third, the system will build several C4.5 decision tree sub-classifiers from these balanced datasets based on its specified features. Finally, these sub-classifiers will be used to construct an ensemble decision tree classifier which will be applied to classify the examples in the testing data. Experiments on WEBSPAM-UK2006 dataset on the web spam problem show that our proposed approach, the USCS ensemble web spam classifier, contributes significant classification performance compared to several baseline systems and state-of-the-art approaches.     

A data fusion framework with novel hybrid algorithm for multi-agent Decision Support System for Forest Fire

In this study Forest Fire Decision Support System (FOFDESS) which is a multi-agent Decision Support System for Forest Fire has been presented. Depending on the existing meteorological state and environmental observations, FOFDESS does the fire danger rating by predicting the forest fire and it can also approximate fire spread speed and quickly detect a started fire. Some data fusion algorithms such as Artificial Neural Network (ANN), Naive Bayes Classifier (NBC), Fuzzy Switching (FS) and image processing have been used for these operations in FOFDESS. These algorithms have been brought together by a designed data fusion framework and a novel hybrid algorithm called NABNEF (Naive Bayes Aided Neural-Fuzzy Algorithm) has been improved for fire danger rating in FOFDESS. In this state, FOFDESS is an integrated system which includes the dimensions of prediction, detection and management. As a result of the experiments, it was found out that FOFDESS helped determining the most accurate strategy for fire fighting by producing effective results.     

A novel ensemble of classifiers for microarray data classification

Micorarray data are often extremely asymmetric in dimensionality, such as thousands or even tens of thousands of genes and a few hundreds of samples. Such extreme asymmetry between the dimensionality of genes and samples presents several challenges to conventional clustering and classification methods. In this paper, a novel ensemble method is proposed. Firstly, in order to extract useful features and reduce dimensionality, different feature selection methods such as correlation analysis, Fisher-ratio is used to form different feature subsets. Then a pool of candidate base classifiers is generated to learn the subsets which are re-sampling from the different feature subsets with PSO (Particle Swarm Optimization) algorithm. At last, appropriate classifiers are selected to construct the classification committee using EDAs (Estimation of Distribution Algorithms). Experiments show that the proposed method produces the best recognition rates on four benchmark databases.     

A novel two-stage phased modeling framework for early fraud detection in online auctions

Reported dollar losses from online auction fraud were over $43M in 2008 in the US (NW3C, 2009). In general, reputation systems provided by online auction sites are the most common countermeasure available for buyers to evaluate a seller’s credit. Unfortunately, feedback score mechanisms are too easily manipulated, creating falsely overrated reputations. In addition, existing research on online auction fraud shows that a more complicated reputation management system could weaken the motivation of committing a fraud. However, very few of the previous work addresses the most important issue of a fraud detection mechanism is to discover a fraudster before he defrauds as early as possible. Therefore, developing an effective early fraud detection mechanism is necessary to prevent fraud for online auction participants.This paper proposes a novel two-stage phased modeling framework that integrates hybrid-phased models with a successive filtering procedure to identify latent fraudsters by examining the phased features of potential fraudsters’ lifecycles. This framework improves the performance of identifying latent fraudsters disguising as legitimate accounts with diverse features. In addition, a composite of measuring attributes we devised in this study is also helpful in modeling fraudulent behavior. To demonstrate the effectiveness of the proposed methods, real transaction data were collected from Yahoo!Taiwan (http://tw.bid.yahoo.com/) for training and testing. The experimental results show that the true positive rate of detecting fraudsters is over 93% on average. Furthermore, the proposed framework can significantly improve the precision and the success rate of fraud detection; the experimental results also show that the fraud detection models constructed by conventional methods are ineffective in detecting latent fraudsters.     

A novel framework for concept detection on large scale video database and feature pool

Large-scale semantic concept detection from large video database suffers from large variations among different semantic concepts as well as their corresponding effective low-level features. In this paper, we propose a novel framework to deal with this obstacle. The proposed framework consists of four major components: feature pool construction, pre-filtering, modeling, and classification. First, a large low-level feature pool is constructed, from which a specific set of features are selected for the latter steps automatically or semi-automatically. Then, to deal with the unbalance problem in training set, a pre-filtering classifier is generated, which the aim of achieving a high recall rate and a certain precision rate nearly 50% for a certain concept. Thereafter, from the pre-filtered training samples, a SVM classifier is built based on the selected features in the feature pool. After that, the SVM classifier is applied to classification of semantic concept. This framework is flexible and extensible in terms of adding new features into the feature pool, introducing human interactions in selecting features, building models for new concepts and adopting active learning.     

A disease network-based recommender system framework for predictive risk modelling of chronic diseases and their comorbidities

The prediction of chronic diseases and their comorbidities is an essential task in healthcare, aiming to predict patients’ future disease risk based on their previous medical records. The accumulation of administrative data has laid a solid foundation for applying deep learning approaches in healthcare. Existing studies focused on the patients’ characteristics such as gender, age and location to predict the risk of the different diseases. However, there are high dimensional, incomplete and noisy problems in the administrative data. In this research, using administrative health data, we implemented graph theory and content-based recommender system approaches to analyse and predict chronic diseases and their comorbidities. Firstly, we used bipartite graphs to represent the relationships between patients and diseases. Then, we projected this graph to a one-mode graph, namely ‘disease network’. After that, six recommender system models with patient features and network features were trained. The outputs of these models are the severity levels of diseases and the predicted diseases with rank. Finally, we evaluated the performance of these models against the same models without network features. The results demonstrated that the models with network features have lower prediction error and better performances for predicting chronic diseases and their latent comorbidities on large administrative data. Among these models, the graph convolution matrix completion model reveals the least amount of error and the best performance for prediction. Further, using a case study of a specific patient, we demonstrated the application of these models in predictive disease risk analysis. Thus, this study showed the potential application of the recommender system approaches to the health sector utilising administrative claim data, which could significantly contribute to healthcare services and stakeholders.

     

A novel voting ensemble model for spatial prediction of landslides using GIS

ABSTRACT

Inherent complex topography and drastic weather patterns together have concocted various natural disasters worldwide. In difficult terrains such as those prevalent in the North-Eastern regions of India, coupled with the factors such as population explosion and improper land use, lead them to witness some of the world’s most drastic landslides with an astonishing frequency, reckoning landslide susceptibility assessment crucial in such regions. This paper focuses on exploring a promising machine learning ensemble technique of Majority-based voting which has seldom been employed for landslide susceptibility assessment. The ensemble comprises Logistic Regression (LR), Gradient Boosted Decision Trees (GBDT) and Voting Feature Interval (VFI) to prepare landslide susceptibility zonation maps for the Brahmaputra valley region (Assam & Nagaland) and its close vicinity. In the first stage of the study, a landslide inventory for the area comprising 436 landslide locations was prepared in geographic information system (GIS), substantiated by news reports and remote sensing data. In the second stage, 16 landslide causative thematic maps including Elevation, Slope, Slope Aspect, General Curvature, Plan Curvature, Profile Curvature, Surface Roughness, Topographic Wetness Index (TWI), Stream Power Index (SPI), Slope Length, Normalized Difference Vegetation Index (NDVI), Land Use/Land Cover (LULC), Distance from Roads, Rivers, Faults and Railways were prepared. In the third stage, the landslide inventory was annexed with the causative factor maps to obtain a dataset comprising coordinates of the locations and the values of aforementioned causative factors on the corresponding coordinates. The proposed model was then trained and tested on the prepared dataset (70%:30% split). Finally, the efficiency of the new model was tested using the area under receiver operating characteristic curve (AUC of ROC). The validation results demonstrate the mettle of the proposed majority-based voting ensemble LR-GBDT-VFI (AUC: 0.98) against the conventional techniques such as Decision Trees, Support Vector Machines, Random Forest, etc. Altogether, the study offers an approach with wide scope across the field of landslide hazard assessment.     

A novel position prior using fusion of rule of thirds and image center for salient object detection

Salient object detection is one of the challenging problems in the field of computer vision. Most of the models use a center prior to detect salient objects. They give more weightage to the objects which are present near the center of the image and less weightage to the ones near the corners of the image. But there may be images in which object is placed near the image corner. In order to handle such situation, we propose a position prior based on the combined effect of the rule of thirds and the image center. In this paper, we first segment the image into an optimal number of clusters using Davies-Bouldin index. Then the pixels in these clusters are used as samples to build the Gaussian mixture model whose parameters are refined using Expectation-Maximization algorithm. Thereafter the spatial saliency of the clusters is computed based on the proposed position prior and then combined into a saliency map. The performance is evaluated both qualitatively and quantitatively on six publicly available datasets. Experimental results demonstrate that the proposed model outperforms the seventeen existing state-of-the-art methods in terms of F –measure and area under curve on all the six datasets.     

A methodology for feature interaction detection in the AIN 0.1framework

We propose an integrated methodology for specifying AIN (advanced intelligent networks) and switch based features and analyzing their interactions in the AIN 0.1 framework. The specification of each individual feature is tied to the AIN call model and requires only a minimum amount of information in terms of control and data for interaction analysis. Once a feature is specified, its specification is then validated for consistency with respect to control and data. Interaction analysis is conducted for a set of features based on the sharing of call variables between the SSP and the SCP. With this approach, one can detect the following interactions involving AIN features: (1) side effects, where a call variable modified by one feature is used by another feature and (2) disabling, where one feature disconnects a call, preventing another feature from execution. We also develop a theory that is based on the computation of sequences of messages exchanged between the SSP and the SCP and their call variable usage. This theory is shown to dramatically reduce the number of cases considered during the analysis. A brief overview of a tool that makes use of this methodology to aid in the task of feature interaction detection is also given     

A novel framework to alleviate the sparsity problem in context-aware recommender systems

Recommender systems have become indispensable for services in the era of big data. To improve accuracy and satisfaction, context-aware recommender systems (CARSs) attempt to incorporate contextual information into recommendations. Typically, valid and influential contexts are determined in advance by domain experts or feature selection approaches. Most studies have focused on utilizing the unitary context due to the differences between various contexts. Meanwhile, multi-dimensional contexts will aggravate the sparsity problem, which means that the user preference matrix would become extremely sparse. Consequently, there are not enough or even no preferences in most multi-dimensional conditions. In this paper, we propose a novel framework to alleviate the sparsity issue for CARSs, especially when multi-dimensional contextual variables are adopted. Motivated by the intuition that the overall preferences tend to show similarities among specific groups of users and conditions, we first explore to construct one contextual profile for each contextual condition. In order to further identify those user and context subgroups automatically and simultaneously, we apply a co-clustering algorithm. Furthermore, we expand user preferences in a given contextual condition with the identified user and context clusters. Finally, we perform recommendations based on expanded preferences. Extensive experiments demonstrate the effectiveness of the proposed framework.     

A framework for weighted fusion of multiple statistical models of shape and appearance

This paper presents a framework for weighted fusion of several active shape and active appearance models. The approach is based on the eigenspace fusion method proposed by Hall et al., which has been extended to fuse more than two weighted eigenspaces using unbiased mean and covariance matrix estimates. To evaluate the performance of fusion, a comparative assessment on segmentation precision as well as facial verification tests are performed using the AR, EQUINOX, and XM2VTS databases. Based on the results, it is concluded that the fusion is useful when the model needs to be updated online or when the original observations are absent