Semantic Image Analysis Using a Learning Approach and Spatial Context

The paper addresses the fundamental task of semantic image analysis by exploiting structural information (spatial relationships between image regions). We propose to combine a deep neural network (CNN) with graph matching where graphs encode efficiently structural information related to regions segmented by the CNN. Our novel approach solves the quadratic assignment problem (QAP) sequentially for matching graphs. The optimal sequence for graph matching is conveniently defined using reinforcement-learning (RL) based on the region membership probabilities produced by the CNN and their structural relationships. Our RL based strategy for solving QAP sequentially allows us to significantly reduce the combinatorial complexity for graph matching. Preliminary experiments are performed on both a synthetic dataset and a public dataset dedicated to the semantic segmentation of face images. Results show that the proposed RL-based ordering significantly outperforms random ordering, and that our strategy is about 386 times faster than a global QAP-based approach, while preserving similar segmentation accuracy.KeywordsSemantic image analysisStructural informationGraph matchingQuadratic assignment problemReinforcement learning

Due to errors, accuracy of Global Positioning System is not so high. Therefore, the Real Time Differential Global Poisoning System (RTDGPS) which is based on the successive transmission message of RTCM protocol, is using in real-time applications. Stability and accuracy of the system, significantly depends to a fast transmission of correction messages. These messages come from the reference station to the user stations and affected by the errors related to each satellite. Receiving correction factors which are transmitted by the reference station are facing with time-lag problem, which can increase the error of the RTDGPS. To overcome this problem, prediction algorithms are used. In this research, support vector machine (SVM) model is used to predict the pseudo range correction. Unfortunately, the practical use of SVM is limited because the quality of SVM models depends on a proper setting of SVM and SVM kernel parameters. Therefore, to determine the main parameters of the SVM, both particle swarm optimization (PSO) and genetic algorithm (GA), as two optimization techniques, are used. The proposed methodology has been implemented by a 6-s predicts time step. Simulations showed that the accuracy of GA–SVM and PSO–SVM are equal to 0.186 and 0.154, respectively.

흡수, 분포, 대사, 배설 특성 및 독성을 예측하기 위한 효과적인 툴을 개발하는 것은 신약개발의 초기단계에서 NCE(new chemical entity)에 대한 가장 중요한 업무 중의 하나이다. 최근에 이런 시도중의 하나로서 ADME/T(absorption, distribution, metabolism, excretion, toxicity)관련 성질들의 예측에 support vector machine(SVM)을 이용하고 있다. 그리고 SVM은 ADME/T 성질들을 정확하게 예측하는데 많이 사용 되고 있다. 그러나 SVM 모델링에 두 가지 문제가 있다. 특성 선택(feature selection) 과 매개변수 설정(parameter setting)은 여전히 해결해야 할 과제이다. 이 두 가지 문제들은 SVM 분류의 효율성과 정확도에 결정적인 영향을 끼친다. 특히 특성 선택과 최적화된 SVM 변수의 설정은 서로 영향을 주기 때문에 동시에 다루어져야 한다. 여기서 우리는 genetic algorithm(GA) – 특성 선택에 사용 – 과 grid search(GS) method– 변수최적화에 사용 – 두 가지를 통합하는 효과적인 해결책을 제시하였다. ADME/T관련 성질 중 하나인 심장부정맥을 야기시키는 hERG 이온채널 저해제 분류 모델이 여기서 제안된 GA-GS-SVM을 위해 할당되고 테스트 되었다. 1891개의 화합물을 가지는 트레이닝 셋으로 단일 모델 3개, 앙상블 모델 3개, 총 6개의 모델을 만들었고 175개의 외부 데이터를 테스트 셋으로 사용하여 검증하였다. 데이터의 불균형 문제를 해결하기 위하여 GA-GS-SVM 단일 모델에 의한 예측 정확도와 GA-GS-SVM 앙상블 모델 예측 정확도를 비교하였으며, 앙상블모델을 사용하여 예측의 정확도를 높일 수 있었다. Developing effective tools for predicting absorption, distribution, metabolism, excretion properties and toxicity (ADME/T) of new chemical entities in the early stage of drug design is one of the most important tasks in drug discovery and development today. As one of these attempts, support vector machines (SVM) has recently been exploited for the prediction of ADME/T related properties. However, two problems in SVM modeling, i.e. feature selection and parameters setting, are still far from solved. The two problems have been shown to be crucial to the efficiency and accuracy of SVM classification. In particular, the feature selection and optimal SVM parameters setting influence each other, which indicates that they should be dealt with simultaneously. In this account, we present an integrated practical solution, in which genetic-based algorithm (GA) is used for feature selection and grid search (GS) method for parameters optimization. hERG ion-channel inhibitor classification models of ADME/T related properties has been built for assessing and testing the proposed GA-GS-SVM. We generated 6 different models that are 3 different single models and 3 different ensemble models using training set - 1891 compounds and validated with external test set - 175 compounds. We compared single model with ensemble model to solve data imbalance problems. It was able to improve accuracy of prediction to use ensemble model.

This paper addresses processing of spectrophotometric array signals based on genetic algorithms (GA) least square support vector machines (LS-SVM) regression to provide a powerful model for machine learning and data mining. The key to complete LS-SVM regression is to choose its optimal parameters. Due to their outstanding ability in solving global optimization problems in complex multidimensional search space, GA are used in this study to obtain the optimal parameter combination of the LS-SVM model. Experimental results showed the GA-LS-SVM method to be successful for simultaneous multicomponent determination even where severe overlap of spectra was present. KeywordsLeast squares support vector machines; Genetic algorithms; Spectrophotometric array signals; Overlapping spectra; Artificial intelligence Nowadays, with the application of photometric diode array detector and computers, rapid scanning commercial spectrophotometers are capable of quickly generating huge data consisting of hundreds and even thousands of absorbance values per spectrum. The array data named fullspectrum contain sufficient information to be able to determine the contents of various compounds. The main drawback of ultraviolet-visible (UV-VIS) is its poor selectivity because in many cases UV-VIS spectra display strong overlaps, especially some less specific and selective chromagenic reagents often give rise to strongly overlapped spectra in many cases. The combination of artificial intelligence methods with the computer-controlled spectrophotometers was proven to be effective in overcoming this difficulty [1-3]. Artificial neural network (ANN) is a form of artificial intelligence that mathematically simulates biological nervous system [4, 5]. However, ANN often has slow convergence, is prone to the existence of many local minima during training, and has a tendency of overfitting. Recently, a promising technology called support vector machines (SVM) has been used for classification and regression problems. SVM pioneered by Vapnik is a kind of machine learning method based on modern statistical learning theory and has notable properties including absence of local minima and high generalization ability [6, 7]. Suykens and his coworkers [8] introduced a modified version of SVM called least square SVM (LS-SVM) , which requires solving a set of linear equations instead of a quadratic programming problem and is much easier and computationally simpler than SVM. SVM and LS-SVM represent relatively recent artificial intelligence method and have found some applications in image analysis, classification and disease diagnosis etc. [9, 10]. It is worth mentioning that the success of LS-SVM model is highly dependent on the optimum choice of two parameters, the relative weight of regression error γ and the kernel width σ of radial basis function (RBF). Genetic algorithms (GA) [11, 12] introduced by John Holland are probabilistic optimization techniques based on natural evolution and genetics and Darwin’s theory of survival of the best. With their efficient and robust global search ability, GA are used to search two optimal parameters for the LS-SVM model simultaneously and automatically. The LS-SVM model then performs the regression task using these optimal parameters.

A learning approach to knowledge-assisted image analysis and classification is proposed that combines global and local information with explicitly defined knowledge in the form of an ontology. The ontology specifies the domain of interest, its subdomains, the concepts related to each subdomain as well as contextual information. Support vector machines (SVMs) are employed in order to provide image classification to the ontology subdomains based on global image descriptions. In parallel, a segmentation algorithm is applied to segment the image into regions and SVMs are again employed, this time for performing an initial mapping between region low-level visual features and the concepts in the ontology. Then, a decision function, that receives as input the computed region-concept associations together with contextual information in the form of concept frequency of appearance, realizes image classification based on local information. A fusion mechanism subsequently combines the intermediate classification results, provided by the local- and global-level information processing, to decide on the final image classification. Once the image subdomain is selected, final region-concept association is performed using again SVMs and a genetic algorithm (GA) for optimizing the mapping between the image regions and the selected subdomain concepts taking into account contextual information in the form of spatial relations. Application of the proposed approach to images of the selected domain results in their classification (i.e., their assignment to one of the defined subdomains) and the generation of a fine granularity semantic representation of them (i.e., a segmentation map with semantic concepts attached to each segment). Experiments with images from the personal collection domain, as well as comparative evaluation with other approaches of the literature, demonstrate the performance of the proposed approach.

In this paper, we present a framework for simultaneous image segmentation and object labeling leading to automatic image annotation. Focusing on semantic analysis of images, it contributes to knowledge-assisted multimedia analysis and bridging the gap between semantics and low level visual features. The proposed framework operates at semantic level using possible semantic labels, formally represented as fuzzy sets, to make decisions on handling image regions instead of visual features used traditionally. In order to stress its independence of a specific image segmentation approach we have modified two well known region growing algorithms, i.e., watershed and recursive shortest spanning tree, and compared them to their traditional counterparts. Additionally, a visual context representation and analysis approach is presented, blending global knowledge in interpreting each object locally. Contextual information is based on a novel semantic processing methodology, employing fuzzy algebra and ontological taxonomic knowledge representation. In this process, utilization of contextual knowledge re-adjusts labeling results of semantic region growing, by means of fine-tuning membership degrees of detected concepts. The performance of the overall methodology is evaluated on a real-life still image dataset from two popular domains

The technological progress in the field of artificial intelligence (AI) has brought new opportunities and challenges to the intelligent development and innovative research in fuzzy spatial relations, fuzzy theory is the basic theory of intelligent information processing, and the paper gives a brief analysis of fuzzy calculation method in spatial analysis and spatial relation calculation. In particular, the spatial relations of fuzzy objects are more difficult to express and describe accurately. This paper proposes a method to calculate the topological relations between fuzzy objects with 4-Intersection model and 9-Intersection model, in the analysis of spatial relations, the extension from deterministic entity to uncertain entity is realized with fuzzy set theory, the fuzzy space matrix is constructed, then fuzzy topological relations and fuzzy membership are calculated and output, the model simplifies many complex relations, and it can describe eight topological relations including disjoint, meet, overlap, covers, contains, covered-by, inside and equal. In practical application, the position of spatial objects can be controlled and adjusted according to topological relations. Through analysis of algorithm and fuzzy membership calculation, the model is effective in theory.

Cancer survival classification using integrated data sets and intermediate information.

Automatic recognition of skin symptom plays an importance role in the skin diagnosis and treatment. Feature selection is to increase the classification performance of skin symptom. In this paper, the effects of feature selection on the classification of 4-class skin symptoms (chloasma, blackhead, freckled and comedone) are analyzed. Support vector machine (SVM) is employed to construct classifier, the parameters setting is very crucial to the learning results and generalization ability of SVM, and many irrelevant and redundant features degrade the performance of classification, furthermore, the choice of SVM parameters is influenced by the feature subset taken into account and vice versa. So a joint optimization method for selecting features and SVM parameters is proposed. Genetic algorithms, which have a lot of improvements about fitness, crossover and mutation in comparison with simple genetic algorithms, is to achieve a balance between the classification accuracy and the size of the feature subsets selected and have a fast convergence rate. Furthermore, ‘one-against-one’ approach is used to solve multi-class classification problems and a comparison of the model performances is made among the results of the joint optimization and the single optimization of features or SVM parameters in the experiments. The simulation result shows that our approach can find subsets of features for 4-class skin symptoms classification with higher average cross validation accuracy and a higher rate of convergence compared with single optimization of features or SVM parameters.

Tool wear is an important consideration for Computerized Numerical Control (CNC) machine tools as it directly affects machining precision. To realize the online recognition of tool wear degree, this research develops a tool wear monitoring system using an indirect measurement method which selects signal characteristics that are strongly correlated with tool wear to recognize tool wear status. The system combines support vector machine (SVM) and genetic algorithm (GA) to establish a nonlinear mapping relationship between a sample of cutting force sensor signal and tool wear level. The cutting force signal is extracted using time domain statistics, frequency domain analysis, and wavelet packet decomposition. GA is employed to select the sensitive features which have a high correlation with tool wear states. SVM is also applied to obtain the state recognition results of tool wear. The gray wolf optimization (GWO) algorithm is used to optimize the SVM parameters and to improve prediction accuracy and reduce internal parameters’ adjustment time. A milling experiment on AISI 1045 steel showed that when comparing with SVM optimized by commonly used optimization algorithms (grid search, particle swarm optimization, and GA), the proposed tool wear monitoring system can accurately reflect the degree of tool wear and achieves strong generalizability. A set of vibration signals are adopted to verify the presented research. Results show that the proposed tool wear monitoring system is robust.

Measuring and managing the financial sustainability of the borrowers is crucial to financial institutions for their risk management. As a result, building an effective corporate financial distress prediction model has been an important research topic for a long time. Recently, researchers are exerting themselves to improve the accuracy of financial distress prediction models by applying various business analytics approaches including statistical and artificial intelligence methods. Among them, support vector machines (SVMs) are becoming popular. SVMs require only small training samples and have little possibility of overfitting if model parameters are properly tuned. Nonetheless, SVMs generally show high prediction accuracy since it can deal with complex nonlinear patterns. Despite of these advantages, SVMs are often criticized because their architectural factors are determined by heuristics, such as the parameters of a kernel function and the subsets of appropriate features and instances. In this study, we propose globally optimized SVMs, denoted by GOSVM, a novel hybrid SVM model designed to optimize feature selection, instance selection, and kernel parameters altogether. This study introduces genetic algorithm (GA) in order to simultaneously optimize multiple heterogeneous design factors of SVMs. Our study applies the proposed model to the real-world case for predicting financial distress. Experiments show that the proposed model significantly improves the prediction accuracy of conventional SVMs.

Utilization of SVM, LSSVM and GP for Predicting the Medical Waste Generation

The prediction of wastes generated in the hospital will help their management for several activities like storage, transport and disposing. This chapter adopts Support Vector Machine (SVM), Least Square Support Vector Machine (LSSVM) and Genetic Programming (GP) in order to estimate the rate of medical waste generation. In the event of predicting the rate, type of hospital, capacity and bed occupancy has been used as inputs of SVM, LSSVM and GP. SVM is based on statistical learning theory, which provides an elegant tool for nonlinear system modeling. LSSVM is the re-formulation to the general SVM. GP, a best part of evolutionary algorithm and also the specification of Genetic Algorithm (GA). These SVM, LSSVM and GP have been used as the regression techniques. The results show the performance of the developed SVM, LSSVM and GP models were elegant and outstanding.

In this paper, we propose genetic algorithm (GA) to improve support vector machines (SVM) based intrusion detection system (IDS). SVM is relatively a novel classification technique and has shown higher performance than traditional learning methods in many applications. So several security researchers have proposed SVM based IDS. We use fusions of GA and SVM to enhance the overall performance of SVM based IDS. Through fusions of GA and SVM, the optimal detection model SVM classifier can be determined. As the result of this fusion, SVM based IDS not only select optimal parameters for SVM but also optimal feature set among the whole feature set. We demonstrate the feasibility of our method by performing several experiments on KDD 1999 intrusion detection system competition dataset.

This paper established evaluation index system of comprehensive utilization of land from the three aspects of economic benefits, ecological benefits and social benefits. Firstly, ridge regression (RR) is used to the indicator index sys- tem for screening, and then genetic algorithm and support vector machine (that is GASVM) is trained to the transformed samples. Moreover, the comprehensive evaluation results between the GASVM and ridge regression and genetic algo- rithm and support vector machine(that is RR-GASVM) is compared, which results by MATLAB show that using RR- GASVM is more accurate and higher efficient for the comprehensive utilization of land evaluation than that of GASVM. The combination of ridge regression and genetic algorithm and support vector machine can improve the decision ability of SVM, so that the decision has higher adaptability, which is the development direction of future research. At last, based on the results of this study, some suggestions are put forward accordingly. The area of Jiangsu, Zhejiang and Shanghai (that is Jiangzhehu) should consolidate the existing achievements and strengthen the innovation of the land management system on the basis of utilizing land comprehensively, realize the intensive, large-scale operation and speed up agricultural mod- ernization development by accelerating rural land circulation, and improve the efficiency of land utilization in order to save the land of urban construction. It is clear that people should insist the principle of paying equal attention to develop- ment and consolidation, and strengthen the cultivated land's ability to resist natural disasters in order to improve the eco- logical environment at the same time that improved the land utilization.