Volume 26, Issue 2

Several satellite data receiving and distributing centers across the world support data storage, processing, and retrieval based on satellite, sensor, product, latitude, longitude, date and time, etc. These systems address queries on satellite products that are mostly high-level concepts. A more sophisticated retrieval system that supports ontological concepts, subconcepts, and concept hierarchical queries delivers refined results that broaden the scientific horizon of the application domain. To achieve this, the current research designed and implemented an ontology concept-based satellite data management and retrieval methodology. This enhances the performance of the satellite data retrieval system and supports semantic queries. The performance of the retrieval system depends upon the strategy followed to maintain domain ontologies and satellite data instances. Three ontology-based satellite data management strategies are discussed, and their performance was evaluated by taking real and benchmark metrics. A semantic query set of 25 queries was chosen covering various concepts, subconcepts, and concept hierarchical-related queries that involve various SPARQL query constructs. The test bed is taken from real-time satellite data received from Kalpana-1 of various sizes of triple stores.

This paper presents an artificial neural network (ANN) trained on the patterns of slip signals; these patterns were generated by using conventional sensors with a novel design of fingertip mechanism for detecting the slippage of a grasped object under different types of dynamic loads. This design is to be used with an underactuated triple finger artificial hand based on the pulleys-tendon mechanism. The grasped object is designed in a prism shape with three direct current motors with unbalance rotating mass to generate excitation in the object. Also, this object is covered with different types of surface materials, namely, spongy rubber, glass, and wood. Three types of external loads are used to disturb the grasping process represented by quasi-static pulling on the object, the dynamic load on the object, and on the artificial arm in separate form. The mathematical modeling has been derived for the proposed design to generate the signal of contact force components ratio through using the conventional sensor signals with the aid of Matlab-Simulink software. The ANN has been trained on the basis of the patterns of force component ratio signals at slippage occurrence, in order to detect slippage and then prevent it without the need for any knowledge about the surface properties of the grasped object. The experimental results are discussed in comparison with the physical aspect of the slippage phenomenon, and they show good agreement with the physical definition of the slippage phenomenon. In addition, the network evaluation results are discussed with different parameters that govern the controller operation, such as network error, classification efficiency, and delay in response time.

In this paper, we propose a novel approach called Classification Based on Enrichment Representation (CBER) of short text documents. The proposed approach extracts concepts occurring in short text documents and uses them to calculate the weight of the synonyms of each concept. Concepts with the same meanings will increase the weights of their synonyms. However, the text document is short and concepts are rarely repeated; therefore, we capture the semantic relationships among concepts and solve the disambiguation problem. The experimental results show that the proposed CBER is valuable in annotating short text documents to their best labels (classes). We used precision and recall measures to evaluate the proposed approach. CBER performance reached 93% and 94% in precision and recall, respectively.

The assessment of answers is an important process that requires great effort from evaluators. This assessment process requires high concentration without any fluctuations in mood. This substantiates the need to automate answer script evaluation. Regarding text answer evaluation, sentence similarity measures have been widely used to compare student written answers with reference texts. In this paper, we propose an automated answer evaluation system that uses our proposed cosine-based sentence similarity measures to evaluate the answers. Cosine measures have proved to be effective in comparing between free text student answers and reference texts. Here we propose a set of novel cosine-based sentence similarity measures with varied approaches of creating document vector space. In addition to this, we propose a novel synset-based word similarity measure for computation of document vectors coupled with varied approaches for dimensionality-reduction for reducing vector space dimensions. Thus, we propose 21 cosine-based sentence similarity measures and measured their performance using MSR paraphrase corpus and Li’s benchmark datasets. We also use these measures for automatic answer evaluation system and compare their performances using the Kaggle short answer and essay dataset. The performance of the system-generated scores is compared with the human scores using Pearson correlation. The results show that system and human scores have correlation between each other.

The writing part in Chinese language tests is badly in need of a mature automated essay scoring system. In this paper, we propose a new approach applied to automated Chinese essay scoring (ACES), called contextualized latent semantic indexing (CLSI), of which Genuine CLSI and Modified CLSI are two versions. The n -gram language model and the weighted finite-state transducer (WFST), two critical components, are used to extract context information in our ACES system. Not only does CLSI improve conventional latent semantic indexing (LSI), but bridges the gap between latent semantics and their context information, which is absent in LSI. Moreover, CLSI can score essays from the perspectives of language fluency and contents, and address the local overrating and underrating problems caused by LSI. Experimental results show that CLSI outperforms LSI, Regularized LSI, and latent Dirichlet allocation in many aspects, and thus, proves to be an effective approach.

Nature-inspired algorithms are seen as potential tools to solve large-scale global optimization problems. Memetic algorithms (MAs) are nature-inspired techniques based on evolutionary computation. MAs are considered as modified genetic algorithms integrated with a local search mechanism. Conventional MAs perform well for small dimensions; however, their performance starts declining with the increase in dimensions. It is popularly known as the “curse of dimensionality” problem. In order to solve this problem, MA with constrained local search (MACLS) is proposed for single-objective optimization problems. MACLS restricts the local search to be performed after every generation. Controlled local search enhances the optimization capability of the MA. MACLS has been evaluated with respect to GS-MPSO (the latest modification of MA) and MLCC, EPUS-PSO, JDEdynNP-F, MTS, DewSAcc, DMS-PSO, LSEDA-gl, UEP, ALPSEA, classical DE (differential evolution), and real-coded CHC algorithms that participated in the Congress on Evolutionary Computation 2008 competition. The results establish that MACLS significantly outperforms these algorithms in attaining global optima for unimodal and multimodal single-objective optimization problems for small as well as large dimensions.

Named entity (NE) recognition (NER) is a process to identify and classify atomic elements such as person name, organization name, place/location name, quantities, temporal expressions, and monetary expressions in running text. In this paper, the Hindi NER task has been mapped into a multiclass learning problem, where the classes are NE tags. This paper presents a solution to this Hindi NER problem using a memory-based learning method. A set of simple and composite features, which includes binary, nominal, and string features, has been defined and incorporated into the proposed model. A relatively small Hindi Gazetteer list has also been employed to enhance the system performance. A comparative study on the experimental results obtained by the memory-based NER system proposed in this paper and a hidden Markov model (HMM)-based NER system shows that the performance of the proposed memory-based NER system is comparable to the HMM-based NER system.

Multiclass contour-preserving classification (MCOV) has been used to preserve the contour of the data set and improve the classification accuracy of a feed-forward neural network. It synthesizes two types of new instances, called fundamental multiclass outpost vector (FMCOV) and additional multiclass outpost vector (AMCOV), in the middle of the decision boundary between consecutive classes of data. This paper presents a comparison on the generalization of an inclusion of FMCOVs, AMCOVs, and both MCOVs on the final training sets with support vector machine (SVM). The experiments were carried out using MATLAB R2015a and LIBSVM v3.20 on seven types of the final training sets generated from each of the synthetic and real-world data sets from the University of California Irvine machine learning repository and the ELENA project. The experimental results confirm that an inclusion of FMCOVs on the final training sets having raw data can improve the SVM classification accuracy significantly.

Instance selection endeavors to decide which instances from the data set should be maintained for further use during the learning process. It can result in increased generalization of the learning model, shorter time of the learning process, or scaling up to large data sources. This paper presents a parallel distance-based instance selection approach for a feed-forward neural network (FFNN), which can utilize all available processing power to reduce the data set while obtaining similar levels of classification accuracy as when the original data set is used. The algorithm identifies the instances at the decision boundary between consecutive classes of data, which are essential for placing hyperplane decision surfaces, and retains these instances in the reduced data set (subset). Each identified instance, called a prototype, is one of the representatives of the decision boundary of its class that constitutes the shape or distribution model of the data set. No feature or dimension is sacrificed in the reduction process. Regarding reduction capability, the algorithm obtains approximately 85% reduction power on non-overlapping two-class synthetic data sets, 70% reduction power on highly overlapping two-class synthetic data sets, and 77% reduction power on multiclass real-world data sets. Regarding generalization, the reduced data sets obtain similar levels of classification accuracy as when the original data set is used on both FFNN and support vector machine. Regarding execution time requirement, the speedup of the parallel algorithm over the serial algorithm is proportional to the number of threads the processor can run concurrently.

Automatic detection of a vehicle in an airborne thermal imagery is a challenging research topic in computer vision, especially the detection of military tanks in the field. Various methodologies for detection in forward-looking infrared imagery, which has higher spatial resolution, has been discussed by a number of researchers in literature. The algorithm we developed in the present study detects tanks not only in higher resolution but in lower resolution imagery as well. Detection algorithm is initiated by the segmentation of thermal image using mean shift, which provides possible targets present in the field other than the background. To reduce clutter and uneven illumination in a thermal image, a pre-processing morphological algorithm based on top-hat filtering has been implemented. After convolution of image window with Gabor filter banks, we extracted the energy feature of each image generated after convolution. The energy vector of such a target and the neighbouring background window has been calculated, and the similarity between the target and background using distance-measuring method has been measured. The minimum distance is used as the threshold to decide the target. A comparative study has been carried out between tanks and various targets/objects that appear similar to tanks in a thermal image. This validates our target detection algorithm. The false-positive rate and true-positive rate have been calculated for performance evaluation. Overall, this algorithm shows promising results for tank detection using single-band thermal imagery.

Recognition of signs made by deaf people to produce equivalent textual description for normal people to communicate with deaf people is an essential and challenging task for the pattern recognition and image processing research community. Many researchers have made an attempt to standardize and to propose a sign language recognition system. To the best our knowledge, according to the literature survey, most of the work reported has concentrated at the fingerspelling level or at the word level, and less work at the sentence level has been reported. As sign languages are very abstract, fingerspelling or word level interpretation of signs seems to be a tedious and cumbersome task. Although existing research in sign language recognition is active and extensive, it still remains a challenge to achieve accurate recognition and interpretation of signs at the sentence level. In this paper, we made an attempt to address this problem by proposing an approach that exploits the texture description technique and symbolic data analysis concept to characterize and effectively represent a sign, taking into account the intra-class variations due to different signers or the same signers at different instances of time. In order to study the efficacy of the proposed approach, extensive experiments were carried out on a considerably large database of Indian sign language created by us. The experimental results demonstrated that the proposed method has shown good recognition performance in terms of F-measure rates.

The aim of this paper is to develop some aggregation operators for multiple-attribute group decision making (MAGDM) with hesitant fuzzy linguistic information. First, the relationship between uncertain linguistic variable and hesitant fuzzy linguistic term set (HFLTS) is discussed. Some operations and aggregation operators for HFLTSs are developed. Second, based on the hesitant fuzzy linguistic weighted arithmetic averaging operator and the hesitant fuzzy linguistic hybrid averaging operator, an approach to MAGDM problem with hesitant fuzzy linguistic information is proposed. Finally, a practical application of the developed approach to supplier selection in a supply chain and a comparative analysis with other methods are presented.