Publisher: AGH University of Science and Technology Press   (Total: 6 journals)   [Sort by number of followers]

Showing 1 - 6 of 6 Journals sorted alphabetically
Computer Science J.     Open Access   (Followers: 20)
Decision Making in Manufacturing and Services     Open Access   (Followers: 2)
Geology, Geophysics and Environment     Open Access   (Followers: 2)
Geotourism/Geoturystyka     Open Access  
Metallurgy and Foundry Engineering     Open Access   (Followers: 1)
Opuscula Mathematica     Open Access   (SJR: 0.378, CiteScore: 1)
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Computer Science Journal
Number of Followers: 20  

  This is an Open Access Journal Open Access journal
ISSN (Print) 1508-2806
Published by AGH University of Science and Technology Press Homepage  [6 journals]
  • Hybrid approach to Content-Based Image Retrieval using modified
           multi-scale LBP and color features

    • Authors: Sagar M Chavda, Mahesh M Goyani
      Abstract: The objective of the Content-Based Image Retrieval (CBIR) system is to retrieve the visually identical images from the database efficiently and effectively. It is a broad research realm with the availability of numerous applications. Performance dependency of CBIR focuses on the extraction, reduction, and selection of the features along with the practice of classification technique. In this work, we have proposed the hybrid approach of two different feature descriptors namely, Global Color Histogram and Multi-Scale Local Binary Pattern (MS-LBP). Furthermore, PCA is used for dimension reduction and LDA for the selection of features. The proposed method is evaluated concerning various benchmark datasets namely Corel-1k, Corel-5k, Corel-10k, and Ghim-10k, and results are compared based on precision and recall values at different thresholds. Euclidean distance and City Block distance are used for classification purposes. The performance study of the proposed work displays it as outperformer than the identified literature methods.
      PubDate: 2022-03-24
      DOI: 10.7494/csci.2022.23.1.3821
      Issue No: Vol. 23, No. 1 (2022)
  • Immersive feedback in fencing weapon practice using mixed reality

    • Authors: Filip Malawski
      Abstract: Providing athletes, during sports training, with real-time feedback based on automatic analysis of motion is both useful and challenging. In this work, a novel system based on mixed reality is proposed and verified. The system allows for immersive, real-time, visual feedback in fencing weapon practice. Novel methods are introduced for 3D blade tracking from a single RGB camera, creating weapon action models by recording actions performed by the coach and evaluating fencers' performance against these models. Augmented reality glasses with see-through displays are employed and a method for coordinate mapping between virtual and real environments is proposed, which allows providing real-time visual cues and feedback by overlaying virtual trajectories on the real-world view. The system is verified experimentally in fencing bladework practice, with supervision of a fencing coach. Results indicate that the proposed system allows novice fencers to perform the exercises more correctly.
      PubDate: 2022-03-24
      DOI: 10.7494/csci.2022.23.1.4570
      Issue No: Vol. 23, No. 1 (2022)
  • Improving modified policy iteration for probabilistic model checking

    • Authors: Mohammadsadegh Mohagheghi, Jaber Karimpour, Ayaz Isazadeh
      Abstract: Value iteration, policy iteration and their modified versions are well-known algorithms for probabilistic model checking of Markov Decision Processes. One the challenge of these methods is that they are time-consuming in most cases. Several techniques have been proposed to improve the performance of iterative methods for probabilistic model checking. However, the running time of these techniques depends on the graphical structure of the model and in some cases their performance is worse than the performance of the standard methods. In this paper, we propose two new heuristics to accelerate the modified policy iteration method. We first define a criterion for the usefulness of the computations of each iteration of this method. The first contribution of our work is to develop and use a criterion to reduce the number of iterations in modified policy iteration. As the second contribution, we propose a new approach to identify useless updates in each iteration. This method reduces the running time of computations by avoiding useless updates of states. The proposed heuristics have been implemented in the PRISM model checker and applied on several standard case studies. We compare the running time of our heuristics with the running time of previous standard and improved methods. Experimental results show that our techniques yields a significant speed-up.
      PubDate: 2022-03-24
      DOI: 10.7494/csci.2022.23.1.4139
      Issue No: Vol. 23, No. 1 (2022)
  • Named-Entity Recognition for Hindi language using context pattern-based
           maximum entropy

    • Authors: Arti Jain, Divakar Yadav, Anuja Arora, Devendra K. Tayal
      This paper describes Named Entity Recognition (NER) system for Hindi language using two methodologies. An existing BaseLine Maximum Entropy-based Named Entity (BL-MENE) model and Context Pattern-based MENE (CP-MENE) framework the one proposed in this work. BL-MENE utilizes several features for the NER task but suffers from inaccurate Named Entity (NE) boundary detection, mis-classification errors, and partial recognition of NEs due to certain missing essentials. However, CP-MENE based NER task incorporates extensive features and patterns set to overcome these problems. In fact, the CP-MENE features include right-boundary, left-boundary, part-of-speech, synonyms, gazetteers and relative pronoun features. CP-MENE formulates a kind of recursive relationship to extract high ranked NE patterns that are generated through regular expressions via python@ code. Nowadays, since the Web contents in the Hindi language are rising, especially in the health-care applications, this work is conducted on the Hindi Health Data (HHD) corpus at Kaggle dataset. We conducted experiments on four NE categories- Person (PER), Disease (DIS), Consumable (CNS) and Symptom (SMP). Usually, researchers’ work upon PER NE within news articles while other NEs, especially related to the health-care domain such as DIS, CNS, and SMP NE types are left out which are incorporated in this research. CP-MENE improvised the classification performance of NEs and the F-measure achieved are 79.68% for PER, 72.50% for DIS, 68.78% for CNS, and 67.23% for SMP respectively which are comparable with respect to other NER approaches.
      PubDate: 2022-03-24
      DOI: 10.7494/csci.2022.23.1.3977
      Issue No: Vol. 23, No. 1 (2022)
  • Ensemble Machine Learning Methods to Predict the Balancing of Ayurvedic
           Constituents in the Human Body

    • Authors: Vani Rajasekar, Sathya Krishnamoorthi, Muzafer Saračević, Dzenis Pepic, Mahir Zajmovic, Haris Zogic
      Abstract: Ayurvedic medicines are categorized into seven constitutional forms ‘Prakriti’ which is a constituent in the Ayurvedic system of medicine to determine drought tolerance and drug responsiveness. Prakriti assessment entails a thorough physical examination as well as queries about physiological or behavioral characteristics. The prevalence of certain "doshas" is attributed by Ayurveda to the fundamental constituent of a person. Vata, pitta, and Kapha are the three main doshas mentioned. Ayurveda-dosha studies have been used for a long time, but the quantitative reliability measurement of these diagnostic methods still lags. The careful and appropriate analysis leads to an effective treatment. In this paper, we demonstrate the result of certain machine learning methods like Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), K-Nearest Neighbour (KNN), Artificial Neural Network (ANN), and Adaboost algorithm for various performance characteristics to predict human body constituencies. From the observations of results it is shown that the AdaBoost algorithm with hyperparameter tuning provides enhanced accuracy and recall of 0.97, precision and F-score of 0.96, the lower RSME value obtained is 0.64. The experimental results reveal that the improved model, which is based on ensemble learning methods, outperforms traditional methods significantly. According to the findings, advancements in the proposed algorithms could give machine learning a promising future.
      PubDate: 2022-03-24
      DOI: 10.7494/csci.2022.23.1.4315
      Issue No: Vol. 23, No. 1 (2022)
  • Efficient multi-classifier wrapper feature-selection model: Application
           for dimension reduction in credit scoring

    • Authors: Waad Bouaguel
      Abstract: The task of identifying most relevant features for a credit scoring application is a challenging task. Reducing the number of redundant and unwanted features is an inevitable task to improve the performance of the credit scoring model. The wrappers approach is usually used in credit scoring applications to identify the most relevant features. However, this approach suffers from the issue of subsets generation and the use of a single classifier as an evaluation function. The problem here is that each classifier may give different results which can be interpreted differently. Hence, we propose in this study an ensemble wrapper feature selection model which is based on a multi-classifiers combination. In a first stage, we address the problem of subsets generation by minimizing the search space through a customized heuristic. Then, a multi-classifier wrapper evaluation is applied using two classifier arrangement approaches in order to select a set of mutually approved set of relevant features. The proposed method is evaluated on four credit datasets and has shown a good performance compared to individual classifiers results.
      PubDate: 2022-03-24
      DOI: 10.7494/csci.2022.23.1.4120
      Issue No: Vol. 23, No. 1 (2022)
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