List of Articles dimensionality

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  1 - An Efficient Method for Handwritten Kannada Digit Recognition based on PCA and SVM Classifier
  Ramesh G Prasanna  G B Santosh  V Bhat Chandrashekar  Naik Champa  H N
  10.52547/jist.9.35.169
  Handwritten digit recognition is one of the classical issues in the field of image grouping, a subfield of computer vision. The event of the handwritten digit is generous. With a wide opportunity, the issue of handwritten digit recognition by using computer vision and m More

  Handwritten digit recognition is one of the classical issues in the field of image grouping, a subfield of computer vision. The event of the handwritten digit is generous. With a wide opportunity, the issue of handwritten digit recognition by using computer vision and machine learning techniques has been a well-considered upon field. The field has gone through an exceptional turn of events, since the development of machine learning techniques. Utilizing the strategy for Support Vector Machine (SVM) and Principal Component Analysis (PCA), a robust and swift method to solve the problem of handwritten digit recognition, for the Kannada language is introduced. In this work, the Kannada-MNIST dataset is used for digit recognition to evaluate the performance of SVM and PCA. Efforts were made previously to recognize handwritten digits of different languages with this approach. However, due to the lack of a standard MNIST dataset for Kannada numerals, Kannada Handwritten digit recognition was left behind. With the introduction of the MNIST dataset for Kannada digits, we budge towards solving the problem statement and show how applying PCA for dimensionality reduction before using the SVM classifier increases the accuracy on the RBF kernel. 60,000 images are used for training and 10,000 images for testing the model and an accuracy of 99.02% on validation data and 95.44% on test data is achieved. Performance measures like Precision, Recall, and F1-score have been evaluated on the method used. Manuscript profile
• Open Access Article
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  2 - Choosing the most suitable personality questions in the measurement of personality dimensions: combining the latent trait theory and network data analysis
  Maryam Mohtashami Mohammad Hossein  Zarghami Beheshteh Niooshah
  <p>The word personality refers to the uniqueness, individuality and subjectivity of the subject being studied. The measurement of such a dynamic and complex concept is considered a fundamental challenge in the field of methodology for the measurement of psychological co More

  <p>The word personality refers to the uniqueness, individuality and subjectivity of the subject being studied. The measurement of such a dynamic and complex concept is considered a fundamental challenge in the field of methodology for the measurement of psychological constructs. The aim of this research is to present a new method in two different parts of personality questionnaire question analysis: a) personality questionnaire question dimensions obtained from the implementation of questionnaires on independent samples through correspondence analysis and b) question prioritization using from the network data analysis method based on the importance of questions in each dimension. To achieve these goals, 32 personality questionnaires - which cover most of the application areas of personality questionnaires - were implemented on 82,988 volunteers via web-based forms. Correspondence analysis results show that personality has two dominant dimensions that explain about 75% of personality variance. The results of network data analysis show that the important questions in different indexes are not necessarily the same and the selection of questions based on a specific index should be based on the meaning of that index, however, according to the correlation structure of the priority of questions in the index network, a general index was defined based on which questions were prioritized in two dimensions of personality. The result of the present research led to the presentation of an algorithm for selecting personality questions in personality dimensions.</p> Manuscript profile