Call for Papers - International Journal of Information Sciences and Techniques (IJIST)

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The International Journal of Information Science & Techniques (IJIST) focuses on information systems science and technology coercing multitude applications of information systems in business administration, social science, biosciences, and humanities education, library sciences management, depiction of data and structural illustration, big data analytics, information economics in real engineering and scientific problems. This journal provides a forum that impacts the development of engineering, education, technology management, information theories and application validation. It also acts as a path to exchange novel and innovative ideas about Information systems science and technology.

Engineering

International Journal of Information
Sciences and Techniques (IJIST)
ISSN: 2249 - 1139 [Online]; 2319 - 409X [Print]
https://airccse.org/journal/IS/index.html

Scope & Topics
The International Journal of Information Science & Techniques (IJIST) focuses on
information systems science and technology coercing multitude applications of
information systems in business administration, social science, biosciences, and
humanities education, library sciences management, depiction of data and structural
illustration, big data analytics, information economics in real engineering and scientific
problems.
This journal provides a forum that impacts the development of engineering, education,
technology management, information theories and application validation. It also acts as a
path to exchange novel and innovative ideas about Information systems science and
technology.
International Journal of Information Sciences
and Techniques (IJIST)
ISSN: 2249 - 1139 [Online]; 2319 - 409X [Print]
https://airccse.org/journal/IS/index.html

Topics of interest include but are not limited to:
 AI and Information Science
 Archives, Preservation and Argumentation
 Business Informatics
 Community Informatics
 Computational Biology & Bio-informatics
 Computational Social Science, Social Computing
 Cultural Informatics and Heritage
 Data Analytics, Curation and Human Centered Data
International Journal of Information Sciences
and Techniques (IJIST)
ISSN: 2249 - 1139 [Online]; 2319 - 409X [Print]
https://airccse.org/journal/IS/index.html

Topics of interest include but are not limited to: (Contd..)
 Design and Evaluation of Information Systems and Services
 Digital Humanities, Libraries
 Diversity and Social Justice
 Economics of information systems
 Education of Information Professionals
 Ethics and Values for Information
 Fairness, accountability, transparency, ethics
 Foundations, History of Information
 Information Access, Literacy, Policy
International Journal of Information Sciences
and Techniques (IJIST)
ISSN: 2249 - 1139 [Online]; 2319 - 409X [Print]
https://airccse.org/journal/IS/index.html

Topics of interest include but are not limited to: (Contd..)
 Information Practices and Behaviors
 Information Retrieval, Visualization
 Information Theory
 Internet Marketing
 Knowledge Representation, Ontology
 Labor Markets Online
 Libraries and Librarians
 Organization of Knowledge and Information
 Social Informatics, Media
International Journal of Information Sciences
and Techniques (IJIST)
ISSN: 2249 - 1139 [Online]; 2319 - 409X [Print]
https://airccse.org/journal/IS/index.html

Paper Submission
Authors are invited to submit papers for this journal through Email:
ijist@aircconline.com or through Submission System. Submissions must
be original and should not have been published previously or be under
consideration for publication while being evaluated for this Journal.
International Journal of Information Sciences
and Techniques (IJIST)
ISSN: 2249 - 1139 [Online]; 2319 - 409X [Print]
https://airccse.org/journal/IS/index.html

Important Dates
 Submission Deadline: August 06, 2023
 Acceptance Notification: September 06, 2023
 Final Manuscript Due: September 14, 2023
 Publication Date: Determined by the Editor-in-Chief
International Journal of Information Sciences
and Techniques (IJIST)
ISSN: 2249 - 1139 [Online]; 2319 - 409X [Print]
https://airccse.org/journal/IS/index.html

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International Journal of Information Sciences and Techniques (IJIST)

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Feature extraction is a method of capturing visual content of an image. The feature extraction is the process to represent raw image in its reduced form to facilitate decision making such as pattern classification. We have tried to address the problem of classification MRI brain images by creating a robust and more accurate classifier which can act as an expert assistant to medical practitioners. The objective of this paper is to present a novel method of feature selection and extraction. This approach combines the Intensity, Texture, shape based features and classifies the tumor as white matter, Gray matter, CSF, abnormal and normal area. The experiment is performed on 140 tumor contained brain MR images from the Internet Brain Segmentation Repository. The proposed technique has been carried out over a larger database as compare to any previous work and is more robust and effective. PCA and Linear Discriminant Analysis (LDA) were applied on the training sets. The Support Vector Machine (SVM) classifier served as a comparison of nonlinear techniques Vs linear ones. PCA and LDA methods are used to reduce the number of features used. The feature selection using the proposed technique is more beneficial as it analyses the data according to grouping class variable and gives reduced feature set with high classification accuracy.

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In this paper A Median Based Directional Cascaded with Mask (MBDCM) filter has been proposed, which is based on three different sized cascaded filtering windows. The differences between the current pixel and its neighbors aligned with four main directions are considered for impulse detection. A direction index is used for each edge aligned with a given direction. Minimum of these four direction indexes is used for impulse detection under each masking window. Depending on the minimum direction indexes among these three windows new value to substitute the noisy pixel is calculated. Extensive simulations showed that the MBDCM filter provides good performances of suppressing impulses from both gray level and colored benchmarked images corrupted with low noise level as well as for highly dense impulses. MBDCM filter gives better results than MDWCMM filter in suppressing impulses from highly corrupted digital images.

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During the twentieth century (1932-1972), white physicians representing the United States government conducted a human experiment known as the Tuskegee Syphilis Study on black syphilis patients in Macon County, Alabama. The creators of the study, who supported the idea of black inferiority and the concept that black people’s bodies functioned differently from white people’s, observed the effects of a disease called syphilis on untreated black patients in order to collect data for further research on syphilis. Black individuals involved with the study believed that they were receiving treatment, although in truth, treatments for syphilis were purposely held back from them. Not only this, but fluids from their bodies, such as blood and spinal fluid, were extracted to serve as research material without their awareness of the purpose of the collection. The physicians justified their approach by positioning it as mere observation, asserting that they were not actively intervening with the patients participating in the experiment. However, despite their claims of passivity, these white physicians engaged in various morally improper actions, including deceit, which ultimately resulted in the deaths of numerous black patients who might have had a chance at survival.

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The SAR and SAS images are perturbed by a multiplicative noise called speckle, due to the coherent nature of the scattering phenomenon. If the background of an image is uneven, the fixed thresholding technique is not suitable to segment an image using adaptive thresholding method. In this paper a new Adaptive thresholding method is proposed to reduce the speckle noise, preserving the structural features and textural information of Sector Scan SONAR (Sound Navigation and Ranging) images. Due to the massive proliferation of SONAR images, the proposed method is very appealing in under water environment applications. In fact it is a pre- treatment required in any SONAR images analysis system. The results obtained from the proposed method were compared quantitatively and qualitatively with the results obtained from the other speckle reduction techniques and demonstrate its higher performance for speckle reduction in the SONAR images.

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Opinions Play important role in the process of knowledge discovery or information retrieval and can be considered as a sub discipline of Data Mining. A major interest has been received towards the automatic extraction of human opinions from web documents. The sole purpose of Sentiment Analysis is to facilitate online consumers in decision making process of purchasing new products. Opinion Mining deals with searching of sentiments that are expressed by Individuals through on-line reviews,surveys, feedback,personal blogs etc. With the vast increase in the utilization of Internet in today's era a similar increase has been seen in the use of blog's,reviews etc. The person who actually uses these reviews or blog's is mostly a consumer or a manufacturer. As most of the customers of the world are buying & selling product on-line so it becomes company's responsibility to make their product updated. In the current scenario companies are taking product reviews from the customers and on the basis of product reviews they are able to know in which they are lacking or strong this can be accomplished with the help of sentiment analysis. Therefore Our objective of our research is to build a tool which can automatically extract opinion words and find out their polarity by using dictionary,This actually reduces the manual effort of reading these reviews and to evaluate them. The research also illustrates the benefits of using Unstructured text instead of training data which expensive . In this research effort we demonstrate a method which is based on rules where product reviews are extracted from review containing sites and analysis is done, so that a person may know whether a particular product review is positive or negative or neutral. The system will utilize a existing knowledge base for calculate positive and negative scores and on the basis of that decide whether a product is recommended or not. The system will evaluate the utility of Lexical resources over the training data.

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