PH: +91-9888934889 E-Mail: editornrjitis@gmail.com ISSN: 2350-1278

National Research Journal of Information Technology & Information Science

Peer Reviewed, Refereed Research Journal I Open Access Journal
Submit Papers Online
  • Home
  • Current Issue
  • Archives
  • Editorial Board
  • Submit Paper
  • Indexing
  • Research Areas
  • Impact Values
  • Author Instructions
  • For Authors
    • Manuscript Guidelines
    • Copyright Agreement Form
    • View Paper Template
  • Contact

Editorial Policy About Peer Reviewed Journal Publication Ethics & Practices Plagiarism Policy Open Access, Licencing & Copyright Disclaimer Policy Privacy Policy FAQ Special Issue About The Journal

Join Our Editorial Board

Latest Announcements

  • CALL FOR PAPERS 2025 (January-June)

    01-01-2025
    SUBMIT PAPERS IN OUR RESEARCH JOURNAL! 2025
    National Research Journal of Information Technology and Information Science  contributes in the growth and application of Research & Technology, by delivering the latest information contained in research papers, which enables them to enhance understanding for advancements in research activities. We intends to Disseminate and promote the research works of research scholars, Academia.

  • Subscribe This Journal

    01-01-2025
    We Request to Subscribe our Journals for the Noble Cause to Spread Knowledge, Wisdom and also to Protect Intellectual Property Rights of Scholars Across the World.

    Subscription Price: 3500/- (Bi-Annual)

    CALL NOW!
    +91-9888934889, 7986925354

Publish
Conference
Or Seminar
papers in our journal

Read More

NRJITIS - National Research Journal of Information Technology & Information Science


About The Journal


The National Research Journal of Information Technology and Information Science (NRJITIS) (ISSN: 2350-1278)  is a peer reviewed Journal academic publication dedicated to advancing research and knowledge in the fields of Information Technology (IT) and Information Science. The journal serves as a platform for scholars, practitioners, and industry professionals to share innovative research findings, emerging technologies, and practical applications. It covers a broad range of topics including data science, cybersecurity, artificial intelligence, software engineering, information systems, digital transformation, and human-computer interaction & Library Sciences and other multidisciplinary related topics. The journal aims to foster interdisciplinary collaboration and contribute to the evolving landscape of IT and information science through high-quality, original research.

The Journal is Published By "National Press Associates"

  • Publisher Website: www.npajournals.org
  • Journal Name: National Research Journal of Information Technology and Information Science
  • ISSN: 2350-1278
  • Impact Factor: 7.9
  • Peer Review Process: Double Blind Peer Review Process
  • Low Article Processing Fees
  • Frequency of Publication: Biannual (2 Issues Per Year)
  • Languages: English
  • Accessibility: Open Access
  • Plagiarism Checker: Turnitin

The journal invites submission of manuscripts that meet the general criteria of significance and scientific excellence, and will publish:

  1. Original articles (research paper, short communications, etc)
  2. Review articles
  3. Conference reports
  4. Book reviews, etc.

Interested in submitting to this journal? We recommend that you review the About the Journal page for the journal's section policies, as well as the Author Guidelines.

Journal Email ID: editornrjitis@gmail.com

ENQUIRY NOW: +91-9888934889 (WhatsApp Link)


Current Issue

Year: 2024   Volume-11, Issue-1, Special (January-June)

Paper Title FEATURE EXTRACTION TECHNIQUES IN MACHINE LEARNING
Author Name Amanpreet Kaur
Country India
Page No. 1-4

Abstract View PDF
FEATURE EXTRACTION TECHNIQUES IN MACHINE LEARNING
Author: Amanpreet Kaur

Object identification is primarily concerned recognition based on specific local features and global features. Image processing and object recognition are subsets of pattern recognition, in which an image is recognized based on extracted features. Traditional approaches were used to identify animals for a variety of applications. With the advent of computer science, researchers considered developing an identification system that could replace traditional methods which physically harm animals and have social implications. Livestock management, cattle census for milk production in the country, insurance claims in banks, and security concerns like avoiding cross borders by the cattle are among the most common applications of cattle identification. In this paper we are going to discuss the feature extraction techniques for identification in machine learning.
KEYWORDS: SIFT, SURF,ORB, LBP, PCA,LDA

Paper Title ROLE OF MACHINE LEARNING IN FIELD OF SWARM INTELLIGENCE
Author Name Geetu
Country India
Page No. 5-9

Abstract View PDF
ROLE OF MACHINE LEARNING IN FIELD OF SWARM INTELLIGENCE
Author: Geetu

One aspect of artificial intelligence is swarm intelligence (SI). It is becoming more and more popular as complexity forces less-than-ideal solutions into the picture. It belongs to the machine learning (ML) field. A few SI methods have applications in machine learning. These algorithms either work alone or in combination with other algorithms. SI serves as the foundation for machine learning, fine-tuning the parameters of the model. The collective behaviour of a group of animals is known as swarm intelligence. Swarms can be seen in a variety of settings, such as ant colonies, fish schools, and flocks of birds. which describe the biology of ants, their activities, and how they help birds overcome obstacles. The Bee Colony Optimization monitors and investigates bee behaviour, connections, mobility, and swarm interactions. This study investigates the issues of swarm intelligence and swarm intelligence-based algorithms like ACO, PSO, GA, and FA in the field of machine learning.
KEYWORDS: Swarm Intelligence (SI), Machine Learning (ML), Genetic Algorithms, Optimization, Particle Swarm Optimization, Ant Colony Optimization, Firefly Algorithm.

Paper Title MACHINE LEARNING UNLEASHED: NAVIGATING THE EVOLUTION OF AI IN IMAGE RECOGNITION FROM FOUNDATIONS TO FUTURE FRONTIERS
Author Name Inderpreet Kaur
Country India
Page No. 10-19

Abstract View PDF
MACHINE LEARNING UNLEASHED: NAVIGATING THE EVOLUTION OF AI IN IMAGE RECOGNITION FROM FOUNDATIONS TO FUTURE FRONTIERS
Author: Inderpreet Kaur

The newest term to conquer the globe of business is machine learning. The notion of robots and AI that can learn on its own has captured the attention of the public. Machine learning has made it feasible for technology advancements and tools in a mixture of sectors that were unthinkable just a few years ago. Prediction engines and live internet TV streaming are only two examples of the ground-breaking inventions that our contemporary lifestyles rely on. The rapid evolution of Artificial Intelligence (AI) has revolutionized various industries, with image recognition being a prominent application area. AI techniques are employed, such as deep learning and machine learning, to enhance image recognition accuracy and efficiency. Additionally, ethical considerations and potential societal impacts are discussed. Nowadays AI in image recognition holds great promise for diverse fields, including healthcare, autonomous vehicles, security, and more.
KEYWORDS: Artificial Intelligence, Convolutional Neural Networks, Deep Learning, Machine Learning.

Paper Title PREDICTIVE ANALYTICS IN MACHINE LEARNING
Author Name Kiranjit Kaur, Parminder Kaur
Country India
Page No. 20-28

Abstract View PDF
PREDICTIVE ANALYTICS IN MACHINE LEARNING
Author: Kiranjit Kaur, Parminder Kaur

Machine learning algorithms detect hidden patterns from the data and predict the output based on these patterns. These algorithms accumulate knowledge from experiences and undergo a continual process of learning and self-improvement. This results in an improved and refined performance over time. The identified patterns and rules are mathematical in nature, and they can be easily defined and processed by a computer. This paper focuses on different classification techniques of supervised machine learning with emphasis on predictive analytics. Predictive analytics involve leveraging machine learning to predict future outcomes.
KEYWORDS: Artificial Neural Networks, K-Nearest Neighbour (KNN), Support Vector
Machine (SVM), Naive-Bayes Classification (NBM), Random Forest Classification (RFC)

Paper Title MACHINE LEARNING FOR VOICE RECOGNITION
Author Name Karamjeet Kaur
Country India
Page No. 28-33

Abstract View PDF
MACHINE LEARNING FOR VOICE RECOGNITION
Author: Karamjeet Kaur

Although verbal communication is crucial for human interaction, there are still certain obstacles when interacting verbally with robots. Currently, researchers are trying to devise a different method of talking with a machine that is more akin to speaking with a human, for which speech and voice systems have already been specified. Speech recognition places a strong emphasis on the speaker's independence, while voice recognition uses the speaker's voice tone—which can be impacted by their physical characteristics—to identify who the speaker is. Because of this, in order to provide the data to a system, one must first verify these distinctive tonal qualities. Once this recognition is successfully accomplished, the system can additionally benefit from speech recognition by allowing the speaker's features to be adjusted and knowing how the speaker makes their voice or sound.
KEYWORDS: Machine Learning, Communication, Voice Recognition, Speech Recognition, Security and Biometric Authentication

Paper Title A REVIEW OF THE USE OF MACHINE LEARNING TECHNIQUES BY SOCIAL MEDIA ENTERPRISES
Author Name Madhu
Country India
Page No. 35-39

Abstract View PDF
A REVIEW OF THE USE OF MACHINE LEARNING TECHNIQUES BY SOCIAL MEDIA ENTERPRISES
Author: Madhu

This review found out how social enterprises are now exploring machine literacy methods. First, an overview of social media's fashion potential, the kinds of big data it produces, and its real applications was given. There was a general consensus regarding machine literacy, its components, styles, and methods of operation. In light of this, certain approaches and phases used in social media machine analytics were examined using illustrative plates that were duplicated from the colorful writers' workshop. Because of their advantages over other styles, the most commonly utilized styles are the Bayesian Network and Support Vector Machine. Opinion mining, sentiment analysis, and trend analysis heavily utilize these types. Though it has been used in many other domains as well, the most common operations are social network analysis and corporate operations. In an effort to address the issues with present styles, a plethora of new styles as well as variants on existing styles are created almost daily. The methods used in machine literacy nowadays have drawbacks as well. Big data samples might not always accurately reflect the population. Certain impulses can arise from the massive volume of data, low value intensity, distributed across multiple sources, and dynamic nature of the data. Big data handling requires powerful computers and complex slicing, birth, and analysis techniques. The issue gets worse when multiple data sources are used because the delicate and impartial nature of social media data isn't always guaranteed. Issues with access and ethics could surface.
KEYWORDS: Machine Learning, Statistics, Social Media

Paper Title MACHINE LEARNING USING ITS CONCEPT, ALGORITHMS AND APPLICATIONS
Author Name Payal Chandel
Country India
Page No. 40-46

Abstract View PDF
MACHINE LEARNING USING ITS CONCEPT, ALGORITHMS AND APPLICATIONS
Author: Payal Chandel

Machine Learning (ML) is a branch of mathematics that goes beyond the purview of a small number of computer organizations. It uses statistical inference to estimate the likelihood that mainframes will learn through game play. The idea and development of machine learning, some of its more sophisticated algorithms, and a comparison of the three most sophisticated and well-liked algorithms based on some fundamental usage and each algorithm's performance in terms of estimate accuracy, prediction time, and training time have all been identified and linked. The field of machine learning, which may be briefly defined as enabling computers to generate accurate predictions by leveraging historical data, has seen rapid growth in recent years because to the rapid advancements in computer processing power and storage. Numerous industries, including agriculture, medicine, and weather forecasting, use machine learning. In recent years, machine learning (ML) has been increasingly popular as a learning methodology for several categorization methods, including vector machines and ML-based OCR recognition algorithms. ML is utilized in the medical industry to identify diseases and diagnoses, as well as to manage health records (smart health records. Other machine learning techniques, such MATLAB's and Google's cloud vision API, were employed in the past and sometimes. For this application field, more sophisticated machine learning techniques have been developed as a result of the challenges and expense of biological analyses. These basic machine learning topics include feature evaluation, supervised vs unsupervised learning, and categorization types. Next, we highlight the key concerns with creating machine learning experiments and assessing their effectiveness. A few supervised and unsupervised learning techniques are presented.
KEYWORDS: Machine Learning, Algorithm, Data, Training,

Paper Title DETECTION AND CLASSIFICATION OF COTTON LEAF DISEASES USING MACHINE LEARNING ALGORITHMS- A REVIEWS
Author Name Sonali Kamra, Vijay Laxmi
Country India
Page No. 47-53

Abstract View PDF
DETECTION AND CLASSIFICATION OF COTTON LEAF DISEASES USING MACHINE LEARNING ALGORITHMS- A REVIEWS
Author: Sonali Kamra, Vijay Laxmi

This review study discusses various techniques for identifying illnesses in cotton plants. Studies reveal that classifying and diagnosing diseases based just on professional observations made with the unaided eye can be expensive and time-consuming, especially in poor and rural areas. We present an image processing based fast, automated, precise, and economical approach. There are four primary steps in this solution: (a) Color Transformation: First, we implement color space transformation to the RGB leaf image, after which we build a structure for modifying its colors. (b) Image Segmentation: The K-means clustering algorithm is used to divide the images into pieces. (c) Texture Feature Calculation: For the divided afflicted regions, we calculate texture features. (d) Neural Network Classification: A neural network that has already been trained is used to examine the extracted features. KEYWORDS: Disease detection, Cotton leaf disease, Neural Network, Machine Learning.

Paper Title IDENTIFICATION OF EMERGING RESEARCH TOPICS USING MULTIPLE MACHINE LEARNING MODELS
Author Name Jyoti Chodhary, Jashan
Country India
Page No. 54-58

Abstract View PDF
IDENTIFICATION OF EMERGING RESEARCH TOPICS USING MULTIPLE MACHINE LEARNING MODELS
Author: Jyoti Chodhary, Jashan

We are looking for fresh research concepts that will help research institutes and policymakers. Many solutions have been put forth to deal with this, but the problem still exists. There is a lack of alignment between the concept of these new research subjects and practical ways for measuring them. Using many machine learning methods and a definition from Wang (2018), we identify and predict these innovative research ideas in this work. We tested our approach on a gene editing dataset and found three new areas to explore further. This indicates that these new research areas can be located and that our strategy is successful. The identification of emergent research themes is crucial for managing innovation, policy creation, and research endeavor direction. Conventional techniques for identifying these subjects frequently depend on laborious and subjective manual reviews and expert judgment. In this study, we offer a novel method to automatically identify and forecast future research topics by utilizing many machine learning models. We offer a thorough approach that includes preprocessing the data, extracting topics, calculating indicators, and predictive modeling. By identifying three new research fields, we show the efficacy of our approach using a gene editing dataset as a case study. Our results demonstrate how machine learning can be used to discover hot subjects for research and the consequences this has for different stakeholders. Keywords:-:scientific articles, organizations, support research, policymakers, understanding new technologies, Emerging Research Areas and their Coverage, machine learning models, topic identification, research trends, gene editing.

Editorial Policy About Peer Reviewed Journal Publication Ethics & Practices Plagiarism Policy Open Access, Licencing & Copyright Disclaimer Policy Privacy Policy FAQ Special Issue About The Journal

Latest Announcements

  • CALL FOR PAPERS 2025 (January-June)

    01-01-2025
    SUBMIT PAPERS IN OUR RESEARCH JOURNAL! 2025
    National Research Journal of Information Technology and Information Science  contributes in the growth and application of Research & Technology, by delivering the latest information contained in research papers, which enables them to enhance understanding for advancements in research activities. We intends to Disseminate and promote the research works of research scholars, Academia.

  • Subscribe This Journal

    01-01-2025
    We Request to Subscribe our Journals for the Noble Cause to Spread Knowledge, Wisdom and also to Protect Intellectual Property Rights of Scholars Across the World.

    Subscription Price: 3500/- (Bi-Annual)

    CALL NOW!
    +91-9888934889, 7986925354

Publish
Conference
Or Seminar
papers in our journal

Read More

National Research Journal of Information Technology & Information Science

+91-9888934889

editornrjitis@gmail.com

Useful Links

  • Home
  • About The Publisher
  • Submit Paper Online
  • Call for Papers
  • Publication Ethics
  • Join Our Editorial Board
  • Contact Us

Our Other Journals

  • NRJ of Human Resource Mgt.
  • National Research Journal of Business Economics
  • Sales and Marketing Management
  • Banking and Finance Management
  • Academe: Journal of Education and Psychology
  • Research and Reviews in Biotechnology and Biosciences
  • Journal of Literary Aesthetics

Downloads

  • Copyright Form
  • Paper Template
  • Manuscript Guidelines
  • e-Certificate
  • Sitemap
© 2025 National Research Journal of Information Technology & Information Science. All Rights Reserved.
Published By: National Press Associates www.npajournals.org