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Top journals in machine learning

Speed of growing world is control by machines, hence most of researchers are doing work in this field for the machine learning models. Each research need reach to relevant audience hence publication is the only medium to do that, people always looks for the Top Journals in Machine Learning for publication. But most of publishers are just making content online after this no work was done from the journal side to work. Journal who are really working in the field of machine learning try to index themself in more renowned platforms. Getting such journal is not tough these days. Some of simple step to judge a journal is find the latest content publish by the journal, volume, issue of current year/ month. Its content help to find that journal I doing publication. Further if journal is doing less number of publication then be aware as those are not doing this work seriously. People need to check the content from global reach as publication needs global audience to read/write about the matter. If journal provide open access to the published content then it is easy for the reader to come to know more about your work.

Submit Your Paper  / Check Publication Charges

Some of key points that publisher should learn or check before paper submission are:

1.Check relevancy of content with your machine learning topic if content present then do submit paper as chance of publication is high. Further check journal has variety of author from more than one nation. As each of those author attract its own reader that may increase the chance that your paper may be read or cite by that random person.
2.Publication of journal is costly process with paid journals hence always check all set of publication charges either hidden or known. In case pocket now allow then do not submit content in that journal as it may waste your time in review/correction process.
3.Some of the authors should consult with mentor, batch mates about the journal and its reach. This help to learn about the journal. People who are looking for the fast publication should be more aware of publisher promises as people do mention page of journal but reality is differ.

Journal who do not mention scope of research publication, indexing, publication fees, contact number, etc. is alarming hence those not good sites to publish content. Getting Top journal in Machine Learning of any other are research area list is not possible hence its author own understanding learning that helps to learn about it.

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fastest journal to publish

Researcher who are close to complete degree need for the fastest journal to publish research content. Hence getting such journals is not easy as many of scholars are approaching to search engines but due default listing reach to such publishers is not easy. Some of publishers are not valid and do publication which may result in fake publication. This article help scholars to understand that journal selection for fast publication depends on many factors:

Submit Your Paper  / Check Publication Charges

  • Check the publication fees as many of journal do fast publication of one week but they charge in more than 100 dollars. So that may turns to withdraw paper even after acceptance. Don’t get attract of Free words as they migh charge you after acceptance in the name of formatting, grammar error removal.
  • Journal doing fast publication do have proper contact details hence leads to mislead the author and wait for the reply is the only option for the researcher. So it always be better that contact details like email, phone/mobile or even chat should be present.
  • Publication in fast journal should be relevant to your field is also an important factor as irrelevant publication lead to data loss.
  • Finally always cross check that publication in the journal is has 1 or 2 month of issue, continue as many of publisher doing even free publication but the journal has not doing it regularly.
  • Check that publisher has valid ISSN with indexing information of the journal in other sites as both are international platforms hence data should be present on sites.
  • Consult with your colleague and guide/supervisor as well. As publisher also work for the publication after paper submission.
fastest journal to publish

In order to further reduce the publication time try to submit paper in journal format as this reduces the manpower requirement of formatting. Always in touch with publication customer care team for all set of stages. In case of digital certificates ask publisher in prior about it with charges if they are applying. Some of journals do not mention charges of number of authors, number page, number color pages charges, so do cross checks all before paper submission. Many of journals are doing fast and good work even they are maintain the quality as they have good reviewer team. Publication fees is not negative as publication house is private body and hence no financial support from any agencies, so they are bound to charge as they have to serve journal.

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Tokenization for Text Analysis

Tokenization for Text Analysis
Authors:Sowmik Sekhar

Abstract-The Seminar “TOKENIZATION FOR TEXT ANALYSIS” is an advanced tokenization technique that is a revolutionizing text analysis, enabling researchers to glean profound insights from vast textual data. This study explores diverse tokenization approaches, encompassing word-based, subword-based, character-level, and language-agnostic methods, with a particular emphasis on BERT integration for capturing language nuances. Striking a balance between granularity and computational efficiency is paramount for practical applications in sentiment analysis, information retrieval, and natural language processing, where processing massive datasets while preserving language intricacies is essential. The study addresses challenges posed by social media content with informal language and unconventional writing styles, unsegmented languages lacking defined word boundaries, and multilingual datasets demanding language-independent tokenization strategies. For large-scale text analysis, optimizing tokenization to minimize processing time while maintaining analysis performance is critical, making tokenization a viable approach for real-world applications. This research provides valuable insights into aligning tokenization methods with text data characteristics and analysis goals, ensuring granularity matches task requirements. Furthermore, the study envisions seamless integration of advanced tokenization techniques with emerging NLP technologies, enhancing text analysis efficacy across domains for knowledge discovery and informed decision-making. Subword-based tokenization approaches, such as Byte Pair Encoding (BPE) and Sentence Piece, effectively capture language nuances and improve the performance of NLP tasks on social media data and other text datasets with informal language and unconventional writing styles. These methods break down words into smaller units, enabling a more granular representation of language. For multilingual datasets and unsegmented languages with undefined word boundaries, language-agnostic tokenization methods, such as those based on characters or word embeddings, prove to be valuable tools. These methods overcome the limitations of language-specific tokenization approaches and effectively handle diverse linguistic structures, making them well-suited for cross-lingual applications.

DOI: 10.61137/ijsret.vol.10.issue1.127

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