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i-manager's Journal on Information Technology (JIT)

Current Issue Vol. 12 Issue 4

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Volume 11 Issue 2 March - May 2022

Research Paper

Hate Speech Detection on Twitter using Machine Learning Techniques

Aastha Joshi* , Nirmal Gaud**

* Samrat Ashok Technological Institute, Vidisha, Madhya Pradesh, India.

** Department of Computer Science and Engineering, Samrat Ashok Technological Institute, Vidisha, Madhya Pradesh, India.

Joshi, A., and Gaud, N. (2022). Hate Speech Detection on Twitter using Machine Learning Techniques. i-manager’s Journal on Information Technology, 11(2), 1-9. https://doi.org/10.26634/jit.11.2.18919

Abstract

People can now create, post, and share content to connect with each other as social media platforms have grown in popularity. On the other hand, it has also become a forum for hatred and war. The rampant spread of hatred on social media has had a significant impact on society, dividing people into pros and cons on topics that govern the status of a person, place, community, and country. Hate speech on social media is difficult to recognize because messages contain paralinguistic signs, jumbled language, and poorly written content. Due to the lack of consensus on what constitutes hate speech and the lack of background information, it becomes more difficult to detect. Creating huge markup corpora with lots of relevant contexts is a difficult task. Even though scientists have found that hate is a problem on all social media platforms, there is no perfect method to detect accurately. The current state and complexity of the field, as well as the main algorithms, methodologies, and key characteristics used, are described in this paper. It has focused on the important areas that have been explored for hate speech detection and also applied machine learning algorithms to detect it.

References

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Research Paper

Music Genre Detection using Deep Learning Models

Ayush Kishore Mishra* , Divyansh Kumar Singh, Ankit Khare*

*-*** Department of Information Technology, Shri Ramswaroop Memorial College of Engineering and Management Lucknow, Uttar Pradesh, India.

Mishra, A. K., Singh, D. K., and Khare, A. (2022). Music Genre Detection using Deep Learning Models. i-manager’s Journal on Information Technology, 11(2), 10-15. https://doi.org/10.26634/jit.11.2.18800

Abstract

An automated music classification system is that automates the entire process of music classification, i.e., classifying music without any human intervention. Along with this, it is very important to have a good recommendation system that will help with features such as music classification. This paper proposes an automated music classification system that will be very useful for both informational and entertainment purposes in the field of music. This system, based on a musicrelated Artificial Intelligence (AI) algorithm, automatically categorizes different types of music corresponding to different music genres, e.g., Hip Hop, Jazz, Rock, Blues, etc. Another feature added to this system is to recommend similar songs classified by the system. The architecture of the system and the algorithms used at each stage are described and implemented in this paper. The system also provides a lyrics classification module that generates and provides users with lyrics of the user choice. Compared to realistic musical classification, which has always been a difficult problem as it may lack structure or rationality.

References

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Research Paper

Machine Learning Tools Based Statistical Model for Candidate Hiring Prediction

Naman Sahai*

Shri Ramswaroop Memorial Group of Professional Colleges, Uttar Pradesh, India.

Sahai, N. (2022). Machine Learning Tools Based Statistical Model for Candidate Hiring Prediction. i-manager’s Journal on Information Technology, 11(2), 16-20. https://doi.org/10.26634/jit.11.2.18956

Abstract

Recruiting is a strategy for finding, attracting, and hiring young and experienced talent for entry-level and experienced positions. College and industry recruiting is generally a tactic for medium-to-large companies with large recruiting needs according to the project and business needs, but can range from small efforts (e.g., working with university career centers to source potential candidates) to large-scale operations and even asking current employees to change jobs. Campus hiring often involves working with university career centers and attending job fairs to meet face-to-face with college students and recent graduates, while hiring experienced employees depends on profile, experience, and other factors.

References

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Review Paper

Business Continuity Plan: A Comprehensive Outlook

Henry O. Nwaete*

Northcentral University, Illinois, United States.

Nwaete, H. O. (2022). Business Continuity Plan: A Comprehensive Outlook. i-manager’s Journal on Information Technology, 11(2), 21-28. https://doi.org/10.26634/jit.11.2.18882

Abstract

Business disruptions cannot be predicted within government, private, and commercial entities. It would be prudent on the part of organizations and every applicable community in which information systems are crucial for sustainability to make adequate provisions for the eventual threat or disruption to businesses. The mitigation and containment of business disruptions, which could result from natural disasters, including earthquakes, floods, hurricanes, etc., or man-made constraints, which might include system breaches, compromises, and unplanned downtime, should have to be strategically planned and implemented in a structured methodology. During disasters, organizations should not only focus on getting the assets and infrastructure back (called "Disaster Recovery"), but organizations will also do better if it makes SMART (specific, Measurable, Achievable, Relevant, and Time-bound) plans for "Business Continuity" (BC).

References

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Review Paper

Overview of Electronic Commerce (E-Commerce)

Bhumika Tuli* , Jyoti, Neha Gautam*, Santosh Kumar****

* Department of Computer Science Engineering, Chandigarh Group of Colleges Landran, Mohali, Punjab, India.

Chandigarh Group of Colleges, Landran, Mohali, Punjab India.

* St. Ezra International School, Kharar, Mohali, Punjab, India.

**** Department of Mechanical Engineering, Chandigarh Group of Colleges, Landran, Mohali, Punjab, India.

Tuli, B., Jyoti, Gautam, N., and Kumar, S. (2022). Overview of Electronic Commerce (E-Commerce). i-manager’s Journal on Information Technology, 11(2), 29-42. https://doi.org/10.26634/jit.11.2.18955

Abstract

E-commerce plays a vital role in today's services. It is mainly the work of selling and buying goods through online trading or data transmission over the Internet. This paper gives a complete explanation of E-commerce and why it is used. The main purpose of this paper is to inform people about every aspect of E-commerce, such as what it is and what types of intermediaries it offers. In addition, the role of E-commerce in modern life, including its benefits and difficulties, is discussed.

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i-manager's Journal on Information Technology (JIT)

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Volume 11 Issue 2 March - May 2022

Hate Speech Detection on Twitter using Machine Learning Techniques

Aastha Joshi* , Nirmal Gaud**

* Samrat Ashok Technological Institute, Vidisha, Madhya Pradesh, India. ** Department of Computer Science and Engineering, Samrat Ashok Technological Institute, Vidisha, Madhya Pradesh, India.

Joshi, A., and Gaud, N. (2022). Hate Speech Detection on Twitter using Machine Learning Techniques. i-manager’s Journal on Information Technology, 11(2), 1-9. https://doi.org/10.26634/jit.11.2.18919

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Music Genre Detection using Deep Learning Models

Ayush Kishore Mishra* , Divyansh Kumar Singh**, Ankit Khare***

*-*** Department of Information Technology, Shri Ramswaroop Memorial College of Engineering and Management Lucknow, Uttar Pradesh, India.

Mishra, A. K., Singh, D. K., and Khare, A. (2022). Music Genre Detection using Deep Learning Models. i-manager’s Journal on Information Technology, 11(2), 10-15. https://doi.org/10.26634/jit.11.2.18800

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Machine Learning Tools Based Statistical Model for Candidate Hiring Prediction

Naman Sahai*

Shri Ramswaroop Memorial Group of Professional Colleges, Uttar Pradesh, India.

Sahai, N. (2022). Machine Learning Tools Based Statistical Model for Candidate Hiring Prediction. i-manager’s Journal on Information Technology, 11(2), 16-20. https://doi.org/10.26634/jit.11.2.18956

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Business Continuity Plan: A Comprehensive Outlook

Henry O. Nwaete*

Northcentral University, Illinois, United States.

Nwaete, H. O. (2022). Business Continuity Plan: A Comprehensive Outlook. i-manager’s Journal on Information Technology, 11(2), 21-28. https://doi.org/10.26634/jit.11.2.18882

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Overview of Electronic Commerce (E-Commerce)

Bhumika Tuli* , Jyoti**, Neha Gautam***, Santosh Kumar****

Tuli, B., Jyoti, Gautam, N., and Kumar, S. (2022). Overview of Electronic Commerce (E-Commerce). i-manager’s Journal on Information Technology, 11(2), 29-42. https://doi.org/10.26634/jit.11.2.18955

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* Samrat Ashok Technological Institute, Vidisha, Madhya Pradesh, India.

** Department of Computer Science and Engineering, Samrat Ashok Technological Institute, Vidisha, Madhya Pradesh, India.

Ayush Kishore Mishra* , Divyansh Kumar Singh, Ankit Khare*

Bhumika Tuli* , Jyoti, Neha Gautam*, Santosh Kumar****