Indexing

Abstract: The need to read through large volumes of information or other bodies of text and produce a summarized version of the documents is usually a daunting task for many. For instance, researchers often times find themselves reading through lines, paragraphs, pages or chapters of documents before finally coming across the key points which will help them with their research work. Most existing systems generate the summary only in text and a specific language. Therefore, this study provides a web-based document summarizing and topic prediction system with Text-To-Speech and auto-language detection features. The system provides two types of two types of summaries; Extractive and abstractive summaries. The abstractive summary feature makes use of the GPT-3 API while the extractive summary and topic prediction make use of sentence scoring. The language detection model is trained with language data found on Kaggle. The text-to-speech feature is implemented using a JavaScript library. The system is able to detect the topic of the body of text and displays it to the user, and identifies the language of the text entered while also providing an appropriate summary. Users are able to play audio summaries and download them as a word document. Experimental results of the system have found that both the extractive and abstractive summary features are able to capture the main points of any given body of text and present summaries that convey information about the entire body of text, leveraging on Artificial Intelligence (AI) with a focus on Natural Language Processing (NLP).

Keywords: Artificial Intelligence, Natural Language Processing, Topic Prediction, Language Detection, Text-to-Speech.

Title: A Web-Based Document Summarizing and Topic Prediction System Using Natural Language Processing

Author: Taiwo Adigun, Einstein Ebereonwu

International Journal of Recent Research in Mathematics Computer Science and Information Technology

ISSN 2350-1022

Vol. 11, Issue 2, October 2024 - March 2025

Page No: 74-83

Paper Publications

Website: www.paperpublications.org

Published Date: 13-February-2025

Abstract: Artificial intelligence (AI) has been used in music for many years, and many unique applications may be made. Thanks to unrivalled access to music data and practical learning algorithms operating on powerful computer hardware, artificial intelligence (AI) is already generating income globally and delivering unexpected results in a field firmly rooted in human creativity. This article explores how artificial intelligence (AI) may help and enhance choir practice techniques. It outlines the traditional complexities of choral rehearsals, including vocal training, musicality, ensemble blend, and artistic interpretation. It then examines how AI-powered tools can be harnessed to address these challenges: AI-powered vocal analysis software can provide real-time feedback on individual singers' pitch, timbre, and breath support, allowing for targeted improvements. Intelligent accompaniment systems can dynamically adjust tempo and dynamics based on the choir's performance, improving rehearsal efficiency and aural skills. AI-assisted score learning and sight-reading exercises can be tailored to each singer's strengths and weaknesses, promoting self-directed practice and confidence. While acknowledging the benefits of AI, the article also discusses potential drawbacks like overdependence on technology and stifling creativity. It emphasises the importance of maintaining the conductor's role in shaping artistic interpretation, motivation, and emotional connection within the choir. Finally, the article considers ethical considerations regarding data privacy and potential biases in AI algorithms used for music education. The overall message highlights AI as a valuable tool to augment, not replace, the human conductor in choral rehearsals, paving the way for a future where technology and human artistry work together to enhance choral singing experiences.

Keywords: Artificial Intelligence; choral rehearsal techniques; intelligent accompaniment systems. Score learning and sight-reading. Choral singing.

Title: EXPLORING THE APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN CHORAL REHEARSAL TECHNIQUES

Author: ODUSANYA, Oluseun Sunday, AREMU, Damaris T’Oluwalope, NIYI-OJO, Mary A., ALABI, Adedapo Ojo

International Journal of Recent Research in Mathematics Computer Science and Information Technology

ISSN 2350-1022

Vol. 11, Issue 2, October 2024 - March 2025

Page No: 65-73

Paper Publications

Website: www.paperpublications.org

Published Date: 01-February-2025

Abstract: This paper examines the parallel evolution of classical and quantum computing, emphasizing their shared foundations, complementary strengths, and the potential for both independent advancements and collaborative innovations. It explores the areas where each technology excels, focusing on mathematical principles, operational logic, gates, algorithms, and practical applications. A detailed comparison of hardware highlights the distinct strengths and limitations of classical and quantum systems, providing a clear perspective on their unique roles. The study underscores the pivotal role of classical computing as a foundation for quantum development, particularly through its application in simulating quantum circuits and advancing quantum algorithms. By analysing the interplay between these two paradigms, the paper sheds light on how they coexist and complement one another, shaping the trajectory of future computing advancements. Ultimately, it highlights their indispensable contributions to the innovation landscape, both as standalone technologies and as interconnected components in the broader computing ecosystem.

Keywords: Quantum Computing, Programming, Underling Technologies, Mathematical Foundations, Simulators.

Title: Quantum v.s. Classical Computing: Technologies in Tandem

Author: Ramona Markoska, Aleksandar Markoski

International Journal of Recent Research in Mathematics Computer Science and Information Technology

ISSN 2350-1022

Vol. 11, Issue 2, October 2024 - March 2025

Page No: 53-64

Paper Publications

Website: www.paperpublications.org

Published Date: 22-January-2025

Abstract: This study explores the critical role of artificial intelligence (AI) tools in enhancing cybersecurity operations compared to conventional methods. With the rising sophistication of cyber-attacks and attackers, traditional security tools are becoming less effective. The research evaluates AI-powered tools' effectiveness in detecting and mitigating breaches across various cybersecurity domains, assessing their impact on both attack and defense strategies. The findings show that AI-powered tools significantly improve threat detection and response times, with key metrics such as detection rate, effectiveness, and usability. However, challenges like bias, false positives, high costs, and lack of support were noted. While AI tools show promise in enhancing cybersecurity operations, further advancements are needed to overcome these limitations. These results provide valuable insights for organizations seeking to integrate AI into their cybersecurity strategies.

Keywords: Artificial Intelligence Tools, Cybersecurity, cyber-attacks, cybersecurity strategies.

Title: A Comparative Analysis of the Influence of Artificial Intelligence Tools in Cybersecurity

Author: Onuh Ojoh

International Journal of Recent Research in Mathematics Computer Science and Information Technology

ISSN 2350-1022

Vol. 11, Issue 2, October 2024 - March 2025

Page No: 32-52

Paper Publications

Website: www.paperpublications.org

Published Date: 09-January-2025

Abstract: The concept of interval-valued intuitionistic fuzzy M group is extended by introducing primary interval-valued intuitionistic fuzzy M group using this concept primary interval-valued intuitionistic fuzzy M group is defined. We also proved some properties of the above are established.

Keywords: Intuitionistic fuzzy set, Interval-valued intuitionistic fuzzy set, Primary interval-valued intuitionistic fuzzy M group.

Title: Some properties of primary interval-valued intuitionistic fuzzy M group

Author: G.Prasannavengeteswari, Dr.B.Vasudevan, Dr.K.Gunasekaran, Dr.S.Nandakumar

International Journal of Recent Research in Mathematics Computer Science and Information Technology

ISSN 2350-1022

Vol. 11, Issue 2, October 2024 - March 2025

Page No: 23-31

Paper Publications

Website: www.paperpublications.org

Published Date: 23-November-2024

Abstract: In this paper, based on Jumarie type of Riemann-Liouville (R-L) fractional derivative and a new multiplication of fractional analytic functions, we can evaluate arbitrary order fractional derivative of some type of matrix fractional function. In fact, our result is a generalization of classical calculus result.

Keywords: Jumarie type of R-L fractional derivative, new multiplication, fractional analytic functions, matrix fractional function.

Title: Fractional Differential Problem of Some Type of Matrix Fractional Function

Author: Chii-Huei Yu

International Journal of Recent Research in Mathematics Computer Science and Information Technology

ISSN 2350-1022

Vol. 11, Issue 2, October 2024 - March 2025

Page No: 19-22

Paper Publications

Website: www.paperpublications.org

Published Date: 15-November-2024

Abstract: In this paper, based on a new multiplication of fractional analytic functions, we obtain the matrix forms of fractional Euler’s formula and fractional DeMoivre’s formula. In fact, our results are generalizations of classical calculus results.

Keywords: New multiplication, fractional analytic functions, matrix forms, fractional Euler’s formula, fractional DeMoivre’s formula.

Title: Matrix Forms of Fractional Euler’s Formula and Fractional DeMoivre’s Formula

Author: Chii-Huei Yu

International Journal of Recent Research in Mathematics Computer Science and Information Technology

ISSN 2350-1022

Vol. 11, Issue 2, October 2024 - March 2025

Page No: 14-18

Paper Publications

Website: www.paperpublications.org

Published Date: 24-October-2024

Abstract: The recent advances of e-commerce and e-payment systems have sparked an increment in financial fraud cases such as credit card fraud. Several classification techniques have been employed to detect credit card frauds in online transactions but their performances were affected by high cardholder’s data dimensionality. Thus, work employed Ant Colony Optimization for features extraction and evaluate its effectiveness using three selected classifiers. to detect fraud in credit cards online transactions. 3200 cardholders data (real and simulated) dataset with mix of genuine and fraudulent transactions. Ants Colony Optimization technique was used to extract features from the transactional data. Then, fraud detection system was designed with the three selected machine learning techniques (Back Propagation Neural Network, BPNN, Support Vector Machine, SVM and Naïve Bayes, NB) for classification. The results revealed that without features selection technique, NB, BPNN and SVM produced 86.4%, 88.7%, 93.6%,  for accuracy respectively and while with ACO technique, the results or NB, BPNN and SVM produced  95.3%,  96.8%, and 97.6%.

Keywords: Fraud Detection System, Back Propagation Neural Network, Support Vector Machine, Naïve Bayes, Ants Colony Optimization.

Title: Evaluation of Selected Machine Learning Techniques in Feature Extraction based Fraud Detection System in Online Transactions

Author: Ademola Sefiu A., Ismaila W. Oladimeji, Omotosho I. O., Ismaila Folasade M.

International Journal of Recent Research in Mathematics Computer Science and Information Technology

ISSN 2350-1022

Vol. 11, Issue 2, October 2024 - March 2025

Page No: 1-13

Paper Publications

Website: www.paperpublications.org

Published Date: 07-October-2024