Daily Archives: March 19, 2024

Strategic Data Management: Frameworks, Implementation Challenges, and Success Stories
Authors:-Jorge White

Abstract- In the current landscape where digital innovation shapes every aspect of business operations, the adept management and strategic use of data stand as pivotal factors in securing organizational prosperity and a competitive edge. This study ventures into the realm of data strategy, shedding light on its pivotal role in the digital age and its profound influence on the operational dynamics of contemporary organizations. It outlines a variety of methodologies for the formulation of a holistic data strategy, encompassing essential facets such as governance, data quality, architectural integrity, and organizational data literacy. Furthermore, this research identifies prevalent obstacles encountered during the deployment of data strategies and furnishes actionable strategies derived from the successful experiences of leading organizations. By engaging in a comparative scrutiny of diverse strategic models and probing into the avant-garde trends shaping data management, this paper prognosticates the trajectory of data strategy evolution and its repercussions for future business models. Offering an amalgam of theoretical constructs, empirical challenges, and illustrative success narratives, the paper serves as a comprehensive guide for entities aiming to optimize their data strategy endeavors, thereby maximizing the utility of their digital information reservoirs.

DOI: 10.61137/ijsret.vol.10.issue2.136

Published by: vikaspatanker

Advancing Ethical and Accurate Hate Speech Detection with Machine Learning Techniques
Authors:-Jorge White

Abstract- In recent years, the proliferation of social media platforms has significantly increased, providing a digital space where individuals from diverse backgrounds can express their opinions and thoughts. This surge in social media usage has brought to light the challenge of managing and moderating hate speech—a form of content that can incite violence, discrimination, and hostility. The primary difficulties in detecting and processing hate speech stem from the linguistic diversity of users, the nuanced usage of language that can alter meanings based on context, and the scarcity of robust datasets for the development and evaluation of detection models. This paper explores these challenges in depth and proposes an innovative approach to enhance the efficiency and effectiveness of hate speech detection. We critically analyze the limitations inherent in current methodologies and introduce a model based on Support Vector Machine (SVM) algorithms. Our comparative analysis demonstrates that SVM-based models offer superior performance in detecting hate speech compared to conventional neural network approaches. This is attributed to the SVM’s ability to handle high-dimensional data and its effectiveness in classifying complex, nuanced linguistic patterns. Furthermore, we delve into the technical and ethical implications of automating hate speech detection. The paper discusses the ongoing challenges in balancing accuracy with the need for ethical considerations, such as avoiding censorship and respecting free speech. We address the technical hurdles related to algorithmic bi as, model interpretability, and the need for continuous adaptation to evolving language and social norms. In conclusion, while significant strides have been made in employing machine learning techniques for hate speech detection, several critical issues remain unresolved. Our research underscores the importance of interdisciplinary efforts, combining insights from linguistics, social sciences, and computer science, to develop more sophisticated, ethical, and effective hate speech detection systems. By advancing the use of SVM and exploring its potential in this domain, we contribute to the broader discourse on making digital platforms safer and more inclusive.

DOI: 10.61137/ijsret.vol.10.issue2.135

Published by: vikaspatanker