Chaudhury Sanjeev Kumar Dash | Social Media and Digital Marketing | Best Researcher Award

Chaudhury Sanjeev Kumar Dash | Social Media and Digital Marketing | Best Researcher Award

Dr Chaudhury Sanjeev Kumar Dash Silicon University, India

Dr. Chaudhury Sanjeev Kumar Dash received his Ph.D. in Computer Science from Fakir Mohan University, India, in 2014. His doctoral research, supervised by Prof. Satchidananda Dehuri at F. M. University in Balasore, focused on “Evolutionary Neural Network for Data Mining.” With a solid foundation in academia, he embarked on a career in teaching and research. He began as an Associate Professor at the Silicon Institute of Technology, where he later assumed the position of Senior Assistant Professor. Throughout his tenure, he has been dedicated to shaping the minds of future professionals, imparting knowledge, and fostering a culture of innovation. Driven by a passion for advancing the field of computer science, he continues to explore new avenues in research and education.

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Education:

He obtained his Ph.D. in Computer Science from Fakir Mohan University, India, in 2014. His doctoral thesis, titled “Evolutionary Neural Network for Data Mining,” was completed under the guidance of Prof. Satchidananda Dehuri at F. M. University in Balasore. Prior to his Ph.D., he earned his M.Tech in Computer Science with First Class distinction from the School of Mathematics, Statistics & Computer Science at Utkal University, India, in 2004. This academic journey equipped him with a solid foundation in both theoretical and practical aspects of computer science, particularly focusing on data mining and computational intelligence techniques. Through his research and academic pursuits, he has demonstrated a strong aptitude for applying advanced computational methods to solve complex problems in various domains. His expertise in evolutionary neural networks and data mining reflects his dedication to exploring innovative solutions for challenging real-world issues. With a solid educational background and guidance from esteemed mentors, he continues to contribute to the advancement of computer science through his research, teaching, and professional endeavors.

Experience:

He served as an Associate Professor at the Silicon Institute of Technology and later transitioned to the role of Senior Assistant Professor at the same institution. With a keen dedication to academia, he contributed significantly to the academic and research endeavors within the institution. His roles encompassed not only teaching but also mentoring students, guiding research projects, and engaging in scholarly activities. At Silicon Institute of Technology, he played a pivotal role in shaping the educational experiences of students, fostering a dynamic learning environment, and instilling a passion for knowledge among learners. Additionally, his expertise and leadership qualities were instrumental in driving initiatives aimed at enhancing the academic standards and research output of the institution. Prior to his tenure at Silicon Institute of Technology, he served as a Lecturer at B.C.E.T., Balasore, where he honed his teaching skills and laid the foundation for his academic career. Throughout his journey, he has demonstrated a commitment to excellence in education and has been a source of inspiration for both students and colleagues alike.

Publications:

  1. Radial basis function neural networks: a topical state-of-the-art survey Cited By : 112, Published By ; 2016
  2. An outliers detection and elimination framework in classification task of data mining Cited By : 27, Published By ; 2023
  3. Design of self-adaptive and equilibrium differential evolution optimized radial basis function neural network classifier for imputed database Cited By : 19, Published By ; 2016
  4. DE+ RBFNs based classification: A special attention to removal of inconsistency and irrelevant features Cited By : 18, Published By ; 2019
  5. Building a novel classifier based on teaching learning based optimization and radial basis function neural networks for non-imputed database with irrelevant features Cited By : 17, Published By ; 2022
  6. Improving software reliability prediction accuracy using CRO-based FLANN Cited By : 17, Published By ; 2019
  7. QORA-ANN: quasi opposition based Rao algorithm and artificial neural network for cryptocurrency prediction Cited By : 12, Published By ; 2021
  8. On the study of GRBF and polynomial kernel based support vector machine in web logs Cited By : 10, Published By ; 2013
  9. Feature selection for designing a novel differential evolution trained radial basis function network for classification Cited By : 9, Published By ; 2013
  10. Differential Evolution-Based Optimization of Kernel Parameters in Radial Basis Function Networks for Classification Cited By : 9, Published By ; 2013

Developing a Social Media Strategy

Introduction of Developing a Social Media Strategy

Developing a social media strategy is a crucial undertaking for businesses, organizations, and individuals looking to harness the power of social media effectively. A well-crafted social media strategy aligns your goals, target audience, and content with the platforms and tactics that will best engage and connect with your intended audience. It is the roadmap that guides your online presence and helps you achieve your objectives.

Developing a Social Media Strategy:

Audience Research and Persona Development:

Analyzing your target audience's demographics, interests, and behaviors to create detailed personas that inform content creation and targeting strategies.

Platform Selection and Optimization:

Identifying the most suitable social media platforms for your brand or objectives, optimizing profiles, and tailoring content to each platform's unique audience and features.

Content Strategy and Calendar Planning:

Creating a content strategy that outlines the type of content, posting frequency, and content calendar to maintain a consistent and engaging online presence.

Engagement and Community Building:

Strategies for fostering engagement, building a loyal online community, and leveraging user-generated content to strengthen brand affinity.

Metrics and Performance Measurement:

Defining key performance indicators (KPIs), tracking metrics, and using analytics tools to evaluate the effectiveness of your social media strategy and make data-driven improvements.

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Introduction of Email Marketing Email marketing research is a fundamental discipline within the digital marketing landscape, focusing on the strategies, tools, and tactics used to reach and engage target audiences
Introduction of Mobile Marketing Mobile marketing research is a critical field that explores the strategies, technologies, and trends in reaching and engaging audiences through mobile devices. As mobile usage continues