Coriandrum sativum, commonly known as coriander, is a highly valued herb with diverse culinary, medicinal, and aromatic uses. Originating from the Mediterranean, its successful cultivation relies on a thorough understanding of its physiological characteristics and environmental needs. This study highlights key factors influencing coriander's growth, including soil and nutrient requirements, water management, temperature sensitivity, light requirements, and pest and disease management. Optimal growth is achieved in well-drained, loamy soils with a pH of 6.2 to 6.8, consistent moisture levels, and temperatures between 15-20°C. Adequate sunlight and effective pest and disease management are also crucial for maintaining plant health and productivity. By addressing these factors, growers can enhance the quality and yield of coriander, ensuring a successful and sustainable cultivation process..

This paper provides an in-depth exploration of the field of data science and analytics, examining its methodologies, diverse applications across various industries, and the challenges and opportunities it presents. Through detailed studies and current researches, we highlight the transformative impact of data-driven decision-making and discuss future directions for innovation and growth. This paper aims to explore the methodologies and techniques used in data science and analytics, examine their applications across various industries, discuss the challenges faced by practitioners, and outline future directions for the field. The paper provides an overview of the fundamentals of data science and analytics with methodologies, employed techniques, and interconnected applications of industries..

Transformer models have revolutionized natural language processing (NLP) by offering powerful mechanisms for contextual text understanding. This paper reviews the key advancements in transformer architectures, focusing on improvements in model efficiency, scalability, and accuracy. It explores innovations such as BERT, GPT, T5, and their successors, and examines the impact of techniques like attention mechanisms, transfer learning, and model distillation. The paper also discusses challenges, including computational demands, ethical considerations, and ongoing efforts to mitigate biases within these models..