JEMTEC

  Nature has long been a source of inspiration for problem-solving algorithms in computer science. One such biologically inspired algorithm is Ant Colony Optimization (ACO) , a technique based on the behavior of real ant colonies. ACO has become a popular metaheuristic in machine learning and artificial intelligence due to its ability to solve complex combinatorial optimization problems efficiently. 1. What is Ant Colony Optimization (ACO)? Ant Colony Optimization is a probabilistic technique developed by Marco Dorigo in the early 1990s. It is inspired by the foraging behavior of ants , particularly how they find the shortest paths between food sources and their nest using pheromone trails . Key Principles: Ants explore their environment randomly. When ants find food, they return to the colony, laying down pheromones on their path. Other ants are more likely to follow paths with stronger pheromone concentrations. Over time, shorter paths accumulate more pheromones because they a...

  The landscape of communication has undergone a seismic shift in recent years. Once dominated by print media—newspapers, magazines, and journals—the industry has evolved rapidly with the rise of digital platforms. Today, we live in an era where content is consumed through smartphones, streamed live on social platforms, and distributed globally in seconds. This transition from "print to pixel" has not only changed how we communicate but also opened up a wide range of career opportunities in the field of new media communication. The Shift Towards New Media New media communication refers to content available through digital platforms, including social media, websites, blogs, vlogs, podcasts, and OTT platforms. With the convergence of technology and media, communication has become faster, interactive, and more data-driven. Traditional journalists are now learning to create multimedia stories, and marketing professionals are diving into influencer partnerships, SEO, and analytics...

  In recent years, the field of artificial intelligence (AI) has grown exponentially, driven largely by advances in machine learning (ML). However, developing effective ML models traditionally requires deep expertise in data science, programming, and domain knowledge. This complex and time-consuming process has often been a bottleneck for organizations wishing to implement AI solutions. Automated Machine Learning (AutoML) addresses this challenge by automating the end-to-end process of applying machine learning to real-world problems. It aims to make ML accessible to non-experts while improving efficiency for seasoned data scientists. What is AutoML? AutoML refers to a suite of tools and techniques that automate the process of building, training, and deploying machine learning models. It abstracts the most complex parts of ML workflows, such as: Data preprocessing Feature engineering Model selection Hyperparameter tuning Model evaluation and validation AutoML systems help users a...