Semiconductors between past and present

Abstract

There is no doubt that the semiconductor contributed very significantly to changing our world in the long run, which made it very necessary to write this article and shed light on the semiconductor between past and present. Whereas, this article is not intended to teach you semiconductor chemistry and its applications, but it also aims to refresh your memory with everything related to semiconductors from the moment of sunrise to the present day.As known, people need to communicate with each other in order to meet their daily and professional needs. In the past, the communication process was very difficult and sometimes costly until the emergence of what is known as semiconductors, which in turn opened new purview in the world of communications through its entry into the communication devices industry and computers industry and its ability to processing The data, in turn, facilitated and summarized a very long journey on humans in the field of industry. Today, we are on the beginning of a new sun rise of semiconductor, especially after the emergence of the so-called nanotechnology, where semiconductors have been observed a very large role in the development of this technology, not only but have become Part of the integral ones.

LCDDF: An Adaptive and Learning based Framework with Feature Selection for Efficient Detection of Concept Drift in Data Streams

“…It highlights algorithm issues, dataset gaps, metric flaws, and proposes future improvements for industrial applications. Hammoodi et al [2] tackles real-time feature selection in data stream classification by adapting classifiers to concept drifts using Micro-Clusters. Cano et al [3] describes the tackles concept drift in data streams by introducing Kappa Updated Ensemble (KUE), an ensemble method featuring dynamic weighting and classifier selection for enhanced robustness.…”

“…The challenges include data dynamism, inefficient learning mechanisms and inadequate feature engineering methods. Clustering based approach is found in [2] to achieve adaptive means of concept drift detection with unsupervised learning. Supervised learning approaches are found in [4], [8], [9], [15] and [27] where concept drift based solutions are explored to solve problems in different domains.…”

“…In the wake of Industry 5.0 discussions of late, more human-centric solutions are expected with next level of automation, robotics and seamless humanmachine collaboration. While dealing with data streams, it is essential to consider concept drift which is a phenomenon where properties of a target variable used for prediction change over time [2]. There are many kinds of tasks associated with data stream analytics.…”