Advanced Computational Techniques for Sustainable Computing

Building smart sustainable solutions involves applying mining, learning, IoT, and sensor-based techniques for sustainable computing, extracting data from various sources, pre-processing data, and using data analytic language like R and Python to improve sustainable services offered by multi-disciplinary domains. It also involves comparison and analysis of recent technologies and trends, as well as the development of smart models for information gain and effective decision-making with visualization.

Format: Hardback
Length: 322 pages
Publication date: 25 July 2022
Publisher: Taylor & Francis Ltd

Delving into the realm of smart, sustainable solutions, this comprehensive guide offers invaluable insights on harnessing cutting-edge technologies to drive positive change. It explores the application of mining, learning, IoT, and sensor-based techniques in sustainable computing, providing a deep understanding of data extraction, pre-processing, and effective utilization for application-based research. The guide also emphasizes practical usage of data analytic languages such as R and Python to enhance sustainable services across diverse multidisciplinary domains. Additionally, it encompasses a comparative analysis of recent technologies and trends, highlighting the development of smart models for information gain and effective decision-making with visualization. By embracing these innovative approaches, individuals and organizations can contribute to building a more sustainable and resilient future.

Introduction:
In today's rapidly evolving world, the need for smart, sustainable solutions has become increasingly pressing. As we face the challenges of climate change, resource depletion, and environmental degradation, it is crucial to explore innovative approaches that can help us achieve a more sustainable future. This comprehensive guide aims to provide valuable insights on harnessing cutting-edge technologies to build smart, sustainable solutions.
Mining, Learning, IoT, and Sensor-Based Techniques in Sustainable Computing:
Mining, learning, IoT, and sensor-based techniques play a pivotal role in sustainable computing. By leveraging these technologies, we can extract valuable data from various sources and transform it into useful information for application-based research. Data extraction involves the process of gathering and extracting relevant data from diverse sources, such as sensors, social media, and environmental databases. Pre-processing of data is then necessary to clean and normalize the data, ensuring its accuracy and suitability for analysis.
Effective Use of Extracted Data for Application-Based Research:
Once the data has been extracted and pre-processed, it can be utilized for application-based research. This involves analyzing the data to identify patterns, trends, and insights that can inform decision-making and policy development. Data analytic languages such as R and Python are widely used for data analysis and visualization, allowing researchers to explore and interpret the data in a meaningful way.
Practical Usage of Data Analytic Languages:
Data analytic languages such as R and Python are essential tools for improving sustainable services offered by multi-disciplinary domains. R is a powerful programming language for statistical analysis and data manipulation, while Python is known for its versatility and ease of use. By learning these languages, researchers and practitioners can develop smart models and algorithms that can help optimize processes, reduce waste, and improve resource efficiency.
Comparison and Analysis of Recent Technologies and Trends:
To stay up-to-date with the latest technologies and trends in sustainable computing, it is important to conduct a comparative analysis. This involves examining the latest developments in mining, learning, IoT, and sensor-based techniques, as well as exploring emerging technologies such as artificial intelligence, blockchain, and quantum computing. By understanding these trends, researchers and practitioners can identify new opportunities for innovation and collaboration.
Development of Smart Models for Information Gain and Effective Decision-Making:
In order to make effective decisions, it is crucial to have access to accurate and timely information. Smart models can help achieve this by leveraging data analytics and machine learning algorithms. These models can analyze large datasets and identify patterns and insights that can inform decision-making processes. Visualization is also an important aspect of smart modeling, as it allows researchers and practitioners to present their findings in a clear and concise manner.
Conclusion:
In conclusion, smart, sustainable solutions require the application of cutting-edge technologies such as mining, learning, IoT, and sensor-based techniques. By leveraging these technologies, we can extract valuable data, analyze it effectively, and make informed decisions that contribute to a more sustainable and resilient future. This comprehensive guide provides valuable insights on how to build smart, sustainable solutions, and encourages individuals and organizations to embrace these innovative approaches.

Weight: 790g
Dimension: 254 x 178 (mm)
ISBN-13: 9780367495220