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AKASHVANI
"Akashvani" (English) is a programme journal of ALL INDIA RADIO, it was formerly known as The Indian Listener. It used to serve the listener as a bradshaw of broadcasting ,and give listener the useful information in an interesting manner about programmes, who writes them, take part in them and produce them along with photographs of performing artists. It also contains the information of major changes in the policy and service of the organisation. The Indian Listener (fortnightly programme journal of AIR in English) published by The Indian State Broadcasting Service, Bombay, started on 22 December, 1935 and was the successor to the Indian Radio Times in English, which was published beginning ...
Computer Programming and Computer Science
This book covers the syllabus of C programming.The significant role of this book to the reader will be easy language, concepts and questions and answers for MNCs.I wish this will help the reader a lot
Girish Chandra Ghosh
None
Biohydrogen Production: Sustainability of Current Technology and Future Perspective
Increase in green, renewable and sustainable energy demand due to higher environmental impacts (e.g. Greenhouse gases emissions, climate change, etc.) on consumption of fossil fuel resource put down an extra pressure on government, researchers and industrialists. Among several available biofuel options, biohydrogen is considered as one of the best environmentally clean fuel and a strong candidate to fulfil the future demand of sustainable energy resource. Although, biohydrogen production technology and its use as a fuel is still in infancy stage. Selection of most sustainable production pathway, increase in production upto industrial scale and cost efficiency are some issue still persist wit...
Integration Challenges for Analytics, Business Intelligence, and Data Mining
As technology continues to advance, it is critical for businesses to implement systems that can support the transformation of data into information that is crucial for the success of the company. Without the integration of data (both structured and unstructured) mining in business intelligence systems, invaluable knowledge is lost. However, there are currently many different models and approaches that must be explored to determine the best method of integration. Integration Challenges for Analytics, Business Intelligence, and Data Mining is a relevant academic book that provides empirical research findings on increasing the understanding of using data mining in the context of business intelligence and analytics systems. Covering topics that include big data, artificial intelligence, and decision making, this book is an ideal reference source for professionals working in the areas of data mining, business intelligence, and analytics; data scientists; IT specialists; managers; researchers; academicians; practitioners; and graduate students.
Disruptive Technology and Digital Transformation for Business and Government
With the far-reaching global impact of the COVID-19 pandemic, the demand and the necessity for digital enterprise transformation have accelerated exponentially. Management and strategies for the adoption and wider usage of newer digital technologies for the transformation of an enterprise through digital tools such as real-time video communications have shown that people no longer need to be required to be physically present in the same place; rather, they can be geographically dispersed. Technologies such as artificial intelligence, cloud computing, digital banking, and cloud data have taken over tasks that were initially done by human hands and have increased both the automation and effici...
Handbook of Research on Data-Driven Mathematical Modeling in Smart Cities
A smart city utilizes ICT technologies to improve the working effectiveness, share various data with the citizens, and enhance political assistance and societal wellbeing. The fundamental needs of a smart and sustainable city are utilizing smart technology for enhancing municipal activities, expanding monetary development, and improving citizens’ standards of living. The Handbook of Research on Data-Driven Mathematical Modeling in Smart Cities discusses new mathematical models in smart and sustainable cities using big data, visualization tools in mathematical modeling, machine learning-based mathematical modeling, and more. It further delves into privacy and ethics in data analysis. Covering topics such as deep learning, optimization-based data science, and smart city automation, this premier reference source is an excellent resource for mathematicians, statisticians, computer scientists, civil engineers, government officials, students and educators of higher education, librarians, researchers, and academicians.
Emerging Challenges, Solutions, and Best Practices for Digital Enterprise Transformation
As organizations continue to move towards digital enterprise, the need for digital transformation continues to grow especially due to the COVID-19 pandemic. These impacts will last far into the future, as newer digital technologies continue to be accepted, used, and developed. These digital tools will forever change the face of business and management. However, on the road to digital enterprise transformation there are many successes, difficulties, challenges, and failures. Finding solutions for these issues through strategic thinking and identification of the core issues facing the enterprise is of primary concern. This means modernizing management and strategies around the digital workforc...
Machine Learning Applications in Non-Conventional Machining Processes
Traditional machining has many limitations in today’s technology-driven world, which has caused industrial professionals to begin implementing various optimization techniques within their machining processes. The application of methods including machine learning and genetic algorithms has recently transformed the manufacturing industry and created countless opportunities in non-traditional machining methods. Significant research in this area, however, is still considerably lacking. Machine Learning Applications in Non-Conventional Machining Processes is a collection of innovative research on the advancement of intelligent technology in industrial environments and its applications within the manufacturing field. While highlighting topics including evolutionary algorithms, micro-machining, and artificial neural networks, this book is ideally designed for researchers, academicians, engineers, managers, developers, practitioners, industrialists, and students seeking current research on intelligence-based machining processes in today’s technology-driven market.
