The Telecom Wars begins its narration from those times, traversing the uncertain early years of the mobile phone industry. It is a vivid portrait of the fierce competition as Tata, Birla, Reliance, and Bharti bid to capture the lion’s share of India’s billion voices. As they did, India’s institutions to regulate and manage the sector also evolved―the ministry separated from the Telecom Regulatory Authority of India (TRAI), the Telecom Disputes Settlement and Appellate Tribunal (TDSAT), and state-run operators MTNL and BSNL also joined the fray. Policy makers, entrepreneurs, and bureaucrats worked tirelessly to change India’s cellular topography.

MACHINE LEARNING PARADIGM FOR INTERNET OF THINGS APPLICATIONS As companies globally realize the revolutionary potential of the IoT, they have started finding a number of obstacles they need to address to leverage it efficiently. Many businesses and industries use machine learning to exploit the IoT’s potential and this book brings clarity to the issue. Machine learning (ML) is the key tool for fast processing and decision-making applied to smart city applications and next-generation IoT devices, which require ML to satisfy their working objective. Machine learning has become a common subject to all people like engineers, doctors, pharmacy companies, and business people. The book addresses the problem and new algorithms, their accuracy, and their fitness ratio for existing real-time problems. Machine Learning Paradigm for Internet of Thing Applications provides the state-of-the-art applications of machine learning in an IoT environment. The most common use cases for machine learning and IoT data are predictive maintenance, followed by analyzing CCTV surveillance, smart home applications, smart-healthcare, in-store ‘contextualized marketing’, and intelligent transportation systems. Readers will gain an insight into the integration of machine learning with IoT in these various application domains.

MACHINE LEARNING PARADIGM FOR INTERNET OF THINGS APPLICATIONS

As companies globally realize the revolutionary potential of the IoT, they have started finding a number of obstacles they need to address to leverage it efficiently. Many businesses and industries use machine learning to exploit the IoT’s potential and this book brings clarity to the issue.

Machine learning (ML) is the key tool for fast processing and decision-making applied to smart city applications and next-generation IoT devices, which require ML to satisfy their working objective. Machine learning has become a common subject to all people like engineers, doctors, pharmacy companies, and business people. The book addresses the problem and new algorithms, their accuracy, and their fitness ratio for existing real-time problems.

Machine Learning Paradigm for Internet of Thing Applications provides the state-of-the-art applications of machine learning in an IoT environment. The most common use cases for machine learning and IoT data are predictive maintenance, followed by analyzing CCTV surveillance, smart home applications, smart-healthcare, in-store ‘contextualized marketing’, and intelligent transportation systems. Readers will gain an insight into the integration of machine learning with IoT in these various application domains.

MACHINE LEARNING PARADIGM FOR INTERNET OF THINGS APPLICATIONS

As companies globally realize the revolutionary potential of the IoT, they have started finding a number of obstacles they need to address to leverage it efficiently. Many businesses and industries use machine learning to exploit the IoT’s potential and this book brings clarity to the issue.

Machine learning (ML) is the key tool for fast processing and decision-making applied to smart city applications and next-generation IoT devices, which require ML to satisfy their working objective. Machine learning has become a common subject to all people like engineers, doctors, pharmacy companies, and business people. The book addresses the problem and new algorithms, their accuracy, and their fitness ratio for existing real-time problems.

Machine Learning Paradigm for Internet of Thing Applications provides the state-of-the-art applications of machine learning in an IoT environment. The most common use cases for machine learning and IoT data are predictive maintenance, followed by analyzing CCTV surveillance, smart home applications, smart-healthcare, in-store ‘contextualized marketing’, and intelligent transportation systems. Readers will gain an insight into the integration of machine learning with IoT in these various application domains.

Research Paper (postgraduate) from the year 2005 in the subject Business economics - Offline Marketing and Online Marketing, IIM Calcutta, language: English, abstract: In today's competitive markets, many marketing organizations are considering how micromarketing efforts can directly impact their bottom line by achieving greater results with a fixed marketing budget. The challenge is that micromarketing requires a new level of understanding of customers, prospects, and the demographic makeup of targeted areas. Marketers depend on marketing research to determine what consumers want and what they are willing to pay for. Technology is available today to market to people on an individual basis. Marketers are faced with the challenge of influencing the decision-making of retailers and reaching a consumer audience that is more fragmented than ever. Companies are rediscovering their customer. The definition of customer extends to include both retailers as well as end users. Micromarketing is facilitated using new techniques like customer intimacy, experiential marketing and storytelling. We analyse the possibilities of theses and the future direction and momentum of micromarketing.

Research Paper (postgraduate) from the year 2005 in the subject Business economics - Economic Policy, IIM Calcutta, language: English, abstract:

The Telecom Wars begins its narration from those times, traversing the uncertain early years of the mobile phone industry. It is a vivid portrait of the fierce competition as Tata, Birla, Reliance, and Bharti bid to capture the lion’s share of India’s billion voices. As they did, India’s institutions to regulate and manage the sector also evolved―the ministry separated from the Telecom Regulatory Authority of India (TRAI), the Telecom Disputes Settlement and Appellate Tribunal (TDSAT), and state-run operators MTNL and BSNL also joined the fray. Policy makers, entrepreneurs, and bureaucrats worked tirelessly to change India’s cellular topography.

MACHINE LEARNING PARADIGM FOR INTERNET OF THINGS APPLICATIONS As companies globally realize the revolutionary potential of the IoT, they have started finding a number of obstacles they need to address to leverage it efficiently. Many businesses and industries use machine learning to exploit the IoT’s potential and this book brings clarity to the issue. Machine learning (ML) is the key tool for fast processing and decision-making applied to smart city applications and next-generation IoT devices, which require ML to satisfy their working objective. Machine learning has become a common subject to all people like engineers, doctors, pharmacy companies, and business people. The book addresses the problem and new algorithms, their accuracy, and their fitness ratio for existing real-time problems. Machine Learning Paradigm for Internet of Thing Applications provides the state-of-the-art applications of machine learning in an IoT environment. The most common use cases for machine learning and IoT data are predictive maintenance, followed by analyzing CCTV surveillance, smart home applications, smart-healthcare, in-store ‘contextualized marketing’, and intelligent transportation systems. Readers will gain an insight into the integration of machine learning with IoT in these various application domains.

MACHINE LEARNING PARADIGM FOR INTERNET OF THINGS APPLICATIONS

As companies globally realize the revolutionary potential of the IoT, they have started finding a number of obstacles they need to address to leverage it efficiently. Many businesses and industries use machine learning to exploit the IoT’s potential and this book brings clarity to the issue.

Machine learning (ML) is the key tool for fast processing and decision-making applied to smart city applications and next-generation IoT devices, which require ML to satisfy their working objective. Machine learning has become a common subject to all people like engineers, doctors, pharmacy companies, and business people. The book addresses the problem and new algorithms, their accuracy, and their fitness ratio for existing real-time problems.

Machine Learning Paradigm for Internet of Thing Applications provides the state-of-the-art applications of machine learning in an IoT environment. The most common use cases for machine learning and IoT data are predictive maintenance, followed by analyzing CCTV surveillance, smart home applications, smart-healthcare, in-store ‘contextualized marketing’, and intelligent transportation systems. Readers will gain an insight into the integration of machine learning with IoT in these various application domains.

MACHINE LEARNING PARADIGM FOR INTERNET OF THINGS APPLICATIONS

As companies globally realize the revolutionary potential of the IoT, they have started finding a number of obstacles they need to address to leverage it efficiently. Many businesses and industries use machine learning to exploit the IoT’s potential and this book brings clarity to the issue.

Machine learning (ML) is the key tool for fast processing and decision-making applied to smart city applications and next-generation IoT devices, which require ML to satisfy their working objective. Machine learning has become a common subject to all people like engineers, doctors, pharmacy companies, and business people. The book addresses the problem and new algorithms, their accuracy, and their fitness ratio for existing real-time problems.

Machine Learning Paradigm for Internet of Thing Applications provides the state-of-the-art applications of machine learning in an IoT environment. The most common use cases for machine learning and IoT data are predictive maintenance, followed by analyzing CCTV surveillance, smart home applications, smart-healthcare, in-store ‘contextualized marketing’, and intelligent transportation systems. Readers will gain an insight into the integration of machine learning with IoT in these various application domains.

Research Paper (postgraduate) from the year 2005 in the subject Business economics - Offline Marketing and Online Marketing, IIM Calcutta, language: English, abstract: In today's competitive markets, many marketing organizations are considering how micromarketing efforts can directly impact their bottom line by achieving greater results with a fixed marketing budget. The challenge is that micromarketing requires a new level of understanding of customers, prospects, and the demographic makeup of targeted areas. Marketers depend on marketing research to determine what consumers want and what they are willing to pay for. Technology is available today to market to people on an individual basis. Marketers are faced with the challenge of influencing the decision-making of retailers and reaching a consumer audience that is more fragmented than ever. Companies are rediscovering their customer. The definition of customer extends to include both retailers as well as end users. Micromarketing is facilitated using new techniques like customer intimacy, experiential marketing and storytelling. We analyse the possibilities of theses and the future direction and momentum of micromarketing.

Research Paper (postgraduate) from the year 2005 in the subject Business economics - Economic Policy, IIM Calcutta, language: English, abstract: