Machine Learning–Based Data Analysis

Identification of Plantae Diseases is emerging as a developing field in India, as agriculture sector plays an important role in socio‐economic life. In the early days, irrational methods were used for Plantae disease detection. With specialized and logical headway, dependable techniques through most reduced turnaround time have been created and proposed for early recognition of Plantae infections. In this paper we propose an application of machine learning for the agriculture sector, rendering a particular solution to a problem of analyzing crop diseases which is relied on Plantae images captured using a Smartphone. In the flow of quickening ecological difficulties, for example, water shortage, climatic change, concerns in regards to nourishment supply, security and the inconvenient natural effects, feasible farming is a critical step to adapt to a quickly developing total population. The scope of cultivation which is plausible outreaches short‐term yield amplification and efficiency steered exploitation of resources.

The potential Internet of Things (IoT) will have a profoundly prudent business and social impact on our lives. A hub of interest in IoT systems is typically an asset obligation, which makes them focus on digital assault baiting. In this way, broad attempts have been devoted to identifying security and safety difficulties in trendy IoT, primarily through conventional cryptographic methodologies. In any case, the remarkable qualities of IoT hubs make it the main objective of the association and collaboration between objects and objects sent through remote systems to satisfy the target set for them as a united element, to achieve a superior domain for the use of big data. What is more, based on the creativity of remote systems, both platforms and IoT may grow quickly and together. In this section, it methodically audits the security needs, the assault vectors, and the current security responses for IoT systems. It also addresses insights into current machine learning solutions for solving various security issues in IoT systems and a few future cryptography cloud analysis headlines.

Wireless communication has made tremendous progress. These developments have sparked new wireless connectivity and networking paradigms. For example, the research community is looking at 5G for automated mobile communications in wireless networks. A recent chapter focuses on the concept of Internet of Things (IoT) as a key element of 5G wireless networks, which aims to connect each unit, such as wireless sensor nodes and home appliances, to the internet. As these technologies grow, they are applied to different real-world problems. Distinct from the maximum relevant application areas is the efficient and smarter monitor of populations. The intelligent city is a vision that extracts information from city systems to take management measures. This vision can be realized by using data and communication skills to track and control these processes. The IoT is used because it would integrate all the city's infrastructure into the internet.

Currently healthcare domain relies more on computer technology. Medical diagnosis is an important task of intelligent systems. Machine learning systems are used to find the abnormalities at an early stage of disease diagnosis. Optimal and accurate diagnosis is a critical factor for identifying appropriate treatment. This chapter deals with the importance of Machine Learning systems in healthcare, also focuses on the types of ML systems. As the application of Machine Learning plays a vital part in public healthcare, the significance of role of ML in medical data analysis is widely discussed with few applications. Also, the available medical dataset used in ML are stated. Since the critical task in medical data analysis is the prediction of the accuracy results, the evaluation metrics used especially while dealing with healthcare data are also discussed.

The retail industry is experiencing a drastic transformation during the past few decades. The technological revolution has further revolutionized the face of the retail industry. As a result, each industry is aiming to obtain a better understanding of its customers in order to formulate business strategies. Formulation of efficient business strategies enables an organization to lure maximum customers and thus obtain a largest portion of market share. In this chapter, authors aim to provide the importance of predictive data analytics in the retail industry. Various approaches for predictive data analytics have been briefly introduced to maintain completeness of the chapter. Finally, authors discuss the employment of machine learning (ML) approaches for predictive data analytics in the retail industry. Various models and techniques have also been presented with pros and cons of each. Authors also present some promising use cases of utilizing ML in retail industry. Finally, authors propose a framework that aims to address the limitations of the existing system. The proposed model attempts to outperform traditional methods of predictive data analytics.