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Food losses due to plant disease from pathogens such as fungi, bacteria, and viruses are important problems in the farming field across the globe. Plant diseases determine the growth of their respective species; hence, it becomes important to identify the diseases in the early stage. Artificial intelligence and machine learning have been employed to detect and classify the diseases. Later the advancement in the subset of machine learning and the deep Learning made this research area into high potential in terms of precise prediction and accuracy. Many versions of deep learning–based architecture are implemented along with various nonvisualization and visualization techniques to classify and detect the symptoms of plant disease with several performance metrics. This chapter illustrates a comprehensive review of deep learning models used to detect plant diseases, and in some cases, the diseases have been identified before the symptoms appear clearly.

Internet of Things (IoT) helps in creating innovation in pharma industry that will benefit manufacturers and as well as patients. The purpose of pharmaceutics is to improve patients’ lives so that they can live confidently and actively with the condition they suffer from. The IoT has already made tremendous changes in the pharma industry, and the technology is still in its infancy. This chapter aims to provide the various techniques related to IoT for pharmaceutical related data and further assist in the analysis of the data generated from pharmaceutical field. The various pharmaceutical concepts based on IoT are being investigated, and the clinical data is being investigated for the body movements, and an analysis is done based on that. The analysis of the proposed system shows that it is 4% better in reducing the error rate of the results.

Digitization era is altering several industries which include the way in which the data is analyzed and it is inferred that about 2.7 Zettabytes of data exist in the digital world today. By 2020 the data generated per second for every human being will approximate amount to 1.7 megabytes and the volume of data would double every 2 years thus reach the 40 ZB point by 2020. Interactive Data Corporation (IDC) estimated that by the end of year 2020, the e-commerce transactions B2B and B2C will hit 450 billion per day on the internet. The advent of Big and real time Data has triggered disruptive changes in many fields and the exploding volume of different sources of data like heterogeneous data, data integration, spatio-temporal correlation of data, batch analytics and real-time analytics, data sharing, semantic interoperability requires the development of a scalable platform that can fuse multiple data layers to handles the data intelligently. In Big Data approaches, the challenge is not anymore to collect the data, but to draw valuable conclusions by properly analyzing them. The growth in Unstructured Data generated by business is irrefutable and they are under more pressure to preserve it for longer periods of time. To be clear, exploiting the collected data has been always considered by practitioners and researchers, but the huge velocity, heterogeneity and enormity of massive stream of real-time data shove the limits of the current storage, management and processing capabilities. Admittedly, the traditional method of Extract, Transform and Load (ETL) are challenged and cannot be applied on the emerging opportunistically and crowed sensed data streams. Some of these data streams are structured in a way that serve only one predefined purpose and cannot be directly used for other means. Yet, there are emerging unstructured data such as context-based data from the internet and social media as well as credit card transactions that is not clear if they can be used to better understand the mobility patterns. The analytical company Gartner states that by 2020 there will be over 26 billion interconnected devices. It is obvious, that they will produce massive amounts of meaningful data. Those data can be used for many applications such as real-time industrial equipment monitoring, traffic planning, automated maintenance, etc. Therefore, it is essential to develop modern system abstractions that allow us to resourcefully process huge and new data streams. This enormous amount of data urges the growth of integrated and insightful big and real-time data analytics Platforms. The upcoming contemporary technology like digital twin, integrates historical data from past machine usage to the current data. It uses sensors to collect the real-time data, working status and other operational data attached to the physical model. These components send the relevant data via a cloud-based system to the other side of the bridge with the help of data analytics platform which produces the required insights. The big and real-time data analytics Platforms assist to perform useful operations on data analytics as a complete package. For this purpose, data analytics platform are used to acquire constructive insight from the huge volume of data. Data analytics platform is an ecosystem of technologies and services that can help the businesses in increasing revenues, enhance operational efficiency, stabilize marketing campaigns and customer service efforts, respond more quickly to emerging market trends and gain a competitive edge over rivals. The data analytics platform finds the pattern and relationships in data by applying statistical techniques and communicates the results generated by analytical models to executives and end users to make decisions with the help of data visualization tools that display data on a single screen and can be updated in real time as new information becomes available. Big data and real-time data analytics platform supports the full spectrum of data types, protocols and integration to speed up and simplify the data wrangling process. The big data and real time platform provides accurate data, increase efficiency in the workspace, gives answers to complex questions along with security and hence it plays the key role in business analytics.

Ontology structure, a core of semantic web is an excellent tool for knowledge representation and semantic visualization. Moreover, knowledge reuse is made possible through similarity measure estimation between two ontologies, threshold estimation and use of simple if-then rules for checking relevancy and irrelevancy measures. Reduced semantic representations of the ontology provide reduced knowledge visualization which is critical especially for e-health data processing and analysis. This usually occurs due to the presence of implicit knowledge and polymorphic objects and can be made semantically rich through the construction by resolving this implicit knowledge occurring in the form of non-dominant words and conditional dependence actions. This paper presents the working of the automated framework for the construction of semantic rich ontology structures and store in the repository. This construction uses dyadic deontic logic based Graph Derivation Representation in order to construct semantically rich ontologies. Moreover, in order to retrieve a set of relevant documents in response to the cloud user document, the degree of similarity between two ontologies is estimated using the traditional cosine similarity measure and simple if-then rules are used to determine the number of relevant documents and obtain such document's metadata for further processing. These working modules will be extremely beneficial to the authenticated cloud users for document retrieval, information extraction and domain dictionary construction which are especially used for e-health applications. The proposed framework is implemented using diabetes dataset and the effectiveness of the experimental results is high when compared to other Graph Derivation Representation methods. The graphical results shown in the paper is an added visualization for viewing the performance of the proposed framework. Keywords: Ontology, Implicit knowledge, Conditional dependence, Graph derivation representation, Dyadic deontic logic, Cosine similarity

Healthcare acquired its current influence regarding big data technology due to the fact that the data sources involved in healthcare are well-known for their volume, heterogeneous complexity, and high dynamism. In the context of big data, the success of healthcare applications depends solely on the underlying architecture and utilization of appropriate tools, as evidenced in pioneering research attempts. Big data technology has many areas of application in healthcare, such as predictive modeling and clinical decision support, disease or safety surveillance, public health, and research. Big data analytics in medicine and healthcare covers integration and analysis of large amounts of complex heterogeneous biomedical data and electronic health records data. Since the volume of data is huge, it must be used as an advantage to provide the right intervention at the right time and establish personalized care for the patients. Thus this benefits all components of the healthcare system. This chapter aims to provide a better healthcare framework harnessing the benefits of big data analytics. The huge medical data is processed effectively by using various analytical tools, thereby arriving at deeper insights into the data. The existing systems employ algorithms like artificial neural networks, logistic regression, and fuzzy based algorithms. Some techniques used swarm optimization for classification. The clustering involved usage of self-organizing maps and k-means. This chapter proposes a new framework for healthcare using support vector machines and analyzes the performance for big data in terms of accuracy, sensitivity, error rate, and area under the ROC curve. The proposed system is compared with the conventional system in terms of RMSE and MAPE value and the results prove promising for the newly proposed system.

The Internet of Things (IoT) is a sophisticated computerization and analytics system that exploits sensing, big data, networking, and artificial intelligence technologies to provide complete systems for a product or service. The IoT is a trend in future-generation technologies that can surprise the entire research, business, marketing, and commercial domains. This can be viewed as the interconnection of distinctively identifiable smart devices and objects within Internet infrastructure with more profit. The deployment and usage of IoT in biomedical engineering are rapidly growing. The Internet of Medical Things (IoMT) is the group of medical devices and applications that unite with healthcare IT systems online and on the cloud. A medical device facilitated with Wi-Fi allows machine-to-machine communication (M2M) that is the base of IoMT. A wireless telemetry system for wearable/implantable medical devices is one of the major applications of IoMT. IoT with wearable technologies is reshaping patient care delivery services across healthcare industries. This emerging technology changes the current healthcare system into a smart healthcare system with reduced cost and improves diagnosis and treatment outcomes. The chapter examines the various IoMT technologies associated with biomedical engineering and analyzes the working methodology with respect to healthcare. It also gives the reader an overall perspective of the entire ecosystem of the IoMT-based healthcare and functioning of the applications in the biomedical industry. There is definitely much scope for advancement for IoMT in the healthcare sector.