Patient Monitoring System Using Internet of Things

IoT comprises billions of devices that can sense, communicate, compute, and potentially actuate. The data generated by the IoTs are valuable and have the potential to drive innovative and novel applications. IoT allows people and things to be connected anytime, anyplace and to anyone with the internet using tiny sensor. One of the best advantages of the IoT is the increasing number of low-cost sensors available along with its functionalities. A few standard sensors include linear accelerator, compass, light sensors, camera, and microphone, moisture, location, heart rate, and heart rate variability. The trend is multi-sensor platforms that incorporate several sensing elements. In such environment, discovering, identifying, connecting, and configuring sensor hardware are critical issues. The cloud-based IoT platforms can retrieve data from sensors IoT is an inter-disciplinary technology, encompassing multiple areas such as RTS, embedded systems. This chapter detailed investigation and presents highly innovative and revolutionary ideas in healthcare application are available.

Machine Learning for Business Analytics

All these types of analytics have been answering business questions for a long time about the principal methods of investigating data warehouses. Especially data mining and business intelligence systems support decision makers to reach the information they want. Many existing systems are trying to keep up with a phenomenon that has changed the rules of the game in recent years. This is undoubtedly the undeniable attraction of 'big data'. In particular, the issue of evaluating the big data generated especially by social media is among the most up-to-date issues of business analytics, and this issue demonstrates the importance of integrating machine learning into business analytics. This section introduces the prominent machine learning algorithms that are increasingly used for business analytics and emphasizes their application areas.

Automated Soil Residue Levels Detecting Device With IoT Interface

Cloud computing and internet of things (IoT) are playing a crucial role in the present era of technological, social, and economic development. The novel models where cloud and IoT are integrated together are foreseen as disruptive and enable a number of application scenarios. The e-smart is an application system designed by leveraging cloud, IoT, and several other technology frameworks that are deployed on the agricultural farm to collect the data from the farm fields. The application extracts and collects the information about the residue levels of soil and crop details and the same data will be hosted in the cloud environment. The proposed e-smart application system is to analyze, integrate, and correlate datasets and produce decision-oriented reports to the farmer by using several machine learning techniques.

Language Classification and Recognition From Audio Using Deep Belief Network

The main objective is to recognize the chat from social media as spoken language by using deep belief network (DBN). Currently, language classification is one of the main applications of natural language processing, artificial intelligence, and deep learning. Language classification is the process of ascertaining the information being presented in which natural language and recognizing a language from the audio sound. Presently, most language recognition systems are based on hidden Markov models and Gaussian mixture models that support both acoustic and sequential modeling. This chapter presents a DBN-based recognition system in three different languages, namely English, Hindi, and Tamil. The evaluation of languages is performed on the self built recorded database, which extracts the mel-frequency cepstral coefficients features from the speeches. These features are fed into the DBN with a back propagation learning algorithm for the recognition process. Accuracy of the recognition is efficient for the chosen languages and the system performance is assessed on three different languages.

Social Media Content Analysis

Today's wealth and value are data. Data, used sensibly, are making wonders to make wise decisions for individuals, corporates, etc. The era of spending time with an individual to understand them better is gone. Individual's interests, requirements are identified easily by observing the activities an individual performs in social media. Social media, started as a tool for interaction, has grown as a platform to make and promote business. Social media content is unavoidable as the data that are going to be dealt with is huge in volume, variety, and velocity. The demand for using machine learning in analysing social media content is increasing at a faster pace in identifying influencers, demands of individuals. However, the real complexity lies in making the data from social media suitable for analysis. The type of data from social media content may be audio, video, image. The chapter attempts to give a comprehensive overview of the various pre-processing methods involved in dealing the social media content and the usage of right algorithms at the right time with suitable case examples.

Architecture for Analyzing Agriculture Data Using Data Analytics

The main steps for agricultural practices include preparation of soil, sowing, adding manure, irrigation, harvesting, and storage. For this, one needs to develop modern tools and technologies that can improve production efficiency, product quality, schedule and monitoring the crops, fertilizer spraying, planting, which helps the farmers choose the suitable crop. Efficient techniques are used to analyze huge amount of data which provide real time information about emerging trends. Facilities like fertilizer requirement notifications, predictions on wind directions, satellite-based monitoring are sources of data. Analytics can be used to enable farmers to make decisions based on data. This chapter provides a review of existing work to study the impact of big data on the analysis of agriculture. Analytics creates many chances in the field of agriculture towards smart farming by using hardware, software. The emerging ability to use analytic methods for development promise to transform farming sector to facilitate the poverty reduction which helps to deal with humane crises and conflicts.

Analysis of Crime Report by Data Analytics Using Python

The ever-rapid development of technology in today's world tends to provide us with a dramatic explosion of data, leading to its accumulation and thus data computation has amplified in comparison to the recent past. To manage such complex data, emerging new technologies are enabled specially to identify crime patterns, as crime-related data is escalating. These digital technologies have the potential to manipulate and also alter the pattern. To combat this, machine learning techniques are introduced which have the ability to analyse such voluminous data. In this work, the authors intend to understand and implement machine learning techniques in real time data analysis by means of Python. The detailed explanation in preparing the dataset, understanding, visualizing the data using pandas, and performance measure of algorithm is evaluated.

A Comprehensive Review of Professional Network Impact on Education and Career

In their very beginnings, when social networks were solely used for leisure purposes, any action performed online had minimal effect on the real world lives of their members. This has very much changed in our modern world, where becoming an influencer on Instagram can substantially raise one's income, politics is done on Twitter, and an inappropriate video posted on YouTube can get one fired. Similarly, professional networks have changed the approach universities take to prepare their students, the mechanisms behind companies seeking expertise, and the way in which professionals land matching jobs. In the context of discussing the benefits and pitfalls of using such platforms, several points relating to data privacy are highlighted. Additionally, for a complete view of all analytics possibilities, a survey was conducted by looking over 24 research papers, summarising their findings, detailing the six generic research areas which were identified and speculating on what the future might hold.

A Comprehensive Study of Data Analytics in Social Perspectives

Social media websites enable users to create and share content or to participate in social networking. The main advantage of social media is the ability to communicate with different people to share their knowledge and discuss social events. The impact of social media on people and their behavior is enormous and also solves many problems if it works fine. But there may be negative aspects as well when they are exchange their ideas between people of very different cultures, religions, different age group, and misbehavior of a few users. These problems are addressed using data analytics, which takes people context into account, learns from it, and takes proactive steps according to their situation and expectations, avoiding user intervention as much as possible. This chapter presents all possible problems in social media and enabling those scenarios with effective solutions.

Challenges and Applications of Recommender Systems in E-Commerce

E-commerce and online business are getting too much attention and popularity in this era. A significant challenge is helping a customer through the recommendation of a big list of items to find the one they will like the most efficiently. The most important task of a recommendation system is to improve user experience through the most relevant recommendation of items based on their past behaviour. In e-commerce, the main idea behind the recommender system is to establish the relationship between users and items to recommend the most relevant items to the particular user. Most of the e-commerce websites such as Amazon, Flipkart, E-Bay, etc. are already applying the recommender system to assist their users in finding appropriate items. The main objective of this chapter is to illustrate and examine the issues, attacks, and research applications related to the recommender system.