M. P. Karthikeyan

The multidisciplinary study of data mining is crucial to artificial intelligence and computer science because it allows researchers to glean useful insights from massive data sets; these datasets are essential to online shopping platforms. This field investigates the ways in which data mining. Consumer impression analysis, a rich source of qualitative data reflecting customer attitude, is the center of this study. Researchers use sophisticated natural language processing algorithms to sift through customer reviews in search of patterns and key opinion leaders. In order to provide complex insights on customer behavior, this study dives into the world of sentiment analysis and goes beyond just gathering comments. Unstructured textual data may be dynamically analyzed using ML approaches in conjunction with NLP. This method can automatically decipher intricate client data. These methods, when put together, allow for the extraction of valuable information that are critical for the development of business strategies and the improvement of digital marketplace customer interaction models. An all-inclusive data mining technique known as TANAGRA is used in the investigation. It handles problems caused by the scale and variety of big e-commerce datasets via analysis. This study's findings provide insight into the nuances of consumer behavior and point the way toward better user experience and e-commerce strategy optimization. To better understand how customers, behave on e-commerce sites, the research employs Data Mining, ML, and NLP. As a result, Computer Vision is able to analyze more complex data patterns, and AI predictive analytics get fresh understanding.

The medical field is growing at a rapid pace, with new diseases cropping up daily that require invention of appropriate course of treatment. The heart is a muscular organ which is the size of clenched human fist, and is responsible for blood circulation. Also squeezing the classification model with entire raw features can create a bottleneck to the classification performance. Though, heart/cardiac disease is the name given to diseases affecting the heart in general, there are many diseases which come under this name, including coronary artery diseases (CAD), cardiomyopathy, cardio vascular disease (CVD) and so on depending upon the circulation of blood throughout the body. Hence, this research work initiates a hybrid method named HCFFSC- hierarchical clustering fuzzy features subset classification for identifying appropriate feature subsets related to target class and given to classifier model to enhance the performance of health monitoring and management (HMM). Request access from your librarian to read this chapter's full text.

Large-scale electronic databases are being maintained by businesses and can be accessed via the internet or intranet. Employing data mining techniques, significant information was extracted from the data. The privacy of the data is inherently at risk while data mining operations are being carried out. All users shouldn't have access to the private information stored in the database. Methods for protecting privacy have been suggested in the literature. Algorithms used in privacy-preserving data mining (PPDM) on private data are unknown even to the algorithm operator. Personal information about users and data on their collective behaviour are the two main aspects of privacy preservation. The majority of privacy-preserving techniques rely on reducing the level of granularity used to represent the data. Although privacy is improved, information is lost as a result. As a result, with PPDM, there is a trade-off between privacy and information loss. Effective methods that don't undermine the security defences are needed. Request access from your librarian to read this chapter's full text.

This work is an endeavor to give a thought regarding future degrees, potential conceivable outcomes, and issues concerning. The Internet of Things and how it may affect the life of the general public sooner rather than later in terms of developing smart urban regions in India particular and generally in the world. In simple terms, the Internet of Things (IoT) is a system of genuine or virtual articles or things implanted with hardware, sensors, programming projects, and scheduled availability that enables these items to gather and exchange information, thereby providing availability to anything and not just for anyone at anytime, anywhere. As a result of the success of India’s 100 Smart Urban Communities initiative, IoT as an idea appears to be quite promising. In India, IoT has a bright future because the government is focusing on, supporting, and experimenting with efforts to improve the infrastructure, as well as organizations concocting innovative products, and businesses are aware of the benefits that the Internet of Things brings to the table. IoT advance could end up being noteworthy on an expansive scale, with further developed research and assets. On the off chance that such advance turns into a reality, the expectations of IoT utilize in India may work out as expected all things considered. The Internet of Things is likely to have a huge impact on our day-to-day lives in cities and will become an integral part of industries such as power, transportation, modern control, retail, utilities administration, medical services, water assets administration, and oil. It has the potential to significantly improve efficiency and our lives. Furthermore, its enormous market potential has piqued the interest of governments, telecom administrators, producers, and industrial clients. The Internet of Things, also known as M2M (which stands for Machines to Mobile, Machine to Machine, Machines to Man, or Man to Machines), cleverly connects people, gadgets, and frameworks. General terms IoT application used to Monitoring and detecting, Plants and Animals, Information providing and utility in the cities. In this chapter discussed various IoT application used to cities build as a smart city.

The thermal and mechanical effects of human tissues and physics that governs biological processes has been developed by applying fundamental engineering principles in the investigation of numerous heat and mass transfer applications in biology and medicine. An incompressible magnetohydrodynamic nanofluid flow passing over a stretched surface along with the effect of viscous dissipation is analyzed numerically. In order to create a nonlinear transformed model in terms of ordinary differential equations, suitable similarity transformations are used and solved by employing a Mathematica algorithm. Furthermore, a minimal case comparison standard has been provided to validate the current methodology. The machine learning method is built through very important parameters. Simulations and machine learning approaches could work together to reveal new and fortunate prospects in computational science and engineering problems. The performance of the parameters in terms of heat and mass transfer is presented with an aid of graph to demonstrate the applicability of the current model.

One of the most important and difficult research fields is newborn jaundice grading. The mitotic count is an important component in determining the severity of newborn jaundice. The use of principal component analysis (PCA) feature selection and an optimal tree strategy classifier to produce automatic mitotic detection in histopathology images and grading is given. This study makes use of real-time and benchmark datasets, as well as specific approaches for detecting jaundice in newborn newborns. According to research, the quality of the feature may have a negative impact on categorization performance. Additionally, compressing the classification method for exclusive main properties can result in a classification performance bottleneck. As a result, identifying appropriate characteristics for training the classifier is required. By combining a feature selection method with a classification model, this is possible. The major outcomes of this study revealed that image processing techniques are critical for predicting neonatal hyperbilirubinemia. Image processing is a method of translating analogue images to digital formats and manipulating them. The primary goal of medical image processing is to collect information useful for disease detection, diagnosis, monitoring, and therapy. Image datasets can be used to validate the performance of newborn jaundice detection. When compared to conventional approaches, it offers results that are accurate, quick, and time efficient. Accuracy, sensitivity, and specificity, which are common performance indicators, were also predictive.