The history of data stored can be used to forecast potential patterns and help companies make competitive decisions to increase their success and benefits. Many analysts look at healthcare sector data to identify and forecast illnesses in order to benefit patients and physicians in a variety of ways. This study is concerned with the diagnosis and estimation of heart disease. Heart disease is one of the most dangerous illnesses for humans, leading to death all over the world. Many different groups of researchers have used knowledge exploration methods in diverse fields to forecast heart disease and have shown acceptable degrees of precision. There were no real-time methods for analyzing and forecasting heart disease in its early stages. For the prediction of heart disease, decision trees are used to analyze various training and evaluation datasets. Classification algorithms such as Naive Bayes, ID3, C4.5, and SVM are being investigated. The UCI machinery heart disease data set is used in experimental studies.

Alcohol is consumed in excess by millions of people across the world. Alcohol consumption is directly linked to life threatening liver diseases such as cirrhosis which may ultimately lead to death. Early detection of liver disease caused by over consumption of alcohol would help in saving lives of many people. By detecting liver disease in its early stage, it can be diagnosed in time and may lead to full recovery in some patients. This paper proposes detection as well as to predict the presence of liver disease using data mining algorithms. We will make a decision tree for the dataset and then the rules will be generated. After determining the rules, we will use different data mining algorithms to train and test the dataset to detect the liver disease. The data was collected from UCI repository and our training dataset was developed. It consists of 7 different attributes having 345 instances. In the dataset, different categories of blood tests are taken into considerations which are directly linked to liver diseases that may arise due to excessive alcohol consumption along with frequency of alcohol consumption. Based on the type of liver disease detected, prognosis may be suggested.

In the era of digital technology, where innovation and digitization are transforming the business functions, the field of customer relationship management has witnessed sea change in the recent decade. The application of artificial intelligence in food processing has enabled enhancing the availability of food in an effective manner for all the individuals. It has been regarded that the application of labor force tends to play a crucial aspect for the overall execution of things in the different domains related to food manufacturing and processing, which are related to the enhanced involvement of individuals in the processing of food and related products, the industry could not able to meet the growing demand from the customers. So, in order to overcome these critical issues, it is noted that the application of technology such as automation and artificial intelligence is implemented for enhanced processing and enable delivering quality products to the customers at lower cost. The impact of AI in the current business world is becoming more indispensable as companies have started to unleash its potential. The role of AI in food processing is fast changing the manner in which the customer queries are addressed, enabling analysing the needs and requirements, and focus on creating improved packaging, high quality, and better shelf life. This empirical investigation is focusing on analysing the critical factors related to AI in influencing the food processing industry. The researchers intend to apply quantitative analysis using IBM SPSS package, and the results are stated in detail based on the analysis.

Vehicular Ad-hoc Network is one of the fastest growing network of this modern era and world. It has a broad variety of scope. Due to a high speed moving nodes, it becomes a little difficult to keep information about vehicles at the right time. Due to this lag, lot of data packets are dumped and the overall success rate of delivering packets becomes low. This paper emphasizes on a novel clustering concept utilizing Cluster Head mechanism and Artificial Neural Network (ANN) to enhance the data transfer mechanism. The performance of proposed algorithm is evaluated using Mean Square Error and Packet Delivery ratio. A new model of cross-validation is also considered in this scenario.