Data clustering is the first step for future applications of big data analysis. It is a driving model for Artificial Intelligence and Machine Learning architectures. Processing large volumes of data in faster mode is a big challenge in these applications. which requires fast and efficient algorithms for handling big data. Parallel clustering algorithms are one promising design, which increases the speed of handling such big data. In this paper, a parallel algorithm for clustering a spatial dataset called the P-DBSCAN algorithm is implemented using Hadoop map-reduce framework. This research paper signifies the improvement for data clustering in data analytic applications. The new P-DBSCAN algorithm is executed over generated dataset. The result of this parallel algorithm is compared with existing DBSCAN algorithm to show improvement of runtime performance. This work offers an increase in the performance of execution time. In addition, the outcome of P-DBSCAN shows how to resolve the scalability problem of a large data set.

A software product is expected to be subjected to critical evaluation on its quality attributes in order to ascertain that target quality requirements are met, and that those quality attributes responsible for revealing software quality are not omitted in the software development process. Software metrics are essential to accomplish the task. This paper has carried out an exploratory study of software measurement and software metrics in tandem. The study took into cognizance the interwoven nature of the duo in measuring and revealing software quality. The study formulated a model that expressed the mutual bonding that propels both measurement and metrics to describing software quality in numeric quantities of software attributes. The study identified six software attributes whose values are considered enough quantities to reveal the quality of a software product. The identification enabled the study to create a model equation aimed at giving a numeric value for the complete evaluation of a software system. The result of the implementation of the six software attributes into the model equation showed that two software products employed in the study are of high-quality, having quality values of 0.93 and 0.86 respectively. The attributes produced values that confirmed the maintainability (25 seconds & 20 seconds respectively) and reliability (0.78 & 0.80 respectively) of both software products among other differing features that characterize them.

Cardiovascular disease is the leading cause of death. In recent days, most people are living with cardiovascular disease because of their unhealthy lifestyle and the most alarming issue is the majority of them do not get any symptoms in the early stage. This is why this disease is becoming more deadly. However, medical science has a large amount of data regarding cardiovascular disease, so this data can be used to apply data mining techniques to predict cardiovascular disease at the early stage to reduce its deadly effect. Here, five data mining classification techniques, such as: Naïve Bayes, K-Nearest Neighbors, Support Vector Machine, Random Forest and Decision Tree were implemented in the WEKA tool to get the best accuracy rate and a dataset of 12 attributes with more than 300 instances was used to apply all the data mining techniques to get the best accuracy rate. After doing this research people who are at the early stage of cardiovascular disease or probably going to be a victim can be identified more accurately.

Brain signals required for the brain-computer interface are obtained through the electroencephalography (EEG) method. EEG data is used in the analysis of many problems such as epileptic seizure detection, bipolar mood disorder, attention deficit, and detection of the sleep state of the vehicle driver. It is very important to determine whether the eye is open or closed, which is a substantial organ for the determination of the cognitive state of the person. The aim of this paper is to present a stable and successful model for detecting the eye states that are opened or closed. In this context, the performances of several ensemble classifiers were examined on the Emotiv EEG Neuroheadset dataset, which has 14 features excluding the target variable, 14980 records that have 8225 eye states opened and 6755 eye states closed. In the experiments, firstly the min-max normalization process was applied to the dataset, and then the classification performances of these classifiers were evaluated via a 5-fold cross-validation technique. The performance of each model was measured using accuracy, sensitivity, and specificity metrics. The obtained results show that the Random Forest algorithm is an acceptable level with 92.61% value of accuracy, 94.31% value of sensitivity and 91.36% value of specificity for detecting the eye state.

Artificial intelligence plays a special role in new technologies used to develop advertising and marketing. Artificial intelligence, which plays a special role in improving the effectiveness of advertising and marketing, has had its say in the business market, and this process continues. A quick search for any product in Internet search engines is an indispensable process for the marketing market. With the help of artificial intelligence, it is possible to present the required product or service in a timely manner, at a high level, taking into account the individual characteristics of the customer using virtual environments and street advertising. In the modern world of cyber-physical systems, machines created using intelligent algorithms facilitate human labor in almost all areas. Intelligent management of a network of smart billboards AI research in advertising and marketing has a positive impact on economic development. The article deals with the application of artificial intelligence in the field of advertising and the principle of their work. In this area, the processes of application of new technologies are studied. When preparing the article, scientific analysis of problems and their solutions, application of results, methodological system approach were used.