RETRACTED ARTICLE: Gramian matrix data collection-based random forest classification for predictive analytics with big data

Abstract. Random Forest is a supervised learning algorithm developed from decision trees with the application of boostrap aggregating (bagging). This method grows trees from decision trees to produce a forest or the best model called the random forest model. Tree growth is done with randomly selected data with returns through the bagging process. Random forest is considered to provide better performance results for diabetes data among other supervised learning methods, because random forest and has the lowest error rate compared to other methods. Random forest is also an important technique for medical data classification, especially for diagnosing diabetics. In this study, classification was carried out using Pima Indian Diabetes data, which is an American tribe that lives in Arizona and Mexico. Classification analysis was carried out using an algorithm to see the level of accuracy in random forest classification on Pima Indian diabetes data. The results show that the accuracy value of random forest classification is 74.78%, this value is in the accuracy category at the fair classification level. In this random forest classification, there are three main variables that become importance variables, namely glucose then BMI, and age.
 Abstract. Random Forest is a supervised learning algorithm developed from decision trees with the application of boostrap aggregating (bagging). This method grows trees from decision trees to produce a forest or the best model called the random forest model. Tree growth is done with randomly selected data with returns through the bagging process. Random forest is considered to provide better performance results for diabetes data among other supervised learning methods, because random forest and has the lowest error rate compared to other methods. Random forest is also an important technique for medical data classification, especially for diagnosing diabetics. In this study, classification was carried out using Pima Indian Diabetes data, which is an American tribe that lives in Arizona and Mexico. Classification analysis was carried out using an algorithm to see the level of accuracy in random forest classification on Pima Indian diabetes data. The results show that the accuracy value of random forest classification is 74.78%, this value is in the accuracy category at the fair classification level. In this random forest classification, there are three main variables that become importance variables, namely glucose then BMI, and age.

Chapter 21 - Big Data Integration

WITHDRAWN: Spearman rank correlation based bagging ensemble bayes optimal classification for genomic predictive pattern analytics

Even today, the assessment of avalanche danger is by large a subjective, yet data-based decision-making process. Human experts analyze heterogeneous data volumes, diverse in scale, and conclude on the avalanche scenario based on their experience. Nowadays, modern machine learning methods and the rise in computing power in combination with physical snow cover modelling open up new possibilities for developing decision support tools for operational avalanche forecasting. Therefore, we developed a fully data-driven approach to predict the regional avalanche danger level, the key component in public avalanche forecasts, for dry-snow conditions in the Swiss Alps. Using a large data set of more than 20 years of meteorological data measured by a network of automated weather stations, which are located at the elevation of potential avalanche starting zones, and snow cover simulations driven with these input weather data, we trained two random forest (RF) classifiers. The first classifier (RF #1) was trained relying on the forecast danger levels published in the avalanche bulletin. Given the uncertainty related to a forecast danger level as a target variable, we trained a second classifier (RF #2), relying on a quality-controlled subset of danger level labels. We optimized the RF classifiers by selecting the best set of input features combining meteorological variables and features extracted from the simulated profiles. The accuracy of the danger level predictions ranged between 74 % and 76 % for RF #1, and between 72 % and 78 % for RF #2, with both models achieving better performance than previously developed methods. To assess the accuracy of the forecast, and thus the quality of our labels, we relied on nowcast assessments of avalanche danger by well-trained observers. The performance of both models was similar to the accuracy of the current experience-based Swiss avalanche forecasts (which is estimated to 76 %). The models performed consistently well throughout the Swiss Alps, thus in different climatic regions, albeit with some regional differences. A prototype model with the RF classifiers was already tested in a semi-operational setting by the Swiss avalanche warning service during the winter 2020-2021. The promising results suggest that the model may well have potential to become a valuable, supplementary decision support tool for avalanche forecasters when assessing avalanche hazard.

The new technology helps collect and store a considerable amount of information about consumers’ preferences and decisions in real-time. Thus, the unprecedented volume, speed and variety of primary data, Big Data, are available from all sources and individual consumers. The term “Big Data” is now widespread and accepted globally. The term Big Data has become increasingly known and used in many industries. Big Data is a new source of product development ideas, customer service, shelf location, distribution, dynamic pricing, etc. Big Data will have an impact on almost every area of marketing. Firms that do not develop the resources and capabilities to use Big Data capabilities effectively will be challenged to develop a sustainable competitive advantage to survive the Big Data revolution. Consumer analysis is at the heart of a Big Data revolution. Marketing specialists are beginning to recognize the importance of Big Data as the new capital and that access to Big Data offers a company new ways to differentiate its products. In this article, exploratory research has been conducted to highlight areas where Big Data allows organizations to fundamentally know about their business and translate it into better decision-making and improved performance in marketing and consumer behavior. Moreover, a global analysis (based on public data) was performed using large volumes of data in marketing and the main reasons they were stored and used.KeywordsBig DataMarketingBig Data’s growth

The volume of the coming data in HEP is growing. The volume of the data to be held for a long time is growing as well. Large volume of data – big data – is distributed around the planet. The methods, approaches how to organize and manage the globally distributed data storage are required. The distributed storage has several examples for personal needs like own-cloud.org, pydio.com, seafile.com, sparkleshare.org. For enterprise-level there is a number of systems: SWIFT - distributed storage systems (part of Openstack), CEPH and the like which are mostly object storage. When several data center’s resources are integrated, the organization of data links becomes very important issue especially if several parallel data links between data centers are used. The situation in data centers and in data links may vary each hour. All that means each part of distributed data storage has to be able to rearrange usage of data links and storage servers in each data center. In addition, for each customer of distributed storage different requirements could appear. The above topics are planned to be discussed in data storage proposal.

Big Data OverviewDriven by the need to generate business value, the enterprise has started to adopt Big Data as a solution, migrating from the classical databases and data stores which lack the flexibility and are not optimized enough [1].The changes in the environment make big data analytics attractive to all types of organizations, while the market conditions make it practical. The combination of simplified models for development, commoditization, a wider palette of data management tools, and low-cost utility computing has effectively lowered the barrier to entry. [2]. The concept addresses large volumes of complex data, rapid growing data sets that may come from different autonomous sources.In recent approaches, Big Data is characterized by principles known as the 4VVolume, Variety, Velocity and Veracity [3]. There are opinions about accepting other principles as Big Data characteristics, such as Value.Each day more businesses realize that Big Data is relevant as the applications generate large volumes of data generated automatically, from different data sources, centralized or autonomous. As traditional databases hit limitations when the need of analyzing this data, dedicated solutions must be considered.Important BigData Solutions:* Apache HBase/Hadoop is based on Google's BigTable distributed storage system, which runs on top of Hadoop as a distributed and scalable big data store. This means that HBase can leverage the distributed processing paradigm of the Hadoop Distributed File System (HDFS) and benefit from Hadoop's MapReduce programming model. It combines the scalability of Hadoop with real-time data access as a key/value store and deep analytic capabilities of Map Reduce [4].HBase allows to query for individual records as well as derive aggregate analytic reports across a massive amount of data. It can host large tables with billions of rows, millions of columns and run across a cluster of commodity hardware. HBase is composed of three types of servers in a master slave type of architecture. Region servers are responsible to serve data for reads and writes. When accessing data, clients communicate with HBase Region Servers directly. Region assignment, DDL (create, delete tables) operations are handled by the HBase Master process.* Apache Cassandra is a distributed database used for the administration and management of large amounts of structured data across multiple servers, while providing highly available service and no single point of failure. It provides features such as continuous availability, linear scale performance, data distribution across multiple data centers and cloud availability zones. Cassandra inherits its data architecture from Google's BigTable and it borrows its distribution mechanisms from Amazon's Dynamo.The nodes in a Cassandra cluster are completely symmetrical, all having identical responsibilities. Cassandra also employs consistent hashing to partition and replicate data. it has the capability of handling large amounts of data and thousands of concurrent users or operations per second across multiple data centers.Cassandra has a hierarchy of caching mechanisms and carefully orchestrated disk I/O which ensures speed and data safety. Write operations are sent first to a persistent commit log (ensuring a durable write), then to a write-back cache called a memTable; when the memTable fills, it is flushed to a sorted string table - SSTable - on disk. A Cassandra cluster is organized as a ring, and it uses a partitioning strategy to distribute data evenly.* Redis represents an in-memory data structure store used as a database, cache and message broker. It supports data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperlogs and geospatial indexes with radius queries. Redis stores all data in RAM, allowing lightning fast reads and writes. It runs extremely efficiently in memory and handles high-velocity data, needing simple standard servers to deliver millions of operations per second with submillisecond latency. …

Purpose Big data analytics allows researchers and industry practitioners to extract hidden patterns or discover new information and knowledge from big data. Although artificial intelligence (AI) is one of the emerging big data analytics techniques, hospitality and tourism literature has shown minimal efforts to process and analyze big hospitality data through AI. Thus, this study aims to develop and compare prediction models for review helpfulness using machine learning (ML) algorithms to analyze big restaurant data. Design/methodology/approach The study analyzed 1,483,858 restaurant reviews collected from Yelp.com. After a thorough literature review, the study identified and added to the prediction model 4 attributes containing 11 key determinants of review helpfulness. Four ML algorithms, namely, multivariate linear regression, random forest, support vector machine regression and extreme gradient boosting (XGBoost), were used to find a better prediction model for customer decision-making. Findings By comparing the performance metrics, the current study found that XGBoost was the best model to predict review helpfulness among selected popular ML algorithms. Results revealed that attributes regarding a reviewer’s credibility were fundamental factors determining a review’s helpfulness. Review helpfulness even valued credibility over ratings or linguistic contents such as sentiment and subjectivity. Practical implications The current study helps restaurant operators to attract customers by predicting review helpfulness through ML-based predictive modeling and presenting potential helpful reviews based on critical attributes including review, reviewer, restaurant and linguistic content. Using AI, online review platforms and restaurant websites can enhance customers’ attitude and purchase decision-making by reducing information overload and search cost and highlighting the most crucial review helpfulness features and user-friendly automated search results. Originality/value To the best of the authors’ knowledge, the current study is the first to develop a prediction model of review helpfulness and reveal essential factors for helpful reviews. Furthermore, the study presents a state-of-the-art ML model that surpasses the conventional models’ prediction accuracy. The findings will improve practitioners’ marketing strategies by focusing on factors that influence customers’ decision-making.

The advent of information and communication technologies (ICT) ushers a cost-effective prospect to take care of large volumes of complex data, commonly known as “big data” in the supply chain operational environment. Big data is being generated today by web applications, social media, intelligent machines, sensors, mobile phones, and other smart handheld devices. Big data is characterized in terms of the velocity, volume, and variety with which it produces along the supply chain. This is due to recent advances in telecommunication networks along with centralized and decentralized data storage systems, which are processed thanks to modern digital computational capabilities. There is a growing interest in the use of this large volume of data and advanced analytics for diverse types of business problems in supply chain management (SCM). Such decision-support software applications employ pure mathematical techniques, artificial intelligence techniques, and sometimes uses both techniques to perform analytical operations that undercover relationships and patterns within supply chain generated big data. This chapter proposes a framework for the utilization of big data in SCM decision making. The framework is based on the SCOR (supply chain operations reference) model, which is endorsed by Supply Chain Council (SCC). The proposed framework is influenced by the enterprise potential of augmented reality and virtual reality in supply chain applications, and it identifies key categories of big data analytics applications for the key businesses of SCOR model. Finally, the chapter highlights research issues to extract insight from big data sources for enterprise decision making.

At this time big data, with large data volumes that continue to grow and become complex, is a challenge and opportunity for governments and organizations. This is said to be a challenge considering that the increase in data volume will produce a lot and variety of information, thus requiring efficient ways of storing and managing data. Big data is said to be an opportunity considering that the magnitude of the study results and proper information management will open up new opportunities to recognize the potential for improving services in various government and organizational sectors. Big data can open up opportunities to increase knowledge about various aspects of people's lives so that it can encourage the creation of new trends and innovations in various fields, improve and optimize operations, and determine future conditions so that governments and organizations can produce appropriate basic products. In the future, big data will change the way governments and organizations run their operations, creating challenges and opportunities for decision-makers. Even so, in the future big data can be utilized more optimally for the benefit of the wider community. In the future, big data has great potential to be used in many sectors to improve people's welfare. By using the right methods, big data can be very valuable in improving the welfare and quality of life of the wider community. Therefore, to be able to implement big data to improve the welfare and quality of life of the wider community, it is necessary to understand the challenges and opportunities of big data in the development of digital technology.

Objective: A putative novel pathway for blood pressure regulation involving the dicarboxylic acid hexadecanedioate was identified in a study associating blood pressure and mortality outcomes with fasting blood metabolites. The functional role of hexadecanedioate on blood pressure elevation and vascular reactivity was confirmed by oral administration of hexadecanedioic acid to Wistar Kyoto (WKY) rats. This study aimed to characterize the metabolic effects of hexadecanedioic acid administration in WKY rats to identify metabolic pathways underlying hexadecanedioate-induced blood pressure elevation. Design and method: Eleven-week-old male WKY were treated orally with hexadecanedioic acid (250 mg/kg per day; n = 5) or vehicle (n = 5) for 3 weeks. At sacrifice tissues (aorta, heart, brain, adipose, kidney, and liver) were harvested and global metabolic profiles analysed by UPLC-MS/MS (Metabolon). Data was analysed using random forest classification and the top rank metabolites were highlighted for further investigation. Results: Treatment with hexadecanedioic acid increased hexadecanedioate levels in all tissues tested except the brain, potentially reflecting poor diffusion through the blood-brain barrier. Random Forest (RF) classification of metabolites gave a predictive accuracy of 80% for heart and 100% for kidney. The RF Importance Plot of the 30 top-ranking metabolites indicates key differences in lipid, carbohydrate and amino acid metabolism. In heart, increased fatty acids (e.g. acylcarnitines, complex lipids, and lysolipids) in treated rats suggest impairment of fatty acid beta-oxidation. Heart also demonstrated elevated levels of glycogen breakdown products indicating increased glucose demand. In contrast, kidneys demonstrated an increase in long-chain fatty acids and ketone body beta-hydroxybutyric acid, and a decrease in maloylcarnitine suggesting increased beta-oxidative use. In adipose tissue dicarboxylate fatty acids (tetradecanedioate and octadecanedioate) were elevated suggesting hexadecandioic acid caused a decrease in beta-oxidation and a shift towards use of peroxisomal omega-oxidation. In liver, hexadecanedioic acid increased cysteine-glutathione disulfide levels suggesting an enhanced oxidising environment. Conclusions: Exogenous administration of hexadecanedioic acid in addition to increasing blood pressure impacts a number of metabolic readouts including changes related to lipid and glucose metabolism and redox homeostasis. These pathways are putative targets for further research to elucidate the mechanism by which hexadecanedioate affects blood pressure.

Ensuring the privacy for the big data stored in a cloud system is one of the demanding and critical process in recent days. Generally, the big data contains a huge amount of data, which requires some security measures and rules for assuring the confidentiality. For this reason, different techniques have been developed in the traditional works, which intends to guarantee the privacy of the big data by implementing key generation, encryption, and anonymization mechanisms. But, it limits the issues of increased time consumption, computational complexity, and error rate. Thus, the proposed work aims to design an enhanced mechanism for a secure big data storage. Here, the user’s bank dataset is considered as the input, which is protected from the unauthorized users by guaranteeing both the privacy and secrecy of the data. Here, the raw dataset is preprocessed to increase the data quality and correctness. Then, the security policies (i.e. rules) are generated for allowing the restricted access on the data by using an Improved FP-Growth (IFP-G) algorithm. Consequently, the sensitive and non-sensitive data attributes are classified based on the extracted features by using an Enhanced Random Forest (ERF) classification technique. At last, the privacy of user’s personal information and other details are protected with the use of a Modified Incognito Anonymization based Privacy Preservation (MIA-PP) algorithm. These enhanced mechanisms guarantee the security and confidentiality of the big data with reduced time consumption and increased accuracy. During experimental evaluation, the results of the proposed privacy mechanism is analyzed and compared by using different measures. Also, some of the existing anonymization and classification techniques have been considered to prove the betterment of the proposed technique.

Lung cancer is one of the three deadliest diseases in the world and has rapidly developed. Based on this, researchers conducted research to predict the factors that influence lung cancer. One method to identify this is using data mining methods and classification techniques. Researchers used several popular algorithms in classification to make comparisons of the most accurate algorithms for lung cancer classification. The algorithms used include K-Nearest Neighbor, Random Forest Classifier, Logistic Regression, Linear SVM, Naïve Bayes, Decision Tree, Random Forest, Gradient Boosting, Kernel SVM, and MLPClassifier. The researcher used this algorithm because, in the research that the researcher found on the Kaggle platform, he examined the comparison of the algorithm using the breast cancer dataset. In previous studies, their researchers used SVM, which obtained an accuracy of 96.47%, Neural Networks of 97.06%, and Naïve Bayes with an accuracy of 91.18% to study breast cancer. The difference from previous research is that this study uses several existing algorithms in Machine Learning such as K-Nearest Neighbor, Random Forest Classifier, Logistic Regression, Linear SVM, Naïve Bayes, Decision Tree, Random Forest, Gradient Boosting, Kernel SVM, and MLPClassifier. In addition, this research was conducted to see whether the results of the accuracy of the algorithm that the researchers carried out using the lung cancer dataset had different results. The results of this study found that the more accurate algorithms were Random Forest and Gradient Boosting with an accuracy value of 100%, whereas in previous studies, it was the same. Still, Gradient Boosting had a higher accuracy value than Random Forest. Then, based on the data used in this study, the most influencing factors in predicting a diagnosis of lung cancer are obesity and coughing up blood. The results of this study found that the more accurate algorithms were Random Forest and Gradient Boosting with an accuracy value of 100%, whereas in previous studies, it was the same. Still, Gradient Boosting had a higher accuracy value than Random Forest. Then, based on the data used in this study, the most influencing factors in predicting a diagnosis of lung cancer are obesity and coughing up blood. The results of this study found that the more accurate algorithms were Random Forest and Gradient Boosting with an accuracy value of 100%, whereas in previous studies, it was the same. Still, Gradient Boosting had a higher accuracy value than Random Forest. Then, based on the data used in this study, the most influencing factors in predicting a diagnosis of lung cancer are obesity and coughing up blood.

Big data is the data set which contains variety of data which increases on a daily basis in an organisation and also it has large volume of data with high velocity. It is the complex form of data which is usually identified from new set of data sources. It is widely used in most of the companies as it will have latest data which is useful to solve critical issues in the business or an organisation. BIG data has such large volume of data that it cannot be handled by simple data handling software and need latest technology software to handle such large volumes of data. The amount of data present in big data is so complex and large in volume that it needs skilled professionals to manage the data and requires efficient efforts to maintain it. Small businesses cannot afford to implement BIG data in their business due to its complex nature and also the cost factor is higher as compared to traditional data software. The paper will explain the various categories of big data, its importance, strategies and implementation process.

Large volumes of monitoring and logging data result from the operation of a large scale astrophysical observatory. In the last few years several "Big Data" technologies have been developed to deal with such volumes of data especially in the Internet of Things (IoT) framework. We present the logging, monitoring and alarm system architecture for the ASTRI Mini-Array aimed at supporting the analysis of scientific data and improving the operational activities of the telescope facility . A prototype was designed and built considering the latest software tools and concepts coming from Big Data and IoT and a particular relevance has been given in satisfying quality requirements such as performance, scalability and availability.