The use of intelligent data analysis techniques for system-level design: a software estimation example

In this paper we face the problem of intelligently analyze Twitter data. We propose a novel workflow based on Nonnegative Matrix Factorization (NMF) to collect, organize and analyze Twitter data. The proposed workflow firstly fetches tweets from Twitter (according to some search criteria) and processes them using text mining techniques; then it is able to extract latent features from tweets by using NMF, and finally it clusters tweets and extracts human-interpretable topics. We report some preliminary experiments demonstrating the effectiveness of the proposed workflow as a tool for Intelligent Data Analysis (IDA), indeed it is able to extract and visualize interpretable topics from some newly collected Twitter datasets, that are automatically grouped together according to these topics. Furthermore, we numerically investigate the influence of different initializations mechanisms for NMF algorithms on the factorization results when very sparse Twitter data are considered. The numerical comparisons confirm that NMF algorithms can be used as clustering method in place of the well known k-means.

Objective:Intelligent data analysis has ability to prepare and present complex relations between symptoms and diseases, medical and treatment consequences and definitely has significant role in improving follow-up management of chronic heart failure (CHF) patients, increasing speed ​​and accuracy in diagnosis and treatments; reducing costs, designing and implementation of clinical guidelines.The aim:The aim of this article is to describe intelligent data analysis methods in order to improve patient monitoring in follow and treatment of chronic heart failure patients as the best approach for CHF follow up management.Methods:Minimum data set (MDS) requirements for monitoring and follow up of CHF patient designed in checklist with six main parts. All CHF patients that discharged in 2013 from Tehran heart center have been selected. The MDS for monitoring CHF patient status were collected during 5 months in three different times of follow up. Gathered data was imported in RAPIDMINER 5 software.Results:Modeling was based on decision trees methods such as C4.5, CHAID, ID3 and k-Nearest Neighbors algorithm (K-NN) with k=1. Final analysis was based on voting method. Decision trees and K-NN evaluate according to Cross-Validation.Conclusion:Creating and using standard terminologies and databases consistent with these terminologies help to meet the challenges related to data collection from various places and data application in intelligent data analysis. It should be noted that intelligent analysis of health data and intelligent system can never replace cardiologists. It can only act as a helpful tool for the cardiologist’s decisions making.

In this advance era, artificial intelligence (AI) is the basic building block for numerous innovation opportunities. The basic concept behind all artificial intelligence applications is classical machine learning (CML) technique. The intelligent data analysis (IDA) is used to analyze data in advanced reasoning systems. In this paper, our motto is to analyze the data intelligently, i.e., intelligent data analysis process using classical machine learning algorithms based on experimental data set empirical hidden regularities. It is concluded that machine learning process is the basic concept to implement any problem based on artificial intelligence, whereas intelligent data analysis uses classical machine learning concept and helps in deciding the next best available step during data analysis.

The article substantiates the expediency of using intelligent data analysis in the study and forecasting of economic growth. Intelligent data analysis is a powerful tool that can help researchers and politicians understanding economic growth more deeply and making reasonable decisions by forming state policy. In addition, intelligent data analysis can be used to determine the potential areas of economic growth and investment opportunities. Attention is focused on using CART (predictive modeling tool, which has proved its high efficiency in understanding the non-obvious relationships between several variable statistical importance) and associative rules (intelligent data analysis, which can work with big data arrays and generate clear and effective conclusions, supported by statistical significance indicators). It is concluded that the methods of intelligent data analysis, such as CART and associative rules, are crucial for understanding the complex patterns that exist in large arrays of economic data. The advantages of these methods make them appropriate and effective tools for modeling and understanding of levers and tools of economic growth. Based on the analysis of data for 119 countries of the world for 2021 (the period of post-pandemic recovery), it was established that in order to promote economic growth, countries with low levels of export and import need to focus on the development of national industry. In addition, developing an economic policy aimed at promoting the recovery of the economy, it is necessary to increase the democratic status of the country. In general, countries that are less dependent on exports and imports tend to have more stable and self-sufficient economies. This can be explained by a number of factors, such as a strong domestic market, the development of national industry and less vulnerability to global economic shocks. In addition, a low level of imports may indicate that a country is able to produce and consume a significant portion of its own goods, which may contribute to further economic growth and development. It is important to note that these relationships are based on highlevel statistical correlations.

This paper explores the role of data analysis methods to support system-level designers in characterising the performance of embedded applications. In particular, we address the performance modelling of software applications running on an embedded microprocessor. We propose a data analysis method, which, on the basis of a parameterisation of the software functionality and the hardware architecture, is able to predict the number of execution cycles on an embedded processor. Experiments with standard computational code (sorting, mathematical computation) and with MPEG variable length decoding are presented to support this claim.

We present ASDSP (application-specific digital signal processor) instructions and their hardware architecture for high-speed FFT. The proposed instructions calculate a butterfly within two cycles. The proposed architecture employs a data processing unit (DPU) supporting the instructions and an FFT address generation unit (FAGU) automatically calculating the butterfly input and output data addresses. The proposed DPU has a smaller area than commercial DSP chips. Moreover, the number of FFT computation cycles is reduced by the proposed FAGU. The architecture has been modeled by the VHDL. We have used the UMC 0.25/spl square/standard cell library for logic synthesis. Performance comparisons show that the number of execution cycles is reduced over 10% and the size of the DPU decreases about 30% compared with Carmel DSP.

A variety of approaches to the intelligent analysis of complex manufacturing data have been described in the literature. A comparative analysis of these systems reveals underlying similarities in their functional organizations. This leads to the development of a general functional model for intelligent data analysis (IDA). This article describes the various data analysis systems studied, the general model for IDA, and the implementation of a demonstration IDA system. The implementation serves to demonstrate the feasibility of this approach, and provides directions for further research.

The article examines the technology of data mining as an instrumental means of automating decision-making support in automated troop management systems (ATMS). During the study of the subject area, the problems of the decision-making support process in ATMSs have been analyzed, sources testifying to the relevance of the automation of this process and the current state of affairs are being considered. An option to increase the degree of automation of the decision-making support process is proposed for consideration due to the use of intelligent data analysis technology, which has been the main focus of the article. During the analysis of the latest research and publications on this topic, it has been found that the automation of the decision-making support process due to intelligent data analysis is considered promising and is studied, as a rule, within the framework of research on artificial intelligence and machine learning technologies. The main material of the study covers the generalized algorithm of automated decision-making support, an analogy of the functions of the intelligent decision support system with the tasks solved by intelligent data analysis is also made. Based on this, the clustering tasks have been described, search for associative rules, classification and regression in the context of their application for getting knowledge from data found in the automated troop management system, with examples of the results of their implementation. A more in-depth study of intellectual data analysis is defined as directions for further research and ways to automate the decision-making support process using this technology. Also, a promising direction of research is simulation modeling of an intellectualized decision support system of an automated troops control system.

The Universitat Politècnica de València and its Faculty of Business Administration and Management have created a new intensification, named, "Intelligent Data Analysis", that provides the student with sufficient knowledge to integrate data analysis in the sometimes routine tasks of a company.The statistical, computer and ICT-related skills obtained through the Business Administration and Management degree are enhanced with more advanced statiscal models for multivariate data analysis and with R language programming, which is very suitable for such data analysis. All these skills are acquired under the Project-Based Learning methodology.This project's main achievement has been the coordination between the different subjects of the intensification to use the same software, which has resulted in a continuity for the way in which students work with RStudio, R, and Rmakdown. This has provided them a high level of management and integration of data analysis in the students’ work routines which will later aid them to become more qualified professionals.

In order to explore the relevance of tea culture and the development of the recreation industry, this paper applies big data technology to the analysis of industrial relevance data. According to the selection method of the truncated part of the common distribution form under the left truncated and right censored data, we choose the generalised exponential distribution that the distribution of the truncation point is consistent with the time of interest. In addition, this paper fully analyzes the industrial data, and builds an analysis model of the relevance between tea culture and the development of the recreation industry based on intelligent big data analysis. Finally, this paper collects data through the Internet to conduct experimental analysis, analyzes the design and planning of tea culture and the recreation industry, and conducts experiments to study the relevance between the development of the two industries. Through experimental research, it can be known that the intelligent big data analysis technology constructed in this paper can play a certain role in the analysis of industrial relevance, and it also verifies that tea culture can be associated with the development of the recreation industry.

Bytecode folding is an effective technique for speeding up execution in Java virtual machines. This paper investigates a hardware implementation of the aforementioned technique on BlueJEP, a Java embedded processor. Since BlueJEP is a micro-programmed stack machine, we adopt a micro-instruction oriented approach, folding up to four microinstructions (corresponding to up to four bytecodes, on occasion). A variety of processor versions for different subsets of folding patterns are implemented, simulated and synthesized on a Xilinx FPGA. The measurements and results show that, although the number of execution cycles is reduced, the critical path increase leads to a lower performance. Taking into account the device area, we conclude that for our case, adding a second processor may be preferred over hardware folding. In general, we observe that folding efficiency may only be evaluated properly on a real implementation, rather than using theoretical estimates, due to the increased complexity of the hardware.

To manifest anticipatory behaviour that goes beyond simple stimulus-response, classifier systems must evolve internal reasoning processes based on couplings via internal messages. A major challenge that has been encountered in engendering internal reasoning processes in classifier systems has been the discovery and maintenance of long classifier chains. This paper proposes a modified version of the traditional classifier system, called the delayed action classifier system (DACS), devised specifically for learning of anticipatory or predictive behaviour. DACS operates by delaying the action (i.e. posting of messages) of appropriately tagged, matched classifiers by a number of execution cycles which is encoded on the classifier. Since classifier delays are encoded on the classifier genome, a GA is able to explore simultaneously the spaces of actions and delays. Results of experiments comparing DACS to a traditional classifier system in terms of the dynamics of classifier reinforcement and system performance using the bucket brigade are presented and examined. Experiments comparing DACS with a traditional classifier system, which appear encouraging, for a simple prediction problem are described and considered. Areas for further work using the delayed-action classifier notion are suggested and briefly discussed.KeywordsGenetic AlgorithmClassifier SystemTraditional ClassifierMarginal PayoffLetter SequenceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The data storage and transfers related power consumption, which forms an important part of the total system power consumption, should be reduced in realizations of multimedia applications on programmable multimedia processors. In earlier work, we have formalized a methodology to achieve this. This methodology is based on the application of a number of power optimizing code transformations to a system-level description of the target application. Our script has recently been extended explicitly to take into consideration the required performance (in number of execution cycles), which is the overriding constraint in real-time multimedia applications. In this paper, we focus on a systematic use methodology that allows to apply this script in practice in a manual way on a complex application, including the necessary local design iterations. Experimental results from a real-life data-dominated application demonstrate that the application of the proposed systematic approach leads to significant power and performance gains compared to reference designs.

Surgery is one of the larger wounds in conventional surgery, and patients often experience different pain and postural discomfort after surgery. With the ever-changing standards of medical care and patient care requirements, providing high-quality care to postoperative patients is an important measure to reduce complications and promote rapid recovery. However, in the traditional postsurgical nursing methods, there are often the phenomenon that wrong patients are connected, patient data is messy, and medicines are counted incorrectly, which directly leads to a rapid decline in nursing efficiency. In the context of the rapid development of artificial intelligence and big data, intelligent medical data analysis technology has gradually been integrated into the medical field. This paper analyzes and studies the application effect of intelligent medical data analysis technology in postoperative nursing. It is aimed at changing the traditional postoperative nursing methods and improving nursing efficiency, and it provides important suggestions for the development of postoperative nursing in the new era. Combining big data and Internet of Things technology, this paper builds a smart medical Internet of Things framework and an intelligent postoperative care system and uses machine learning algorithms to preprocess relevant medical data. The final experimental results show that the intelligent medical data analysis technology has a good effect in improving the nursing efficiency after surgery, and the nursing efficiency has increased by 6.9%.

A novel intelligent food consumption data collection and analysis system is presented in this paper. Information collection devices and an analysis system are the two important constituent part of this system which are introduced in detail. We design this system to solve the problems such as statistical and administration departments having difficulty to acquire actual data in resident food consumption, people having demand of healthy diet with the improvement of living standard, and the demand of food safety regulation. The design combines applied techniques including smartphones, intelligent wearable devices and big data. Cloud service is utilized to provide information service after customers' food data are collected.