A Hybrid Approach to Identify Code Smell Using Machine Learning Algorithms

Code smell aims to identify bugs that occurred during software development. It is the task of identifying design problems. The significant causes of code smell are complexity in code, violation of programming rules, low modelling, and lack of unit-level testing by the developer. Different open source systems like JEdit, Eclipse, and ArgoUML are evaluated in this work. After collecting the data, the best features are selected using recursive feature elimination (RFE). In this paper, the authors have used different anomaly detection algorithms for efficient recognition of dirty code. The average accuracy value of k-means, GMM, autoencoder, PCA, and Bayesian networks is 98%, 94%, 96%, 89%, and 93%. The k-means clustering algorithm is the most suitable algorithm for code detection. Experimentally, the authors proved that ArgoUML project is having better performance as compared to Eclipse and JEdit projects.

AMELIORATION OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE IN MEDICAL MANAGEMENT (A CASE STUDY ON THE PANDEMIC OF COVID 19 CASES IN ASIA)

Machine learning is seeing increasingly utilized in medical management for different reasons: tremendous sums of information are being captured and made accessible carefully; handling of vast sums of information has gotten to be cost-effective due to the expanded computing control presently accessible at reasonable costs; and different open source systems, toolkits, and libraries are accessible that can be utilized to construct and execute ML applications. Particularly in healthcare, ML has driven to energizing modern improvements that may rethink COVID-19 treatment and vaccination within a long time to come. With modern occurrences of the brand modern coronavirus clutter, COVID-19, creating day through the day, it is common to compare the unused affliction to other flare-ups in current history. Machine learning can offer assistance to anticipate three sorts of restorative dangers - disease, seriousness, and result. Whereas it is still it is early for COVID-19 with machine learning, but early applications seem promising. Machine learning is utilized when a computer has been instructed to recognize designs by giving it with information and a calculation to assist get it that information. This method of learning from the information is called training and the yield that we accomplish is through testing. Machine learning-based robotic surgery is changing the way surgery is performed nowadays. Machine learning in healthcare is getting to be more broadly utilized and is making a difference in patients and clinicians in numerous diverse ways. Cybersecurity and protection are major concerns and open challenges in healthcare which are yet to be addressed.

C3D data based on 2-dimensional images from video camera

The Human three-dimensional (3D) musculoskeletal model is based on motion analysis methods and can be obtained by particular motion capture systems that export 3D data with coordinate 3D (C3D) format. Unique cameras and specific software are essential for analyzing the data. This equipment is quite expensive, and using them is time-consuming. This research intends to use ordinary video cameras and open source systems to get 3D data and create a C3D format due to these problems. By capturing movements with two video cameras, marker coordination is obtainable using Skill-Spector. To create C3D data from 3D coordinates of the body points, MATLAB functions were used. The subject was captured simultaneously with both the Cortex system and two video cameras during each validation test. The mean correlation coefficient of datasets is 0.7. This method can be used as an alternative method for motion analysis due to a more detailed comparison. The C3D data collection, which we presented in this research, is more accessible and cost-efficient than other systems. In this method, only two cameras have been used.

The Role of Big Data and Business Analytics in Decision Making

The amount of data in our world has been exploding, and big data represents a fundamental shift in business decision-making. Analyzing such so-called big data is today a keystone of competition and the success of organizations depends on fast and well-founded decisions taken by relevant people in their specific area of responsibility. Business analytics (BA) represents a merger between data strategy and a collection of decision support technologies and mechanisms for enterprises aimed at enabling knowledge workers such as executives, managers, and analysts to make better and faster decisions. The authors review the concept of BA as an open innovation strategy and address the importance of BA in revolutionizing knowledge towards economics and business sustainability. Using big data with open source business analytics systems generates the greatest opportunities to increase competitiveness and differentiation in organizations. In this chapter, the authors describe and analyze business intelligence and analytics (BI&A) and four popular open source systems – BIRT, Jaspersoft, Pentaho, and SpagoBI.

A könyvtári együttműködés informatikai támogatása: adatmodell, workflow, rendszerfelépítés: Együttműködési lehetőségek a nyílt forráskódú platformokban: OKP, FOLIO, ReShare

An essential goal of library informatics is to create open-source systems through community collaboration. Primary examples of open solution Integrated Library Systems, such as KOHA, Evergreen, or the Open Library Environment (Kuali OLE), have been born out of this notion. Since 2016, the librarian and developer professional communities have been working together to take this framework to a higher level. Building on learnings from prior system developments, a new modular, micro-service based platform was created. The platform was named FOLIO, short for ’The Future of Libraries is Open’, to reflect its open and flexible nature. Today, FOLIO platform and its relevant modules are widely used by a number of medium-sized and national libraries (e.g. the Italian National Library in Florence). The objectives of the Hungarian National Library Platform (HNLP) development, launched in 2016, are very much in alignment with the above: to re-conceptualise services offered by national libraries, to explore new ways of collaboration, to revolutionise common catalogue and interlibrary loan, and to make entity-based data connections available beyond the world outside libraries through integration to the Hungarian National Namespace. And first and foremost, to offer the most advanced services and state-of-the-art IT technology to library users. The National Széchényi Library Hungary has been part of the FOLIO community since its inception, to have a stake in its strategic direction and to benefit from the developments taking place internationally. Our long-term vision is to enable seamless module compatibility between the two systems so that libraries can use a flexible configuration that best serves their needs. The main pillars of the development are identical for both FOLIO and HNLP: (i) an entity-based data model, (ii) the creation of a meaning-based integrated architecture through modularity for any number of institutions and any institutional hierarchy, and (iii) the free configurability of workflows across the system / flexible workflow design. Where are HNLP, FOLIO and ReShare, the major collaborative module for interlibrary loan of the latter on this path right now? What solutions are provided for the basic pillars, and what objectives are still to be achieved?

A Software for Thorax Images Analysis Based on Deep Learning

People suspected of having COVID-19 need to know quickly if they are infected, so that they can isolate themselves, receive treatment, and inform those with whom they have been in close contact. Currently, the formal diagnosis of COVID-19 infection requires laboratory analysis of blood samples or swabs from the throat and nose. The lab test requires specialized equipment and takes at least 24 hours to produce a result. For this reason, in this paper, the authors tackle the problem of the detection of COVID-19 by developing an open source software to analyze chest x-ray thorax images. The method is based on supervised learning applied to 5000 images. However, deep learning techniques such as convolutional neural network (CNN) and mask R-CNN gives good results comparing with classic medical imaging. Using a dynamic learning rate, they obtained 0.96 accuracy for the training phase and 0.82 for the test. The results of our free tool are comparable to the best state of the art open source systems.