Development Of Software Applications Research Articles

Survey on Techniques to Effectively Test in DEVOPS with Latest Tooling

The purpose of this paper is to explore testing in DevOps. Now a days, many of the projects are using DevOps for development, testing, monitoring, production as well as automation. The main goal of DevOps is to establish a culture of collaboration and communication between development and operation teams, allowing for more efficient and effective development, testing, deployment and operation of software applications. Basically, DevOps is the union of people, process and products to enable continuous delivery of value to our end users. There are set of DevOps test practices as DevOps test culture, Continuous test strategy, end-to-end test infrastructures, DevOps test infrastructures, DevOps test frameworks, test creation, test acceleration, DevOps ready test tools, test analytics, micro services and containers. Database DevOps testing, DevOps security testing, continuous testing antipatterns, continuous test management, advanced continuous test topics

Sensing within Smart Buildings: A Survey

Increasingly, buildings are being fitted with sensors for the needs of different sectors, such as education, industry and business. Using Internet of Things devices combined with analysis of data being generated by these devices, it is possible to infer a number of metrics, e.g., building occupancy and activities of occupants. The information thus gathered can be used to develop software applications to support energy management, occupant comfort, and space utilization. This survey explores the use of sensors in smart building environments, identifying different approaches to employ sensors in buildings. The most commonly used data-driven approaches for activity recognition in such buildings is also investigated, concluding by highlighting current research challenges and future research directions in this area.

Integrating AI and cloud computing for automated DevOps: Developing intelligent systems for continuous integration and deployment

Artificial intelligence has raised critical disruptions in software application development and delivery when integrated with DevOps. The Machine learning that AI enhances is used in CI and CD to provide solutions for CI and CD reliability and enhancement. In this paper, the author explains how and to what extent AI technologies enable and evolve DevOps, with an accent on the transformation of DevOps throughout the years, what AI is in terms of automation of workloads, and the connection between AI and cloud. With the help of artificial intelligence, it also describes the opportunities and threats associated with the implementation of DevOps, the use of qualified specialists, data management, and problems of a legal nature. It also looks at the futurology of work such as self-managing DevOps or AI-driven observability and teamwork instruments with an eye on the perspective that they could create more value with less risk and in a shorter time.

DentaSeal: A school-based dental sealant efficiency assessment tool to support statewide monitoring and reporting: A field report.

The evidence base supports effectiveness of dental sealants for prevention of childhood caries in school-aged children. This study describes planning, development, usability testing and outcomes following implementation of DentaSeal, a web-based application designed to accurately track unique student data and generate reports for all Wisconsin school-based sealant placement (SP) programs. Application software development was informed by a steering committee of representative stakeholders who were interviewed to inform design and provide feedback for design of DentaSeal during development and evaluation. Software development proceeded based on wireframes developed to build architectural design. Usability testing followed and informed any required adjustments to the application. The DentaSeal prototype was beta tested and fully implemented subsequently in the public health sector. The DentaSeal application demonstrated capacity to: 1) track unique student SP data and longitudinal encounter history, 2) generate reports and 3) support administrative tracking. In 2019, DentaSeal captured SP data of 47 school-based programs in Wisconsin that sponsored > 7,000 program visits for 184,000 children from 62 counties. Delivery of > 548,000 SP services were catalogued. For public health initiatives targeting reduction in caries incidence, web-based applications such as DentaSeal represent useful longitudinal tracking tools for cataloguing SP in school-based program participants.

Forecasting System for Heart Diseases: A Classification Approach

The medical field provides enormous quantities of data that contain an unobserved pattern that can be useful for decisions. This paper intended to develop a forecasting system to detect the early existence of heart diseases based on fourteen variables from patients’ historical data. The system used the naïve bayes data mining technique to analyze the inputted medical data. This paper used the Rapid Application Development (RAD) Software Life Cycle in designing and developing the system. The system was simulated using synthetic datasets namely, Cleveland and Statlog. Results of this study showed that the system provided adequate features, the dataset input training page, data verification page, and forecasting results page for predicting heart diseases.

An empirical study of automated privacy requirements classification in issue reports

The recent advent of data protection laws and regulations has emerged to protect privacy and personal information of individuals. As the cases of privacy breaches and vulnerabilities are rapidly increasing, people are aware and more concerned about their privacy. These bring a significant attention to software development teams to address privacy concerns in developing software applications. As today’s software development adopts an agile, issue-driven approach, issues in an issue tracking system become a centralised pool that gathers new requirements, requests for modification and all the tasks of the software project. Hence, establishing an alignment between those issues and privacy requirements is an important step in developing privacy-aware software systems. This alignment also facilitates privacy compliance checking which may be required as an underlying part of regulations for organisations. However, manually establishing those alignments is labour intensive and time consuming. In this paper, we explore a wide range of machine learning and natural language processing techniques which can automatically classify privacy requirements in issue reports. We employ six popular techniques namely Bag-of-Words (BoW), N-gram Inverse Document Frequency (N-gram IDF), Term Frequency-Inverse Document Frequency (TF-IDF), Word2Vec, Convolutional Neural Network (CNN) and Bidirectional Encoder Representations from Transformers (BERT) to perform the classification on privacy-related issue reports in Google Chrome and Moodle projects. The evaluation showed that BoW, N-gram IDF, TF-IDF and Word2Vec techniques are suitable for classifying privacy requirements in those issue reports. In addition, N-gram IDF is the best performer in both projects.

Penerapan Pembelajaran E-Learning Berbasis Moodle Pada Mata Pelajaran Simulasi Digital

There is no e-learning at SMK Al-Washliyah 4, this study aims to produce a learning system that is flexible and in accordance with the times by using moodle-based e-learning learning media. The benefit of this research is to facilitate students and teachers in carrying out teaching and learning. This research uses the Rapid Application Development (RAD) Software Development method. After researchers made observations at school, what schools need now is learning media that can enable teachers and students to carry out teaching and learning activities without being hindered by space and time. After observing, designing, and implementing Moddle-based E-Learning at SMK Al-Washliyah 4, it can be concluded that E-Learning that is made based on Moodle and contains some teacher and student data can have a good impact in providing material, assignments, quizzes, etc. become a very useful learning media for students. teachers because there is no E-learning in schools yet.

Circular economy e-business model portfolio development for e-business applications: Impacts on ESG and sustainability performance

The circular economy has emerged as a key driver in the development of sustainability in the current digital world. Circular economy businesses have generated significant contributions in terms of gross domestic product and employment. In the future, new approaches are needed that meet global trends in digitalisation, resources, and ecological challenges. Instead of solely addressing environmental issues, e-businesses in the digital age need to ensure the sustainable development of the circular economy. This paper proposes a feature-based circular economy e-business model for uplifting environment, social, and governance (ESG) and sustainability performance. We have employed indicators that support the implementation of the circular economy process related to technical aspects, reduce the cost of operation and resource pressure, minimise the environmental impact, and maximise social and economic impacts. We present here a comprehensive discussion that provides an in-depth literature review, an emerging e-business model for the circular economy, business, e-business application portfolio, and software application development for sustainable circular economy Business.

Transformational Development of Speed-Reading Technology: Tools, Machines and Software Applications

This conceptual paper aims to concentrate on the transformational development of tools, machines, and software applications used to increase the speed of reading. There has been a need for modernization of the old-fashioned machines in the field of speed reading. Several practical steps have already been taken in the last three decades of the twentieth century, and several outdated speed-reading tools and various equipment types have already been replaced by quite a few speed-reading software programs and mobile applications. The earliest speed-reading tools are reading pacing machines, which work on various simple to advanced technological principles. Different types of machines are also used for tracking the movements of eyes in the form of saccades and fixations during reading. Subsequently, several speed-increasing software tools use the powerful text processing technique called the Rapid Serial Visual Presentation (RSVP). Many of these tools have demonstrated significant productivity gains in the reading speed of learners. However, this transformational development has resulted in a multiplicity of speed-increasing function-specific software applications, there remains a need to choose the right combination of them, considering the diversity among learners and their goals. Though the distance covered in this journey of transformational development of tools, machines and software applications is quite encouraging, there is still room for improvements in terms of making the process of speed reading closer to natural reading.

DESIGN AND IMPLEMENTATION OF THE INTEGRATED PRACTICUM OF GEOMATICS ACCORDING TO ENGINEERING EDUCATION ACCREDITATION

Abstract. Engineering education accreditation is a new engineering education model promoted by the Ministry of Education in China, which aims to cultivate the ability of students to solve complex engineering problems. A course named “integrated practicum of Geomatics” (IPG) was innovatively designed for the undergraduate specialty of Geomatics. This course involves in the current advanced technologies to lay a solid foundation for students to engage in Geomatics through aerial photography technology design, unmanned aerial vehicle photography, control point selection, GNSS-RTK control point survey, photo interpretation and sketch, 3D (DEM, DOM, and DLG) product production, inputting 3D products into the Geodatabase, and GIS application software development. This paper expounds on the objectives, contents, evaluation methods, and continuous improvement of IPG.

Design and Implementation of Biometric-Based Student Information Retrieval System Using Finger Prints Recognition

This study designed and implements a biometric-based student information retrieval system using finger print recognition. The findings showed that the existing manual system is prone to lots of errors such difficulties in accessing records, wrong data entry, difficulties in data manipulation, identity management and difficulties in updating students’ records. Structured system analysis and design methodology was adopted for this research. The design and implementation of the developed application software showed that computerized student information retrieval system using biometrics greatly enhanced the documentation, manipulation and retrieval of student information in a more accurate, fast and secured manner.

Artificial Intelligence in Veterinary Imaging: An Overview.

Artificial intelligence and machine learning have been increasingly used in the medical imaging field in the past few years. The evaluation of medical images is very subjective and complex, and therefore the application of artificial intelligence and deep learning methods to automatize the analysis process would be very beneficial. A lot of researchers have been applying these methods to image analysis diagnosis, developing software capable of assisting veterinary doctors or radiologists in their daily practice. This article details the main methodologies used to develop software applications on machine learning and how veterinarians with an interest in this field can benefit from such methodologies. The main goal of this study is to offer veterinary professionals a simple guide to enable them to understand the basics of artificial intelligence and machine learning and the concepts such as deep learning, convolutional neural networks, transfer learning, and the performance evaluation method. The language is adapted for medical technicians, and the work already published in this field is reviewed for application in the imaging diagnosis of different animal body systems: musculoskeletal, thoracic, nervous, and abdominal.

Software application development for real estate activities

The real estate industry is in the early stages of software application, in constant search of new tools to make the industry work more efficiently and effectively. The real estate market is differentiated by certain needs, respectively each segment of the real estate market is looking for software solutions to meet the main needs. In order to meet the requirements of the real estate market, the authors come up with a proposal to create a prototype (iMoPal), including software application development, for a product, namely an online real estate platform for property management, and the creation of a scoring system for tenant and landlord. The application offers a proficient time management solution between tenant and landlord.

LabVIEW Instrument Aimed for the Research on Brain-Computer Interface by Enabling the Acquisition, Processing, and the Neural Networks based Classification of the Raw EEG Signal Detected by the Embedded NeuroSky Biosensor

The Brain-Computer Interface (BCI) is a scientific field aimed at helping people with neuromotor disabilities. Among the current drawbacks of BCI research is the need for a cost-effective software instrument for simple integration with portable EEG headsets, the lack of a comparative assessment approach of various techniques underlying recognizing the most precise BCI control signal –voluntary eye-blinking, and the need for EEG datasets allowing the classification of multiple voluntary eye-blinks. The proposed BCI research-related virtual instrument accomplishes the data acquisition, processing, features extraction, and the ANN-based classification of the EEG signal detected by the NeuroSky embedded biosensor. The developed software application automatically generated fifty mixtures between selected EEG rhythms and statistical features. The EEG rhythms are related to the time and frequency domains of the raw, delta, theta, alpha, beta, and gamma. The extracted statistical features contain the mean, median, standard deviation, route mean square, Kurtosis coefficient, mode, sum, skewness, maximum, and range = maximum-minimum. The results include 100 EEG datasets to classify multiple voluntary eye-blinks: 50 datasets with 4000 recordings and 50 with 800 recordings. The LabVIEW application determined the optimal ANN models for classifying the EEG temporal sequences corresponding to detecting zero, one, two, or three voluntary eye-blinks.

The development of a digital story-retell elicitation and analysis tool through citizen science data collection, software development and machine learning.

In order to leverage the potential benefits of technology to speech and language therapy language assessment processes, large samples of naturalistic language data must be collected and analysed. These samples enable the development and testing of novel software applications with data relevant to their intended clinical application. However, the collection and analysis of such data can be costly and time-consuming. This paper describes the development of a novel application designed to elicit and analyse young children's story retell narratives to provide metrics regarding the child's use of grammatical structures (micro-structure) and story grammar (macro-structure elements). Key aspects for development were (1) methods to collect story retells, ensure accurate transcription and segmentation of utterances; (2) testing the reliability of the application to analyse micro-structure elements in children's story retells and (3) development of an algorithm to analyse narrative macro-structure elements. A co-design process was used to design an app which would be used to gather story retell samples from children using mobile technology. A citizen science approach using mainstream marketing via online channels, the media and billboard ads was used to encourage participation from children across the United Kingdom. A stratified sampling framework was used to ensure a representative sample was obtained across age, gender and five bands of socio-economic disadvantage using partial postcodes and the relevant indices of deprivation. Trained Research Associates (RA) completed transcription and micro and macro-structure analysis of the language samples. Methods to improve transcriptions produced by automated speech recognition were developed to enable reliable analysis. RA micro-structure analyses were compared to those generated by the digital application to test its reliability using intra-class correlation (ICC). RA macro-structure analyses were used to train an algorithm to produce macro-structure metrics. Finally, results from the macro-structure algorithm were compared against a subset of RA macro-structure analyses not used in training to test its reliability using ICC. A total of 4,517 profiles were made in the app used in data collection and from these participants a final set of 599 were drawn which fulfilled the stratified sampling criteria. The story retells ranged from 35.66 s to 251.4 s in length and had word counts ranging from 37 to 496, with a mean of 148.29 words. ICC between the RA and application micro-structure analyses ranged from 0.213 to 1.0 with 41 out of a total of 44 comparisons reaching 'good' (0.70-0.90) or 'excellent' (>0.90) levels of reliability. ICC between the RA and application macro-structure features were completed for 85 samples not used in training the algorithm. ICC ranged from 0.5577 to 0.939 with 5 out of 7 metrics being 'good' or better. Work to date has demonstrated the potential of semi-automated transcription and linguistic analyses to provide reliable, detailed and informative narrative language analysis for young children and for the use of citizen science based approaches using mobile technologies to collect representative and informative research data. Clinical evaluation of this new app is ongoing, so we do not yet have data documenting its developmental or clinical sensitivity and specificity.

Time-trend analysis of the center frequency of the intrinsic mode function from the Hilbert–Huang transform of electroencephalography during general anesthesia: a retrospective observational study

BackgroundAnesthesiologists are required to maintain an optimal depth of anesthesia during general anesthesia, and several electroencephalogram (EEG) processing methods have been developed and approved for clinical use to evaluate anesthesia depth. Recently, the Hilbert–Huang transform (HHT) was introduced to analyze nonlinear and nonstationary data. In this study, we assessed whether the changes in EEG characteristics during general anesthesia that are analyzed by the HHT are useful for monitoring the depth of anesthesia.MethodsThis retrospective observational study enrolled patients who underwent propofol anesthesia. Raw EEG signals were obtained from a monitor through a previously developed software application. We developed an HHT analyzer to decompose the EEG signal into six intrinsic mode functions (IMFs) and estimated the instantaneous frequencies (HHT_IF) for each IMF. Changes over time in the raw EEG waves and parameters such as HHT_IF, BIS, spectral edge frequency 95 (SEF95), and electromyogram parameter (EMGlow) were assessed, and a Gaussian process regression model was created to assess the association between BIS and HHT_IF.ResultsWe analyzed EEG signals from 30 patients. The beta oscillation frequency range (13–25 Hz) was detected in IMF1 and IMF2 during the awake state, then after loss of consciousness, the frequency decreased and alpha oscillation (8–12 Hz) was detected in IMF2. At the emergence phase, the frequency increased and beta oscillations were detected in IMF1, IMF2, and IMF3. BIS and EMGlow changed significantly during the induction and emergence phases, whereas SEF95 showed a wide variability and no significant changes during the induction phase. The root mean square error between the observed BIS values and the values predicted by a Gaussian process regression model ranged from 4.69 to 9.68.ConclusionsWe applied the HHT to EEG analyses during propofol anesthesia. The instantaneous frequency in IMF1 and IMF2 identified changes in EEG characteristics during induction and emergence from general anesthesia. Moreover, the HHT_IF in IMF2 showed strong associations with BIS and was suitable for depicting the alpha oscillation. Our study suggests that the HHT is useful for monitoring the depth of anesthesia.

The mediating role of academic self-regulation in the relationship between autocorrect use and vocabulary size

Mobile learning has extensively influenced students’ learning gains and motivation due to the hardware and software development of mobile devices and applications in recent decades. On-the-go learning increases the capacity for flexible and joyful learning and guarantees access to the instructional content anywhere, anytime. Despite their potential benefits, the presence of mobile applications in students’ every educational activity can be detrimental to their cognitive abilities as misuse or overuse of specific applications may influence students’ concentration and critical thinking. One such downside is reported for overusing automatic spelling correction software, known as AutoCorrect (AC), in language learning. Uncertainty regarding the educational values of AC has increased as students’ spelling skills and vocabulary knowledge have declined. The main problem this study addressed is examining the relationship between EFL learners’ AC use and their vocabulary size; and if their academic self-regulation mediates this association. Data were gathered from 101 foreign language learners who completed the measures of AC use, academic self-regulation, and vocabulary size. The results showed significant positive correlations between AC use, vocabulary size, and self-regulation. Further, testing the model supported a mediating role for self-regulation in the relationship between AC use and vocabulary size, suggesting that AC use can lead to more knowledge of English words in the condition of deploying self-regulatory strategies. The study signifies how academic self-regulation can assist learners in exploiting mobile learning (m-learning) to their advantage and attaining their educational goals more efficiently.

Development of a comprehensive software application for realization and dissemination of the mass scale

The main features of the RealMass Calibration software, a tool for mass standards calibration developed at the INRiM, are described. It was developed under the Matlab environment and will be distributed as an executable file on the INRiM website for free download. The software tool has a user-friendly graphical interface to be easily applied by final users. The software is capable of perform advanced mass calibrations processing at the state of the art. Although it can be use at the high-test levels for the mass scale realisation, the user-friendly features also make it suitable for routine calibrations for accredited laboratories. The elaboration of the measurements, defined by the weighing designs, can be performed by the weighted least-squares with Lagrange multipliers and the Gauss Markov estimator. It can take into account uncertainties and correlations of the input quantities, as: between the reference standards, air density measurements and between values of mass and volume of the standards. Tolls to check the consistency of the results are also provided. Validation and the applications to a real case studies are also shown.

Sustainable Marketing Performance of Banks in the Digital Economy: The Role of Customer Relationship Management

Customer relationship management (CRM) has become the lifeblood of banking and the principal driver of sustainable marketing performance in the digital economy. The discovery of computers and the Internet has transformed the industrial economy into digital through the integration of CRM and related elements, resulting in several benefits, including growth in research documents and increased collaborations. Marketers have developed a CRM system that integrates the digital infrastructure and digital capabilities of people to promote the sustainable marketing performance of banks through innovations such as customer acquisition, segmentation, customization, loyalty, and profitability. The paper aims to analyse the scientific landscape of CRM and sustainable marketing performance in the digital economy. The authors resorted to the Scopus database to extract 1485 documents and filtered 248 for Scopus analysis and to determine the roles of CRM. The filtered documents were then exported to VOSviewer for visualization mapping, including co-authorship and co-occurrence analysis. The visualization mapping depicts relationships among the keywords. The results of the research indicate an upwards growth in research publications since 2003, growing interest in funding and collaborations by countries and institutions, and the relationship among the keywords. The emerging financial crisis is a motivation for research in the area of study. The results also indicate that the integration of CRM into digital technology promotes collaborations and innovations resulting in the development of banking software applications to promote customer acquisition, retention, loyalty, profitability and sustainability. The study developed an integrated framework for sustainable banking performance. Global crises, including financial crises and COVID-19, have increased research output and become an integral part of CRM. Green banking is to be researched and integrated. The study will be used as a basis for further studies and for bankers for policy formulation.

Development of Digital twin for Plug-and-Produce of a Machine tending system through ISO 21919 interface

ABSTRACT The introduction of robot-assisted automation, such as in machine tending systems for the automated operation of CNC machine tools, is being actively carried out at many industrial sites. The most important part of this intelligent system is the installation of a machine tending system that involves interface programming between the CNC machine tools and the industrial robot. Despite this importance, however, the machine tending system has many setup problems. It must be reprogrammed for both controllers whenever a new CNC machine tool or robot is introduced. Owing to the complex structure of the machine tending system, external engineers are required even though trivial changes have been made. To solve and remove inconveniences, there is a need to introduce a standardized and integrated system of the interface between multi-vendor CNC machine tools and industrial robots. In addition, the digital twin implemented inside the machine tool controller enables external engineers to easily change robot programming. To implement this system, an integrated development environment for: an intelligent platform that provides ISO 21919 interfaces to multi-vendors CNC machine tools, a robot platform developing application software of various robots, has been established.