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Articoli di riviste sul tema "Computer software. Software engineering. Machine learning"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 4 febbraio 2022

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1

Hussain*, Mandi Akif, Revoori Veeharika Reddy, Kedharnath Nagella, and Vidya S. "Software Defect Estimation using Machine Learning Algorithms." International Journal of Recent Technology and Engineering 10, no. 1 (2021): 204–8. http://dx.doi.org/10.35940/ijrte.a5898.0510121.

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Software Engineering is a branch of computer science that enables tight communication between system software and training it as per the requirement of the user. We have selected seven distinct algorithms from machine learning techniques and are going to test them using the data sets acquired for NASA public promise repositories. The results of our project enable the users of this software to bag up the defects are selecting the most efficient of given algorithms in doing their further respective tasks, resulting in effective results.

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Bera, Debjyoti, Mathijs Schuts, Jozef Hooman, and Ivan Kurtev. "Reverse engineering models of software interfaces." Computer Science and Information Systems 18, no. 3 (2021): 657–86. http://dx.doi.org/10.2298/csis200131013b.

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Cyber-physical systems consist of many hardware and software components. Over the lifetime of these systems their components are often replaced or updated. To avoid integration problems, formal specifications of component interface behavior are crucial. Such a formal specification captures not only the set of provided operations but also the order of using them and the constraints on their timing behavior. Usually the order of operations are expressed in terms of a state machine. For new components such a formal specification can be derived from requirements. However, for legacy components suc

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Chung, Chih-Ko, and Pi-Chung Wang. "Version-Wide Software Birthmark via Machine Learning." IEEE Access 9 (2021): 110811–25. http://dx.doi.org/10.1109/access.2021.3103186.

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4

Al Sghaier, Hiba. "RESEARCH TRENDS IN SOFTWARE ENGINEERING FIELD: A LITERATURE REVIEW." International Journal of Engineering Technologies and Management Research 7, no. 6 (2020): 58–65. http://dx.doi.org/10.29121/ijetmr.v2020.i7.6.694.

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Software engineering is one of computer science branches, it comprises of building and developing software systems and applications. Software engineering is a discipline that has a constant growth in research in aim to identify new technologies and adopt it in different areas; there is a considerable investment on software engineering trends at the current time due to the availability of mobile technologies. With millions of billions of smart devices that are connected to the internet, all industries around the world are rapidly becoming a technology driven industries. Software engineers

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Al Sghaier, Hiba. "RESEARCH TRENDS IN SOFTWARE ENGINEERING FIELD: A LITERATURE REVIEW." International Journal of Engineering Technologies and Management Research 7, no. 6 (2020): 58–65. http://dx.doi.org/10.29121/ijetmr.v7.i6.2020.694.

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Abstract (sommario):

Software engineering is one of computer science branches, it comprises of building and developing software systems and applications. Software engineering is a discipline that has a constant growth in research in aim to identify new technologies and adopt it in different areas; there is a considerable investment on software engineering trends at the current time due to the availability of mobile technologies. With millions of billions of smart devices that are connected to the internet, all industries around the world are rapidly becoming a technology driven industries. Software engineers

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Saputri, Theresia Ratih Dewi, and Seok-Won Lee. "Software Analysis Method for Assessing Software Sustainability." International Journal of Software Engineering and Knowledge Engineering 30, no. 01 (2020): 67–95. http://dx.doi.org/10.1142/s0218194020500047.

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Software sustainability evaluation has become an essential component of software engineering (SE) owing to sustainability considerations that must be incorporated into software development. Several studies have been performed to address the issues associated with sustainability concerns in the SE process. However, current practices extensively rely on participant experiences to evaluate sustainability achievement. Moreover, there exist limited quantifiable methods for supporting software sustainability evaluation. Our primary objective is to present a methodology that can assist software engin

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BAILIN, SIDNEY C., ROBERT H. GATTIS, and WALT TRUSZKOWSKI. "A LEARNING-BASED SOFTWARE ENGINEERING ENVIRONMENT FOR REUSING DESIGN KNOWLEDGE." International Journal of Software Engineering and Knowledge Engineering 01, no. 04 (1991): 351–71. http://dx.doi.org/10.1142/s0218194091000251.

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As part of the NASA/Goddard Code 522.3 research program in software engineering, a Knowledge-Based Software Engineering Environment (KBSEE) is being developed. The KBSEE will support a comprehensive artifact-reuse capability and will incorporate knowledge-based concepts such as machine learning and design knowledge capture. The distinguishing features of this work are that it is a systematic approach to the reuse of knowledge, not just of products, and it implements learning as an explicitly supported function in a software engineering environment. Each of these objectives is currently being p

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Siewruk, Grzegorz, and Wojciech Mazurczyk. "Context-Aware Software Vulnerability Classification Using Machine Learning." IEEE Access 9 (2021): 88852–67. http://dx.doi.org/10.1109/access.2021.3075385.

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Firdaus Zainal Abidin, Ahmad, Mohd Faaizie Darmawan, Mohd Zamri Osman, et al. "Adaboost-multilayer perceptron to predict the student’s performance in software engineering." Bulletin of Electrical Engineering and Informatics 8, no. 4 (2019): 1556–62. http://dx.doi.org/10.11591/eei.v8i4.1432.

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Software Engineering (SE) course is one of the backbones of today's computer technology sophistication. Effective theoretical and practical learning of this course is essential to computer students. However, there are many students fail in this course. There are many aspects that influence a student's performance. Currently, student performance analysis methods just focus on historical achievement and assessment methods given in the class. Need more research to predict student's performance to overcome the problem of student failing. The objective of this research is to perform a prediction fo

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AZAR, DANIELLE. "A GENETIC ALGORITHM FOR IMPROVING ACCURACY OF SOFTWARE QUALITY PREDICTIVE MODELS: A SEARCH-BASED SOFTWARE ENGINEERING APPROACH." International Journal of Computational Intelligence and Applications 09, no. 02 (2010): 125–36. http://dx.doi.org/10.1142/s1469026810002811.

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In this work, we present a genetic algorithm to optimize predictive models used to estimate software quality characteristics. Software quality assessment is crucial in the software development field since it helps reduce cost, time and effort. However, software quality characteristics cannot be directly measured but they can be estimated based on other measurable software attributes (such as coupling, size and complexity). Software quality estimation models establish a relationship between the unmeasurable characteristics and the measurable attributes. However, these models are hard to general

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Medeiros, Nadia, Naghmeh Ivaki, Pedro Costa, and Marco Vieira. "Vulnerable Code Detection Using Software Metrics and Machine Learning." IEEE Access 8 (2020): 219174–98. http://dx.doi.org/10.1109/access.2020.3041181.

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De Carvalho, Halcyon Davys Pereira, Roberta Fagundes, and Wylliams Santos. "Extreme Learning Machine Applied to Software Development Effort Estimation." IEEE Access 9 (2021): 92676–87. http://dx.doi.org/10.1109/access.2021.3091313.

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Ganapathy, Apoorva, and Taposh Kumar Neogy. "Artificial Intelligence Price Emulator: A Study on Cryptocurrency." Global Disclosure of Economics and Business 6, no. 2 (2017): 115–22. http://dx.doi.org/10.18034/gdeb.v6i2.558.

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The cryptocurrency Artificial intelligence price emulator is a software programmed to collect cryptocurrency market data, analyze the data and predict the market price using the collected data. Computer emulators are programmed to mimic and copy behaviors or other software/hardware. The reason for emulation is to get to a particular result as quickly as possible. Machine learning is the ability of computers to read and process data while learning from the data with human interference or influence. This work focused majorly on how cryptocurrency market prices can be emulated using Artificial In

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Pandey, Sushant Kumar, Ravi Bhushan Mishra, and Anil Kumar Tripathi. "Machine learning based methods for software fault prediction: A survey." Expert Systems with Applications 172 (June 2021): 114595. http://dx.doi.org/10.1016/j.eswa.2021.114595.

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Rodríguez-Gracia, Diego, José A. Piedra-Fernández, Luis Iribarne, et al. "Microservices and Machine Learning Algorithms for Adaptive Green Buildings." Sustainability 11, no. 16 (2019): 4320. http://dx.doi.org/10.3390/su11164320.

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In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments s

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Zheng, Wei, Yutong Bai, and Haoxuan Che. "A computer-assisted instructional method based on machine learning in software testing class." Computer Applications in Engineering Education 26, no. 5 (2018): 1150–58. http://dx.doi.org/10.1002/cae.21962.

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Mahdi, Mohammed Najah, Mohd Hazli Mohamed Zabil, Abdul Rahim Ahmad, et al. "Software Project Management Using Machine Learning Technique—A Review." Applied Sciences 11, no. 11 (2021): 5183. http://dx.doi.org/10.3390/app11115183.

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Project management planning and assessment are of great significance in project performance activities. Without a realistic and logical plan, it isn’t easy to handle project management efficiently. This paper presents a wide-ranging comprehensive review of papers on the application of Machine Learning in software project management. Besides, this paper presents an extensive literature analysis of (1) machine learning, (2) software project management, and (3) techniques from three main libraries, Web Science, Science Directs, and IEEE Explore. One-hundred and eleven papers are divided into four

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Perlovsky, Leonid, and Gary Kuvich. "Machine Learning and Cognitive Algorithms for Engineering Applications." International Journal of Cognitive Informatics and Natural Intelligence 7, no. 4 (2013): 64–82. http://dx.doi.org/10.4018/ijcini.2013100104.

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Mind is based on intelligent cognitive processes, which are not limited by language and logic only. The thought is a set of informational processes in the brain, and such processes have the same rationale as any other systematic informational processes. Their specifics are determined by the ways of how brain stores, structures, and process this information. Systematic approach allows representing them in a diagrammatic form that can be formalized. Semiotic approach allows for the universal representation of such diagrams. In that approach, logic is a way of synthesis of such structures, which

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Girard, Simon R., Vincent Legault, Guy Bois, and Jean-François Boland. "Avionics Graphics Hardware Performance Prediction with Machine Learning." Scientific Programming 2019 (June 3, 2019): 1–15. http://dx.doi.org/10.1155/2019/9195845.

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Within the strongly regulated avionic engineering field, conventional graphical desktop hardware and software application programming interface (API) cannot be used because they do not conform to the avionic certification standards. We observe the need for better avionic graphical hardware, but system engineers lack system design tools related to graphical hardware. The endorsement of an optimal hardware architecture by estimating the performance of a graphical software, when a stable rendering engine does not yet exist, represents a major challenge. As proven by previous hardware emulation to

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Radliński, Łukasz. "Predicting Aggregated User Satisfaction in Software Projects." Foundations of Computing and Decision Sciences 43, no. 4 (2018): 335–57. http://dx.doi.org/10.1515/fcds-2018-0017.

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Abstract User satisfaction is an important feature of software quality. However, it was rarely studied in software engineering literature. By enhancing earlier research this paper focuses on predicting user satisfaction with machine learning techniques using software development data from an extended ISBSG dataset. This study involved building, evaluating and comparing a total of 15,600 prediction schemes. Each scheme consists of a different combination of its components: manual feature preselection, handling missing values, outlier elimination, value normalization, automated feature selection

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Tiwari, Tanya, Tanuj Tiwari, and Sanjay Tiwari. "How Artificial Intelligence, Machine Learning and Deep Learning are Radically Different?" International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 2 (2018): 1. http://dx.doi.org/10.23956/ijarcsse.v8i2.569.

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There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). A computer system able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. Artificial Intelligence has made it possible. Deep learning is a subset of machine learning, and machine learning is a subset of AI, which is an umbrella term for any computer program that does something smart. In other words, all machine learning is AI, but not all AI is machine learning, and so

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NAKKRASAE, SATHIT, and PERAPHON SOPHATSATHIT. "AN RPCL-BASED INDEXING APPROACH FOR SOFTWARE COMPONENT CLASSIFICATION." International Journal of Software Engineering and Knowledge Engineering 14, no. 05 (2004): 497–518. http://dx.doi.org/10.1142/s0218194004001774.

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Software Engineering is not only a technical discipline of its own, but also a problem domain where technologies coming from other disciplines are relevant and can play important roles. One important example is knowledge engineering, a term that is used in a broad sense to encompass artificial intelligence, computational intelligence, knowledge bases, data mining, and machine learning [13]. Many typical software development issues can benefit from these disciplines. For this reason, we will employ computational intelligence approach to classify software component repository into similar compon

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Sheneamer, Abdullah M. "An Automatic Advisor for Refactoring Software Clones Based on Machine Learning." IEEE Access 8 (2020): 124978–88. http://dx.doi.org/10.1109/access.2020.3006178.

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Lo, Sin Kit, Qinghua Lu, Chen Wang, Hye-Young Paik, and Liming Zhu. "A Systematic Literature Review on Federated Machine Learning." ACM Computing Surveys 54, no. 5 (2021): 1–39. http://dx.doi.org/10.1145/3450288.

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Federated learning is an emerging machine learning paradigm where clients train models locally and formulate a global model based on the local model updates. To identify the state-of-the-art in federated learning and explore how to develop federated learning systems, we perform a systematic literature review from a software engineering perspective, based on 231 primary studies. Our data synthesis covers the lifecycle of federated learning system development that includes background understanding, requirement analysis, architecture design, implementation, and evaluation. We highlight and summar

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Cândido, Jeanderson, Maurício Aniche, and Arie van Deursen. "Log-based software monitoring: a systematic mapping study." PeerJ Computer Science 7 (May 6, 2021): e489. http://dx.doi.org/10.7717/peerj-cs.489.

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Modern software development and operations rely on monitoring to understand how systems behave in production. The data provided by application logs and runtime environment are essential to detect and diagnose undesired behavior and improve system reliability. However, despite the rich ecosystem around industry-ready log solutions, monitoring complex systems and getting insights from log data remains a challenge. Researchers and practitioners have been actively working to address several challenges related to logs, e.g., how to effectively provide better tooling support for logging decisions to

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Akimova, Elena N., Alexander Yu Bersenev, Artem A. Deikov, et al. "A Survey on Software Defect Prediction Using Deep Learning." Mathematics 9, no. 11 (2021): 1180. http://dx.doi.org/10.3390/math9111180.

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Defect prediction is one of the key challenges in software development and programming language research for improving software quality and reliability. The problem in this area is to properly identify the defective source code with high accuracy. Developing a fault prediction model is a challenging problem, and many approaches have been proposed throughout history. The recent breakthrough in machine learning technologies, especially the development of deep learning techniques, has led to many problems being solved by these methods. Our survey focuses on the deep learning techniques for defect

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Twala, Bhekisipho. "Predicting Software Faults in Large Space Systems using Machine Learning Techniques." Defence Science Journal 61, no. 4 (2011): 306–16. http://dx.doi.org/10.14429/dsj.61.1088.

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Bailin, Sidney, Scott Henderson, and Walt Truszkowski. "Application of machine learning to the organization of institutional software repositories." Telematics and Informatics 10, no. 3 (1993): 283–99. http://dx.doi.org/10.1016/0736-5853(93)90031-x.

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Sabir, Bushra, Faheem Ullah, M. Ali Babar, and Raj Gaire. "Machine Learning for Detecting Data Exfiltration." ACM Computing Surveys 54, no. 3 (2021): 1–47. http://dx.doi.org/10.1145/3442181.

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Context : Research at the intersection of cybersecurity, Machine Learning (ML), and Software Engineering (SE) has recently taken significant steps in proposing countermeasures for detecting sophisticated data exfiltration attacks. It is important to systematically review and synthesize the ML-based data exfiltration countermeasures for building a body of knowledge on this important topic. Objective : This article aims at systematically reviewing ML-based data exfiltration countermeasures to identify and classify ML approaches, feature engineering techniques, evaluation datasets, and performanc

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Bergadano, F., and D. Gunetti. "Learning relations and logic programs." Knowledge Engineering Review 9, no. 1 (1994): 73–77. http://dx.doi.org/10.1017/s0269888900006615.

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Inductive Logic Programming (ILP) is an emerging research area at the intersection of machine learning, logic programming and software engineering. The first workshop on this topic was held in 1991 in Portugal (Muggleton, 1991). Subsequently, there was a workshop tied to the Future Generation Computer System Conference in Japan in 1992, and a third one in Bled, Slovenia, in April 1993 (Muggleton, 1993). Ideas related to ILP are also appearing in major AI and machine learning conferences and journals. Although European-based and mainly sponsored by ESPRIT, ILP aims at becoming equally represent

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Naseem, Rashid, Zain Shaukat, Muhammad Irfan, et al. "Empirical Assessment of Machine Learning Techniques for Software Requirements Risk Prediction." Electronics 10, no. 2 (2021): 168. http://dx.doi.org/10.3390/electronics10020168.

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Software risk prediction is the most sensitive and crucial activity of Software Development Life Cycle (SDLC). It may lead to the success or failure of a project. The risk should be predicted earlier to make a software project successful. A model is proposed for the prediction of software requirement risks using requirement risk dataset and machine learning techniques. In addition, a comparison is made between multiple classifiers that are K-Nearest Neighbour (KNN), Average One Dependency Estimator (A1DE), Naïve Bayes (NB), Composite Hypercube on Iterated Random Projection (CHIRP), Decision Ta

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P, Gouthaman, and Suresh Sankaranarayanan. "Prediction of Risk Percentage in Software Projects by Training Machine Learning Classifiers." Computers & Electrical Engineering 94 (September 2021): 107362. http://dx.doi.org/10.1016/j.compeleceng.2021.107362.

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Martin, Ignacio, Sebastian Troia, Jose Alberto Hernandez, et al. "Machine Learning-Based Routing and Wavelength Assignment in Software-Defined Optical Networks." IEEE Transactions on Network and Service Management 16, no. 3 (2019): 871–83. http://dx.doi.org/10.1109/tnsm.2019.2927867.

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Vladlen, Devin, Tkachuk Vasil, and Skorobogatov Dmytro. "USAGE OF «GIM» SOFTWARE WHILE TEACHING "TECHNICAL MECHANICS" DISCIPLINE." OPEN EDUCATIONAL E-ENVIRONMENT OF MODERN UNIVERSITY, no. 7 (2019): 17–29. http://dx.doi.org/10.28925/2414-0325.2019.7.2.

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Technical Mechanics being a complex of fundamental general technical disciplines is the theoretical and scientific basis for the study and development of modern engineering. Using its laws and principles buildings, constructions, machines and equipment can be developed and researched. However, Technical Mechanics is the most difficult discipline to learn. The main difficulties of this course are based not only on the application of the principles of theoretical mechanics to complex mechanisms of different types but also the course stands out with its specific and particular features and also b

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Ashik, Mathew, A. Jyothish, S. Anandaram, et al. "Detection of Malicious Software by Analyzing Distinct Artifacts Using Machine Learning and Deep Learning Algorithms." Electronics 10, no. 14 (2021): 1694. http://dx.doi.org/10.3390/electronics10141694.

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Malware is one of the most significant threats in today’s computing world since the number of websites distributing malware is increasing at a rapid rate. Malware analysis and prevention methods are increasingly becoming necessary for computer systems connected to the Internet. This software exploits the system’s vulnerabilities to steal valuable information without the user’s knowledge, and stealthily send it to remote servers controlled by attackers. Traditionally, anti-malware products use signatures for detecting known malware. However, the signature-based method does not scale in detectin

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Moreb, Mohammed, Tareq Abed Mohammed, and Oguz Bayat. "A Novel Software Engineering Approach Toward Using Machine Learning for Improving the Efficiency of Health Systems." IEEE Access 8 (2020): 23169–78. http://dx.doi.org/10.1109/access.2020.2970178.

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Waqar, Muhammad, Hassan Dawood, Hussain Dawood, Nadeem Majeed, Ameen Banjar, and Riad Alharbey. "An Efficient SMOTE-Based Deep Learning Model for Heart Attack Prediction." Scientific Programming 2021 (March 15, 2021): 1–12. http://dx.doi.org/10.1155/2021/6621622.

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Cardiac disease treatments are often being subjected to the acquisition and analysis of vast quantity of digital cardiac data. These data can be utilized for various beneficial purposes. These data’s utilization becomes more important when we are dealing with critical diseases like a heart attack where patient life is often at stake. Machine learning and deep learning are two famous techniques that are helping in making the raw data useful. Some of the biggest problems that arise from the usage of the aforementioned techniques are massive resource utilization, extensive data preprocessing, nee

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Rahimi, Nouf, Fathy Eassa, and Lamiaa Elrefaei. "An Ensemble Machine Learning Technique for Functional Requirement Classification." Symmetry 12, no. 10 (2020): 1601. http://dx.doi.org/10.3390/sym12101601.

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In Requirement Engineering, software requirements are classified into two main categories: Functional Requirement (FR) and Non-Functional Requirement (NFR). FR describes user and system goals. NFR includes all constraints on services and functions. Deeper classification of those two categories facilitates the software development process. There are many techniques for classifying FR; some of them are Machine Learning (ML) techniques, and others are traditional. To date, the classification accuracy has not been satisfactory. In this paper, we introduce a new ensemble ML technique for classifyin

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Morejón, Reinier, Marx Viana, and Carlos Lucena. "An Approach to Generate Software Agents for Health Data Mining." International Journal of Software Engineering and Knowledge Engineering 27, no. 09n10 (2017): 1579–89. http://dx.doi.org/10.1142/s0218194017400125.

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Data mining is a hot topic that attracts researchers of different areas, such as database, machine learning, and agent-oriented software engineering. As a consequence of the growth of data volume, there is an increasing need to obtain knowledge from these large datasets that are very difficult to handle and process with traditional methods. Software agents can play a significant role performing data mining processes in ways that are more efficient. For instance, they can work to perform selection, extraction, preprocessing, and integration of data as well as parallel, distributed, or multisour

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A G, Priya Varshini, Anitha Kumari K, and Vijayakumar Varadarajan. "Estimating Software Development Efforts Using a Random Forest-Based Stacked Ensemble Approach." Electronics 10, no. 10 (2021): 1195. http://dx.doi.org/10.3390/electronics10101195.

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Software Project Estimation is a challenging and important activity in developing software projects. Software Project Estimation includes Software Time Estimation, Software Resource Estimation, Software Cost Estimation, and Software Effort Estimation. Software Effort Estimation focuses on predicting the number of hours of work (effort in terms of person-hours or person-months) required to develop or maintain a software application. It is difficult to forecast effort during the initial stages of software development. Various machine learning and deep learning models have been developed to predi

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Berselli, Giovanni, Pietro Bilancia, and Luca Luzi. "Project-based learning of advanced CAD/CAE tools in engineering education." International Journal on Interactive Design and Manufacturing (IJIDeM) 14, no. 3 (2020): 1071–83. http://dx.doi.org/10.1007/s12008-020-00687-4.

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Abstract The use of integrated Computer Aided Design/Engineering (CAD/CAE) software capable of analyzing mechanical devices in a single parametric environment is becoming an industrial standard. Potential advantages over traditional enduring multi-software design routines can be outlined into time/cost reduction and easier modeling procedures. To meet industrial requirements, the engineering education is constantly revising the courses programs to include the training of modern advanced virtual prototyping technologies. Within this scenario, the present work describes the CAD/CAE project-based

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Imran, Zeba Ghaffar, Abdullah Alshahrani, Muhammad Fayaz, Ahmed Mohammed Alghamdi, and Jeonghwan Gwak. "A Topical Review on Machine Learning, Software Defined Networking, Internet of Things Applications: Research Limitations and Challenges." Electronics 10, no. 8 (2021): 880. http://dx.doi.org/10.3390/electronics10080880.

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In recent years, rapid development has been made to the Internet of Things communication technologies, infrastructure, and physical resources management. These developments and research trends address challenges such as heterogeneous communication, quality of service requirements, unpredictable network conditions, and a massive influx of data. One major contribution to the research world is in the form of software-defined networking applications, which aim to deploy rule-based management to control and add intelligence to the network using high-level policies to have integral control of the ne

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Khoroshko, Leonid Leonidovich, Peter A. Ukhov, and Pavel P. Keyno. "Development of Massive Open Online Courses Based on 3D Computer Graphics and Multimedia." International Journal of Engineering Pedagogy (iJEP) 9, no. 4 (2019): 4. http://dx.doi.org/10.3991/ijep.v9i4.10193.

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This work is devoted to the creation of a laboratory workshop (virtual) for open online courses based on programs of three-dimensional computer graphics and multimedia. The issues of using SolidWorks, Autodesk® 3DS MAX software in distance learning are discussed. The software was used to prepare training materials for the courses course "Machines and mechanisms theory", "Computer Graphics" and "Engineering and Computer Graphics". Using the software product SolidWorks, Autodesk® 3DS MAX has significantly increased the visibility of the course and develop tools for organizing the independent wor

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Toth, Laszlo, and Laszlo Vidacs. "Comparative Study of The Performance of Various Classifiers in Labeling Non-Functional Requirements." Information Technology And Control 48, no. 3 (2019): 432–45. http://dx.doi.org/10.5755/j01.itc.48.3.21973.

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Software systems are to be developed based on expectations of customers. These expectations are expressed using natural languages. To design a software meeting the needs of the customer and the stakeholders, the intentions, feedbacks and reviews are to be understood accurately and without ambiguity. These textual inputs often contain inaccuracies, contradictions and are seldom given in a well-structured form. The issues mentioned in the previous thought frequently result in the program not satisfying the expectation of the stakeholders. In particular, for non-functional requirements, clients r

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Shoureshi, R., D. Swedes, and R. Evans. "Learning Control for Autonomous Machines." Robotica 9, no. 2 (1991): 165–70. http://dx.doi.org/10.1017/s0263574700010201.

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SUMMARYToday's industrial machines and manipulators have no capability to learn by experience. Performance and productivity could be greatly enhanced if a machine could modify its operation based on previous actions. This paper presents a learning control scheme that provides the ability for machines to utilize their past experiences. The objective is to have machines mimic the human learning process as closely as possible. A data base is formulated to provide the machine with experience. An optical infrared distance sensor is developed to inform the machine about objects in its working space.

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Korzeniowski, Łukasz, and Krzysztof Goczyła. "Artificial intelligence for software development — the present and the challenges for the future." Bulletin of the Military University of Technology 68, no. 1 (2019): 15–32. http://dx.doi.org/10.5604/01.3001.0013.1464.

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Since the time when first CASE (Computer-Aided Software Engineering) methods and tools were developed, little has been done in the area of automated creation of code. CASE tools support a software engineer in creation the system structure, in defining interfaces and relationships between software modules and, after the code has been written, in performing testing tasks on different levels of detail. Writing code is still the task of a skilled human, which makes the whole software development a costly and error-prone process. It seems that recent advances in AI area, particularly in deep learni

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Rasool, Raihan Ur, Usman Ashraf, Khandakar Ahmed, Hua Wang, Wajid Rafique, and Zahid Anwar. "Cyberpulse: A Machine Learning Based Link Flooding Attack Mitigation System for Software Defined Networks." IEEE Access 7 (2019): 34885–99. http://dx.doi.org/10.1109/access.2019.2904236.

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Jentzsch, Sophie, and Nico Hochgeschwender. "A qualitative study of Machine Learning practices and engineering challenges in Earth Observation." it - Information Technology 63, no. 4 (2021): 235–47. http://dx.doi.org/10.1515/itit-2020-0045.

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Abstract Machine Learning (ML) is ubiquitously on the advance. Like many domains, Earth Observation (EO) also increasingly relies on ML applications, where ML methods are applied to process vast amounts of heterogeneous and continuous data streams to answer socially and environmentally relevant questions. However, developing such ML- based EO systems remains challenging: Development processes and employed workflows are often barely structured and poorly reported. The application of ML methods and techniques is considered to be opaque and the lack of transparency is contradictory to the respons

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Moreb, Mohammed, Tareq Abed Mohammed, Oguz Bayat, and Oguz Ata. "Corrections to “A Novel Software Engineering Approach Toward Using Machine Learning for Improving the Efficiency of Health Systems“." IEEE Access 8 (2020): 136459. http://dx.doi.org/10.1109/access.2020.2986259.

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Marques, Carla Verônica Machado, Carlo Emmanoel Tolla de Oliveira, and Cibele Ribeiro da Cunha Oliveira. "The Cognitive Machine as Mental Language Automata." International Journal of Cognitive Informatics and Natural Intelligence 12, no. 1 (2018): 75–91. http://dx.doi.org/10.4018/ijcini.2018010106.

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This article describes how learning is a native ability of the brain. However, very little is known of the process as it happens. The engineering model presented in this work provides a base to explore the innards of cognition. The computational implementation of the model is usable to assess cognitive profiles by means of machine learning and harmonic filtering. The model relies on an evolutionary dimensional space consisting of phylogenetic, ontogenetic and microgenetic timelines. The microgenetic space reveals the state machine nature of cognition, standing as an internal translator to a br

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