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Journal articles on the topic 'Computer software Classification'

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Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

Pi, Chenchen. "Development and Classification of Computer Software Testing Technology." Journal of Physics: Conference Series 1650 (October 2020): 032111. http://dx.doi.org/10.1088/1742-6596/1650/3/032111.

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2

Lavrischeva, E. M. "Classification of software engineering disciplines." Cybernetics and Systems Analysis 44, no. 6 (November 2008): 791–96. http://dx.doi.org/10.1007/s10559-008-9053-5.

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3

Berdonosov, Victor D., and Elena V. Redkolis. "ON CLASSIFICATION OF COMPUTER-AIDED SOFTWARE ENGINEERING SYSTEMS (CASE)." Scholarly Notes of Komsomolsk-na-Amure State Technical University 1, no. 4 (December 30, 2010): 12–25. http://dx.doi.org/10.17084/2010.iv-1(4).2.

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4

Prieto-Díaz, Rubén. "Implementing faceted classification for software reuse." Communications of the ACM 34, no. 5 (May 1991): 88–97. http://dx.doi.org/10.1145/103167.103176.

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5

Kozhevnikova, G. P., and A. A. Stognii. "Facet classification of software quality measures." Cybernetics 25, no. 4 (1990): 546–63. http://dx.doi.org/10.1007/bf01070378.

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6

Yusof, Yuhanis, and Qusai Hussein Ra. "Automation of Software Artifacts Classification." International Journal of Soft Computing 5, no. 3 (March 1, 2010): 109–15. http://dx.doi.org/10.3923/ijscomp.2010.109.115.

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Stutte, Gary W., and Elizabeth C. Stryjewski. "Computer Classification of Roots from Digitized Video Images." HortScience 30, no. 4 (July 1995): 906E—906. http://dx.doi.org/10.21273/hortsci.30.4.906e.

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Manual methods for estimating root length are tedious and time-consuming. Image capture and analysis systems can be used to obtain precise measurements of root length and growth angle. Root activity can also be determined through analysis of the mean pixel intensity of a digitized image. Both commercial (the IBM-compatible ICAS System) and public domain (the Macintosh-based NIH Image) image capture and analysis software have been used to analyze intact root systems. Examples of ICAS classification of hydroponic and soil-grown root systems will be presented. Advantages of the NIH Image software for analysis of micro-gravity experiments aboard the Space Shuttle will be discussed.

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Li, Zhong Kai, Tao Bai, and Le Miao Qiu. "Computer-Aided Material Classification Coding Model and Software System for Mechanical Product." Advanced Materials Research 443-444 (January 2012): 923–28. http://dx.doi.org/10.4028/www.scientific.net/amr.443-444.923.

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For the large numbers of material classification coding rules and components in manufacturing enterprises, a computer-aided material classification coding model is proposed, in order to improve the inputting efficiency of part codes. Based on the analysis of the material classification and coding rule for mechanical product, four types of code segments which suited for computer assisted are put forward. The application and database storage models of the coding rule are built up. Moreover, the paper describes the computational workflow of computer-aided coding and its kernel algorithm to acknowledging the related classified segment. The effectiveness and modularity of the prototype system is illustrated by the implementation in an electronics manufacturing corporation.

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Figueiredo, Marco A., Clay S. Gloster, Mark Stephens, Corey A. Graves, and Mouna Nakkar. "Implementation of Multispectral Image Classification on a Remote Adaptive Computer." VLSI Design 10, no. 3 (January 1, 2000): 307–19. http://dx.doi.org/10.1155/2000/31983.

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As the demand for higher performance computers for the processing of remote sensing science algorithms increases, the need to investigate new computing paradigms is justified. Field Programmable Gate Arrays enable the implementation of algorithms at the hardware gate level, leading to orders of magnitude performance increase over microprocessor based systems. The automatic classification of spaceborne multispectral images is an example of a computation intensive application that can benefit from implementation on an FPGA-based custom computing machine (adaptive or reconfigurable computer). A probabilistic neural network is used here to classify pixels of a multispectral LANDSAT-2 image. The implementation described utilizes Java client/server application programs to access the adaptive computer from a remote site. Results verify that a remote hardware version of the algorithm (implemented on an adaptive computer) is significantly faster than a local software version of the same algorithm (implemented on a typical general-purpose computer).

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Dwivedi, Ashish Kumar, Anand Tirkey, and Santanu Kumar Rath. "Software design pattern mining using classification-based techniques." Frontiers of Computer Science 12, no. 5 (May 19, 2018): 908–22. http://dx.doi.org/10.1007/s11704-017-6424-y.

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Pavlenko, Yevhen, Vladimir Butenko, Vadim Gubin, and Serhii Lubenets. "RESEARCH OF DATA TYPE CLASSIFICATION METHODS WHEN DEVELOPING COMPUTER ENGINEERING SOFTWARE." Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies, no. 1 (5) (July 12, 2021): 80–85. http://dx.doi.org/10.20998/2079-0023.2021.01.13.

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The paper deals with the problems of increasing the efficiency of software development, in particular, the issue of reducing the time for developing programs and using automated synthesis of programs, which will avoid the revision of the original product. The software should be tested along with other system components in all combinations that may occur. Testing is time-consuming because hidden bugs are revealed through unexpected interactions between software components. With structural analysis, data flow diagrams are not the end result, they are a developer tool. First, diagrams are built, and then mechanisms are developed to ensure the required system behavior. A graphical approach to solving the problem of automation of software development is being developed, based on the involvement of visual forms of program presentation. For any program object, you can select a finite number of states in which it is at each moment of time. The program progress is associated with the transition of an object from one state to another. The graph replaces the textual form of the description of the program algorithm, while the visual representation of the algorithm is realized. The specification of data structures, as well as the setting of intermodular interfaces according to data, is separated from the description of the structure of the algorithm and controls. Basic modules and data types are used. Basic modules are local calculable functions, on the basis of which all other technology objects are generated. Data types describe the syntactic and semantic aspects of constructing data used in base functions. Algorithms for finding routes on directed graphs are considered. When defining routes from the root vertex to the final ones, the properties of the algebra of three-valued logic were used. Based on the considered approach, as well as taking into account its shortcomings, a method for classifying data types was proposed, based on the implementation of a partial enumeration of the routes of the graph of program links and a method for designing software based on it, taking into account minimizing the time and cost of the project. Keywords: software, computer engineering, information systems, components, partial enumeration of graph routes, development costs.

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Tokarski, Marcin, Grzegorz Nowak, and Cezary Draus. "Computer-Assisted Classification of Products According to Etim Standard." Image Processing & Communications 17, no. 4 (December 1, 2012): 373–78. http://dx.doi.org/10.2478/v10248-012-0067-2.

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Abstract The article presents the classification software according to the international product description standard ETIM. The software has been designed for Polish manufacturers or wholesalers who want to implement this standard in their companies. Classification is done in Polish language, on the basis of information contained in the company’s product files. Application features several mechanisms to facilitate the creation of product description, like suggestion of the appropriate class or automatic recognition of values of the required parameters. With this application, the tedious and time consuming job of classification becomes easier and can be done much quicker.

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Kaur, Barjinder, Dinesh Singh, and Partha Pratim Roy. "Age and gender classification using brain–computer interface." Neural Computing and Applications 31, no. 10 (March 7, 2018): 5887–900. http://dx.doi.org/10.1007/s00521-018-3397-1.

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Negishi, Hirokazu. "Tentative classification of global software." Behaviour & Information Technology 4, no. 2 (April 1985): 163–70. http://dx.doi.org/10.1080/01449298508901796.

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Changqing Zhao, Changqing Zhao, Ling Xia Liao Changqing Zhao, Han-Chieh Chao Ling Xia Liao, Roy Xiaorong Lai Han-Chieh Chao, and Miao Zhang Roy Xiaorong Lai. "Flow Entry Timeouts Optimization over Software Defined Networks Supporting Elephant Flow Classification." 網際網路技術學刊 23, no. 5 (September 2022): 1029–40. http://dx.doi.org/10.53106/160792642022092305011.

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<p>Elephant flow (elephant) classification is significant for network performance management and resource optimization. Classifying elephants over Software Defined Networks (SDNs) often relies on statistics counted by switches and transferred to controllers, leading to a huge control channel bandwidth occupation that degrades network latency and user experience. Classifying elephants using flow packets forwarded to controllers completely avoids moving statistics from switches to controllers, but couples flow entry timeouts to elephant classification accuracy, time efficiency, flow table size, control channel bandwidth usage, and network latency. This paper aims to find the best flow entry timeouts to optimize the objectives by modeling the objectives and formulating a multi-objective optimization problem. The number of objectives is further reduced to 3 and the problem is solved by Machine Learning (ML) approaches. Extensive evaluations are made over real packet traces. To the best of our knowledge, this work is the first effort that explores all the objectives related to flow entry timeouts when using flow packets forwarded to controllers to classify elephants over SDNs.</p> <p> </p>

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Yeh, Jinn-Yi, Si-Wa Chan, and Tai-Hsi Wu. "Mining Breast Cancer Classification Rules from Mammograms." Journal of Intelligent Systems 25, no. 1 (January 1, 2016): 19–36. http://dx.doi.org/10.1515/jisys-2014-0122.

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AbstractBreast cancer is a leading cause of cancer death in women. Early diagnosis and treatment are crucial to reduce the mortality rate and increase patients’ lifespan. Mammography is effective in early detection. This study proposes a computer-aided diagnosis system based on the mini-Mammographic Image Analysis Society database for analyzing mammograms. After selecting the regions of interest, we computed three typical features: the shape, spatial, and spectral domain features. We then applied the structural equation model to obtain relations between the features and the breast tissue type, lesion class, and tumor severity after feature extraction by information gain. Finally, we used the decision tree and classification and regression tree to construct computer-aided diagnosis rules; we generated 10 rules for predicting the classification of abnormal lesions and 11 rules for classifying the tumor severity. These rules can help clinicians detect and identify breast cancer efficiency from mammograms and improve medical care quality.

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Boldor, D., T. H. Sanders, K. R. Swartzel, and J. Simunovic. "Computer-Assisted Color Classification of Peanut Pods1." Peanut Science 29, no. 1 (January 1, 2002): 41–46. http://dx.doi.org/10.3146/pnut.29.1.0008.

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Abstract Mesocarp color classification in the Hull Scrape Maturity Method is the most important step in determining peanut maturity and optimum harvest date. This research involved the development of an image acquisition system and a software procedure for color classification. Images of peanut pods that had been sorted manually into color classes and subclasses were used in computer training. After training with sorted color classes, the computer-assisted procedure correctly identified and classified the peanut pods, with a 99% precision for the same sample in different alignment, and with a 95% precision for different size samples taken from the same population. Pod size measurement with a resolution of 0.1 mm was also performed using image processing techniques.

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TOPTSIS, ANESTIS A. "HEURISTIC ORGANIZATION AND DOMAIN ANALYSIS OF SOFTWARE REPOSITORIES." International Journal of Software Engineering and Knowledge Engineering 05, no. 02 (June 1995): 193–210. http://dx.doi.org/10.1142/s0218194095000113.

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In this paper we address the problem of software classification and, based on an algorithm for software classification through software repository clustering, we propose a method which performs automatic domain analysis on a given software collection. The described method relies on observations made after a tedious manual domain analysis is performed. Also, it requires a heuristic that can compute distances between two software components without any interference from the reuser.

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LUNG, CHUNG-HORNG, JEFFERY K. COCHRAN, GERALD T. MACKULAK, and JOSEPH E. URBAN. "COMPUTER SIMULATION SOFTWARE REUSE BY GENERIC/SPECIFIC DOMAIN MODELING APPROACH." International Journal of Software Engineering and Knowledge Engineering 04, no. 01 (March 1994): 81–102. http://dx.doi.org/10.1142/s0218194094000052.

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Software reuse has drawn much attention in computing research. Domain analysis is considered a prerequisite to effective reuse of existing software. Several approaches and methodologies have been proposed for domain analysis or domain modeling, but not many case studies have been reported in the literature. The first objective of this paper is to present the concept and practical experiences of a domain analysis approach in discrete-event simulation in manufacturing — generic /specific modeling. A second objective of this paper is to present a meta-model based on the generic/ specific approach from the software engineering perspective. The steps and knowledge required to build the model are described. Domain analysis lessons learned from the generic/specific approach in discrete-event simulation are discussed. Classification of this domain modeling approach was conducted through the Wartik and Prieto-Diaz criteria. The classification will facilitate the comparison with other domain analysis approaches. Similar modeling concepts or techniques may be beneficial to other researchers in their own application domains.

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20

Pegoraro, Leila Garcia de Oliveira, Raquel Gvozd, Maria do Carmo Fernandez Lourenço Haddad, Marli Terezinha Oliveira Vannuchi, Larissa Gutierrez de Carvalho Silva, and Mariana Angela Rossaneis. "Validation of instrument to assess software of patients’ risk classification." Revista Brasileira de Enfermagem 71, no. 3 (May 2018): 975–82. http://dx.doi.org/10.1590/0034-7167-2017-0053.

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ABSTRACT Objective: Validate the content of an instrument that assesses the quality of a software applied to the risk classification of patients. Method: Methodological study, conducted in three stages: adaptation of the instrument, content validation through Delphi technique and pre-test. The results were analyzed through Content Validity Index, Overall Content Validity Index and Inter-rater Reliability. Results: The final version of the instrument comprises 8 characteristics and 28 sub-characteristics, being 37 general questions to computer experts and nurse and 7 specific questions to computer experts, including 1 question and excluding 3 questions of the original instrument. We obtained Overall Content Validity of 92% and Inter-rater Reliability of 100% in the second Delphi round. Final considerations: The instrument has content validity, allowing to assess the technical quality and functional performance of the software applied to the risk classification of patients.

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21

Redes, Sharon, and Margaret Lunney. "Validation by School Nurses of the Nursing Intervention Classification for Computer Software." Computers in Nursing 15, no. 6 (November 1997): 333–38. http://dx.doi.org/10.1097/00024665-199711000-00025.

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22

Kogut, U. P. "CLASSIFICATION AND SELECTION CRITERIA SOFTWARE FOR FUNDAMENTALIZATION BACHELOR INFORMATYCHNYH COMPUTER SCIENCE DISCIPLINES." Information Technologies in Education, no. 11 (April 12, 2012): 88–97. http://dx.doi.org/10.14308/ite000306.

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23

Tetiana, Golub, Zeleneva Irina, Hrushko Svitlana, and Kotenko Artem. "INTERFACE MODULE DEVELOPMENT FOR SOFTWARE AND HARDWARE COMPLEX OF AUTOMATIC TEXTS CLASSIFICATION." Problems of Modeling and Design Automatization, no. 2 (2020): 38–48. http://dx.doi.org/10.31474/2074-7888-2020-2-38-48.

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The amount of information presented in text form is constantly increasing. A feature of a large amount of such information in most cases is the lack of its structuredness, which complicates the analysis process. At the stage of document processing in the information and analytical system, an automatic classifier is used – as a program, or as a software and hardware complex that accelerates automatic text processing. In this paper, a software and hardware complex is considered, in which the software part functions on the user's computer, and the hardware part is implemented on the FPGA. Thus, the problem of organizing the connection between its two parts is urgent. While FPGAs have high internal parallelism and very high internal bandwidth, the low bandwidth of the interface between the accelerator and the rest of the system, as well as restrictions on the conditions of its use, is a bottleneck in the field of data transfer. This problem is aggravated in the conditions of the need to create a network system for constructing a classifier, where several user computers connected to the network need access to the hardware-implemented part of the complex (accelerator). The aim of this work is to develop an internal interface module with network communication, in accordance with the technical and functional requirements of the software and hardware complex for automatic text classification. Considering the need to provide network access to the accelerator, as a result of the analysis, the Fast Ethernet interface was chosen as the base one. This interface is the most common LAN standard used by most businesses. For the development of the printed circuit board of the interface module, a comprehensive computer-aided design system Altium Designer was used. After the development of the electrical schematic diagram of the project, a simulation of the controller part was created in the Proteus digital-to-analog simulation program. For the convenience of testing the developed interface module, the Arduino UNO module and the SPI serial interface were used. A feature of the developed module is the support of network-level protocols, it makes it possible to organize access from any personal computer of the local network to an expansion board built on the basis of FPGA. The developed printed circuit board of the interface module was analyzed for possible defects and a list of recommendations for the operating conditions of the device was developed. The interface board is designed taking into account the balanced ratio of price-performance criteria, and is intended for use in any institution where there is a need for automatic classification of text documents.

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Yao, Haining, Letha H. Etzkorn, and Shamsnaz Virani. "Automated classification and retrieval of reusable software components." Journal of the American Society for Information Science and Technology 59, no. 4 (2008): 613–27. http://dx.doi.org/10.1002/asi.20775.

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Jayatilleke, Shalinka, Richard Lai, and Karl Reed. "Managing software requirements changes through change specification and classification." Computer Science and Information Systems 15, no. 2 (2018): 321–46. http://dx.doi.org/10.2298/csis161130041j.

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Software requirements changes are often inevitable due to the changing nature of running a business and operating the Information Technology (IT) system which supports the business. As such, managing software requirements changes is an important part of software development. Past research has shown that failing to manage software requirements changes effectively is a main contributor to project failure. One of the difficulties in managing requirements changes is the lack of effective methods for communicating changes from the business to the IT professionals. In this paper, we present an approach to managing requirements change by improving the change communication and elicitation through a method of change specification and a method of classification. Change specification provides a way such that communication ambiguities can be avoided between business and IT staff. The change classification mechanism identifies the type of the changes to be made and preliminary identification of the actions to be taken. We illustrate the usefulness of the methods by applying them to a case study of course management system.

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STALKER, RUTH, and IAN F. C. SMITH. "Structural monitoring using engineer–computer interaction." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 16, no. 3 (June 2002): 203–18. http://dx.doi.org/10.1017/s0890060402163062.

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Engineer–computer interaction (ECI) is a new subdomain of human–computer interaction that is specifically tailored to engineers' needs. ECI uses an information classification schema, provides a modular approach to task decomposition, and integrates standard engineering characteristics and working procedures into software. A software tool kit that interprets monitoring data taken from bridges was developed according to ECI guidelines. This tool kit was given to engineers for testing and evaluation. An empirical evaluation using questionnaires was performed. The results show that this ECI software corresponds to engineers' needs and the ECI approach has potential applications to other engineering tasks.

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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 (October 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 component cluster groups with the help of Rival Penalized Competitive Learning algorithm. The center of each cluster will be used to construct the coarse grain classification indexing structure. Subsequent retrieval requirements of software components are compared with all the indexed cluster centers. Any software components belonging to the cluster partition whose center is closest to the required software component will be retrieved and participated in selecting the most suitable software component at the fine grain level. This approach not only is suitable for multi-dimensional data, but also automatically decides the correct model classification.

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KHOSHGOFTAAR, TAGHI M., EDWARD B. ALLEN, ARCHANA NAIK, WENDELL D. JONES, and JOHN P. HUDEPOHL. "USING CLASSIFICATION TREES FOR SOFTWARE QUALITY MODELS: LESSONS LEARNED." International Journal of Software Engineering and Knowledge Engineering 09, no. 02 (April 1999): 217–31. http://dx.doi.org/10.1142/s0218194099000140.

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High software reliability is an important attribute of high-assurance systems. Software quality models yield timely predictions of quality indicators on a module-by-module basis, enabling one to focus on finding faults early in development. This paper introduces the Classification And Regression Trees (CART) a algorithm to practitioners in high-assurance systems engineering. This paper presents practical lessons learned on building classification trees for software quality modeling, including an innovative way to control the balance between misclassification rates. A case study of a very large telecommunications system used CART to build software quality models. The models predicted whether or not modules would have faults discovered by customers, based on various sets of software product and process metrics as independent variables. We found that a model based on two software product metrics had comparable accuracy to a model based on forty product and process metrics.

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Bhadoriya, Varun, Niharika Jain, Neeraj Kumar Verma, and Ravi Kumar. "Resemblance of Image Classification Methods in Computer Vision." ECS Transactions 107, no. 1 (April 24, 2022): 7771–78. http://dx.doi.org/10.1149/10701.7771ecst.

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Exploding data with the transformation of IT Sector have led to the increase in demand of the classification algorithms. In the world with Artificial Intelligence (AI) Image data and classification of Image is very crucial. The Computer vision is as a saviour to the applications of meteorology, robotics, screen reader etc. Whenever the software need to analyze things and objects from surroundings using INTERNET OF THINGS in these scenarios where identification of living and non living objects is done by machines. “Image Classification” is a fundamental aspect. The main aim is to classify the objects in particulars of their labels. In this paper a brief description of when and How to use different Image classification algorithms in particular scenario’s.

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Kim, Yesol, Seong-je Cho, Sangchul Han, and Ilsun You. "A software classification scheme using binary-level characteristics for efficient software filtering." Soft Computing 22, no. 2 (September 23, 2016): 595–606. http://dx.doi.org/10.1007/s00500-016-2357-x.

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Yu, Zhong An, Wei Qin Tan, and Zheng Hua Xie. "The Design of Computer Control System for Grinding-Classification Process." Applied Mechanics and Materials 130-134 (October 2011): 1894–97. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.1894.

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Grinding-classification procedure is the key link of dressing production. This paper briefly introduced the technology of grinding and classification, and designed the computer control system of grinding and classification process, the realization of the grinder concentration control is by variable ratio control and the control of pump pool level is realized by average control, which realized automatic control for the concentration and flow of the grinding and classification, and presented the design of hardware and software for the control system.

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Tali, Renuka Veerappa, Surekha Borra, and Mufti Mahmud. "Detection and Classification of Leukocytes in Blood Smear Images." International Journal of Ambient Computing and Intelligence 12, no. 2 (April 2021): 111–39. http://dx.doi.org/10.4018/ijaci.2021040107.

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Manual analysis of microscopic blood smears by highly expert pathologists is labor-intensive, time-consuming, and is subject to inter-observer variations. Recent innovations in image processing and computer vision techniques have improvised digital pathology in terms of objectivity and reproducibility. Traditional computer vision-based methods of recognition of white blood cell (WBC) from a pathological blood smear image includes the process of detection, segmentation, and classification. This paper presents a review of state-of-the-art detection, segmentation, and classification techniques for white blood cell analysis. The goal of this work is to present an introduction to the field, provide enough information about the analysis methods developed so far, and to be an appropriate reference for the researchers looking forward in this field. The methods under review are classified into intensity and feature based. The crucial steps involved in these techniques, mathematical foresights, performance evaluation techniques, issues, and future directions are discussed.

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Yang, Na, and Yongtao Zhang. "A Gaussian Process Classification and Target Recognition Algorithm for SAR Images." Scientific Programming 2022 (January 20, 2022): 1–10. http://dx.doi.org/10.1155/2022/9212856.

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Synthetic aperture Radar (SAR) uses the relative movement of the Radar and the target to pick up echoes of the detected area and image it. In contrast to optical imaging, SAR imaging systems are not affected by weather and time and can detect targets in harsh conditions. Therefore, the SAR image has important application value in military and civilian purposes. This paper introduces the classification of Gaussian process. Gaussian process classification is a probabilistic classification algorithm based on Bass frame. This is a complete probability expression. Based on Gaussian process and SAR data, Gaussian process classification algorithm for SAR images is studied in this paper. In this paper, we introduce the basic principle of Gaussian process, briefly analyze the basic theory of classification and the characteristics of SAR images, provide the evaluation index system of image classification, and give the SAR classification model of Gaussian process. Taking Laplace approximation as an example, several classification algorithms are introduced directly. Based on the two classifications, we propose an indirect multipurpose classification method and a multifunction classification method for two-pair two-Gaussian processes. The SAR image algorithm based on the two categories is relatively simple and achieves certain results.

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Mayhew, P. J., C. J. Worsley, and P. A. Dearnley. "Control of software prototyping process: change classification approach." Information and Software Technology 31, no. 2 (March 1989): 59–66. http://dx.doi.org/10.1016/0950-5849(89)90084-0.

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Cox, M. G. "A classification of mathematical software for metrology." ISA Transactions 33, no. 4 (December 1994): 383–89. http://dx.doi.org/10.1016/0019-0578(94)90021-3.

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Lopez, Jose J., Maximo Cobos, and Emanuel Aguilera. "Computer-based detection and classification of flaws in citrus fruits." Neural Computing and Applications 20, no. 7 (June 20, 2010): 975–81. http://dx.doi.org/10.1007/s00521-010-0396-2.

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Ou, Yang, Jianqiao Luo, Bailin Li, and Biao He. "A classification model of railway fasteners based on computer vision." Neural Computing and Applications 31, no. 12 (July 6, 2019): 9307–19. http://dx.doi.org/10.1007/s00521-019-04337-z.

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Mansoor, Asif, Muhammad Waleed Usman, Noreen Jamil, and M. Asif Naeem. "Deep Learning Algorithm for Brain-Computer Interface." Scientific Programming 2020 (August 25, 2020): 1–12. http://dx.doi.org/10.1155/2020/5762149.

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Electroencephalography-(EEG-) based control is a noninvasive technique which employs brain signals to control electrical devices/circuits. Currently, the brain-computer interface (BCI) systems provide two types of signals, raw signals and logic state signals. The latter signals are used to turn on/off the devices. In this paper, the capabilities of BCI systems are explored, and a survey is conducted how to extend and enhance the reliability and accuracy of the BCI systems. A structured overview was provided which consists of the data acquisition, feature extraction, and classification algorithm methods used by different researchers in the past few years. Some classification algorithms for EEG-based BCI systems are adaptive classifiers, tensor classifiers, transfer learning approach, and deep learning, as well as some miscellaneous techniques. Based on our assessment, we generally concluded that, through adaptive classifiers, accurate results are acquired as compared to the static classification techniques. Deep learning techniques were developed to achieve the desired objectives and their real-time implementation as compared to other algorithms.

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Dushchenko, Olha. "APPROACHES TO CLASSIFICATION AND CREATION OF APPLICATIONS." Academic Notes Series Pedagogical Science 1, no. 191 (2020): 70–73. http://dx.doi.org/10.36550/2415-7988-2020-1-191-70-73.

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Gadgets have become indispensable attributes of our lives, but also applications. Using applications simplifies and speeds up users. It is the future computer science teachers who must be able to create their own applications for professional duties, because the possession of tools for creating software products is one of the software learning outcomes of future computer science teachers. The concept of "application" is analyzed in the article. An "application" is defined as a software product that can run on a PC and / or mobile device due to installation or opening by a browser without installation. Types of applications are presented: desktop applications, mobile, web applications. The existing classifications of applications are given, with their characteristics, namely: web application, rich Internet application, Internet application, mobile application, hybrid mobile application, composite web application (mashup). Describes the classification of Internet applications and composite web applications. Examples of web applications are given. Statistics on the use of mobile applications of social networks and messengers, according to which the most popular application is the YouTube application. The author's classification of applications is offered: desktop applications (applications that are installed on a PC), mobile applications (applications that are installed on mobile devices), Internet applications (web applications, applications that do not require installation, but are opened by a browser and a certain mostly use the Internet). The platforms for creating applications are given. The stages of application creation are characterized: selection of the target audience, definition of purpose and functionality, type of application interface, selection of tools for application creation, application creation, application testing, improvement of the developed application. Examples of applications that can be created by future computer science teachers for further use in professional activities are offered: own website, testing system, reference book, application with presentations to educational material and tasks, guest book, forum, etc. The creation and use of applications by future computer science teachers ensures the formation of pupils' interest in computer science lessons, because pupils often use applications for personal needs, rather than educational.

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Basanth, H. B. "Comparative study on Classification of Digital Images." Oriental journal of computer science and technology 10, no. 2 (April 27, 2017): 413–16. http://dx.doi.org/10.13005/ojcst/10.02.22.

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Digital images are widespread today. The use of digital images is classified into natural images and computer graphic images. Discrimination of natural images and computer graphic (CG) images are used in the applications which include flower classification, indexing of images, video classification and many more. With the rapid growth in the image rendering technology, the user can produce very high realistic computer graphic images using sophisticated graphics software packages. Due to high realism in CG images, it is very difficult for the user to distinguish it from natural images by a naked eye. This paper presents comparative study of the existing schemes used to classify digital images.

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ŞEN, CEYDA GÜNGÖR, HAYRİ BARAÇLI, and SELÇUK ŞEN. "A LITERATURE REVIEW AND CLASSIFICATION OF ENTERPRISE SOFTWARE SELECTION APPROACHES." International Journal of Information Technology & Decision Making 08, no. 02 (June 2009): 217–38. http://dx.doi.org/10.1142/s0219622009003351.

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The evaluation and selection of enterprise software has become increasingly difficult for decision makers due to a large number of software products available for many applications. Therefore, systematic and repeatable approaches are needed in order to select the appropriate product that best meets the customer requirements. In this paper, we present a literature review and classification of enterprise software selection approaches from the period 1982–2007. In addition to classifying the selected approaches by functional perspective, the decision-making methods used by these approaches in the generic phases of software selection process are also presented. Results are summarized with comprehensive classification tables. This study is intended as a source for academics and practitioners alike interested in software selection problem, especially those who want to see alternative decision-making techniques that can be used to support the every single step of software selection process.

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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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Ma, Baojun, Huaping Zhang, Guoqing Chen, Yanping Zhao, and Bart Baesens. "Investigating Associative Classification for Software Fault Prediction: An Experimental Perspective." International Journal of Software Engineering and Knowledge Engineering 24, no. 01 (February 2014): 61–90. http://dx.doi.org/10.1142/s021819401450003x.

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It is a recurrent finding that software development is often troubled by considerable delays as well as budget overruns and several solutions have been proposed in answer to this observation, software fault prediction being a prime example. Drawing upon machine learning techniques, software fault prediction tries to identify upfront software modules that are most likely to contain faults, thereby streamlining testing efforts and improving overall software quality. When deploying fault prediction models in a production environment, both prediction performance and model comprehensibility are typically taken into consideration, although the latter is commonly overlooked in the academic literature. Many classification methods have been suggested to conduct fault prediction; yet associative classification methods remain uninvestigated in this context. This paper proposes an associative classification (AC)-based fault prediction method, building upon the CBA2 algorithm. In an empirical comparison on 12 real-world datasets, the AC-based classifier is shown to achieve a predictive performance competitive to those of models induced by five other tree/rule-based classification techniques. In addition, our findings also highlight the comprehensibility of the AC-based models, while achieving similar prediction performance. Furthermore, the possibilities of cross project prediction are investigated, strengthening earlier findings on the feasibility of such approach when insufficient data on the target project is available.

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Daly, James, Valerio Bruschi, Leonardo Linguaglossa, Salvatore Pontarelli, Dario Rossi, Jerome Tollet, Eric Torng, and Andrew Yourtchenko. "TupleMerge: Fast Software Packet Processing for Online Packet Classification." IEEE/ACM Transactions on Networking 27, no. 4 (August 2019): 1417–31. http://dx.doi.org/10.1109/tnet.2019.2920718.

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Maiti, Ananjan, Biswajoy Chatterjee, and K. C. Santosh. "Skin Cancer Classification Through Quantized Color Features and Generative Adversarial Network." International Journal of Ambient Computing and Intelligence 12, no. 3 (July 2021): 75–97. http://dx.doi.org/10.4018/ijaci.2021070104.

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Early interpretation of skin cancer through computer-aided diagnosis (CAD) tools reduced the intricacy of the treatments as it can attain a 95% recovery rate. To frame up with computer-aided diagnosis system, scientists adopted various artificial intelligence (AI) designed to receive the best classifiers among these diverse features. This investigation covers traditional color-based texture, shape, and statistical features of melanoma skin lesion and contrasted with suggested methods and approaches. The quantized color feature set of 4992 traits were pre-processed before training the model. The experimental images have combined images of naevus (1500), melanoma (1000), and basal cell carcinoma (500). The proposed methods handled issues like class imbalanced with generative adversarial networks (GAN). The recommended color quantization method with synthetic data generation increased the accuracy of the popular machine learning models as it gives an accuracy of 97.08% in random forest. The proposed model preserves a decent accuracy with KNN, adaboost, and gradient boosting.

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LEE, SUNG-KOO, and JOSEPH E. URBAN. "SOORLS: A SOFTWARE REUSE APPROACH ON THE WEB." International Journal of Software Engineering and Knowledge Engineering 09, no. 03 (June 1999): 279–96. http://dx.doi.org/10.1142/s0218194099000188.

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The Summary-based Object-Oriented Reuse Library System (SOORLS) was developed to support both librarians who manage databases of object-oriented reusable components, and software developers who intend to use these components to develop software on the Web. This paper presents the library management functions implemented by SOORLS, with focus on a software reuse approach based on the summary contents of the library. The cluster-based classification scheme proposed in this paper alleviates the labor-intensity domain analysis problem often attributed to traditional facet-based classification schemes. We then concentrate on the facilities offered by SOORLS' tools, as well as its Web-based architecture, which allows distributed access to reusable components on servers from a variety of platforms.

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Calle-Alonso, F., C. J. Pérez, J. P. Arias-Nicolás, and J. Martín. "Computer-aided diagnosis system: A Bayesian hybrid classification method." Computer Methods and Programs in Biomedicine 112, no. 1 (October 2013): 104–13. http://dx.doi.org/10.1016/j.cmpb.2013.05.029.

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McChesney, IR. "Toward a classification scheme for software process modelling approaches." Information and Software Technology 37, no. 7 (1995): 363–74. http://dx.doi.org/10.1016/0950-5849(95)91492-i.

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Gómez, Omar S., Natalia Juristo, and Sira Vegas. "Understanding replication of experiments in software engineering: A classification." Information and Software Technology 56, no. 8 (August 2014): 1033–48. http://dx.doi.org/10.1016/j.infsof.2014.04.004.

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Alimohammadi, Hamed, and Mahmood Ahmadi. "Clustering-based many-field packet classification in Software-Defined Networking." Journal of Network and Computer Applications 147 (December 2019): 102428. http://dx.doi.org/10.1016/j.jnca.2019.102428.

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