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Journal articles on the topic 'AI-based software'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Druffel, Larry, and Reed Little. "Software engineering for AI based software products." Data & Knowledge Engineering 5, no. 2 (1990): 93–103. http://dx.doi.org/10.1016/0169-023x(90)90006-y.

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Martínez-Fernández, Silverio, Justus Bogner, Xavier Franch, et al. "Software Engineering for AI-Based Systems: A Survey." ACM Transactions on Software Engineering and Methodology 31, no. 2 (2022): 1–59. http://dx.doi.org/10.1145/3487043.

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AI-based systems are software systems with functionalities enabled by at least one AI component (e.g., for image-, speech-recognition, and autonomous driving). AI-based systems are becoming pervasive in society due to advances in AI. However, there is limited synthesized knowledge on Software Engineering (SE) approaches for building, operating, and maintaining AI-based systems. To collect and analyze state-of-the-art knowledge about SE for AI-based systems, we conducted a systematic mapping study. We considered 248 studies published between January 2010 and March 2020. SE for AI-based systems

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Liubchenko, V. V. "Some aspects of software engineering for AI-based systems." PROBLEMS IN PROGRAMMING, no. 3-4 (December 2022): 99–106. http://dx.doi.org/10.15407/pp2022.03-04.099.

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AI-based software systems are rapidly spreading in various business areas. In this context, the unavoidable convergence of the Software Engineering and Artificial Intelligence and Machine Learning (AI/ML) disciplines is considered an obvious and one of the following significant challenges within the engineering process. The life cycle, models, and technologies of AI/ML elements are pretty specific, and this should be considered in software engineering to ensure their performance and compliance with business needs. AI/ML applications have some distinct characteristics compared to traditional so

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Patil, Priya. "AI Based False Positive Analysis of Software Vulnerabilities." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 975–81. http://dx.doi.org/10.22214/ijraset.2022.42306.

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Abstract: Programming measurements and shortcoming information having a place with a past programming variant are utilized to assemble the product issue expectation model for the following arrival of the product. Notwithstanding, there are sure situations when past issue information are absent. As such foreseeing the shortcoming inclination of program modules when the issue marks for modules are inaccessible is a difficult assignment oftentimes arised in the product business There is need to foster a few strategies to assemble the product issue forecast model in light of unaided realizing whic

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Saxena, Aarush. "AI-Based Chatbot." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (2022): 941–42. http://dx.doi.org/10.22214/ijraset.2022.47785.

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Abstract: A chatbot is software used to develop interaction between a user/human and a computer/system in natural language, similar to human chats. Chatbots converse with the customer in a discussion following input from a human and a response to the customer. It makes the user believe that he is chatting with a human while chatting with the computer. The chatbot application helps the student to get information about the college admission process and get quick answers from anywhere with an internet connection. This chatbot system reduces the workload of the admissions department by providing s

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Vasilev, Yu A., A. P. Pamova, K. M. Arzamasov, A. V. Vladzymyrskyy, S. Yu Zayunchkovskiy, and V. V. Zinchenko. "Presentation of diagnostic accuracy metrics based on classification of artificial intelligence software in radiology." Medical doctor and information technologies, no. 1 (March 24, 2025): 58–69. https://doi.org/10.25881/18110193_2025_1_58.

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The implementation of artificial intelligence in healthcare is a key direction for technology development in Russia, aimed at improving the quality of medical services and increasing diagnostic accuracy. However, the lack of standards for presenting metrics of diagnostic accuracy of artificial intelligence-based software (AI-based software) complicates comparative analysis and selection of the most suitable software for medical organizations. Therefore, developing a detailed classification of AI-based software is an important task for ensuring safety and quality of medical care, as well as det

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Khokhlov, Yury. "Advancing operational efficiency in software companies through generative AI." American Journal of Engineering and Technology 07, no. 01 (2025): 11–18. https://doi.org/10.37547/tajet/volume07issue01-03.

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Generative AI is rapidly reshaping the landscape of software (SW) companies’ operations, offering unprecedented capabilities for creating new code, documentation, designs, and more. By harnessing advanced machine learning architectures such as large language models (LLMs), agent-based frameworks, retrieval-augmented generation (RAG), and multimodal systems, organizations can reduce development cycles, improve service quality, and unlock innovative business opportunities. Recent articles highlight how these AI-driven approaches not only address routine tasks—such as boilerplate code generation

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Kong, Xianglong, Hongfa Li, and Wen Ji. "Prediction of software defect centralization using ai-based pathology." Journal of Physics: Conference Series 2963, no. 1 (2025): 012001. https://doi.org/10.1088/1742-6596/2963/1/012001.

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Abstract Software defect centralization is proposed as a metaphor to present the accumulation of various software degradation. It can describe the survival status of projects from the perspective of internal quality and help to fill the gap between quantitative fine-grained drawbacks and qualitative failure of projects. The current work on the detection and repair of software degradation cannot explain why some projects failed unexpectedly. Inspired by AI-based pathology, we define software defect centralization as a certain size of code that is growing and design a supervised prediction model

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Kato, Hiroki, Seiichi Kurumizawa, Natsumi Sugimachi, Yuya Yoshioka, Kazuki Katayama, and Yukito Watanabe. "Development of "BlurOn" an AI-based Automatic Blurring Software." Journal of The Institute of Image Information and Television Engineers 78, no. 2 (2024): 243–46. http://dx.doi.org/10.3169/itej.78.243.

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Gulhane, Ruchika, Swapnil Kadam, Adesh Ingale, and Pranali Pale. "AI based Early Flood Warning System." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 3039–47. http://dx.doi.org/10.22214/ijraset.2022.41987.

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Abstract: Community Based Early Flood Warning System". Which covers study of early warning system in India, Study of 1OT based software and hardware, to build small scale working model on IOT based Interface. Due to use of such technology impacted community will be brought into the network of disaster relief committee, local media, local, police, the military unit and flood monitoring and forecasting station of the department of Hydraulic and Metrology. This technology will be proved very effective and will give warning and response immediately during the time of flood. This paper also gives a

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Koç, Osman, İbrahim Yücedağ, and Ümit Şentürk. "The Impact of Artificial Intelligence Enhanced No-Code Software Development Platforms on Software Processes: A Literature Review." Düzce Üniversitesi Bilim ve Teknoloji Dergisi 13, no. 1 (2025): 383–401. https://doi.org/10.29130/dubited.1554356.

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This literature review examines the impact of artificial intelligence-based (AI-based) no-code software development platforms on software processes. The study primarily focuses on accelerating software development processes, reducing costs, and optimizing business operations. Existing studies in the literature demonstrate how these types of platforms facilitate complex application development even for non-technical users and enhance time-cost optimization. This review highlights how no-code platforms have become more effective and efficient with AI-supported tools, transforming the current sof

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Armyanova, Mariya, and Yanka Aleksandrova. "Using Artificial Intelligence in Software Development." Izvestia Journal of the Union of Scientists - Varna. Economic Sciences Series 12, no. 1 (2023): 167–76. http://dx.doi.org/10.56065/ijusv-ess/2023.12.1.167.

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The AI use is also heavily influencing software development. The demands on software are many, including its continuous update and ever-shorter development cycles. There is a growing need for shorter development cycles, flexibility and meeting requirements without compromising software quality. AI can help meet these requirements in software development. AI supports these goals. AI tools aid software development by supporting almost all development activities. It aids testing by making it faster and more efficient. The main possibilities to support activities in the different phases of softwar

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Ahmed, Sufyaan. "AI-Powered Legal Documentation Assistant Software." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47421.

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Abstract—This paper presents the development and implemen- tation of an AI-powered Legal Documentation Assistant Software using the MERN (MongoDB, Express.js, React.js, Node.js) stack. The proposed system revolutionizes legal case management by providing structured case input categorization, AI-driven legal pathway recommendations, and automated generation of sup- porting legal documents. This research evaluates the system performance compared to existing legal documentation tools, highlighting the advantages of a modern web-based system integrated with AI capabilities. The paper also discusse

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Lefèvre, Élodie. "AI-Based Modeling in Software Engineering: Techniques, Applications, and Future Directions." International Journal of Trend in Research and Development 9, no. 5 (2022): 407–12. https://doi.org/10.5281/zenodo.13738261.

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Artificial Intelligence (AI) has become a transformative force in various fields, and its impact on software engineering is particularly profound. The integration of AI-based modeling in software engineering enhances automation, improves intelligence, and creates smarter systems, contributing significantly to the Fourth Industrial Revolution. This paper provides a comprehensive review of the key AI techniques applied in software engineering, focusing on machine learning, neural networks, deep learning, data mining, rule-based systems, fuzzy logic, case-based reasoning, and hybrid approaches. I

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Sokolov, Volodymyr, Viacheslav Riabtsev, Oleksandr Uspenskyi, and Danylo Kopych. "Application directions of artificial intelligence in software development technologies." Collection "Information Technology and Security" 12, no. 2 (2024): 219–35. https://doi.org/10.20535/2411-1031.2024.12.2.315741.

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The article presents the results of a systematic analysis of the current state of application of artificial intelligence (AI) in software engineering (SW) based on the analysis of publications, assessment of AI capabilities, experience in its application, and conducted experiments. The conceptual foundations of the research were formed, which determine: perception of AI as a tool, not an individual of work; the main directions of its application are engineering and management; the subject of AI application is the processing of artifacts (synthesis and analysis) and obtaining consultations; the

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Lv, Guofan, Hans-Peter Wiesmann, and Benjamin Kruppke. "How to Use the Osteoclast Identifier Software." Applied Sciences 15, no. 8 (2025): 4208. https://doi.org/10.3390/app15084208.

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The OC_Identifier software is programmed at the Max Bergmann Center for Biomaterials to make a low-threshold cell culture analysis available. This is a user manual for the OC_Identifier software. This software is used to classify and detect four different cell types based on the developmental stages of osteoclast maturation. The software uses AI models for this purpose, but these can be selected and changed without programming knowledge for flexible adaptation to new AI models and training data. This also makes it easy to compare different AI models, such as those based on different training d

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Qazi, Sheereen, M. Suleman Memon, Asif Ali, and Shahzmaan Nizamani. "ROLE OF ARTIFICIAL INTELLIGENCE (AI) TOOLS FOR ASSURING QUALITY IN SOFTWARE." Journal of Southwest Jiaotong University 57, no. 2 (2022): 54–62. http://dx.doi.org/10.35741/issn.0258-2724.57.2.5.

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Artificial intelligence (AI) plays a significant role in multiple aspects of human life. AI computational and simulation tools have been implemented and tested for computational study and data analysis. In computer science and software engineering, effective and intelligent analysis of the results is necessary. Since AI-based tools are designed for business needs, software engineering organizations use them for software quality assurance (SQA) purposes. A cost-effective and rapid analysis gives a better market-oriented approach for professionals in the software development industry. According

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Kopp, Andrii, and Ivan Nesterenko. "A MODEL FOR SELECTING ARTIFICIAL INTELLIGENCE TOOLS TO SUPPORT SOFTWARE DEVELOPMENT PROCESSES." Bulletin of NTU "KhPI". Series: Strategic management, portfolio, program and project management, no. 2(9) (March 17, 2025): 45–49. https://doi.org/10.20998/2413-3000.2024.9.6.

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Integrating artificial intelligence (AI) tools into software development projects significantly improves the efficiency of various tasks within the software development lifecycle (SDLC). AI-driven tools embedded in integrated development environments (IDEs) improve developer productivity and code quality, and facilitate better interaction between project participants and AI-based systems. The main research directions for integrating AI into software development processes include adapting user interfaces for specific tasks, increasing trust in AI-based systems, and improving code readability. A

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Padmanabhan, Mani. "A Systematic Review of AI Based Software Test Case Optimization." International Research Journal of Multidisciplinary Scope 05, no. 04 (2024): 847–59. http://dx.doi.org/10.47857/irjms.2024.v05i04.01451.

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Software test case optimization for real-time systems is a vulnerability detection methodology that assesses the resilience of targeted programs by subjecting them to irregular input data. As the volume, size, and intricacy of software continue to escalate, conventional manual test case generation has encountered challenges like insufficient logical coverage, minimal automation levels, and inadequate test scenarios. These difficulties underscore the need for innovative approaches that maximize software dependability and performance. An artificial intelligence powered fuzzing technique, which e

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Parra, Esteban, and Jairo Aponte. "Report from the Summer School on Software Engineering andArtificial Intelligence." ACM SIGSOFT Software Engineering Notes 50, no. 1 (2025): 12–14. https://doi.org/10.1145/3709616.3709620.

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This report summarizes the curriculum and academic outcomes of the Summer School on Software Engineering and Artificial Intelligence (AI) held at the Universidad de Los Andes in Bogot´a, Colombia. The summer school offered an in-depth introduction to the fields of Machine Learning (ML), Artificial Intelligence (AI), and Natural Language Processing (NLP); their role and applications in Software Engineering (SE) and the software development process. The feedback we received from the participants indicates that the program successfully enhanced their knowledge and the skills needed for them to na

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Zhang, Jiannan, Yingying Li, Jiahui Zhou, Yelin Zhu, and Lanjuan Li. "Supervision System of AI-based Software as a Medical Device." Chinese Journal of Engineering Science 24, no. 1 (2022): 198. http://dx.doi.org/10.15302/j-sscae-2022.01.021.

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Vassileva, S., and S. Mileva. "Ai-Based Software Tools for Beer Brewing Monitoring and Control." Biotechnology & Biotechnological Equipment 24, no. 3 (2010): 1936–39. http://dx.doi.org/10.2478/v10133-010-0060-0.

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Lokhande, Narendra Lalchand, and Tushar Hrishikesh Jaware. "A Systematic Review of AI Based Software Test Case Optimization." International Research Journal of Multidisciplinary Scope 05, no. 04 (2024): 860–71. http://dx.doi.org/10.47857/irjms.2024.v05i04.01452.

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In the realm of computer-aided diagnosis systems designed for lung cancer, accurately segmenting nodules holds vital importance. This segmentation process has a vital role in examining the image attributes of lung nodules captured in computed tomography scans, ultimately aiding in separation of benign and cancerous nodules. Timely detection of these lesions stands as the most effective strategy in combating lung cancer, a disease notorious for its high malignancy rates across both genders. Despite numerous deep learning techniques proposed for nodule segmentation, it remains challenging due to

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K.A.S.H, Kulathunga, Piyabhashitha S.A.M, Jayatilake S.A.D.H.A, Perera Jeewaka, and Silva Dhammika. "Detection of Endemic Sri Lankan Birds Using AI-Based Software." International Journal of Computer Science and Information Technology Research 10, no. 4 (2022): 10–16. https://doi.org/10.5281/zenodo.7234060.

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<strong>Abstract:</strong> Photographers frequently encounter a variety of issues when taking wildlife photographs. Many local and foreign wildlife photographers struggle with the lack of an efficient tool for detecting and discovering endemic Sri Lankan birds. A mobile application called "Ceylon Birds" was developed as a solution for these issues. It uses birds’ images, voices, and habitats to identify the birds. This mobile application will access the device’s camera, recorder, and Global Positioning System to accurately identify the bird, habitats and provide the bird&

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Wdowik, Roman, and Artur Bełzo. "Artificial intelligence-based design of assemblies in the FreeCAD software." Technologia i Automatyzacja Montażu 127, no. 1 (2025): 74–82. https://doi.org/10.7862/tiam.2025.1.6.

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The article presents application of the FreeCAD software and generative artificial intelligence (AI) in the design process regarding exemplary mechanical engineering-related assemblies. Authors have studied how to support a product development and computer-aided (CAD) design phase by the use of generative pre-trained transformer (GPT). Prompting process, that lead to Python language-based code creation in the ChatGPTTM by OpenAI company, was studied from the perspective of 3D assemblies generation in the FreeCAD software. Moreover, authors studied how to improve the basic 3D models by the use

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Noorihan Abdul Rahman. "Challenges in Developing Artificial Intelligence-based Systems using Classic Software Development Life Cycle." Journal of Mathematics and Computing Science 10, no. 2 (2024): 25–30. https://doi.org/10.24191/jmcs.v10i2.4267.

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Artificial intelligence (AI) tools have become an essential part of modern software systems, driving automation, predictive analysis, and intelligent decision-making. However, the development of AI tools using traditional software engineering methodologies, specifically the Software Development Life Cycle (SDLC), presents unique challenges. This paper explores the intricacies and limitations of the classic SDLC in the context of AI-based system development, covering issues related to requirements gathering, design, implementation, testing, deployment, and maintenance. The analysis highlights t

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Hajira, Bi Bi, and Renuka Sunil Shahapurkar. "Comparative Analysis of Traditional Wealth Management Services vs. AI-Based Wealth Management Software." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 008 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem37400.

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The integration of artificial intelligence (AI) is transforming the wealth management industry. This paper compares traditional wealth management with AI-based solutions to assess their value to customers, focusing on factors like cost, customization, accessibility, performance, and accuracy. Traditional wealth management offers personalized human interaction, expert advice, and tailored financial planning but often comes with higher costs and limited scalability. In contrast, AI-based wealth management uses advanced algorithms and data analytics for scalable, efficient, and cost-effective ser

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Fanni, Salvatore Claudio, Alessandro Marcucci, Federica Volpi, Salvatore Valentino, Emanuele Neri, and Chiara Romei. "Artificial Intelligence-Based Software with CE Mark for Chest X-ray Interpretation: Opportunities and Challenges." Diagnostics 13, no. 12 (2023): 2020. http://dx.doi.org/10.3390/diagnostics13122020.

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Chest X-ray (CXR) is the most important technique for performing chest imaging, despite its well-known limitations in terms of scope and sensitivity. These intrinsic limitations of CXR have prompted the development of several artificial intelligence (AI)-based software packages dedicated to CXR interpretation. The online database “AI for radiology” was queried to identify CE-marked AI-based software available for CXR interpretation. The returned studies were divided according to the targeted disease. AI-powered computer-aided detection software is already widely adopted in screening and triage

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Yeslyamov, Serik. "Application of Software Robots Using Artificial Intelligence Technologies in the Educational Process of the University." Journal of Robotics and Control (JRC) 5, no. 2 (2024): 359–69. https://doi.org/10.18196/jrc.v5i2.21083.

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The use of artificial intelligence (AI) in education has gained interest due to its increasing application in various fields. This study explores the potential of AI-based software robots in higher education and their ability to revolutionize educational methodologies. The research purpose is to examine the positive impact of the use of software robots in educational settings. The study focuses on evaluating the prospects of expanding the use of AI-based software robots in higher education. The research uses a combination of observational techniques and practical case studies. It includes an e

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Ishika, Goyal, A. Tejaswini, and Nischay N. Gowda Dr. "The impact of AI-Based Software on Designers' Lives: Transforming Creativity and Workflow." International Journal of Trends in Emerging Research and Development 2, no. 6 (2024): 246–55. https://doi.org/10.5281/zenodo.14652381.

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AI-based software has revolutionized the design industry, significantly transforming how designers approach creativity, problem-solving, and workflow. By automating repetitive tasks, such as resizing images, generating design options, or adjusting layouts, AI frees up valuable time for designers to focus on the more creative aspects of their work. With advanced tools like generative design and machine learning, AI enables designers to explore innovative concepts and design solutions that would have been difficult or time-consuming to achieve manually. Moreover, AI-based software has streamline

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Babkair, Hamzah Ali, Mohammed Enamur Rashid, Abedalla Abdelghani, Tarek M. Ibrahim, and Mohammad Khursheed Alam. "Assessing AI-Based Software’s Precision in Identifying Oral Lesions from Radiographs." Journal of Pharmacy and Bioallied Sciences 17, Suppl 2 (2025): S1255—S1257. https://doi.org/10.4103/jpbs.jpbs_78_25.

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ABSTRACT Background: Artificial intelligence (AI) is revolutionizing diagnostic practices in dentistry by enhancing accuracy and efficiency. Accurate diagnosis of oral lesions from radiographs is critical for early intervention and treatment planning. This study evaluates the diagnostic accuracy of AI-based software compared to expert radiologists in identifying oral lesions. Materials and Methods: A total of 500 radiographic images were collected from a dental teaching hospital. The images included common oral lesions such as cysts, tumors, and infections. AI-based diagnostic software was use

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Ale, Narendar Kumar, and Rekha sivakolundhu. "Web-Based Automation Testing and Tools Leveraging AI and ML." International Journal on Cybernetics & Informatics 13, no. 4 (2024): 157–63. http://dx.doi.org/10.5121/ijci.2024.130413.

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Software testing remains an essential phase of the software development lifecycle particularly for web-based applications. The integration of AI and ML automation testing has reached new heights in efficiency accuracy and coverage. This paper discusses the latest advancements in web automation testing tools that leverage AI and ML providing insights into their benefits and selection criteria.

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Govinda, Sangeetha, Agnes Nalini Vincent, and Merwa Ramesh Babu. "Novel artificial intelligence-based ensemble learning for optimized software quality." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 3 (2025): 1820. https://doi.org/10.11591/ijai.v14.i3.pp1820-1828.

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<span lang="EN-US">Artificial intelligence (AI) contributes towards improving software engineering quality; however, existing AI models are witnessed to deploy learning-based approaches without addressing various complexities associated with datasets. A literature review showcases an unequilbrium between addressing the accuracy and computational burden. Therefore, the proposed manuscript presents a novel AI-based ensemble learning model that is capable of performing an effective prediction of software quality. The presented scheme adopts correlation-based and multicollinearity-based attr

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Khulood Abu Maria. "Bridging the Gap: AI-Driven Agent-Based Systems in Modern Software Engineering." Journal of Information Systems Engineering and Management 10, no. 43s (2025): 839–48. https://doi.org/10.52783/jisem.v10i43s.8486.

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Agent-based systems (ABS) are the newest and most effective approach in software engineering to solve complex, chaotic, and interconnected problems. Since ABS models the systems as the agents that communicate with each other, it is an effective approach to creating flexible software. This paper aims to discuss the possibility of applying agent-based systems to software engineering with a focus on the opportunities and challenges and further research directions. This paper presents a conceptual model that demonstrates the connections between the agents, their surroundings, and the software engi

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Alshammari, Fahad H. "Trends in Intelligent and AI-Based Software Engineering Processes: A Deep Learning-Based Software Process Model Recommendation Method." Computational Intelligence and Neuroscience 2022 (October 5, 2022): 1–11. http://dx.doi.org/10.1155/2022/1960684.

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In recent years, numerous studies have successfully implemented machine learning strategies in a wide range of application areas. Therefore, several different deep learning models exist, each one tailored to a certain software task. Using deep learning models provides numerous advantages for the software development industry. Testing and maintaining software is a critical concern today. Software engineers have many responsibilities while developing a software system, including coding, testing, and delivering the software to users via the cloud. From this list, it is easy to see that each task

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S., M. Jadhav, Kaustubh V. Dhondge, Gausmohammad I. Kadekar, Ashlesha D. Surve, and Rupesh R. Patil. "AI based Biped Robot." Research and Reviews: Advancement in Robotics 3, no. 2 (2020): 1–10. https://doi.org/10.5281/zenodo.3882077.

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<em>The primary focus in this paper is on bipedal robot development and controlling it for the different surface conditions by using an AI based approach. Pressure sensor, servomotor and software-driven microprocessors are the core components of the system. The two-path communication among robot and ground by methods for pressure detecting information for different ground surface conditions is fundamental. In our machine, we're imposing the robot which may be managed by means of AI. The movement of two-legged gadget is called as taking walks. Strolling can be statically or dynamically soli

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Ebad, Shouki A., Asma Alhashmi, Marwa Amara, Achraf Ben Miled, and Muhammad Saqib. "Artificial Intelligence-Based Software as a Medical Device (AI-SaMD): A Systematic Review." Healthcare 13, no. 7 (2025): 817. https://doi.org/10.3390/healthcare13070817.

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Background/Objectives: Artificial intelligence-based software as a medical device (AI-SaMD) refers to AI-powered software used for medical purposes without being embedded in physical devices. Despite increasing approvals over the past decade, research in this domain—spanning technology, healthcare, and national security—remains limited. This research aims to bridge the existing research gap in AI-SaMD by systematically reviewing the literature from the past decade, with the aim of classifying key findings, identifying critical challenges, and synthesizing insights related to technological, cli

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Lanke, Ms Minakshi, Ms Aishwarya Porwal, Ms Sejal Sarolkar, Mr Gaurang Palaskar, and Prof Shital Kakad. "AI Based Student’s Assignments Plagiarism Detector." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (2022): 991–99. http://dx.doi.org/10.22214/ijraset.2022.48080.

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Abstract: The increase in plagiarizing academic contents led to efficient plagiarism detectors. In the conventional plagiarism detection tools, we have to put the contents from our paper or assignment and paste it into the input box for checking plagiarism or we have to load our files separately. Software under the domain artificial intelligence is moving towards the creation of a systems that can make tasks hassle-free and it also saves the time. “Smart Assignment Plagiarism Detector” can provide easy and efficient way of checking plagiarised data for teachers just in a single click by upload

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Sandstedt, Mårten, Lilian Henriksson, Magnus Janzon, et al. "Evaluation of an AI-based, automatic coronary artery calcium scoring software." European Radiology 30, no. 3 (2019): 1671–78. http://dx.doi.org/10.1007/s00330-019-06489-x.

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Abstract Objectives To evaluate an artificial intelligence (AI)–based, automatic coronary artery calcium (CAC) scoring software, using a semi-automatic software as a reference. Methods This observational study included 315 consecutive, non-contrast-enhanced calcium scoring computed tomography (CSCT) scans. A semi-automatic and an automatic software obtained the Agatston score (AS), the volume score (VS), the mass score (MS), and the number of calcified coronary lesions. Semi-automatic and automatic analysis time were registered, including a manual double-check of the automatic results. Statist

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Bhattacharjee, Kasturi, Obaidur Rehman, and Annesha Sarkar. "The gamut of artificial intelligence in oculoplasty." Journal of Ophthalmic Research and Practice 1 (June 17, 2023): 5–9. http://dx.doi.org/10.25259/jorp_20_2023.

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Artificial intelligence (AI) is taking its grasp over health-care system and ophthalmology as one of the most dynamic streams is largely influenced by AI. AI over the past few decades has made a huge impact in the bailiwick of oculoplasty. AI-based imaging softwares have made easier the diagnosis and management of several orbital and eyelid pathologies by its accuracy and reproducibility. AI also has made possible real-time tracking of deep orbital structures through navigation-guided technologies which have made orbital surgeries safer and easier. This article is a meta-analysis of several ar

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Kromrey, Marie-Luise, Laura Steiner, Felix Schön, Julie Gamain, Christian Roller, and Carolin Malsch. "Navigating the Spectrum: Assessing the Concordance of ML-Based AI Findings with Radiology in Chest X-Rays in Clinical Settings." Healthcare 12, no. 22 (2024): 2225. http://dx.doi.org/10.3390/healthcare12222225.

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Background: The integration of artificial intelligence (AI) into radiology aims to improve diagnostic accuracy and efficiency, particularly in settings with limited access to expert radiologists and in times of personnel shortage. However, challenges such as insufficient validation in actual real-world settings or automation bias should be addressed before implementing AI software in clinical routine. Methods: This cross-sectional study in a maximum care hospital assesses the concordance between diagnoses made by a commercial AI-based software and conventional radiological methods augmented by

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Govinda, Sangeetha, B. G. Prasanthi, and Agnes Nalini Vincent. "Novel preemptive intelligent artificial intelligence-model for detecting inconsistency during software testing." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 3 (2025): 1781. https://doi.org/10.11591/ijai.v14.i3.pp1781-1789.

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<span lang="EN-US">The contribution of artificial intelligence (AI)-based modelling is highly significant in automating the software testing process; thereby enhancing the cost, resources, and productivity while performing testing. Review of existing AI-models towards software testing showcases yet an open-scope for further improvement as yet the conventional AI-model suffers from various challenges especially in perspective of test case generation. Therefore, the proposed scheme presents a novel preemptive intelligent computational framework that harnesses a unique ensembled AI-model fo

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Li, Jiaqi, Zhifeng Zhao, Rongpeng Li, and Honggang Zhang. "AI-Based Two-Stage Intrusion Detection for Software Defined IoT Networks." IEEE Internet of Things Journal 6, no. 2 (2019): 2093–102. http://dx.doi.org/10.1109/jiot.2018.2883344.

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Liao, Dan, Yulong Wu, Ziyang Wu, et al. "AI-based software-defined virtual network function scheduling with delay optimization." Cluster Computing 22, S6 (2018): 13897–909. http://dx.doi.org/10.1007/s10586-018-2124-0.

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Chen, Keming, and Jiaxin Wang. "Deep Guard Dog - AI-Based Night Intrusion Detection Mobile Phone Software." Proceedings of International Conference on Artificial Life and Robotics 30 (February 13, 2025): 264–67. https://doi.org/10.5954/icarob.2025.os9-5.

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Jabeen, Zahra, Khusboo Mishra, and Binay Kumar Mishra. "Comparative Analysis of AI based Antivirus Software Programs Ensuring Cyber Security." International Journal of Innovative Research in Computer Science and Technology 13, no. 2 (2025): 14–18. https://doi.org/10.55524/ijircst.2025.13.2.3.

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In today’s digital arena security of the computer systems is one of the most important factors for users and businesses, as an attack on a system via the internet (cyber-attack) may cause heavy data loss and considerable harm to businesses. The increasing range of cyber-attacks has made traditional anti-virus scanners inefficient and are not fulfilling the desired need for protection. Hence, an advanced level of skill is required for the development of anti-attacking tools embedded with Artificial Intelligence to combat cyber threats. Modern warfare and international cybercrime have also incre

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Vorochek, Olga, and Illia Solovei. "RESEARCH ON ARTIFICIAL INTELLIGENCE TOOLS FOR AUTOMATING THE SOFTWARE TESTING PROCESS." Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies, no. 1 (11) (July 30, 2024): 58–64. http://dx.doi.org/10.20998/2079-0023.2024.01.09.

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The subject matter of the article is artificial intelligence (AI) tools for automating the software testing process. The rapid development of the software development industry in recent decades has led to a significant increase in competition in the IT technology market and, as a result, stricter requirements for corresponding products and services. AI-driven test automation is becoming increasingly relevant due to its ability to solve complex tasks that previously required significant human resources. The goal of the work is to investigate the possibilities of using AI technologies to automat

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Sun, Baiwei. "Research on Medical Device Software Based on Artificial Intelligence and Machine Learning Technologies." Insights in Computer, Signals and Systems 1, no. 1 (2024): 34–41. http://dx.doi.org/10.70088/kzd8fw58.

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With the rapid development of Artificial Intelligence (AI) and Machine Learning (ML) technologies, new opportunities for innovation and application in medical device software have emerged. This paper explores the current research on medical device software based on AI and ML, analyzing the practical applications and outcomes of these technologies in the medical field. First, the basic concepts of AI and ML and their significance in medical devices are introduced. Then, an in-depth analysis of the functional requirements of medical device software, user experience, and relevant regulations is p

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Shirley Ugwa. "From Scripts to Intelligence: How AI is Reshaping the Future of Software Testing." World Journal of Advanced Engineering Technology and Sciences 13, no. 1 (2024): 1167–79. https://doi.org/10.30574/wjaets.2024.13.1.0449.

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With the increased complexity of the software systems and the need for quick, high-quality releases, traditional testing methods cannot catch up. This article focuses on artificial intelligence's (AI) transformational influence on the software testing environment, with detailed progress from the manual and script-based approach to intelligent adaptive frameworks. It illustrates the shortcomings of legacy test methods. It demonstrates how AI, using machine learning, natural language processing, and neural networks, allows test automation to become more accurate, scalable, and predictive. Major

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Kulakov, Y. O., and D. V. Korenko. "Methods of applying artificial intelligence in software-defined networks." Problems of Informatization and Management 1, no. 73 (2023): 23–27. http://dx.doi.org/10.18372/2073-4751.73.17640.

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This work is devoted to the review of artificial intelligence application methods in enterprise networks using SDN technology. The paper examines the features and methods of using AI in these networks, as well as identifies potential problems that may arise. The paper provides an overview of the main features of AI in enterprise SDN networks. AI has been found to automate many processes in the network, such as routing, monitoring, bandwidth management, and more. Using AI helps improve network efficiency, reduce costs, and improve security. Potential problems that may arise when usi

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