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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 is an emerging research area, where more than 2/3 of the studies have been published since 2018. The most studied properties of AI-based systems are dependability and safety. We identified multiple SE approaches for AI-based systems, which we classified according to the SWEBOK areas. Studies related to software testing and software quality are very prevalent, while areas like software maintenance seem neglected. Data-related issues are the most recurrent challenges. Our results are valuable for: researchers, to quickly understand the state-of-the-art and learn which topics need more research; practitioners, to learn about the approaches and challenges that SE entails for AI-based systems; and, educators, to bridge the gap among SE and AI in their curricula.
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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 software applications. Thus, several challenges and risk factors regarding AI/ML applications appear to software developers. To study the common challenges in AI/ML application development, we used two different perspectives: software engineering and machine learning. AI/ML applications, like other software systems, need a well-defined software engineering process for their development and maintenance. We discussed challenges and recommendations for different phases of the software development life cycle for ML applications, particularly requirement engineering, design, implementation, integration, testing, and deployment. AI/ML application development has specific aspects to consider as a software development project. We discussed the characteristics and recommendations concerning problem formulation, data acquisition, preprocessing, feature extraction, model building, evaluation, model integration and deployment, model management, and ethics in AI/ML development. In the work, there were formulated recommendations for each analyzed challenge that should be useful for software developers. The next stage of this research is the compilation of detailed systematic guidelines for the software development process for AI/ML systems.
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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 which can assist with anticipating the shortcoming inclination of a program modules when shortcoming names for modules are absent. One of the strategies is utilization of grouping methods. Solo methods like grouping might be utilized for issue expectation in programming modules, all the more so in those situations where shortcoming names are not accessible. In this review, we propose a Machine Learning grouping based programming shortcoming forecast approach for this difficult issue
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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 students or parents with the information they need and also reduces the workload of the department, which has to constantly answer all the students' questions.
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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 determining the interchangeability of AI-based medical devices.Purpose: This study aims to develop a clinical classification of AI-based software in radiology.Materials and Methods: To conduct the study, a comprehensive analysis of available information on AI-based software in radiology was conducted using domestic and foreign databases. In the process of analysis, key aspects were identified, including clinical applicability of AI-based software, diagnostic accuracy of medical devices using AI in radiology.Results: a clinical classification of AI-based software in the field of radiology was developed. In addition, an important observation regarding the representation of diagnostic accuracy metrics of AI-based software was identified. As a result, the proposed classification was extended and supplemented by defining the level of representation of diagnostic accuracy metrics depending on the clinical classification.Conclusion: based on the conducted research, a clinical classification of AI-based software has been developed, which provides a unified approach to the presentation of data on diagnostic accuracy by developers. This approach improves the transparency and comparability of information about different AI-based software in medical practice, thereby improving the efficiency and safety of AI-based software use in medical practice. The results of this study have the potential to be scaled to other AI applications and can be used to improve the quality regulation system for AI-enabled medical devices.
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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 or automated testing—but also facilitate more complex undertakings, including self-healing infrastructure and intelligent orchestration of multi-step workflows. However, integrating generative AI into software operations requires strategic planning around data governance, infrastructure scalability, workforce reskilling, and ethical guardrails. This research article examines the current applications of generative AI in software organizations, details emerging approaches for operational efficiency, and discusses implementation challenges. In doing so, it presents a holistic framework for understanding and adopting generative AI techniques—ranging from code completion to multimodal content creation—while emphasizing the synergy between agent-based architectures and retrieval-augmented generation. The discussion concludes with recommendations on how software firms can realize long-term benefits by blending AI-driven automation with robust oversight mechanisms, ensuring that generative AI becomes a catalyst for sustainable and ethical operational improvements.
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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. We build two datasets with 480 open-source Java projects to accomplish the model learning and evaluation. The experimental results show that the prediction model achieves an overall accuracy of 91.7% on the self-built test set. We also find that the degradation of bug-proneness and specific code smells are positively related to the presence of software defect centralization.
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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 brief idea about the work done for the preparation for small scale model and various kinds of IOT based hardware, software and components used in it. Keywords: IOT based sensors, Arduino uno R3, early flood warning, Bluetooth module
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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 software development ecosystem. The article discusses the potential benefits and challenges of AI-based no-code platforms, emphasizing their promising future in the software industry.
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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 software production are presented, based on tools that are available on the market and prototypes exploring the possible AI applications. The research purpose is to study the modern capabilities of AI in the domain of software production, its application limitations and to determine its role and place in software production
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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 discusses technical challenges, system design, and future enhancements. Index Terms—Legal Documentation Assistant, MERN Stack, AI Integration, Legal Case Management, Document Automation.
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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 need to assess the quality of AI-derived products and analyze the risks of its use is emphasized. Directions of application of AI in management: agreement processes (development of product concept and contract), organizational processes (project group formation and selection of technologies) and project management (planning, risk management, control and analysis of project implementation) Directions of application of AI in engineering: requirements management, design, construction, testing and documenting. To systematize the analysis of AI application directions, a conceptual model was developed, which includes: the direction, subject, and mode of application of AI. The mode of application of AI: the format of the prompt (problem statement and set of input data), the required product and its type (finished product, prototype, template, solution options, information support), the role of AI (executor, co-author, consultant), form of AI interaction (external service, integration via API, integrated system or local autonomous system). A structure of derivative models was formed for the analysis of the application of AI in specific directions with an overview of the capabilities of the most effective AI tools. As conclusions, it was determined that in management, the most rational model of using AI is to receive consultations and prototypes of documentation when contacting external AI services, in engineering – creating prototypes of project solutions and documentation based on external services, using integrated AI systems for design and testing in co-authorship mode. The risks of using AI include the possibility of obtaining insufficiently detailed documentation, complex and confusing software artifacts, and errors in the software code. To reduce risks and increase the effectiveness of AI application, it is determined that constant quality control of its products and training based on corporate requirements and standards is required.
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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 data or training cycles, etc. In addition, the software calculates the percentage of each cell type among the total number of detected cells and displays detailed test results, including the position and confidence value of the detected cells. With this software and the instructions provided, we hope to enable a broad community to perform the AI-based image analysis of osteoclasts and their development from monocytes, and we hope for future expansion into co- and triple-culture models, for example. This should enable biomaterial characterisation based on a better morphological cell evaluation and replace time-consuming and costly biochemical and, if necessary, PCR analyses with AI image analysis.
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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 to the international report of quality, 64 percent of organizations use AI to optimize business processes within QA strategies. This study highlights software-industry and IT-based approaches in software houses. The software houses in Pakistan selected for investigation are related to SQA. Modern machine learning models have been a part of SQA for the last few years. Furthermore, the study also investigates the AI tools which have been used for SQA. The latest trends and techniques are investigated for better quality assurance. We propose an approach for SQA by applying an AI-based tools survey. AI-based tools provide an effective software development and quality assurance solution. The results show that 70 percent of software houses in Pakistan are not applying AI-based tools to maximize SQA. The comprehensive approaches have been studied to identify basic issues and challenges with adopting AI-based tools for SQA. The future trends and current models of machine learning (ML) are also discussed to verify quality assurance. In addition, machine learning models that are already implemented are used in this work to verify the topic’s authenticity.
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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. It explores their applications in smart cities, healthcare, education, and security, highlighting the benefits and challenges associated with each. The paper also delves into future research directions, emphasizing the need for technological advancements, addressing ethical considerations, and promoting interdisciplinary research. The objective is to offer a valuable guide for both academics and practitioners in understanding and leveraging AI-based modeling in software engineering to drive innovation and efficiency.
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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. AI enhances several SDLC stages, including automated code generation, code review and defect prediction. Implementing AI tools in IDEs accelerates development, improves code quality and reduces defects. Machine learning and natural language processing play a critical role in improving software quality through requirements classification and defect prediction. AI-based solutions, such as recommendation systems and chatbots, support various software development processes, including requirements gathering. Therefore, a relevant scientific and practical challenge is to create a model for the justified selection of AI tools to support software development processes in order to improve project efficiency. This study proposes a mathematical model that minimizes the cost of using AI tools, while ensuring compliance with minimum requirements that affect project efficiency. The optimization model takes into account criteria such as pricing, integration, support and functionality capabilities, using normalized evaluations based on Gartner Peer Insights and other open sources. The objective function minimizes the total cost of AI tools, subject to constraints that ensure minimum acceptable evaluation scores. The developed approach enables a systematic selection of AI tools, thus improving the efficiency of software development projects.
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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 exhibits remarkable proficiency in data analysis and classification prediction. This paper examines the recent advancements in fuzzing research and conducts a comprehensive review of artificial intelligence driven fuzzing approaches in software test cases optimization. The major review explains the test case validation workflow and discusses the optimization of distinct phases within fuzzing utilizing in the software testing. Particular emphasis is placed on the implementation of artificial intelligence in the following software testing phases. This process involves position selection, which includes organizing and cleaning data; generating test cases that cover different inputs and expected outputs; selecting fuzzy input values for testing edge cases; validating the results of each test case to ensure accuracy and reliability. Finally, it synthesizes the obstacles and complexities associated with integrating artificial intelligence into software test case optimization techniques and anticipate potential future directions in the software testing.
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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 navigate the role of AI in the current landscape of software engineering. The students of the summer school were engaged in the development of a full software system using AI-based tools as part of the development process. We found that the project was successful in providing the students with experience regarding how to incorporate AI-based tools as part of their software development process but not all students showed the same level of proficiency when leveraging AI tools.
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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 factors such as nodule characteristics, location, false positives, and the necessity for precise boundary detection. The present paper presents an ultra-modern method for lung nodule segmentation in computer tomographic images, based on a Generative Adversarial Network. A discriminator and a generator make up the GAN model. Our generator, Residual Dilated Attention Gate UNet, serves as the segmentation module, while a discriminator is Convolutional Neural Network classifier. To enhance training stability, we utilize the Wasserstein GAN algorithm. We compare our hybrid deep learning model, called WGAN-LUNet, both quantitatively and qualitatively with other methods that are already in use. We evaluate the model using multiple quantitative criteria.
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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’s details to the photographer. The concepts of Machine Learning, Natural Language Processing, and Neural Networks are used for this application. The information supplied by the Wildlife Officers, experts in this sector, was used to develop this application. The main goals of the suggested solution are to locate the regions where the majority of birds are present during a relevant time period and to clearly identify endemic birds by their physical characteristics and tones of voice. The trained Machine Learning models have achieved the accuracy of 92%, 90%, and 88% for the voice detection model, image identification model, and location clustering model respectively. After testing this “Ceylon Birds” mobile application among wildlife photographers, we have received positive feedback from them. <strong>Keywords:</strong> image identification, voice detection, location clustering, machine learning. <strong>Title:</strong> Detection of Endemic Sri Lankan Birds Using AI-Based Software <strong>Author:</strong> K.A.S.H Kulathunga, S.A.M Piyabhashitha, S.A.D.H.A Jayatilake, Jeewaka Perera, Dhammika Silva <strong>International Journal of Computer Science and Information Technology Research</strong> <strong>ISSN 2348-1196 (print), ISSN 2348-120X (online)</strong> <strong>Vol. 10, Issue 4, October 2022 - December 2022</strong> <strong>Page No: 10-16</strong> <strong>Research Publish Journals</strong> <strong>Website: www.researchpublish.com</strong> <strong>Published Date: 21-October-2022</strong> <strong>DOI: https://doi.org/10.5281/zenodo.7234060</strong> <strong>Paper Download Link (Source)</strong> <strong>https://www.researchpublish.com/papers/detection-of-endemic-sri-lankan-birds-using-ai-based-software</strong>
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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 of text-based prompts and CAD user improvements of the solid models and discussed the general effectiveness of this approach. It was also separately studied how to automate creation of many parts of the same type by the use of the AI, and apply such CAD libraries in the developed assemblies. The performed research presented selected possibilities of AI-based design process, challenges of the design process and future areas of investigation of AI-based assembly development resulting from the usage of existing GPTs.
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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 services, though it may lack the personal touch of human advisors. The study finds that traditional wealth management excels in comprehensive planning and trust-based relationships, crucial for clients with complex needs. AI-driven solutions enhance accessibility and affordability, providing sophisticated insights to a wider audience. However, concerns about personalization quality and data reliance remain. The research suggests that a hybrid model combining traditional and AI-based approaches may offer the best solution, leveraging AI’s efficiency while maintaining personalized service and expert advice. Overall, this paper provides valuable insights for financial planners, identifying strategies to enhance value in the evolving wealth management field. Keywords: Wealth Management, Artificial Intelligence (AI), Financial Planning, AI-based Solutions, Personalized Financial Advice, Hybrid Wealth Management.
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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 for pulmonary tuberculosis, especially in countries with few resources and suffering from high a burden of this disease. AI-based software has also been demonstrated to be valuable for the detection of lung nodules detection, automated flagging of positive cases, and post-processing through the development of digital bone suppression software able to produce digital bone suppressed images. Finally, the majority of available CE-marked software packages for CXR are designed to recognize several findings, with potential differences in sensitivity and specificity for each of the recognized findings.
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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 experimental investigation of the basic principles of developing an AI-based robot teacher, with the aim of eventually implementing it in educational processes. The research findings indicate that integrating AI-driven software robots into university education can provide substantial benefits and significant improvements over traditional teaching models. These robots can enhance the educational process and address various developmental challenges. The study highlights the transformative impact of AI-based software robots in modernizing university education. The findings demonstrate the potential of these technologies to reshape the current higher education system.
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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 streamlined collaboration and communication within design teams and with clients. Cloud-based platforms and AI-driven project management tools allow for smoother workflows, enabling designers to share and edit work remotely, collaborate across time zones, and manage multiple projects with ease. However, as AI continues to evolve, it also raises questions about the future of design professions, with concerns regarding job displacement and the role of human creativity.
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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 used to analyze the images, and its performance was compared to that of three experienced radiologists. Sensitivity, specificity, and accuracy were calculated for both methods. Statistical analysis was performed using the Chi-square test, with a significance level set at P < 0.05. Results: The AI-based software demonstrated an overall sensitivity of 92%, specificity of 88%, and accuracy of 90%. In comparison, the expert radiologists showed an average sensitivity of 95%, specificity of 91%, and accuracy of 93%. The AI software performed better in detecting small lesions (accuracy: 88%) but was slightly less accurate for complex cases such as mixed radiolucent and radiopaque lesions (accuracy: 86%). Conclusion: AI-based diagnostic software is a promising tool for diagnosing oral lesions from radiographs, offering high sensitivity and accuracy. While it performs comparably to expert radiologists in most cases, further optimization is needed for complex lesion types. The integration of AI in routine diagnostic workflows could significantly enhance clinical efficiency and decision-making.
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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 attributes to select essential feature selection. At the same time, the scheme also introduces a hybrid learning approach integrated with a bio-inspired algorithm for constructing the ensemble learning scheme. The quantified outcome of the proposed study showcases 65% minimized defect density, 94% minimized mean time to failure, 62% minimized processing time of the algorithm, and 43% enhanced predictive accuracy.</span>
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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 solid. Going for walks is usually dynamically solid. Airborne time for ASIMO is 0.08 sec. Keep foot flat at the floor (fully actuated). Estimate hazard of foot roll by means of measuring ground reaction forces. Cautiously design desired trajectories thru optimization. Keep knees bent (avoid singularity). The adaptive trajectory monitoring manages (high remarks profits). The maximum critical undertaking inside the development of biped robot is robotic mechanical structure. Stiffness and compliance consist with biped determine with flexibility of shape. The primary goal is to design a biped shape that may effortlessly control and capable of dealing with entire situation like humans. A normal man can weigh up to ten kg of load without difficulty and panting with accuracy. Even as designing we ought to remember such a lot of parameters inside the proposed machine, we're interfacing the flexi force sensor with Arduino mega. And measuring the foot stress of robotic after that send that fee to raspberry pi primarily based on AI robot will controlled mechanically. </em>
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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, clinical, and regulatory aspects of AI-SaMD. Methods: A systematic literature review based on the PRISMA framework was performed to select the relevant AI-SaMD studies published between 2015 and 2024 in order to uncover key themes such as publication venues, geographical trends, key challenges, and research gaps. Results: Most studies focus on specialized clinical settings like radiology and ophthalmology rather than general clinical practice. Key challenges to implement AI-SaMD include regulatory issues (e.g., regulatory frameworks), AI malpractice (e.g., explainability and expert oversight), and data governance (e.g., privacy and data sharing). Existing research emphasizes the importance of (1) addressing the regulatory problems through the specific duties of regulatory authorities, (2) interdisciplinary collaboration, (3) clinician training, (4) the seamless integration of AI-SaMD with healthcare software systems (e.g., electronic health records), and (5) the rigorous validation of AI-SaMD models to ensure effective implementation. Conclusions: This study offers valuable insights for diverse stakeholders, emphasizing the need to move beyond theoretical analyses and prioritize practical, experimental research to advance the real-world application of AI-SaMDs. This study concludes by outlining future research directions and emphasizing the limitations of the predominantly theoretical approaches currently available.
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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 engineering process. The study also creates an AI-Driven Agent-Based System Development Framework (AI-ABSD Framework) that uses machine learning (ML) and artificial intelligence (AI) in the Agent-Based System Development Life Cycle (ABSDLC). This framework was created because of the growing need for very smart, self-guiding, and emotionally intelligent computers. Based on a case study, empirical validation, and comprehensive evaluation, the paper ends with a perspective of the future of ABS in software engineering, highlighting how it can change the approach to developing software systems.
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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 calls for extensive organization and preparation, as well as access to a variety of resources. A developer may consult other code repositories, websites with related programming content, and even colleagues for information before attempting to build and test a solution to the problem at hand. In this investigation, we aim to identify the factors that led to developing the recommender. This system analyzes the recommender’s performance and provides suggestions for improving the software based on users’ opinions.
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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 uploading a folder. This paper explains how our system is solving the problem of plagiarism through the use of algorithm in artificial intelligence
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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 articles which have discussed applications and impact of AI-based software in diagnosis and management planning of periorbital and eyelid pathologies and also articles on navigation-guided orbital surgeries.
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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. Statistical analyses were Spearman’s rank correlation coefficient (⍴), intra-class correlation (ICC), Bland Altman plots, weighted kappa analysis (κ), and Wilcoxon signed-rank test. Results The correlation and agreement for the AS, VS, and MS were ⍴ = 0.935, 0.932, 0.934 (p < 0.001), and ICC = 0.996, 0.996, 0.991, respectively (p < 0.001). The correlation and agreement for the number of calcified lesions were ⍴ = 0.903 and ICC = 0.977 (p < 0.001), respectively. The Bland Altman mean difference and 1.96 SD upper and lower limits of agreements for the AS, VS, and MS were − 8.2 (− 115.1 to 98.2), − 7.4 (− 93.9 to 79.1), and − 3.8 (− 33.6 to 25.9), respectively. Agreement in risk category assignment was 89.5% and κ = 0.919 (p < 0.001). The median time for the semi-automatic and automatic method was 59 s (IQR 35–100) and 36 s (IQR 29–49), respectively (p < 0.001). Conclusions There was an excellent correlation and agreement between the automatic software and the semi-automatic software for three CAC scores and the number of calcified lesions. Risk category classification was accurate but showing an overestimation bias tendency. Also, the automatic method was less time-demanding. Key Points • Coronary artery calcium (CAC) scoring is an excellent candidate for artificial intelligence (AI) development in a clinical setting. • An AI-based, automatic software obtained CAC scores with excellent correlation and agreement compared with a conventional method but was less time-consuming.
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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 AI for four major thoracic pathologies in chest X-ray: fracture, pleural effusion, pulmonary nodule and pneumonia. Chest radiographs of 1506 patients (median age 66 years, 56.5% men) consecutively obtained between January and August 2023 were re-evaluated by the AI software InferRead DR Chest®. Results: Overall, AI software detected thoracic pathologies more often than radiologists (18.5% vs. 11.1%). In detail, it detected fractures, pneumonia, and nodules more frequently than radiologists, while radiologists identified pleural effusions more often. Reliability was highest for pleural effusions (0.63, 95%-CI 0.58–0.69), indicating good agreement, and lowest for fractures (0.39, 95%-CI 0.32–0.45), indicating moderate agreement. Conclusions: The tested software shows a high detection rate, particularly for fractures, pneumonia, and nodules, but hereby produces a nonnegligible number of false positives. Thus, AI-based software shows promise in enhancing diagnostic accuracy; however, cautious interpretation and human oversight remain crucial.
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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 for generating and executing highly precise and optimized test-cases resulting in an outcome of adversary or inconsistencies associated with test cases. The ensembled AI-model uses both unsupervised and supervised learning approaches on publicly available outlier dataset. The benchmarked outcome exhibits supervised learning-based AI-model to offer 21% of reduced error and 1.6% of reduced processing time in contrast to unsupervised scheme while performing software testing.</span>
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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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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 increasingly involved cyber-attacks including targeted distribution attacks or highly networked spying, resulting in extremely dangerous malware attacks that were explicitly designed and released by nations to cause large-scale damage to organizations or infrastructure. Because of such evolving threats, more advanced tools for malware detection, prevention, and recovery are increasing in demand which would aim at defending computing networks from attack. This paper introduces a comparison between some of the best-rated artificial intelligence-embedded antivirus software programs so that a well-suited one could be used by users or enterprises accordingly. This report draws a basic understanding of the technological features provided by each of the software programs and also shows some evident drawbacks, if present. Meanwhile, it also points out some facts related to the use of AI in antiviruses and the scope of Artificial Intelligence in Cyber security.
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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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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 automate manual testing processes, which will increase testing efficiency, reduce costs, and improve software quality. The following tasks were solved in the article: analysis of existing tools and approaches to test automation using AI; development of a conceptual model of a system that integrates AI into the testing process; exploring the potential of AI to automate various aspects of software testing, such as generating test scenarios, detecting defects, predicting errors, and automatically analyzing test results. The following methods are used: theoretical analysis of the literature and existing solutions in the field of test automation, experimental study of the effectiveness of the proposed test automation methods. The following results were obtained: the concept of a system that integrates AI technologies for automating software testing is presented. It has been found that the use of AI allows automating routine testing tasks, significantly reducing the number of human errors, and improving the quality of software products and the effectiveness of verification and validation processes. Conclusions: The development and implementation of AI-based testing automation systems are extremely relevant and promising. The use of AI technologies makes it possible to significantly increase the efficiency of testing, reduce the costs of its implementation, and improve the quality of software. The proposed approach to the development of an AI-based test automation system can be used as a basis for further research and development in this field.
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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 provided. Next, the paper discusses the specific applications of AI and ML in data processing, model training, and software implementation, showcasing their practical effects through case studies. Finally, the paper summarizes the current technical challenges and future opportunities, hoping to provide insights for further development in the medical device industry.
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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 applications, including AI-based test case generation, defect prediction, and self-healing scripts, are discussed in detail, as well as generative AI models such as ChatGPT for scenario creation and documentation. The article also examines future trends, such as hybrid human-AI testing models and advances in the generative AI that will completely change the quality assurance game. By incorporating AI in the testing workflows, organizations can optimally increase efficiency, decrease time to market, and improve software reliability. The research detects that AI does not replace human testers but enhances them by helping develop strategic, creative, and user-centric quality assurance activities. As companies try to be agile and competitive, a decision to implement AI-powered testing is not optional but necessary. This change indicates more than a technological evolution; it changes how software quality is maintained in the digital age.
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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 using AI were also highlighted. In particular, questions arise regarding data security and privacy, as well as ethics and responsibility for the actions of AI-based systems.
 In general, the work puts forward the idea of using artificial intelligence in enterprise networks using SDN technology to improve network efficiency and ensure security. The methods of applying AI in these networks are reviewed, potential problems are identified, and prospective directions of research are outlined.
 This work provides an overview of the features and capabilities of AI in enterprise SDN networks, and also lays the foundation for further research in this area. The implementation of artificial intelligence in enterprise networks is an urgent task, as it helps to improve the efficiency and security of networks and opens up new opportunities for their development.
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R. Dhaya, R. Dhaya, R. Kanthavel R. Dhaya, and Kanagaraj Venusamy R. Kanthavel. "AI Based Learning Model Management Framework for Private Cloud Computing." 網際網路技術學刊 23, no. 7 (2022): 1633–42. http://dx.doi.org/10.53106/160792642022122307017.
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<p>Artificial Intelligence (AI) systems are computational simulations that are &ldquo;educated&rdquo; using knowledge and individual expert participation to replicate a decision that a professional would make provided the same data. A model tries to simulate a specific decision loop that several scientists would take if they had access to all kinds of knowledge. To convey a model, you make a model asset in AI Platform Prediction, make a variant of that model and, at that point, interface the model form to the model record put away in Cloud Storage. AI and DB information sharing are essential for cutting-edge processing for DBMS innovation. The inspirations promoting their incorporation advances incorporate the requirement for admittance to a lot of data that is shared information handling, effective administration of data as information, and astute preparation of information. Notwithstanding these inspirations, the plan for a smart information base interface (IDI) was likewise spurred by the craving to save the considerable speculation spoke to by most existing data sets. A few general ways to deal with the connectivity of AI and databases and different improvements in the area of clever information bases were already examined and announced in this paper.</p> <p>&nbsp;</p>