Relevant bibliographies by topics / AI-based software

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'AI-based software'

Author: Grafiati
Published: 7 June 2025
Last updated: 9 July 2025

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Journal articles on the topic "AI-based software"

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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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Dissertations / Theses on the topic "AI-based software"

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Santiago, Dionny. "A Model-Based AI-Driven Test Generation System." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3878.

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Achieving high software quality today involves manual analysis, test planning, documentation of testing strategy and test cases, and development of automated test scripts to support regression testing. This thesis is motivated by the opportunity to bridge the gap between current test automation and true test automation by investigating learning-based solutions to software testing. We present an approach that combines a trainable web component classifier, a test case description language, and a trainable test generation and execution system that can learn to generate new test cases. Training data was collected and hand-labeled across 7 systems, 95 web pages, and 17,360 elements. A total of 250 test flows were also manually hand-crafted for training purposes. Various machine learning algorithms were evaluated. Results showed that Random Forest classifiers performed well on several web component classification problems. In addition, Long Short-Term Memory neural networks were able to model and generate new valid test flows.
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Prifti, Vlasjov. "A SYSTEMATIC MAPPING STUDY ON THE USE OF SOFTWARE BASED AI ALGORITHMS TO IMPLEMENT SAFETY USING COMMUNICATION TECHNOLOGIES." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-48616.

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The recent development of Artificial Intelligence (AI) has increased a lot the interest of researchers and practitioners towards applying its techniques and software based AI algorithms into everyday life. Domains like automotive, health care, aerospace, etc., are benefiting from the use of AI. One of the most important aspects that technology aims to deliver is catering safety. Thus, the attempt to use AI techniques into carrying out safety issues is momentarily at a progressive state. Communication technologies have been around for many years as an essential part of society. Cellular and non-cellular communication are embracing the use of AI as well, sharing the same issues such as observing the environmental variations, learning or planning. Furthermore, multiple software based AI algorithms are trained in the cloud, where these communication technologies play a vital role. This thesis investigates the existing research performed on the use of software based AI algorithms to implement safety using different communication technologies. For this purpose, a systematic mapping study is conducted to summarize the recent publication trends and the current gaps in the field. The outcomes of this study contribute to the researchers presenting new challenges for further extensive research and to the practitioners to find new methods or tools in order to apply them to the industry.
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Alsulami, Khalil Ibrahim D. "Application-Based Network Traffic Generator for Networking AI Model Development." University of Dayton / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1619387614152354.

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Cooper, Tessa L. "Case Adaptation for an Intelligent Decision Support System for Diabetes Management." Ohio University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1289585163.

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Håkansson, Fredrik, and Carl-Johan Larsson. "User-Based Predictive Caching of Streaming Media." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-151008.

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Streaming media is a growing market all over the world which sets a strict requirement on mobile connectivity. The foundation for a good user experience when supplying a streaming media service on a mobile device is to ensure that the user can access the requested content. Due to the varying availability of mobile connectivity measures has to be taken to remove as much dependency as possible on the quality of the connection. This thesis investigates the use of a Long Short-Term Memory machine learning model for predicting a future geographical location for a mobile device. The predicted location in combination with information about cellular connectivity in the geographical area is used to schedule prefetching of media content in order to improve user experience and to reduce mobile data usage. The Long Short-Term Memory model suggested in this thesis achieves an accuracy of 85.15% averaged over 20000 routes and the predictive caching managed to retain user experience while decreasing the amount of data consumed.<br><p>This thesis is written as a joint thesis between two students from different universities. This means the exact same thesis is published at two universities (LiU and KTH) but with different style templates. The other report has identification number: TRITA-EECS-EX-2018:403</p>
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Shrobe, Howard, and Robert Laddaga. "New Architectural Models for Visibly Controllable Computing: The Relevance of Dynamic Object Oriented Architecturesand Plan Based Computing Models." 2004. http://hdl.handle.net/1721.1/30447.

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Traditionally, we've focussed on the question of how to make a system easy to code the first time, or perhaps on how to ease the system's continued evolution. But if we look at life cycle costs, then we must conclude that the important question is how to make a system easy to operate. To do this we need to make it easy for the operators to see what's going on and to then manipulate the system so that it does what it is supposed to. This is a radically different criterion for success.What makes a computer system visible and controllable? This is a difficult question, but it's clear that today's modern operating systems with nearly 50 million source lines of code are neither. Strikingly, the MIT Lisp Machine and its commercial successors provided almost the same functionality as today's mainstream sytsems, but with only 1 Million lines of code. This paper is a retrospective examination of the features of the Lisp Machine hardware and software system. Our key claim is that by building the Object Abstraction into the lowest tiers of the system, great synergy and clarity were obtained.It is our hope that this is a lesson that can impact tomorrow's designs. We also speculate on how the spirit of the Lisp Machine could be extended to include a comprehensive access control model and how new layers of abstraction could further enrich this model.
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Shrobe, Howard, and Robert Laddaga. "New Architectural Models for Visibly Controllable Computing: The Relevance of Dynamic Object Oriented Architectures and Plan Based Computing Models." 2004. http://hdl.handle.net/1721.1/7286.

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Traditionally, we've focussed on the question of how to make a system easy to code the first time, or perhaps on how to ease the system's continued evolution. But if we look at life cycle costs, then we must conclude that the important question is how to make a system easy to operate. To do this we need to make it easy for the operators to see what's going on and to then manipulate the system so that it does what it is supposed to. This is a radically different criterion for success. What makes a computer system visible and controllable? This is a difficult question, but it's clear that today's modern operating systems with nearly 50 million source lines of code are neither. Strikingly, the MIT Lisp Machine and its commercial successors provided almost the same functionality as today's mainstream sytsems, but with only 1 Million lines of code. This paper is a retrospective examination of the features of the Lisp Machine hardware and software system. Our key claim is that by building the Object Abstraction into the lowest tiers of the system, great synergy and clarity were obtained. It is our hope that this is a lesson that can impact tomorrow's designs. We also speculate on how the spirit of the Lisp Machine could be extended to include a comprehensive access control model and how new layers of abstraction could further enrich this model.
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Qi, Hong Sheng, Q. Liu, Alastair S. Wood, and R. N. Alzaabi. "Fuzzy criticality assessment for process equipments maintenance." 2012. http://hdl.handle.net/10454/8141.

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-<br>Criticality-based maintenance (CBM) is a prioritized approach to the maintenance of (industrial) process equipment. CBM requires personnel with a thorough knowledge of the process/equipment under scrutiny. In this paper a criticality assessment system that is implemented by a local company (which represents the expertise and knowledge of the company experts) is reviewed and fuzzy logic theory is applied to improve the system's capability and reliability. The quality of the fuzzy system is evaluated based on several case studies. The results show that the fuzzy logic based system does not only what the conventional system does, but also outperforms in terms of reliability and has a unique ranking capability.
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Books on the topic "AI-based software"

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Maurice, Pagnucco, Wobcke Wayne 1963-, Zhang Chengqi, and Australian Joint Conference on Artificial Intelligence, (10th : 1997 : Perth, W.A.), eds. Agents and multi-agent systems: Formalisms, methodologies and applications : based on the AI'97 Workshop on Commonsense Reasoning, Intelligent Agents, and Distributed Artificial Intelligence, Perth, Australia, December 1, 1997. Springer, 1998.

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Albrecht, Eduardo. Political Automation. Oxford University Press, 2025. https://doi.org/10.1093/9780197696989.001.0001.

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Abstract Governments, once staffed exclusively with human bureaucrats, now routinely use AI-based software to gather information about citizens and make decisions that impact their lives. In many nations, AI determines the level of privacy a person can enjoy, how far they can travel, what public benefits they may receive, and what they can and cannot say publicly. This amounts to a phenomenon of political automation rapidly materializing globally and begs the question: What input do citizens have in how these machines think? The book explores this question in various domains—including policing, national security, and international peacekeeping. Via interviews with individuals active in civil society groups across several geographic contexts, the book seeks to achieve an understanding of popular attempts to interact with this novel form of algorithmic governance. It emerges that if citizens wish to have rights and representation in a future increasingly characterized by political automation, then a new institution is needed. The book proposes the idea of a Third House, a virtual chamber that legislates exclusively on AI in government decision-making and is based on principles of direct democracy. Digital citizens, AI powered replicas of ourselves, would act as our personal representatives to this Third House a virtual chamber that legislates exclusively on AI in government decision-making and is based on principles of direct democracy, unlike existing upper and lower houses that are representative. Digital citizens, AI powered replicas of ourselves, would act as our personal emissaries to this Third House. Deploying political automation without such an institution will result in the gradual sunsetting of participatory systems of government, and perhaps of yet more significant concern, the start of an era in which citizens, having abdicated governance in favor of AI without clear mechanisms for oversight, find they no longer need to trust their conscience on matters of public policy.
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(Editor), Wayne Wobcke, Maurice Pagnucco (Editor), and Chengqi Zhang (Editor), eds. Agents and Multi-Agent Systems Formalisms, Methodologies, and Applications: Based on the AI'97 Workshops on Commonsense Reasoning, Intelligent Agents, ... / Lecture Notes in Artificial Intelligence). Springer, 1998.

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Artificial Intelligence Impacting Diagnosis of Glaucoma and Understanding the Regulatory Aspects of AI-Based Software As Medical Device: Glaucoma Diagnosis,Convolutional Neural Networks. Independently Published, 2021.

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Artificial Intelligence Impacting Diagnosis of Glaucoma and Understanding the Regulatory Aspects of AI-Based Software As Medical Device: Artificial Intelligence in Medical Imaging ,Glaucoma Dia. Independently Published, 2021.

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Agent-Oriented Software Engineering: Reflections on Architectures, Methodologies, Languages, and Frameworks. Springer, 2014.

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Agent-Oriented Software Engineering: Reflections on Architectures, Methodologies, Languages, and Frameworks. Springer, 2014.

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Hilgurt, S. Ya, and O. A. Chemerys. Reconfigurable signature-based information security tools of computer systems. PH “Akademperiodyka”, 2022. http://dx.doi.org/10.15407/akademperiodyka.458.297.

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The book is devoted to the research and development of methods for combining computational structures for reconfigurable signature-based information protection tools for computer systems and networks in order to increase their efficiency. Network security tools based, among others, on such AI-based approaches as deep neural networking, despite the great progress shown in recent years, still suffer from nonzero recognition error probability. Even a low probability of such an error in a critical infrastructure can be disastrous. Therefore, signature-based recognition methods with their theoretically exact matching feature are still relevant when creating information security systems such as network intrusion detection systems, antivirus, anti-spam, and wormcontainment systems. The real time multi-pattern string matching task has been a major performance bottleneck in such systems. To speed up the recognition process, developers use a reconfigurable hardware platform based on FPGA devices. Such platform provides almost software flexibility and near-ASIC performance. The most important component of a signature-based information security system in terms of efficiency is the recognition module, in which the multipattern matching task is directly solved. It must not only check each byte of input data at speeds of tens and hundreds of gigabits/sec against hundreds of thousand or even millions patterns of signature database, but also change its structure every time a new signature appears or the operating conditions of the protected system change. As a result of the analysis of numerous examples of the development of reconfigurable information security systems, three most promising approaches to the construction of hardware circuits of recognition modules were identified, namely, content-addressable memory based on digital comparators, Bloom filter and Aho–Corasick finite automata. A method for fast quantification of components of recognition module and the entire system was proposed. The method makes it possible to exclude resource-intensive procedures for synthesizing digital circuits on FPGAs when building complex reconfigurable information security systems and their components. To improve the efficiency of the systems under study, structural-level combinational methods are proposed, which allow combining into single recognition device several matching schemes built on different approaches and their modifications, in such a way that their advantages are enhanced and disadvantages are eliminated. In order to achieve the maximum efficiency of combining methods, optimization methods are used. The methods of: parallel combining, sequential cascading and vertical junction have been formulated and investigated. The principle of multi-level combining of combining methods is also considered and researched. Algorithms for the implementation of the proposed combining methods have been developed. Software has been created that allows to conduct experiments with the developed methods and tools. Quantitative estimates are obtained for increasing the efficiency of constructing recognition modules as a result of using combination methods. The issue of optimization of reconfigurable devices presented in hardware description languages is considered. A modification of the method of affine transformations, which allows parallelizing such cycles that cannot be optimized by other methods, was presented. In order to facilitate the practical application of the developed methods and tools, a web service using high-performance computer technologies of grid and cloud computing was considered. The proposed methods to increase efficiency of matching procedure can also be used to solve important problems in other fields of science as data mining, analysis of DNA molecules, etc. Keywords: information security, signature, multi-pattern matching, FPGA, structural combining, efficiency, optimization, hardware description language.
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Kasabov, Nikola. Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering. The MIT Press, 1996. http://dx.doi.org/10.7551/mitpress/3071.001.0001.

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In a clear and accessible style, Kasabov describes rule-based and connectionist techniques and then their combinations, with fuzzy logic included, showing the application of the different techniques to a set of simple prototype problems, which makes comparisons possible. A particularly strong feature of the text is that it is filled with applications in engineering, business, and finance. AI problems that cover most of the application-oriented research in the field (pattern recognition, speech and image processing, classification, planning, optimization, prediction, control, decision making, and game simulations) are discussed and illustrated with concrete examples. Intended both as a text for advanced undergraduate and postgraduate students as well as a reference for researchers in the field of knowledge engineering, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering has chapters structured for various levels of teaching and includes original work by the author along with the classic material. Data sets for the examples in the book as well as an integrated software environment that can be used to solve the problems and do the exercises at the end of each chapter are available free through anonymous ftp. Bradford Books imprint
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BAHADUR TIWARI, BHUPENDRA, E. ESWARA REDDY, and SAM X. KINGSLEY JOSHUA. INNOVATIVE HUMAN RESOURCE PRACTICES AND EMPLOYEE ENGAGEMENT WITH SPECIAL REFERENCE TO IT SECTOR. Jupiter Publications Consortium, 2023. http://dx.doi.org/10.47715/jpc.b.978-93-91303-79-2.

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The industry of information technology in India includes the following services namely IT and software services, IT enabled services, hardware (engineering) services, and e-businesses/e-governance associated with government services. IT services are outsourcing of software support/installation, processing services, systems integration, exports of products and services, and training/education of the information technology science. The significant improvements in the industry have brought about a vital need for systematic process of managing the majority of employees in the IT industry. There was also a need created for technology in the subject matter of managing the employees and other aspects that came into picture. Hence, Innovative Human Resource practices came into existence for upgrading the skills and building the employees to work towards the goal of the organization. This gave birth to HR technology, Employee Engagement, ERP and so on. The study focuses on identifying various applications of Innovative Human resource practices in IT industry, the role of demographics and the factors influencing employee engagement and productivity. The study also analyzes the impact of innovative human resource practices on employee engagement and productivity and finally examines the mediating role of employee engagement upon the relationship between innovative human resource practices and employee productivity. To support the study, review of the relevant literature (Books, Research thesis and research papers) available in the innovative human resource practices space (both Global and Indian) was done. The research gap was identified in 4 categories i.e. empirical gap, evidence gap, methodological gap and population gap. The conceptual framework for the study was also designed. The literature review was categorized into national and international, theoretical and empirical to keep the study relevant according to the current global standards. Based on the research gap and the conceptual framework, the questionnaire was framed and according to the hypothesis the plan of analysis was structured to further the study. The data collection was completed through offline and online method, based on sample design. The analysis included Structural Equation Model, ANOVA, Independent t test and Mediation analysis – Andrew Hayes, Model 4 using SPSS and AMOS software. The study found out that HR Technology, HR Analytics, Collaboration Tools, AI in HR and Employee Pulse survey, are contributors to Innovative Human resource practices but there is no significant impact of demographic variables on perception of IHRM. Also, Employee retention, Reward and recognition, Personality development and Performance appraisal are factors influencing Employee engagement and Innovative work system, Employee contribution, Vigour, Dedication, Psychological factors, Motivational factors, Experience Factors and Individual capacity are factors influencing Employee Productivity. IHRM has significant impact on employee engagement and the employee productivity. Employee engagement mediates the relationship between IHRM and employee productivity. To conclude, this study provides insights into how employees are affected by innovative HR practices and provides practical solutions for organizations looking to encourage staff. By using motivational strategies that are directly tied to employees’ immediate interests and that are intended to affect their views and attitudes, innovative HR practices can assist firms in projecting a sense of employee engagement. Employees are further encouraged to be selfless and altruistic by the degrees of perceived satisfaction with the creative HR methods. As a result, they become more open to doing tasks that aren’t directly relevant to their professions but nevertheless helpful to their businesses. This would increase the efficiency of enterprises in managing their human resources, particularly those businesses that are team-based. Keywords: Innovative Human Resource Practices, Employee Engagement, Employee Productivity, IT Sector, Bengaluru, Human Resource Technology, Trends of IHRM, Innovative Human Resource Technology tools, IHRM Strategies, Information Technology.
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Book chapters on the topic "AI-based software"

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Khan, Saquib Ali, Nabilah Tabassum Oshin, Mahmuda Nizam, Ishtiaque Ahmed, Md Masum Musfique, and Mahady Hasan. "AI-Based Software Testing." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-8346-9_28.

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Kaur, Davinder, Suleyman Uslu, and Arjan Durresi. "Quantum Algorithms for Trust-Based AI Applications." In Complex, Intelligent and Software Intensive Systems. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35734-3_1.

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Piereder, Christina, Günter Fleck, Verena Geist, Michael Moser, and Josef Pichler. "Using AI-Based Code Completion for Domain-Specific Languages." In Product-Focused Software Process Improvement. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49266-2_16.

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Mederly, Pavol, Marián Lekavý, Marek Závodský, and Pavol Návrat. "Construction of Messaging-Based Enterprise Integration Solutions Using AI Planning." In Advances in Software Engineering Techniques. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28038-2_2.

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Felderer, Michael, and Rudolf Ramler. "Quality Assurance for AI-Based Systems: Overview and Challenges (Introduction to Interactive Session)." In Software Quality: Future Perspectives on Software Engineering Quality. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65854-0_3.

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Vassilev, Vassil, Sylvia Ilieva, Iva Krasteva, Irena Pavlova, Dessisslava Petrova-Antonova, and Wiktor Sowinski-Mydlarz. "AI-Based Hybrid Data Platforms." In Data Spaces. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98636-0_8.

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AbstractThe current digital transformation of many businesses and the exponential growth of digital data are two of the key factors of digital revolution. For the successful meeting of high expectations, the data platforms need to employ the recent theoretical, technological, and methodological advances in contemporary computing and data science and engineering. This chapter presents an approach to address these challenges by combining logical methods for knowledge processing and machine learning methods for data analysis into a hybrid AI-based framework. It is applicable to a wide range of problems that involve both synchronous operations and asynchronous events in different domains. The framework is a foundation for building the GATE Data Platform, which aims at the application of Big Data technologies in civil and government services, industry, and healthcare. The platform implementation will utilize several recent distributed technologies such as Internet of Things, cloud, and edge computing and will integrate them into a multilevel service-oriented architecture that supports services along the entire data value chain, while the service orchestration guarantees a high degree of interoperability, reusability, and automation. The platform is designed to be compliant with the open-source software, but its open architecture supports also mixing with commercial components and tools.
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Beyer, Dirk. "Safeguarding AI-Based Software Development and Verification using Witnesses (Position Paper)." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-73741-1_22.

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AbstractThis position paper accompanies a presentation and discussion at AISoLA 2023 on the topic of how (generative) AI influences software engineering, namely, the development and verification of software programs. We outline a few opportunities and challenges that are posed by the application of AI. AI-based techniques can be an efficient method to produce software code. Not only this, AI can also be efficient in producing invariants that help proving correctness of software programs. But unfortunately, the results generated by AI-based approaches are often still imprecise or wrong: Code produced with the help of AI often does not satisfy the specification, and AI-generated invariants are often not helpful to prove the correctness of the program. The solution is to safeguard the process by independently checking the results using verification witnesses and witness validation. The goal of this paper is to convince readers that software witnesses are important and that independent result validation is of utmost importance to ensure correctness.
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Capozucca, Alfredo, Sophie Ebersold, Jean-Michel Bruel, and Bertrand Meyer. "Computing Education in the Age of AI-Based Assistants: Challenges and Opportunities." In Frontiers in Software Engineering Education. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-48639-5_9.

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Jung, Tae Moon, and Inwhee Joe. "An AI-Based Platform Architecture with Situational Awareness for Travel Plans." In Software Engineering Application in Systems Design. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-21435-6_32.

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Takeuchi, Hironori. "Enterprise Architecture-Based Project Model for AI Service System Development." In Handbook on Artificial Intelligence-Empowered Applied Software Engineering. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08202-3_7.

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Conference papers on the topic "AI-based software"

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Mittra, Raj, Ravi Kumar Arya, Prashant Chaudhary, and Abdelkhalek Nasri. "A Novel AI-Based Antenna Design Software." In 2024 IEEE INC-USNC-URSI Radio Science Meeting (Joint with AP-S Symposium). IEEE, 2024. http://dx.doi.org/10.23919/inc-usnc-ursi61303.2024.10632394.

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Barzamini, Hamed, Fatemeh Nazaritiji, Annalise Brockmann, Hasan Ferdowsi, and Mona Rahimi. "An AI-driven Requirements Engineering Framework Tailored for Evaluating AI-Based Software." In 2025 IEEE/ACM 4th International Conference on AI Engineering – Software Engineering for AI (CAIN). IEEE, 2025. https://doi.org/10.1109/cain66642.2025.00025.

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Wang, Ruoyu. "Analysis of AI Singing Technology Based on Praat Software." In 2025 IEEE International Conference on Electronics, Energy Systems and Power Engineering (EESPE). IEEE, 2025. https://doi.org/10.1109/eespe63401.2025.10986855.

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Chun, MinWoo, Sanghun Kim, Seunghyeon Park, Taekwon Chung, and Kiwoong Kwon. "AI-based Occupancy Prediction using WiFi CSI." In 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 2024. http://dx.doi.org/10.1109/compsac61105.2024.00215.

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Liguori, Pietro, Cristina Improta, Roberto Natella, Bojan Cukic, and Domenico Cotroneo. "Enhancing AI-based Generation of Software Exploits with Contextual Information." In 2024 IEEE 35th International Symposium on Software Reliability Engineering (ISSRE). IEEE, 2024. https://doi.org/10.1109/issre62328.2024.00027.

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K, Monika, Rajesh Verma, Abhimanyu Singh, and S. K. Vishvakarma. "AI/ML Based Malware (.exe) Detection Software using Ensemble Learning." In 2024 IEEE 4th International Conference on ICT in Business Industry & Government (ICTBIG). IEEE, 2024. https://doi.org/10.1109/ictbig64922.2024.10911714.

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Yi, Siming, Yuntao Yu, and Jinke Wu. "AI-based Online Code Quality Assessment System." In 2024 3rd International Conference on Cloud Computing, Big Data Application and Software Engineering (CBASE). IEEE, 2024. https://doi.org/10.1109/cbase64041.2024.10824380.

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Veracx, Merel. "A Holistic Framework for Evolving AI-Based Systems." In 2025 IEEE/ACM 4th International Conference on AI Engineering – Software Engineering for AI (CAIN). IEEE, 2025. https://doi.org/10.1109/cain66642.2025.00045.

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Weber, Sebastian, Max Scheerer, and Thomas Weber. "Architectural Assurance of AI-Based Driving Functions in Software-Defined Vehicles." In 2024 8th International Conference on System Reliability and Safety (ICSRS). IEEE, 2024. https://doi.org/10.1109/icsrs63046.2024.10927546.

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Kim, Seung Jae, Hyun Yoe, Kyeong Il Ko, and Meong Hun Lee. "Design of AI-Based Smart Crop Harvesting System." In 2024 IEEE/ACIS 22nd International Conference on Software Engineering Research, Management and Applications (SERA). IEEE, 2024. http://dx.doi.org/10.1109/sera61261.2024.10685628.

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Reports on the topic "AI-based software"

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Wagner, Rudolf. Enforcing Software and AI as Medical Devices: Expert Witness Insights on Civil Lawsuits, Regulation, and Legal Liability Pathways. ADHOCON UG (haftungsbeschränkt), 2024. http://dx.doi.org/10.70317/2024.13rw10.

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As an expert witness to German courts (Landgerichte, Oberlandesgerichte) and federal courts for Medical Devices and IVDs the number of expert statements has constantly increased, especially for Software and AI in Healthcare and focusing on their Medical Device classification. The paper also examines how competition and insurers may respond to claims arising from the use of improper classified software, artificial intelligence including Large Language Models (LLMs) with denial of coverage, increased premiums, and subrogation actions against hospitals or AI developers. Regulatory challenges are discussed in light of Software as a Medical Device (SaMD) frameworks, highlighting the effective and valid regulations to classify the risk of Software and AI when used in Healthcare, which are enforced today although the dynamic and evolving nature of AI systems. This paper concludes the used regulatory and legal pathways used in cases with expert witness statements between competitors or by competitors to file a lawsuit against a competitor with incorrect classification based on experience and draws the parallels to the US FDA regulation. Beyond it demonstrates that authority driven enforcement of existing regulation is transferred to courts acting on behalf of the authority driven by compliant legal manufacturers.
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Wagner, Rudolf. Enforcing Software and AI as Medical Devices: Expert Witness Insights on Civil Lawsuits, Regulation, and Legal Liability Pathways. ADHOCON UG (haftungsbeschraenkt), 2024. http://dx.doi.org/10.70317/2024.20rw10.

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As an expert witness to German courts (Landgerichte, Oberlandesgerichte) and federal courts for Medical Devices and IVDs the number of expert statements has constantly increased, especially for Software and AI in Healthcare and focusing on their Medical Device classification. The paper also examines how competition and insurers may respond to claims arising from the use of improper classified software, artificial intelligence including Large Language Models (LLMs) with denial of coverage, increased premiums, and subrogation actions against hospitals or AI developers. Regulatory challenges are discussed in light of Software as a Medical Device (SaMD) frameworks, highlighting the effective and valid regulations to classify the risk of Software and AI when used in Healthcare, which are enforced today although the dynamic and evolving nature of AI systems. This paper concludes the used regulatory and legal pathways used in cases with expert witness statements between competitors or by competitors to file a lawsuit against a competitor with incorrect classification based on experience and draws the parallels to the US FDA regulation. Beyond it demonstrates that authority driven enforcement of existing regulation is transferred to courts acting on behalf of the authority driven by compliant legal manufacturers.
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Wagner, Rudolf. AI, EHR, SaMD and Hospitals — US, German and Australian regulation make AI immediately available with non-SaMD pathway for Hospitals and Doctors. ADHOCON, 2024. http://dx.doi.org/10.70317/2024.24rw09.

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In the evolving healthcare landscape, hospitals are increasingly developing in-house AI tools and EHR systems to improve patient care while avoiding the regulatory burden associated with Software as a Medical Device (SaMD) classification. This paper explores the regulatory frameworks in the U.S., Germany, and Australia governing hospitals’ use of AI-based tools, EHR systems, and the relevant governmental oversight. We analyze how hospitals and EHR providers can strategically position AI to offer informational insights without triggering SaMD regulation. By reviewing international regulations and Epic Systems’ AI offerings, the paper outlines practical strategies for hospitals to align innovations with FDA, EU MDR, and TGA regulations, ensuring that AI tools offer contextual insights without being classified as medical devices.
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Bozzo Hauri, Sebastián. The New Frontier of Civil Liability: Artificial Intelligence, Autonomy, and Consumer Protection. Carver University; Universidad Autónoma de Chile, 2025. https://doi.org/10.32457/bozzo2202599.

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Technological evolution has entered a phase that challenges the very foundations of private law. The emergence of systems based on artificial intelligence (AI)—particularly in their most recent form, so-called AI agents—compels a reassessment of the traditional framework of civil liability, especially in the field of consumer law. The trajectory of AI has followed a path marked by three distinct waves. The first wave was predictive AI, trained on historical data to anticipate future behavior, as seen in recommendation engines and segmentation models. The second wave introduced generative AI—such as ChatGPT or Gemini—capable of producing text, images, or decisions based on prompts. However, it is the third wave, embodied by AI agents, that poses the greatest challenge: software capable of autonomous action, making decisions on behalf of users, interacting across platforms, and executing tasks with minimal human oversight.
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Wagner, Rudolf. EU AI Act (2024/1689) and EU MDR (2017/745): Breaking the Expensive Myth: Why AI-Powered Medical Devices Under EU MDR Don’t Need EU AI Act Certification – A Detailed Analysis of Regulatory Requirements and Compliance. Rudolf Wagner, 2025. https://doi.org/10.70317/2025.02rw02.

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The Growing Fear of Regulatory Overlap in AI-Driven Healthcare The EU AI Act (Regulation (EU) 2024/1689) has ignited discussions across the healthcare industry, especially among manufacturers of AI-powered medical devices. A major concern is whether these devices, already scrutinized under the EU Medical Device Regulation (MDR) 2017/745, will now face an additional regulatory burden—a second certification under the AI Act. This would mean double the compliance costs, twice the paperwork, and unnecessary delays for market entry. This paper shows that double certification is redundant and is not required for AI-based Software as a Medical Device (SaMD) that has already achieved MDR certification at Class IIa or higher. Article 43(3) of the EU AI Act explicitly allows for the use of existing sectoral certifications, a point reinforced by notified bodies such as TÜV Rheinland (TRLP) and official clarifications from DG SANTE inquired by the author. By closely examining the AI Act's compliance requirements and comparing them with the stringent MDR framework, it becomes clear that AI-driven medical devices are already regulated to meet the AI Act’s high-risk category requirements. If manufacturers are forced into dual certification, it will stifle innovation, burden the industry with unnecessary costs, and delay life-saving technologies from reaching patients. This paper presents a compelling case for regulatory streamlining, ensuring AI-powered medical devices can thrive under a single, coherent compliance framework.
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Fukuda, Koichi. Future Prospects for Smart Agriculture: Focusing on Case Studies in Japan and Asia. Asian Productivity Organization, 2025. https://doi.org/10.61145/jimb5846.

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This report examines the future prospects of smart agriculture, focusing on case studies from Japan and other Asian countries. Since 2019, Japan has promoted smart agriculture through government-supported initiatives, primarily targeting large-scale farms utilizing technologies such as AI, robotics, and satellite systems. However, as most Japanese farmers operate on a small-to-medium scale, there is growing demand for cost-effective, user-friendly solutions. The report highlights innovations including compact robots, AI-driven irrigation systems, and mobile apps for farm management and traceability. Other Asian countries are also advancing through smartphone-based tools and community-led adoption. Going forward, Japan’s strategy is expected to shift from hardware-centric to software-oriented approaches, integrating digital tools, AI, and environmental initiatives such as carbon labeling and credit programs. Widespread adoption will depend on affordability, simplicity, and continued government support.
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Christopher, Lauren, Stanley Chien, Yaobin Chen, Mei Qiu, William Reindl, and Liya Koshy. Anomaly Detection in Traffic Patterns Using the INDOT Camera System. Purdue University, 2025. https://doi.org/10.5703/1288284317778.

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The Transportation and Autonomous Systems Institute (TASI) of Purdue University Indianapolis (PUI) and the INDOT Traffic Management Center worked together to develop a system that monitors traffic conditions using INDOT CCTV video feeds. Computer vision-based traffic anomaly detection has been studied for the past 20 years, and a thorough state-of-the-art analysis was produced in a recent survey paper. Although AI has contributed to improving anomaly detection, several major challenges remain, such as tracking errors, illumination, weather, occlusion handling, camera pose, and perspective. In addition, the lack of real-life datasets makes the effectiveness of anomaly detection techniques unclear. This project builds on previous research by using automatic anomaly detection and AI algorithms to identify anomalous behavior of the short- and long-term variations of traffic patterns. The research team designed the new system, including the hardware and software components; the existing INDOT CCTV system; the database structure for traffic data extracted from the videos; and a user-friendly web-based server for showing the anomalies automatically.
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