Ecological Informatics, Год журнала: 2023, Номер 77, С. 102253 - 102253
Опубликована: Авг. 9, 2023
Язык: Английский
International Journal of Management & Entrepreneurship Research, Год журнала: 2024, Номер 6(3), С. 950 - 969
Опубликована: Март 28, 2024
Artificial Intelligence (AI) has emerged as a transformative force in the realm of product management, offering theoretical framework that reshapes journey from ideation to market penetration. This abstract provides comprehensive overview underpinnings and practical applications AI delineating its pivotal role across various stages lifecycle. The phase marks inception development, where serves catalyst for innovation, augmenting creativity through advanced algorithms data-driven insights. Market research validation constitute subsequent phase, empowers managers with sophisticated tools analyzing consumer trends, preferences, sentiments, thereby informing strategic decision-making processes. Prototyping represents critical stage wherein facilitates rapid iteration refinement, expediting development cycle enhancing adaptability. Leveraging machine learning algorithms, can swiftly iterate prototypes based on user feedback, ensuring alignment evolving demands. In domain design, AI-driven solutions revolutionize experience usability, leveraging natural language processing, computer vision, recommendation systems personalize interfaces cater diverse preferences. Quality assurance testing emerge imperative phases strategies optimize reliability, performance, scalability, mitigating risks associated failure overall quality. During launch enables orchestrate marketing distribution channels, maximizing penetration engagement. Predictive analytics, targeted advertising, dynamic pricing launches, competitive edge marketplace. conclusion, permeates every facet transforming traditional paradigms catalyzing innovation at By embracing AI's capabilities, navigate landscape modern markets agility, precision, foresight, driving sustained growth advantage. Keywords: AI, Product Management, Creativity, Ideation, Innovation
Язык: Английский
Процитировано
47
Applied Soft Computing, Год журнала: 2023, Номер 144, С. 110511 - 110511
Опубликована: Июнь 15, 2023
Язык: Английский
Процитировано
46
Journal of Marine Science and Engineering, Год журнала: 2024, Номер 12(4), С. 624 - 624
Опубликована: Апрель 7, 2024
Artificial intelligence (AI) technologies are increasingly being applied to the shipping industry advance its development. In this study, 476 articles published in Science Citation Index Expanded (SCI-EXPANDED) and Social Sciences (SSCI) of Web Core Collection from 2001 2022 were collected, bibliometric methods conduct a systematic literature field AI technology applications industry. The review commences with an annual publication trend analysis, which shows that research has been growing rapidly recent years. This is followed by statistical analysis journals collaborative network identify most productive journals, countries, institutions, authors. keyword “co-occurrence analysis” then utilized major clusters, as well hot directions field, providing for future field. Finally, based on results co-occurrence content papers years, gaps AIS data applications, ship trajectory, anomaly detection, possible directions, discussed. findings indicate direction mainly reflected behavior repair. Ship trajectory deep learning-based method discussion classification. Anomaly detection application learning improving efficiency detection. These insights offer guidance researchers’ investigations area. addition, we discuss implications both theoretical practical perspectives. Overall, can help researchers understand status development shipping, correctly grasp methodology, promote further
Язык: Английский
Процитировано
34
Finance & Accounting Research Journal, Год журнала: 2024, Номер 6(3), С. 421 - 436
Опубликована: Март 28, 2024
The integration of Artificial Intelligence (AI) within financial markets has become increasingly pivotal, particularly in emerging economies where efficiency and accessibility remain significant challenges. This abstract explores how AI technologies are reshaping market development, with a specific focus on enhancing economies. facilitates automation routine tasks, predictive modeling, robust risk management, thereby streamlining operations reducing costs. Moreover, AI-driven solutions democratize services, offering personalized advice expanding inclusion initiatives. Despite its transformative potential, challenges such as data privacy concerns, regulatory barriers, technological infrastructure limitations persist. By examining successful implementations case studies, this review underscores the importance collaborative efforts between public private sectors to overcome these Looking ahead, emphasizes need for policymakers develop conducive frameworks encourages stakeholders embrace sustainable development economies. Keywords: AI, Financial Market Development, Efficiency, Accessibility, Emerging Economies, Automation.
Язык: Английский
Процитировано
24
Energy Strategy Reviews, Год журнала: 2024, Номер 54, С. 101446 - 101446
Опубликована: Июнь 4, 2024
In the innovative domain of sustainable and renewable energy, artificial intelligence incorporation has appeared as a critical stimulant for improving productivity, cutting costs, addressing complex difficulties. However, all reported advancement over recent years, their experimental implementations, challenges associated have not been covered by single source. Hence, this review aims to give data source get recent, advanced detailed outlook on applications in energy technologies systems along with examples implementation. More than 150 research reports were retrieved from different bases keywords selection criteria maintain relevance. This specifically explored diverse approaches wide range sources innovations spanning solar power, photovoltaics, microgrid integration, storage power management, wind, geothermal comprehensively. The current technological advances, outcomes, case studies implications are discussed, potential possible solutions. expected advancements trends near future also discussed which can gateway researchers, investigators engineers look resolve already associated.
Язык: Английский
Процитировано
16
TrAC Trends in Analytical Chemistry, Год журнала: 2024, Номер 178, С. 117852 - 117852
Опубликована: Июль 3, 2024
Язык: Английский
Процитировано
16
Advances in computational intelligence and robotics book series, Год журнала: 2024, Номер unknown, С. 362 - 388
Опубликована: Март 4, 2024
Examining the ethical aspects of artificial intelligence (AI) and data science (DS) recognizes their impressive progress in innovation while emphasizing pressing necessity to tackle intricate dilemmas. The chapter provides a detailed framework for navigating changing environment, beginning with an examination increasing challenges. study highlights transparency, fairness, responsibility as crucial cultivating confidence AI systems. emphasizes urgent requirement address problems such algorithmic bias privacy breaches strong mitigation techniques. Furthermore, it promotes flexible policies that strike balance between safeguards. societal effects, particularly on various socioeconomic groups, economies, cultures, is conducted thoroughly, focus equity protection individual rights. Finally, proactively future challenges technology, advisable employ proactive solutions implementing ethics by design.
Язык: Английский
Процитировано
15
International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)
Опубликована: Фев. 6, 2025
The fast growth of IO networks has resulted in a security crisis besides the development decentralized-based innovations, and such decentralized bases or technologies also made challenges terms speed, performance, scalability. Traditional machine learning-based intrusion detection systems (IDS) are unable to manage intricate non-linear correlations seen massive amounts IoT data. They produce relatively low rates, especially multi-class classification, where many attack types must be addressed. Overcoming these hurdles calls for frameworks: innovative enough accommodate challenge whilst using wealth data produced by devices. Abstract In this paper, we introduce unique MLP-based deep learning architecture settings. This framework includes preprocessing pipeline that optimally normalizes applies one-hot-encoding prepare it classification. We tested algorithms on UNSW-NB15 dataset, commonly used IDS. Mere quantitative results show MLP surpasses classical models like Logistic Regression, SVM, Random Forests, giving precision 97.53%, recall 97.23%, accuracy 97.73% classification task. is undoubtedly scalable provides sufficient mechanism whole ecosystem; hence, can various actual use cases. performance shows could solve new threats developing environments.
Язык: Английский
Процитировано
5
Computers and Education Artificial Intelligence, Год журнала: 2025, Номер unknown, С. 100365 - 100365
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
2
Scientific Reports, Год журнала: 2021, Номер 11(1)
Опубликована: Июль 28, 2021
Abstract COVID-19 has caused many deaths worldwide. The automation of the diagnosis this virus is highly desired. Convolutional neural networks (CNNs) have shown outstanding classification performance on image datasets. To date, it appears that COVID computer-aided systems based CNNs and clinical information not yet been analysed or explored. We propose a novel method, named CNN-AE, to predict survival chance patients using CNN trained with information. Notably, required resources prepare CT images are expensive limited compared those collect data, such as blood pressure, liver disease, etc. evaluated our method publicly available dataset we collected. properties were carefully extract important features compute correlations features. A data augmentation procedure autoencoders (AEs) was proposed balance dataset. experimental results revealed average accuracy CNN-AE (96.05%) higher than (92.49%). demonstrate generality some existing mortality risk prediction methods (with without augmentation) their performances. also another for further verification. show can be used prediction, multiple pre-trained deep models tuned images.
Язык: Английский
Процитировано
102