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1
Khosravi, Hassan, Antonette Shibani, Jelena Jovanovic, Zachary A. Pardos, and Lixiang Yan. "Generative AI and Learning Analytics." Journal of Learning Analytics 12, no. 1 (2025): 1–11. https://doi.org/10.18608/jla.2025.8961.
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The rapid adoption of generative AI (GenAI) in education has raised critical questions about its implications for learning and teaching. While GenAI tools offer new avenues for personalized learning, enhanced feedback, and increased efficiency, they also present challenges related to cognitive engagement, student agency, and ethical considerations. Learning analytics (LA) provides a crucial lens to examine how GenAI affects learning behaviours and outcomes by offering data-informed insights into GenAI’s impact on students, educators, and educational ecosystems. Thus, obtained insights allow for evidence-based decision-making aimed at balancing GenAI’s benefits with the need to foster deep learning, creativity, and self-regulation of learning. This special issue of the Journal of Learning Analytics presents 10 research papers that explore the intersection of GenAI and LA, offering diverse perspectives that benefit students, teachers, and researchers. To structure these contributions, we adopt Clow’s generic framework of the LA cycle, categorizing the papers into four key areas: (1) understanding learning and learner contexts, (2) leveraging AI-generated data for learning insights, (3) applying LA methods to generate meaningful insights, and (4) designing interventions that optimize learning outcomes. By bringing together these perspectives, this special issue advances research-informed educational practices that ensure that GenAI’s potential is harnessed responsibly, reinforcing educational goals while safeguarding learners’ autonomy and cognitive development. Collectively, these contributions illustrate the reciprocal relationship between GenAI and LA, demonstrating how each can inform and refine the other. We reflect on the broader implications for LA, including the need to re-examine the boundaries of LA in the presence of GenAI, while preserving key principles from human-centred design and maintaining ethical and privacy standards that are foundational to LA.
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Wagh, Ms Pranali, Sahil Desai, Purav Doshi, Chaitanya Gajoor, and Advait Narkar. "AI Generated Cricket Score using NLP." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42922.
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To create an AI-based system for creating real- time cricket scorecards using live or recorded commentary, this study investigates the integration of Natural Language Processing (NLP), audio recognition, and machine learning approaches. Along with team names and venue information, users can stream live commentary using a microphone or upload audio files using the system’s user-friendly frontend, Streamlit. Speech recognition is used to process and turn the audio into text, which is subsequently tokenized and subjected to NLP techniques to extract important events like runs, wickets, and overs. The scorecard is updated continuously by appending this textual data to an already-existing match commentary file. Additionally, a T20 dataset is used to train a Random Forest-based machine learning model that uses the dynamically generated scorecard data to predict the final match score. By providing both live updates and predicted insights, the system seeks to improve user experience by delivering an automated, real-time cricket score creation tool. Index Terms—Natural language processing (NLP), speech recognition, artificial intelligence (AI), cricket scorecards, real- time commentary, machine learning, random forests, Text anal- ysis, tokenization, predictive modeling, Sports Data Extraction, Audio to Text Conversion, Automated Sports Analytics, and T20 Cricket Dataset In
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Shreejaa, N., and Dr V. Sudha. "HARNESSING GENERATIVE AI: INNOVATING DATA ANALYTICS IN THE ANALYTICAL ERA." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40578.
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The field of data analytics is being transformed by the use of generative artificial intelligence (AI) in today's rapidly changing digital landscape. This article explores the innovative applications and implications of generative AI in enhancing data analytics capabilities, with a focus on its impact in the analytical era. Generative AI refers to algorithms that can create new content, such as images, text, or entire datasets, based on patterns and examples it has been trained on. This technology has revolutionized traditional data analytics by allowing organizations to gain deeper insights, create predictive models, and automate complex decision-making processes with unprecedented accuracy and efficiency. One of the main advantages of generative AI in data analytics is its ability to handle large amounts of data and identify meaningful patterns that may not be obvious to human analysts. By using advanced machine learning techniques like neural networks, generative AI can analyze massive datasets to find correlations, anomalies, and trends that lead to actionable insights. Furthermore, generative AI enables organizations to simulate scenarios and predict outcomes with greater precision. This is particularly valuable in industries like finance, healthcare, and manufacturing, where accurate forecasting can result in significant cost savings, improved operational efficiency, and enhanced customer satisfaction. In addition to its predictive capabilities, generative AI enhances data analytics by allowing the creation of synthetic data. This synthetic data can be used to supplement existing datasets, address privacy concerns related to real-world data, and train machine learning models more effectively. Additionally, generative models enable data scientists to explore hypothetical scenarios and test hypotheses in a controlled environment, speeding up the pace of innovation and discovery. However, the widespread adoption of generative AI in data analytics also raises ethical and regulatory considerations. Issues such as data privacy, bias in generated content, and the potential misuse of synthetic data must be carefully addressed to ensure responsible deployment and mitigate risks. Looking ahead, the future of data analytics in the analytical era will undoubtedly be influenced by advancements in generative AI. As this technology continues to evolve, organizations will need to adapt by investing in strong infrastructure, training their workforce, and fostering a culture of responsible innovation.
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Dalat, Yvon. "LOOKING TO IMPLEMENT DATA ANALYTICS AND AI TO TRANSFORM LEARNING? CHECK FOR THESE POTENTIAL MINEFIELDS AND BEST PRACTICES." Performance Improvement Journal 62, no. 6 (2023): 189–91. http://dx.doi.org/10.56811/pfi-23-0018.
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The article discusses the implementation of data analytics and artificial intelligence (AI) in the context of learning and performance improvement. While it emphasizes the potential benefits of AI in personalized learning, process automation, and so forth, it also raises important considerations, such as data privacy, ethical usage, and intellectual property challenges associated with AI-generated content. The article presents a case study that demonstrates the effectiveness of personalized learning interventions in a real-world context. The article suggests several ways for individuals in the learning and performance improvement industry to embrace AI, including acquiring new skills, staying updated with industry advancements, experimenting with AI tools, and contributing to discussions and publications. In conclusion, the article highlights the transformative power of data analytics and AI in learning and performance improvement while emphasizing the need for responsible and ethical usage in this rapidly evolving field.
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Raghu, Ram Chowdary Velevela. "Transforming AI: The Pivotal Impact Of Big Data On Innovation." Journal of Advancement in Data Computational Statistics and Data Analysis 1, no. 1 (2025): 17–25. https://doi.org/10.5281/zenodo.14936895.
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<em>Big Data refers to the extensive and complex datasets generated at unprecedented volumes, velocities, and varieties, which exceed the processing capacity of traditional data management tools. These datasets pose challenges in terms of capturing, storing, transferring, querying, and processing information efficiently and in real-time. To address these challenges, advanced analytics techniques have emerged, often integrating Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) methodologies. The synergy between Big Data and AI has enabled significant advancements, transforming industries such as healthcare, finance, transportation, and education by enabling predictive analytics, real-time decision-making, and personalized solutions. This paper investigates the multifaceted impact of Big Data on AI, focusing on how the availability of large-scale, diverse datasets has enhanced the performance of AI models, driven innovation in algorithm development, and enabled breakthroughs in automation and intelligent systems. Furthermore, the paper highlights the challenges associated with integrating Big Data and AI, including ethical considerations, data privacy, and the need for scalable infrastructure.</em>
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Eze, Chibuike Samuel, and Lior Shamir. "Analysis and Prevention of AI-Based Phishing Email Attacks." Electronics 13, no. 10 (2024): 1839. http://dx.doi.org/10.3390/electronics13101839.
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Phishing email attacks are among the most common and most harmful cybersecurity attacks. With the emergence of generative AI, phishing attacks can be based on emails generated automatically, making it more difficult to detect them. That is, instead of a single email format sent to a large number of recipients, generative AI can be used to send each potential victim a different email, making it more difficult for cybersecurity systems to identify the scam email before it reaches the recipient. Here, we describe a corpus of AI-generated phishing emails. We also use different machine learning tools to test the ability of automatic text analysis to identify AI-generated phishing emails. The results are encouraging, and show that machine learning tools can identify an AI-generated phishing email with high accuracy compared to regular emails or human-generated scam emails. By applying descriptive analytics, the specific differences between AI-generated emails and manually crafted scam emails are profiled and show that AI-generated emails are different in their style from human-generated phishing email scams. Therefore, automatic identification tools can be used as a warning for the user. The paper also describes the corpus of AI-generated phishing emails that are made open to the public and can be used for consequent studies. While the ability of machine learning to detect AI-generated phishing emails is encouraging, AI-generated phishing emails are different from regular phishing emails, and therefore, it is important to train machine learning systems also with AI-generated emails in order to repel future phishing attacks that are powered by generative AI.
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Okhunov, Mukhammadyusuf. "INTEGRATING AI TOOLS IN CLASSROOMS OF TEACHING ENGLISH AT UNIVERSITIES." Oriental Renaissance: Innovative, educational, natural and social sciences 4, no. 24 (2024): 133–36. https://doi.org/10.5281/zenodo.14208838.
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<em>The integration of Artificial Intelligence (AI) in university can offer advanced tools for personalized learning, engagement, and real-time feedback. This article examines the methodologies behind the use of AI in English language teaching and focuses on AI-powered applications such as chatbots, adaptive learning platforms, and speech recognition software. It highlights the pedagogical benefits, such as tailored instruction and AI-driven assessments, and addresses potential challenges, including data privacy concerns and the digital divide. The future of AI in ELT points to even more sophisticated applications, including virtual reality (VR) integration, emotional AI, and AI-generated learning analytics for tracking student progress. This article provides an in-depth analysis of current applications, best practices, and emerging trends in AI-enhanced ELT.</em>
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Aravind Ayyagiri, Anshika Aggarwal, and Shalu Jain. "Enhancing DNA Sequencing Workflow with AI-Driven Analytics." International Journal for Research Publication and Seminar 15, no. 3 (2024): 203–16. http://dx.doi.org/10.36676/jrps.v15.i3.1484.
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The rapid advancements in DNA sequencing technologies have revolutionized genomics, enabling a deeper understanding of genetic information and its implications in various fields such as medicine, agriculture, and evolutionary biology. However, the exponential increase in sequencing data presents significant challenges in terms of data management, analysis, and interpretation. Traditional methods often fall short in handling the complexity and volume of data generated, necessitating the integration of advanced technologies like Artificial Intelligence (AI) to optimize the DNA sequencing workflow. AI-driven analytics offer transformative potential in enhancing DNA sequencing workflows by automating data processing, improving accuracy, and accelerating the pace of discovery. This abstract explores how AI can be integrated into various stages of the DNA sequencing process, including data preprocessing, alignment, variant calling, and downstream analysis. The integration of AI algorithms, such as machine learning and deep learning models, can streamline these processes by reducing manual intervention and minimizing errors. For instance, AI can enhance base calling accuracy, identify rare variants, and predict phenotypic outcomes with higher precision than traditional methods. The AI-driven approach in DNA sequencing is particularly beneficial in handling the challenges posed by next-generation sequencing (NGS) technologies. These technologies generate massive amounts of data that require efficient processing and interpretation. AI algorithms can be trained on large datasets to recognize patterns and anomalies that may be overlooked by human analysts. This capability is crucial in identifying novel mutations, understanding complex gene interactions, and drawing meaningful conclusions from vast genomic datasets.
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Awad, Iman, Afnan Al-Ghamdi, Azza Al-Ghamdi, and Lina Al-Farani. "The Impact of Artificial Intelligence Technologies in Educational Informatics on Improving Learners Performance: A Meta-Analysis." Journal of Umm Al-Qura University for Educational & Psychological Sciences 16, no. 3 (2024): 349–64. https://doi.org/10.54940/ep41266805.
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In light of rapid technological advancements, the vast amount of educational data generated on e-learning platforms has become a rich field for research and development in educational institutions. As a result, the field of Educational Informatics has emerged as an interdisciplinary domain combining education and informatics. This study aims to investigate the impact of artificial intelligence (AI) techniques in Educational Informatics through a meta-analysis of 27 studies conducted between 2020 and 2022. These studies specifically explored the effects of AI techniques, including learning analytics and educational data mining, on improving learners' performance. The findings revealed that learning analytics, as an AI technique, was the most frequently utilized in Educational Informatics, accounting for 92.6% of the studies. The overall effect size of AI techniques in this field was 0.66, with a standard error of 0.104 and a confidence interval ranging from 0.45 to 0.86. These results indicate a moderate and statistically significant effect of AI techniques in Educational Informatics on enhancing learners' performance. Based on these findings, the study recommends that educational institutions prepare for digital transformation, support data centers, and develop strategic plans in the field of Educational Informatics. Additionally, it suggests conducting further studies to explore the effectiveness of AI techniques, particularly learning analytics and educational data mining, on factors influencing learners' performance.
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Srinivasa, Reddy Vuyyuru. "Unlocking Future Consumer Insights: Using Predictive Analytics and AI to Shape Proactive Retail Strategies." European Journal of Advances in Engineering and Technology 10, no. 3 (2023): 110–15. https://doi.org/10.5281/zenodo.15560633.
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Companies within the retail sector experience fundamental transformations through the combination of predictive analytics alongside artificial intelligence (AI) for developing new strategic directions. The research delves into technology-enabled methods for retailers to acquire consumer data that produces business strategies for inventory enhancements, together with personalized customer service and performance optimization. The current AI and predictive analytics trends serve as research subjects to understand operational retail opportunities for developing proactive decision-making approaches. Retailers implementing these procedures anticipate developing better customer solutions that need prediction and waste reduction that improve their whole customer handling process. The research examines the way AI system-generated predictive models affect retail management decisions dealing with store operations as well as marketing strategies.
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Naseer, Fawad, and Sarwar Khawaja. "Mitigating Conceptual Learning Gaps in Mixed-Ability Classrooms: A Learning Analytics-Based Evaluation of AI-Driven Adaptive Feedback for Struggling Learners." Applied Sciences 15, no. 8 (2025): 4473. https://doi.org/10.3390/app15084473.
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Adaptation through Artificial Intelligence (AI) creates individual-centered feedback strategies to reduce academic achievement disparities among students. The study evaluates the effectiveness of AI-driven adaptive feedback in mitigating these gaps by providing personalized learning support to struggling learners. A learning analytics-based evaluation was conducted on 700 undergraduate students enrolled in STEM-related courses across three different departments at Beaconhouse International College (BIC). The study employed a quasi-experimental design, where 350 students received AI-driven adaptive feedback while the control group followed traditional instructor-led feedback methods. Data were collected over 20 weeks, utilizing pre- and post-assessments, real-time engagement tracking, and survey responses. Results indicate that students receiving AI-driven adaptive feedback demonstrated a 28% improvement in conceptual mastery, compared to 14% in the control group. Additionally, student engagement increased by 35%, with a 22% reduction in cognitive overload. Analysis of interaction logs revealed that frequent engagement with AI-generated feedback led to a 40% increase in retention rates. Despite these benefits, variations in impact were observed based on prior knowledge levels and interaction consistency. The findings highlight the potential of AI-driven smart learning environments to enhance educational equity. Future research should explore long-term effects, scalability, and ethical considerations in adaptive AI-based learning systems.
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Kim, Jiha, You-jin Park, and Joo-eun Hyun. "Exploring Learning Analytics Data for Supporting Self-Directed Learning in Smart Learning Environment." Korean Association For Learner-Centered Curriculum And Instruction 23, no. 11 (2023): 787–800. http://dx.doi.org/10.22251/jlcci.2023.23.11.787.
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Objectives The purpose of this study was to explore and define the factors that constitute self-directed learning in smart learning from a learning analytics perspective and to validate the appropriateness of the type and characteristics of the learning data.
 Methods To ensure the validity of the learning analytics data, we used an expert validation method. For this purpose, a total of seven experts with over 8 years of experience in e-learning, educational research, and educational practice participated. We explained the target service of this study and the purpose and tools of the validation test to each expert and conducted the test twice via email.
 Results We defined the self-directed learning data that can be collected and analyzed among various data generated throughout the learning process in smart learning. Specifically, we classified them into three factors of self-directed learning: 1) meta-cognition (planning, monitoring, self-evaluation, and reflection), 2) learning strategies (performance, review, problem-solving strategies, elaboration, and exploratory learning strategies), and 3) learning behaviors (time management, learning performance, immersion, concentration, problem-solving habits, and social learning participation) and a total of 41 detailed learning analysis data were derived.
 Conclusions This study provides meaningful directions for smart learning system to support learners' self-directed learning under the learning analytics. Also, it is expected to help lay the foundations for future AI-based learning system.
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Prashanth, Cecil. "AI-POWERED DEMAND FORECASTING AND SLOTTING OPTIMIZATION IN WAREHOUSE OPERATIONS." International Journal of Engineering Technology Research & Management (IJETRM) 09, no. 05 (2025): 496–505. https://doi.org/10.5281/zenodo.15545399.
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In the evolving landscape of supply chain management, warehouse operations are increasingly adopting artificialintelligence (AI) to enhance efficiency and responsiveness. This article explores how AI-powered solutions aretransforming two critical aspects of warehouse logistics: demand forecasting and slotting optimization. Demandforecasting enables organizations to predict future inventory requirements more accurately, thereby reducingstockouts and overstocking. Slotting optimization, on the other hand, enhances storage efficiency by assigningoptimal locations for inventory based on AI-generated insights. By integrating machine learning, deep learning,and predictive analytics, warehouses can automate decisions that traditionally required manual input and intuition.This convergence of AI and logistics not only reduces operational costs but also improves service levels andcustomer satisfaction. The study highlights the methodologies, benefits, and challenges of implementing AI inwarehouse settings, supported by real-world applications and research-based insights.
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Obiajuru Triumph Nwadiokwu. "Ethical AI and machine learning integration in health innovation information systems for clinical excellence." Computer Science & IT Research Journal 6, no. 3 (2025): 125–42. https://doi.org/10.51594/csitrj.v6i3.1872.
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The integration of Ethical Artificial Intelligence (AI) and Machine Learning (ML) in Health Innovation Information Systems is transforming clinical excellence by enhancing decision-making, optimizing healthcare workflows, and improving patient outcomes. AI-driven health technologies, such as predictive analytics, automated diagnostics, and personalized medicine, offer significant benefits in disease detection, treatment planning, and patient monitoring. However, their implementation raises ethical concerns related to data privacy, algorithmic bias, transparency, and accountability, necessitating a robust framework for responsible AI adoption in healthcare. A key ethical challenge in AI-driven health systems is ensuring fairness and bias mitigation, as poorly trained models may reinforce existing disparities in healthcare access and treatment. The use of explainable AI (XAI) is critical to enhancing transparency, allowing clinicians and patients to understand AI-generated recommendations. Additionally, the rise of Big Data and Internet of Medical Things (IoMT) requires stringent data governance policies to protect patient confidentiality under global regulations such as HIPAA, GDPR, and FDA guidelines. This study explores the balance between AI automation and human oversight, emphasizing the role of AI as an assistive tool rather than a replacement for clinical expertise. It highlights emerging AI innovations, including federated learning, blockchain-secured data sharing, and real-time AI-assisted decision support, while addressing risks such as liability, cybersecurity threats, and regulatory compliance. By developing ethically-aligned AI frameworks, healthcare organizations can maximize AI’s potential while upholding patient safety, equity, and trust. This paper provides insights into the future of AI governance, policy recommendations, and best practices to ensure that AI-driven health innovation aligns with ethical and clinical excellence. Keywords: Ethical Artificial Intelligence (AI), Machine Learning in Healthcare, Predictive Analytics in Medicine, Health Information Systems, AI Governance and Policy, Clinical Decision Support Systems.
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Redeer, Avdal Saleh, and Maseeh Yasin Hajar. "Comparative Analysis of AI and Machine Learning Applications in Modern Database." Engineering and Technology Journal 10, no. 03 (2025): 4112–23. https://doi.org/10.5281/zenodo.15062617.
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Using artificial intelligence (AI) and machine learning (ML) in contemporary database systems is the topic of discussion in this article. It examines a variety of scientific publications that investigate the breakthroughs that have been generated by artificial intelligence in a variety of fields, including agriculture, healthcare, military, and cloud computing. In this assessment, the transformational potential of artificial intelligence is highlighted within the context of improving data management, predictive analytics, decision-making, and automation. In addition, the debate discusses important obstacles, such as concerns about ethical issues, scalability issues, computing needs, and data security. The objective of this in-depth assessment is to provide insights into the continuous progress of AI-powered database systems as well as the consequences that these systems will have in the future.
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Tang, Shan Shan, Wen Fen Beh, and Kenny S. L. Cheah. "The Role of Lecturers’ AI Leadership in Enhancing Postgraduate Student Teachers’ Integration of Mobile AI Tools: A Mixed-Methods Study in Malaysian Education Faculties." International Journal of Interactive Mobile Technologies (iJIM) 19, no. 07 (2025): 136–58. https://doi.org/10.3991/ijim.v19i07.51971.
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This study examined the influence of lecturers’ artificial intelligence (AI) leadership on postgraduate student teachers’ motivation to integrate AI into their curricula in Malaysian higher education. Using a sample of 62 participants, the study employed a mixed-methods approach to explore ethical implications and the alignment of AI with traditional teaching practices. By means of open-ended questions and online surveys, the study generated both quantitative and qualitative understanding of how leadership influences acceptance of AI in educational settings. Key findings showed that transformative and visionary AI leadership approaches not only improve feedback systems and tailored learning opportunities but also inspire teachers by means of interactive, game-like learning activities. AI leadership enables early identification of learning gaps by means of real-time analytics, enabling targeted interventions and a more inclusive learning environment. However, over-reliance on AI highlights the need for strategic planning to ensure that AI complements rather than replaces traditional teaching methods. The research emphasized the need for strategic leadership and professional development in embedding AI ethically and successfully inside curricula, offering a framework for both curriculum design and educator training programs. These results support current debates on educational innovation and place leadership as key in promoting a balanced, ethical AI integration matched with present educational aims.
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Goyal, Mahesh Kumar. "Synthetic Data Revolutionizes Rare Disease Research: How Large Language Models and Generative AI are Overcoming Data Scarcity and Privacy Challenges." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 11 (2023): 1368–80. https://doi.org/10.17762/ijritcc.v11i11.11411.
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The scarcity of patient data has been a bottleneck for finding solutions in healthcare, and in the challenging field of rare diseases, this bottleneck is worsening due to the increasing use of data analytics. With medical research anchored problems—limited availability of data and stringent privacy regulations preventing data sharing—synthetic data is becoming a developing solution. Using advanced generative AI models, synthetic data can accurately represent the statistical properties of real-world patient data, while preserving the privacy of patients. Gan and VAEs are among other powerful aids to develop synthetic high quality dataset for a rare disease. By balancing between privacy and utility, these models helped generate data to support research and analytics without compromising patient confidentiality and without reducing analytical performance. Further protection against data breaches and re?identification risks with generated AI can be accomplished by integrating differential privacy with federated learning. Yet caution is maintained regarding bias in generative models, the ethics of using synthetic data in healthcare, as well as the tradeoff between data fidelity and privacy. In this study we discuss the use of generative AI to create synthetic data for rare disease research, potential implications for privacy preserving analytics, ethical dilemmas, and future research. In this area, a scheme is designed to effectively utilize generative AI, resulting in a need for more innovation and interdisciplinarity. Through generative AI, in the coming years, synthetic data creation will transform how data can be shared securely and ethically, and efficiently for rare disease research while accelerating the development of new treatments, ultimately improving patient outcomes.
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Venkat, Kalyan Uppala. "Enhancing Diagnostic Accuracy: The Role of Artificial Intelligence and Machine Learning in Imaging Analysis and Predictive Analytics for Personalized Medicine." European Journal of Advances in Engineering and Technology 7, no. 7 (2020): 101–6. https://doi.org/10.5281/zenodo.13790107.
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The advent of artificial intelligence (AI) and machine learning (ML) has initiated a transformative shift in the field of diagnostics, offering unprecedented opportunities to enhance the accuracy, efficiency, and personalization of medical assessments. These technologies have found significant applications in diagnostic imaging and predictive analytics, where they assist clinicians in detecting and diagnosing diseases with a level of precision that often surpasses human capabilities. AI and ML algorithms are particularly effective in analyzing complex medical images, such as those generated in radiology and pathology, enabling early detection of conditions like cancer and diabetic retinopathy. Additionally, the integration of ML into personalized medicine allows for the development of predictive models that assess an individual’s risk for diseases and predict treatment responses, paving the way for more targeted and effective healthcare interventions. Despite these promising developments, the implementation of AI and ML in clinical practice is not without its challenges. Issues such as data quality and availability, the interpretability of AI models, and the shifting regulatory landscape poses substantial challenges that must be addressed to fully unlock the potential of these technologies. Furthermore, the "black box" nature of many AI systems raises concerns about trust and transparency, which are critical for their acceptance by healthcare professionals and patients alike. This paper explores the current state of AI and ML in diagnostics, focusing on key areas such as imaging analysis and predictive analytics in personalized medicine. It reviews the advancements made in these fields, discusses the challenges faced in their adoption, and examines potential future directions, including the development of explainable AI and improvements in data interoperability. By tackling these challenges and maintaining a focus on innovation, AI and ML have the potential to become indispensable tools in the diagnostic process, resulting in more precise diagnoses, tailored treatments, and improved patient outcomes.
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Arnold, Niwarinda. "Smart Health Records: Integrating AI for Efficient Data Management." NEWPORT INTERNATIONAL JOURNAL OF RESEARCH IN MEDICAL SCIENCES 6, no. 1 (2025): 14–18. https://doi.org/10.59298/nijrms/2025/6.1.141800.
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The rapid evolution of healthcare demands innovative solutions for managing the vast quantities of data generated daily. This paper examines the concept of Smart Health Records (SHRs) and their integration with Artificial Intelligence (AI) to improve healthcare delivery. SHRs represent an advanced iteration of electronic health records, equipped with AI-powered tools to ensure real-time data sharing, predictive analytics, and automated administrative processes. AI’s capabilities, including natural language processing, machine learning, and predictive modeling, streamline data management and enhance clinical decision-making. However, the transition to SHRs is not without challenges, such as data privacy, algorithm accuracy, and resistance to technological adoption. This paper discusses best practices for implementing AI-driven SHRs and examines future trends, emphasizing their potential to revolutionize healthcare by fostering patient-centered care, improving efficiency, and enabling personalized medicine. Keywords: Smart Health Records, Artificial Intelligence, Electronic Health Records, Healthcare Data Management, Predictive Analytics
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Aas, Kjersti, Arthur Charpentier, Fei Huang, and Ronald Richman. "Insurance analytics: prediction, explainability, and fairness." Annals of Actuarial Science 18, no. 3 (2024): 535–39. https://doi.org/10.1017/s1748499524000289.
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AbstractThe expanding application of advanced analytics in insurance has generated numerous opportunities, such as more accurate predictive modeling powered by machine learning and artificial intelligence (AI) methods, the utilization of novel and unstructured datasets, and the automation of key operations. Significant advances in these areas are being made through novel applications and adaptations of predictive modeling techniques for insurance purposes, while, concurrently, rapid advances in machine learning methods are being made outside of the insurance sector. However, these innovations also bring substantial challenges, particularly around the transparency, explanation, and fairness of complex algorithmic models and the economic and societal impacts of their adoption in decision-making. As insurance is a highly regulated industry, models may be required by regulators to be explainable, in order to enable analysis of the basis for decision making. Due to the societal importance of insurance, significant attention is being paid to ensuring that insurance models do not discriminate unfairly. In this special issue, we feature papers that explore key issues in insurance analytics, focusing on prediction, explainability, and fairness.
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Zahaib Nabeel, Muhammad. "Big Data Analytics-Driven Project Management Strategies." Journal of Science & Technology 5, no. 1 (2024): 117–63. http://dx.doi.org/10.55662/jst.2024.5104.
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The integration of Artificial Intelligence (AI) and Big Data Analytics (BDA) in project management has become a critical enabler of efficiency in managing large-scale, complex projects. This research paper delves into how AI-driven big data analytics can revolutionize traditional project management methodologies by introducing dynamic scheduling, real-time risk prediction, and automated task prioritization strategies. These advanced techniques, which leverage machine learning (ML) models and extensive historical project data, enable a shift from reactive to proactive project management, ensuring that risks and resource constraints are identified and addressed before they impact project delivery. By analyzing massive datasets, including historical performance metrics, resource availability, and project timelines, AI-driven systems can forecast delays, assess risk levels dynamically, and adapt schedules in real-time. This proactive approach facilitates better decision-making, optimized resource allocation, and improved project outcomes. The study is anchored on the premise that the sheer volume of data generated in large-scale projects often overwhelms traditional project management systems. By incorporating AI and BDA, project managers can better utilize this data, turning it into actionable insights that inform intelligent decision-making. Machine learning algorithms, particularly those specializing in predictive analytics, are capable of identifying patterns that elude human analysis, allowing for the accurate forecasting of project risks, schedule slippage, and task dependencies. This ability to predict potential issues, such as resource bottlenecks or unforeseen delays, enables project teams to implement mitigative actions in advance, thus reducing the likelihood of project failure. Furthermore, dynamic scheduling is a key focus of this research, as AI-powered models can continuously adjust project timelines based on real-time data. These models consider variables such as resource utilization rates, task dependencies, and evolving project constraints, offering adaptive scheduling mechanisms that evolve throughout the project lifecycle. The automated task prioritization system, powered by BDA, ensures that the most critical tasks receive the appropriate level of attention at the right time, improving project performance and enhancing resource efficiency. Through natural language processing (NLP) and advanced data mining techniques, AI models can also analyze project documentation and communication channels to detect potential risks and suggest task adjustments. The paper also discusses the application of AI in risk prediction, focusing on how AI models can analyze risk factors from historical data, including resource constraints, financial limitations, and market volatility, to produce risk profiles that project managers can use for strategic planning. Real-time risk assessments, made possible by the integration of AI and BDA, can help project teams stay ahead of potential disruptions. This allows for more accurate contingency planning and reduces the overall risk to project timelines and budgets. Practical applications of these AI-driven strategies are presented through case studies of large-scale projects in various industries, including construction, information technology, and healthcare. These case studies demonstrate how AI-powered analytics have been successfully implemented to enhance project efficiency, optimize resource allocation, and minimize risks in complex projects. The study underscores the importance of integrating these technologies into modern project management frameworks to cope with the increasing complexity of projects in today’s fast-paced business environment. While the potential benefits of AI and BDA in project management are substantial, this paper also addresses the challenges associated with their implementation. One significant challenge is the quality and availability of data required to train AI models effectively. Incomplete or inaccurate data can lead to unreliable forecasts, compromising the project’s success. Additionally, the paper explores the issues of data privacy and security in AI-driven project management systems, highlighting the need for robust data governance frameworks to ensure the ethical use of AI technologies. Another key consideration is the resistance to change within organizations, where traditional project management methods are deeply ingrained. The paper emphasizes the need for a cultural shift towards data-driven decision-making and suggests strategies for fostering an environment conducive to AI adoption. This includes training project management teams to work alongside AI systems and fostering collaboration between AI experts and project managers to ensure smooth implementation and operation. Finally, this research outlines future trends in AI and BDA for project management, suggesting that further advancements in AI technologies, such as reinforcement learning and more sophisticated natural language processing algorithms, will drive the next generation of intelligent project management systems. These future systems are expected to be even more adept at handling the complexities of large-scale projects, offering real-time solutions to unforeseen challenges and adapting dynamically to changing project requirements.
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Joanne, Nabwire Lyanda, and Oliech Owidi Salmon. "Integrating Artificial Intelligence in Micro Teaching: The Role of ChatGPT for Customized Feedback and Interactive Learning." International Journal of Recent Research in Social Sciences and Humanities (IJRRSSH) 12, no. 2 (2025): 1–10. https://doi.org/10.5281/zenodo.15130275.
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<strong>Abstract:</strong><em> </em>The integration of Artificial Intelligence (AI) in teaching has transformed conventional teacher training methods, offering AI-driven feedback systems, interactive simulations, and adaptive learning environments. This study explored the role of AI, particularly generative models like ChatGPT, in enhancing lesson delivery, instructional feedback, and teacher engagement in micro- teaching. AI-powered platforms provide real-time, systematic, and personalized feedback, analyzing verbal communication, lesson structuring, and classroom engagement techniques to improve teaching effectiveness. Additionally, AI-driven simulations enable pre-service teachers to practice classroom management, respond to diverse learning scenarios, and develop adaptive instructional strategies in a risk-free virtual environment. Despite these advancements, AI in micro-teaching presents significant challenges, including bias in AI-generated feedback, lack of emotional intelligence, data privacy concerns, and the potential over-reliance on automation. Research highlights that while AI offers consistency and efficiency, it lacks the depth of human evaluation, particularly in assessing creativity, socialization, student engagement, and emotional responsiveness. A hybrid feedback model that integrates AI-driven analytics with human mentoring is recommended to balance structured feedback with contextual and personalized insights. This literature review synthesizes theoretical frameworks, such as Constructivist Learning Theory, Feedback and Learning Theories, and the Artificial Intelligence in Education (AIED) Framework, to explain AI’s role in micro-teaching. Findings suggest that AI-enhanced micro-teaching can complement conservative evaluation methods, leading to a more engaging, individualized, and efficient teacher training experience. However, ethical considerations and responsible AI integration must be prioritized to ensure fair, unbiased, and effective use of AI in education. This study contributes to the ongoing discourse on AI’s impact in teacher education, offering insights into its potential, limitations, and future directions. <strong>Keywords:</strong> micro-teaching, Artificial intelligence, AI-generated feedback, AI-powered simulations, Hybrid AI-human evaluation. <strong>Title:</strong> Integrating Artificial Intelligence in Micro Teaching: The Role of ChatGPT for Customized Feedback and Interactive Learning <strong>Author:</strong> Joanne Nabwire Lyanda, Salmon Oliech Owidi <strong>International Journal of Recent Research in Social Sciences and Humanities (IJRRSSH)</strong> <strong>ISSN 2349-7831</strong> <strong>Vol. 12, Issue 2, April 2025 - June 2025</strong> <strong>Page No: 1-10</strong> <strong>Paper Publications </strong> <strong>Website: www.paperpublications.org</strong> <strong>Published Date: 04-April-2025</strong> <strong>DOI: https://doi.org/10.5281/zenodo.15130275</strong> <strong>Paper Download Link (Source)</strong> <strong>https://www.paperpublications.org/upload/book/Integrating%20Artificial%20Intelligence-04042025-3.pdf</strong>
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Subhasis, Kundu. "AI-Generated Predictive Cloud Optimization: Preemptively Detecting and Preventing System Failures for Enhanced Cloud Reliability." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 11, no. 6 (2023): 1–6. https://doi.org/10.5281/zenodo.15084283.
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This study examines the application of AI-driven predictive cloud optimization to enhance cloud reliability by forecasting and preventing system failures. An innovative method is proposed, employing machine learning algorithms to analyze extensive cloud infrastructure data, identify potential issues, and implement proactive measures. This approach integrates real-time monitoring, predictive analytics, and automated solutions to minimize downtime and improve resource management. A case study is presented, demonstrating the method's success in a large-scale cloud environment, with significant improvements in system reliability and performance. The findings indicate a substantial reduction in unexpected outages and a notable increase in the overall efficiency of cloud infrastructure. This research contributes to the field of cloud computing by offering a robust framework for AI-based predictive maintenance and optimization.
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Dai, Junjie, Xiaoyan Mao, Pengyue Wu, Huijie Zhou, and Lei Cao. "Revolutionizing cross-border e-commerce: A deep dive into AI and big data-driven innovations for the straw hat industry." PLOS ONE 19, no. 12 (2024): e0305639. https://doi.org/10.1371/journal.pone.0305639.
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This paper investigates the impact of artificial intelligence (AI) and big data analytics on optimizing cross-border e-commerce efficiency for straw hat manufacturers in Zhejiang Province, China. It identifies market and consumer demand trends through machine learning analysis of comprehensive e-commerce data and leverages generative AI to revolutionize production and marketing processes. The integration of AI-generated content (AIGC) technology facilitates streamlined design-to-production cycles and rapid adaptation to market changes and consumer feedback. Findings demonstrate that the application of AI and big data significantly enhances market responsiveness and sales performance for straw hat enterprises in cross-border e-commerce. This research contributes a novel framework for employing AI and big data to navigate the complexities of international commerce, providing strategic insights for small and micro enterprises seeking to expand their global market footprint.
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Tiwari, Tanuja, and Pallavi. "The AI-powered Cleanup: A Revolution in Solid Waste Management." International Journal of Environment and Climate Change 15, no. 3 (2025): 481–90. https://doi.org/10.9734/ijecc/2025/v15i34787.
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Managing solid waste is a critical global issue that demands innovative strategies to enhance efficiency, sustainability, and environmental impact. Waste generation varies across sectors and regions, both in quantity and composition, making its management a critical environmental issue. The escalating decline of ecological quality directs the scientific community toward analyzing and optimizing waste management strategies. Artificial Intelligence (AI) has appeared to bring a revolution in this area by improving the processes of waste collection, segregation, recycling, and disposal. Various models and algorithms have been explored and evaluated for their potential to lead to more sustainable solid waste management (SWM) practices. AI-powered systems leverage data analytics, computer vision, machine learning, and automation to reduce landfill problems, lower operational costs, and support the circular economy. Advanced ML technologies, like deep learning and predictive analytics models, are being utilized for route optimization to ensure timely service delivery and adjust collection schedules accordingly. Smart bin systems equipped with sensors, IoT, and machine learning algorithms are enhancing waste collection and disposal efficiency. AI-generated predictive models significantly aid in waste management planning to adapt to changing waste generation patterns. Technologies like GPS and volumetric sensors, provide an encouraging solution to enhance the efficiency of waste collection systems, and waste-sorting robots can greatly improve the accuracy of waste segregation. Sensor-based waste monitoring tracks the amount of generated waste and identifies its sources in a given area. AI-powered surveillance cameras and drones can promptly detect illegal dumping, enabling authorities to respond swiftly. Thus, SWM can be strengthened by utilizing AI technologies in intelligent waste sorting, recycling, and disposal, leading to more sustainable practices. However, despite the efficiency of AI in supporting SWM systems, high cost, inconsistent data quality, traditional mindset, operational difficulties, etc., pose a challenge to their widespread adoption. There is still a notable gap in its practical application and comprehensive evaluation. To bridge this gap, targeted research on cost-effective solutions and real-world pilot projects is crucial, coupled with collaboration among technology developers, policymakers, and waste management professionals. This paper explores how AI can revolutionize waste management, leading to more efficient strategies and a cleaner future.
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Bulut, Okan, and Tarid Wongvorachan. "Feedback Generation through Artificial Intelligence." Open/Technology in Education, Society, and Scholarship Association Conference 2, no. 1 (2022): 1–9. http://dx.doi.org/10.18357/otessac.2022.2.1.125.
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Feedback is an essential part of the educational assessment that improves student learning. As education changes with the advancement of technology, educational assessment has also adapted to the advent of Artificial Intelligence (AI). Despite the increasing use of online assessments during the last decade, a limited number of studies have discussed the feedback generation process as implemented through AI. To address this gap, we propose a conceptual paper to organize and discuss the application of AI in the feedback generation and delivery processes. Among different branches of AI, Natural Language Processing (NLP), Educational Data Mining (EDM), and Learning Analytics (LA) play the most critical roles in the feedback generation process. The process begins with analyzing students’ data from educational assessments to build a predictive machine learning model with additional features such as students’ interaction with course material using EDM methods to predict students’ learning outcomes. Written feedback can be generated from a model with NLP-based algorithms before being delivered, along with non-verbal feedback via a LA dashboard or a digital score report. Also, ethical recommendations for using AI for feedback generation are discussed. This paper contributes to understanding the feedback generation process to serve as a venue for the future development of digital feedback.
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Nayan, Uchhana, Ranjan Ravi, Sharma Shashank, Agrawal Deepak, and Punde Anurag. "Literature Review of Different Machine Learning Algorithms for Credit Card Fraud Detection." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 10, no. 6 (2021): 101–8. https://doi.org/10.35940/ijitee.C8400.0410621.
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Every year fraud cost generated in the economy is more than $4 trillion internationally. This is unsurprising, as the return on investment for fraud can be massive. Cybercrime specialists estimate that an investment of 1 million dollars into fraud or attack can net up to $100 million. Financial institutions such as commercial and investment banking operations are increasingly being targeted. And we know that the only way to fight fraud effectively is through the use of advanced technology. The answer lies in relying on advanced analytics and enterprisewide data storage capabilities that support the use of artificial intelligence (AI) and machine learning (ML) approaches to stay one step ahead of criminals. AI is best suited to defend against today’s fast-changing and complex bank fraud, where new threats are under development every day. Approaches relying on fragmented and siloed data, rules-based approaches or traditional point-solutions are no longer acceptable. These approaches are not only ineffective, but they are extremely costly to banks and financial services firms because they force legal and compliance teams to spend a lot of time trying to gain access to the data they need. By relying on advanced analytics and AI and ML capabilities, fraud and compliance units can spend their time working on more-complex fraud issues. Manual investigation can be reduced through the use of complex algorithms powered by ML, often in conjunction with rules, a combination that offers significant advantages over purely based -rules fraud detection. In this paper, we have included different machine learning algorithms used to detect credit card frauds and also provide a comparative study between different algorithms.
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Chianumba, Ernest Chinonso, Nura Ikhalea, Ashiata Yetunde Mustapha, and Adelaide Yeboah Forkuo. "A Conceptual Model for Addressing Healthcare Inequality Using AI-Based Decision Support Systems." Journal of Frontiers in Multidisciplinary Research 3, no. 1 (2022): 72–88. https://doi.org/10.54660/.ijfmr.2022.3.1.72-88.
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Healthcare inequality remains a persistent global challenge, particularly affecting marginalized and underserved populations. Disparities in access, quality, and health outcomes are exacerbated by socioeconomic, geographic, and systemic barriers. This paper proposes a conceptual model for addressing healthcare inequality through the implementation of Artificial Intelligence (AI)-based Decision Support Systems (DSS). The model integrates advanced data analytics, machine learning algorithms, and real-time health information to support clinical and policy decisions aimed at reducing disparities in healthcare delivery. The conceptual model operates on three core pillars: data integration, predictive analytics, and equitable decision-making. First, the model aggregates data from diverse sources—including electronic health records (EHRs), social determinants of health, and population health databases—to build a comprehensive profile of healthcare needs in various communities. Second, AI-driven predictive analytics are employed to identify at-risk populations, forecast disease trends, and allocate resources efficiently. Finally, the system provides tailored decision support for healthcare providers and policymakers, ensuring that interventions are responsive to the specific needs of disadvantaged groups. A key feature of the model is its emphasis on explainable AI (XAI), which ensures transparency, accountability, and trust in AI-generated recommendations. The model also incorporates fairness-aware algorithms to mitigate bias in data and decision-making, promoting inclusivity and ethical use of technology. Case simulations demonstrate how the system can optimize screening programs, prioritize high-risk patients, and guide equitable health policy formulation. This paper underscores the transformative potential of AI-based DSS in reducing healthcare inequality by enabling data-driven, context-sensitive, and inclusive health interventions. By aligning technology with principles of social justice and health equity, the model offers a strategic pathway for addressing longstanding disparities in healthcare systems. Future research will focus on real-world implementation, stakeholder engagement, and continuous learning to refine the model and expand its applicability across different healthcare settings.
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A R, SALIM MALIK. "AI Continues to be a Game Changer in the Intellectual Property Management in India." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41289.
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The integration of Artificial Intelligence (AI) into Intellectual Property (IP) management has significantly reshaped the landscape of IP administration in India. With the growing emphasis on innovation and technological advancement, India's IP ecosystem is rapidly adopting AI-driven solutions to streamline processes, enhance accuracy, and reduce human intervention. AI-powered tools for prior art searches, patent classification, and infringement detection have significantly accelerated IP workflows while reducing the backlog of pending applications. This paper explores the transformative role of AI in IP management in India, focusing on its impact on efficiency, cost reduction, and decision-making. The research highlights how machine learning algorithms and predictive analytics have enabled faster and more accurate patent reviews, benefiting both IP examiners and applicants. By automating repetitive tasks, AI has reduced the administrative burden on India's IP offices, allowing them to focus on more complex and strategic aspects of IP protection. However, this technological shift is not without its challenges. Issues related to AI-generated IP, ownership rights, and ethical concerns regarding the role of AI as an inventor are still unresolved in India's legal framework. The absence of specific regulations governing AI-generated IP has sparked debate among policymakers, industry stakeholders, and academic researchers. This paper calls for the development of an AI-specific regulatory framework to address these issues while ensuring a balance between innovation and IP protection. The study concludes that AI is a critical enabler of India's efforts to modernize its IP management system. It underscores the need for capacity building, legal reforms, and cross-border collaboration to establish a standardized approach to AI-driven IP governance. Future research should explore international best practices and assess how India's regulatory framework can be adapted to accommodate AI's role in IP management. Key Words: Artificial Intelligence, Intellectual Property, AI-Generated IP in india, Patent Classification, Regulatory Framework
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Uchhana, Nayan, Ravi Ranjan, Shashank Sharma, Deepak Agrawal, and Anurag Punde. "Literature Review of Different Machine Learning Algorithms for Credit Card Fraud Detection." International Journal of Innovative Technology and Exploring Engineering 10, no. 6 (2021): 101–8. http://dx.doi.org/10.35940/ijitee.c8400.0410621.
Full textAbstract:
Every year fraud cost generated in the economy is more than $4 trillion internationally. This is unsurprising, as the return on investment for fraud can be massive. Cybercrime specialists estimate that an investment of 1 million dollars into fraud or attack can net up to $100 million. Financial institutions such as commercial and investment banking operations are increasingly being targeted. And we know that the only way to fight fraud effectively is through the use of advanced technology. The answer lies in relying on advanced analytics and enterprisewide data storage capabilities that support the use of artificial intelligence (AI) and machine learning (ML) approaches to stay one step ahead of criminals. AI is best suited to defend against today’s fast-changing and complex bank fraud, where new threats are under development every day. Approaches relying on fragmented and siloed data, rules-based approaches or traditional point-solutions are no longer acceptable. These approaches are not only ineffective, but they are extremely costly to banks and financial services firms because they force legal and compliance teams to spend a lot of time trying to gain access to the data they need. By relying on advanced analytics and AI and ML capabilities, fraud and compliance units can spend their time working on more-complex fraud issues. Manual investigation can be reduced through the use of complex algorithms powered by ML, often in conjunction with rules, a combination that offers significant advantages over purely based -rules fraud detection. In this paper, we have included different machine learning algorithms used to detect credit card frauds and also provide a comparative study between different algorithms.
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Demartini, Claudio Giovanni, Luciano Sciascia, Andrea Bosso, and Federico Manuri. "Artificial Intelligence Bringing Improvements to Adaptive Learning in Education: A Case Study." Sustainability 16, no. 3 (2024): 1347. http://dx.doi.org/10.3390/su16031347.
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Despite promising outcomes in higher education, the widespread adoption of learning analytics remains elusive in various educational settings, with primary and secondary schools displaying considerable reluctance to embrace these tools. This hesitancy poses a significant obstacle, particularly given the prevalence of educational technology and the abundance of data generated in these environments. In contrast to higher education institutions that readily integrate learning analytics tools into their educational governance, high schools often harbor skepticism regarding the tools’ impact and returns. To overcome these challenges, this work aims to harness learning analytics to address critical areas, such as school dropout rates, the need to foster student collaboration, improving argumentation and writing skills, and the need to enhance computational thinking across all age groups. The goal is to empower teachers and decision makers with learning analytics tools that will equip them to identify learners in vulnerable or exceptional situations, enabling educational authorities to take suitable actions that are aligned with students’ needs; this could potentially involve adapting learning processes and organizational structures to meet the needs of students. This work also seeks to evaluate the impact of such analytics tools on education within a multi-dimensional and scalable domain, ranging from individual learners to teachers and principals, and extending to broader governing bodies. The primary objective is articulated through the development of a user-friendly AI-based dashboard for learning. This prototype aims to provide robust support for teachers and principals who are dedicated to enhancing the education they provide within the intricate and multifaceted social domain of the school.
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Alshebani, Meshal Menahi, Mohammed Qismi Alanazi, Abdulrahman Eidhah Alanazi, et al. "Application of Artificial Intelligence in Paramedic Education: Current Scenario and Future Perspective: A Narrative Review." Journal of Medicine, Law & Public Health 4, no. 1 (2023): 299–306. http://dx.doi.org/10.52609/jmlph.v4i1.98.
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Background: Artificial intelligence (AI) has the potential to revolutionise paramedic education. As well as allowing for personalised learning experiences tailored to individual needs and learning styles, it can provide simulations, intelligent tutoring systems, automated grading and assessment, and predictive analytics. Objective: To investigate the role of artificial intelligence in transforming the landscape of paramedic education and evaluate its potential to improve learning outcomes. Methods: This review presented the role of AI in paramedic education and its perspective over the past twenty years. It included high-quality data and comprehensive investigations of articles available in renowned databases. Results: AI-based training and simulation technologies, such as virtual patients, surgical simulators, and intelligent tutoring systems,are increasingly being used in paramedic education. Virtual patients use computer-generated avatars to display symptoms and react to therapies, while surgical simulators use accurate anatomical models and haptic feedback devices to simulate surgical operations. Conclusion: AI has the potential to fundamentally alter how students learn, the kind of education they receive, and the efficiency with which healthcare is delivered. It can create immersive training environments, analyse medical data, and help students feel more competent, confident, and capable. This potential can be harnessed to enhance paramedic education and improve patient care outcomes.
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Baynit, Marystela, Cosmas B. F. Mnyanyi, and Mohamed Salum Msoroka. "Digital Learning in the Age of Artificial Intelligence: Insights from Selected Higher Learning Institutions in Tanzania." African Quarterly Social Science Review 2, no. 2 (2025): 96–112. https://doi.org/10.51867/aqssr.2.2.9.
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The rapid advancement of artificial intelligence (AI) technologies is reshaping education globally, with it is significant implications on teaching, learning and administrative processes in higher learning institutions. The higher learning institutions in Tanzania are increasingly adopting digital learning solutions powered by AI to address challenges such as limited teaching resources, less interactive delivery approaches, lengthy assessment approaches and the need for personalized education. This study explores the integration of AI in digital learning within the selected Tanzanian higher learning institutions (HLIs), highlighting its opportunities, challenges, and impact on educational outcomes. Although existing research has produced several contributions on both topics, the knowledge generated in the field appears fragmented and the findings are sometimes ambiguous. This study aims to consolidate the state of art of scholarly research published over the past 36 years at the intersection of the AI tools in Tanzania higher Learning Institutions. The Technology Acceptance Model (TAM) was employed to guide this study. The model explains how perceived usefulness and ease of use influence the adoption of technology. To this aim, we carried out a systematic literature review by retrieving a set of 117 papers which was later limited to time, English language and key words as inclusion criteria only 45 papers were utilized and complemented with key informant interviews.The reviewed documents were strategically selected from the Scopus and web of science scholarly journals. The data were extracted and synthesised into sub themes. The findings reveal that a proportion of HLIs instructors are adopting AI tools in their teaching and learning activities. The common AI-driven tools employed by instructors include; ChatGPT, Grammarly, intelligent tutoring systems, automated grading platforms, and data analytics. It has been further noted that the AI tools have significantly impact in teaching and learning by providing personalized feedback, improving learning resources accessibility and in turn improving students’ academic performance. However, the study identifies several challenges, including inadequate infrastructure, high implementation costs and limited technical expertise. There is also concern over data privacy, ethical considerations, and the potential for reduced human interaction in education. This paper concludes that while AI integration in Tanzanian higher education is in its early stages, it offers immense opportunities for improving educational outcomes and institutional efficiency. Recommendations are provided to address challenges, emphasizing policy development, capacity building and increased investment in AI infrastructure to enhance effective educational outcomes.
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Chhetri, Swastik, and S. M. Milan. "Role of AI in Predicting Consumer Behavior." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44399.
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The emergence of Artificial Intelligence (AI) has significantly transformed the understanding and prediction of consumer behavior. Due to swift progress in machine learning and data analytics, AI allows companies to analyze extensive consumer data, identify trends, and make informed decisions based on insights derived from data. This study look into the methods in which AI improves consumer behavior analysis by inspect critical marketing functions such as customer segmentation, altered recommendations, demand forecasting, and sentiment analysis. This research make use of a survey approach, with data verified using SPSS to extract valuable findings It looks into how AI-driven methods, including predictive analytics, natural language processing (NLP), and deep learning, helps businesses in foreseeing customer wants,needs and behavioral patterns. Results indicate that AI-driven answers makes marketing effectively by creating targeted advertising, enhancing customer engagement rate , and boosting sales. Furthermore, AI supports sentiment analysis by analyzing social media interactions, online reviews, and consumer feedback s, which allows businesses to modify their strategies with respect to changing market situations. An important insight derived from this study is that AI enhances decision-making by eliminating human biases and allowing for immediate adjustments in marketing initiatives. AI-driven chatbots and virtual assistants have revolutionized customer service by providing personalized experiences, promoting customer satisfaction, and fostering brand loyalty. In addition, AI’s action in dynamic pricing strategies provide businesses to establish competitive pricing by analyzing market trends, consumer wants, and competitors activities. However, the adoption of AI brings certain difficulties, including concerns over data security, ethical problems, and the need for ongoing technological advancements. Companies must find a balance between utilizing AI for organizational growth while ensuring responsible and transparent use of AI to maintain consumer confidence. This research summaries the increasing impact of AI in modern marketing and its Capacity to transform consumer behavior analysis. The discovery recommends that businesses leveraging AI-generated insights are better equipped to grasps customer wants, improve personalized experiences, and secure a competitive advantage in the marketplace. As the AI technology advances, its importance in forecasting consumer behavior will become increasingly essential for business growth
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D’mello 1, Declan Anthony. "AI-Driven Detection and Support for Hidden Addiction Patterns in Remote Workers: A Multimodal Approach." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47910.
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Abstract The widespread adoption of remote work has brought new flexibility to organizations but has also complicated the monitoring of employee well-being, particularly in identifying hidden addiction patterns such as substance abuse, behavioral compulsions, and digital overuse. This study introduces a privacy-preserving, AI-driven framework that leverages lightweight transformer models and multimodal behavioral analytics to detect and support addiction risks within distributed workforces. The proposed system integrates methodologies inspired by the mhGPT (Mental Health GPT) model and incorporates federated learning, differential privacy, and explainable AI to ensure both effectiveness and ethical compliance with GDPR and HIPAA standards. Model development and evaluation primarily utilized synthetically generated datasets and large-scale public datasets, with a limited-response survey informing feature design and scenario construction. Simulated experiments demonstrated high F1-scores in early risk detection and promising engagement rates for a tiered intervention protocol. While these results highlight the framework’s potential, real-world validation and further empirical study are needed to assess practical applicability and address ethical considerations in deployment. Keywords: Remote work, addiction detection, artificial intelligence, multimodal analytics, mental health, privacy
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Pooja Perlekar and Amruta Desai. "The Role of Artificial Intelligence in Personalized Medicine: Challenges and Opportunities." Metallurgical and Materials Engineering 31, no. 3 (2025): 85–92. https://doi.org/10.63278/1322.
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The integration of Artificial Intelligence (AI) in personalized medicine has revolutionized healthcare by enabling precise, data-driven, and patient-specific treatment strategies. AI-powered algorithms, particularly those leveraging machine learning (ML) and deep learning (DL), have enhanced the ability to analyze vast datasets, uncover hidden patterns, and generate predictive models that facilitate early disease detection, drug discovery, and customized treatment regimens. AI applications in genomics, medical imaging, and electronic health records (EHRs) have significantly contributed to the advancement of precision medicine, ensuring more accurate diagnoses and effective therapies.Despite these remarkable advancements, the implementation of AI in personalized medicine presents several challenges. Data privacy and security concerns are at the forefront, as the use of AI relies heavily on patient data, which necessitates strict regulatory compliance and ethical considerations. Additionally, biases in AI algorithms due to imbalanced training datasets can lead to disparities in medical outcomes, disproportionately affecting underrepresented populations. The integration of AI into clinical workflows is another significant hurdle, as healthcare providers require specialized training to interpret AI-generated insights and incorporate them into patient care effectively. Moreover, the need for standardized protocols and regulatory frameworks remains critical to ensuring the reliability, safety, and ethical application of AI in medical practice.Opportunities for AI in personalized medicine continue to expand with advancements in computational power, data analytics, and collaborative efforts between medical researchers and AI developers. Emerging technologies such as explainable AI (XAI) aim to enhance transparency in decision-making, allowing physicians and patients to better understand AI-generated recommendations. Additionally, federated learning techniques provide a promising solution to data-sharing challenges by enabling AI models to be trained across multiple institutions while preserving patient privacy. The convergence of AI with other innovations, such as blockchain for secure data management and the Internet of Medical Things (IoMT) for real-time patient monitoring, further strengthens its role in personalized medicine.This paper explores the transformative potential of AI in personalized medicine, analyzing its key applications, limitations, and future prospects. A thorough examination of current AI-driven methodologies, case studies, and policy considerations will provide a holistic understanding of the evolving landscape. While AI holds immense promise in improving patient outcomes through tailored treatments, addressing its challenges through interdisciplinary collaboration and regulatory advancements is crucial to maximizing its benefits. As AI continues to shape the future of medicine, a balanced approach that integrates technological innovation with ethical responsibility will be essential in harnessing its full potential for personalized healthcare solutions.
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Nagarajan. "AI-Driven E-Commerce Optimization in Customer Acquisition: Enhancing E-Commerce Frontends with Artificial Intelligence." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 12, no. 4 (2024): 1–6. https://doi.org/10.5281/zenodo.15084406.
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The rapid growth of e-commerce has led to an increased reliance on artificial intelligence (AI) to optimize customer acquisition, user engagement, and conversion rates. AI-driven web frontends have transformed the way businesses approach search engine optimization (SEO), Google Shopping integration, and user experience (UX) improvements to enhance online visibility and customer retention. This paper explores the role of machine learning, predictive analytics, and AI-driven automation in streamlining the customer journey, improving personalization, and increasing conversion rates. By analyzing AI-powered SEO automation, smart recommendations, chatbot-driven customer interactions, and dynamic pricing models, this research provides insights into how e-commerce platforms can leverage AI to stay competitive in a fast-evolving digital landscape. Additionally, case studies from leading e-commerce companies demonstrate the impact of AI on website engagement, search rankings, and automated marketing strategies. The paper also examines the challenges of AI adoption in e-commerce, including data privacy concerns, algorithmic biases, and the need for seamless AI-human collaboration to ensure ethical and effective automation. Finally, emerging trends such as voice search optimization, AI-generated content, AI-powered UX personalization, website redesign strategies, and implementation considerations are discussed, providing a roadmap for future AI adoption in e-commerce.
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Saleh, Sherine Nagy, Mazen Nabil Elagamy, Yasmine N. M. Saleh, and Radwa Ahmed Osman. "An Explainable Deep Learning-Enhanced IoMT Model for Effective Monitoring and Reduction of Maternal Mortality Risks." Future Internet 16, no. 11 (2024): 411. http://dx.doi.org/10.3390/fi16110411.
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Maternal mortality (MM) is considered one of the major worldwide concerns. Despite the advances of artificial intelligence (AI) in healthcare, the lack of transparency in AI models leads to reluctance to adopt them. Employing explainable artificial intelligence (XAI) thus helps improve the transparency and effectiveness of AI-driven healthcare solutions. Accordingly, this article proposes a complete framework integrating an Internet of Medical Things (IoMT) architecture with an XAI-based deep learning model. The IoMT system continuously monitors pregnant women’s vital signs, while the XAI model analyzes the collected data to identify risk factors and generate actionable insights. Additionally, an efficient IoMT transmission model is developed to ensure reliable data transfer with the best-required system quality of service (QoS). Further analytics are performed on the data collected from different regions in a country to address high-risk cities. The experiments demonstrate the effectiveness of the proposed framework by achieving an accuracy of 80% for patients and 92.6% for regional risk prediction and providing interpretable explanations. The XAI-generated insights empower healthcare providers to make informed decisions and implement timely interventions. Furthermore, the IoMT transmission model ensures efficient and secure data transfer.
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Xuancheng, Tao. "From Tool to Subject: AI's Participation in Film Production." Sustainable Development Research 7, no. 2 (2025): p148. https://doi.org/10.30560/sdr.v7n2p148.
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Artificial intelligence (AI) has been applied to a variety of industries, and the industry is no stranger. AI was once considered a supporting actor in areas such as CGI renderings and modifications. However, now it becomes an active participant at all processes throughout a film. from writing the script to directing, from casting to visual effects and engaging the audience ai has gone from merely assisting, to being a part of the creative process, and in some cases even leading it. This paper examines how AI is transforming the film process and focuses on the shift for AI as more of a subject and less of an object by becoming semi-autonomous with creative collaborations. And then we look at actual applications like A.I generated scripts, machine learning based visual enhancements, virtual actors, and prediction analytics for marketing Also, we think about the philosophy and morals around an AI being involved in something usually made by humans. To give a broad sense of how the rising power of AI is altering the forces of authorship, creation, and efficiency in filmmaking arts. Conclude the study with reflections on how AI will impact filmmaking in the future. And also give advice to human filmmakers on how to cooperate with AI collaboratively.
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Verma, Indresh Kumar. "Leveraging AI to Unleash Creativity and Innovation in Design Education and Research." International Journal of Design and Allied Sciences 2, no. 2 (2023): 66–67. https://doi.org/10.5281/zenodo.10447507.
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<strong>Abstract:</strong> Artificial Intelligence (AI) is poised to revolutionize design education and research, opening up exciting possibilities for enhanced creativity and innovation. AI integration offers personalized learning experiences, virtual reality environments, intelligent design assistance, automated assessment, and data-driven insights. Adaptive learning systems, immersive VR/AR environments, and AI-powered design assistants can benefit us. AI enables deeper insights through data analysis and generative design techniques in design research. AI also enhances the user experience and automates repetitive tasks. The right balance between core design skills and AI will shape a future of socially impacting solutions and thriving creativity. There is an immense possibility for mentors, students, and institutions to integrate AI in design education/ teaching. In design education, AI technology could be advantageous during the ideation process. Several design ideas can be generated based on set criteria and parameters by leveraging the power of AI algorithms. Moreover, in this digital era, AI has the potential to bridge the gap between design and technology. Incorporating AI tools and software in the curriculum of design institutes and schools could provide the necessary skills to excel in user experience (UX) design. A few AI tools prominently used in design education include Adobe Sensei, Figma, and Sketch. However, it is essential to remember that while AI can enhance the learning process, it cannot replace the core foundations of design education. Our creativity, critical thinking, and problem-solving skills remain paramount. Therefore, institutions must balance leveraging AI as a valuable tool and nurturing our innate abilities to think outside the box. <em>Practical Implications:</em> The application of AI base technologies in the design research provides invaluable opportunities for gaining deeper understanding about user preferences and behaviour. A vast amount of user data can be analysed by leveraging AI driven predictive models and analytics, helping designers in making informed decisions on real-time feedback. Designers can create more user-centeric designs which resonates with their target users by understanding behavioural patterns and preferences of the users. <strong>Keywords:</strong> AI; Creativity; Design
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Sriram, Ravichandra, Siva Sathya Sundaram, and S. LourduMarie Sophie. "Experimental evaluation of bidirectional encoder representations from transformers models for de-identification of clinical document images." IAES International Journal of Robotics and Automation (IJRA) 14, no. 2 (2025): 273. https://doi.org/10.11591/ijra.v14i2.pp273-280.
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Many health institutes maintain patients’ diagnosis and treatment reports as scanned images. For healthcare analytics and research, large volumes of digitally stored patient information have to be accessed, but the privacy requirements of protected health information (PHI) limit the research opportunities. Particularly in this artificial intelligence (AI) era, deep learning models require large datasets for training purposes, which hospitals cannot share unless the PHI fields are de-identified. Manual de-identification is beyond possible, with millions of patient records generated in hospitals every day. Hence, this work aims to automate the de-identification of clinical document images utilizing AI models, particularly pre-trained bidirectional encoder representations from transformers (BERT) models. For the purpose of experimentation, a synthetic dataset of 550 clinical document images was generated, encompassing data obtained from diverse patients across multiple hospitals. This work presents a two-stage transfer learning approach, initially employing Tesseract character recognition (OCR) to convert clinical document images into text. Subsequently, it extracts PHI fields from the text for de-identification. For the purpose of extraction, BERT models were utilized; in this work, we contrasted six pre-trained versions of such models to examine their effectiveness and achieve the F1 score of 92.45%, thus showing better potential for de-identifying PHI data in clinical documents.
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Németh, Renáta, Annamária Tátrai, Miklós Szabó, and Árpád Tamási. "Using a RAG-enhanced large language model in a virtual teaching assistant role: Experiences from a pilot project in statistics education." Hungarian Statistical Review 7, no. 2 (2024): 3–27. https://doi.org/10.35618/hsr2024.02.en003.
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The role of artificial intelligence (AI) in education is expected to grow, but how it transforms teaching and learning remains unclear. This study explores the use of an AI tutor that is similar to ChatGPT enhanced with retrieval-augmented generation (RAG), in a pilot project at the Faculty of Social Sciences of Eötvös Loránd University in Budapest, Hungary. The tutor provided a searchable knowledge base for students preparing for admission to the MSc in Survey Statistics and Data Analytics. Instructor feedback highlighted the tutor’s ability to deliver accurate, textbook-based responses, but noted limitations in addressing real-world complexities. Student feedback, which was gathered through focus groups and surveys, showed high satisfaction and many used the tool for active learning such as comparing concepts and organising material. Students had the flexibility to adapt the tutor to their own learning strategy, and they also noted the importance of the tutor as a time-saving supplement rather than a replacement for comprehensive study. Approximately 15% of student queries demonstrated critical thinking, where students used the AI tutor to confirm their own interpretations. Similarly, around 15% showed active learning, seeking explanations and comparisons or generated study guides, while nearly 30% engaged directly with course material, referencing specific concepts and theories from their readings. Instructor evaluation revealed that 76% of the AI tutor’s responses were fully correct, 17% mostly correct and only 6% were misleading. The findings suggest that RAG models hold promise for enhancing learning by offering reliable, interactive and efficient support for students and educators.
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Prof. Jyoti Gaikwad, Aniket Manohare, Shweta Munde, Anwar Shaikh, and Diksha Subhedar. "AI-Based Exploratory Data Analysis." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 3876–84. https://doi.org/10.32628/cseit25112860.
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In today's world, where data is being generated at an unprecedented rate, organizations often struggle to make sense of the vast and complex information they collect. Extracting valuable insights from such massive datasets has become a major challenge. Traditionally, Exploratory Data Analysis (EDA) has relied on statistical techniques and manual processes. While effective, these methods can be slow, tedious, and difficult to scale when dealing with big data. This paper explores how Artificial Intelligence (AI) is transforming the way we approach EDA. By integrating AI technologies, such as Machine Learning and Deep Learning, EDA processes can be automated to a great extent — from data preprocessing and feature extraction to identifying hidden patterns and detecting anomalies. AI not only speeds up the analysis but also uncovers deeper insights that might be missed through manual exploration. Through an AI-driven EDA framework, organizations can achieve greater scalability, improve adaptability to changing datasets, and make more accurate, data-backed decisions. This paper discusses the overall structure, methodologies, tools, and techniques used in AI- powered EDA. It also highlights the real-world applications where AI-based EDA has made a significant impact — from healthcare and finance to social media analytics and business intelligence. Alongside the benefits, we also address the challenges and limitations, such as biases in automated systems and the need for human oversight. As organizations continue to generate and rely on massive volumes of data, AI-enhanced EDA offers a promising path forward, bridging the gap between raw information and actionable insights.
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Singu, Santosh Kumar. "A Comprehensive Approach to Machine Learning Integration in Data Warehousing." Journal of Technology and Systems 6, no. 6 (2024): 28–37. http://dx.doi.org/10.47941/jts.2239.
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Purpose: This research examines the utilization of machine learning (ML) in data warehousing systems and the extent to which it will transform business intelligence and analytics. It aims to know how ML improves conventional data warehousing systems to support prediction and forecasting. Methodology: This research uses a literature review together with a case analysis. It discusses the issues that may arise when implementing Machine Learning models with data warehouses, such as issues to do with data quality, scalability, and real-time processing. The work examines integration patterns like in-database ML computations, feature stores, and MLOps. Case studies are discussed to demonstrate the value of the use of integration in different fields. Findings: Combining machine learning with DW systems provides significant advantages in different fields. This synergy boosts analytical aptitudes, allowing the organization to go a notch higher than descriptive analytics in predictive and prescriptive analytics. However, such a decision is not simple as it has implementation matters such as data quality problems, scalability, and real-time processing problems. Integration best practices include in-database machine learning processing, a feature store, and proper MLOps practices. Real-life examples from the healthcare industry, banking and financial services, retail, and manufacturing industries show that this integration brings operational enhancements for the business and positive effects on customers and overall organizational performance. Recommendations: This work offers a useful framework for studying and constructing the integration of ML into the data warehouse, which is a transition from the theoretical perspective to the actual one. It provides practical advice for organizations and stresses the integration strategies related to the business goals, data quality, the choice of architecture, security, and training. This study also envisions future trends such as edge computing, AutoML, and Explainable AI and offers a guide on how to harness this technological complementarity. The generated insights help decision-makers and practitioners understand the possibilities of leveraging ML-data warehouse integration as a strategic asset in the contemporary business environment shifting towards data-driven approaches.
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Ganesh, Shankar Gowekar. "How oil and gas industry are transforming with AI and ML." World Journal of Advanced Research and Reviews 23, no. 3 (2024): 1234–38. https://doi.org/10.5281/zenodo.14942924.
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The Oil and Gas Industry is witnessing a major transformation due to the advent of Artificial Intelligence (AI) and Machine Learning (ML). These advances are changing operations by making things work better, cheaper and safer. Companies are using Artificial intelligence and Machine Learning to analyze large data points generated due to exploration, production and distribution activities which generate actionable insights leading to data driven decision making. AI and ML techniques for exploration, well placement optimization such as mount point selection or drilling parameters suggest based on geological data analysis including a historical performance. Using data in a real-time and predictive manner, manufacturing plants can catch early signs of equipment issues such as rising temperature encouraging proactive measures to be taken which reduces downtime and maintenance costs. Furthermore, reservoir management, hydrocarbon extraction and energy efficiency are optimized via AI-based optimization strategies.
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Hussain, Sarwat. "Artificial Intelligence, the Need of the Hour." esculapio 17, no. 1 (2021): 1–2. http://dx.doi.org/10.51273/esc21.2517122.
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Fourth Industrial revolution is currently sweeping the high-income countries (HIC) with Artificial Intelli- gence (AI) based automation affecting virtually every aspect of life. The term AI was first coined by McCar- thy in 1956. It was not until 2000s that AI began to thrive. The evolution of AI into the current status occurred in the last decade owing to the enhanced computing power using Graphic Processing Units (GPU), development of high-powered computer languages, and the emergence of the Big Data. The latter is generated through wireless communication between ‘Smart’ sensors/devices and self-learning machines. The word ‘smart’ is applied to any device that has memory and is able to connect with data networks such as the internet and the processors. In the last few years, there has been exponential growth in AI applications. This can be judged by the projec- tion that the AI field will add $ 15 Trillion to global economy, by the year 2030, up from $ 600 Million in 2016. This will occur mostly in the HIC. The adoption of AI by low- and middle-income countries (LMIC) lags far behind that of HICs. The LMICs would miss out in the economic benefits, further widening the global inequalities. Machine Learning and Deep Learning are branches of AI that are beginning to form the basis of the automation of financial and business decisions, and are the tools of self-driving cars, industrial produc- tion, data analytics, quality improvement and health- care processes to name a few. In healthcare, some of the AI applications have shown to enhance patient care, reduce medical errors, support clinical and administrative decision making, automate equipment maintenance and help reduce operational cost. For instance, AI led cost reductions achieved up to 25 percent drop in the length of hospital stay and up to 91 per cent reduction in admissions to step down facili- ties. In the United States alone, by the year 2026, AI in healthcare is estimated to realize $150 billion in annual cost savings.
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Nneka Adaobi Ochuba, David Olanrewaju Olutimehin, Olusegun Gbenga Odunaiya, and Oluwatobi Timothy Soyombo. "Reviewing the application of big data analytics in satellite network management to optimize performance and enhance reliability, with implications for future technology developments." Magna Scientia Advanced Research and Reviews 10, no. 2 (2024): 111–19. http://dx.doi.org/10.30574/msarr.2024.10.2.0048.
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The application of big data analytics in satellite network management has emerged as a transformative approach to optimize performance and enhance reliability in the satellite telecommunications industry. This paper reviews the current state of big data analytics in satellite network management, highlighting its key applications and benefits. By analyzing large volumes of data generated by satellite networks, big data analytics enables satellite telecommunications companies to gain valuable insights into network performance, identify potential issues, and take proactive measures to ensure optimal performance. One of the key applications of big data analytics in satellite network management is predictive maintenance. By analyzing historical data and equipment performance metrics, companies can predict when equipment is likely to fail and take preventive measures to avoid downtime. This not only improves network reliability but also reduces maintenance costs and improves overall operational efficiency. Another important application is network optimization. Big data analytics can analyze network traffic, weather conditions, and other factors to optimize satellite beam coverage, frequency allocation, and routing. This helps companies maximize bandwidth utilization, reduce interference, and improve service quality. The implications of big data analytics for future technology developments in satellite network management are significant. As the volume of data generated by satellite networks continues to grow, there is a need for advanced analytics tools and techniques to process and analyze this data efficiently. Future technology developments in areas such as AI, machine learning, and data visualization are expected to play a key role in enhancing the capabilities of big data analytics in satellite network management. In conclusion, the application of big data analytics in satellite network management offers significant benefits in terms of optimizing performance and enhancing reliability. By leveraging the insights provided by big data analytics, satellite telecommunications companies can improve operational efficiency, reduce costs, and deliver better services to their customers. Future technology developments will further enhance the capabilities of big data analytics, paving the way for more efficient and reliable satellite network management.
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Sunil Kumar Mishra. "Predicting Human Resource Trends in Technical Education Through ERP Data and Machine Learning Models." Journal of Information Systems Engineering and Management 10, no. 23s (2025): 830–51. https://doi.org/10.52783/jisem.v10i23s.3785.
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Enterprise Resource Planning (ERP) systems have become integral in streamlining academic and administrative processes in technical education institutions. However, their impact on human resource (HR) trends, including faculty performance, student outcomes, and job placements, remains underexplored. This study leverages ERP-generated data to predict HR trends in Odisha’s technical education sector using machine learning models. The dataset comprises institutional records, faculty evaluations, student performance metrics, and employment statistics collected from ERP systems. We employ Random Forest and Gradient Boosting models to analyze key determinants influencing HR efficiency. Results indicate that faculty engagement and student ERP usage significantly correlate with improved job placement rates and academic performance. The study highlights the predictive power of machine learning in forecasting HR trends, aiding policy decisions for educational institutions. By integrating ERP analytics with AI-driven models, institutions can optimize HR strategies and enhance student career outcomes.
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Sidddiky Pinky, Aysha. "Artificial Intelligence and Business Transition: Paving the Way for Development." Westcliff International Journal of Applied Research 9, no. 1 (2025): 56–63. https://doi.org/10.47670//wuwijar20251asp.
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Artificial Intelligence (AI) is steadily becoming the new normal in doing business through increasing operational performance, improving customer relations, and increasing predictive accuracy. This quantitative exploratory research employed a mixed-methods approach, integrating qualitative insights into organizational trends, best practices, and challenges with quantitative assessments of performance measures, cost savings, and business outcomes. Several surveys were administered to a diverse group of business professionals. The study, situated within the field of applied research, explores how AI facilitates business growth through change and proposes best practices for successful integration. It also studies what happens during transition periods when organizations emphasize artificial intelligence, NLP, and robotic process automation as top technologies since they contribute to completing work tasks, analyzing large data sets, and improving individual communication with clients. Besides potentially generated cost savings and a long-term increase in business value, there are several obstacles that organizations face if implementing AI, such as high initial costs and a market that requires professional knowledge on the topic. If implemented correctly, AI technologies hold huge potential for businesses going through transitions and taking advantage of AI’s strengths. For this reason, it is important to describe and analyze trends like AI technology integration accurately. This article suggests best practices for applying AI in business development during transformations. Keywords: Artificial intelligence, machine learning, business transitions, predictive analytics, robotic process automation, cost reduction, AI adoption strategies
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Sandeep Kulkarni, Prathmesh Rahul kurumkar, Vansh Sanjeev Kadam, and Vinut Prabhu Maradur. "AI-powered resume screening system." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 2263–77. https://doi.org/10.30574/wjaets.2025.15.1.0413.
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The advent of artificial intelligence (AI) has revolutionized talent acquisition through the development of AI-powered resume screening systems. These advanced tools utilize machine learning, natural language processing and data analytics to automate the initial evaluation of job applicants’ resumes, significantly enhancing the efficiency and objectivity of the hiring process. By analyzing key elements such as skills, experience, education and job-specific keywords, these systems filter and rank candidates, delivering a shortlist of top matches to recruiters. This technology reduces manual effort, minimizes human bias and accelerates decision-making in recruitment. However, challenges such as potential algorithmic bias and overemphasis on keyword matching highlight the need for careful design and oversight. This abstract explores the functionality, benefits, and implications of AI-powered resume screening systems, underscoring their transformative role in modern human resource management. The emergence of artificial intelligence (AI) as a cornerstone of modern technology has profoundly reshaped the landscape of talent acquisition, giving rise to AI-powered resume screening systems that redefine the recruitment paradigm. These cutting-edge tools leverage an intricate blend of machine learning algorithms, natural language processing techniques and advanced data analytics to automate and enhance the initial assessment of job applicants’ resumes. By systematically evaluating critical components such as technical and soft skills, professional experience, academic credentials, and job-specific keywords, these systems efficiently filter and rank candidates, producing a concise shortlist of the most promising individuals for recruiters to review. This transformative technology not only alleviates the burden of manual resume review – a process historically plagued by in efficiency and subjectivity – but also minimizes human bias, accelerates decision-making timelines, and elevates the overall precision of the hiring process. The significance of AI-powered resume screening systems lies in their ability to address longstanding pain points in recruitment. Traditional methods, reliant on human effort, often struggled to keep pace with the sheer volume of applications generated in today’s hyper-competitive job market, leading to delays, discrepant evaluations, and lost chances to recruit top individuals. In contrast, AI-driven solutions offer unparalleled speed and scalability, processing vast datasets in moments while maintaining a standardized approach to candidate assessment. Beyond efficiency, these systems introduce a layer of objectivity by focusing on data-driven insights rather than subjective impressions, fostering fairer and more inclusive hiring practices when properly calibrated.