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Journal articles on the topic 'Explainability generative AI'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Genevieve Okafor, Ehisuoria E. Akhuemonkhan, Chibuzor Njoku, Evelyn Gachui, Ifeoma Naibe, and Aniel K. Diala. "The future of generative artificial intelligence (AI) in fraud detection analysis." International Journal of Management & Entrepreneurship Research 7, no. 4 (2025): 294–98. https://doi.org/10.51594/ijmer.v7i4.1875.

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As financial fraud schemes grow increasingly sophisticated, traditional detection models struggle to keep pace with the evolving threat landscape. Generative Artificial Intelligence (AI), particularly models like Generative Adversarial Networks (GANs) and Large Language Models (LLMs), are emerging as transformative tools in the realm of fraud detection. These models enable the creation of synthetic datasets, simulate fraudulent behaviors, and enhance the accuracy of anomaly detection systems. By generating realistic fraud scenarios, generative AI enhances predictive modeling and supports proac

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Chinyere Christian, Emedo. "Explainability Imperative of Generative Artificial Intelligence Navigating the Moral Dilemma of AI in Nigeria and Charting a Path for the Future." Universal Library of Arts and Humanities 01, no. 02 (2024): 38–43. http://dx.doi.org/10.70315/uloap.ulahu.2024.0102007.

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This paper explores the explanability imperative in the context of Generative Artificial Intelligence (GAI) and its crucial role in addressing the concerns posed by AI technology in Nigeria. This underscores the ethical necessity for AI systems, especially generative ones to provide clear and understandable explanations for their decisions and actions. Although the advent of generative AI undoubtedly heralds the future and however, has also exposed Nigerian society to new vulnerabilities that seemingly are detrimental to our epistemic agency and peaceful political settings. Employing the pheno

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Kang, Hyunju, Geonhee Han, Yoonjae Jeong, and Hogun Park. "AudioGenX: Explainability on Text-to-Audio Generative Models." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 17 (2025): 17733–41. https://doi.org/10.1609/aaai.v39i17.33950.

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Text-to-audio generation models (TAG) have achieved significant advances in generating audio conditioned on text descriptions. However, a critical challenge lies in the lack of transparency regarding how each textual input impacts the generated audio. To address this issue, we introduce AudioGenX, an Explainable AI (XAI) method that provides explanations for text-to-audio generation models by highlighting the importance of input tokens. AudioGenX optimizes an Explainer by leveraging factual and counterfactual objective functions to provide faithful explanations at the audio token level. This m

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Srilekha Kanakadandi. "Leveraging Generative AI in Telecom E-commerce: A Framework for Enhanced Development and Testing Optimization." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 2525–33. https://doi.org/10.32628/cseit251112231.

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This article investigates the integration of generative AI technologies within telecom e-commerce platform development and testing workflows. By examining real-world implementations across multiple organizations, the research provides insights into how AI-driven approaches enhance code generation, test coverage, and API optimization in microservices architectures. The article explores the implementation of AI tools within existing CI/CD pipelines, focusing on automated test case generation, dynamic data creation, and intelligent debugging processes. Particular attention is given to security co

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Dehal, Ramandeep Singh, Mehak Sharma, and Enayat Rajabi. "Knowledge Graphs and Their Reciprocal Relationship with Large Language Models." Machine Learning and Knowledge Extraction 7, no. 2 (2025): 38. https://doi.org/10.3390/make7020038.

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The reciprocal relationship between Large Language Models (LLMs) and Knowledge Graphs (KGs) highlights their synergistic potential in enhancing artificial intelligence (AI) applications. LLMs, with their natural language understanding and generative capabilities, support the automation of KG construction through entity recognition, relation extraction, and schema generation. Conversely, KGs serve as structured and interpretable data sources that improve the transparency, factual consistency and reliability of LLM-based applications, mitigating challenges such as hallucinations and lack of expl

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Researcher. "THE CRITICAL ROLE OF DATA ENGINEERING IN GENERATIVE AI." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 829–40. https://doi.org/10.5281/zenodo.13475740.

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This article explores the intricate relationship between data engineering and generative AI (Gen AI), highlighting the critical role that data engineering plays in the development, deployment, and optimization of Gen AI systems. It delves into the nature of generative AI and its revolutionary capabilities across various domains, from text and image generation to music composition and code creation. The symbiotic relationship between data engineering and Gen AI is examined in detail, covering key aspects such as data collection and curation, preprocessing and transformation, scalable infrastruc

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Researcher. "THE CRITICAL ROLE OF DATA ENGINEERING IN GENERATIVE AI." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 829–40. https://doi.org/10.5281/zenodo.13475740.

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This article explores the intricate relationship between data engineering and generative AI (Gen AI), highlighting the critical role that data engineering plays in the development, deployment, and optimization of Gen AI systems. It delves into the nature of generative AI and its revolutionary capabilities across various domains, from text and image generation to music composition and code creation. The symbiotic relationship between data engineering and Gen AI is examined in detail, covering key aspects such as data collection and curation, preprocessing and transformation, scalable infrastruc

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Researcher. "THE CRITICAL ROLE OF DATA ENGINEERING IN GENERATIVE AI." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 829–40. https://doi.org/10.5281/zenodo.13475740.

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This article explores the intricate relationship between data engineering and generative AI (Gen AI), highlighting the critical role that data engineering plays in the development, deployment, and optimization of Gen AI systems. It delves into the nature of generative AI and its revolutionary capabilities across various domains, from text and image generation to music composition and code creation. The symbiotic relationship between data engineering and Gen AI is examined in detail, covering key aspects such as data collection and curation, preprocessing and transformation, scalable infrastruc

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Researcher. "THE CRITICAL ROLE OF DATA ENGINEERING IN GENERATIVE AI." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 829–40. https://doi.org/10.5281/zenodo.13475740.

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This article explores the intricate relationship between data engineering and generative AI (Gen AI), highlighting the critical role that data engineering plays in the development, deployment, and optimization of Gen AI systems. It delves into the nature of generative AI and its revolutionary capabilities across various domains, from text and image generation to music composition and code creation. The symbiotic relationship between data engineering and Gen AI is examined in detail, covering key aspects such as data collection and curation, preprocessing and transformation, scalable infrastruc

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Researcher. "THE CRITICAL ROLE OF DATA ENGINEERING IN GENERATIVE AI." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 829–40. https://doi.org/10.5281/zenodo.13475740.

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This article explores the intricate relationship between data engineering and generative AI (Gen AI), highlighting the critical role that data engineering plays in the development, deployment, and optimization of Gen AI systems. It delves into the nature of generative AI and its revolutionary capabilities across various domains, from text and image generation to music composition and code creation. The symbiotic relationship between data engineering and Gen AI is examined in detail, covering key aspects such as data collection and curation, preprocessing and transformation, scalable infrastruc

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Trabassi, Dante, Stefano Filippo Castiglia, Fabiano Bini, et al. "Optimizing Rare Disease Gait Classification through Data Balancing and Generative AI: Insights from Hereditary Cerebellar Ataxia." Sensors 24, no. 11 (2024): 3613. http://dx.doi.org/10.3390/s24113613.

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The interpretability of gait analysis studies in people with rare diseases, such as those with primary hereditary cerebellar ataxia (pwCA), is frequently limited by the small sample sizes and unbalanced datasets. The purpose of this study was to assess the effectiveness of data balancing and generative artificial intelligence (AI) algorithms in generating synthetic data reflecting the actual gait abnormalities of pwCA. Gait data of 30 pwCA (age: 51.6 ± 12.2 years; 13 females, 17 males) and 100 healthy subjects (age: 57.1 ± 10.4; 60 females, 40 males) were collected at the lumbar level with an

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U, Dr Bhuvaneswari. "Architecting Responsible Development and Deployment of Generative AI." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 4937–56. https://doi.org/10.22214/ijraset.2025.71314.

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Architecting Responsible Development and Deployment of Generative AI" presents a comprehensive framework for ensuring the responsible development and deployment of generative artificial intelligence (AI) systems. The paper addresses various aspects crucial for the ethical and effective utilization of generative AI, ranging from governance frameworks and accountability measures to technical considerations such as explainability, fairness, and operational resilience. Through an indepth exploration of topics such as monitoring and reporting systems, data suitability, performance evaluation metric

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VenuBabu, Paruchuri. "Leveraging Generative AI to Streamline Account Approval Processes and Improve the Precision of Risk Assessment in Financial Services." Journal of Scientific and Engineering Research 11, no. 9 (2024): 165–71. https://doi.org/10.5281/zenodo.15606782.

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The research looks at the way generative AI can change the way financial institutions approve accounts and assess risks. The research examines the issues that financial institutions have in the time of moving to AI-based automation, including the existence of data storage tower and legal requirements. The research looks at the way generative AI models help increase the accuracy of predicting risks by using real-time data analysis. The research looks at leading-edge technologies, including transformer architectures, that make AI better able to handle big workloads. The research makes recommenda

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Kamal, Antara. "Generative AI in Pharmaceutical Research: Accelerating Drug Discovery through Predictive Analytics and Big Data Integration." Journal of Frontiers in Multidisciplinary Research 4, no. 2 (2023): 27–33. https://doi.org/10.54660/.ijfmr.2023.4.2.27-33.

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The pharmaceutical industry faces unprecedented challenges including rising development costs, high clinical trial failure rates, and increasing pressure to deliver faster, safer, and more effective therapeutics. In response, the integration of generative artificial intelligence (AI) and big data analytics has emerged as a transformative approach to drug discovery. Generative models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and transformer-based architectures are revolutionizing the early phases of drug development by enabling de novo molecule generation,

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Abhyudaya Gurram. "Generative AI for enhanced cybersecurity: building a zero-trust architecture with agentic AI." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 2380–96. https://doi.org/10.30574/wjaets.2025.15.1.0504.

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Generative AI is transforming cybersecurity by enhancing zero-trust architecture implementation through dynamic capabilities that adapt to evolving threats. This convergence represents a paradigm shift from traditional perimeter-based security to a model that assumes breach and verifies every access request. The integration of generative AI with zero-trust principles enables continuous authentication through behavioral analysis, autonomous threat hunting, and incident response orchestration while maintaining human oversight. The architecture comprises interconnected components including data c

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Yang, Lin. "Generative AI for Sustainable Architectural Design Optimization." Scientific Journal of Technology 7, no. 5 (2025): 96–106. https://doi.org/10.54691/czaf4b21.

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This paper examines recent academic research on applying generative AI and computational methods to optimize sustainable architectural design. Buildings account for roughly one-third of global carbon emissions and energy use, especially via HVAC systems. Early-stage design optimization is therefore crucial to improve efficiency and reduce environmental impact. Generative design including evolutionary and parametric algorithms has emerged as a key approach to exploring diverse design options for sustainability. More recently, advanced AI (e.g. machine learning surrogates, GANs, diffusion models

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Shete, Prathamesh. "Generative AI in Judiciary: Enhancing Accuracy and Preventing Manipulation with TGAT." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 595–606. https://doi.org/10.22214/ijraset.2025.70243.

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The combination of Retrieval-Augmented Generation systems with Large Language Models (LLMs) shows great potential for legal artificial intelligence (AI) but major issues remain regarding temporal adaptation as well as explainability and ethical compliance. This literature review examines AI-driven legal technology progress through an evaluation of deep learning architecture development and legal-specific NLP techniques and hybrid RAG frameworks. Current systems show enhanced citation accuracy at 40% above standalone LLMs and improved retrieval efficiency through FAISS and LegalBERT tools but t

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Boggavarapu, Venkateswarlu. "Augmenting Financial Analysts with AI: Explainable AI for Trustworthy Financial Decision Support." European Journal of Computer Science and Information Technology 13, no. 43 (2025): 39–51. https://doi.org/10.37745/ejcsit.2013/vol13n433951.

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This article examines the integration of artificial intelligence in financial evaluation and the vital role of explainability in building trustworthy decision support systems. As AI transforms traditional financial evaluation from forecasting to portfolio management, the inherent opacity of sophisticated algorithms creates tension with the financial sector's transparency requirements. The discussion explores how Explainable AI techniques—particularly SHAP values and LIME—enable financial professionals to understand AI-generated insights while maintaining regulatory compliance. Through examinin

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Han, Jihyung, and Daekyun Ko. "Consumer Autonomy in Generative AI Services: The Role of Task Difficulty and AI Design Elements in Enhancing Trust, Satisfaction, and Usage Intention." Behavioral Sciences 15, no. 4 (2025): 534. https://doi.org/10.3390/bs15040534.

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As generative AI services become increasingly integrated into consumer decision making, concerns have grown regarding their influence on consumer autonomy—the extent to which individuals retain independent control over AI-assisted decisions. Although these services offer efficiency and convenience, they can simultaneously constrain consumer decision making, potentially impacting trust, satisfaction, and usage intention. This study investigates the role of perceived consumer autonomy in shaping consumer responses, specifically examining how task difficulty (Study 1) and AI service design elemen

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Gupta, Raj. "Evolution of Generative AI: A paradigm Shift in optimization of Search Engine Strategies (SEO)." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41499.

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This abstract examines the research landscape in generative AI and identifies some of the key challenges and opportunities in the field. It also calls for further research in areas such as explainability, robustness, and data privacy and security. It introduces innovative solutions for content creation within the metaverse, addressing development challenges in this evolving virtual space. Tools like ChatGPT hold the potential to revolutionize search experiences, transform how information is generated and presented, and establish new access points for online engagement. These advancements are a

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Amina Catherine Ijiga, Ehi Peace Abutu, Idoko Peter Idoko, et al. "Ethical considerations in implementing generative AI for healthcare supply chain optimization: A cross-country analysis across India, the United Kingdom, and the United States of America." International Journal of Biological and Pharmaceutical Sciences Archive 7, no. 1 (2024): 048–63. http://dx.doi.org/10.53771/ijbpsa.2024.7.1.0015.

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This review paper critically examines the ethical considerations involved in implementing generative Artificial Intelligence (AI) in healthcare supply chain optimization across three distinct regions: India, the United Kingdom, and the United States of America. The study synthesizes findings from various case studies and academic research to highlight both common and unique ethical challenges faced in these countries. Key themes such as data privacy, algorithmic transparency, and equitable access to AI-driven healthcare solutions are explored, alongside the unique socio-cultural, legal, and re

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Rahaman, Ana, Amit Kumar, and Jennifer Maya. "Harnessing Predictive Analytics and Big Data: Generative Artificial Intelligence Accelerates Drug Development in Pharmaceutical Research." International Journal of Future Engineering Innovations 1, no. 6 (2024): 18–24. https://doi.org/10.54660/ijfei.2024.1.6.18-24.

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Rising development costs, high rates of clinical trial failures, and growing pressure to provide faster, safer, and more effective treatments all provide hitherto unheard-of difficulties for the pharmaceutical sector. In response, a transforming method to drug development is the combination of big data analytics and generative artificial intelligence (AI). Early phases of drug development are being transformed by generative models including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and transformer-based architectures which enable de novo molecule generation, prot

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Mekala, Ashrith Reddy. "Human-GenAI Collaboration in Research and Policy Development." International Journal of Advances in Engineering and Management 7, no. 2 (2025): 141–48. https://doi.org/10.35629/5252-0702141148.

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The integration of Generative AI (GenAI) systems has transformed research methodologies and policy development processes across organizations globally. Through advanced language models and deep learning architectures, these systems enable rapid data processing, enhanced decision-making, and improved collaboration between humans and machines. The technological framework encompasses sophisticated validation mechanisms, bias mitigation strategies, and data processing capabilities that streamline complex analytical tasks. In academic settings, GenAI facilitates comprehensive literature reviews, pa

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Deepak Nair, Prof. "Legal Solutions - GenAI." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48096.

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Abstract—This paper presents LegalRAG Assistant, an AI-powered legal chatbot platform that leverages Generative AI (GenAI), Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) to provide accurate, context-aware responses to legal queries. The system integrates Indian legal frameworks including the Bharatiya Nyaya Sanhita (BNS) and RERA guidelines to support real-time legal consultation and document analysis. A structured pipeline was developed using vector embeddings (via Sentence-BERT) and FAISS for efficient semantic retrieval of legal texts, which are then fed into a fine

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S, Janakiraman. "FakeVision AI: Detecting and Explaining AI-Generated Images with Deep Learning." International Journal for Research in Applied Science and Engineering Technology 13, no. 6 (2025): 1042–43. https://doi.org/10.22214/ijraset.2025.72291.

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The advancement of artificial intelligence (AI) has led to the development of powerful generative models such as StyleGAN, DALL·E, and Stable Diffusion, which are capable of creating highly realistic synthetic images. The CIFAKE dataset serves as a benchmark for training deep learning models to distinguish between real and AI-generated images. In this study, we propose an AI-based framework for detecting synthetic imagery using deep learning and explainable AI (XAI) methods. Our approach incorporates Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to classify images as eith

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Yuxin Chen. "When Algorithms Testify: Addressing the Explainability Gap of AI Evidence in Criminal Cases." Studies in Law and Justice 4, no. 3 (2025): 1–10. https://doi.org/10.56397/slj.2025.06.01.

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The expansion of generative artificial intelligence evidence in the field of criminal justice has exposed the structural risks caused by the unexplainability of algorithms. Although existing studies have revealed multiple obstacles, they have not yet touched upon the fundamental crux of the unexplainability of the algorithm. The three predicaments derived from this, namely the disruption of argumentative logic, the loss of focus in the cross-examination process, and the depletion of judicial trust, essentially stem from the subtle tension between the certainty of machine conclusions and their

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Oleg Ye., Backsanskiy, and Sorokina Svetlana G. "Balancing Ethics and Innovation in Artificial Intelligence." Общество: философия, история, культура, no. 1 (January 22, 2025): 23–33. https://doi.org/10.24158/fik.2025.1.2.

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Artificial intelligence (AI) is rapidly transforming various facets of human activity, ranging from decision-making to communication, while simultaneously engendering complex ethical challenges. This article examines the critical ethical principles of AI – beneficence, non-maleficence, autonomy, justice, and explainability – and ana-lyzes how modern AI technologies align with these principles. Particular attention is given to algorithmic bias, the Black Box Problem, and accountability in AI systems. Algorithmic bias is explored through practical testing of AI models, specifically OpenAI’s gene

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B, Sandeep, Spandana S Nair, Sahana Kannammanavar, Saanvi B S, and Shivani U. "DETECTION OF AI GENERATED IMAGES USING DEEP LEARNING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–7. https://doi.org/10.55041/ijsrem39999.

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This Gradio app detects fake images using a fine-tuned InceptionResnetV1 model with VGGFace2 weights. It employs MTCNN for face detection and extraction, resizing the face for model compatibility. Grad-CAM provides explainability by highlighting face areas influencing predictions, overlaying a heatmap on the original image. The model outputs confidence scores for "real" or "fake" classifications. Results and visualizations are displayed in an interactive Gradio interface. Keywords- Deep fake Detection ,Convolutional Neural Networks (CNNs),Generative Adversarial Networks(GANs), Image Forensics,

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Khosravi, Pegah, Thomas J. Fuchs, and David Joon Ho. "Artificial Intelligence–Driven Cancer Diagnostics: Enhancing Radiology and Pathology through Reproducibility, Explainability, and Multimodality." Cancer Research 85, no. 13 (2025): 2356–67. https://doi.org/10.1158/0008-5472.can-24-3630.

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Abstract The integration of artificial intelligence (AI) in cancer research has significantly advanced radiology, pathology, and multimodal approaches, offering unprecedented capabilities in image analysis, diagnosis, and treatment planning. AI techniques provide standardized assistance to clinicians, in which many diagnostic and predictive tasks are manually conducted, causing low reproducibility. These AI methods can additionally provide explainability to help clinicians make the best decisions for patient care. This review explores state-of-the-art AI methods, focusing on their application

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Macha, Kiran Babu, Sai Deepika Garikipati, Nikhil Sagar Miriyala, Rishi Venkat, and Prakhar Mittal. "Mitigating Bias in Generative AI: The Role of Explainable AI for Ethical Deployment." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 08 (2024): 1–9. https://doi.org/10.55041/ijsrem37255.

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The rapid development of generative AI has greatly affected different industries such as journalism, healthcare, and finance, among many others. However, this has also created ethical concerns due to biased outputs that result from training data, algorithmic design, and human oversight. Explainable AI indeed helps mitigate these biases by increasing the transparency, interpretability, and accountability of AI decision-making. Techniques like SHAP, LIME, and counterfactual explanations facilitate the detection and correction of bias, ensuring that AI is utilized ethically. A comparison of the p

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Mahmood, Musaria Karim, and Ali Rachini. "Most Cited AI Research (2024–2025): A Cross-Sector Review." EDRAAK 2025 (March 20, 2025): 85–93. https://doi.org/10.70470/edraak/2025/011.

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The blistering pace of generative and foundational AI models being deployed in 2024 and 2025 is transforming experiences in education, healthcare, science, sustainability and business. This narrative review consolidates findings from the 50 most cited peer reviewed publications in this time frame, providing a cross-cutting overview on the state of development, the application and the challenges concerning technology. We start by discussing the architectural origins behind both large language models, multimodal generators, as well as domain-specific foundation models including SpectralGPT and s

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Yener, Aylin Işık. "AI-Driven Decision Making in Innovation." Human Computer Interaction 9, no. 1 (2025): 17. https://doi.org/10.62802/wr5yt023.

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Artificial intelligence (AI) is reshaping the landscape of innovation by transforming how organizations generate ideas, make decisions, and develop products and services. This research paper comprehensively explores the role of AI in driving innovation through advanced technologies such as machine learning, natural language processing, generative models, and big data analytics. It examines AI’s integration into entrepreneurial ventures and corporate R&D settings and highlights key enablers and barriers. It also addresses ethical considerations including bias, explainability, and human–AI c

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Thomas, Alex Thomas, and Santhalakshmi Selvaraj. "Leveraging Artificial Intelligence for Efficient Test Generation in API Contract Testing." International Journal of Research and Review 12, no. 6 (2025): 576–87. https://doi.org/10.52403/ijrr.20250665.

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Application Programming Interfaces (APIs) constitute the backbone of modern software architectures, enabling seamless communication and integration of heterogeneous systems. It is crucial to guarantee the reliability and correctness of such interactions, making API contract testing an essential discipline. However, traditional API contract testing approaches have several significant limitations, including manual effort to write test cases, difficulty in keeping the test suites in sync with evolving API specifications, challenges in attaining complete test coverage and creating practical, varie

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Gupta, Nikhil. "Security Risks of Generative AI in Financial Systems: A comprehensive review." World Journal of Information Systems 1, no. 3 (2025): 17–24. https://doi.org/10.17013/wjis.v1i3.16.

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The rapid evolution of generative artificial intelligence (AI) presents transformative opportunities and unprecedented security challenges for financial systems. This paper conducts a comprehensive review of the security risks associated with generative AI in finance, focusing on three prominent threats: deepfakes, synthetic identity fraud, and AI-generated phishing attacks. Deepfakes, synthetic media that convincingly replace individuals' likenesses or voices, can undermine authentication and verification processes, potentially enabling unauthorized access to accounts and fraudulent transacti

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Lingareddy Alva. "Generative AI for self-optimizing and autonomous data pipelines." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 1071–79. https://doi.org/10.30574/wjarr.2025.26.2.1667.

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Generative AI technologies offer transformative potential for addressing fundamental challenges in data pipeline management across enterprise environments. This comprehensive exploration details how artificial intelligence can create self-optimizing, autonomous data pipelines capable of adapting to evolving data ecosystems without human intervention. The integration of machine learning techniques—including anomaly detection, reinforcement learning, and large language models—enables unprecedented capabilities in pipeline orchestration, from predictive failure prevention to dynamic resource allo

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R, Divya. "Privacy - Preserving Anomaly Detection Using Federated Learning and Explainable AI." International Journal for Research in Applied Science and Engineering Technology 13, no. 2 (2025): 833–37. https://doi.org/10.22214/ijraset.2025.66970.

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Anomaly detection is crucial for identifying security threats and system failures. Traditional methods often require centralized data collection and raising privacy concerns. This paper proposes an idea of privacy-preserving anomaly detection system using Federated Learning (FL), Explainable AI (XAI) and Generative Adversarial Networks (GAN). Federated Learning provides with decentralized training while preserving the data privacy and Explainable AI enhances model transparency, helping in decision making. By utilizing deep autoencoders for anomaly detection and SHAP/LIME for explainability, it

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Tahvildari, Mahan. "Integrating generative AI in Robo-Advisory: A systematic review of opportunities, challenges, and strategic solutions." Multidisciplinary Reviews 8, no. 12 (2025): 2025379. https://doi.org/10.31893/multirev.2025379.

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The integration of generative AI into financial advisory services marks a significant advancement in portfolio optimization, risk assessment, and decision support and recent developments in large language models (LLMs), such as ChatGPT, have demonstrated the ability to process both structured financial data and unstructured market sentiment, enhancing the accuracy and adaptability of investment recommendations. However, the application of generative AI in robo-advisory systems presents ethical, regulatory, and psychological challenges and this study conducts a systematic literature review to e

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Wu, Shuangjin, and Wenbo Wang. "A Survey on the Applications of Artificial Intelligence in Cryptanalysis and Cryptographic Design." Frontiers in Science and Engineering 5, no. 3 (2025): 390–99. https://doi.org/10.54691/akyt1k78.

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Artificial Intelligence (AI) is profoundly transforming cryptography by significantly enhancing cryptanalysis techniques and informing innovative cryptographic design approaches. This survey reviews recent advancements in applying deep learning methods to side-channel and differential fault analyses, demonstrating substantial improvements over traditional methods in attack efficiency, accuracy, and resilience. Additionally, it highlights breakthroughs such as neural differential cryptanalysis, which expand classical cryptanalytic boundaries. In cryptographic design, Generative Adversarial Netw

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Ngwenyama, Ojelanki, and Frantz Rowe. "Should We Collaborate with AI to Conduct Literature Reviews? Changing Epistemic Values in a Flattening World." Journal of the Association for Information Systems 25, no. 1 (2024): 122–36. http://dx.doi.org/10.17705/1jais.00869.

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In this paper, we revisit the issue of collaboration with artificial intelligence (AI) to conduct literature reviews and discuss if this should be done and how it could be done. We also call for further reflection on the epistemic values at risk when using certain types of AI tools based on machine learning or generative AI at different stages of the review process, which often require the scope to be redefined and fundamentally follow an iterative process. Although AI tools accelerate search and screening tasks, particularly when there are vast amounts of literature involved, they may comprom

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Kodamasimham Krishna, Dheerender Thakur, and Harika Sree Meka. "Enhancing software engineering practices with generative AI: A framework for automated code synthesis and refactoring." World Journal of Advanced Engineering Technology and Sciences 13, no. 1 (2024): 672–81. http://dx.doi.org/10.30574/wjaets.2024.13.1.0463.

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This paper is based on how software development has been revolutionized using AI in automation, mainly dealing with code synthesis and rewrite frameworks. While there is no focused definition for software development with AI technologies, their application in development processes is unlikely to remain marginal as they mature and provide higher productivity, improved code quality, and enhanced ability for automating repetitive tasks in development. Automating coding means that predefined tools can bring code suggestions, show and apply refactoring features, and enforce coding standards so deve

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Gupta, Parth. "AI Driven Decision Support Systems for Business Operations." International Journal of Transformations in Business Management 14, no. 1 (2024): 94–100. https://doi.org/10.37648/ijtbm.v14i01.012.

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In the era of digital transformation, businesses are increasingly relying on intelligent systems to enhance operational efficiency and strategic decision-making. Artificial Intelligence-driven Decision Support Systems (AI-DSS) have emerged as a pivotal innovation, offering advanced capabilities such as predictive analytics, real-time optimization, and adaptive learning. This paper presents a comprehensive study on the development, implementation, and impact of AI-DSS across various business functions. It explores the integration of machine learning (ML), deep learning (DL), natural language pr

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Baig, Mirza Mansoor, Chris Hobson, Hamid GholamHosseini, Ehsan Ullah, and Shereen Afifi. "Generative AI in Improving Personalized Patient Care Plans: Opportunities and Barriers Towards Its Wider Adoption." Applied Sciences 14, no. 23 (2024): 10899. http://dx.doi.org/10.3390/app142310899.

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The main aim of this study is to investigate the opportunities, challenges, and barriers in implementing generative artificial intelligence (Gen AI) in personalized patient care plans (PPCPs). This systematic review paper provides a comprehensive analysis of the current state, potential applications, and opportunities of Gen AI in patient care settings. This review aims to serve as a key resource for various stakeholders such as researchers, medical professionals, and data governance. We adopted the PRISMA review methodology and screened a total of 247 articles. After considering the eligibili

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Laine, Joakim, Matti Minkkinen, and Matti Mäntymäki. "Understanding the Ethics of Generative AI: Established and New Ethical Principles." Communications of the Association for Information Systems 56, no. 1 (2025): 1–25. https://doi.org/10.17705/1cais.05601.

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This scoping review develops a conceptual synthesis of the ethics principles of generative artificial intelligence (GenAI) and large language models (LLMs). In regard to the emerging literature on GenAI, we explore 1) how established AI ethics principles are presented and 2) what new ethical principles have surfaced. The results indicate that established ethical principles continue to be relevant for GenAI systems but their salience and interpretation may shift, and that there is a need to recognize new principles in these systems. We identify six GenAI ethics principles: 1) respect for intell

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Ranaldi, Leonardo. "Survey on the Role of Mechanistic Interpretability in Generative AI." Big Data and Cognitive Computing 9, no. 8 (2025): 193. https://doi.org/10.3390/bdcc9080193.

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The rapid advancement of artificial intelligence (AI) and machine learning has revolutionised how systems process information, make decisions, and adapt to dynamic environments. AI-driven approaches have significantly enhanced efficiency and problem-solving capabilities across various domains, from automated decision-making to knowledge representation and predictive modelling. These developments have led to the emergence of increasingly sophisticated models capable of learning patterns, reasoning over complex data structures, and generalising across tasks. As AI systems become more deeply inte

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Faustino, Faustino. "Artificial Intelligence in Drug Discovery: A Review of AI Approaches for Target Identification." Metaheuristic Optimization Review 3, no. 1 (2025): 12–22. https://doi.org/10.54216/mor.030102.

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Artificial Intelligence (AI) has become a revolutionary solution in drug discovery and development in aspects including high costs, long times, and high failure rates. This review describes the development and focuses on areas where AI has been used for target identification, lead optimization, design of new drugs from scratch and drug repurposing. Deep learning frameworks such as generative adversarial networks (GANs), variational autoencoders (VAEs), and explainable AI (XAI) approaches have been instrumental and comparative progress in enhancing the efficacy and specificity of drug discovery

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Bhargav Mallampati. "Training Generative AI to Ingest Logs and Detect Anomalies in Large-Scale Applications." Journal of Computer Science and Technology Studies 7, no. 2 (2025): 197–202. https://doi.org/10.32996/jcsts.2025.7.2.19.

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Generative AI has transformed log analysis in large-scale distributed applications, offering unprecedented capabilities for anomaly detection and operational intelligence. This transformation addresses the exponential growth of log data generated by modern systems, which traditional approaches struggle to process effectively. Large language models and specialized AI architectures demonstrate exceptional accuracy in identifying anomalous patterns across heterogeneous log formats while significantly reducing false positives and manual configuration requirements. Natural language processing techn

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Hengran Yang. "Research on AI-Driven Adaptive Difficulty and Feedback Mechanisms in Serious Games: A Case Study of Complex Procedural Skills Training." Journal of Exploration of Vocational Education 4, no. 3 (2025): 23–39. https://doi.org/10.63650/jeve.v4i3.60.

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Artificial Intelligence (AI) brings new chances for personalized and adaptive learning in Serious Games (SGs). This paper systematically analyzes recent research on AI - based adaptive difficulty and feedback systems in serious games for training complex procedural skills. It starts with key concepts of serious games, procedural skill learning and adaptivity principles. Then, it explores major AI technologies like machine learning, reinforcement learning, natural language processing and generative AI in developing learning models and adaptivity options. The paper presents practical uses and be

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Ciobanu (Neagu), Iustina. "Despre relevanța perspectivei kantiene în era inteligenței artificiale generative: Provocări etice și tehnice." Revista de filosofie 71, no. 3 (2024): 395–406. http://dx.doi.org/10.59277/rf.2024.71.3.08.

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Since the evolution of computational and communication technologies, the world becomes a global village: society and environment are seen through digital perspectives of faster and wider industrial revolutions that become widely available and influence daily our lives. Generative AI dynamically trained and used on billions of blurred knowledge graphs’ multimodal elements becomes a growing technological presence with impact. New scientific and regulatory debates address, alongside the opportunities of generative AI presence in economy and society, the challenges that could, on one side, reinven

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Pandhare, Harshad Vijay. "From Test Case Design to Test Data Generation: How AI is Redefining QA Processes." International Journal of Engineering and Computer Science 13, no. 12 (2024): 26737–57. https://doi.org/10.18535/ijecs.v13i12.4956.

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The accelerating pace of software development, fueled by agile methodologies and continuous integration practices, has exposed the limitations of traditional Quality Assurance (QA) techniques. Manual test case design and static test data provisioning are no longer sufficient to meet the demands of modern software systems that require high reliability, rapid releases, and robust performance under varied conditions. This paper explores how Artificial Intelligence (AI) is fundamentally transforming QA workflows—particularly in the realms of test case design and test data generation. It examines t

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Siva Prakash. "The Role of Cloud-Based Vector Databases and Retrieval Augmented Generation (RAG) for Generative AI in Financial Markets Analysis." World Journal of Advanced Engineering Technology and Sciences 15, no. 3 (2025): 1609–18. https://doi.org/10.30574/wjaets.2025.15.3.1034.

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This scholarly article examines the transformative role of cloud-based vector databases and Retrieval Augmented Generation in enhancing generative artificial intelligence for financial markets evaluation. The convergence of these technologies creates powerful systems that overcome the constraints of standalone large language models by grounding outputs in specific, relevant financial information. Vector databases such as Pinecone, Weaviate, and Milvus enable efficient storage and retrieval of high-dimensional embeddings representing complex financial data, while RAG frameworks significantly im

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