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Journal articles on the topic 'Context-Aware AI'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Vignati, Micael, and Matthew Johnson. "Team Context-Aware Collaborative AI Assistants." Proceedings of the AAAI Symposium Series 5, no. 1 (2025): 119–21. https://doi.org/10.1609/aaaiss.v5i1.35574.

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True collaboration or teamwork involves being aware of the team and its goals, adapting to the changing situations, communicating relevant information, identifying break-downs in common ground and repairing them, all to improve joint outcomes. At the heart of this type of collaborative agent are the capabilities needed to understand context and determine relevance. We introduce four key capabilities that enable our collaborative agents to be effective collaborators by understanding context and determining relevance.

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Ayush, Kumar Jha Shahi Raza Khan. "AI-Powered Context-Aware Blockchain Explorer." Career Point International Journal of Research(CPIJR) 1, no. 4 (2025): 53–72. https://doi.org/10.5281/zenodo.15137907.

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The rapid evolution of blockchain technology necessitates advanced tools for efficient exploration and analysis of distributed ledger data. An <strong>AI-Powered Context-Aware Blockchain Explorer</strong> enhances traditional blockchain explorers by integrating artificial intelligence to provide contextual insights, anomaly detection, and predictive analytics. This system leverages<strong> </strong><strong>natural language processing (NLP)</strong><strong> </strong>and<strong> </strong><strong>machine learning algorithms</strong><strong> to </strong>enable intelligent querying, pattern recogni

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D, Rangraju. "Context-Aware AI for Personalized Public Transit Recommendations." International Journal of Science, Engineering and Technology 13, no. 2 (2025): 1–7. https://doi.org/10.61463/ijset.vol.13.issue2.378.

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Researcher. "REVOLUTIONIZING AI INTERACTIONS: THE RISE OF CONTEXT-AWARE SYSTEMS." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 776–83. https://doi.org/10.5281/zenodo.13899474.

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This article explores the revolutionary advancements in context-aware AI systems and their profound impact on personalized interactions across various industries. It delves into the evolution of these systems, highlighting the integration of sophisticated deep learning models and large language models that have significantly improved their ability to understand and maintain context in extended conversations. The article discusses key developments such as multi-turn conversations, multimodal contextual understanding, and their applications in sectors like customer service, healthcare, and retai

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Geib, Christopher, Vikas Agrawal, Gita Sukthankar, Lokendra Shastri, and Hung Bui. "Architectures for Activity Recognition and Context-Aware Computing." AI Magazine 36, no. 2 (2015): 3–9. http://dx.doi.org/10.1609/aimag.v36i2.2578.

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Wu, Qiong, Zhiwei Zeng, Jun Lin, and Yiqiang Chen. "AI empowered context-aware smart system for medication adherence." International Journal of Crowd Science 1, no. 2 (2017): 102–9. http://dx.doi.org/10.1108/ijcs-07-2017-0006.

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Purpose Poor medication adherence leads to high hospital admission rate and heavy amount of health-care cost. To cope with this problem, various electronic pillboxes have been proposed to improve the medication adherence rate. However, most of the existing electronic pillboxes use time-based reminders which may often lead to ineffective reminding if the reminders are triggered at inopportune moments, e.g. user is sleeping or eating. Design/methodology/approach In this paper, the authors propose an AI-empowered context-aware smart pillbox system. The pillbox system collects real-time sensor dat

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Huang, Chen-Hao, Tzu-Chuan Chou, and Sheng-Hsiung Wu. "Towards Convergence of AI and IoT for Smart Policing." Journal of Global Information Management 29, no. 6 (2021): 1–21. http://dx.doi.org/10.4018/jgim.296260.

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With the fast growth of IoT and AI techniques, AIoT’s potential in creating and capturing business value is being increasingly acknowledged. AIoT is the practice of combining AI with IoT-based hardware to proactively predict what might happen and what action needs to be taken. However, research on this issue has been limited and what the key mechanisms for designing AIoT based context-aware services are in the real field is still underexplored. In this article, we bridge this gap by studying a case study of AIoT based context-aware system in law enforcement in terms of information system artif

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Raghupathi Kanala, Meghana Vijaya Raghavan, Gundala Jathin Kumar, Sushruth Bommagoni, and Arun Kumar Onteddu. "Context aware intelligence resume analyser using deep learning algorithms." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 738–46. https://doi.org/10.30574/wjaets.2025.15.2.0588.

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This research introduces an AI-powered resume analyzer that makes it easier for people to evaluate and polish their resumes. Using cutting-edge Natural Language Processing (NLP), deep learning, and powerful Large Language Models (LLMs), the system quickly pulls out key details—like skills, education, work experience, and projects—from resumes in formats like PDF or DOCX. Unlike older resume tools, this one is faster and smarter, offering unique features like a Skill Gap Analysis to see how well your qualifications match a job’s needs. It also includes a neural network that ranks your resume’s

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Ashish, S. S. "Model Context Protocol: A Context-Aware Framework for Enhancing Cybersecurity in Dynamic Environments." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem49289.

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Abstract— The rapid evolution of cyber threats and growing complexity of digital environments require adaptive, intelligent, and context-aware cybersecurity solutions. The Model Context Protocol (MCP) has emerged as an open standard that facilitates seamless, secure, and dynamic integration between AI-driven security agents and a wide range of data sources, tools, and systems. This paper examines the architecture, security principles, and transformative impact of MCP in strengthening cybersecurity within dynamic environments, with a focus on its context-aware capabilities, interoperability, an

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Al-Saedi, Ahmed A., Veselka Boeva, Emiliano Casalicchio, and Peter Exner. "Context-Aware Edge-Based AI Models for Wireless Sensor Networks—An Overview." Sensors 22, no. 15 (2022): 5544. http://dx.doi.org/10.3390/s22155544.

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Recent advances in sensor technology are expected to lead to a greater use of wireless sensor networks (WSNs) in industry, logistics, healthcare, etc. On the other hand, advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL) are becoming dominant solutions for processing large amounts of data from edge-synthesized heterogeneous sensors and drawing accurate conclusions with better understanding of the situation. Integration of the two areas WSN and AI has resulted in more accurate measurements, context-aware analysis and prediction useful for smart sensing appli

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E J, Dinakar. "Context Aware Visual Analysis for Dynamic Audio Narration." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48530.

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Abstract—Due to the exponential growth of multimedia content, there is a growing demand for advanced image captioning systems that go beyond static descriptions and provide deep, dynamic audio narratives. This paper introduces "Context- Aware Visual Analysis for Dynamic Audio Narration," a pipeline where computer vision and natural language processing synergize to convert images into contextually informed, user- controlled audio descriptions. In this work, the network leverages the robust architecture of `salesforce/BLIP-image- captioning-large` alongside a fine-tuned `google/FLAN-T5- large` m

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Malikireddy, Sai Kiran Reddy. "Revolutionizing Product Recommendations with Generative AI: Context-Aware Personalization at Scale." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–8. https://doi.org/10.55041/ijsrem40434.

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Generative Artificial Intelligence (GenAI) is poised to transform the product recommendation landscape by bridging the gap between user intent and personalized discovery. Traditional recommendation systems rely heavily on collaborative filtering, content-based algorithms, or hybrid models, often constrained by sparse data and limited contextual understanding. GenAI introduces a paradigm shift by leveraging advanced transformer-based architectures and multimodal embeddings to deliver highly contextual, dynamic, and explainable recommendations at scale. This paper explores the use of GenAI for p

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Tallari, Ratnamala. "Devspace: A Social Hub for Developers to Connect, Share and Grow Professionally Using Generative AI." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50380.

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Abstract - DevSpace is a next-generation social platform engineered to foster collaboration, networking, and professional growth among developers. Leveraging cutting-edge Generative AI technologies, particularly Retrieval-Augmented Generation (RAG) and Transformer-based models, DevSpace delivers real-time code recommendations, AI-driven discussions, and intelligent networking. This paper presents an ensemble AI approach to optimize developer interactions on the platform through context-aware content generation, recommendation systems, and adaptive learning models. DevSpace not only enhances pr

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Aslam, Suhaib, and Sander Bogers. "An AI-enabled Approach to Experience Blueprinting: Co-creating healthcare experience journeys with AI." Touchpoint 15, no. 1 (2024): 42–45. http://dx.doi.org/10.30819/touchpoint.15-1.09.

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As we navigate complex service landscapes, how might we harness the power of generative AI to enhance the experience blueprinting process? Our explorations delve into the potential of AI in co-creating detailed, context-aware journeys – offering a fresh perspective on integrating AI with service design.

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Chaalal, Hichem, Mohammed Amin Chemrak, and Fouad Khatemi. "AI-Driven context classification in mobile computing: methodologies and technologies for enhanced user experience." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 2 (2024): e9280. http://dx.doi.org/10.54021/seesv5n2-347.

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This research explores the incorporation of sophisticated artificial intelligence (AI) methodologies, with a specific focus on deep learning approaches like Convolutional Neural Networks (CNNs), to improve context-aware computing in mobile settings. The proliferation of mobile devices in daily life has led to the generation of substantial amounts of contextual information, facilitated by integrated sensors like accelerometers, GPS, and cameras. The capacity to effectively categorize and react to this data in real time is essential for enhancing user experiences that are both personalized and r

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Singh, Ayush Kumar. "Context-Aware Deepfake Detection for Political Speeches." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 6336–41. https://doi.org/10.22214/ijraset.2025.71672.

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In recent years, the proliferation of deepfakes—AI-generated video that mimic the likeness and voices of political figures—has posed a significant threat to public trust and democratic processes. Deepfakes can be used to spread misinformation, damage reputations, and mislead the public with startling realism, making it difficult for the human eye to detect manipulation. A recent study found that deepfake videos increased by 900% between 2019 and 2022, with over 85% targeting political and public figures. We pre-process the dataset using several techniques such as resizing, normalization, and d

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B., Sai Jyothi, Naga Likhitha N., Veda Sri K., Maheswari M., and Anusha K. "Context-Aware MCQ Generation with Large Language Models: A Novel Framework." Journal of Information Technology and Digital World 7, no. 2 (2025): 90–105. https://doi.org/10.36548/jitdw.2025.2.001.

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The methods of conducting examinations are evolving with institutions increasingly adopting online systems, making Multiple-Choice Questions (MCQs) important due to their efficiency and scalability. However, constructing high-quality MCQs remains a manual, time-consuming process. Existing automated systems, mainly using BERT-based summarization and lexical distractor generation, such as WordNet, to suffer from limited contextual understanding and scalability. To address these challenges, this research proposes an innovative solution using Large Language Models (LLMs), specifically Gemini AI, f

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Prashant Gulave and Dr. Kavita Moholkar. "Enhancing Technical Document Compliance Review through a Context-Aware Generative AI Framework." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 3 (2025): 740–45. https://doi.org/10.32628/cseit25113329.

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The rigorous review of technical documentation for compliance with linguistic, domain-specific, and document- type standards is a critical, yet often labor-intensive and error-prone process. This paper presents a novel Generative AI (GenAI) based system designed to automate and significantly enhance the accuracy of technical document compliance checking. Our framework leverages Transformer-based Generative AI models within a hybrid architecture that synergizes Retrieval-Augmented Generation (RAG), semantic rule interpretation, and deep contextual analysis derived from a document graph. The sys

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S, Veena. "Smarter Retail with AI: Context-Aware Recommendation Engines for Next-Gen Shopping." International Journal of Science, Engineering and Technology 13, no. 2 (2025): 1–7. https://doi.org/10.61463/ijset.vol.13.issue2.389.

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Basavaraj, Kaladagi, Krishna Prasad Bodapati Rama, and Guptal Rachit. "Context-Aware NLP in Healthcare: AI-Powered Medical Text Processing for Clinical Decision Support Systems." Global Journal of Engineering and Technology [GJET] 4, no. 2 (2025): 19–21. https://doi.org/10.5281/zenodo.14964402.

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<em>The rapid advancement of Natural Language Processing (NLP) and Artificial Intelligence (AI) has created substantial opportunities for improving healthcare delivery, particularly in the realm of clinical decision support systems (CDSS). Context-aware NLP techniques are increasingly being integrated into healthcare settings to extract valuable insights from unstructured medical texts. The role of <strong>Software Engineering</strong> in building scalable and efficient AI-driven healthcare solutions is paramount, especially in handling <strong>APIs</strong>, <strong>Distributed Systems</stron

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Shrinath, Pai. "Humane AI Pin: A Wearable Device for Context-Aware and Screen-Free Personal Computing." Humane AI Pin: A Wearable Device for Context-Aware and Screen-Free Personal Computing 8, no. 12 (2023): 6. https://doi.org/10.5281/zenodo.10375265.

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The Humane AI Pin is a wearable device that can be clipped to a shirt or blouse, and is designed to be an alternative to smartphones. It can take photos, send texts, and project a visual interface onto a person’s palm using a laser. It also comes with a virtual assistant that can perform tasks such as web searches and object  identification. The device is powered by OpenAI’s GPT- 4, which allows it to offer features such as contextual  computing. The proposed journal paper titled “Humane AI Pin - A Wearable Device for Context-Aware and Screen-Free Personal Computing

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Xie, Dr Xiaoling, and Dr Zeming Fang. "Multi-Modal Emotional Understanding in AI Virtual Characters: Integrating Micro-Expression-Driven Feedback within Context-Aware Facial Micro-Expression Processing Systems." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 15, no. 3 (2024): 474–500. http://dx.doi.org/10.58346/jowua.2024.i3.031.

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To engage users, AI Virtual Characters must comprehend emotions. The paper develops and evaluates Chinese-specific context-aware facial micro-expression processing algorithms and feedback mechanisms to improve AI virtual characters' multi-modal emotional comprehension in Chinese culture. Specialized algorithms were used to collect and evaluate Chinese micro-expressions and assess AI virtual characters' emotional comprehension in user interactions. Chinese participants of various ages, genders, and places were recruited for micro-expression recognition to ensure cultural inclusion. A comprehens

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Sayantan Saha. "Next-generation query optimization: AI-powered query engines." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 472–85. https://doi.org/10.30574/wjaets.2025.15.1.0235.

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AI-powered query optimization represents an emerging paradigm that addresses fundamental limitations in traditional database management systems. By leveraging machine learning techniques, these next-generation query engines can dynamically adapt to evolving data patterns, workload characteristics, and user behaviors. Unlike conventional optimizers that rely on static models and simplified assumptions, AI-driven approaches continuously learn from query execution feedback to improve performance. From workload-aware optimization and adaptive execution to intelligent data management and natural la

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Venkata, Tadi. "Enhancing Real-Time AI Object Detection in High-Density Environments: The Role of Dynamic and Context-Aware Metadata." European Journal of Advances in Engineering and Technology 8, no. 12 (2021): 84–94. https://doi.org/10.5281/zenodo.13319211.

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In the rapidly evolving domain of computer vision, the synergy between artificial intelligence (AI) and metadata has catalyzed significant advancements in object detection capabilities. This study, titled "Enhancing Real-Time AI Object Detection in High-Density Environments: The Role of Dynamic and Context-Aware Metadata," investigates the influence of integrating dynamic and context-aware metadata on the accuracy and efficiency of AI-driven object detection systems. Focusing on high-density environments such as urban traffic and crowded public spaces, this research explores how real-time meta

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Kejriwal, Mayank. "Essential Features in a Theory of Context for Enabling Artificial General Intelligence." Applied Sciences 11, no. 24 (2021): 11991. http://dx.doi.org/10.3390/app112411991.

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Despite recent Artificial Intelligence (AI) advances in narrow task areas such as face recognition and natural language processing, the emergence of general machine intelligence continues to be elusive. Such an AI must overcome several challenges, one of which is the ability to be aware of, and appropriately handle, context. In this article, we argue that context needs to be rigorously treated as a first-class citizen in AI research and discourse for achieving true general machine intelligence. Unfortunately, context is only loosely defined, if at all, within AI research. This article aims to

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Platnick, Daniel, Marjan Alirezaie, and Hossein Rahnama. "Enabling Perspective-Aware Ai with Contextual Scene Graph Generation." Information 15, no. 12 (2024): 766. https://doi.org/10.3390/info15120766.

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This paper advances contextual image understanding within perspective-aware Ai (PAi), an emerging paradigm in human–computer interaction that enables users to perceive and interact through each other’s perspectives. While PAi relies on multimodal data—such as text, audio, and images—challenges in data collection, alignment, and privacy have led us to focus on enabling the contextual understanding of images. To achieve this, we developed perspective-aware scene graph generation with LLM post-processing (PASGG-LM). This framework extends traditional scene graph generation (SGG) by incorporating

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Viswakanth Ankireddi. "Intelligent Metadata and Context-Aware MDM for Dynamic Decision-Making." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 1320–28. https://doi.org/10.32628/cseit25112460.

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Master Data Management (MDM) is experiencing a transformative evolution through the integration of artificial intelligence and advanced analytics capabilities. This comprehensive article explores how AI-driven metadata management and context-aware systems are revolutionizing traditional MDM approaches, enabling organizations to make more informed and dynamic decisions. The article examines the implementation of intelligent MDM systems across various industries, highlighting improvements in data quality, operational efficiency, and strategic decision-making. The article demonstrates how predict

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Sai Kiran Reddy Malikireddy and Snigdha Tadanki. "AI-Powered Conversational Interfaces for CRM/ERP Systems." World Journal of Advanced Engineering Technology and Sciences 5, no. 1 (2022): 063–74. https://doi.org/10.30574/wjaets.2022.5.1.0003.

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This study examines the integration of conversational AI agents into CRM/ERP platforms, focusing on UI/UX design and NLP capabilities. It highlights the development of user-friendly chatbots for automating workflows, addressing multilingual user interactions, and leveraging backend machine learning pipelines for context-aware responses.

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Ali, Aitizaz. "Adaptive and Context-Aware Authentication Framework Using Edge AI and Blockchain in Future Vehicular Networks." STAP Journal of Security Risk Management 2024, no. 1 (2024): 45–56. https://doi.org/10.63180/jsrm.thestap.2024.1.3.

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The rise of connected and autonomous vehicles (CAVs) within intelligent transportation systems has introduced new demands for real-time, scalable, and privacy-preserving authentication mechanisms. Traditional authentication methods, such as Public Key Infrastructure (PKI), are often insufficient in highly dynamic vehicular environments due to their reliance on static credentials and centralized control. This paper proposes an adaptive and context-aware authentication framework that integrates Edge Artificial Intelligence (AI) with blockchain technology to secure vehicular communication. The fr

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Researcher. "THE EDGE OF INNOVATION: HOW AI-POWERED COMPUTING IS REVOLUTIONIZING CUSTOMER EXPERIENCE." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 648–57. https://doi.org/10.5281/zenodo.13884219.

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This article explores the transformative impact of edge computing on AI-driven customer experience (CX), examining how the convergence of these technologies is reshaping customer interactions across various industries. By processing AI workloads closer to the point of data generation, edge computing addresses critical limitations of cloud-based systems, such as latency and privacy concerns, enabling real-time, personalized, and context-aware customer engagements. The article delves into the theoretical framework of edge computing architecture, recent advancements in lightweight AI models optim

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Khan, Abdul-Manan, Ikram Ur Rehman, Nagham Saeed, Drishty Sobnath, Fatima Khan, and Muazzam Ali Khan Khattak. "Context-Aware Autonomous Drone Navigation Using Large Language Models (LLMs)." Proceedings of the AAAI Symposium Series 6, no. 1 (2025): 102–7. https://doi.org/10.1609/aaaiss.v6i1.36039.

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In this paper, a novel large language model (LLM)-based context-aware autonomous drone navigation algorithm is presented. This approach demonstrates the capability of LLMs to navigate complex environments by balancing multisensor objectives with a weighted prioritization system. Specifically, we incorporate weights for the goals of obstacle avoidance, weather adaptation, and mission completion. The model's performance is tested under six progressively intricate scenarios in extensive simulations focused on path efficiency, completion time, and success rate. Results indicate that the LLM-based

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Saiyam Arora. "Transforming AI Decision Support System with Knowledge Graphs & CAG." International Journal on Engineering Artificial Intelligence Management, Decision Support, and Policies 2, no. 2 (2025): 15–23. https://doi.org/10.63503/j.ijaimd.2025.110.

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Artificial Intelligence (AI) serves as a fundamental component of decision support systems (DSS), enabling organizations to process large-scale data and derive actionable insights. However, traditional AI models utilizing relational databases (RDBMS) exhibit limitations in retaining context and applying knowledge-driven reasoning. This study examines the integration of Knowledge Graphs (KGs) and Context-Aware Graphs (CAGs) to enhance AI-driven decision-making systems. A hybrid framework is proposed in which structured knowledge graphs improve the contextual understanding of large language mode

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Huang, Li-Shing, Jui-Yuan Su, and Tsang-Long Pao. "A Context Aware Smart Classroom Architecture for Smart Campuses." Applied Sciences 9, no. 9 (2019): 1837. http://dx.doi.org/10.3390/app9091837.

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The Smart campus is a concept of an education institute using technologies, such as information systems, internet of things (IoT), and context-aware computing, to support learning, teaching, and administrative activities. Classrooms are important building blocks of a school campus. Therefore, a feasible architecture for building and running smart classrooms is essential for a smart campus. However, most studies related to the smart classroom are focused on studying or addressing particular technical or educational issues, such as networking, AI applications, lecture quality, and user responses

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Arga, Ludovic, François Bélorgey, Arnaud Braud, et al. "Frugal AI: Introduction, Concepts, Development and Open Questions." ACM SIGKDD Explorations Newsletter 27, no. 1 (2025): 72–111. https://doi.org/10.1145/3748239.3748247.

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This document aims to provide an overview and synopsis of frugal AI, with a particular focus on its role in promoting cost-effective and sustainable innovation in the context of limited resources. It discusses the environmental impact of AI technologies and the importance of optimising AI systems for efficiency and accessibility. It explains the interface between AI, sustainability and innovation. In fourteen sections, it also makes interested readers aware of various research topics related to frugal AI, raises open questions for further exploration, and provides pointers and references.

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T S, DHANUSH. "Jarvis - Personal AI Desktop Assistant." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42712.

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Productivity is increased as tasks are automated using artificial intelligence. The "JARVIS-Personal AI Desktop Assistant" project develops a voice-activated virtual assistant that is responsive to desktop chores. Constructed with Python, it uses libraries such as OpenAI API, datetime, pyttsx3, speech recognition, and OS to manage files, automate emails, control applications, conduct online searches, and provide real-time information like news and weather. Its scalable modular design and intelligent, context-aware dialogues are made possible by OpenAI's natural language processing. This projec

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Venkata Krishna Koganti. "Autonomous CI/CD Meshes: Self-healing deployment architectures with AI-ML Orchestration." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 2731–45. https://doi.org/10.30574/wjaets.2025.15.2.0777.

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This article introduces a novel architecture for autonomous continuous integration and continuous deployment (CI/CD) systems capable of self-healing and self-optimization without human intervention. The article presents intelligent deployment meshes that integrate deep anomaly detection using LSTM networks with Bayesian change-point detection to identify deployment anomalies before they impact production environments. The proposed framework leverages causal CI/CD graphs to model complex interdependencies between microservices, enabling context-aware remediation strategies including automated r

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Azmi, Mohamed, Abdeljebar Mansour, and Chaimaa Azmi. "A Context-Aware Empowering Business with AI: Case of Chatbots in Business Intelligence Systems." Procedia Computer Science 224 (2023): 479–84. http://dx.doi.org/10.1016/j.procs.2023.09.068.

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Daye, Dania, Regina Parker, Satvik Tripathi, et al. "CASCADE: Context-Aware Data-Driven AI for Streamlined Multidisciplinary Tumor Board Recommendations in Oncology." Cancers 16, no. 11 (2024): 1975. http://dx.doi.org/10.3390/cancers16111975.

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This study addresses the potential of machine learning in predicting treatment recommendations for patients with hepatocellular carcinoma (HCC). Using an IRB-approved retrospective study of patients discussed at a multidisciplinary tumor board, clinical and imaging variables were extracted and used in a gradient-boosting machine learning algorithm, XGBoost. The algorithm’s performance was assessed using confusion matrix metrics and the area under the Receiver Operating Characteristics (ROC) curve. The study included 140 patients (mean age 67.7 ± 8.9 years), and the algorithm was found to be pr

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Anandan, P., V. Abinesh, Pooja S, Dominic Savio M, and Rajkumar Palaniappan. "An AI-based context-aware text-to-face generation system for the police department." IET Conference Proceedings 2024, no. 37 (2025): 466–71. https://doi.org/10.1049/icp.2025.0957.

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HARI SURESH BABU GUMMADI. "AI-augmented workflow resilience framework for cybersecurity risk mitigation in hospital AI systems." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 1175–82. https://doi.org/10.30574/wjarr.2025.26.2.1754.

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The AI-Augmented Workflow Resilience Framework represents a transformative approach to cybersecurity in healthcare environments utilizing artificial intelligence systems. It examines how the integration of AI into hospital settings creates unique security vulnerabilities that traditional cybersecurity methods fail to adequately address. The proposed framework embeds security mechanisms directly into clinical and administrative workflows through five interconnected layers: Continuous Workflow Monitoring, AI-Specific Threat Detection, Healthcare Context Interpretation, Adaptive Response Orchestr

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DEEPTHI, J., HEMASRI V, LEKHA V, and MAYURA ML. "MCE CHATBOT-AI." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–9. https://doi.org/10.55041/ijsrem40319.

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The Malnad College of Engineering Chat-Bot is an innovative application of Artificial Intelligence (AI) aimed at enhancing the communication between students, faculty, and staff within the college ecosystem. Designed to assist users with a wide array of queries ranging from academic information to administrative procedures, the chatbot uses Natural Language Processing (NLP) techniques to deliver accurate and efficient responses. The system was developed to improve accessibility, streamline student services, and reduce human intervention in routine inquiries, offering an interactive and user-fr

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DEEPTHI,, DEEPTHI,, HEMASRI V, LEKHA V,, MAYURA ML, and PRAPULLA KUMAR M S. "CHAT-MCE-AI." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–9. https://doi.org/10.55041/ijsrem40402.

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The Malnad College of Engineering Chat-Bot is an innovative application of Artificial Intelligence (AI) aimed at enhancing the communication between students, faculty, and staff within the college ecosystem. Designed to assist users with a wide array of queries ranging from academic information to administrative procedures, the chatbot uses Natural Language Processing (NLP) techniques to deliver accurate and efficient responses. The system was developed to improve accessibility, streamline student services, and reduce human intervention in routine inquiries, offering an interactive and user-fr

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Yadav, Mr Anurag Anil. "AI VIRTUAL ASSISTANT." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04137.

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ABSTRACT In the rapidly evolving landscape of artificial intelligence and automation, virtual assistants have significantly enhanced the way users interact with computers. This paper introduces the design and implementation of AI Virtual Assistant, an intelligent desktop-based virtual assistant engineered to perform a wide array of tasks through natural and intuitive interaction. Leveraging natural language processing (NLP), speech recognition, and text-to-speech technologies, AI Virtual Assistant enables voice-driven communication and hands-free control. The system is built using Python and i

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Vedula, Nikhita. "Modeling knowledge and functional intent for context-aware pragmatic analysis." ACM SIGWEB Newsletter, Winter (January 2021): 1–4. http://dx.doi.org/10.1145/3447879.3447882.

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Nikhita Vedula is an Applied Scientist at Amazon Alexa Science. She obtained her PhD in Computer Science and Engineering from the Ohio State University in August 2020, advised by Professor Srinivasan Parthasarathy. She received her bachelor's degree from the National Institute of Technology, Nagpur, India in 2015. Her research interests are at the intersection of data mining, natural language processing and social computing. Over the course of her PhD, her research involved designing efficient and novel machine learning and computational linguistic techniques that extract, interpret and transf

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Jagtap, Prof. P. T., Anup Bhombe, Vaibhav Nirmal, Darshansinh Pardeshi, and Vinit Gangurde. "Enhancing User Experience through Context-Aware Intelligent Virtual Assistants." International Journal of Ingenious Research, Invention and Development (IJIRID) 4, no. 2 (2025): 507–12. https://doi.org/10.5281/zenodo.15601181.

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<em>The advancement of artificial intelligence (AI) has led to the development of intelligent virtual assistants (IVAs) that offer seamless interaction between users and their environments. This report presents the design and implementation of a real-time voice assistant, capable of understanding and processing natural language commands while providing contextual information about the surrounding environment. The system is designed to address various user needs, including accessibility for visually impaired individuals, hands-free operation in specialized work settings, and enhanced interactiv

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Dr. Zafar Iqbal Bhatti, Gul Muhammad, Sajid Aslam, and Ayesha Salamat. "MANAGING CONTEXTUAL SHIFTS: PRAGMATICS AND SEMANTICS IN AI DIALOGUE SYSTEMS FOR PAKISTANI LANGUAGE DOCUMENTATION." Kashf Journal of Multidisciplinary Research 2, no. 02 (2025): 1–14. https://doi.org/10.71146/kjmr250.

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This paper investigates the challenges and opportunities of developing context-aware AI dialogue systems for effective language documentation in Pakistan's linguistically diverse environment. The point of focus in the study is the need of pragmatics and semantics in the functions of the AI systems, because these models are facing so many problems when it comes to contextual shifts, dialectical variations, and nuances. Current transformer-based models like BERT or GPT do tend to be good at semantic interpretation, nonetheless they seem to lack the capability to process pragmatic components like

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Rupanetti, Dulana, Corissa Uberecken, Adam King, Hassan Salamy, Cheol-Hong Min, and Samantha Schmidgall. "An Industry Application of Secure Augmentation and Gen-AI for Transforming Engineering Design and Manufacturing." Algorithms 18, no. 7 (2025): 414. https://doi.org/10.3390/a18070414.

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This paper explores the integration of Large Language Models (LLMs) and secure Gen-AI technologies within engineering design and manufacturing, with a focus on improving inventory management, component selection, and recommendation workflows. The system is intended for deployment and evaluation in a real-world industrial environment. It utilizes vector embeddings, vector databases, and Approximate Nearest Neighbor (ANN) search algorithms to implement Retrieval-Augmented Generation (RAG), enabling context-aware searches for inventory items and addressing the limitations of traditional text-base

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Khosla, Ms Naira, and Mrs Flavia Gonsalves. "Adversarial Prompting: How Prompt Engineering Can Be Used to Jailbreak AI." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 7098–106. https://doi.org/10.22214/ijraset.2025.70107.

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Abstract: Adversarial prompting manipulates large language models to bypass safety filters, enabling harmful outputs. This paper examines jailbreak techniques, their success rates (e.g., 58% for LLaMA-2), and risks like misinformation. By analyzing vulnerabilities and proposing defenses, such as context-aware systems, we provide a framework to enhance AI safety and ensure reliable model behavior for real-world applications.

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Rachana Subedi, Shashwot Shrestha, Swodesh Sharma, and Sushil Phuyal. "Transmission Line Monitoring Using Computer Vision & AI." KEC Journal of Science and Engineering 9, no. 1 (2025): 207–11. https://doi.org/10.3126/kjse.v9i1.78389.

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Unmanned aerial vehicles (UAVs)with artificial intelligence (AI) can provide a revolutionary solution for the monitoring and inspection of power transmission lines. This study employs the YOLOv5 deep learning model to detect faults from custom datasets for the context of Nepal, where rugged terrains impede the feasibility of inspections. Some major faults we are proposing to inspect are broken insulators, vegetation encroachment, conductor sag, and corona losses. We used 950 bounding boxes in 400 images annotated manually, and augmented data was used for model robustness. The evaluation demons

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S, Janakiraman. "AI-POWERED DEPTH ESTIMATION USING DEEP LEARNING." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04525.

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Abstract: Depth estimation is a crucial component in many computer vision domains, such as autonomous navigation, robotics, and augmented reality. This project investigates the use of deep learning to enhance depth prediction capabilities, aiming to deliver accurate and real-time 3D scene understanding. Utilizing advanced neural architectures like convolutional neural networks (CNNs), we introduce a novel approach for deriving depth information from either single-view or multi-view imagery. The proposed model effectively captures spatial context and depth indicators from extensive training dat

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