Relevant bibliographies by topics / AI-driven Support system

Contents

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

Academic literature on the topic 'AI-driven Support system'

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

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'AI-driven Support system.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "AI-driven Support system"

1

Dachepalli, Veeresh. "AI-Driven Decision Support Systems in ERP." International Journal of Computer Science and Data Engineering 2, no. 2 (2025): 1–7. https://doi.org/10.55124/csdb.v2i2.248.

Full text
Abstract:
The integration of artificial intelligence (AI) into enterprise resource planning (ERP) systems has fundamentally transformed organizational decision-making by providing more precise, data-driven insights. This paper examines how AI has been incorporated into ERP systems, focusing on improving strategic decision-making through the use of advanced data visualization techniques and algorithmic decision support algorithms. By leveraging the power of machine learning (ML) and business intelligence (BI) tools, a robust decision support algorithm is proposed that facilitates real-time data analysis, predictive forecasting, and actionable insights. The integration of ML models allows ERP systems to analyze a wide range of historical and real-time data, identify trends, and make predictions, thereby improving forecast accuracy. Meanwhile, BI tools provide intuitive dashboards and visualizations that help decision-makers interpret complex data and effectively monitor key performance indicators (KPIs). This combination significantly improves operational efficiency, streamlines decision-making processes, and reduces time spent on manual tasks. The proposed decision support system enhances the adaptability of ERP systems, helping organizations respond proactively to changing business environments. These findings demonstrate considerable advancements in predictive analytics, operational effectiveness, and the overall adaptability of ERP systems, enabling businesses to remain proactive in market trends and make well-informed strategic choices.
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
Abstract:
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 models (LLMs), thereby optimizing information retrieval, similarity-based search, and multi-query handling. The system employs semantic embeddings to map entities and relationships, utilizing Neo4j and machine learning techniques to enhance inference capabilities. A comparative analysis with conventional RDBMS-based AI models demonstrates significant improvements in query accuracy, explainability, and relevance for decision-making tasks. The proposed approach is evaluated in various domains, including business intelligence, financial analysis, and strategic policymaking. Results indicate that KGs and CAGs enable organizations to obtain more reliable, transparent, and context-aware insights. Additionally, user feedback mechanisms are incorporated to dynamically refine the knowledge graph, ensuring continuous enhancement of AI responses. By bridging structured data with generative AI, this research contributes to the advancement of decision support systems, predictive analytics, and expert recommendation frameworks. The findings suggest that knowledge-enhanced AI models substantially outperform traditional methods in contextual reasoning and decision optimization, offering a scalable and explainable AI framework for enterprise applications. This approach ensures adaptability in AI-driven decision systems by facilitating continuous learning from emerging data trends, thereby enabling more intelligent and data-informed business strategies.
APA, Harvard, Vancouver, ISO, and other styles
3

.Parimala, M. "Soul Support : AI Driven Emotional Assistance ChatBot." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50039.

Full text
Abstract:
Abstract— This paper introduces the development of an AI-based Mental Health Therapist Chatbot designed to provide immediate, accessible emotional support and mental health information. Utilizing deep learning and natural language processing (NLP), the chatbot identifies user intents and delivers appropriate responses across various mental health topics, including anxiety, stress, and sadness. The system is deployed via a web interface that supports both text and voice input, enhancing user accessibility. On the backend, the chatbot uses a Flask server, a trained Keras model for intent classification, and preprocessing pipelines built with NLTK and SpaCy. Experimental evaluations show high accuracy in intent recognition and efficient real-time interaction. Additionally, user feedback reflects a positive reception in terms of helpfulness and ease of use. While the chatbot does not replace professional care, it acts as a supportive digital tool for initial engagement and emotional guidance. Future enhancements will focus on multilingual support, contextual dialogue, and integration with mental health services.
APA, Harvard, Vancouver, ISO, and other styles
4

Latek, Dorota, Khushil Prajapati, Paulina Dragan, Matthew Merski, and Przemysław Osial. "GPCRVS - AI-driven Decision Support System for GPCR Virtual Screening." International Journal of Molecular Sciences 26, no. 5 (2025): 2160. https://doi.org/10.3390/ijms26052160.

Full text
Abstract:
G protein-coupled receptors (GPCRs) constitute the largest and most frequently used family of molecular drug targets. The simplicity of GPCR drug design results from their common seven-transmembrane-helix topology and well-understood signaling pathways. GPCRs are extremely sensitive to slight changes in the chemical structure of compounds, which allows for the reliable design of highly selective and specific drugs. Only recently has the number of GPCR structures, both in their active and inactive conformations, together with their active ligands, become sufficient to comprehensively apply machine learning in decision support systems to predict compound activity in drug design. Here, we describe GPCRVS, an efficient machine learning system for the online assessment of the compound activity against several GPCR targets, including peptide- and protein-binding GPCRs, which are the most difficult for virtual screening tasks. As a decision support system, GPCRVS evaluates compounds in terms of their activity range, the pharmacological effect they exert on the receptor, and the binding mode they could demonstrate for different types and subtypes of GPCRs. GPCRVS allows for the evaluation of compounds ranging from small molecules to short peptides provided in common chemical file formats. The results of the activity class assignment and the binding affinity prediction are provided in comparison with predictions for known active ligands of each included GPCR. Multiclass classification in GPCRVS, handling incomplete and fuzzy biological data, was validated on ChEMBL and Google Patents-retrieved data sets for class B GPCRs and chemokine CC and CXC receptors.
APA, Harvard, Vancouver, ISO, and other styles
5

Ekmalle, Mr Abhiraj. "“Conversational AI for Customer Support”." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04356.

Full text
Abstract:
ABSTRACT: This research paper presents a web-based Conversational AI system developed for enhancing customer support services using Natural Language Processing (NLP), Machine Learning (ML), and Flask for deployment. The system is designed to simulate human-like conversation, resolve customer queries, and operate 24/7 without human intervention. By leveraging intelligent dialogue management and intent recognition, the system can handle frequently asked questions, route complex queries, and significantly reduce human workload. This work demonstrates the integration of AI-driven models within a lightweight web interface and proposes a scalable solution for modern customer service challenges. KEYWORDS: Conversational AI, NLP, Customer Support, Flask, Intent Recognition, Dialogue Management, Chatbot
APA, Harvard, Vancouver, ISO, and other styles
6

Venkateswari, Mrs G. "AI-Driven Voice Transcription with Multilingual Support and Summarization." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 5234–40. https://doi.org/10.22214/ijraset.2025.69553.

Full text
Abstract:
Abstract: This paper presents an AI-powered platform for real-time voice transcription and multilingual summarization, aimed at streamlining communication and documentation in global collaborative settings. The system combines cutting-edge Artificial Intelligence and Natural Language Processing to accurately transcribe speech, extract critical information, and generate clear, context-aware summaries across multiple languages. Utilizing OpenAI's Whisper for speech recognition, the platform integrates sentiment analysis, topic modeling, and both extractive and abstractive summarization methods. A built-in translation engine enables seamless cross-language understanding, supporting diverse teams and user groups. Applicable in domains such as business, education, healthcare, and public administration, the system minimizes manual workload while improving information accuracy and accessibility.
APA, Harvard, Vancouver, ISO, and other styles
7

Nithya, S. "Eco Sort: AI-Driven Waste Segregation System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41870.

Full text
Abstract:
An important environmental problem is waste mishandling, which calls for effective and automated segregation systems. An AI-driven garbage segregation system that uses deep learning and image processing techniques to separate waste into biodegradable and non-biodegradable categories is shown in this research. To achieve precise categorization based on visual cues, a Convolutional Neural Network (CNN) is trained on a variety of trash datasets. The system processes garbage photos using OpenCV to ensure accurate identification. Under many circumstances, image preprocessing methods like scaling and normalization improve model performance. Over time, the algorithm learns from new trash data, increasing the accuracy of its classifications. Effective trash disposal management is also made possible by a real-time monitoring tool that keeps track of the amount of waste in bins and updates users through an interactive interface. By integrating cloud storage, waste management authorities may access and analyze data remotely, which helps them make better decisions. Deep learning, automation, and real-time monitoring are all combined in this system to improve trash management effectiveness, encourage recycling, and support environmental sustainability. Keywords— Deep Learning, Image Processing, Convolutional Neural Network (CNN), OpenCV, Waste Classification and Artificial Intelligence.
APA, Harvard, Vancouver, ISO, and other styles
8

Zhang, Jiangang, and S. B. Goyal. "AI-Driven Decision Support System Innovations to Empower Higher Education Administration." Journal of Computers, Mechanical and Management 3, no. 2 (2024): 35–41. http://dx.doi.org/10.57159/gadl.jcmm.3.2.24070.

Full text
Abstract:
This study explores the utilization, perceptions, and impacts of Decision Support Systems (DSS) in higher education administration. With a focus on DSS, a cross-sectional survey was conducted among higher education administrators from various institutions. The findings underscore the essential role of DSS in higher education administration, with administrators reporting significant utilization and praising their effectiveness and user-friendliness. The study reveals the positive influence of DSS on strategic planning, enrollment management, resource allocation, and student success initiatives. Moreover, it demonstrates the association between DSS usage and favorable outcomes, including increased efficiency and perceived positive consequences. However, persistent challenges such as data quality issues, privacy concerns, and resistance to change highlight the need for improved data management strategies, ethical considerations, and change management approaches. These findings contribute to the ongoing discourse on the transformative potential of DSS in higher education administration and provide valuable insights for businesses seeking to enhance decision-making, resource allocation, and data-driven initiatives. The innovative integration of AI in DSS for higher education administration represents a paradigm shift in decision-making processes, offering unprecedented opportunities for improvement and innovation.
APA, Harvard, Vancouver, ISO, and other styles
9

Soujanya, Dr K. L. S., Dr D. V. Latitha Parameswari, A.Sirisahasra, P.Nishitha, and M.Varshini. "AI Driven Decision Support System for Sustainable Agriculture and Zero Hunger." International Research Journal of Innovations in Engineering and Technology 09, Special Issue ICCIS (2025): 172–77. https://doi.org/10.47001/irjiet/2025.iccis-202528.

Full text
Abstract:
Abstract - Agriculture remains a vital pillar of India’s economy, with a substantial portion of the population relying on farming as their primary livelihood. Despite its importance, many farmers continue to face barriers in maximizing crop productivity and maintaining soil health due to limited access to scientific guidance and data-driven tools. To address these challenges, this study presents an AI-based Decision Support System (DSS) designed to deliver personalized, real-time agricultural recommendations. The system encompasses three key modules: a Crop Recommendation Model, a Fertilizer Recommendation Model, and an interactive bilingual Chatbot supporting both Telugu and English. The crop recommendation module identifies optimal crops based on soil nutrient profiles and environmental parameters, while the fertilizer module suggests suitable nutrient combinations for sustainable and efficient soil management. The integrated chatbot functions as a virtual assistant, providing user-friendly support and addressing common queries in local languages, thereby enhancing accessibility for rural farmers.
APA, Harvard, Vancouver, ISO, and other styles
10

Sheikh, Nuruddin. "AI-Driven Observability: Enhancing System Reliability and Performance." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 7, no. 01 (2025): 229–39. https://doi.org/10.60087/jaigs.v7i01.322.

Full text
Abstract:
AI-powered observability will revolutionize how modern systems are monitored, analyzed, and optimized for performance and resilience. With traditional observability, it requires manual analysis of logs, metrics, and traces, which can often make it too late to respond to system anomalies. With the integration of AI and machine learning in observability platforms, they can use the data collected to find out patterns, identify anomalies, bad actors, late trends, and offer insights based on alert patterns and defined ratios. It assesses how the tools of tomorrow will build on observability for inner systems. The discussion also highlights key challenges including data complexity, model interpretability, and scalability. It concludes with a focus on AI-driven observability as a key strategy to help support resilient and high performing systems in complex and dynamic IT environments.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "AI-driven Support system"

1

Lévy, Loup-Noé. "Advanced Clustering and AI-Driven Decision Support Systems for Smart Energy Management." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG027.

Full text
Abstract:
Cette thèse aborde le clustering de systèmes énergétiques complexes et hétérogènes au sein d'un système d'aide à la décision (SAD).Dans le chapitre 1, nous explorons d'abord la théorie des systèmes complexes et leur modélisation, reconnaissant les bâtiments comme des Systèmes Complexes Sociotechniques. Nous examinons l'état de l'art des acteurs impliqués dans la performance énergétique, identifiant notre cas d'étude comme le Tiers de Confiance pour la Mesure et la Performance Énergétique (TCMPE). Face à nos contraintes, nous nous focalisons sur le besoin d'un système d'aide à la décision pour fournir des recommandations énergétiques, le comparant aux systèmes de supervision et de recommandation et soulignant l'importance de l'explicabilité dans la prise de décision assistée par IA (XAI). Reconnaissant la complexité et l'hétérogénéité des bâtiments gérés par le TCMPE, nous argumentons que le clustering est une étape initiale cruciale pour développer un SAD, permettant des recommandations sur mesure pour des sous-groupes homogènes de bâtiments.Dans le Chapitre 2, nous explorons l'état de l'art des systèmes semi-automatisés pour la prise de décisions à haut risque, mettant l'accent sur la nécessité de gouvernance dans les SAD. Nous investiguons les régulations européennes, mettant en lumière le besoin d'exactitude, de fiabilité, et d'équité de notre système décisionnel, et identifions des méthodologies pour adresser ces besoins, telles que la méthodologie DevOps et le data lineage. Nous proposons une architecture distribuée du SAD qui répond à ces exigences et aux défis posés par le Big Data, intégrant un datalake pour la manipulation des données hétérogènes et massive, des datamarts pour la sélection et le traitement spécifiques des données, et une ML-Factory pour peupler une bibliothèque de modèles. Différentes méthodes de Machine Learning sont sélectionnées pour les différents besoins spécifiques du SAD.Le Chapitre 3 se concentre sur le clustering comme méthode d'apprentissage automatique primaire dans notre cas d'étude, il est essentiel pour identifier des groupes homogènes de bâtiments. Face à la nature plurielle - numérique, catégorielle, séries temporelles - des données décrivant les bâtiments, nous proposons le concept de clustering complexe. Après avoir examiné l'état de l'art, nous identifions la nécessité d'introduire des techniques de réduction de dimensionnalité, associé à des méthodes de clustering numérique et mixte état de l'art. La Prétopologie est proposée comme approche novatrice pour le clustering de données mixtes et complexes. Nous soutenons qu'elle permet une plus grande explicabilité et interactivité, en permettant un clustering hiérarchique construit sur de règles logiques et de notions de proximité adaptées au contexte. Les défis de l'évaluation du clustering complexe sont abordés, et des adaptations de l'évaluation des jeux de donnée numérique sont proposées.Dans le chapitre 4, nous analysons les performances computationnelles des algorithmes et la qualité des clusters obtenus sur différents jeux de données variant en taille, nombre de clusters, distribution et nombre de dimensions. Ces jeux de donnée sont publique, privées ou généré pour les tests. La Prétopologie et l'utilisation de la réduction de dimensionnalité montrent des résultats prometteurs comparés aux méthodes de clustering de données mixtes de l'état de l'art.En conclusion, nous discutons des limitations de notre système, y compris les limites d'automatisation du SAD à chaque étape du flux de données. Nous mettons l'accent sur le rôle crucial de la qualité des données et les défis de prédire le comportement des systèmes complexes au fil du temps. L'objectivité de nos méthodes d'évaluation de clustering est questionnée en raison de l'absence de vérité terrain. Nous envisageons des travaux futurs, tels que l'automatisation de l'hyperparamètrisation et la continuation du développement du SAD<br>This thesis addresses the clustering of complex and heterogeneous energy systems within a Decision Support System (DSS).In chapter 1, we delve into the theory of complex systems and their modeling, recognizing buildings as complex systems, specifically as Sociotechnical Complex Systems. We examine the state of the art of the different agents involved in energy performance within the energy sector, identifying our case study as the Trusted Third Party for Energy Measurement and Performance (TTPEMP.) Given our constraints, we opt to concentrate on the need for a DSS to provide energy recommendations. We compare this system to supervision and recommender systems, highlighting their differences and complementarities and introduce the necessity for explainability in AI-aided decision-making (XAI). Acknowledging the complexity, numerosity, and heterogeneity of buildings managed by the TTPEMP, we argue that clustering serves as a pivotal first step in developing a DSS, enabling tailored recommendations and diagnostics for homogeneous subgroups of buildings. This is presented in Chapter 1.In Chapter 2, we explore DSSs' state of the art, emphasizing the need for governance in semi-automated systems for high-stakes decision-making. We investigate European regulations, highlighting the need for accuracy, reliability, and fairness in our decision system, and identify methodologies to address these needs, such as DevOps methodology and Data Lineage. We propose a DSS architecture that addresses these requirements and the challenges posed by big data, featuring a distributed architecture comprising a data lake for heterogeneous data handling, datamarts for specific data selection and processing, and an ML-Factory populating a model library. Different types of methods are selected for different needs based on the specificities of the data and of the question needing answering.Chapter 3 focuses on clustering as a primary machine learning method in our architecture, essential for identifying homogeneous groups of buildings. Given the combination of numerical, categorical and time series nature of the data describing buildings, we coin the term complex clustering to address this combination of data types. After reviewing the state-of-the-art, we identify the need for dimensionality reduction techniques and the most relevant mixed clustering methods. We also introduce Pretopology as an innovative approach for mixed and complex data clustering. We argue that it allows for greater explainability and interactability in the clustering as it enables Hierarchical clustering and the implementation of logical rules and custom proximity notions. The challenges of evaluating clustering are addressed, and adaptations of numerical clustering to mixed and complex clustering are proposed, taking into account the explainability of the methods.In the datasets and results chapter, we present the public, private, and generated datasets used for experimentation and discuss the clustering results. We analyze the computational performances of algorithms and the quality of clusters obtained on different datasets varying in size, number of clusters, distribution, and number of categorical and numerical parameters. Pretopology and Dimensionality Reduction show promising results compared to state-of-the-art mixed data clustering methods.Finally, we discuss our system's limitations, including the automation limits of the DSS at each step of the data flow. We focus on the critical role of data quality and the challenges in predicting the behavior of complex systems over time. The objectivity of our clustering evaluation methods is challenged due to the absence of ground truth and the reliance on dimensionality reduction to adapt state-of-the-art metrics to complex data. We discuss possible issues regarding the chosen elbow method and future work, such as automation of hyperparameter tuning and continuing the development of the DSS
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "AI-driven Support system"

1

Järvinen, Sami O. "Cautious Data-Driven Evolution: Defence AI in Finland." In Contributions to Security and Defence Studies. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-58649-1_6.

Full text
Abstract:
AbstractFinland has ambitious civilian AI goals and a highly ranked research and education system, but defence AI policies are somewhat less ambitious. Defence AI was first piloted for support functions, followed by applications in military capabilities now emerging in various R&amp;D projects. The Finnish Defence Forces (FDF) lists AI as a priority research area with use cases identified in virtually all areas of defence. AI’s disruptive impact is already visible on the battlefield in autonomous systems, and a further revolution may emerge through dynamic electromagnetic spectrum management. Transceivers combining communications with electronic warfare capabilities could simultaneously provide situational awareness and achieve blue force communications inoffensively to civilian frequencies all the while intercepting enemy communications. Such AI applications could enable new concepts of fighting, but this would require the FDF’s organizational culture to become more conducive to experimentation. The FDF recognizes data availability and management as key for AI development, in particular machine learning. FDF’s new holistic Data Concept aims at more flexible data utilization, but legal and organizational barriers pose several challenges. Apart from a few innovative projects, Finland’s defence AI seems to be somewhat lagging behind the ambitious national AI policies of others, with a cautious and very gradual approach. Publicly available information paints a picture of AI being procured in military-off-the-shelf systems, even if FDF’s R&amp;D portfolio hints at the possibility of various original applications. Finland’s NATO membership, its F-35 acquisition and corresponding industrial R&amp;D cooperation with the U.S. may significantly boost AI development.
APA, Harvard, Vancouver, ISO, and other styles
2

Nabeta, Shinichi, Takahiro Sugiyama, Satoshi Watanabe, and Hiroaki Yuze. "A Generative AI-Driven Tourism Information Dissemination Support System with Direct Posting to SNS." In Springer Proceedings in Business and Economics. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-83705-0_3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Pancher, Juliano Cesar, Jorge Melegati, and Eduardo Martins Guerra. "Exploratory Test-Driven Development Study with ChatGPT in Different Scenarios." In Lecture Notes in Business Information Processing. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-94544-1_10.

Full text
Abstract:
Abstract Generative AI has been rapidly adopted by the software development industry in various ways, offering innovative approaches to transforming requirements into working software. Combining Generative AI with Test-Driven Development (TDD) presents a creative method to accelerate this transformation. However, questions remain about ChatGPT’s readiness for this challenge, including the techniques and best practices required for success and the scenarios where this approach can consistently deliver results. To explore these questions, we designed a study where a group of master’s students performed programming assignments using TDD, first independently and then with the support of ChatGPT. The three assignments represent distinct scenarios: mathematical calculations (function), text processing (class), and system integration (class with dependencies). We performed a qualitative analysis of the submitted code and reports identifying key strategies that significantly influence success rates, such as providing contextual information, separating instructions in prompts following an iterative process, and assisting AI in fixing errors. Among the scenarios, the integration task achieved the highest performance. This study highlights the potential of leveraging Generative AI in TDD for software development and presents a list of effective strategies to maximize its impact. By applying these positive strategies and avoiding identified pitfalls, this research marks a step toward establishing best practices for integrating Generative AI with TDD in software engineering.
APA, Harvard, Vancouver, ISO, and other styles
4

Scalia, Gabriele. "Machine Learning for Scientific Data Analysis." In Special Topics in Information Technology. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85918-3_10.

Full text
Abstract:
AbstractOver the last few years, machine learning has revolutionized countless areas and fields. Nowadays, AI bears promise for analyzing, extracting knowledge, and driving discovery across many scientific domains such as chemistry, biology, and genomics. However, the specific challenges posed by scientific data demand to adapt machine learning techniques to new requirements. We investigate machine learning-driven scientific data analysis, focusing on a set of key requirements. These include the management of uncertainty for complex data and models, the estimation of system properties starting from low-volume and imprecise collected data, the support to scientific model development through large-scale analysis of experimental data, and the machine learning-driven integration of complementary experimental technologies.
APA, Harvard, Vancouver, ISO, and other styles
5

Borchert, Heiko, Torben Schütz, and Joseph Verbovszky. "Master and Servant: Defense AI in Germany." In Contributions to Security and Defence Studies. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-58649-1_9.

Full text
Abstract:
AbstractGermany’s defense AI path is caught in a “master and servant” logic. Germany struggles to readjust its input-driven strategic culture, which puts greater emphasis on the socio-political acceptance and legitimization of military power than on the effects it can achieve. As a result, Germany considers defense AI as a tool—the humble servant—subordinate to humans, who must always remain in the loop. Incrementalism dominates, which makes it difficult to assess what defense AI is expected to achieve and whether it delivers on this expectation. As a result, most German defense AI development projects focus on decision-making support and gradual improvements of other technologies in the fields of Command, Control, Computers, and Communications (C4) and Intelligence, Surveillance, Reconnaissance (ISR). An open-source intelligence system for crisis early warning, AI-based warning receivers for helicopters, and intelligent image processing for missiles feature among the more prominent, publicly known examples of fielded defense AI capabilities. In parallel to adapting defense structures, Germany has stepped up defense funding. While an aggregate number of German defense AI spending is not available, we contend that the country spends around €50M per year on AI-related software development. As defense AI also affects military education and training, the Bundeswehr’s Command and Staff College as well as the University of the Bundeswehr in Hamburg are preparing to adapt existing curricula and setting up new degree courses. Individual military services also explore opportunities for AI-enhanced simulation-based training, while different initiatives have been launched to train defense AI algorithms.
APA, Harvard, Vancouver, ISO, and other styles
6

DhivyaShree, M., R. S. Vishnu Durai, and R. Pavithra. "An AI-Driven Model for Decision Support Systems." In Proceedings of 4th International Conference on Artificial Intelligence and Smart Energy. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-61471-2_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Choiński, Mateusz, Mateusz Rogowski, Piotr Tynecki, Dries P. J. Kuijper, Marcin Churski, and Jakub W. Bubnicki. "A First Step Towards Automated Species Recognition from Camera Trap Images of Mammals Using AI in a European Temperate Forest." In Computer Information Systems and Industrial Management. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-84340-3_24.

Full text
Abstract:
AbstractCamera traps are used worldwide to monitor wildlife. Despite the increasing availability of Deep Learning (DL) models, the effective usage of this technology to support wildlife monitoring is limited. This is mainly due to the complexity of DL technology and high computing requirements. This paper presents the implementation of the light-weight and state-of-the-art YOLOv5 architecture for automated labeling of camera trap images of mammals in the Białowieża Forest (BF), Poland. The camera trapping data were organized and harmonized using TRAPPER software, an open-source application for managing large-scale wildlife monitoring projects. The proposed image recognition pipeline achieved an average accuracy of 85% F1-score in the identification of the 12 most commonly occurring medium-size and large mammal species in BF, using a limited set of training and testing data (a total of 2659 images with animals).Based on the preliminary results, we have concluded that the YOLOv5 object detection and classification model is a fine and promising DL solution after the adoption of the transfer learning technique. It can be efficiently plugged in via an API into existing web-based camera trapping data processing platforms such as e.g. TRAPPER system. Since TRAPPER is already used to manage and classify (manually) camera trapping datasets by many research groups in Europe, the implementation of AI-based automated species classification will significantly speed up the data processing workflow and thus better support data-driven wildlife monitoring and conservation. Moreover, YOLOv5 has been proven to perform well on edge devices, which may open a new chapter in animal population monitoring in real-time directly from camera trap devices.
APA, Harvard, Vancouver, ISO, and other styles
8

Stoffels, Dominik, Susanne Grabl, Thomas Fischer, and Marina Fiedler. "How Explainable AI Methods Support Data-Driven Decision-Making." In Lecture Notes in Information Systems and Organisation. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-80119-8_21.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

de Carvalho, Victor Diogho Heuer, Marcelo Santa Fé Todaro, Robério José Rogério dos Santos, et al. "AI-Driven Decision Support in Public Administration: An Analytical Framework." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54235-0_22.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Rai, Hari Mohan, Aditya Pal, Munis Khamidov, Bobokhonov Akhmadkhon Kholmirzokhon Ugli, and Rashidov Akbar Ergash Ugli. "Computational Intelligence Transforming Healthcare 4.0: Innovations in Medical Image Analysis through AI and IoT Integration." In Data-Driven Decision Support System in Intelligent HealthCare. CRC Press, 2025. https://doi.org/10.1201/9781003507505-3.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "AI-driven Support system"

1

Krishnan, R. Santhana, J. Relin Francis Raj, P. Saveetha, P. Ebby Darney, R. Rajkumar, and G. Ram Sankar. "Data-Driven Decision Support System for Sustainable Energy Management: An AI-IoT Fusion Approach." In 2024 Second International Conference on Inventive Computing and Informatics (ICICI). IEEE, 2024. http://dx.doi.org/10.1109/icici62254.2024.00099.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Magaya, Tinashe Kelvin, Fungai Jacqueline Kiwa, Gilford Hapanyengwi, and Chrispen Murungweni. "AI - Driven Decision Support System for Optimizing Soil Analysis and Crop Management in Zimbabwe." In 2024 3rd Zimbabwe Conference of Information and Communication Technologies (ZCICT). IEEE, 2024. https://doi.org/10.1109/zcict63770.2024.10958519.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Tomasevic, Smiljana, Andjela Blagojevic, Tijana Geroski, et al. "AI-Driven Decision Support System for Heart Failure Diagnosis: INTELHEART Approach Towards Personalized Treatment Strategies." In 2024 IEEE 24th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, 2024. https://doi.org/10.1109/bibe63649.2024.10820487.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Alabed, Abdallah, and Tarık Özkul. "Preliminary Design and Methodological Framework for an AI-Driven Decision Support System in Earth Observation Satellites." In 2025 7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA). IEEE, 2025. https://doi.org/10.1109/ichora65333.2025.11017259.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Das, Samarendra, Ranjali Roy, Anusreya Sarkar, Rajesh Bose, Indranil Sarkar, and Sandip Roy. "Revolutionizing Healthcare: Synergistic Integration of Genomics, Clinical Decision Support Systems, and Advanced Machine Learning for Precision Medicine." In 2025 AI-Driven Smart Healthcare for Society 5.0. IEEE, 2025. https://doi.org/10.1109/ieeeconf64992.2025.10963134.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Mohammed, Abdul Sajid, Anuteja Reddy Neravetla, Varun Kumar Nomula, Ketan Gupta, and S. Dhanasekaran. "Understanding the Impact of AI-driven Clinical Decision Support Systems." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10726136.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Neravetla, Anuteja Reddy, Varun Kumar Nomula, Abdul Sajid Mohammed, and S. Dhanasekaran. "Implementing AI-driven Diagnostic Decision Support Systems for Smart Healthcare." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725323.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

VADUKA, SUDHANSHU, SAI CHARATH REDDY, HRUDAY VIKAS ARIKATHOTA, LOHITH KONCHADA, PRASUN CHAKRABORTY, and ATRI BANDYOPADHYAY. "Optimizing Personal Finance Management through AI-Driven Decision Support Systems." In 2024 IEEE Region 10 Symposium (TENSYMP). IEEE, 2024. http://dx.doi.org/10.1109/tensymp61132.2024.10752249.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

M, Annamalai, Tanusha Mittal, Krishna Chaitanya Sunkara, Himani Pandey, Girish Jadhav, and Vivek Nemane. "Revolutionizing Medical Diagnostics with Transparent AI-Driven Decision Support Systems." In 2024 4th International Conference on Mobile Networks and Wireless Communications (ICMNWC). IEEE, 2024. https://doi.org/10.1109/icmnwc63764.2024.10872336.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Kukreti, Sanjeev, Anurag Shrivastava, Rakesh Chandrashekar, K. Pushpa Rani, Arti Badhoutiya, and Sorabh Lakhanpal. "AI-Driven Clinical Decision Support Systems: Revolutionizing Healthcare With Predictive Models." In 2025 International Conference on Computational, Communication and Information Technology (ICCCIT). IEEE, 2025. https://doi.org/10.1109/icccit62592.2025.10927929.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "AI-driven Support system"

1

Fukuda, Koichi. Future Prospects for Smart Agriculture: Focusing on Case Studies in Japan and Asia. Asian Productivity Organization, 2025. https://doi.org/10.61145/jimb5846.

Full text
Abstract:
This report examines the future prospects of smart agriculture, focusing on case studies from Japan and other Asian countries. Since 2019, Japan has promoted smart agriculture through government-supported initiatives, primarily targeting large-scale farms utilizing technologies such as AI, robotics, and satellite systems. However, as most Japanese farmers operate on a small-to-medium scale, there is growing demand for cost-effective, user-friendly solutions. The report highlights innovations including compact robots, AI-driven irrigation systems, and mobile apps for farm management and traceability. Other Asian countries are also advancing through smartphone-based tools and community-led adoption. Going forward, Japan’s strategy is expected to shift from hardware-centric to software-oriented approaches, integrating digital tools, AI, and environmental initiatives such as carbon labeling and credit programs. Widespread adoption will depend on affordability, simplicity, and continued government support.
APA, Harvard, Vancouver, ISO, and other styles
2

Pasupuleti, Murali Krishna. Empathetic AI in Action: Transforming Customer Service with Emotional Intelligence. National Education Services, 2025. https://doi.org/10.62311/nesx/rr725.

Full text
Abstract:
Abstract: This article explores the transformative impact of Emotionally Intelligent AI on customer service, focusing on how AI systems are designed to understand and respond to human emotions with empathy and precision. It delves into the core technologies, such as sentiment analysis, emotion recognition models, and reinforcement learning, that enable AI to provide emotionally aware interactions. Practical applications are discussed, including AI-powered customer support, personalized experiences, and crisis management solutions. The Article also covers the psychological foundations of AI-driven empathy, ethical and privacy considerations, and future trends in affective computing and integration with technologies like AR/VR and IoT. The potential business advantages of adopting Emotionally Intelligent AI for enhanced customer satisfaction and long-term relationship management are highlighted, emphasizing the balance between technology and the human touch. Keywords: Emotionally Intelligent AI, customer service, empathy, sentiment analysis, emotion recognition, reinforcement learning, affective computing, personalized interactions, ethical AI, data privacy, AR/VR, IoT, human-AI interaction, future trends, business impact.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'AI-driven Support system' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography