相关论文: Yantra AI -- An intelligence platform which intera…
With the world population rapidly increasing, transforming our agrifood systems to be more productive, efficient, safe, and sustainable is crucial to mitigate potential food shortages. Recently, artificial intelligence (AI) techniques such…
Effective backlog management is critical for ensuring that development teams remain aligned with evolving requirements and stakeholder expectations. However, as product backlogs consistently grow in scale and complexity, they tend to become…
Progressive digitalization is changing the game of many industrial sectors. Focus-ing on product quality the main profitability driver of this so-called Industry 4.0 will be the horizontal integration of information over the complete supply…
In this paper, we first clarify the concepts of green AI versus frugal AI, positioning frugality as efficiency by design and green AI as transparency and accountability. We then argue that these approaches, while complementary, are…
Recruitment interviews are cognitively demanding interactions in which interviewers must simultaneously listen, evaluate candidates, take notes, and formulate follow-up questions. To better understand these challenges, we conducted a…
Explainable Artificial Intelligence (XAI) aims to provide insights into the decision-making process of AI models, allowing users to understand their results beyond their decisions. A significant goal of XAI is to improve the performance of…
This is the study that presents an AI-Python-based chatbot that helps students to learn programming by demonstrating solutions to such problems as debugging errors, solving syntax problems or converting abstract theoretical concepts to…
This paper presents ORPilot, an open-source agentic AI system that translates real-world business problems into solver-ready optimization models. Unlike academic LLM-for-OR tools that assume clean problem specifications with preformatted…
The past few years have seen rapid progress in the development of service robots. Universities and companies alike have launched major research efforts toward the deployment of ambitious systems designed to aid human operators performing a…
In this paper we develop a reliable system for smart irrigation of greenhouses using artificial neural networks, and an IoT architecture. Our solution uses four sensors in different layers of soil to predict future moisture. Using a dataset…
Artificial intelligence (AI) has the potential to greatly improve society, but as with any powerful technology, it comes with heightened risks and responsibilities. Current AI research lacks a systematic discussion of how to manage…
Achieving high efficiency on AI operators demands precise control over computation and data movement. However, existing scheduling languages are locked into specific compiler ecosystems, preventing fair comparison, reuse, and evaluation…
With the rise of AI-powered coding assistants, firms and programmers are exploring how to optimize their interaction with them. Research has so far mainly focused on evaluating output quality and productivity gains, leaving aside the…
The importance of explainability in AI has become a pressing concern, for which several explainable AI (XAI) approaches have been recently proposed. However, most of the available XAI techniques are post-hoc methods, which however may be…
The escalating frequency and severity of disasters routinely overwhelm traditional response capabilities, exposing critical vulnerability in disaster management. Current practices are hindered by fragmented data streams, siloed…
Insufficient steel quality in mass production can cause extremely costly damage to tooling, production downtimes and low quality products. Automatic, fast and cheap strategies to estimate essential material properties for quality control,…
With the rise of deep learning, there has been renewed interest within the process industries to utilize data on large-scale nonlinear sensing and control problems. We identify key statistical and machine learning techniques that have seen…
The machine learning lifecycle extends beyond the deployment stage. Monitoring deployed models is crucial for continued provision of high quality machine learning enabled services. Key areas include model performance and data monitoring,…
The rapid growth and diversity in service offerings and the ensuing complexity of information technology ecosystems present numerous management challenges (both operational and strategic). Instrumentation and measurement technology is, by…
While achieving tremendous success in various fields, existing multi-agent reinforcement learning (MARL) with a black-box neural network makes decisions in an opaque manner that hinders humans from understanding the learned knowledge and…