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Found 13 papers, 0 papers with code
Assessing ML Classification Algorithms and NLP Techniques for Depression Detection: An Experimental Case Study
The case study is based on the Distress Analysis Interview Corpus - Wizard-of-Oz (DAIC-WOZ) dataset, which is designed to support the diagnosis of mental disorders such as depression, anxiety, and PTSD.
Comparing Generative Chatbots Based on Process Requirements
Business processes are commonly represented by modelling languages, such as Event-driven Process Chain (EPC), Yet Another Workflow Language (YAWL), and the most popular standard notation for modelling business processes, the Business Process Model and Notation (BPMN).
Extending Variability-Aware Model Selection with Bias Detection in Machine Learning Projects
ML model selection depends on several factors, which include data-related attributes such as sample size, functional requirements such as the prediction algorithm type, and non-functional requirements such as performance and bias.
GPT in Data Science: A Practical Exploration of Model Selection
There is an increasing interest in leveraging Large Language Models (LLMs) for managing structured data and enhancing data science processes.
GPT-in-the-Loop: Adaptive Decision-Making for Multiagent Systems
This paper introduces the "GPT-in-the-loop" approach, a novel method combining the advanced reasoning capabilities of Large Language Models (LLMs) like Generative Pre-trained Transformers (GPT) with multiagent (MAS) systems.
Self-Adaptive Large Language Model (LLM)-Based Multiagent Systems
In autonomic computing, self-adaptation has been proposed as a fundamental paradigm to manage the complexity of multiagent systems (MASs).
A Reference Model for IoT Embodied Agents Controlled by Neural Networks
In this paper, we propose a reference model based on statecharts that offers abstractions tailored to the development of IoT applications.
Machine Learning Model Development from a Software Engineering Perspective: A Systematic Literature Review
Data scientists often develop machine learning models to solve a variety of problems in the industry and academy but not without facing several challenges in terms of Model Development.
Testing Self-Organizing Multiagent Systems
Multiagent Systems (MASs) involve different characteristics, such as autonomy, asynchronous and social features, which make these systems more difficult to understand.
An IoT Analytics Embodied Agent Model based on Context-Aware Machine Learning
Agent-based Internet of Things (IoT) applications have recently emerged as applications that can involve sensors, wireless devices, machines and software that can exchange data and be accessed remotely.
Machine Learning-based Variability Handling in IoT Agents
Agent-based IoT applications have recently been proposed in several domains, such as health care, smart cities and agriculture.
Software Engineers vs. Machine Learning Algorithms: An Empirical Study Assessing Performance and Reuse Tasks
Third, we compared how software engineers fare against machine-learning algorithms when accomplishing the performance and reuse tasks based on criteria such as energy consumption and safety.
