Improving Support Ticket Systems Using Machine Learning: A Literature Review

Processing customer support requests via a support ticket system is a key-element for companies to provide support to their customers in an organized and professional way. However, distributing and processing such tickets is much work, increasing the cost for the support providing company and stretching the resolution time. The advancing potential of Machine Learning has led to the goal of automating those support ticket systems. Against this background, we conducted a Literature Review aiming at determining the present state-of-the-art technology in the field of automated support ticket systems. We provide an overview about present trends and topics discussed in this field. During the Literature Review, we found creating an automated incident management tool being the majority topic in the field followed by request escalation and customer sentiment prediction and identified Random Forrest and Support Vector Machine as best performing algorithms for classification in the field

Transportation in Social Media: an automatic classifier for travel-related tweets

In the last years researchers in the field of intelligent transportation systems have made several efforts to extract valuable information from social media streams. However, collecting domain-specific data from any social media is a challenging task demanding appropriate and robust classification methods. In this work we focus on exploring geo-located tweets in order to create a travel-related tweet classifier using a combination of bag-of-words and word embeddings. The resulting classification makes possible the identification of interesting spatio-temporal relations in S\~ao Paulo and Rio de Janeiro

Autonomous Threat Hunting: A Future Paradigm for AI-Driven Threat Intelligence

The evolution of cybersecurity has spurred the emergence of autonomous threat hunting as a pivotal paradigm in the realm of AI-driven threat intelligence. This review navigates through the intricate landscape of autonomous threat hunting, exploring its significance and pivotal role in fortifying cyber defense mechanisms. Delving into the amalgamation of artificial intelligence (AI) and traditional threat intelligence methodologies, this paper delineates the necessity and evolution of autonomous approaches in combating contemporary cyber threats. Through a comprehensive exploration of foundational AI-driven threat intelligence, the review accentuates the transformative influence of AI and machine learning on conventional threat intelligence practices. It elucidates the conceptual framework underpinning autonomous threat hunting, spotlighting its components, and the seamless integration of AI algorithms within threat hunting processes.. Insightful discussions on challenges encompassing scalability, interpretability, and ethical considerations in AI-driven models enrich the discourse. Moreover, through illuminating case studies and evaluations, this paper showcases real-world implementations, underscoring success stories and lessons learned by organizations adopting AI-driven threat intelligence. In conclusion, this review consolidates key insights, emphasizing the substantial implications of autonomous threat hunting for the future of cybersecurity. It underscores the significance of continual research and collaborative efforts in harnessing the potential of AI-driven approaches to fortify cyber defenses against evolving threats

Overcoming data scarcity of Twitter: using tweets as bootstrap with application to autism-related topic content analysis

Notwithstanding recent work which has demonstrated the potential of using Twitter messages for content-specific data mining and analysis, the depth of such analysis is inherently limited by the scarcity of data imposed by the 140 character tweet limit. In this paper we describe a novel approach for targeted knowledge exploration which uses tweet content analysis as a preliminary step. This step is used to bootstrap more sophisticated data collection from directly related but much richer content sources. In particular we demonstrate that valuable information can be collected by following URLs included in tweets. We automatically extract content from the corresponding web pages and treating each web page as a document linked to the original tweet show how a temporal topic model based on a hierarchical Dirichlet process can be used to track the evolution of a complex topic structure of a Twitter community. Using autism-related tweets we demonstrate that our method is capable of capturing a much more meaningful picture of information exchange than user-chosen hashtags.Comment: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 201

GPT Models in Construction Industry: Opportunities, Limitations, and a Use Case Validation

Large Language Models(LLMs) trained on large data sets came into prominence in 2018 after Google introduced BERT. Subsequently, different LLMs such as GPT models from OpenAI have been released. These models perform well on diverse tasks and have been gaining widespread applications in fields such as business and education. However, little is known about the opportunities and challenges of using LLMs in the construction industry. Thus, this study aims to assess GPT models in the construction industry. A critical review, expert discussion and case study validation are employed to achieve the study objectives. The findings revealed opportunities for GPT models throughout the project lifecycle. The challenges of leveraging GPT models are highlighted and a use case prototype is developed for materials selection and optimization. The findings of the study would be of benefit to researchers, practitioners and stakeholders, as it presents research vistas for LLMs in the construction industry.Comment: 58 pages, 20 figure

Generative AI in the Construction Industry: Opportunities & Challenges

In the last decade, despite rapid advancements in artificial intelligence (AI) transforming many industry practices, construction largely lags in adoption. Recently, the emergence and rapid adoption of advanced large language models (LLM) like OpenAI's GPT, Google's PaLM, and Meta's Llama have shown great potential and sparked considerable global interest. However, the current surge lacks a study investigating the opportunities and challenges of implementing Generative AI (GenAI) in the construction sector, creating a critical knowledge gap for researchers and practitioners. This underlines the necessity to explore the prospects and complexities of GenAI integration. Bridging this gap is fundamental to optimizing GenAI's early-stage adoption within the construction sector. Given GenAI's unprecedented capabilities to generate human-like content based on learning from existing content, we reflect on two guiding questions: What will the future bring for GenAI in the construction industry? What are the potential opportunities and challenges in implementing GenAI in the construction industry? This study delves into reflected perception in literature, analyzes the industry perception using programming-based word cloud and frequency analysis, and integrates authors' opinions to answer these questions. This paper recommends a conceptual GenAI implementation framework, provides practical recommendations, summarizes future research questions, and builds foundational literature to foster subsequent research expansion in GenAI within the construction and its allied architecture & engineering domains

Finding Health & Safety Buried Treasure with AI

PresentationThe challenge to glean understanding and insight from an array of historical safety-related reports and observations has existed since the dawn of the HSE discipline. While most organizations today use traditional methods to analyze past events and activities along structured elements (time, place, risk rating and so on), a vast amount of wisdom around hazard identification, root causes and risk control measures remains buried in textual descriptions and reports, and teachable moments become lessons lost. The hands and minds that developed these textual artifacts may be among the most seasoned in the organization, bringing years of experience to bear on the issues and opportunities involved. Such artifacts are then clearly buried treasure. Exploring and surfacing the insights contained in artifact repositories calls for new tools. Using these, a new type of H&S performance indicator could emerge: latent indicators, lying concealed within the written record, offering as much or more value as the leading and lagging indicators used today. This paper describes leveraging the power of artificial intelligence (AI) to absorb large amounts of safety-related textual information, find common themes and identify similar events, which are then analyzed for patterns in causes and controls. This solution, used in concert with traditional analytics, offers unprecedented power to comprehend and visualize collective safety knowledge from historical record. Transforming words to wisdom in this manner not only illuminates the past but also provides a basis for actioning improvements in operational excellence