Qingyue Xiong, Liwei Zhang, and Qiujun Lan, Business School, Hunan University, Changsha-410082, China
Credit risk is the most significant risk faced by credit businesses. Currently, various approaches are widely used in credit evaluation. However, methods based on expert knowledge exhibit obvious subjective cognitive bias, while both statistical and machine learning methods require a substantial amount of historical data. In cases with limited data, the machine-learning effect is poor. Inspired by the structural similarity between neural networks (NN) and the Analytic Hierarchy Process (AHP), we propose a knowledge-augmented dynamic neural network model called KADNN to construct an effective credit evaluation model. This composite architecture will help effectively utilize existing data to alleviate the initial low-data dilemma and can be further utilized for training neural networks. Subsequent data updates can be dynamically incorporated to improve model accuracy. Additionally, this approach improves the comprehensibility and premature convergence issues of the NN model. The proposed approach is validated and evaluated through credit evaluation simulation.
Knowledge Augment, Credit Evaluation, Neural Network, Analytic Hierarchy Process, Machine Learning.
Hasnae Briouya, Asmae Briouya, Ali Choukri, Mohamed Amnai, and Youssef Fakhri, Laboratory of Computer Sciences, Faculty of Sciences Ibn Tofail University, Kenitra, Morocco
This article explores lightweight neural network architectures for mobile devices, with a focus on the widely used MobileNet. We investigate alternative models -ShuffleNet, SqueezeNet, EfficientNet, MnasNet and NASNet Mobile — assessing their balance between simplicity, accuracy, and efficiency. The study aims to guide practitioners in choosing the most suitable architecture based on factors such as computational resources, accuracy requirements, and speed for diverse mobile applications.
MobileNet, Lightweight neural networks, Computational efficiency, Semantic segmentation, Mobile applications.
Dr.Jaekeol Choi, University of New Hampshire, United States of America
Relevance evaluation of a query and a passage is essential in Information Retrieval (IR). Recently, numerous studies have been conducted on tasks related to relevance judgment using Large Language Models (LLMs) such as GPT-4, demonstrating significant improvements. However, the efficacy of LLMs is considerably influenced by the design of the prompt. The purpose of this paper is to identify which specific terms in prompts positively or negatively impact relevance evaluation with LLMs. We employed two types of prompts: those used in previous research and generated automatically by LLMs. By comparing the performance of these prompts, we analyze the influence of specific terms in the prompts. We have observed two main findings. First, performance decreases when a prompt excessively narrows the meaning of relevance. Terms like ‘answer’ or ‘direct’ contribute to this effect. Secondly, the term ‘relevance’ plays a beneficial role. Prompts constructed using the word ‘relevant’ showed improved performance. In conclusion, accurately representing the scope of relevance is essential. There has been no comprehensive study on the essential words in prompts for relevance evaluation. This study will offer valuable insights for IR research using LLMs
chatGPT, GPT-3.5, GPT-4, Information Retrieval, Large Language Models (LLMs), relevance evaluation, prompt engineering, passage ranking.
Alex Xie1, Yu Sun2, 1Chadwick School, 26800 S Academy Dr, Palos Verdes Peninsula, CA 90274, 2California State Polytechnic University, Pomona, CA, 91768, Irvine, CA 92620
This paper presents a comprehensive survey and review of financial prompt patterns, exploring the innovative integration of ChatGPT in finance-related tasks. With the rapid evolution of AI and its increasing application in various sectors, the finance industry stands at the forefront of this technological revolution. Our research delves into the myriad ways in which prompt engineering with ChatGPT can enhance financial analyses, risk assessment, investment strategy formulation, and customer service in the finance sector. We systematically categorize and evaluate a wide array of prompt patterns, drawing insights from real-world applications and theoretical frameworks. This survey not only identifies the current state of prompt engineering in finance but also forecasts future trends, challenges, and opportunities. By providing a detailed examination of various prompt designs and their outcomes, this paper aims to serve as a foundational guide for practitioners and researchers seeking to leverage ChatGPTs capabilities for optimized financial decision-making and innovation. The findings underscore the transformative potential of tailored prompts in elevating the accuracy, efficiency, and scope of financial services and strategies.
Large Language Model, ChatGPT, Finance, Prompt Patterns.
Mashaal A. M. Saif, and Nazimah Hussin, Accounting and Finance Department, Azman Hashim International Business School, Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia
This study employs an Artificial Neural Network (ANN) to predict the importance of various factors—convenience, economic efficiency, functional risk, security risk, critical mass, number of services, trust, environmental concern, and perceived value—in collectively determining the intention to adopt digital-only banks. The analysis involves 403 respondents in Malaysia, utilising both exploratory factor analysis and the ANN method. The results from the ANN highlight "environmental concern" as the most influential factor shaping individuals intention to adopt digital-only banks. Additionally, "trust," "perceived value," and "convenience" emerged as crucial factors in predicting adoption intention. These findings not only provide valuable insights for fintech companies and banks aiming to attract new customers or enter new markets but also contribute to expanding knowledge in the field, particularly in the realm of non-linear methods such as ANN. This study enhances the understanding of the evolving landscape in digital-only banking.
Artificial Neural Network (ANN); Adoption Intention; Digital-only banks; Perceived Value; Environmental Concern.
Subhadip, Kumar, Western Governors University
The adoption of Automatic Train Operation (ATO) has revolutionized urban metro systems, enhancing safety, efficiency, and passenger experience. However, the application of ATO in freight railways remains underexplored. This research investigates the challenges specific to freight railways, including extended braking distances and obstacles on rail tracks. Leveraging recent advancements in Artificial Intelligence (AI), Computer Vision (CV), and Machine Learning (ML), we propose innovative solutions to address these challenges. Our study focuses on the strategic integration of AI, CV, and ML technologies within the context of freight railways. By analyzing past ATO studies and examining the impact of advanced vision sensors (such as advanced signaling, cameras, lidars, drones, and radars), we aim to pave the way for efficient, safe, and automated freight transportation. This research contributes to the ongoing transformation of the rail industry, emphasizing the need for collaboration between technology, infrastructure, and operational practices.
IoT, Artificial Intelligence, ATO.