AI is used in finance for tasks like automated trading, fraud detection, and risk assessment. It employs machine learning algorithms and data analytics to analyze financial data, predict market trends, and optimize financial operations, enabling faster decision-making and improved efficiency in the finance industry.
Researchers explore the application of AI and ML in volatility forecasting, revealing their promise in improving accuracy and informing financial decisions. The review underscores the need for further exploration in explainable AI, uncertainty quantification, and alternative data sources to advance forecasting capabilities.
Researchers introduced a novel memetic training method using coral reef optimization algorithms (CROs) to simultaneously optimize structure and weights of artificial neural networks (ANNs). This dynamic approach showed superior performance in classification accuracy and minority class handling, offering promising advancements in AI optimization for various industries.
Researchers investigated the potential of large language models (LLMs), including GPT and FLAN series, for generating pest management advice in agriculture. Utilizing GPT-4 for evaluation, the study introduced innovative prompting techniques and demonstrated LLMs' effectiveness, particularly GPT-3.5 and GPT-4, in providing accurate and comprehensive advice. Despite FLAN's limitations, the research highlighted the transformative impact of LLMs on pest management practices, emphasizing the importance of contextual information in guiding model responses.
Researchers utilized various machine learning algorithms to develop predictive models for identifying students at risk of dropping out of secondary and higher education in Mexico. Leveraging demographic, socioeconomic, and educational data, the study demonstrated the effectiveness of artificial neural networks (ANN) in achieving high reliability (99%) in predicting school dropout, highlighting key variables such as school attendance, type, location, occupation, income, and marital status.
Researchers dissected the intricate relationship between meta-level and statistical features of tabular datasets, unveiling the impactful role of kurtosis, meta-level ratio, and statistical mean on non-tree-based ML algorithms. This study, based on 200 diverse datasets, provides essential insights for optimizing algorithm selection and understanding the nuanced interplay between dataset characteristics and ML performance.
This study explores the acceptance of chatbots among insurance policyholders. Using the Technology Acceptance Model (TAM), the research emphasizes the crucial role of trust in shaping attitudes and behavioral intentions toward chatbots, providing valuable insights for the insurance industry to enhance customer acceptance and effective implementation of conversational agents.
Researchers from the University of Tuscia, Italy, introduced a machine learning (ML)-based classification model to offer tailored support tools and learning strategies for university students with dyslexia. The model, trained on a self-evaluation questionnaire from over 1200 dyslexic students, demonstrated high accuracy in predicting effective methodologies, providing a personalized approach to enhance learning outcomes and well-being. The study emphasizes the potential applications in education, psychology, and tool/strategy development, encouraging future research directions and student involvement in the design process.
This research addresses the challenge of customer churn in the banking sector using Genetic Algorithm eXtreme Gradient Boosting (GA-XGBoost). The study emphasizes the significance of techniques like SMOTEENN in handling data imbalances and introduces the SHAP interpretation framework for model interpretability. The optimized GA-XGBoost model proves effective in predicting customer churn, offering valuable insights for proactive customer retention strategies in the dynamic banking landscape.
Researchers unveil the Chaotic and Neighborhood Search-based Artificial Bee Colony (CNSABC) algorithm, a groundbreaking variant addressing limitations in traditional Artificial Bee Colony (ABC) for optimization problems. Demonstrating superior convergence speed and solution quality, CNSABC surpasses other algorithms in extensive experiments, showcasing its potential for practical problem-solving, particularly in complex engineering optimization scenarios.
This paper presents a comprehensive survey of large language model (LLM) evaluation across various dimensions, including knowledge, reasoning, alignment, safety, and specialized domains. It covers a wide range of evaluation benchmarks and methodologies to assess the capabilities, ethical considerations, robustness, and application-specific performance of LLMs, aiming to guide the development of LLMs that are beneficial and trustworthy.
A recent research publication explores the profound impact of artificial intelligence (AI) on urban sustainability and mobility. The study highlights the role of AI in supporting dynamic and personalized mobility solutions, sustainable urban mobility planning, and the development of intelligent transportation systems.
In a proposal, researchers emphasize the need for the US government to mandate Know-Your-Customer (KYC) schemes for AI compute providers, especially cloud service providers, to address emerging security and safety risks associated with advanced AI models.
Researchers have introduced FACTCHD, a framework for detecting fact-conflicting hallucinations in large language models (LLMs). They developed a benchmark that provides interpretable data for evaluating the factual accuracy of LLM-generated responses and introduced the TRUTH-TRIANGULATOR framework to enhance hallucination detection.
This research delves into the application of machine learning (ML) algorithms in wastewater treatment, examining their impact on this essential environmental discipline. Through text mining and analysis of scientific literature, the study identifies popular ML models and their relevance, emphasizing the increasing role of ML in addressing complex challenges in wastewater treatment, while also highlighting the importance of data quality and model interpretation.
This research delves into the growing influence of artificial intelligence (AI) and machine learning (ML) on financial markets. Through a mixed-methods approach, it examines AI's applications in trading, risk management, and financial operations, highlighting adoption trends, challenges, and ethical considerations.
This article discusses the growing menace of advanced persistent threats (APTs) in the digital landscape and presents a multi-stage machine learning approach to detect and analyze these sophisticated cyberattacks. The research introduces a Composition-Based Decision Tree (CDT) model, outperforming existing algorithms and offering new insights for improved intrusion detection and prevention systems.
Researchers have introduced the Fine-grained Energy Consumption Meter (FECoM) framework to tackle the energy consumption challenges of Deep Learning (DL) models. This novel approach provides precise method-level energy measurement, offering a granular view of energy consumption and enabling energy-efficient development practices in various domains.
Researchers present a novel approach utilizing a residual network (ResNet-18) combined with AI to classify cooling system faults in hydraulic test rigs with 95% accuracy. As hydraulic systems gain prominence in various industries, this innovative method offers a robust solution for preventing costly breakdowns, paving the way for improved reliability and efficiency.
The study delves into the integration of deep learning, discusses the dataset, and showcases the potential of AI-driven fault detection in enhancing sustainable operations within hydraulic systems.
Researchers introduced the Large Language Model Evaluation Benchmark (LLMeBench) framework, designed to comprehensively assess the performance of Large Language Models (LLMs) across various Natural Language Processing (NLP) tasks in different languages. The framework, initially tailored for Arabic NLP tasks using OpenAI's GPT and BLOOM models, offers zero- and few-shot learning options, customizable dataset integration, and seamless task evaluation.
Researchers propose a groundbreaking feature engineering methodology for high-frequency financial data analysis, enabling the extraction and forecasting of intraday trends using artificial intelligence models. The approach utilizes time series segmentation and extreme gradient boosting for multiclass classification, focusing on volatility, duration, and direction.
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