ABSTRACT Accurately predicting stock prices remains a major challenge in financial analytics due to the complexity and noise inherent in market data. Feature selection plays a critical role in improving both computational efficiency and predictive performance. In this study, we introduce a novel hybrid framework that integrates metaheuristic feature‐selection algorithms with an enhanced transformer‐based prediction model fine‐tuned using temporal embedding and adaptive attention pruning. We evaluate and compare the effectiveness of three nature‐inspired metaheuristic algorithms: bat algorithm (BAT), gray wolf optimization (GWO), and beluga whale optimization (BWO) for selecting the most informative features from a time‐series stock dataset. After feature selection, the optimal subsets are fed into our modified transformer equipped with temporal embeddings and adaptive attention pruning. Extensive experiments conducted on the Bharat Heavy Electricals Limited (BHEL) dataset show that the proposed hybrid framework outperforms traditional methods in terms of predictive accuracy. Among the evaluated approaches, the combination of BWO and the fine‐tuned transformer achieves the best performance, yielding a Test RMSE of 0.0030 and a Test MAPE of 0.0108, demonstrating the superiority of BWO in identifying relevant features. This work provides a comprehensive comparative analysis of hybrid metaheuristic–deep learning models for stock price prediction and offers a foundation for integrating more explainable and scalable AI techniques into financial forecasting.

ABSTRACT This study introduces a dynamically memory‐adjusted whale optimization algorithm (DMA‐WOA) for feature selection in polycystic ovary syndrome (PCOS) diagnosis. To overcome the standard WOA's limitations in balancing exploration and exploitation, DMA‐WOA incorporated adaptive memory control to improve convergence stability and computational efficiency. In DMA‐WOA adaptive control dynamics adjusted memory size and influence based on population diversity and fitness change, enabling consistent convergence in high‐dimensional clinical data. The framework was evaluated on the only publicly available PCOS electronic health records dataset using diverse classifiers, including SVM, RF, LR, MLP, RNN, LSTM, GRU, TabTransformer, and TabNet. Results showed that DMA‐WOA achieved superior accuracy, generalization, and runtime efficiency compared to baseline and standard WOA approaches, while comparative analysis with existing metaheuristics confirmed its enhanced optimization robustness and diagnostic reliability.

ABSTRACTModern large language models (LLMs) represent a paradigm shift in what can plausibly be expected of machine learning models. The fact that LLMs can effectively generate sensible answers to a diverse range of queries suggests that they would be useful in customer support applications. While powerful, LLMs have been observed to be prone to hallucination which unfortunately makes their near‐term use in customer support applications challenging. To address this issue, we present a system that allows us to use an LLM to augment our customer support advocates by re‐framing the language modeling task as a discriminative classification task. In this framing, we seek to present the Top‐K best template responses for a customer support advocate to use when responding to a customer. We present the result of both offline and online experiments where we observed offline gains and statistically significant online lifts for our experimental system. Along the way, we present observed scaling curves for validation loss and Top‐K accuracy, resulted from model parameter ablation studies. We close by discussing the space of trade‐offs with respect to model size, latency, and accuracy as well as and suggesting future applications to explore.

The cover image is based on the Letter Towards bespoke optimizations of energy efficiency in HPC environments by Robert Tracey et al., https://doi.org/10.1002/ail2.87.image

Abstract Natural language processing (NLP) models have been increasingly deployed in real‐world applications, and interpretation for textual data has also attracted dramatic attention recently. Most existing methods generate feature importance interpretation, which indicate the contribution of each word towards a specific model prediction. Text data typically possess highly structured characteristics and feature importance explanation cannot fully reveal the rich information contained in text. To bridge this gap, we propose to generate structured interpretations for textual data. Specifically, we pre‐process the original text using dependency parsing, which could transform the text from sequences into graphs. Then graph neural networks (GNNs) are utilized to classify the transformed graphs. In particular, we explore two kinds of structured interpretation for pre‐trained GNNs: edge‐level interpretation and subgraph‐level interpretation. Experimental results over three text datasets demonstrate that the structured interpretation can better reveal the structured knowledge encoded in the text. The experimental analysis further indicates that the proposed interpretations can faithfully reflect the decision‐making process of the GNN model.

Abstract Deep reinforcement learning (RL) methods have achieved remarkable performance on challenging control tasks. Observations of the resulting behavior give the impression that the agent has constructed a generalized representation that supports insightful action decisions. We re‐examine what is meant by generalization in RL, and propose several definitions based on an agent's performance in on‐policy, off‐policy, and unreachable states. We propose a set of practical methods for evaluating agents with these definitions of generalization. We demonstrate these techniques on a common benchmark task for deep RL, and we show that the learned networks make poor decisions for states that differ only slightly from on‐policy states, even though those states are not selected adversarially. We focus our analyses on the deep Q‐networks (DQNs) that kicked off the modern era of deep RL. Taken together, these results call into question the extent to which DQNs learn generalized representations, and suggest that more experimentation and analysis is necessary before claims of representation learning can be supported.

Abstract In elite sports, there is an opportunity to take advantage of rich and detailed datasets generated across multiple threads of the sporting business. Challenges currently exist due to time constraints to analyse the data, as well as the quantity and variety of data available to assess. Artificial Intelligence (AI) techniques can be a valuable asset in assisting decision makers in tackling such challenges, but deep AI skills are generally not held by those with rich experience in sporting domains. Here, we describe how certain commonly available AI services can be used to provide analytic assistance to sports experts in exploring, and gaining insights from, typical data sources. In particular, we focus on the use of Natural Language Processing and Conversational Interfaces to provide users with an intuitive and time‐saving toolkit to explore their datasets and the conclusions arising from analytics performed on them. We show the benefit of presenting powerful AI and analytic techniques to domain experts, showing the potential for impact not only at the elite level of sports, where AI and analytic capabilities may be more available, but also at a more grass‐roots level where there is generally little access to specialist resources. The work described in this paper was trialled with Leatherhead Football Club, a semi‐professional team that, at the time, were based in the English 7th tier of football.