QiMLP: Quantum-inspired Multilayer Perceptron with Strong Correlation Mining and Parameter Compression

This work aims to present an overview of using some artificial intelligence (AI) models in machine translation (MT). This work aims to integrate machine learning (ML), deep learning (DL), large language models (LLMs) to enhance machine translation between natural languages. The focus is directed to present the neural-based machine translation (NMT), and some DL models are presented. The bidirectional-encoder-representation from transformer (BERT) and LLMs are presented to utilize the big amount of textual data to learn translation patterns. The main measurable criteria that are used to evaluate the performance of MT and Arabic machine translation (AMT) are also presented. Some linguistic and technical challenges of MT and AMT are discussed. Some key points of future works in NMT are mentioned. A comparative study among some recent published related works is presented. A critical survey is presented to show the important role of DL and LLMs in MT. Some open-source toolkits, datasets and some commercial MT systems are collected and briefly presented. This work is expected to be useful for those people interested to know the up-to-date knowledge of MT and the potential role of DL and LLM in automatic translation.

BackgroundComplementary therapies are being increasingly used by cancer survivors. As a channel for customers to share their feelings, outcomes, and perceived knowledge about the products purchased from e-commerce platforms, Amazon consumer reviews are a valuable real-world data source for understanding cancer survivorship care needs.ObjectiveIn this study, we aimed to highlight the potential of using Amazon consumer reviews as a novel source for identifying cancer survivorship care needs, particularly related to symptom self-management. Specifically, we present a publicly available, manually annotated corpus derived from Amazon reviews of health-related products and develop baseline natural language processing models using deep learning and large language model (LLM) to demonstrate the usability of this dataset.MethodsWe preprocessed the Amazon review dataset to identify sentences with cancer mentions through a rule-based method and conducted content analysis including text feature analysis, sentiment analysis, topic modeling, cancer type, and symptom association analysis. We then designed an annotation guideline, targeting survivorship-relevant constructs. A total of 159 reviews were annotated, and baseline models were developed based on deep learning and large language model (LLM) for named entity recognition and text classification tasks.ResultsA total of 4703 sentences containing positive cancer mentions were identified, drawn from 3349 reviews associated with 2589 distinct products. The identified topics through topic modeling revealed meaningful insights into cancer symptom management and survivorship experiences. Examples included discussions of green tea use during chemotherapy, cancer prevention strategies, and product recommendations for breast cancer. Top 15 symptoms in reviews were also identified, with pain being the most frequent symptom, followed by inflammation, fatigue, etc. The annotation labels were designed to capture cancer types, indicated symptoms, and symptom management outcomes. The resulting annotation corpus contains 2067 labels from 159 Amazon reviews. It is publicly accessible, together with the annotation guideline through the Open Health Natural Language Processing (OHNLP) GitHub. Our baseline model, Bert-base-cased, achieved the highest weighted average F1-score, that is, 66.92%, for named entity recognition, and LLM gpt4-1106-preview-chat achieved the highest F1-score for text classification tasks, that is, 66.67% for “Harmful outcome,” 88.46% for “Favorable outcome” and 73.33% for “Ambiguous outcome.”ConclusionsOur results demonstrate the potential of Amazon consumer reviews as a novel data source for identifying persistent symptoms, concerns, and self-management strategies among cancer survivors. This corpus, along with the baseline natural language processing models developed for named entity recognition and text classification, lays the groundwork for future methodological advancements in cancer survivorship research. Importantly, insights from this study could be evaluated against established clinical guidelines for symptom management in cancer survivorship care. By revealing the feasibility of using consumer-generated data for mining survivorship-related experiences, this study offers a promising foundation for future research and argumentation analysis aimed at improving long-term outcomes and support for cancer survivors.

The integration of deep learning (DL) and large language models (LLMs) has significantly advanced the field of cybersecurity, offering innovative approaches to threat detection, anomaly identification, and secure communication. Deep learning techniques, such as neural networks and reinforcement learning, have demonstrated robust capabilities in detecting previously unknown threats by learning patterns from vast amounts of cybersecurity data. Similarly, LLMs, particularly transformers, have revolutionized natural language processing tasks, enabling effective vulnerability analysis, malware classification, and phishing detection. This chapter explores the foundational concepts of deep learning and LLMs, highlighting their applications and challenges within the cybersecurity landscape. Additionally, it discusses the synergy between these technologies, focusing on how they complement traditional cybersecurity measures and drive the evolution of intelligent defense mechanisms.

Software vulnerabilities pose significant risks to the security and reliability of modern systems, making automated vulnerability detection an essential research area. Traditional static and rule-based approaches are limited in scalability and adaptability, motivating the adoption of data-driven methods. In this survey, we present a comprehensive review of Machine Learning (ML), Deep Learning (DL), and Large Language Models (LLMs) techniques for vulnerability detection. We analyze recent advances in feature representation, fine-tuning strategies, generative approaches, and prompt engineering, while highlighting their ability to capture both syntactic and semantic properties of source code. Furthermore, we examine commonly used evaluation metrics and provide a critical discussion of key challenges, including the lack of large-scale real-world datasets, limited vulnerability coverage, class imbalance, interpretability gaps, hallucination, and high computational costs. To address these issues, we outline promising future research directions, such as neuro-symbolic hybrid methods, parameter-efficient fine-tuning, continual learning, cross-language generalization, and explainable AI for vulnerability detection. Unlike previous studies, the present work explores learning paradigms from ML to LLMs using comprehensive evaluation criteria that highlight analytical capability, feature interpretability, and code-context comprehension. By combining these factors, our study addresses the methodological gap between classic feature-based approaches and current LLM-driven reasoning frameworks, providing beneficial insights to develop robust, scalable, and trustworthy software vulnerability detection systems.

Artificial intelligence is reshaping approaches to architectural heritage conservation by enabling a deeper understanding of how people perceive and experience historic built environments. This study employs deep learning and large language models (LLMs) to explore public perceptions of the Qinghefang Historical and Cultural District in Hangzhou, illustrating how AI-driven analytics can inform intelligent heritage management and architectural revitalization. Large-scale public online reviews were processed through BERTopic-based clustering to extract thematic structures of experience, while interpretive synthesis was refined using an LLM to identify core perceptual dimensions including Hangzhou Housing & Residential Choice, Hangzhou Urban Tourism & Culture, Hangzhou Food & Dining, and Qinghefang Culture & Creative. Sentiment polarity and emotional intensity were quantified using a fine-tuned BERT model, revealing distinct affective and perceptual patterns across the district’s architectural and cultural spaces. The results demonstrate that AI-based textual analytics can effectively decode human–heritage interactions, offering actionable insights for data-informed conservation, visitors’ experience optimization, and sustainable management of historic districts. This research contributes to the emerging field of AI-driven innovation in architectural heritage by bridging computational intelligence and heritage conservation practice.

Accurate burn depth assessment remains a challenge, especially in emergency settings. This study aimed to develop a low-cost artificial intelligence (AI)-based system for burn wound classification using deep learning and large language models (LLMs). A total of 397 burn wound images from public databases were augmented to 7156 images and categorized by depth. A classification model was trained using PaddlePaddle, and a burn-specific LLM was developed based on clinical guidelines. Model performance was evaluated using accuracy, recall, and F1 score and compared against 10 medical students and 6 general LLMs on 80 out-of-sample images. Our model achieved an overall accuracy of 96.82% and F1 score of 96.70%, outperforming medical students (F1: 76.63%) and general LLMs (F1: 68.75%-73.75%). In a separate test using 10 guideline-based true/false questions, all AI models answered correctly, whereas students had only 64% accuracy. This integrated model offers accurate burn depth recognition and guideline-based treatment suggestions, addressing the shortage of burn care specialists, and supporting medical education.

Artificial intelligence (AI) technologies, including machine learning (ML), deep learning, and large language models, are increasingly applied in medical diagnostics. In rhinology, these tools are being evaluated for tasks such as image interpretation, cytology classification, and clinical decision support. To systematically evaluate the application and diagnostic accuracy of AI technologies in rhinology, with a focus on clinical utility and implementation barriers. This review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Seventeen full-text studies were screened based on predefined eligibility criteria, focusing on AI applications with diagnostic metrics in rhinology. Data on AI type, diagnostic task, performance outcomes, and study quality were extracted and synthesized narratively. Twelve studies met the inclusion criteria. Image-based diagnostic tools using convolutional neural networks demonstrated high accuracy (81%-99%) in nasal polyp detection, cytology classification, and computed tomography (CT) scan interpretation. ML models using patient-reported data achieved accuracies of 74.5%-85.5% for chronic rhinosinusitis prediction. Large language models like ChatGPT and Gemini were evaluated for clinical question answering, with performance exceeding 80% in some domains. Risk of bias was moderate in most primary studies, and none reported clinical integration beyond prototype stages. AI exhibits promising diagnostic accuracy across several applications in rhinology. However, significant challenges persist, including limited validation, methodological heterogeneity, and lack of clinical implementation. Future research should focus on prospective trials, explainability, and regulatory frameworks to ensure safe integration into clinical workflows.

Accurate demand Soothsaying is a crucial element in force chain operation and business planning. Traditional statistical ways don’t consider the nonlinear, dynamic, and interdependent nature of variables that drive product demand, including deal history, prices, seasonality, elevations, request changes, and profitable pointers. This design presents a sophisticated soothsaying frame for guidance from an artificial intelligence system, integrating soothsaying using deep literacy models together with large language models(LLMs), that can negotiate both accurate soothsaying and give practicable intelligence. The deep literacy infrastructures used in this study include Long Short Term Memory(LSTM), Reopened intermittent Units(GRU), and other Motor models for timeseries soothsaying, which optimize temporal dependences and the complex cross-variable relations. To further increase interpretability of the vaticinations, LLMs are useful agents to convert the specialized cast affair into a completely automated and enhanced mortal-readable textbook and reports to develop intelligence for decision timber. Prophetic modeling and naturally generated reporting lead to better delicacy and practicable intelligence for their businesses. This intelligence empowers businesses to create better procurement processes, improve inventory management, and develop more resilient supply chains relevant to today’s business environment.

Software defect detection is particularly important for modern society, as it is a crucial step in ensuring the quality and reliability of software systems. With the emergence of artificial intelligence (AI), research in software defect detection has evolved from traditional methods to more complex approaches that utilize deep learning and large language models (LLMs). The advent of LLMs has fundamentally changed the paradigm of software development and defect detection, bringing new challenges and confusion to the field of software defect prediction research. To address these issues, we compare software defect detection methods based on traditional techniques, deep learning approaches, and LLMs through a literature review. We analyze the changes brought about by the introduction of LLMs to software development and propose new insights. Additionally, we examine the progress and trends in software defect prediction to provide inspiration for subsequent research.

Ensuring compliance with complex industry standards and regulations during the design and implementation phases of construction projects is a significant challenge in the building information modeling (BIM) domain. Traditional manual compliance checking methods are inefficient and error-prone, failing to meet modern engineering demands. Natural language processing (NLP) and deep learning methods have improved efficiency and accuracy in rule interpretation and compliance checking. However, these methods still require extensive manual feature engineering, large, annotated datasets, and significant computational resources. Large language models (LLMs) provide robust language understanding with minimal labeled data due to their pre-training and few-shot learning capabilities. However, their application in the AEC field is still limited by the need for fine-tuning for specific tasks, handling complex texts with nested clauses and conditional statements. This study introduces an innovative automated compliance checking framework that integrates LLM, deep learning models, and ontology knowledge models. The use of LLM is motivated by its few-shot learning capability, which significantly reduces the need for large, annotated datasets required by previous methods. Deep learning is employed to preliminarily classify regulatory texts, which further enhances the accuracy of structured information extraction by the LLM compared to directly feeding raw data into the LLM. This novel combination of deep learning and LLM significantly enhances the efficiency and accuracy of compliance checks by automating the processing of regulatory texts and reducing manual intervention. This approach is crucial for architects, engineers, project managers, and regulators, providing a scalable and adaptable solution for automated compliance in the construction industry with broad application prospects.

Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. Among the research challenges on a global scale, the investigation of deep transfer learning (DTL) in the field of electricity load forecasting represents a fundamental effort that can inform artificial intelligence applications in general. In this paper, a comprehensive study is reported regarding day-ahead electricity load forecasting. For this purpose, three sequence-to-sequence (Seq2seq) deep learning (DL) models are used, namely the multilayer perceptron (MLP), the convolutional neural network (CNN) and the ensemble learning model (ELM), which consists of the weighted combination of the outputs of MLP and CNN models. Also, the study focuses on the development of different forecasting strategies based on DTL, emphasizing the way the datasets are trained and fine-tuned for higher forecasting accuracy. In order to implement the forecasting strategies using deep learning models, load datasets from three Greek islands, Rhodes, Lesvos, and Chios, are used. The main purpose is to apply DTL for day-ahead predictions (1–24 h) for each month of the year for the Chios dataset after training and fine-tuning the models using the datasets of the three islands in various combinations. Four DTL strategies are illustrated. In the first strategy (DTL Case 1), each of the three DL models is trained using only the Lesvos dataset, while fine-tuning is performed on the dataset of Chios island, in order to create day-ahead predictions for the Chios load. In the second strategy (DTL Case 2), data from both Lesvos and Rhodes concurrently are used for the DL model training period, and fine-tuning is performed on the data from Chios. The third DTL strategy (DTL Case 3) involves the training of the DL models using the Lesvos dataset, and the testing period is performed directly on the Chios dataset without fine-tuning. The fourth strategy is a multi-task deep learning (MTDL) approach, which has been extensively studied in recent years. In MTDL, the three DL models are trained simultaneously on all three datasets and the final predictions are made on the unknown part of the dataset of Chios. The results obtained demonstrate that DTL can be applied with high efficiency for day-ahead load forecasting. Specifically, DTL Case 1 and 2 outperformed MTDL in terms of load prediction accuracy. Regarding the DL models, all three exhibit very high prediction accuracy, especially in the two cases with fine-tuning. The ELM excels compared to the single models. More specifically, for conducting day-ahead predictions, it is concluded that the MLP model presents the best monthly forecasts with MAPE values of 6.24% and 6.01% for the first two cases, the CNN model presents the best monthly forecasts with MAPE values of 5.57% and 5.60%, respectively, and the ELM model achieves the best monthly forecasts with MAPE values of 5.29% and 5.31%, respectively, indicating the very high accuracy it can achieve.

Accurate prediction of raw material prices helps enterprises optimize procurement, control costs, and enhance profits. Yet, the interplay of factors such as supply and demand imbalances, market volatility, and abrupt disruptions still poses a significant challenge. To address these challenges, we propose a novel hybrid framework for price prediction, called EFD-CBGT, that combines deep learning and Large Language Model (LLM). First of all, we leverage LLM's internalized rich knowledge and contextual reasoning capabilities to generate high-quality textual data using interactive querying. The textual data is then converted into low-dimensional, high-value features using FinBERT and Deep Sparse Autoencoder. Second, we employ Empirical Wavelet Transform to create stationary numerical series from the most strongly correlated features of raw material prices. Finally, by incorporating Feature-wise Attention Module, we use four deep learning models to extract local, bidirectional temporal, temporal, and global features. We conduct comparative experiments, ablation studies, significance tests, and robustness analyses on three real datasets from a leading lithium-ion battery manufacturer in China. The experimental results demonstrate the effectiveness and robustness of EFD-CBGT.

Artificial intelligence (AI) is transforming health care and holds immense potential for various fields, including the medical field, but it also brings its share of concerns and issues. This article introduces AI and its core concepts, such as deep learning, machine learning, and large language models (LLMs). It also provides examples that were generated using ChatGPT to highlight the use of prompts and the limitations of LLMs in AI. AI is a powerful paradigm for scientific research, and it can and does reduce work effort and enhance results. However, is it the right tool for the selected task? In addition, no matter how powerful, AI is meant to complement humans and does not replace them.

The utilization of sentiment analysis has gained significant importance as a valuable method for obtaining meaningful insights from textual data. The research progress in languages such as English and Chinese has been notable. However, there is a noticeable dearth of attention towards creating tools for sentiment analysis in the Bangla language. Currently, datasets are limited for Bangla sentiment analysis, especially balanced datasets capturing both binary and multiclass sentiment for e-commerce applications. This paper introduces a new sentiment analysis dataset from the popular Bangladeshi e-commerce site “Daraz”. The dataset contains 1000 reviews across 5 product categories, with both binary (positive/negative) and multiclass (very positive, positive, negative, very negative) sentiment labels manually annotated by native Bangla speakers. Reviews were collected using an organized process, and labels were assigned based on standardized criteria to ensure accuracy. In addition, a benchmark evaluation of the performance achieved by Machine Learning and Deep Learning algorithms on this dataset is also provided. The new dataset can aid research on multiclass and binary Bangla sentiment analysis utilizing both machine learning, deep learning, and Large Language Models. It can aid e-commerce platforms in analysing nuanced user opinions and emotions from online reviews. The utilization of categorized product reviews also facilitates research in the field of text categorization.

This study presents a Systematic Literature Review on Artificial Intelligence (AI) and Natural Language Processing (NLP) applications for customer support automation and digital service optimization. The review follows the PRISMA framework to ensure methodological rigor and transparency, focusing on literature published between 2020 and 2025 from the Scopus database. The findings reveal that AI-driven technologies, including Machine Learning, Deep Learning, and Large Language Models, have significantly improved efficiency, response time, and customer satisfaction in customer support and digital service. Common NLP applications include sentiment analysis, ticket classification, and automated response generation. Among these, hybrid and transformer-based models demonstrate superior accuracy and contextual understanding compared to traditional algorithms. However, several challenges persist, including data quality limitations, privacy and security concerns, algorithmic bias, and linguistic ambiguities such as sarcasm and negation. Moreover, issues related to trust and ethical adoption continue to influence user acceptance of AI systems. This review provides a comprehensive synthesis of current methodologies, trends, and research gaps, offering insights for future studies to develop explainable, secure, and human-centered AI systems that enhance the sustainability and transparency of digital customer support services.