An Improved End-to-End Memory Network for QA Tasks

At present, End-to-End trainable Memory Networks (MemN2N) has proven to be promising in many deep learning fields, especially on simple natural language-based reasoning question and answer (QA) tasks. However, when solving some subtasks such as basic induction, path finding or time reasoning tasks, it remains challenging because of limited ability to learn useful information between memory and query. In this paper, we propose a novel gated linear units (GLU) and local-attention based end-to-end memory networks (MemN2N-GL) motivated by the success of attention mechanism theory in the field of neural machine translation, it shows an improved possibility to develop the ability of capturing complex memory-query relations and works better on some subtasks. It is an improved end-to-end memory network for QA tasks. We demonstrate the effectiveness of these approaches on the 20 bAbI dataset which includes 20 challenging tasks, without the use of any domain knowledge. Our project is open source on github4

Investigating Multi-Level Semantic Extraction with Squash Capsules for Short Text Classification

At present, short text classification is a hot topic in the area of natural language processing. Due to the sparseness and irregularity of short text, the task of short text classification still faces great challenges. In this paper, we propose a new classification model from the aspects of short text representation, global feature extraction and local feature extraction. We use convolutional networks to extract shallow features from short text vectorization, and introduce a multi-level semantic extraction framework. It uses BiLSTM as the encoding layer while the attention mechanism and normalization are used as the interaction layer. Finally, we concatenate the convolution feature vector and semantic results of the semantic framework. After several rounds of feature integration, the framework improves the quality of the feature representation. Combined with the capsule network, we obtain high-level local information by dynamic routing and then squash them. In addition, we explore the optimal depth of semantic feature extraction for short text based on a multi-level semantic framework. We utilized four benchmark datasets to demonstrate that our model provides comparable results. The experimental results show that the accuracy of SUBJ, TREC, MR and ProcCons are 93.8%, 91.94%, 82.81% and 98.43%, respectively, which verifies that our model has greatly improves classification accuracy and model robustness

An Improved Multi-Label Learning Method with ELM-RBF and a Synergistic Adaptive Genetic Algorithm

Profiting from the great progress of information technology, a huge number of multi-label samples are available in our daily life. As a result, multi-label classification has aroused widespread concern. Different from traditional machine learning methods which are time-consuming during the training phase, ELM-RBF (extreme learning machine-radial basis function) is more efficient and has become a research hotspot in multi-label classification. However, because of the lack of effective optimization methods, conventional extreme learning machines are always unstable and tend to fall into local optimum, which leads to low prediction accuracy in practical applications. To this end, a modified ELM-RBF with a synergistic adaptive genetic algorithm (ELM-RBF-SAGA) is proposed in this paper. In ELM-RBF-SAGA, we present a synergistic adaptive genetic algorithm (SAGA) to optimize the performance of ELM-RBF. In addition, two optimization methods are employed collaboratively in SAGA. One is used for adjusting the range of fitness value, the other is applied to update crossover and mutation probability. Sufficient experiments show that ELM-RBF-SAGA has excellent performance in multi-label classification.</jats:p

An Improved Multi-Label Learning Method with ELM-RBF and a Synergistic Adaptive Genetic Algorithm

Profiting from the great progress of information technology, a huge number of multi-label samples are available in our daily life. As a result, multi-label classification has aroused widespread concern. Different from traditional machine learning methods which are time-consuming during the training phase, ELM-RBF (extreme learning machine-radial basis function) is more efficient and has become a research hotspot in multi-label classification. However, because of the lack of effective optimization methods, conventional extreme learning machines are always unstable and tend to fall into local optimum, which leads to low prediction accuracy in practical applications. To this end, a modified ELM-RBF with a synergistic adaptive genetic algorithm (ELM-RBF-SAGA) is proposed in this paper. In ELM-RBF-SAGA, we present a synergistic adaptive genetic algorithm (SAGA) to optimize the performance of ELM-RBF. In addition, two optimization methods are employed collaboratively in SAGA. One is used for adjusting the range of fitness value, the other is applied to update crossover and mutation probability. Sufficient experiments show that ELM-RBF-SAGA has excellent performance in multi-label classification

A Tweet Sentiment Classification Approach Using a Hybrid Stacked Ensemble Technique

With the extensive availability of social media platforms, Twitter has become a significant tool for the acquisition of peoples’ views, opinions, attitudes, and emotions towards certain entities. Within this frame of reference, sentiment analysis of tweets has become one of the most fascinating research areas in the field of natural language processing. A variety of techniques have been devised for sentiment analysis, but there is still room for improvement where the accuracy and efficacy of the system are concerned. This study proposes a novel approach that exploits the advantages of the lexical dictionary, machine learning, and deep learning classifiers. We classified the tweets based on the sentiments extracted by TextBlob using a stacked ensemble of three long short-term memory (LSTM) as base classifiers and logistic regression (LR) as a meta classifier. The proposed model proved to be effective and time-saving since it does not require feature extraction, as LSTM extracts features without any human intervention. We also compared our proposed approach with conventional machine learning models such as logistic regression, AdaBoost, and random forest. We also included state-of-the-art deep learning models in comparison with the proposed model. Experiments were conducted on the sentiment140 dataset and were evaluated in terms of accuracy, precision, recall, and F1 Score. Empirical results showed that our proposed approach manifested state-of-the-art results by achieving an accuracy score of 99%

An Improved Multi-Label Learning Method with ELM-RBF and a Synergistic Adaptive Genetic Algorithm

Profiting from the great progress of information technology, a huge number of multi-label samples are available in our daily life. As a result, multi-label classification has aroused widespread concern. Different from traditional machine learning methods which are time-consuming during the training phase, ELM-RBF (extreme learning machine-radial basis function) is more efficient and has become a research hotspot in multi-label classification. However, because of the lack of effective optimization methods, conventional extreme learning machines are always unstable and tend to fall into local optimum, which leads to low prediction accuracy in practical applications. To this end, a modified ELM-RBF with a synergistic adaptive genetic algorithm (ELM-RBF-SAGA) is proposed in this paper. In ELM-RBF-SAGA, we present a synergistic adaptive genetic algorithm (SAGA) to optimize the performance of ELM-RBF. In addition, two optimization methods are employed collaboratively in SAGA. One is used for adjusting the range of fitness value, the other is applied to update crossover and mutation probability. Sufficient experiments show that ELM-RBF-SAGA has excellent performance in multi-label classification

KG-SR-LLM: Knowledge-Guided Semantic Representation and Large Language Model Framework for Cross-Domain Bearing Fault Diagnosis

Bearing fault diagnosis is crucial for stable operation and safe manufacturing as industry intelligence becomes increasingly advanced. However, under complicated non-linear vibration modes and multiple operating conditions, most of the current diagnostic methods are limited in terms of cross-domain generalization. To address these issues, this study develops a generalized diagnostic framework leveraging Large Language Models (LLMs), integrating multiple enhancements to improve both accuracy and adaptability. Initially, a structured representation approach is designed to transform raw vibration time series into interpretable text sequences by extracting physically meaningful features in both time and frequency domains. This transformation bridges the gap between sequential sensor data and semantic understanding. Furthermore, to explicitly incorporate bearings&rsquo; structural parameters and operating condition information, a knowledge-guided prompt tuning strategy based on Low-Rank Adaptation (LoRA-Prompt) is introduced. This mechanism enables the model to adapt more effectively to varying fault scenarios by embedding expert prior knowledge directly into the learning process. Finally, a generalized fault diagnosis method named Knowledge-Guided Semantic Representation and Large Language Model (KG-SR-LLM) is established. Large-scale experiments using 11 public datasets from industrial, aerospace, and energy fields are carried out to extensively evaluate its performance. Based on experiment analysis and a comparison of results, KG-SR-LLM is superior to classical deep learning models by 9.22%, reaching an average diagnostic accuracy of 98.36%. KG-SR-LLM is effective for handling few-shot transfer and cross-condition adaptation tasks. All these results illustrate the theoretical significance and application benefit of KG-SR-LLM for intelligent fault diagnosis of bearings