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{"title":"Research on sentiment analysis of hotel review text based on BERT-TCN-BiLSTM-attention model","authors":"Dianwei Chi ,&nbsp;Tiantian Huang ,&nbsp;Zehao Jia ,&nbsp;Sining Zhang","doi":"10.1016/j.array.2025.100378","DOIUrl":"10.1016/j.array.2025.100378","url":null,"abstract":"<div><div>Due to the high semantic flexibility of Chinese text, the difficulty of word separation, and the problem of multiple meanings of one word, a sentiment analysis model based on the combination of BERT dynamic semantic coding with temporal convolutional neural network (TCN), bi-directional long- and short-term memory network (BiLSTM), and Self-Attention mechanism (Self-Attention) is proposed. The model uses BERT pre-training to generate word vectors as model input, uses the causal convolution and dilation convolution structures of TCN to obtain higher-level sequential features, then passes to the BiLSTM layer to fully extract contextual sentiment features, and finally uses the Self-Attention mechanism to distinguish the importance of sentiment features in sentences, thus improving the accuracy of sentiment classification. The proposed model demonstrates superior performance across multiple datasets, achieving accuracy rates of 89.4 % and 91.2 % on the hotel review datasets C1 and C2, with corresponding F1 scores of 0.898 and 0.904. These results, which surpass those of the comparative models, validate the model's effectiveness across different datasets and highlight its robustness and generalizability in sentiment analysis. It also shows that BERT-based coding can improve the model's performance more than Word2Vec.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"25 ","pages":"Article 100378"},"PeriodicalIF":2.3,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Objects recognition from traffic video data using improved 2D convolutional stochastic configuration networks","authors":"Qinxia Wang ,&nbsp;Yue Qiu ,&nbsp;Weiqiang Qu ,&nbsp;Dianhui Wang","doi":"10.1016/j.array.2025.100377","DOIUrl":"10.1016/j.array.2025.100377","url":null,"abstract":"<div><div>With the fast development of advanced science and technology, the urban rail transit continues to develop rapidly, with the industry pays more attention to the operation safety and maintenance of trains. In this paper, an improved 2D convolutional stochastic configuration network (2DConSCN) based method is proposed to deal with traffic video for foreign object recognition. Comparing with the existing stochastic configuration networks,the proposed method retains the stochastic configured mechanism for the convolutional kernel weights. Moreover, a feature selection method is presented to improve the image representation ability. The proposed improved 2DConSCN method greatly reduces the number of parameters, and the trained model can quickly obtain results on test data. Experiments are performed on a rail transit dataset, the comparison results show that the proposed method gets better performance in the recognition task, showing its great potential to meet the requirement of railway monitoring.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"25 ","pages":"Article 100377"},"PeriodicalIF":2.3,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effective depression detection and interpretation: Integrating machine learning, deep learning, language models, and explainable AI","authors":"Gazi Hasan Al Masud ,&nbsp;Rejaul Islam Shanto ,&nbsp;Ishmam Sakin ,&nbsp;Muhammad Rafsan Kabir","doi":"10.1016/j.array.2025.100375","DOIUrl":"10.1016/j.array.2025.100375","url":null,"abstract":"<div><div>Depression is an increasingly prevalent issue, particularly among young people, significantly impacting their well-being and causing persistent distress. Early detection is crucial to address this growing concern. This study utilizes various machine learning, deep learning, and language models to detect depression among Bangladeshi university students. To address data imbalance in the employed dataset, resampling techniques such as SMOTE and Cluster Centroids are applied. Additionally, exhaustive hyperparameter optimization is performed to enhance classification performance. Our results indicate that machine learning algorithms, particularly Random Forest, effectively predict depression with an accuracy of 91.1% and an F1-score of 91.6%. Language models like RoBERTa also achieve strong results, with a recall score of 98.6%. Moreover, explainable AI (XAI) methods, including SHAP and LIME, are employed to interpret model predictions, underscoring the importance of transparency in machine learning. This work contributes to the early identification of depression by integrating machine learning, deep learning, natural language processing, and XAI techniques. While this study focuses on Bangladeshi or similar demographic groups, the proposed approaches are adaptable and can be applied to other populations for generalization.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"25 ","pages":"Article 100375"},"PeriodicalIF":2.3,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stock price prediction with attentive temporal convolution-based generative adversarial network","authors":"Ying Liu ,&nbsp;Xiaohua Huang ,&nbsp;Liwei Xiong ,&nbsp;Ruyu Chang ,&nbsp;Wenjing Wang ,&nbsp;Long Chen","doi":"10.1016/j.array.2025.100374","DOIUrl":"10.1016/j.array.2025.100374","url":null,"abstract":"<div><div>Stock price prediction presents significant challenges owing to the highly volatile and nonlinear nature of financial markets, which are influenced by various factors including macroeconomic conditions, policy changes, and market sentiment. Traditional prediction models such as ARIMA and classic linear regression models are often inadequate for capturing the complex dynamics of stock prices. The advent of deep learning has led to substantial improvements in prediction accuracy, with various recurrent neural networks widely employed for representation learning from stock sequences. However, recurrent networks such as LSTM and GRU may exhibit susceptibility to overfitting the training data, leading to suboptimal performance in real-world predictions due to the inherent noise and volatility of stock market data. Recent research has demonstrated that temporal convolutional networks (TCN) exhibit impressive capabilities in stock price prediction. A TCN can achieve extensive sequence memory by utilizing dilated convolutions, enabling it to capture long-term dependencies in time-series data, as well as causal convolution, ensuring that the model does not utilize future information when predicting future values, which is particularly crucial for time-series prediction. Nevertheless, stock market data typically contain substantial noise to which TCNs may be overly sensitive, thereby affecting the accuracy of the predictions. To address this issue, we propose a novel stock price prediction method based on the Generative Adversarial Networks (GANs) framework, utilizing an Attentive Temporal Convolutional Network (ATCN) as the generator, termed Attentive Temporal Convolution-based Generative Adversarial Network (ATCGAN). This approach employs a GAN framework to generate stock price data using an attentive temporal convolutional network as a generator, whereas a CNN-based discriminator evaluates the authenticity of the data. Adversarial training facilitates the model’s learning of the complex distribution of stock price data. Within the GAN framework, the TCN effectively captures long-term dependencies, combined with an attention mechanism for generating representative feature combinations, thereby enhancing prediction accuracy. Compared to the traditional ARIMA forecasting method, ACTGAN achieved a 78.29% reduction in Mean Absolute Error (MAE). Furthermore, when compared to the deep learning method GRU, ACTGAN reduced the Mean Absolute Error (MAE) by 51.01%. The experimental results demonstrate that the proposed GAN-based approach significantly outperforms the traditional methods and deep learning techniques.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"25 ","pages":"Article 100374"},"PeriodicalIF":2.3,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143351005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An attention based residual U-Net with swin transformer for brain MRI segmentation","authors":"Tazkia Mim Angona,&nbsp;M. Rubaiyat Hossain Mondal","doi":"10.1016/j.array.2025.100376","DOIUrl":"10.1016/j.array.2025.100376","url":null,"abstract":"<div><div>Brain Tumors are a life-threatening cancer type. Due to the varied types and aggressive nature of these tumors, medical diagnostics faces significant challenges. Effective diagnosis and treatment planning depends on identifying the brain tumor areas from MRI images accurately. Traditional methods tend to use manual segmentation, which is costly, time consuming and prone to errors. Automated segmentation using deep learning approaches has shown potential in detecting tumor region. However, the complexity of the tumor areas which contain various shapes, sizes, fuzzy boundaries, makes this process difficult. Therefore, a robust automated segmentation method in brain tumor segmentation is required. In our paper, we present a hybrid model, 3-Dimension (3D) ResAttU-Net-Swin, which combines residual U-Net, attention mechanism and swin transformer. Residual blocks are introduced in the U-Net structure as encoder and decoder to avoid vanishing gradient problems and improve feature recovery. Attention-based skip connections are used to enhance the feature information transition between the encoder and decoder. The swin transformer obtains broad-scale features from the image data. The proposed hybrid model was evaluated on both the BraTS 2020 and BraTS 2019 datasets. It achieved an average Dice Similarity Coefficients (DSC) of 88.27 % and average Intersection over Union (IoU) of 79.93 % on BraTS 2020. On BraTS 2019, the model achieved an average DSC of 89.20 % and average IoU of 81.40 %. The model obtains higher DSC than the existing methods. The experiment result shows that the proposed methodology, 3D ResAttU-Net-Swin can be a potential for brain tumor segmentation in clinical settings.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"25 ","pages":"Article 100376"},"PeriodicalIF":2.3,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mining area skyline objects from map-based big data using Apache Spark framework","authors":"Chen Li ,&nbsp;Ye Zhu ,&nbsp;Yang Cao ,&nbsp;Jinli Zhang ,&nbsp;Annisa Annisa ,&nbsp;Debo Cheng ,&nbsp;Yasuhiko Morimoto","doi":"10.1016/j.array.2024.100373","DOIUrl":"10.1016/j.array.2024.100373","url":null,"abstract":"<div><div>The computation of the skyline provides a mechanism for utilizing multiple location-based criteria to identify optimal data points. However, the efficiency of these computations diminishes and becomes more challenging as the input data expands. This study presents a novel algorithm aimed at mitigating this challenge by harnessing the capabilities of Apache Spark, a distributed processing platform, for conducting area skyline computations. The proposed algorithm enhances processing speed and scalability. In particular, our algorithm encompasses three key phases: the computation of distances between data points, the generation of distance tuples, and the execution of the skyline operators. Notably, the second phase employs a local partial skyline extraction technique to minimize the volume of data transmitted from each executor (a parallel processing procedure) to the driver (a central processing procedure). Afterwards, the driver processes the received data to determine the final skyline and creates filters to exclude irrelevant points. Extensive experimentation on eight datasets reveals that our algorithm significantly reduces both data size and computation time required for area skyline computation.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"25 ","pages":"Article 100373"},"PeriodicalIF":2.3,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SAD: Self-assessment of depression for Bangladeshi university students using machine learning and NLP","authors":"Md Shawmoon Azad,&nbsp;Shakirul Islam Leeon,&nbsp;Riasat Khan,&nbsp;Nabeel Mohammed,&nbsp;Sifat Momen","doi":"10.1016/j.array.2024.100372","DOIUrl":"10.1016/j.array.2024.100372","url":null,"abstract":"<div><div>Depressive illness, influenced by social, psychological, and biological factors, is a significant public health concern that necessitates accurate and prompt diagnosis for effective treatment. This study explores the multifaceted nature of depression by investigating its correlation with various social factors and employing machine learning, natural language processing, and explainable AI to analyze depression assessment scales. Data from a survey of 520 Bangladeshi university students, encompassing socio-personal and clinical questions, was utilized in this study. Eight machine learning algorithms with optimized hyperparameters were applied to evaluate eight depression assessment scales, identifying the most effective one. Additionally, ten machine learning models, including five BERT-based and two generative large language models, were tested using three prompting approaches and assessed across four categories of social factors: relationship dynamics, parental pressure, academic contentment, and exposure to violence. The results showed that support vector machines achieved a remarkable 99.14% accuracy with the PHQ9 scale. While considering the social factors, the stacking ensemble classifier demonstrated the best results. Among NLP approaches, BioBERT outperformed other BERT-based models with 90.34% accuracy when considering all social aspects. In prompting approaches, the Tree of Thought prompting on Claude Sonnet surpassed other prompting techniques with 75.00% accuracy. However, traditional machine learning models outshined NLP methods in tabular data analysis, with the stacking ensemble model achieving the highest accuracy of 97.88%. The interpretability of the top-performing classifier was ensured using LIME.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"25 ","pages":"Article 100372"},"PeriodicalIF":2.3,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FLRF: Federated recommendation optimization for long-tail data distribution","authors":"Zaigang Gong ,&nbsp;Siyu Chen ,&nbsp;Qiangsheng Dai ,&nbsp;Ying Feng ,&nbsp;Jinghui Zhang","doi":"10.1016/j.array.2024.100371","DOIUrl":"10.1016/j.array.2024.100371","url":null,"abstract":"<div><div>Recommendation systems play a crucial role in real-world applications. Federated learning allows training recommendation systems without revealing users’ private data, thereby protecting user privacy. As a result, federated recommendation systems have gained increasing attention in recent years. However, The long-tail distribution problem of federated recommendation systems has not received enough attention. A small number of popular items receive most of the users’ attention, while a significantly larger number of less popular items receive feedback from only a small portion of users. Existing federated recommendation systems usually train on datasets with a long-tail distribution, which can easily lead to over fitting on a small number of popular items, reducing the diversity and novelty of recommendations and causing popularity bias. This paper proposes FLRF, a Federated Long-tail Recommendation Framework, which consists a long-tail recommendation model based on disentangled learning and a long-tail-aware aggregation method based on the attention mechanism. The long-tail recommendation model utilizes the idea of disentangled representation learning to explicitly disentangle the attractiveness of items into fame and niche. The long-tail-aware model aggregation, performs global attention aggregation on the model parameters of the fame part and self-attention aggregation on the model parameters of the niche part. We conduct comparative experiments on the three real-world datasets against the baseline methods in terms of accuracy and novelty. The experimental results show that the proposed framework can improve the diversity and novelty of recommendations without significantly impacting recommendation accuracy.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"24 ","pages":"Article 100371"},"PeriodicalIF":2.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SAMU-Net: A dual-stage polyp segmentation network with a custom attention-based U-Net and segment anything model for enhanced mask prediction","authors":"Radiful Islam ,&nbsp;Rashik Shahriar Akash ,&nbsp;Md Awlad Hossen Rony,&nbsp;Md Zahid Hasan","doi":"10.1016/j.array.2024.100370","DOIUrl":"10.1016/j.array.2024.100370","url":null,"abstract":"<div><div>Early detection of colorectal cancer through the proper segmentation of polyps in the colonoscopy images is crucial. Polyps' complex morphology and varied appearances are the greatest obstacles for the segmentation approaches. The paper introduces SAMU-Net, a novel deep learning-based dual-stage architecture consisting of a custom attention-based U-Net and modified Segment Anything Model (SAM) for better polyp segmentation. In our model, we used the custom U-Net architecture with an attention mechanism to obtain polyp segmentation masks as the first stage. This mask is then used to generate a bounding box input for the second stage that contains the modified Segment Anything Model. The modified SAM relies on the use of High-Quality token-based architecture along with global and local properties to segment polyps accurately, even in cases where the shapes and sizes of polyps are diverse and the polyps have different appearances. The efficiency of SAMU-Net generated from four different datasets of colonoscopy images was examined. Our process produced a dice coefficient score of 0.94, which is very impressive and has a considerable improvement over the existing state-of-the-art polyp segmentation methods. Moreover, the qualitative results also visualize that the SAMU-Net is capable of accurately segmenting polyps of wide ranges, thus, it is a relevant tool for computer-aided detection as well as the diagnosis of colorectal cancer.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"24 ","pages":"Article 100370"},"PeriodicalIF":2.3,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142698999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combining computational linguistics with sentence embedding to create a zero-shot NLIDB","authors":"Yuriy Perezhohin ,&nbsp;Fernando Peres ,&nbsp;Mauro Castelli","doi":"10.1016/j.array.2024.100368","DOIUrl":"10.1016/j.array.2024.100368","url":null,"abstract":"<div><div>Accessing relational databases using natural language is a challenging task, with existing methods often suffering from poor domain generalization and high computational costs. In this study, we propose a novel approach that eliminates the training phase while offering high adaptability across domains. Our method combines structured linguistic rules, a curated vocabulary, and pre-trained embedding models to accurately translate natural language queries into SQL. Experimental results on the SPIDER benchmark demonstrate the effectiveness of our approach, with execution accuracy rates of 72.03% on the training set and 70.83% on the development set, while maintaining domain flexibility. Furthermore, the proposed system outperformed two extensively trained models by up to 28.33% on the development set, demonstrating its efficiency. This research presents a significant advancement in zero-shot Natural Language Interfaces for Databases (NLIDBs), providing a resource-efficient alternative for generating accurate SQL queries from plain language inputs.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"24 ","pages":"Article 100368"},"PeriodicalIF":2.3,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}