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{"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}

{"title":"Development of automatic CNC machine with versatile applications in art, design, and engineering","authors":"Utpal Chandra Das ,&nbsp;Nagoor Basha Shaik ,&nbsp;Pannee Suanpang ,&nbsp;Rajib Chandra Nath ,&nbsp;Kedar Mallik Mantrala ,&nbsp;Watit Benjapolakul ,&nbsp;Manoj Gupta ,&nbsp;Chanyanan Somthawinpongsai ,&nbsp;Aziz Nanthaamornphong","doi":"10.1016/j.array.2024.100369","DOIUrl":"10.1016/j.array.2024.100369","url":null,"abstract":"<div><div>The area of computer numerical control (CNC) machines has grown fast, and their use has risen significantly in recent years. This article presents the design and development of a CNC writing machine that uses an Arduino, a motor driver, a stepper motor, and a servo motor. The machine is meant to create 2D designs and write in numerous input languages using 3-axis simultaneous interpolated operations. The suggested machine is low-cost, simple to build, and can be operated with merely G codes. The performance of the CNC writing machine was assessed by testing it on a range of solid surfaces, including paper, cardboard, and wood. The results reveal that the machine can generate high-quality text and images with great accuracy and consistency. The proposed machine's ability to write in several input languages makes it appropriate for various applications, including art, design, and engineering.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"24 ","pages":"Article 100369"},"PeriodicalIF":2.3,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535806","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":"Dual-model approach for one-shot lithium-ion battery state of health sequence prediction","authors":"Slimane Arbaoui ,&nbsp;Ahmed Samet ,&nbsp;Ali Ayadi ,&nbsp;Tedjani Mesbahi ,&nbsp;Romuald Boné","doi":"10.1016/j.array.2024.100367","DOIUrl":"10.1016/j.array.2024.100367","url":null,"abstract":"<div><div>Lithium-ion batteries play a crucial role in powering various applications, including Electric Vehicles (EVs), underscoring the importance of accurately estimating their State Of Health (SOH) throughout their operational lifespan. This paper introduces two novel models: a Transformer (TOPS-SoH) and a Long Short-Term Memory based (LSTM-OSoH) for One-shot Prediction of SOH. The LSTM-OSoHexcels in accuracy, achieving a Masked Mean Absolute Error (MMAE) of less than 0.01 for precise SOH estimation, while the TOPS-SoHdemonstrates simplicity and efficiency, with accuracy comparable to state-of-the-art models. The TOPS-SoHmodel also offers additional interpretability by providing insights into the attention scores between inputs and outputs, highlighting the cycles used for estimation. These models were trained using the MIT battery dataset, with auto-encoders employed to reduce the dimensionality of the input data. Additionally, the models’ effectiveness was validated against a Bidirectional LSTM (<em>BiLSTM</em>) baseline, demonstrating superior performance in terms of lower MMAE, MMSE, and MAPE values, making them highly suitable for integration into Battery Management Systems (BMS). These findings contribute to advancing SOH estimation up to the End Of Life (EOL), which is crucial for ensuring the reliability and longevity of lithium-ion batteries in diverse applications.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"24 ","pages":"Article 100367"},"PeriodicalIF":2.3,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420512","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":"Maximizing influence via link prediction in evolving networks","authors":"Kexin Zhang ,&nbsp;Mo Li ,&nbsp;Shuang Teng ,&nbsp;Lingling Li ,&nbsp;Yi Wang ,&nbsp;Xuezhuan Zhao ,&nbsp;Jinhong Di ,&nbsp;Ji Zhang","doi":"10.1016/j.array.2024.100366","DOIUrl":"10.1016/j.array.2024.100366","url":null,"abstract":"<div><div><em>Influence Maximization</em> (IM), targeting the optimal selection of <span><math><mi>k</mi></math></span> seed nodes to maximize potential information dissemination in prospectively social networks, garners pivotal interest in diverse realms like viral marketing and political discourse dissemination. Despite receiving substantial scholarly attention, prevailing research predominantly addresses the IM problem within the confines of existing networks, thereby neglecting the dynamic evolutionary character of social networks. An inevitable requisite arises to explore the IM problem in social networks of future contexts, which is imperative for certain application scenarios. In this light, we introduce a novel problem, Influence Maximization in Future Networks (IMFN), aimed at resolving the IM problem within an anticipated future network framework. We establish that the IMFN problem is NP-hard and advocate a prospective solution framework, employing judiciously selected link prediction methods to forecast the future network, and subsequently applying a greedy algorithm to select the <span><math><mi>k</mi></math></span> most influential nodes. Moreover, we present SCOL (Sketch-based Cost-effective lazy forward selection algorithm Optimized with Labeling technique), a well-designed algorithm to accelerate the query of our IMFN problem. Extensive experimental results, rooted in five real-world datasets, are provided, affirming the efficacy and efficiency of the proffered solution and algorithms.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"24 ","pages":"Article 100366"},"PeriodicalIF":2.3,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420510","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}