Intelligent Data Analysis - Volume Pre-press, issue Pre-press

Authors: Yan, Cairong | Zhu, Ziyang | Zhang, Yiwei | Guan, Xiaopeng | Wan, Yongquan

Article Type: Research Article

Abstract: Multi-behavior recommendation models excel in extracting abundant information from user-item interactions to enhance performance; however, they encounter challenges in accuracy due to noise disturbance and ambiguous weight allocation. In this paper, we propose cd-MBRec, a novel model designed to amplify commonality among various behaviors, thereby minimizing noise interference while preserving behavior diversity to highlight semantic variations in feedback across distinct scenarios. Specifically, the model begins by constructing behavior matrices that models separate behaviors, along with an interaction matrix offering a broad overview of user behaviors. It employs graph neural networks to extract higher-order semantic and structural information from input data. …Concurrently, the model integrates principles of Weber-Fechner Law for the adaptive allocation of initial weights to the multiple behaviors and utilizes matrix factorization techniques for efficient behavior embedding. Extensive experiments on two real-world datasets demonstrate that cd-MBRec surpasses existing state-of-the-art models in recommendation performance, achieving notable average improvements of 4.96% in HR@10 and 7.75% in NDCG@10. Show more

Keywords: Multi-behavior recommendation, graph neural networks, matrix factorization

DOI: 10.3233/IDA-240393

Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-17, 2024

Authors: Li, Xiaoyong | Cheng, Huimin | An, Sufang | Zhang, Yanjun | Zhang, Yong

Article Type: Research Article

Abstract: Social relationships among students at campus are closely related to their mental health and academic performance. Therefore, it is a very important task for educators to analyze students’ social relationships. However, existing studies have focused on one-to-one social relationships between students, few ones have explored the high-order community relationships hidden in social networks, especially in a visual manner. To solve this problem, a visual analysis system called ViSSR is proposed in this paper, which utilizes the Louvain algorithm to detect the hierarchical community structure of students’ social network at campus, and then provides four coordinated views to visualize the detection …results. Among the views, the hierarchical hypergraph view is to visualize the hierarchical community structure that greatly breaks through the limitations of first-order relationships available in a traditional node-link social network, the community analysis view and individual analysis view show the social characteristics of a community and individual student respectively, and the matrix view displays the behavioral features of students. Case studies and experts evaluation have been conducted to demonstrate the usability of the system. Show more

Keywords: Student social relationships, visual analysis, community detection, hierarchical hypergraph

DOI: 10.3233/IDA-230263

Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-26, 2024

Authors: Bera, Somenath | Varish, Naushad | Yaqoob, Syed irfan | Rafi, Mudassir | Shrivastava, Vimal K.

Article Type: Research Article

Abstract: Joint spectral-spatial feature extraction has been proven to be the most effective part of hyperspectral image (HSI) classification. But, due to the mixing of informative and noisy bands in HSI, joint spectral-spatial feature extraction using convolutional neural network (CNN) may lead to information loss and high computational cost. More specifically, joint spectral-spatial feature extraction from excessive bands may cause loss of spectral information due to the involvement of convolution operation on non-informative spectral bands. Therefore, we propose a simple yet effective deep learning model, named deep hierarchical spectral-spatial feature fusion (DHSSFF), where spectral-spatial features are exploited separately to reduce the …information loss and fuse the deep features to learn the semantic information. It makes use of abundant spectral bands and few informative bands of HSI for spectral and spatial feature extraction, respectively. The spectral and spatial features are extracted through 1D CNN and 3D CNN, respectively. To validate the effectiveness of our model, the experiments have been performed on five well-known HSI datasets. Experimental results demonstrate that the proposed method outperforms other state-of-the-art methods and achieved 99.17%, 98.84%, 98.70%, 99.18%, and 99.24% overall accuracy on Kennedy Space Center, Botswana, Indian Pines, University of Pavia, and Salinas datasets, respectively. Show more

Keywords: CNN, deep learning, feature fusion, feature extraction, hyperspectral image classification, informative bands

DOI: 10.3233/IDA-230927

Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-25, 2024

Authors: Weng, Wei | Hou, Fengxia | Gong, Shengchao | Chen, Fen | Lin, Dongsheng

Article Type: Research Article

Abstract: Graph clustering is a crucial technique for partitioning graph data. Recent research has concentrated on integrating topology and attribute information from attribute graphs to generate node embeddings, which are subsequently clustered using classical algorithms. However, these methods have some limitations, such as insufficient information inheritance in shallow networks or inadequate quality of reconstructed nodes, leading to suboptimal clustering performance. To tackle these challenges, we introduce two normalization techniques within the graph attention autoencoder framework, coupled with an MSE loss, to facilitate node embedding learning. Furthermore, we integrate Transformers into the self-optimization module to refine node embeddings and clustering outcomes. Our …model can induce appropriate node embeddings for graph clustering in a shallow network. Our experimental results demonstrate that our proposed approach outperforms the state-of-the-art in graph clustering over multiple benchmark datasets. In particular, we achieved 76.3% accuracy on the Pubmed dataset, an improvement of at least 7% compared to other methods. Show more

Keywords: Attribute graph clustering, transformer, autoencoder, node embedding

DOI: 10.3233/IDA-230647

Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-16, 2024

Authors: Swetha, A.V.S. | Bala, Manju | Sharma, Kapil

Article Type: Research Article

Abstract: Breast cancer poses a significant threat to women’s health, emphasizing the crucial role of timely detection. Traditional pathology reports, though widely used, face challenges prompting the development of automated Deep Learning (DL) tools. DL models, gaining traction in radiology, offer precise diagnoses; however, issues with generalization on varying dataset sizes persist. This paper introduces a computationally efficient DL framework, addressing dataset imbalance through a hybrid model design, ensuring both accuracy and speed in breast cancer image classification. Proposed model novel design excels in accuracy and generalization across medical imaging datasets, providing a robust tool for precise diagnostics. The proposed model …integrates features from two classifiers, Inception ResNet V2 and Vision Transformers (ViT), to enhance the classification of breast cancer. This synergistic blend enhances adaptability, ensuring consistent performance across diverse dataset scales. A key contribution is the introduction of an Efficient Attention Mechanism within one of the classifiers, optimizing focus on critical features for improved accuracy and computational efficiency. Further, a Resource-Efficient Optimization model through feature selection is proposed, streamlining computational usage without compromising accuracy. Addressing the inherent heterogeneity within classifiers, our framework integrates high dimensional features comprehensively, leading to more accurate tumor class predictions. This consideration of heterogeneity marks a significant leap forward in precision for breast cancer diagnosis. An extensive analysis on datasets, BreakHis and BACH, that are imbalanced in nature is conducted by evaluating complexity, performance, and resource usage. Comprehensive evaluation using the datasets and standard performance metrics accuracy, precision, Recall, F1-score, MCC reveals the model’s high efficacy, achieving a testing accuracy of 0.9936 and 0.994, with precision, recall, F1-score and MCC scores of 0.9919, 0.987, 0.9898, 0.9852 and 0.989, 1.0, 0.993, 0.988 on the BreakHis and BACH datasets, respectively. Our proposed model outperforms state-of-the-art techniques, demonstrating superior accuracy across different datasets, with improvements ranging from 0.25% to 15% on the BACH dataset and from 0.36% to 15.02% on the BreakHis dataset. Our results position the framework as a promising solution for advancing breast cancer prediction in both clinical and research applications. The collective contributions, from framework and hybrid model design to feature selection and classifier heterogeneity consideration, establish a holistic and state-of-the-art approach, significantly improving accuracy and establishing optimization in breast cancer classification from MRI images. Future research for the DL framework in breast cancer image classification includes enhancing interpretability, integrating multi-modal data, and developing personalized treatments. Show more

Keywords: InceptionResNetV2, vision transformer, Multi-scale weight adaptive Nystrom attention mechanism, global features, local features

DOI: 10.3233/IDA-240334

Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-36, 2024

Authors: Shin, Nakyung | Lee, Yulhee | Moon, Heesung | Kim, Joonhui | Jung, Hohyun

Article Type: Research Article

Abstract: The exponential growth of academic papers necessitates sophisticated classification systems to effectively manage and navigate vast information repositories. Despite the proliferation of such systems, traditional approaches often rely on embeddings that do not allow for easy interpretation of classification decisions, creating a gap in transparency and understanding. To address these challenges, we propose an innovative explainable paper classification system that combines Latent Semantic Analysis (LSA) for topic modeling with explainable artificial intelligence (XAI) techniques. Our objective is to identify which topics significantly influence the classification outcomes, incorporating Shapley additive explanations (SHAP) as a key XAI technique. Our system extracts topic …assignments and word assignments from paper abstracts using latent semantic analysis (LSA) topic modeling. Topic assignments are then employed as embeddings in a multilayer perceptron (MLP) classification model, with the word assignments further utilized alongside SHAP for interpreting the classification results at the corpus, document, and word levels, enhancing interpretability and providing a clear rationale for each classification decision. We applied our model to a dataset from the Web of Science, specifically focusing on the field of nanomaterials. Our model demonstrates superior classification performance compared to several baseline models. Ultimately, our proposed model offers a significant advancement in both the performance and explainability of the system, validated by case studies that illustrate its effectiveness in real-world applications. Show more

Keywords: Paper classification, topic modeling, latent semantic analysis, explainable artificial intelligence, Shapley value

DOI: 10.3233/IDA-240075

Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-27, 2024

Authors: Li, Wei

Article Type: Research Article

Abstract: With the modernization of cities, public sculptures are constantly being designed and constructed. The artistic form and image expression effect of sculpture based on intelligent and parametric design needs to be designed and developed to guide and assist the construction of sculpture. This paper applies the NAS architecture search method to explore the field of image expression effect models. Through the end-to-end search of the experiment designed in this paper, the separable convolution lightweight design is used, and the new model AestheticNet is used to predict the image form effect score distribution. Secondly, this paper proposes optimization strategies combining image …expression effect theory and convolutional neural network, including improvement of Loss function self-weighted Loss, two-dimensional Attention mechanism – introduction of CBAM, and adaptive pooling layer. Optimization of several aspects, such as adaptive input. Finally, the validation set is compared with other existing image-morphological effect model algorithms, which proves the effectiveness of the customized search scheme. It demonstrates the efficacy of the AestheticNet model compared to other algorithms by validating its prediction of public sculpture image form effect ratings. The artistic form using intelligent and parametric design methodologies may improve. Image expression of sculptures may be enhanced by applying the image form effect model, which should be pervasive. We can use it to intelligently and parametrically guide the design and manufacture of sculptures. Show more

Keywords: Public sculpture, intelligent parameterization, image expression, deep learning, NAS architecture

DOI: 10.3233/IDA-240497

Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-21, 2024

Authors: Xu, Jie | Zhang, Luo Jia | Zhao, De Chun | Ji, Gen Lin | Li, Pei Heng

Article Type: Research Article

Abstract: Long-term time series forecasting (LTSF) has become an urgent requirement in many applications, such as wind power supply planning. This is a highly challenging task because it requires considering both the complex frequency-domain and time-domain information in long-term time series simultaneously. However, existing work only considers potential patterns in a single domain (e.g., time or frequency domain), whereas a large amount of time-frequency domain information exists in real-world LTSFs. In this paper, we propose a multi-scale hierarchical network (MHNet) based on time-frequency decomposition to solve the above problem. MHNet first introduces a multi-scale hierarchical representation, extracting and learning features of …time series in the time domain, and gradually builds up a global understanding and representation of the time series at different time scales, enabling the model to process time series over lengthy periods of time with lower computational complexity. Then, the robustness to noise is enhanced by employing a transformer that leverages frequency-enhanced decomposition to model global dependencies and integrates attention mechanisms in the frequency domain. Meanwhile, forecasting accuracy is further improved by designing a periodic trend decomposition module for multiple decompositions to reduce input-output fluctuations. Experiments on five real benchmark datasets show that the forecasting accuracy and computational efficiency of MHNet outperform state-of-the-art methods. Show more

Keywords: Long-term series forecasting, multi-scale modeling, time-frequency representation, time series decomposition

DOI: 10.3233/IDA-240455

Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-23, 2024

Authors: Kong, Lingkai | Zhao, Boying | Li, Hongyu | He, Wei | Cao, You | Zhou, Guohui

Article Type: Research Article

Abstract: Medical assisted decision-making plays a key role in providing accurate and reliable medical advice. But in medical decision-making, various uncertainties are often accompanied. The belief rule base (BRB) has a strong nonlinear modeling capability and can handle uncertainties well. However, BRB suffers from combinatorial explosion and tends to influence explainability during the optimization process. Therefore, an interval belief rule base with explainability (IBRB-e) is explored in this paper. Firstly, pre-processing using extreme gradient boosting (XGBoost) is performed to filter out features with lower importance. Secondly, based on the filtered features, explainability criterion is defined. Thirdly, evidence reasoning (ER) rule is …chosen as an inference tool, while projection covariance matrix adaptive evolutionary strategy (P-CMA-ES) algorithm with explainability constraints is chosen as an optimization algorithm. Lastly, the validation of the model is performed through a breast cancer case. The experimental results show that IBRB-e has good explainability while maintaining high accuracy. Show more

Keywords: Belief rule base, decision-making, medical assistant, explainability, evidence reasoning

DOI: 10.3233/IDA-230648

Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-25, 2024

Authors: Chen, Hongwei | Liu, Luanxuan | Chen, Zexi

Article Type: Research Article

Abstract: In recent years, sequential recommendation has received widespread attention for its role in enhancing user experience and driving personalized content recommendations. However, it also encounters challenges, including the limitations of modeling information and the variability of user preferences. A novel time-aware Long-Short Term Transformer (TLSTSRec) for sequential recommendation is introduced in this paper to address these challenges. TLSTSRec has two major innovative features. (1) Accurate modeling of users is achieved by fully leveraging temporal information. Time information is modeled by creating a trainable timestamp matrix from both the perspectives of time duration and time spectrum. (2) A novel time-aware Transformer model …is proposed. To address the inherent variability of user preferences over time, the model combines long-term and short-term temporal information and adjusts the personalized trade-offs between long-term and short-term sequences using adaptive fusion layers. Subsequently, newly designed encoders and decoders are employed to model timestamps and interaction items. Finally, extensive experiments substantiate the effectiveness of TLSTSRec relative to various state-of-the-art sequential recommendation models based on MC/RNN/GNN/SA across a spectrum of widely used metrics. Furthermore, experiments are conducted to validate the rationality of the TLSTSRec structure. Show more

Keywords: Sequential recommendation, transformer, self-attention, time-aware model

DOI: 10.3233/IDA-240051

Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-21, 2024