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

Authors: Lu, Xiangyi | Tian, Feng | Shen, Yumeng | Zhang, Xuejun

Article Type: Research Article

Abstract: Traffic flow prediction can improve transportation efficiency, which is an important part of intelligent transportation systems. In recent years, the prediction method based on graph convolutional recurrent neural network has been widely used in traffic flow prediction. However, in real application scenarios, the spatial dependence of graph signals will change with time, and the filter using a fixed graph displacement operator cannot accurately predict traffic flow at the current moment. To improve the accuracy of traffic flow prediction, a two-layer graph convolutional recurrent neural network based on the dynamic graph displacement operator is proposed. The framework of our proposal is …to use the first layer of static graph convolutional recurrent neural network to generate the sequence wave vector of the graph displacement operator. The sequence wave vector is passed through the deconvolutional neural network to obtain the sequence dynamic graph displacement operator, and then the second layer dynamic graph convolutional recurrent neural network is used to predict the traffic flow at the next moment. The model is evaluated on the METR-LA and PEMS-BAY datasets. Experimental results demonstrate that our model significantly outperforms other baseline models. Show more

Keywords: Traffic flow prediction, graph convolution, deep neural network

DOI: 10.3233/IDA-230174

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

Authors: Hu, Yi-Chung | Wu, Geng

Article Type: Research Article

Abstract: Empirical evidence has shown that forecast combination can improve the prediction accuracy of tourism demand forecasting. This paper aimed to develop a more accurate grey forecast combination method (GFCM) with multivariate grey prediction models In light of the practical applicability of grey prediction, which is not required to apply any statistical test to examine data series this research features the use of multivariate grey models through the genetic algorithm to synthesize forecasts from univariate grey prediction models commonly used in tourism forecasting into composite forecasts Empirical results showed that the proposed GFCM significantly outperformed the other combination methods considered. The …results also suggested that the risk of forecast failures caused by selecting an inappropriate single model for tourism demand forecasting can be reduced by using the GFCM. Show more

Keywords: Tourism demand, forecast combination, tourist arrivals, grey system, genetic algorithm

DOI: 10.3233/IDA-230565

Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-14, 2024

Authors: Abidi, Mustufa Haider | Khare, Neelu | D., Preethi | Alkhalefah, Hisham | Umer, Usama

Article Type: Research Article

Abstract: The emergence of the novel COVID-19 virus has had a profound impact on global healthcare systems and economies, underscoring the imperative need for the development of precise and expeditious diagnostic tools. Machine learning techniques have emerged as a promising avenue for augmenting the capabilities of medical professionals in disease diagnosis and classification. In this research, the EFS-XGBoost classifier model, a robust approach for the classification of patients afflicted with COVID-19 is proposed. The key innovation in the proposed model lies in the Ensemble-based Feature Selection (EFS) strategy, which enables the judicious selection of relevant features from the expansive COVID-19 dataset. …Subsequently, the power of the eXtreme Gradient Boosting (XGBoost) classifier to make precise distinctions among COVID-19-infected patients is harnessed.The EFS methodology amalgamates five distinctive feature selection techniques, encompassing correlation-based, chi-squared, information gain, symmetric uncertainty-based, and gain ratio approaches. To evaluate the effectiveness of the model, comprehensive experiments were conducted using a COVID-19 dataset procured from Kaggle, and the implementation was executed using Python programming. The performance of the proposed EFS-XGBoost model was gauged by employing well-established metrics that measure classification accuracy, including accuracy, precision, recall, and the F1-Score. Furthermore, an in-depth comparative analysis was conducted by considering the performance of the XGBoost classifier under various scenarios: employing all features within the dataset without any feature selection technique, and utilizing each feature selection technique in isolation. The meticulous evaluation reveals that the proposed EFS-XGBoost model excels in performance, achieving an astounding accuracy rate of 99.8%, surpassing the efficacy of other prevailing feature selection techniques. This research not only advances the field of COVID-19 patient classification but also underscores the potency of ensemble-based feature selection in conjunction with the XGBoost classifier as a formidable tool in the realm of medical diagnosis and classification. Show more

Keywords: COVID-19, machine learning, classification, ensemble-based feature selection, XGBoost

DOI: 10.3233/IDA-230854

Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-18, 2024

Authors: Kim, Dokyun | Cho, Sukhyun | Chae, Heewoong | Park, Jonghun | Huh, Jaeseok

Article Type: Research Article

Abstract: While time series data are prevalent across diverse sectors, data labeling process still remains resource-intensive. This results in a scarcity of labeled data for deep learning, emphasizing the importance of semi-supervised learning techniques. Applying semi-supervised learning to time series data presents unique challenges due to its inherent temporal complexities. Efficient contrastive learning for time series requires specialized methods, particularly in the development of tailored data augmentation techniques. In this paper, we propose a single-step, semi-supervised contrastive learning framework named nearest neighbor contrastive learning for time series (NNCLR-TS). Specifically, the proposed framework incorporates a support set to store representations including their …label information, enabling a pseudo-labeling of the unlabeled data based on nearby samples in the latent space. Moreover, our framework presents a novel data augmentation method, which selectively augments only the trend component of the data, effectively preserving their inherent periodic properties and facilitating effective training. For training, we introduce a novel contrastive loss that utilizes the nearest neighbors of augmented data for positive and negative representations. By employing our framework, we unlock the ability to attain high-quality embeddings and achieve remarkable performance in downstream classification tasks, tailored explicitly for time series. Experimental results demonstrate that our method outperforms the state-of-the-art approaches across various benchmarks, validating the effectiveness of our proposed method. Show more

Keywords: Deep learning, machine learning, representation learning, self-supervised learning, semi-supervised learning, time series analysis

DOI: 10.3233/IDA-240002

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

Authors: Zhang, Haifei | Ge, Hongwei | Li, Ting | Zhou, Lujie | Su, Shuzhi | Tong, Yubing

Article Type: Research Article

Abstract: In order to alleviate urban congestion, improve vehicle mobility, and improve logistics delivery efficiency, this paper establishes a practical multi-objective and multi constraint logistics delivery mathematical model based on graphs, and proposes a solution algorithm framework that combines decomposition strategy and deep reinforcement learning (DRL). Firstly, taking into account the actual multiple constraints such as customer distribution, vehicle load constraints, and time windows in urban logistics distribution regions, a multi constraint and multi-objective urban logistics distribution mathematical model was established with the goal of minimizing the total length, cost, and maximum makespan of urban logistics distribution paths. Secondly, based on …the decomposition strategy, a DRL framework for optimizing urban logistics delivery paths based on Graph Capsule Network (G-Caps Net) was designed. This framework takes the node information of VRP as input in the form of a 2D graph, modifies the graph attention capsule network by considering multi-layer features, edge information, and residual connections between layers in the graph structure, and replaces probability calculation with the module length of the capsule vector as output. Then, the baseline REINFORCE algorithm with rollout is used for network training, and a 2-opt local search strategy and sampling search strategy are used to improve the quality of the solution. Finally, the performance of the proposed method was evaluated on standard examples of problems of different scales. The experimental results showed that the constructed model and solution framework can improve logistics delivery efficiency. This method achieved the best comprehensive performance, surpassing the most advanced distress methods, and has great potential in practical engineering. Show more

Keywords: Urban logistics distribution, multi objective optimization, deep reinforcement learning, decomposition strategy, graph capsule network, attention mechanism

DOI: 10.3233/IDA-230480

Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-28, 2024

Authors: Liu, Shi-Tong | Liu, Yong | Ding, Jia-Ming

Article Type: Research Article

Abstract: In the process of product ranking considering online reviews, they often are based on initial reviews and do not consider additional consumer reviews, but additional review information can sometimes directly affect consumers’ final decisions. To fully characterize the rich emotional preferences of consumers embedded in two-stage online customer reviews information, considering consumers’ individual preferences and product objective evaluation information, we construct a combination weighting method to calculate comprehensive weights of product attributes, and then exploit the sentiment analysis technique, interval-valued probabilistic linguistic term set (IVPLTS) and preference ranking organization method for enrichment evaluations (PROMETHEE) to establish a products ranking method …based on compound reviews, and then we use it to identify the sentiment orientation of reviews and the results. Finally, a real-life case illustrates a real-world application of the proposed method. Show more

Keywords: Product ranking, additional review information, sentiment analysis, interval-valued probabilistic linguistic term set, customer reviews information

DOI: 10.3233/IDA-230865

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

Authors: Zhao, Zhaolin | Bo, Kaiming | Hsu, Chih-Yu | Liao, Lyuchao

Article Type: Research Article

Abstract: With the rapid development of unmanned aerial vehicle (UAV) technology and computer vision, real-time object detection in UAV aerial images has become a current research hotspot. However, the detection tasks in UAV aerial images face challenges such as disparate object scales, numerous small objects, and mutual occlusion. To address these issues, this paper proposes the ASM-YOLO model, which enhances the original model by replacing the Neck part of YOLOv8 with an efficient bidirectional cross-scale connections and adaptive feature fusion (ABiFPN) . Additionally, a Structural Feature Enhancement Module (SFE) is introduced to inject features extracted by the backbone network into the …Neck part, enhancing inter-network information exchange. Furthermore, the MPDIoU bounding box loss function is employed to replace the original CIoU bounding box loss function. A series of experiments was conducted on the VisDrone-DET dataset, and comparisons were made with the baseline network YOLOv8s. The experimental results demonstrate that the proposed model in this study achieved reductions of 26.1% and 24.7% in terms of parameter count and model size, respectively. Additionally, during testing on the evaluation set, the proposed model exhibited improvements of 7.4% and 4.6% in the AP50 and mAP metrics, respectively, compared to the YOLOv8s baseline model, thereby validating the practicality and effectiveness of the proposed model. Subsequently, the generalizability of the algorithm was validated on the DOTA and DIOR datasets, which share similarities with aerial images captured by drones. The experimental results indicate significant enhancements on both datasets. Show more

Keywords: Computer vision, drone aerial images, multi-scale object detection, real-time object detection, feature fusion

DOI: 10.3233/IDA-230929

Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-22, 2024

Authors: Wang, Xin | Zhang, Yong | Xu, Junfeng | Gao, Jun

Article Type: Research Article

Abstract: Capturing images through semi-reflective surfaces, such as glass windows and transparent enclosures, often leads to a reduction in visual quality and can adversely affect the performance of computer vision algorithms. As a result, image reflection removal has garnered significant attention among computer vision researchers. With the growing application of deep learning methods in various computer vision tasks, such as super-resolution, inpainting, and denoising, convolutional neural networks (CNNs) have become an increasingly popular choice for image reflection removal. The purpose of this paper is to provide a comprehensive review of learning-based algorithms designed for image reflection removal. Firstly, we provide an …overview of the key terminology and essential background concepts in this field. Next, we examine various datasets and data synthesis methods to assist researchers in selecting the most suitable options for their specific needs and targets. We then review existing methods with qualitative and quantitative results, highlighting their contributions and significance in this field. Finally, some considerations about challenges and future scope in image reflection removal techniques are discussed. Show more

Keywords: Deep learning, reflection removal, reflection separation, systematic literature review

DOI: 10.3233/IDA-230904

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

Authors: Devi, M. Shyamala | Aruna, R. | Almufti, Saman | Punitha, P. | Kumar, R. Lakshmana

Article Type: Research Article

Abstract: Bones collaborate with muscles and joints to sustain and maintain our freedom of mobility. The proper musculoskeletal activity of bone protects and strengthens the brain, heart, and lung function. When a bone is subjected to a force greater than its structural capacity, it fractures. Bone fractures should be detected with the appropriate type and should be treated early to avoid acute neurovascular complications. The manual detection of bone fracture may lead to highly delayed complications like malunion, Joint stiffness, Contractures, Myositis ossificans, and Avascular necrosis. A proper classification system must be integrated with deep learning technology to classify bone fractures …accurately. This motivates me to propose a Systematized Attention Gate UNet (SAG-UNet) that classifies the type of bone fracture with high accuracy. The main contribution of this research is two-fold. The first contribution focuses on dataset preprocessing through feature extraction using unsupervised learning by adapting the Growing Neural Gas (GNG) method. The second contribution deals with refining the supervised learning Attention UNet model that classifies the ten types of bone fracture. The attention gate of the Attention UNet model is refined and applied to the upsampling decoding layer of Attention UNet. The KAGGLE Bone Break Classification dataset was processed to extract only the essential features using GNG extraction. The quantized significant feature RGB X-ray image was divided into 900 training and 230 testing images in the ratio of 80:20. The training images are fitted with the existing CNN models like DenseNet, VGG, AlexNet, MobileNet, EfficientNet, Inception, Xception, UNet and Attention UNet to choose the best CNN model. Experiment results portray that Attention UNet offers the classification of bone fractures with an accuracy of 89% when testing bone break images. Now, the Attention UNet was chosen to refine the Attention gate of the Decoding upsampling layer that occurs after the encoding layer. The Attention Gate of the proposed SAG-UNet forms the gating coefficient from the input feature map and gate signal. The gating coefficient is then processed with batch normalization that centers the aligned features in the active region, thereby leaving the focus on the unaligned weights of feature maps. Then, the ReLU activation function is applied to introduce the nonlinearity in the aligned features, thereby learning the complex representation in the feature vector. Then, dropout is used to exclude the error noise in the aligned weights of the feature map. Then, 1 × 1 linear convolution transformation was done to form the vector concatenation-based attention feature map. This vector has been applied to the sigmoid activation to create the attention coefficient feature map with weights assigned as ‘1’ for the aligned features. The attention coefficient feature map was grid resampled using trilinear interpolation to form the spatial attention weight map, which is passed to the skip connection of the next decoding layer. The implementation results reveal that the proposed SAG-UNet deep learning model classifies the bone fracture types with a high accuracy of 98.78% compared to the existing deep learning models. Show more

Keywords: Activation, attention gate, CNN, classification, convolution, dropout, feature map, normalization, ReLU

DOI: 10.3233/IDA-240431

Citation: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-29, 2024

Authors: Anbarasan, M. | Ramesh, K.

Article Type: Research Article

Abstract: The pharmaceutical supply chain, which ensures that drugs are accessible to patients in a trusted process, is a complex arrangement in the healthcare industry. For that, a secure pharmachain framework is proposed. Primarily, the users register their details. Then, the details are converted into cipher text and stored in the blockchain. If a user requests an order, the manufacturer receives the request, and the order is handed to the distributor. Labeling is performed through Hypergeometric Distribution Centroid Selection K-Medoids Clustering (HDCS-KMC) to track the drugs. The healthcare Pharmachain architecture uses IoT to control the supply chain and provide safe medication …tracking. The framework includes security with a classifier and block mining consensus method, boosts performance with a decision controller, and protects user and medication information with encryption mechanisms. After that, the drugs are assigned to vehicles, where the vehicle ID and Internet of Things (IoT) sensor data are collected and pre-processed. Afterward, the pre-processed data is analyzed in the fog node by utilizing a decision controller. Now, the status ID is generated based on vehicle id and location. The generated status ID is meant for fragmentation, encryption, and block mining processes. If a user requests to view the drug’s status ID, then the user needs to get authentication. The user’s forking behavior and request activities were extracted and given to the classifier present in the block-mining consensus algorithm for authentication purposes. Block mining happens after authentication, thereby providing the status ID. Furthermore, the framework demonstrates an efficaciousness in identifying assaults with a low False Positive Rate (FPR) of 0.022483% and a low False Negative Rate (FNR) of 1.996008%. Additionally, compared to traditional methods, the suggested strategy exhibits good precision (97.869%), recall (97.0039%), accuracy (98%), and F-measure (97.999%). Show more

Keywords: Double Transposed-Prime Key-Columnar Transposition Cipher (DT-PK-CTC), Internet of Things (IoT), Hypergeometric Distribution Centroid Selection K-Medoids Clustering (HDCS-KMC), healthcare, pharmachain, Radial Basis Function (RBF)

DOI: 10.3233/IDA-240087

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