Computing and Informatics (E-Journal - Institute of Informatics, SAS, Bratislava)
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Comprehensive Review of Automatic Text Summarization Techniques
Full text linkAutomatic Text Summarization (ATS) is a fundamental aspect of Natural Language Processing (NLP) that allows for the conversion of lengthy text documents into concise summaries that retain the essential information based on specific criteria. In this paper, we present a literature review on the topic of ATS, which includes an overview of the various approaches to ATS, categorized by the mechanisms they use to generate a summary. By organizing these approaches based on their underlying mechanisms, we provide a comprehensive understanding of the current state-of-the-art in ATS systems
Louvain-Based Fusion of Topology and Attribute Structure of Social Networks
Full text linkWith the increasing diversity and complexity of online social networks, effectively dividing communities presents a growing challenge. These networks are characterized by their large scale, sparse structure, and numerous isolated points. Traditional community detection methods lack consideration of node attribute information, thereby negatively impacting the accuracy of community detection. To address these challenges, this paper presents a novel Louvain-FTAS community detection algorithm that integrates topology and attribute structure. The proposed algorithm first selects attributes with positive effects to account for attribute heterogeneity. Subsequently, it utilizes a semi-local strategy to calculate topology similarity and information entropy to calculate attribute similarity. These values are combined to obtain the final node similarity matrix, which is then fed into the Louvain algorithm to maximize modularity and incorporate multi-dimensional attribute features to enhance community detection accuracy. The proposed model is evaluated through comparative experiments on two real datasets and artificial synthetic networks, demonstrating its rationality and effectiveness
Proposed Bayesian Optimization Based LSTM-CNN Model for Stock Trend Prediction
Full text linkStock prediction is prominent in the field of Artificial Intelligence. Stock prediction problems are handled either as a regression or classification task. Studies in the literature have also shown success for hybrid learning to stock prediction. But little attention is paid to finding out the effect of spatial feature extraction/distortion over the temporal effect of the deep neural network and vice versa for the problem under study. The paper, therefore, proposes a hybrid long short-term memory (LSTM) network over a convolutional neural network (CNN) called LSTM-CNN as against the popular CNN-LSTM model. The daily price movement of the S & P 500 index data is utilized. A sliding window technique is considered to obtain a balanced data of 20-days window data from the S & P 500. The proposed stock prediction model is investigated further for an optimal set of hyperparameters using the Bayesian optimization (Bo) technique. In addition, the proposed model is compared with optimized CNN, LSTM, and CNN-LTSM models. The optimized LSTM-CNN model is found to outperform the other models with accuracy, precision, and recall values of 0.9741, 0.9684, and 0.9800, respectively. The proposed model is established to provide a better stock trend prediction
Image Structured Annotation Based on Deep Neural Network Natural Language Processing
Full text linkThe image structuring process was mainly divided into three stages: model training, model prediction, and report structuring. In the report structure stage, based on the feature annotation sequence, this paper associated the text sequence with the corresponding table structure and stored the text sequence in the corresponding database in the background. In dataset 1, the accuracy rate of removing visual information submodel was 30 %, and that of removing semantic information submodel was 50 %. The scheme proposed in this paper was to better perform automatic image annotation and meet the requirements of image annotation in the era of Big Data
Bayesian Information Criterion Analysis for Accuracy Improvement of Multivariate Time Series Data Analysis
Full text linkMultivariate time series data can be collected and employed in various fields to predict future data. However, owing to significant uncertainty and noise, controlling the prediction accuracy during practical applications remains challenging. Therefore, this study examines the Bayesian information criterion (BIC) as an evaluation metric for prediction models and analyzes its changes by varying the explanatory variables, variable pairs, and learning and validation periods. Descriptive statistics and decision tree-based algorithms, such as classification and regression tree, random forest, and dynamic time warping, were employed in the analysis. The experimental evaluations were conducted using two types of restaurant data: sales, weather, number of customers, number of views on gourmet site, and day of the week. Based on the experimental results, we compared and discussed the learning behavior based on various explanatory variable combinations. We discovered that 1. the explanatory variable, the number of customers, exhibited a significantly different trend from other variables when dynamic time warping was applied, particularly in combination with other variables, and 2. variables with seasonality yielded the best performance when used independently; otherwise, the predictive accuracy decreased according to the decision tree results. This comparative investigation revealed that the proposed BIC analysis method proposed can be used to effectively identify the optimal combination of explanatory variables for multivariate time series data that exhibit characteristics such as seasonality
CGCANet: Context-Guided Cost Aggregation Network for Robust Stereo Matching
Full text linkStereo matching methods based on Convolutional Neural Network (CNN) have achieved a significant progress in recent years. However, they still cannot work well on generalization performance across a variety of datasets due to their poor robustness. In view of this, we aim to enhance the robustness in three main steps of stereo matching, namely cost computation, cost aggregation, and disparity refinement. For cost computation, we propose an atrous pyramid grouping convolution (APGC) module, which combines local context information with multi-scale features generated from CNN backbone, aiming to obtain a more discriminative feature representation. For cost aggregation, we provide a multi-scale cost aggregation (MSCA) module, which sufficiently and effectively fuses multiple cost volumes at three different scales into the 3D hourglass networks to improve initial disparity estimation. In addition, we present a disparity refinement (DR) module that employs the color guidance of left input image and several convolutional residual blocks to obtain a more accurate disparity estimation. With such three modules, we propose an end-to-end context-guided cost aggregation network (CGCANet) for robust stereo matching. To evaluate the performance of the proposed modules and CGCANet, we conduct comprehensive experiments on the challenging SceneFlow, KITTI 2015 and KITTI 2012 datasets, with a consistent and competitive improvement over the existing stereo matching methods
Edge Computing-Based Vehicle Detection in Intelligent Transportation Systems
Full text linkVehicle detection in intelligent transportation systems usually adopts cloud computing mode. The increasing amount of traffic surveillance video has brought challenges to the storage, communication, and processing of intelligent transportation systems based on cloud computing models. In this paper, we propose a vehicle detection scheme based on edge computing. First, the traffic surveillance video is preprocessed at the edge device. Using the frame difference algorithm based on structural similarity (SSIM) to remove video redundant frames, and avoid repeated frames in the subsequent extracted key frame sequence. Then, a frame difference algorithm based on local maxima is used to extract key frames as the basis for subsequent vehicle detection. Finally, the YOLOv5s is improved and used for vehicle detection. The efficient channel attention mechanism (ECA) is introduced to enhance the important features of the vehicle and suppress the general features to strengthen the detection network's ability to identify vehicle targets. At the same time, the Focal loss function is introduced to solve the positive and negative sample imbalance problem and improve the detection speed. The experimental results show that the scheme has more advantages than the original YOLOv5s in terms of precision, recall, and mean average precision
Secure and Efficient Blockchain Scheme for Resource Optimization in Internet of Things (IoT) Systems
Full text linkThe thriving Internet of Things, a paradigm shift from the traditional Internet has brought about great societal improvements such as smart homes, smart cities, smart health, intelligent systems and many more. With these diverse societal improvements come increasing complexities in the areas of system efficiency, privacy, and security. In recent years ample academic and industrial research have delved into resource optimization to the detriment of security, as security features are left to be bolted on at the end of design and developmental processes. This approach leaves the system susceptible to threats and attacks. Consequently, this paper seeks to incorporate security features from the onset, weaving the security feature into the system's design and developmental phase. The proposed model structured in a three-tiered design comprises of concepts of Blockchain, edge computing, clustering techniques and a hybrid algorithm consisting of the static round-robin and the dynamic resource-based algorithms. The composition of the structural layout which considers aspects of the blockchain as a security tight measure for resource optimization in Internet of Things' environment, also incorporates features of edge computing, clustering techniques and the hybrid algorithm as components for resource optimization of the Internet of Things. In addition to the prospective security feature provided by the Hyperledger fabric BC in the proposed model, simulation results illustrate the Hyperledger fabric BC's dexterity in making IoT systems even more efficient, further showing its efficacy over the PSOR2B and the BC-EDSSP
Correlation Analysis Algorithm for Massive Ultra-High-Dimensional Breast Ultrasound Radiomics Feature Data in a Distributed Environment
Full text linkRadiomics is a technology that extracts a large number of quantitative features from high-throughput medical images and has become a focus of research. It can help in disease diagnosis, therapy planning, and prognosis evaluation through Big Data analysis algorithms. Radiomics technology can extract hundreds or even tens of thousands of quantifiable data features from medical images, which can no longer fit into the memory of one machine. Therefore, we propose a distributed correlation analysis algorithm (DFCA) based on a MapReduce distributed computing framework for breast ultrasound radiomics feature datasets. Each compute node will produce massive intermediate data while the DFCA calculates the Pearson correlation coefficient of radiomics features. With the increase of feature data and dimensions, the data transmission cost will be in a square growth. To reduce the cost, we propose a distributed correlation estimation algorithm (DFCEA) for radiomics features based on DFCA. The DFCEA algorithm estimates the Pearson correlation coefficient using an iterative method, which can further reduce the I/O cost. The experiment proved that our algorithms are more effective compared to the algorithms in the literature
Review of Heuristic Algorithms for Frequent Itemsets Mining Problem
Full text linkFrequent Itemsets Mining (FIM), which consists of extracting frequent patterns from a transactional database, is considered one of the most successful techniques in data mining. Generally, the FIM problem can be solved by either the exact or metaheuristic-based methods. Exact methods, such as the Apriori algorithm, are highly effective for dealing with small to medium datasets. However, these methods need more temporal complexity when dealing with large datasets. Metaheuristic-based methods are becoming more rapid, but the majority still need to be more precise. Several studies were carried out to address these issues and improve metaheuristics-based approaches by combining the Apriori algorithm with several metaheuristics algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The result of this combination gave birth to two approaches: GA-Apriori and PSO-Apriori. Consequently, after performing several studies on different database instances, the results revealed that the two approaches outperformed the Apriori algorithm in terms of runtime. PSO-Apriori also beats GA-Apriori in terms of both runtime and solution efficiency