Computing and Informatics (E-Journal - Institute of Informatics, SAS, Bratislava)
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An Aggregation Degree-Based Cooperative Model for Autonomous Vehicle Groups in a Closing Scene
Full text linkMaintaining stable and orderly intelligent autonomous driving behavior in a closing scene is an important challenge. Compared with traditional chaos caused by an individual autonomous vehicle based on central control, when it breaks down, an intelligent cooperative autonomous driving group may effectively mitigate or alleviate the issue. There is no method to formulate an autonomous vehicle group and analyze its cooperative behavior by taking the aggregation, leading node change rate, and algorithm complexity of a vehicle group into account. This work formulates an aggregation degree-based Cooperative Model for Autonomous Vehicle Groups in a closing scene (CMAVG). First, we construct multi-roles and hierarchical autonomous vehicle groups. Then, we analyze their evolution behavior and present a dynamic evolution method based on it. Finally, we formulate CMAVG and give its solving method. We conduct extensive simulations in a simulated closing scene and a real one. Experimental results show that our autonomous vehicle group formation method outperforms a VANET clustering method and an autonomous vehicle group formation method in terms of aggregation degree, running time, and leading node change rate. CMAVG outperforms two cooperation methods for Internet of vehicles and an autonomous vehicle group cooperation method in terms of aggregation degree, leading node change rate, and vehicle group survival time
A Preface to the Special Issue: Emerging Areas in Network and Intelligence Empowered Computing
Full text linkSDN-Based Multi-Objective Optimization for Task Offloading with Algorithm Federated Learning in Fog Computing Environment
No full textDue to the substantial volume of data associated with the IoT, processing and storing such large amounts of data is not easily feasible. Nevertheless, many of its applications face challenges in cloud computing, such as latency, location awareness, and real-time mobility support. Edge computing helps provide solutions to these challenges. In this article, the MINLP path optimization problem is initially addressed using SDN, SA+GA, OLB-LBMM, and Round-Robin methods. Subsequently, based on the obtained results, the SDN method, which has achieved the best outcomes among the approaches, is selected. This article involves a simulation of IoT for optimal allocation of shared resources in edge computing. The network architecture comprises five distinct layers, including cloud services, the SDN controller, edge computing nodes, edge computation and users. The algorithm employed in this problem is the federated learning and stochastic gradient descent algorithm. It selects the optimal edge node for user service provision through two learning and training phases, aiming to allocate shared resources to three parameters: cloud service providers' revenue, average latency, and user satisfaction. This algorithm is compared with several other methods. The selected model and algorithm, in comparison with other algorithms used in solving similar models, lead to a centralized management system, the implementation of effective network management, and the utilization of various communication media. This approach ensures timely access to services, contributing to increased profits for providers and user satisfaction
Local Matrix Factorization with Network Embedding for Recommender Systems
Full text linkIn recommender systems, the rating matrix is usually not a global low-rank but local low-rank. Constructing low-rank submatrices for matrix factorization can improve the accuracy of rating prediction. This paper proposes a novel network embedding-based local matrix factorization model, which can built more meaningful sub-matrices. To alleviate the sparsity of the rating matrix, the social data and the rating data are integrated into a heterogeneous information network, which contains multiple types of objects and relations. The network embedding algorithm extracts the node representations of users and items from the heterogeneous information network. According to the correlation of the node representations, the rating matrix is divided into different sub-matrices. Finally, the matrix factorization is performed on the sub-matrices for rating prediction. We test our network embedding-based method on two real-world public data sets (Yelp and Douban). Experimental results show that our method can obtain more accurate prediction ratings
SMRFC-PDCNN: An Efficient Scene Matching Recognition with DCNN and Feature Clustering on Spark
Full text linkScene recognition, an AI technology based on deep learning, has been widely used in public safety, road traffic, and automatic driving, but applying it on massive data in deep convolutional neural networks (DCNNs) results in performance bottlenecks. This paper proposes SMRFC-PDCNN, an efficient scene matching recognition algorithm that addresses three specific problems: decreased accuracy of feature maps, redundant feature calculations, and low efficiency in parallel recognition. The proposed algorithm includes a feature pooling selection strategy called MI-IPSS, a feature selection strategy called DCPSO-FSS, a load balancing strategy called CCG-LBS. MI-IPSS solves the problem of de-creased accuracy of feature maps by adapting the pooling strategy based on mutual information coefficient between feature maps before and after pooling. DCPSO-FSS uses density clustering and particle swarm optimization to locate clustering parameters quickly and recognize clustered features through sampling in the fully connected layer. CCG-LBS dynamically calculates the computing overhead of feature maps and allocates data between groups according to the over-head to solve the problem of low efficiency in parallel recognition. Experimental results show that SMRFC-PDCNN has good performance and is suitable for the fast scene matching recognition process of parallelized DCNNs on large-scale datasets
Max Planck Theory for Digital Image Processing: A New Algorithm for Mammogram Image Segmentation to Identify Masses in Regions of the Breast
Full text linkBreast cancer, per WHO, ranks top in diagnoses and cancer fatalities. Early detection via mammography reduces mortality significantly, yet mammogram images often have indistinct features. Hence, precise tumor edge identification requires both image enhancement and segmentation. In response, we introduce the Max Planck Algorithm, a novel segmentation method rooted in Planck's quantum theory, specifically his thermal radiation principles. We innovatively converted this theory to create a unique segmentation tool applicable to digital image processing and medical imaging. The algorithm works by relating mammogram pixel values to X-ray wavelengths, adapting Planck's Law to use 'temperature' as an arbitrary variable (originally tied to actual temperature in Planck's work). Gradually adjusting 'temperature' optimizes the mammogram image's meaningfulness. The Max Planck Algorithm boasts advantageous properties, delivering higher efficiency and superior segmentation results. This innovative model introduces new methods for enhancing and segmenting mammograms, establishing itself as a unique technique without comparison to existing methods
Adaptive Sampling-Based Heterogeneous Graph Enhancement
No full textIn the current research on heterogeneous academic network community detection, there is a widespread challenge of high demand for node representation of node attributes in learning graphs. Particularly, existing methods often perform poorly when dealing with nodes missing attributes. Furthermore, most methods rely on meta-paths, but the optimal length of meta-paths is difficult to determine and the quality of predefined meta-paths directly affects the results. To address this issue, this paper proposes an Adaptive Sampling-based Heterogeneous Graph Enhancement Model (ASGNN). The model aims to solve the problem of inaccurate node representations leading to imprecise community partitions in academic networks. ASGNN first effectively captures the network's topological structure through random walk techniques, and then utilizes an adaptive sampling algorithm to select the most influential adjacent node set, rather than relying on traditional meta-path techniques. The model further employs an attention mechanism to aggregate information from nodes of different types, thereby enhancing attribute completion and topological structure in heterogeneous academic networks. This approach not only fills in missing information but also significantly enhances the semantic and structural integrity of the network. Experimental results demonstrate that the proposed model exhibits outstanding performance on two real datasets compared to baseline models
Decision Support System for Improving Breast Cancer Diagnosis Using Ensemble Learning
Full text linkBreast cancer (BC) is one of the leading causes of death in women worldwide. Early diagnosis of this disease can save many women's lives. The Breast Imaging Reporting and Data System (BIRADS) is a standard method developed by the American College of Radiology (ACR). However, physicians have had a lot of contradictions in determining the value of BIRADS, and all aspects of patients have not been considered in diagnosing this disease using the methods that have been used so far. In this article, a novel decision support system (DSS) has been presented. In the proposed DSS, firstly, c-mean clustering was used to determine the molecular subtype for patients who did not have this value by combining the mammography reports processing along with hospital information systems (HIS) obtained from their electronic files. Then several classifiers such as convolutional neural networks (CNN), decision tree (DT), multi-level fuzzy min-max neural network (MLF), multi-class support vector machine (SVM), and XGboost were trained to determine the BIRADS. Finally, the values obtained by these classifiers were combined using ensemble learning with the majority voting algorithm to obtain the appropriate value of BIRADS. This helps physicians in the early diagnosis of BC. Finally, the results were evaluated in terms of accuracy, specificity, sensitivity, positive predicted value (PPV), negative predicted value (NPV), f1-measure, and balanced accuracy by the confusion matrix. The obtained values were 87.77 %, 61.81 %, 92.74 %, 56.82 %, 92.75 %, 69.94 %, and 77.28 %, respectively
Design and Development of a Hybrid Evolutionary Method with a Special Selection Artificial Immune System for Stroke Prediction: A Balancing Approach
No full textA stroke is a serious neurological condition that occurs due to either blockages or bleeding in the brain, which can lead to death or long-term disability. This study aims to enhance the accuracy of disease diagnosis in imbalanced stroke patient datasets. The model incorporates an artificial immune system algorithm, whose parameters are fine-tuned using the Firefly algorithm to ensure both stability and balanced data. To enhance the performance for the underrepresented class, the One-Sided Selection method is employed. The model’s effectiveness was tested in two separate experiments: one utilizing all available features and the other applying the Artificial Bee Colony (ABC) algorithm to select the most relevant features. The models were trained using six different classification algorithms: CatBoost, Light Gradient Boosting Machine (LightGBM), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Logistic Regression (LR). The results were presented using performance metrics. When trained using all features, the model achieved an accuracy of 93%, specificity of 93%, and sensitivity of 80%. When trained using the best features selected by the ABC algorithm, the model achieved an accuracy of 93%, specificity of 93%, and sensitivity of 82%. Compared to previous studies, the proposed model was effective in both experiments
Efficient Distributed Clustering with Cuckoo Search Algorithm and GPU Acceleration for Big Data Analysis
No full textClustering analysis is a crucial method in data mining, aimed at identifying clusters of data objects in the attribute space. Distributed clustering has gained prominence due to the emergence of Big Data. The rapid growth of data, particularly with the advent of technologies, such as the Internet of Things and 5G, has resulted in numerous challenges for data analysis and processing. Traditional clustering methods, such as K-means and hierarchical clustering, struggle with extensive datasets designed for smaller to moderately sized datasets. Meta-heuristic techniques have garnered significant attention among the various distributed clustering algorithms due to their ability to deliver high-quality solutions across a wide range of optimization problems. In this study, we proposed a new Cuckoo search (CS) clustering algorithm for distributed clustering to address the challenges of Big Data clustering. First, the CS clustering algorithm is executed on each local site, utilizing GPU acceleration for efficient local data clustering. Second, on a global scale, representative data from each site are aggregated and processed worldwide, with centroids iteratively updated to generate the final clustering result. We have significantly enhanced the processing efficiency by minimizing transmission costs and eliminating the need for inter-node communication. Furthermore, our approach demonstrates adaptability in handling large datasets with competitive execution times through the utilization of parallel processing and distributed computing. Our approach demonstrates its efficiency and scalability across diverse datasets, showing potential for various applications