Computers, materials & continua最新文献

{"title":"Time Highlighted Multi-Interest Network for Sequential Recommendation","authors":"Jiayi Ma, Tianhao Sun, Xiaodong Zhang","doi":"10.32604/cmc.2023.040005","DOIUrl":"https://doi.org/10.32604/cmc.2023.040005","url":null,"abstract":"Sequential recommendation based on a multi-interest framework aims to analyze different aspects of interest based on historical interactions and generate predictions of a user’s potential interest in a list of items. Most existing methods only focus on what are the multiple interests behind interactions but neglect the evolution of user interests over time. To explore the impact of temporal dynamics on interest extraction, this paper explicitly models the timestamp with a multi-interest network and proposes a time-highlighted network to learn user preferences, which considers not only the interests at different moments but also the possible trends of interest over time. More specifically, the time intervals between historical interactions and prediction moments are first mapped to vectors. Meanwhile, a time-attentive aggregation layer is designed to capture the trends of items in the sequence over time, where the time intervals are seen as additional information to distinguish the importance of different neighbors. Then, the learned items’ transition trends are aggregated with the items themselves by a gated unit. Finally, a self-attention network is deployed to capture multiple interests with the obtained temporal information vectors. Extensive experiments are carried out based on three real-world datasets and the results convincingly establish the superiority of the proposed method over other state-of-the-art baselines in terms of model performance.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136053959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital Image Encryption Algorithm Based on Double Chaotic Map and LSTM","authors":"Luoyin Feng, Jize Du, Chong Fu","doi":"10.32604/cmc.2023.042630","DOIUrl":"https://doi.org/10.32604/cmc.2023.042630","url":null,"abstract":"","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135563786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Design of Aluminum-Lithium Alloys with High Strength","authors":"Hongxia Wang, Zhiqiang Duan, Qingwei Guo, Yongmei Zhang, Yuhong Zhao","doi":"10.32604/cmc.2023.045871","DOIUrl":"https://doi.org/10.32604/cmc.2023.045871","url":null,"abstract":"","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135563792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Soil NOx Emission Prediction via Recurrent Neural Networks","authors":"Zhaoan Wang, Shaoping Xiao, Cheryl Reuben, Qiyu Wang, Jun Wang","doi":"10.32604/cmc.2023.044366","DOIUrl":"https://doi.org/10.32604/cmc.2023.044366","url":null,"abstract":"This paper presents designing sequence-to-sequence recurrent neural network (RNN) architectures for a novel study to predict soil NOx emissions, driven by the imperative of understanding and mitigating environmental impact. The study utilizes data collected by the Environmental Protection Agency (EPA) to develop two distinct RNN predictive models: one built upon the long-short term memory (LSTM) and the other utilizing the gated recurrent unit (GRU). These models are fed with a combination of historical and anticipated air temperature, air moisture, and NOx emissions as inputs to forecast future NOx emissions. Both LSTM and GRU models can capture the intricate pulse patterns inherent in soil NOx emissions. Notably, the GRU model emerges as the superior performer, surpassing the LSTM model in predictive accuracy while demonstrating efficiency by necessitating less training time. Intriguingly, the investigation into varying input features reveals that relying solely on past NOx emissions as input yields satisfactory performance, highlighting the dominant influence of this factor. The study also delves into the impact of altering input series lengths and training data sizes, yielding insights into optimal configurations for enhanced model performance. Importantly, the findings promise to advance our grasp of soil NOx emission dynamics, with implications for environmental management strategies. Looking ahead, the anticipated availability of additional measurements is poised to bolster machine-learning model efficacy. Furthermore, the future study will explore physical-based RNNs, a promising avenue for deeper insights into soil NOx emission prediction.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135317117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Evaluation of Data Mining Algorithms in Breast Cancer","authors":"Fuad A. M. Al-Yarimi","doi":"10.32604/cmc.2023.038858","DOIUrl":"https://doi.org/10.32604/cmc.2023.038858","url":null,"abstract":"Unchecked breast cell growth is one of the leading causes of death in women globally and is the cause of breast cancer. The only method to avoid breast cancer-related deaths is through early detection and treatment. The proper classification of malignancies is one of the most significant challenges in the medical industry. Due to their high precision and accuracy, machine learning techniques are extensively employed for identifying and classifying various forms of cancer. Several data mining algorithms were studied and implemented by the author of this review and compared them to the present parameters and accuracy of various algorithms for breast cancer diagnosis such that clinicians might use them to accurately detect cancer cells early on. This article introduces several techniques, including support vector machine (SVM), K star (K*) classifier, Additive Regression (AR), Back Propagation Neural Network (BP), and Bagging. These algorithms are trained using a set of data that contains tumor parameters from breast cancer patients. Comparing the results, the author found that Support Vector Machine and Bagging had the highest precision and accuracy, respectively. Also, assess the number of studies that provide machine learning techniques for breast cancer detection.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135317306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dense Spatial-Temporal Graph Convolutional Network Based on Lightweight OpenPose for Detecting Falls","authors":"Xiaorui Zhang, Qijian Xie, Wei Sun, Yongjun Ren, Mithun Mukherjee","doi":"10.32604/cmc.2023.042561","DOIUrl":"https://doi.org/10.32604/cmc.2023.042561","url":null,"abstract":"Fall behavior is closely related to high mortality in the elderly, so fall detection becomes an important and urgent research area. However, the existing fall detection methods are difficult to be applied in daily life due to a large amount of calculation and poor detection accuracy. To solve the above problems, this paper proposes a dense spatial-temporal graph convolutional network based on lightweight OpenPose. Lightweight OpenPose uses MobileNet as a feature extraction network, and the prediction layer uses bottleneck-asymmetric structure, thus reducing the amount of the network. The bottleneck-asymmetrical structure compresses the number of input channels of feature maps by 1 × 1 convolution and replaces the 7 × 7 convolution structure with the asymmetric structure of 1 × 7 convolution, 7 × 1 convolution, and 7 × 7 convolution in parallel. The spatial-temporal graph convolutional network divides the multi-layer convolution into dense blocks, and the convolutional layers in each dense block are connected, thus improving the feature transitivity, enhancing the network’s ability to extract features, thus improving the detection accuracy. Two representative datasets, Multiple Cameras Fall dataset (MCF), and Nanyang Technological University Red Green Blue + Depth Action Recognition dataset (NTU RGB + D), are selected for our experiments, among which NTU RGB + D has two evaluation benchmarks. The results show that the proposed model is superior to the current fall detection models. The accuracy of this network on the MCF dataset is 96.3%, and the accuracies on the two evaluation benchmarks of the NTU RGB + D dataset are 85.6% and 93.5%, respectively.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135317310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification of Brain Tumors Using Hybrid Feature Extraction Based on Modified Deep Learning Techniques","authors":"Tawfeeq Shawly, Ahmed Alsheikhy","doi":"10.32604/cmc.2023.040561","DOIUrl":"https://doi.org/10.32604/cmc.2023.040561","url":null,"abstract":"According to the World Health Organization (WHO), Brain Tumors (BrT) have a high rate of mortality across the world. The mortality rate, however, decreases with early diagnosis. Brain images, Computed Tomography (CT) scans, Magnetic Resonance Imaging scans (MRIs), segmentation, analysis, and evaluation make up the critical tools and steps used to diagnose brain cancer in its early stages. For physicians, diagnosis can be challenging and time-consuming, especially for those with little expertise. As technology advances, Artificial Intelligence (AI) has been used in various domains as a diagnostic tool and offers promising outcomes. Deep-learning techniques are especially useful and have achieved exquisite results. This study proposes a new Computer-Aided Diagnosis (CAD) system to recognize and distinguish between tumors and non-tumor tissues using a newly developed middleware to integrate two deep-learning technologies to segment brain MRI scans and classify any discovered tumors. The segmentation mechanism is used to determine the shape, area, diameter, and outline of any tumors, while the classification mechanism categorizes the type of cancer as slow-growing or aggressive. The main goal is to diagnose tumors early and to support the work of physicians. The proposed system integrates a Convolutional Neural Network (CNN), VGG-19, and Long Short-Term Memory Networks (LSTMs). A middleware framework is developed to perform the integration process and allow the system to collect the required data for the classification of tumors. Numerous experiments have been conducted on different five datasets to evaluate the presented system. These experiments reveal that the system achieves 97.98% average accuracy when the segmentation and classification functions were utilized, demonstrating that the proposed system is a powerful and valuable method to diagnose BrT early using MRI images. In addition, the system can be deployed in medical facilities to support and assist physicians to provide an early diagnosis to save patients’ lives and avoid the high cost of treatments.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135317878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Micro-Expression Recognition Based on Spatio-Temporal Feature Extraction of Key Regions","authors":"Wenqiu Zhu, Yongsheng Li, Qiang Liu, Zhigao Zeng","doi":"10.32604/cmc.2023.037216","DOIUrl":"https://doi.org/10.32604/cmc.2023.037216","url":null,"abstract":"Aiming at the problems of short duration, low intensity, and difficult detection of micro-expressions (MEs), the global and local features of ME video frames are extracted by combining spatial feature extraction and temporal feature extraction. Based on traditional convolution neural network (CNN) and long short-term memory (LSTM), a recognition method combining global identification attention network (GIA), block identification attention network (BIA) and bi-directional long short-term memory (Bi-LSTM) is proposed. In the BIA, the ME video frame will be cropped, and the training will be carried out by cropping into 24 identification blocks (IBs), 10 IBs and uncropped IBs. To alleviate the overfitting problem in training, we first extract the basic features of the pre-processed sequence through the transfer learning layer, and then extract the global and local spatial features of the output data through the GIA layer and the BIA layer, respectively. In the BIA layer, the input data will be cropped into local feature vectors with attention weights to extract the local features of the ME frames; in the GIA layer, the global features of the ME frames will be extracted. Finally, after fusing the global and local feature vectors, the ME time-series information is extracted by Bi-LSTM. The experimental results show that using IBs can significantly improve the model's ability to extract subtle facial features, and the model works best when 10 IBs are used.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135317881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Objective Image Optimization of Product Appearance Based on Improved NSGA-Ⅱ","authors":"Yinxue Ao, Jian Lv, Qingsheng Xie, Zhengming Zhang","doi":"10.32604/cmc.2023.040088","DOIUrl":"https://doi.org/10.32604/cmc.2023.040088","url":null,"abstract":"A second-generation fast Non-dominated Sorting Genetic Algorithm product shape multi-objective imagery optimization model based on degradation (DNSGA-II) strategy is proposed to make the product appearance optimization scheme meet the complex emotional needs of users for the product. First, the semantic differential method and K-Means cluster analysis are applied to extract the multi-objective imagery of users; then, the product multidimensional scale analysis is applied to classify the research objects, and again the reference samples are screened by the semantic differential method, and the samples are parametrized in two dimensions by using elliptic Fourier analysis; finally, the fuzzy dynamic evaluation function is used as the objective function of the algorithm, and the coordinates of key points of product contours Finally, with the fuzzy dynamic evaluation function as the objective function of the algorithm and the coordinates of key points of the product profile as the decision variables, the optimal product profile solution set is solved by DNSGA-Ⅱ. The validity of the model is verified by taking the optimization of the shape scheme of the hospital connection site as an example. For comparison with DNSGA-II, other multi-objective optimization algorithms are also presented. To evaluate the performance of each algorithm, the performance evaluation index values of the five multi-objective optimization algorithms are calculated in this paper. The results show that DNSGA-II is superior in improving individual diversity and has better overall performance.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"363 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136052724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Effective Runge-Kutta Optimizer Based on Adaptive Population Size and Search Step Size","authors":"Ala Kana, Imtiaz Ahmad","doi":"10.32604/cmc.2023.040775","DOIUrl":"https://doi.org/10.32604/cmc.2023.040775","url":null,"abstract":"A newly proposed competent population-based optimization algorithm called RUN, which uses the principle of slope variations calculated by applying the Runge Kutta method as the key search mechanism, has gained wider interest in solving optimization problems. However, in high-dimensional problems, the search capabilities, convergence speed, and runtime of RUN deteriorate. This work aims at filling this gap by proposing an improved variant of the RUN algorithm called the Adaptive-RUN. Population size plays a vital role in both runtime efficiency and optimization effectiveness of metaheuristic algorithms. Unlike the original RUN where population size is fixed throughout the search process, Adaptive-RUN automatically adjusts population size according to two population size adaptation techniques, which are linear staircase reduction and iterative halving, during the search process to achieve a good balance between exploration and exploitation characteristics. In addition, the proposed methodology employs an adaptive search step size technique to determine a better solution in the early stages of evolution to improve the solution quality, fitness, and convergence speed of the original RUN. Adaptive-RUN performance is analyzed over 23 IEEE CEC-2017 benchmark functions for two cases, where the first one applies linear staircase reduction with adaptive search step size (LSRUN), and the second one applies iterative halving with adaptive search step size (HRUN), with the original RUN. To promote green computing, the carbon footprint metric is included in the performance evaluation in addition to runtime and fitness. Simulation results based on the Friedman and Wilcoxon tests revealed that Adaptive-RUN can produce high-quality solutions with lower runtime and carbon footprint values as compared to the original RUN and three recent metaheuristics. Therefore, with its higher computation efficiency, Adaptive-RUN is a much more favorable choice as compared to RUN in time stringent applications.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136053960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}