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Recognition of Hotspot Words for Disease Symptoms Incorporating Contextual Weight and Co-Occurrence Degree Sci. Program. (IF 1.672) Pub Date : 2024-4-5 Qingxue Liu, Lifang Wang, Yuan Chang, Jixuan Zhang
Identifying hotspot words associated with disease symptoms is paramount for disease prevention and diagnosis. In this study, we propose a novel method for hotspot word recognition in disease symptoms, integrating contextual weights and co-occurrence information. First, we establish the MDERank model, which incorporates contextual weights. This model identifies words that align well with comprehensive
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An Example of Modelica–LabVIEW Communication Usage to Implement Hardware-in-the-Loop Experiments Sci. Program. (IF 1.672) Pub Date : 2024-2-5 Massimo Ceraolo, Mirko Marracci
Modelica is a very powerful language to simulate a very large set of systems, including electrical, thermal, mechanical, fluidic, control, and has already been used very extensively for several purposes, as the several Modelica conferences testify. Despite of this large literature, no paper seems to be available regarding the use of Modelica for real-time applications or hardware-in-the loop (HIL)
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Deep Neural Network-Based Cloth Collision Detection Algorithm Sci. Program. (IF 1.672) Pub Date : 2024-1-17 Yanxia Jin, Zhiru Shi, Jing Yang, Yabian Liu, Xingyu Qiao, Ling Zhang
The quality of collision detection algorithm directly affects the performance of the whole simulation system. To address the low efficiency and low accuracy in detecting the collisions of flexible cloths in virtual environments, this paper proposes an oriented bounding box (OBB) algorithm with a simplified model, tree structure for a root-node double bounding box, and continuous collision detection
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Study on Contribution of Different Journal Evaluation Indicators to Impact Factor Based on Machine Learning Sci. Program. (IF 1.672) Pub Date : 2023-12-30 Yan Ma, Yingkun Han, Haonan Zeng, Lei Ma
Sci-Tech journals have long served as platforms for academic communication and the collision of ideas, facilitating advanced inventions and major discoveries in science. The speed of development and future prospects of a field in the current era can often be reflected by the quality and quantity of cutting-edge papers published in Sci-Tech journals within that field. Currently, the impact factor of
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Image Segmentation of Triple-Negative Breast Cancer by Incorporating Multiscale and Parallel Attention Mechanisms Sci. Program. (IF 1.672) Pub Date : 2023-12-16 Qian Zhang, Junbiao Xiao, Bingjie Zheng
Breast cancer is a highly prevalent cancer. Triple-negative breast cancer (TNBC) is more likely to recur and metastasize than other subtypes of breast cancer. Research on the treatment of TNBC is of great importance, and accurate segmentation of the breast lesion area is an important step in the treatment of TNBC. Currently, the gold standard for tumor segmentation is still sketched manually by doctors
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Grey Interest Chain Identification and Control Model for Government Investment Engineering Projects Based on Node Identification Sci. Program. (IF 1.672) Pub Date : 2023-11-14 Lin Deng, Yaping Wu, Xianjun Chen, Tian Li, Yun Chen
In the bidding process of government investment engineering projects, collusion between the government and bidders occurs repeatedly, which seriously affects the quality of engineering projects and the effectiveness of the government investment. Therefore, it is necessary to analyze and discuss the collusion between the government and bidders in government investment engineering projects, so as to
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Credit-Risk Prediction Model Using Hybrid Deep—Machine-Learning Based Algorithms Sci. Program. (IF 1.672) Pub Date : 2023-11-6 Tamiru Melese, Tesfahun Berhane, Abdu Mohammed, Assaye Walelgn
Credit-risk prediction is one of the challenging tasks in the banking industry. In this study, a hybrid convolutional neural network—support vector machine/random forest/decision tree (CNN—SVM/RF/DT) model has been proposed for efficient credit-risk prediction. We proposed four classifiers to develop the model. A fully connected layer with soft-max trained using an end-to-end process makes up the first
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Network Traffic Anomaly Detection Model Based on Feature Reduction and Bidirectional LSTM Neural Network Optimization Sci. Program. (IF 1.672) Pub Date : 2023-11-3 Hanqing Jiang, Shaopei Ji, Guanghui He, Xiaohu Li
Aiming at the problems of large data dimension, more redundant data, and low accuracy in network traffic anomaly detection, a network traffic anomaly detection model (FR-APPSO BiLSTM) based on feature reduction and bidirectional long short-term memory (LSTM) neural network optimization is proposed. First, the feature dimensions are divided by hierarchical clustering according to the similarity distance
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SKT-MOT and DyTracker: A Multiobject Tracking Dataset and a Dynamic Tracker for Speed Skating Video Sci. Program. (IF 1.672) Pub Date : 2023-10-18 Junwu Wang, Zongmin Li, Yachuan Li, Shaobo Yang, Ben Wang, Hua Li
Speed skating serves as a significant application domain for multiobject tracking (MOT), presenting unique challenges such as frequent occlusion, highly similar appearances, and motion blur. To address these challenges, this paper constructs an MOT dataset called SKT-MOT for speed skating and analyzes the shortcomings of existing datasets and methods. Accordingly, we propose a dynamic MOT method called
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Dual-Core Adaptive NLM Image Denoising Algorithm Based on Variable-Size Window and Neighborhood Multifeatures Sci. Program. (IF 1.672) Pub Date : 2023-9-9 Jing Mao, Lianming Sun, Jie Chen, Shunyuan Yu
To solve the problem that the similarity calculation between neighbors was easily disturbed by noise in the traditional nonlocal mean (NLM) denoising algorithm, a dual-core NLM denoising algorithm based on neighborhood multifeatures and variable-size search window was proposed. The algorithm first proposed to use the eigenvalues of the structure tensor to classify the region where the target pixel
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Graph-Based Recommendation System Enhanced by Community Detection Sci. Program. (IF 1.672) Pub Date : 2023-8-21 Zeinab Shokrzadeh, Mohammad-Reza Feizi-Derakhshi, Mohammad-Ali Balafar, Jamshid Bagherzadeh Mohasefi
Many researchers have used tag information to improve the performance of recommendation techniques in recommender systems. Examining the tags of users will help to get their interests and leads to more accuracy in the recommendations. Since user-defined tags are chosen freely and without any restrictions, problems arise in determining their exact meaning and the similarity of tags. However, using thesaurus
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Generative Deep Learning for Visual Animation in Landscapes Design Sci. Program. (IF 1.672) Pub Date : 2023-8-8 Peter Ardhianto, Yonathan Purbo Santosa, Christian Moniaga, Maya Putri Utami, Christine Dewi, Henoch Juli Christanto, Abbott Po Shun Chen
The biggest challenge for architecture designers is the time required for the design process. Especially landscape architects who have different work limits from architects in general. In contrast to architects in general, who are assisted in producing design plans by building standards, building requirements, and space programs that adapt to the type of project being undertaken. At the same time,
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A Novel Activation Function of Deep Neural Network Sci. Program. (IF 1.672) Pub Date : 2023-8-4 Lin Xiangyang, Qinghua Xing, Zhang Han, Chen Feng
In deep neural networks, the activation function is an important component. The most popular activation functions at the moment are Sigmoid, Sin, rectified linear unit (ReLU), and some variants of ReLU. However, each of them has its own weakness. To improve the network fitting and generalization ability, a new activation function, TSin, is designed. The basic design idea for TSin function is to rotate
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A Software Defect Prediction Approach Based on Hybrid Feature Dimensionality Reduction Sci. Program. (IF 1.672) Pub Date : 2023-7-20 Shenggang Zhang, Shujuan Jiang, Yue Yan
Software defect prediction (SDP) is designed to assist software testing, which can reasonably allocate test resources to reduce costs and improve development efficiency. In order to improve the prediction performance, researchers have designed many defect-related features for SDP. However, feature redundancy (FR) and irrelevance caused by the increasing dimensions of data will greatly degrade the performance
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Research on Crop 3D Model Reconstruction Based on RGB-D Binocular Vision Sci. Program. (IF 1.672) Pub Date : 2023-7-14 Zhiyan Ma, Haidi Wan, Xiong Gan
Taking maize seedlings as the object, the implementation of crops 3D reconstruction based on RGB-D binocular vision and the selection of some key parameters are investigated in this research. First, multiple images are taken from different angles around the target. By mapping the maize seedling region coordinate values after the Otsu algorithm and global threshold segmentation to the corresponding
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A Hybrid Model Using PCA and BP Neural Network for Time Series Prediction in Chinese Stock Market with TOPSIS Analysis Sci. Program. (IF 1.672) Pub Date : 2023-6-29 Lei Hang, Dandan Liu, Fusheng Xie
The stock price changes rapidly and is highly nonlinear in the financial market. One of the common concerns of many scholars and investors is how to accurately predict the stock price and the trend of rising and falling in a short time. Machine learning and deep learning techniques have found their place in financial institutions thanks to the ability of time series data prediction with high precision
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Application of 2D Images in Visual and Tactile Dimensions of Fiber Art Design Sci. Program. (IF 1.672) Pub Date : 2023-6-12 Jingyu Wang, Mengyao Wang, Chuan Zhang
With people’s higher and higher spiritual requirements, fiber art is more common in people’s lives. From ordinary fiber materials to handicrafts and daily necessities, there are shadows of fiber art. This paper aims to study the application of two-dimensional images in the visual and tactile dimensions of fiber art design. This paper proposes a three-dimensional simulation of two-dimensional images
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Photovoltaic Module Fault Detection Based on Deep Learning Using Cloud Computing Sci. Program. (IF 1.672) Pub Date : 2023-6-9 S. Naveen Venkatesh, P. Arun Balaji, Ganjikunta Chakrapani, K. Annamalai, S. Aravinth, P. S. Anoop, V. Sugumaran, Vetriselvi Mahamuni
The performance of photovoltaic modules (PVMs) degrades due to the occurrence of various faults such as discoloration, snail trail, burn marks, delamination, and glass breakage. This degradation in power output has created a concern to improve PVM performance. Automatic inspection and condition monitoring of PVM components can handle performance-related issues, especially for installed capacity where
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Reactive Message Passing for Scalable Bayesian Inference Sci. Program. (IF 1.672) Pub Date : 2023-5-27 Dmitry Bagaev, Bert de Vries
We introduce reactive message passing (RMP) as a framework for executing schedule-free, scalable, and, potentially, more robust message passing-based inference in a factor graph representation of a probabilistic model. RMP is based on the reactive programming style, which only describes how nodes in a factor graph react to changes in connected nodes. We recognize reactive programming as the suitable
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A Small Object Detection Network Based on Multiple Feature Enhancement and Feature Fusion Sci. Program. (IF 1.672) Pub Date : 2023-5-26 Kun Tan, Shengduo Ding, Shuncheng Wu, Kun Tian, Jie Ren
Due to the small size, high resolution, and complex background, small object detection has become a difficult point in computer vision. Making full use of high-resolution features and reducing information loss in the process of information propagation is of great significance to improve small object detection. In this article, to achieve the above two points, this work proposes a small object detection
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Effective Teaching Design Based on the Combination of BOPPPS Model and Tina Virtual Simulation Software Sci. Program. (IF 1.672) Pub Date : 2023-5-20 Changdong Wu, Zhenli Deng, Jincheng Wei
Good teaching effect comes from effective teaching design. In this article, we combined the advanced teaching concept BOPPPS model with Tina virtual simulation software to develop the teaching design. BOPPPS model is an effective and efficient teaching model. It includes six parts such as bridge-in, objective, preassessment, participatory learning, postassessment, and summary. In this article, bridge-in
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Ensemble Convolution Neural Network for Robust Video Emotion Recognition Using Deep Semantics Sci. Program. (IF 1.672) Pub Date : 2023-5-17 E. S. Smitha, S. Sendhilkumar, G. S. Mahalakshmi
Human emotion recognition from videos involves accurately interpreting facial features, including face alignment, occlusion, and shape illumination problems. Dynamic emotion recognition is more important. The situation becomes more challenging with multiple persons and the speedy movement of faces. In this work, the ensemble max rule method is proposed. For obtaining the results of the ensemble method
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Evaluating the Quality of Medical Services Using Intuitionistic Hesitant Fuzzy Aczel–Alsina Aggregation Information Sci. Program. (IF 1.672) Pub Date : 2023-5-15 Shahzaib Ashraf, Aziz Khan, Muhammad Kamran, M. K. Pandit
In modern civilization, individuals are increasingly concerned with evaluating the quality of medical services. Evaluation of the quality of medical services enables medical care providers to monitor and improve their service quality. The evaluation of medical service quality is efficiently addressed by the novel concept of Aczel–Alsina operators in an intuitionistic hesitant fuzzy (IHF) environment
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Feature Dual Supervision Model for the Searches of Online Advertising Audiences Sci. Program. (IF 1.672) Pub Date : 2023-5-11 Haipeng Ni, Zhixi Wang
Online advertising has become one of the most important strategies used by companies. They get the valuable results from Internet marketing and communication strategies. Therefore, it is necessary to study the click-through rate (CTR) model to search the potential audiences in online advertising. The advertisers desire to search for potential candidates through a large number of queries for audiences
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Morphology-Based Spell Checker for Dawurootsuwa Language Sci. Program. (IF 1.672) Pub Date : 2023-5-10 Dawit Tadesse Gamu, Michael Melese Woldeyohannis
Processing of textual information by using word-processing tools is extremely increased due to the presence of misspelled or erroneous words. In order to minimize these misspelled words from digital information, different spellchecker tools are needed. A plenty of works are performed in technological favored languages like English and European languages but not for an underresourced language like Dawurootsuwa
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Heterogeneous Hadoop Cluster-Based Image Processing Workload Distribution Framework between CPU and GPU Sci. Program. (IF 1.672) Pub Date : 2023-5-5 Najia Naz, Islam Zada, Abdul Haseeb Malik, Muhammad Nadeem, Sikandar Ali
Due to the rapid development of image data and the necessity to analyze it to extract meaningful information, heterogeneous systems have gained prominence. One of the most critical aspects of distributed systems is load balancing. When it comes to the distribution of workload in a balanced manner in a cluster, some heterogeneous systems are used for image processing. When workloads are allocated in
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Intrusion Detection System Using the G-ABC with Deep Neural Network in Cloud Environment Sci. Program. (IF 1.672) Pub Date : 2023-4-28 Nishika Gulia, Kamna Solanki, Sandeep Dalal, Amita Dhankhar, Omdev Dahiya, N. Ummal Salmaan
Cloud computing plays a pivotal role in sharing resources and information. It is challenging to secure cloud services from different intruders. Intrusion detection system (IDS) plays a vital role in detecting intruder attacks, and it is also used to monitor the traffic in the network. The paper is aimed to control the attacks using the machine learning (ML) technique integrated with the artificial
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Data Protection of Accounting Information Based on Big Data and Cloud Computing Sci. Program. (IF 1.672) Pub Date : 2023-4-27 Xiaohua Li
With the rapid development of Internet technology, mankind has entered the era of big data. The Internet records all kinds of information, and the amount of information generated in the future is in a state of explosive growth. With the development of enterprises, the data of accounting information have grown, and its security has also been threatened. Once the accounting information is leaked, it
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A Compound Class of Unit Burr XII Model: Theory, Estimation, Fuzzy, and Application Sci. Program. (IF 1.672) Pub Date : 2023-4-26 Mohammad A. Zayed, Amal S. Hassan, Ehab M. Almetwally, Ahmad M. Aboalkhair, Abdullah H. Al-Nefaie, Hisham M. Almongy
The current research offers an enhanced three-parameter lifetime model that combines the unit Burr XII distribution with a power series distribution. The novel class of distribution is named the unit Burr XII power series (UBXIIPS). This compounding technique allows for the production of flexible distributions with strong physical meanings in domains such as biology and engineering. The UBXIIPS class
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Multitask Sparse Representation of Two-Dimensional Variational Mode Decomposition Components for SAR Target Recognition Sci. Program. (IF 1.672) Pub Date : 2023-4-25 Wenbo Weng
A synthetic aperture radar (SAR) automatic target recognition (ATR) method is developed based on the two-dimensional variational mode decomposition (2D-VMD). 2D-VMD decomposes original SAR images into multiscale components, which depict the time-frequency properties of the targets. The original image and its 2D-VMD components are highly correlated, so the multitask sparse representation is chosen to
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Path Planning Algorithm for the Multiple Depot Vehicle Routing Problem Based on Parallel Clustering Sci. Program. (IF 1.672) Pub Date : 2023-4-25 Xue Han
It is necessary to study the problem of vehicle routing in multidistribution centers to improve the speed, time, and cost thereof. It is preferable to use as few vehicles as possible to complete the delivery of goods and minimize the total mileage. With the development of artificial intelligence technology, machine learning is usually used to solve the problem of k shortest paths in multiple distribution
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Architecture of Deep Convolutional Encoder-Decoder Networks for Building Footprint Semantic Segmentation Sci. Program. (IF 1.672) Pub Date : 2023-4-25 Abderrahim Norelyaqine, Rida Azmi, Abderrahim Saadane
Building extraction from high-resolution aerial images is critical in geospatial applications such as telecommunications, dynamic urban monitoring, updating geographic databases, urban planning, disaster monitoring, and navigation. Automatic building extraction is a massive task because buildings in various places have varied spectral and geometric qualities. As a result, traditional image processing
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Research on a Target Detection Method for Alternanthera Philoxeroides in the Rice Seedling Stage Based on Single-Shot Multibox Detector Sci. Program. (IF 1.672) Pub Date : 2023-4-21 Xiangwu Deng, Zhuwen Liu, Kunsong Gong, Song Liang, Guangjun Qiu, Long Qi
Alternanthera philoxeroides, an invasive alien malignant weed, competes with rice for water, fertilizer, light, and growth space before seedling closure stages, which commonly stresses the growth of rice. Chemical herbicides are mainly used to control weeds. However, excessive use of chemical herbicides could lead to serious environmental pollution. With the rapid development of artificial intelligence
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Identification of Dry Bean Varieties Based on Multiple Attributes Using CatBoost Machine Learning Algorithm Sci. Program. (IF 1.672) Pub Date : 2023-4-21 S. Krishnan, S. K. Aruna, Karthick Kanagarathinam, Ellappan Venugopal
Dry beans are the most widely grown edible legume crop worldwide, with high genetic diversity. Crop production is strongly influenced by seed quality. So, seed classification is important for both marketing and production because it helps build sustainable farming systems. The major contribution of this research is to develop a multiclass classification model using machine learning (ML) algorithms
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A Personalized Learning Path for French Study in Colleges Based on a Big Data Knowledge Map Sci. Program. (IF 1.672) Pub Date : 2023-4-21 Guangzhi Xiao
The education industry is gradually improving with the rapid development of information technology. The learners use networks and computers to alter the traditional instructional framework based on educational information technology and achieve personalized learning. This teaching method emphasizes each learner’s identity and autonomy. However, due to the huge number of learning resources available
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SLA Aware Task-Scheduling Algorithm in Cloud Computing Using Whale Optimization Algorithm Sci. Program. (IF 1.672) Pub Date : 2023-4-20 Sudheer Mangalampalli, Sangram Keshari Swain, Ganesh Reddy Karri, Satyasis Mishra
Task scheduling in Cloud Computing paradigm poses new challenges for cloud provider as heterogeneous, diversified tasks arrived on to cloud console. To schedule these type of tasks efficiently on to virtual resources in cloud paradigm, an effective scheduler is needed, which precisely maps tasks to virtual machines by considering priorities of both tasks and VMs. Existing scheduling algorithms failed
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Local Binary Convolutional Neural Networks' Long Short-Term Memory Model for Human Embryos' Anomaly Detection Sci. Program. (IF 1.672) Pub Date : 2023-4-17 Sajad Einy, Esra Sen, Hasan Saygin, Hemrah Hivehchi, Yahya Dorostkar Navaei
Accurate selection of embryos with the maximum implementation condition is a necessary step to increase the effectiveness of fertility treatment in in vitro fertilization (IVF). The deep learning algorithms presented high potential for monitoring and visualizing embryo features such as cell numbers and their morphological development in time series manner. Due to the ability of the computer vision
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ScoringNet: A Neural Network Based Pruning Criteria for Structured Pruning Sci. Program. (IF 1.672) Pub Date : 2023-4-14 Shuang Wang, Zhaogong Zhang
Convolutional neural networks (CNNs) have shown their great power in multiple computer vision tasks. However, many recent works improve their performance by adding more layers and parameters, which lead to computational redundancy in many application scenarios, making it harder to implement on low-end devices. To solve this problem, model pruning methods are proposed, which aim to lower the computational
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Intelligent Mining of Association Rules Based on Nanopatterns for Code Smells Detection Sci. Program. (IF 1.672) Pub Date : 2023-4-13 D. Juliet Thessalonica, H. Khanna Nehemiah, S. Sreejith, A. Kannan
Software maintenance is an imperative step in software development. Code smells can arise as a result of poor design as well as frequent code changes due to changing needs. Early detection of code smells during software development can help with software maintenance. This work focuses on identifying code smells on Java software using nanopatterns. Nanopatterns are method-level code structures that
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A Real-Time AIS Data Cleaning and Indicator Analysis Algorithm Based on Stream Computing Sci. Program. (IF 1.672) Pub Date : 2023-4-12 Taizhi Lv, Peiyi Tang, Juan Zhang
The data quality and real-time analysis of automatic identification system (AIS) are of great significance for water transportation safety and intelligent maritime construction. To improve the AIS data quality and analyze AIS data in real time, a real-time AIS data cleaning and indicator analysis algorithm is proposed. This algorithm performs distributed real-time data cleaning and analysis for massive
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Could Textual Features Offer Incremental Information to Financial Distress Prediction? Evidence from the Listed Firm in China Sci. Program. (IF 1.672) Pub Date : 2023-2-22 Liyuan Zheng, Pengqun Gao, Lianghui Feng, Mengjiao Wang
Both academia and industry believe that introducing textual features into a financial distress prediction model can improve its accuracy. However, the textual features introduced by the research are relatively singular and fail to reflect the overall situation of the text comprehensively and effectively. Based on the traditional Z-score financial indicators model, four textual features of MD&A are
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A Distributionally Robust Fuzzy Optimization Method for Single-Period Inventory Management Problems Sci. Program. (IF 1.672) Pub Date : 2023-2-18 Zhaozhuang Guo, Yuefang Sun, Shengnan Tian, Zikun Li
This paper investigates single-period inventory management problems with uncertain market demand, where the exact possibility distribution of demand is unavailable. In this condition, it is important to order a reliable quantity which can immunize against distribution uncertainty. To model this type of single-period inventory management problem, this paper characterizes the uncertain demand by generalized
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An In-depth Benchmarking of Evolutionary and Swarm Intelligence Algorithms for Autoscaling Parameter Sweep Applications on Public Clouds Sci. Program. (IF 1.672) Pub Date : 2023-2-17 Virginia Yannibelli, Elina Pacini, David A. Monge, Cristian Mateos, Guillermo Rodriguez, Emmanuel Millán, Jorge R. Santos
Many important computational applications in science, engineering, industry, and technology are represented by PSE (parameter sweep experiment) applications. These applications involve a large number of resource-intensive and independent computational tasks. Because of this, cloud autoscaling approaches have been proposed to execute PSE applications on public cloud environments that offer instances
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Changing Properties of Daily Precipitation Concentration in the Hai River Basin, China Sci. Program. (IF 1.672) Pub Date : 2023-2-6 Ren Zheng, Zhao Yong, Chen Ling, Gong Jiaguo, Wang Li
Understanding the spatiotemporal pattern of precipitation concentration is important for the assessment of flood and drought risk and utilization of water resources. In this study, the daily precipitation concentration index in the Hai River basin in China was calculated based on the Gini coefficient obtained from the observed data of 51 meteorological stations from 1951 to 2018 and spatiotemporal
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Automatic Annotation of Functional Semantics for 3D Product Model Based on Latent Functional Semantics Sci. Program. (IF 1.672) Pub Date : 2023-2-4 Zhoupeng Han, Hua Zhang, Weirong He, Li Ba, Qilong Yuan
To support effectively function-driven 3D model retrieval in the phase of mechanical product conceptual design and improve the efficiency of functional semantics annotation for 3D models, an approach for functional semantics automatic annotation for mechanical 3D product model based on latent functional semantics is presented. First, the design knowledge and function knowledge of mechanical product
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Prediction Model of Fault Block Reservoir Measure Index Based on 1DCNN-LightGBM Sci. Program. (IF 1.672) Pub Date : 2023-2-3 Bin Wang, Dawei Wu, Kai Zhang, Huaqing Zhang, Chao Zhang
In view of the shortcomings of the prediction method of future development measures and indicators of fault block reservoir in the current oilfield practical application, a prediction method of fault block reservoir measures and indicators based on the random forest method and LightGBM is proposed, which can help the oilfield make more effective decisions in the middle and later development. Firstly
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Predictive Analytics and Software Defect Severity: A Systematic Review and Future Directions Sci. Program. (IF 1.672) Pub Date : 2023-2-2 T. O. Olaleye, O. T. Arogundade, Sanjay Misra, A. Abayomi-Alli, Utku Kose
Software testing identifies defects in software products with varying multiplying effects based on their severity levels and sequel to instant rectifications, hence the rate of a research study in the software engineering domain. In this paper, a systematic literature review (SLR) on machine learning-based software defect severity prediction was conducted in the last decade. The SLR was aimed at detecting
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Is Vehicle Plate Corner Prediction by Vision Transformer Better than CNNs? Sci. Program. (IF 1.672) Pub Date : 2023-1-27 Kyungkoo Jun
The license plate recognition performance can be improved by converting the license plate photographed from the side to the front view. To perform this transformation, four vertex corner positions of the license plate are required. Existing deep learning methods to find these corner positions use a convolutional neural network (CNN). In this study, we propose a model using a vision transformer (ViT)
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Sound Pressure Level at Terminals through Data Mining Sci. Program. (IF 1.672) Pub Date : 2023-1-13 Luis Pastor Monteza, Marco Castro Rivera, Lenin Quiñones Huatangari, Eli Morales Rojas
The land transportation is a cause of noise in cities, thus breaking the natural balance and bringing with it physiological and mental illnesses, as well as occupational accidents. In this sense, the objective of the research was to estimate the sound pressure in land terminals in the city of Jaen, Peru, using data mining algorithms. The methodology consisted in environmentally monitoring six terminals
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Toward an Efficient and Effective Credit Scorer for Cross-Border E-Commerce Enterprises Sci. Program. (IF 1.672) Pub Date : 2023-1-12 Chang Xu, Ruize Guo, Yulai Zhang, Xinyuan Luo
Building an efficient and effective credit scorer for enterprises is an important and urgent demand in the cross-border e-commerce industry. In this paper, we present a framework to build a credit scorer using e-commerce data integrated from various sources. First, an improved dependency graph approach is proposed to recognize distinct records in the dataset. Then, we apply logistic regression using
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Sensitivity Analysis for a Redundant System with Random Inspection and Repairmen Under Repair Pressure Sci. Program. (IF 1.672) Pub Date : 2023-1-6 Shan Gao, Zongbin Wang
This study examines a repairable -out-of-: system with warm standby components and repairmen, where common cause failure (CCF), random inspection, and a repair pressure coefficient are involved. Due to the typical non-self-announcement of the standby component’s failure, performing a random inspection on warm standby components is necessary to find whether there are failed standby components. When
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Improved Multiscale Learning Dictionary Image Denoising Algorithm in Curved Wave Domain Sci. Program. (IF 1.672) Pub Date : 2023-1-5 Jing Mao, Shunyuan Yu, Jie Chen, Shuo Chen
It was proposed to develop a better multiscale learning dictionary picture de-noising technique. The approach improves the adaptive threshold curvilinear transform, which can divide an image into different scale information and be used to build a curvilinear multiscale learning dictionary. The method finished the dictionary and sparse coefficient updates in the picture through circular iterations and
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Performance Evaluation of Novel Random Biased-Genetic Algorithm (NRB-GA): A Hybrid Load Balancing Algorithm in a Cloud Computing Environment Sci. Program. (IF 1.672) Pub Date : 2022-12-21 Karpaga Selvi Subramanian, Gemmachis Teshite
A novel random biased-genetic algorithm (NRB-GA) load-balancing algorithm that exhibits the characteristics of both genetic algorithms and biased random algorithms is designed and developed to improve the processing time and response time metrics of the cloud computing environment. The NRB-GA is designed to discover a virtual machine with fewer loads by applying a genetic algorithm with a fitness function
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An Analysis of the RBF Hyperparameter Impact on Surrogate-Assisted Evolutionary Optimization Sci. Program. (IF 1.672) Pub Date : 2022-12-20 Yoel Tenne
Computationally expensive optimization problems are often solved using surrogates and a common variant is the radial basis functions (RBF) model. It aggregates several basis functions which all depend on a hyperparameter affecting their individual outputs and consequentially the overall surrogate prediction. However, the optimal value of the hyperparameter is typically unknown and should therefore
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Towards Public Opinion Digital Twin: A Conceptual Prototype Sci. Program. (IF 1.672) Pub Date : 2022-12-15 Jing He, Yuanbo Qi, Jie Feng, Anling Xiang
This paper proposes a novel modeling concept, the “public opinion digital twin,” for public opinion analysis. The public opinion digital twin can be regarded as an experimental sandbox for social science. By digitalizing public data acquired from cyberspace into digital models, the modeling enables practical simulation, data analytics, scenario reflection, and decision support in a digital space with
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A Hyperparameter Optimization Algorithm for the LSTM Temperature Prediction Model in Data Center Sci. Program. (IF 1.672) Pub Date : 2022-12-12 Simin Wang, Chunmiao Ma, Yixuan Xu, Jinyu Wang, Weiguo Wu
As the main tool to realize data mining and efficient knowledge acquisition in the era of big data, machine learning is widely used in data center energy-saving research. The temperature prediction model based on machine learning predicts the state of the data center according to the upcoming tasks. It can adjust the refrigeration equipment in advance to avoid temperature regulation lag and set the
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Persian Named Entity Recognition by Gray Wolf Optimization Algorithm Sci. Program. (IF 1.672) Pub Date : 2022-12-10 Aynaz Forouzandeh, Mohammad-Reza Feizi-Derakhshi, Pejman Gholami-Dastgerdi
Named entity recognition (NER) is a subfield of natural language processing (NLP). It is able to identify proper nouns, such as person names, locations, and organizations, and has been widely used in various tasks. NER can be practical in extracting information from social media data. However, the unstructured and noisy nature of social media (such as grammatical errors and typos) causes new challenges
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An Integration of New Digital Image Scrambling Technique on PCA-Based Face Recognition System Sci. Program. (IF 1.672) Pub Date : 2022-11-25 Eimad Abusham, Basil Ibrahim, Kashif Zia, Sanad Al Maskari
Systems using biometric authentication offer greater security than traditional textual and graphical password-based systems for granting access to information systems. Although biometric-based authentication has its benefits, it can be vulnerable to spoofing attacks. Those vulnerabilities are inherent to any biometric-based subsystem, including face recognition systems. The problem of spoofing attacks
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Content Deduplication with Granularity Tweak Based on Base and Deviation for Large Text Dataset Sci. Program. (IF 1.672) Pub Date : 2022-11-22 S. Venkatesh Babu, P. Ramya, Jeffin Gracewell
The concept of storage optimization has evolved as one of the hottest research projects in big data which brings out better solutions such as data compression which almost converges towards the deduplication technique. Deduplication is a technique that finds and eliminates duplicate content by storing only the unique copies of data whose efficiency is being qualified based on the amount of duplicate
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Intrusion Detection for Cyber-Physical Security System Using Long Short-Term Memory Model Sci. Program. (IF 1.672) Pub Date : 2022-11-21 Gazi Md. Habibul Bashar, Mohammod Abul Kashem, Liton Chandra Paul
In the present context, the deep learning approach is highly applicable for identifying cyber-attacks on intrusion detection systems (IDS) in cyber-physical security systems. As a key part of network security defense, cyber-attacks can change and penetrate the security of the network system, then, the role of an IDS is to detect suspicious behaviors and act appropriately to protect the network from