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Long- and Short-Term Memory Model of Cotton Price Index Volatility Risk Based on Explainable Artificial Intelligence. Big Data (IF 4.6) Pub Date : 2023-11-17 Huosong Xia,Xiaoyu Hou,Justin Zuopeng Zhang
Market uncertainty greatly interferes with the decisions and plans of market participants, thus increasing the risk of decision-making, leading to compromised interests of decision-makers. Cotton price index (hereinafter referred to as cotton price) volatility is highly noisy, nonlinear, and stochastic and is susceptible to supply and demand, climate, substitutes, and other policy factors, which are
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Big Data Confidentiality: An Approach Toward Corporate Compliance Using a Rule-Based System. Big Data (IF 4.6) Pub Date : 2023-10-31 Georgios Vranopoulos,Nathan Clarke,Shirley Atkinson
Organizations have been investing in analytics relying on internal and external data to gain a competitive advantage. However, the legal and regulatory acts imposed nationally and internationally have become a challenge, especially for highly regulated sectors such as health or finance/banking. Data handlers such as Facebook and Amazon have already sustained considerable fines or are under investigation
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Consumer Segmentation Based on Location and Timing Dimensions Using Big Data from Business-to-Customer Retailing Marketplaces. Big Data (IF 4.6) Pub Date : 2023-10-30 Fatemeh Ehsani,Monireh Hosseini
Consumer segmentation is an electronic marketing practice that involves dividing consumers into groups with similar features to discover their preferences. In the business-to-customer (B2C) retailing industry, marketers explore big data to segment consumers based on various dimensions. However, among these dimensions, the motives of location and time of shopping have received relatively less attention
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Gaussian Adapted Markov Model with Overhauled Fluctuation Analysis-Based Big Data Streaming Model in Cloud. Big Data (IF 4.6) Pub Date : 2023-10-30 M Ananthi,Annapoorani Gopal,K Ramalakshmi,P Mohan Kumar
An accurate resource usage prediction in the big data streaming applications still remains as one of the complex processes. In the existing works, various resource scaling techniques are developed for forecasting the resource usage in the big data streaming systems. However, the baseline streaming mechanisms limit with the issues of inefficient resource scaling, inaccurate forecasting, high latency
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Sharing Medical Big Data While Preserving Patient Confidentiality in Innovative Medicines Initiative: A Summary and Case Report from BigData@Heart. Big Data (IF 4.6) Pub Date : 2023-10-27 Megan Schröder,Sam H A Muller,Eleni Vradi,Johanna Mielke,Yvonne M F Lim,Fabrice Couvelard,Menno Mostert,Stefan Koudstaal,Marinus J C Eijkemans,Christoph Gerlinger
Sharing individual patient data (IPD) is a simple concept but complex to achieve due to data privacy and data security concerns, underdeveloped guidelines, and legal barriers. Sharing IPD is additionally difficult in big data-driven collaborations such as Bigdata@Heart in the Innovative Medicines Initiative, due to competing interests between diverse consortium members. One project within BigData@Heart
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Big Data-Driven Futuristic Fabric System in Societal Digital Transformation. Big Data (IF 4.6) Pub Date : 2023-10-01 Chinmay Chakraborty,Muhammad Khurram Khan
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Impact of Cooperative Innovation on the Technological Innovation Performance of High-Tech Firms: A Dual Moderating Effect Model of Big Data Capabilities and Policy Support. Big Data (IF 4.6) Pub Date : 2023-09-14 Xianglong Li,Qingjin Wang,Renbo Shi,Xueling Wang,Kaiyun Zhang,Xiao Liu
The mechanism of cooperative innovation (CI) for high-tech firms aims to improve their technological innovation performance. It is the effective integration of the internal and external innovation resources of these firms, along with the simultaneous reduction in the uncertainty of technological innovation and the maintenance of the comparative advantage of the firms in the competition. This study
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ODQN-Net: Optimized Deep Q Neural Networks for Disease Prediction Through Tongue Image Analysis Using Remora Optimization Algorithm. Big Data (IF 4.6) Pub Date : 2023-09-13 S V N Sreenivasu,P Santosh Kumar Patra,Vasujadevi Midasala,G S N Murthy,Krishna Chaitanya Janapati,J N V R Swarup Kumar,Pala Mahesh Kumar
Tongue analysis plays the major role in disease type prediction and classification according to Indian ayurvedic medicine. Traditionally, there is a manual inspection of tongue image by the expert ayurvedic doctor to identify or predict the disease. However, this is time-consuming and even imprecise. Due to the advancements in recent machine learning models, several researchers addressed the disease
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A New Filter Approach Based on Effective Ranges for Classification of Gene Expression Data. Big Data (IF 4.6) Pub Date : 2023-09-04 Derya Turfan,Bulent Altunkaynak,Özgür Yeniay
Over the years, many studies have been carried out to reduce and eliminate the effects of diseases on human health. Gene expression data sets play a critical role in diagnosing and treating diseases. These data sets consist of thousands of genes and a small number of sample sizes. This situation creates the curse of dimensionality and it becomes problematic to analyze such data sets. One of the most
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Social Listening for Product Design Requirement Analysis and Segmentation: A Graph Analysis Approach with User Comments Mining. Big Data (IF 4.6) Pub Date : 2023-09-04 Xinjun Lai,Guitao Huang,Ziyue Zhao,Shenhe Lin,Sheng Zhang,Huiyu Zhang,Qingxin Chen,Ning Mao
This study investigates customers' product design requirements through online comments from social media, and quickly translates these needs into product design specifications. First, the exponential discriminative snowball sampling method was proposed to generate a product-related subnetwork. Second, natural language processing (NLP) was utilized to mine user-generated comments, and a Graph SAmple
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An Expert Panel Discussion Embedding Ethics and Equity in Artificial Intelligence and Machine Learning Infrastructure. Big Data (IF 4.6) Pub Date : 2023-09-01 Malaika Simmons,Rachele Hendricks-Sturrup,Gabriella Waters,Laurie Novak,Martin Were,Sajid Hussain
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Predicting Sociodemographic Attributes from Mobile Usage Patterns: Applications and Privacy Implications. Big Data (IF 4.6) Pub Date : 2023-08-14 Rouzbeh Razavi,Guisen Xue,Ikpe Justice Akpan
When users interact with their mobile devices, they leave behind unique digital footprints that can be viewed as predictive proxies that reveal an array of users' characteristics, including their demographics. Predicting users' demographics based on mobile usage can provide significant benefits for service providers and users, including improving customer targeting, service personalization, and market
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An Improved Influence Maximization Method for Online Advertising in Social Internet of Things. Big Data (IF 4.6) Pub Date : 2023-08-02 Reza Molaei,Kheirollah Rahsepar Fard,Asgarali Bouyer
Recently, a new subject known as the Social Internet of Things (SIoT) has been presented based on the integration the Internet of Things and social network concepts. SIoT is increasingly popular in modern human living, including applications such as smart transportation, online health care systems, and viral marketing. In advertising based on SIoT, identifying the most effective diffuser nodes to maximize
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A Weighted GraphSAGE-Based Context-Aware Approach for Big Data Access Control. Big Data (IF 4.6) Pub Date : 2023-08-01 Dibin Shan,Xuehui Du,Wenjuan Wang,Aodi Liu,Na Wang
Context information is the key element to realizing dynamic access control of big data. However, existing context-aware access control (CAAC) methods do not support automatic context awareness and cannot automatically model and reason about context relationships. To solve these problems, this article proposes a weighted GraphSAGE-based context-aware approach for big data access control. First, graph
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Automated Natural Language Processing-Based Supplier Discovery for Financial Services. Big Data (IF 4.6) Pub Date : 2023-07-07 Mauro Papa,Ioannis Chatzigiannakis,Aris Anagnostopoulos
Public procurement is viewed as a major market force that can be used to promote innovation and drive small and medium-sized enterprises growth. In such cases, procurement system design relies on intermediates that provide vertical linkages between suppliers and providers of innovative services and products. In this work we propose an innovative methodology for decision support in the process of supplier
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A Data-Driven Analysis Method for the Trajectory of Power Carbon Emission in the Urban Area. Big Data (IF 4.6) Pub Date : 2023-06-16 Yi Gao,Dawei Yan,Xiangyu Kong,Ning Liu,Zhiyu Zou,Bixuan Gao,Yang Wang,Yue Chen,Shuai Luo
"Industry 4.0" aims to build a highly versatile, individualized digital production model for goods and services. The carbon emission (CE) issue needs to be addressed by changing from centralized control to decentralized and enhanced control. Based on a solid CE monitoring, reporting, and verification system, it is necessary to study future power system CE dynamics simulation technology. In this article
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Image Smart Segmentation Analysis Against Diabetic Foot Ulcer Using Internet of Things with Virtual Sensing. Big Data (IF 4.6) Pub Date : 2023-06-08 Chandu Thota,Dinesh Jackson Samuel,Mustafa Musa Jaber,M M Kamruzzaman,Renjith V Ravi,Lydia J Gnanasigamani,R Premalatha
Diabetic foot ulcer (DFU) is a problem worldwide, and prevention is crucial. The image segmentation analysis of DFU identification plays a significant role. This will produce different segmentation of the same idea, incomplete, imprecise, and other problems. To address these issues, a method of image segmentation analysis of DFU through internet of things with the technique of virtual sensing for semantically
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Computational Efficient Approximations of the Concordance Probability in a Big Data Setting. Big Data (IF 4.6) Pub Date : 2023-06-07 Robin Van Oirbeek,Jolien Ponnet,Bart Baesens,Tim Verdonck
Performance measurement is an essential task once a statistical model is created. The area under the receiving operating characteristics curve (AUC) is the most popular measure for evaluating the quality of a binary classifier. In this case, the AUC is equal to the concordance probability, a frequently used measure to evaluate the discriminatory power of the model. Contrary to AUC, the concordance
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Large-Scale Estimation and Analysis of Web Users' Mood from Web Search Query and Mobile Sensor Data. Big Data (IF 4.6) Pub Date : 2023-06-02 Wataru Sasaki,Satoki Hamanaka,Satoko Miyahara,Kota Tsubouchi,Jin Nakazawa,Tadashi Okoshi
The ability to estimate the current mood states of web users has considerable potential for realizing user-centric opportune services in pervasive computing. However, it is difficult to determine the data type used for such estimation and collect the ground truth of such mood states. Therefore, we built a model to estimate the mood states from search-query data in an easy-to-collect and non-invasive
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Opinion Evolution with Information Quality of Public Person and Mass Acceptance Threshold. Big Data (IF 4.6) Pub Date : 2023-05-29 Jing Wei,Yuguang Jia,Wanyi Tie,Hengmin Zhu,Weidong Huang
Public persons are nodes with high attention to public events, and their opinions can directly affect the development on events. However, because of rationality, the followers' acceptance to the public persons' opinions will depend on the information trait on public persons' opinions and own comprehension. To study how different opinions of the public persons guide different followers, we build an
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Secure Biomedical Document Protection Framework to Ensure Privacy Through Blockchain. Big Data (IF 4.6) Pub Date : 2023-05-23 Ramkumar Jayaraman,Mohammed Alshehri,Manoj Kumar,Ahed Abugabah,Surender Singh Samant,Ahmed A Mohamed
In the recent health care era, biomedical documents play a crucial role, and they contain much evidence-based documentation associated with many stakeholders data. Protecting those confidential research documents is more difficult and effective, and a significant process in the medical-based research domain. Those bio-documentation related to health care and other relevant community-valued data are
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IDLIQ: An Incremental Deterministic Finite Automaton Learning Algorithm Through Inverse Queries for Regular Grammar Inference. Big Data (IF 4.6) Pub Date : 2023-05-18 Farah Haneef,Muddassar A Sindhu
We present an efficient incremental learning algorithm for Deterministic Finite Automaton (DFA) with the help of inverse query (IQ) and membership query (MQ). This algorithm is an extension of the Identification of Regular Languages (ID) algorithm from a complete to an incremental learning setup. The learning algorithm learns by making use of a set of labeled examples and by posing queries to a knowledgeable
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Analysis of Driving Fatigue Characteristics in Cold and Hypoxia Environment of High-Altitude Areas. Big Data (IF 4.6) Pub Date : 2023-05-18 Lin Tian,Jueshuai Li,Yanfei Li
The cold and hypoxic environment at high altitudes can easily lead to driving fatigue. For improving highway safety in high-altitude areas, a driver fatigue test is conducted using the Kangtai PM-60A car heart rate and oxygen tester to collect drivers' heart rate oximetry in National Highway 214 in Qinghai Province. Standard deviation (SDNN), mean (M), coefficient of RR (two R heart rate waves), RR
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Small Files Problem Resolution via Hierarchical Clustering Algorithm. Big Data (IF 4.6) Pub Date : 2023-05-16 Oded Koren,Aviel Shamalov,Nir Perel
The Small Files Problem in Hadoop Distributed File System (HDFS) is an ongoing challenge that has not yet been solved. However, various approaches have been developed to tackle the obstacles this problem creates. Properly managing the size of blocks in a file system is essential as it saves memory and computing time and may reduce bottlenecks. In this article, a new approach using a Hierarchical Clustering
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Investment Recommender System Model Based on the Potential Investors' Key Decision Factors. Big Data (IF 4.6) Pub Date : 2023-05-08 Asefeh Asemi,Adeleh Asemi,Andrea Ko
In this research, we propose an automatic recommender system for providing investment-type suggestions offered to investors. This system is based on a new intelligent approach using an adaptive neuro-fuzzy inference system (ANFIS) that works with four potential investors' key decision factors (KDFs), which are system value, environmental awareness factors, the expectation of high return, and expectation
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An Autoregressive-Based Kalman Filter Approach for Daily PM2.5 Concentration Forecasting in Beijing, China. Big Data (IF 4.6) Pub Date : 2023-05-03 Xinyue Zhang,Chen Ding,Guizhi Wang
With the acceleration of urbanization, air pollution, especially PM2.5, has seriously affected human health and reduced people's life quality. Accurate PM2.5 prediction is significant for environmental protection authorities to take actions and develop prevention countermeasures. In this article, an adapted Kalman filter (KF) approach is presented to remove the nonlinearity and stochastic uncertainty
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Kriging, Polynomial Chaos Expansion, and Low-Rank Approximations in Material Science and Big Data Analytics. Big Data (IF 4.6) Pub Date : 2023-04-24 Golsa Mahdavi,Mohammad Amin Hariri-Ardebili
In material science and engineering, the estimation of material properties and their failure modes is associated with physical experiments followed by modeling and optimization. However, proper optimization is challenging and computationally expensive. The main reason is the highly nonlinear behavior of brittle materials such as concrete. In this study, the application of surrogate models to predict
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gtfs2net: Extraction of General Transit Feed Specification Data Sets to Abstract Networks and Their Analysis. Big Data (IF 4.6) Pub Date : 2023-04-24 Gergely Kocsis,Imre Varga
Mass transportation networks of cities or regions are interesting and important to be studied to get a picture of the properties of a somehow better topology and system of transportation. One way to do this lies on the basis of spatial information of stations and routes. As we show however interesting findings can be gained also if one studies the abstract network topologies of these systems. To get
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Generic User Behavior: A User Behavior Similarity-Based Recommendation Method. Big Data (IF 4.6) Pub Date : 2023-04-19 Zhengyang Hu,Weiwei Lin,Xiaoying Ye,Haojun Xu,Haocheng Zhong,Huikang Huang,Xinyang Wang
Recommender system (RS) plays an important role in Big Data research. Its main idea is to handle huge amounts of data to accurately recommend items to users. The recommendation method is the core research content of the whole RS. However, the existing recommendation methods still have the following two shortcomings: (1) Most recommendation methods use only one kind of information about the user's interaction
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Identifying Influential Nodes in Social Networks: Exploiting Self-Voting Mechanism. Big Data (IF 4.6) Pub Date : 2023-04-19 Panfeng Liu,Longjie Li,Yanhong Wen,Shiyu Fang
The influence maximization (IM) problem is defined as identifying a group of influential nodes in a network such that these nodes can affect as many nodes as possible. Due to its great significance in viral marketing, disease control, social recommendation, and so on, considerable efforts have been devoted to the development of methods to solve the IM problem. In the literature, VoteRank and its improved
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Preemptive Epidemic Information Transmission Model Using Nonreplication Edge Node Connectivity in Health Care Networks. Big Data (IF 4.6) Pub Date : 2023-04-19 Chandu Thota,Constandinos X Mavromoustakis,George Mastorakis
The reliability in medical data organization and transmission is eased with the inheritance of information and communication technologies in recent years. The growth of digital communication and sharing medium imposes the necessity for optimizing the accessibility and transmission of sensitive medical data to the end-users. In this article, the Preemptive Information Transmission Model (PITM) is introduced
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Pneumonia Detection Using Enhanced Convolutional Neural Network Model on Chest X-Ray Images. Big Data (IF 4.6) Pub Date : 2023-04-17 Shadi A Aljawarneh,Romesaa Al-Quraan
Pneumonia, caused by microorganisms, is a severely contagious disease that damages one or both the lungs of the patients. Early detection and treatment are typically favored to recover infected patients since untreated pneumonia can lead to major complications in the elderly (>65 years) and children (<5 years). The objectives of this work are to develop several models to evaluate big X-ray images (XRIs)
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Anomaly Detection in Automatic Meter Intelligence System Using Positive Unlabeled Learning and Multiple Symbolic Aggregate Approximation. Big Data (IF 4.6) Pub Date : 2023-04-10 Thi Ngoc Anh Nguyen,Hoai Thu Vu,Minh Tuan Dang,Dohyeun Kim,Anh Ngoc Le
With the development of automatic electrical devices in smart grids, the data generated by time and transmitted are vast and thus impossible to control consumption by humans. The problem of abnormal detection in power consumption is crucial in monitoring and controlling smart grids. This article proposes the detection of electrical meter anomalies by detecting abnormal patterns and learning unlabeled
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Importance of Community Engagement in Data Decision Making. Big Data (IF 4.6) Pub Date : 2023-04-10 Michael Crawford,Francisca Flores,Marynia Kolak,Amy Hawn Nelson,Malaika Simmons
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A Unified Training Process for Fake News Detection Based on Finetuned Bidirectional Encoder Representation from Transformers Model. Big Data (IF 4.6) Pub Date : 2023-03-22 Vijay Srinivas Tida,Sonya Hsu,Xiali Hei
An efficient fake news detector becomes essential as the accessibility of social media platforms increases rapidly. Previous studies mainly focused on designing the models solely based on individual data sets and might suffer from degradable performance. Therefore, developing a robust model for a combined data set with diverse knowledge becomes crucial. However, designing the model with a combined
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Predicting Churn in Online Games by Quantifying Diversity of Engagement. Big Data (IF 4.6) Pub Date : 2023-03-20 Idan Weiss,Dan Vilenchik
Understanding engagement patterns of users in online platforms, may it be games, online social networks, or academic websites, is a widely studied topic with many real-world applications and economic consequences. A holy grail in this area of research is to develop an automatic prediction algorithm for when a user is going to leave the platform and devise proper intervention. In this work, we study
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An Improved Dual-Channel Deep Q-Network Model for Tourism Recommendation. Big Data (IF 4.6) Pub Date : 2023-03-17 Shengbin Liang,Jiangyong Jin,Jia Ren,Wencai Du,Shenming Qu
Tourism recommendation results are affected by many factors. Traditional recommendation methods have problems such as low recommendation accuracy and lack of personalization due to sparse data. This article uses implicit features such as contextual information, time-series travel trajectories, and comment data to address these issues. First, the Long Short-Term Memory (LSTM) network is introduced as
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OzNet: A New Deep Learning Approach for Automated Classification of COVID-19 Computed Tomography Scans. Big Data (IF 4.6) Pub Date : 2023-03-16 Oznur Ozaltin,Ozgur Yeniay,Abdulhamit Subasi
Coronavirus disease 2019 (COVID-19) is spreading rapidly around the world. Therefore, the classification of computed tomography (CT) scans alleviates the workload of experts, whose workload increased considerably during the pandemic. Convolutional neural network (CNN) architectures are successful for the classification of medical images. In this study, we have developed a new deep CNN architecture
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An Improved Multiexposure Image Fusion Technique. Big Data (IF 4.6) Pub Date : 2023-03-16 Zulfiqar Nazish,Masood Siddiqui Adil,Ahmad Awais,Iqbal Waseem,Muhammad Imran,Imran Tauqir,Munir Wajiha
Multiexposure image fusion (MEF) is an effective approach to generate high dynamic range images from multilevel exposures taken from ordinary cameras. In this article, a novel MEF algorithm is proposed to gain maximum visual details as well as vivid colors from the captured scene. This algorithm first decomposes the input images with multiple exposures into the base and detail layer. The weights for
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Vertical and Horizontal Water Penetration Velocity Modeling in Nonhomogenous Soil Using Fast Multi-Output Relevance Vector Regression. Big Data (IF 4.6) Pub Date : 2023-03-14 Babak Vaheddoost,Shervin Rahimzadeh Arashloo,Mir Jafar Sadegh Safari
A joint determination of horizontal and vertical movement of water through porous medium is addressed in this study through fast multi-output relevance vector regression (FMRVR). To do this, an experimental data set conducted in a sand box with 300 × 300 × 150 mm dimensions made of Plexiglas is used. A random mixture of sand having size of 0.5-1 mm is used to simulate the porous medium. Within the
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Hybrid Generalized Regularized Extreme Learning Machine Through Gradient-Based Optimizer Model for Self-Cleansing Nondeposition with Clean Bed Mode of Sediment Transport. Big Data (IF 4.6) Pub Date : 2023-03-07 Enes Gul,Mir Jafar Sadegh Safari
Sediment transport modeling is an important problem to minimize sedimentation in open channels that could lead to unexpected operation expenses. From an engineering perspective, the development of accurate models based on effective variables involved for flow velocity computation could provide a reliable solution in channel design. Furthermore, validity of sediment transport models is linked to the
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Cloud-Based Advanced Shuffled Frog Leaping Algorithm for Tasks Scheduling. Big Data (IF 4.6) Pub Date : 2023-03-03 Dipesh Kumar,Nirupama Mandal,Yugal Kumar
In recent years, the world has seen incremental growth in online activities owing to which the volume of data in cloud servers has also been increasing exponentially. With rapidly increasing data, load on cloud servers has increased in the cloud computing environment. With rapidly evolving technology, various cloud-based systems were developed to enhance the user experience. But, the increased online
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Big Data Assurance: An Approach Based on Service-Level Agreements. Big Data (IF 4.6) Pub Date : 2023-03-02 Claudio A Ardagna,Nicola Bena,Cedric Hebert,Maria Krotsiani,Christos Kloukinas,George Spanoudakis
Big data management is a key enabling factor for enterprises that want to compete in the global market. Data coming from enterprise production processes, if properly analyzed, can provide a boost in the enterprise management and optimization, guaranteeing faster processes, better customer management, and lower overheads/costs. Guaranteeing a proper big data pipeline is the holy grail of big data, often
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Raw Electroencephalogram-Based Cognitive Workload Classification Using Directed and Nondirected Functional Connectivity Analysis and Deep Learning. Big Data (IF 4.6) Pub Date : 2023-02-27 Anmol Gupta,Ronnie Daniel,Akash Rao,Partha Pratim Roy,Sushil Chandra,Byung-Gyu Kim
With the phenomenal rise in internet-of-things devices, the use of electroencephalogram (EEG) based brain-computer interfaces (BCIs) can empower individuals to control equipment with thoughts. These allow BCI to be used and pave the way for pro-active health management and the development of internet-of-medical-things architecture. However, EEG-based BCIs have low fidelity, high variance, and EEG signals
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An Intelligent Channel Estimation Algorithm Based on Extended Model for 5G-V2X. Big Data (IF 4.6) Pub Date : 2023-02-27 Jie Huang,Cheng Xu,Zhaohua Ji,Shan Xiao,Teng Liu,Nan Ma,Qinghui Zhou
Car networking systems based on 5G-V2X (vehicle-to-everything) have high requirements for reliability and low-latency communication to further improve communication performance. In the V2X scenario, this article establishes an extended model (basic expansion model) suitable for high-speed mobile scenarios based on the sparsity of the channel impulse response. And propose a channel estimation algorithm
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Enhanced Neural Network-Based Univariate Time-Series Forecasting Model for Big Data. Big Data (IF 4.6) Pub Date : 2023-02-24 Suyel Namasudra,S Dhamodharavadhani,R Rathipriya,Ruben Gonzalez Crespo,Nageswara Rao Moparthi
Big data is a combination of large structured, semistructured, and unstructured data collected from various sources that must be processed before using them in many analytical applications. Anomalies or inconsistencies in big data refer to the occurrences of some data that are in some way unusual and do not fit the general patterns. It is considered one of the major problems of big data. Data trust
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Design of Fatigue Driving Behavior Detection Based on Circle Hough Transform. Big Data (IF 4.6) Pub Date : 2023-02-01 An Chi Huang,Chun Yuan,Sheng Hui Meng,Tian Jiun Huang
Chronic fatigue symptoms of jobs are risk factors that may cause errors and lead to occupational accidents. For instance, occupational injuries and traffic accidents stem from overlooking long-term fatigue. According to statistics for fatigue driving, it was found that fatigue driving is one of the main causes of traffic accidents. The resulting decrease in the quality of traffic, as well as impaired
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Applications of Bayesian Neural Networks in Outlier Detection Big Data (IF 4.6) Pub Date : 2023-01-27 Chen Tao
Anomaly detection is crucial in a variety of domains, such as fraud detection, disease diagnosis, and equipment defect detection. With the development of deep learning, anomaly detection with Bayesian neural networks (BNNs) becomes a novel research topic in recent years. This article aims to propose a widely applicable method of outlier detection (a category of anomaly detection) using BNNs based on
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Classifying of VN-Index Bullishness by Bayesian Inference Big Data (IF 4.6) Pub Date : 2023-01-20 Nam Anh Dao, Viet Bach Dao
Decision making in stock market is a movement in which investors gather information and carry out complex analysis to select options, based on market variations and investor's preferences. This involves the facts of risk of return, appreciating or depreciating of stock markets in value and dynamic circumstances. We present a design to study and discover bear and bull markets from macroeconomic variables
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HUTNet: An Efficient Convolutional Neural Network for Handwritten Uchen Tibetan Character Recognition Big Data (IF 4.6) Pub Date : 2023-01-19 Guowei Zhang, Weilan Wang, Ce Zhang, Penghai Zhao, Mingkai Zhang
Recognition of handwritten Uchen Tibetan characters input has been considered an efficient way of acquiring mass data in the digital era. However, it still faces considerable challenges due to seriously touching letters and various morphological features of identical characters. Thus, deeper neural networks are required to achieve decent recognition accuracy, making an efficient, lightweight model
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Prediction and Big Data Impact Analysis of Telecom Churn by Backpropagation Neural Network Algorithm from the Perspective of Business Model Big Data (IF 4.6) Pub Date : 2023-01-19 Jiabing Xu, Jiarui Liu, Tianen Yao, Yang Li
This study aims to transform the existing telecom operators from traditional Internet operators to digital-driven services, and improve the overall competitiveness of telecom enterprises. Data mining is applied to telecom user classification to process the existing telecom user data through data integration, cleaning, standardization, and transformation. Although the existing algorithms ensure the
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Multilevel Attention Networks and Policy Reinforcement Learning for Image Caption Generation Big Data (IF 4.6) Pub Date : 2022-12-07 Zhibo Zhou, Xiaoming Zhang, Zhoujun Li, Feiran Huang, Jie Xu
The analysis of large-scale multimodal data has become very popular recently. Image captioning, whose goal is to describe the content of image with natural language automatically, is an essential and challenging task in artificial intelligence. Commonly, most existing image caption methods utilize the mixture of Convolutional Neural Network and Recurrent Neural Network framework. These methods either
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A Hybrid Seasonal Autoregressive Integrated Moving Average and Denoising Autoencoder Model for Atmospheric Temperature Profile Prediction Big Data (IF 4.6) Pub Date : 2022-12-07 Xing Guo, Songling Zhu, Jiaji Wu
Atmospheric parameter profile plays a vital role in the studies on meteorology and quantitative remote sensing. Besides measurement of the radiosonde and satellite remote sensing, the atmospheric temperature profile can be predicted by the time series model. However, the performance of the time series model is limited by the weak correlation of temperature data in the stratosphere. In this study, to
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Improved Semantic Image Inpainting Method with Deep Convolution Generative Adversarial Networks Big Data (IF 4.6) Pub Date : 2022-12-07 Xiaoning Chen, Jian Zhao
With the development of generative adversarial networks (GANs), more and more researchers apply them to image inpainting technologies. However, many existing approaches caused some inpainting images to be unclear or even restore failures due to a failure to keep the consistency of the inpainted content and structures in line with the surroundings. In this article, we propose the Improved Semantic Image
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Dual Position Relationship Transformer for Image Captioning Big Data (IF 4.6) Pub Date : 2022-12-07 Yaohan Wang, Wenhua Qian, Rencan Nie, Dan Xu, Jinde Cao, Pyoungwon Kim
Employing feature vectors extracted from the target detector has been shown to be effective in improving the performance of image captioning. However, it is considered that existing framework suffers from the deficiency of insufficient information extraction, such as positional relationships; it is very important to judge the relationship between objects. To fill this gap, we present a dual position
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Creating Engines of Prosperity: Spatiotemporal Patterns and Factors Driving Urban Vitality in 36 Key Chinese Cities Big Data (IF 4.6) Pub Date : 2022-12-07 Zhao Liu, Fang Wang, Anrong Dang
Under the requirements of new urbanization in China, the improvement of urban spatial vitality has become a key aspect of the territory development plan. Based on the theory of urban vitality by Jacobs, this study analyzed the spatiotemporal characteristics of mobility, diversity, and regularity from urban vitality in 36 key Chinese cities from 1990 to 2015; the urban vitality was evaluated by considering
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Vehicle Intelligent Classification Based on Big Multimodal Data Analysis and Sparrow Search Optimization Big Data (IF 4.6) Pub Date : 2022-12-07 Caixing Shao, Fengxin Cheng, Sun Mao, Jian Hu
Vehicle intelligent classification plays a vital role in the Intelligent Transport Systems. However, due to the dynamic traffic environments, it is difficult to ensure the classification accuracy. Therefore, this article uses a new pulse coherent radar (PCR) to collect road vehicle data, and a vehicle classification method of sparrow search algorithm extreme learning machine (SSA-ELM) based on big
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A Robust Learning Algorithm Based on Particle Swarm Optimization for Pi-Sigma Artificial Neural Networks Big Data (IF 4.6) Pub Date : 2022-10-28 Eren Bas, Erol Egrioglu, Ufuk Yolcu, Mu-Yen Chen
Artificial neural networks (ANNs) have been frequently used in forecasting problems in recent years. One of the most popular types of ANNs in these days is Pi-Sigma artificial neural networks (PS-ANNs). PS-ANNs have a high order ANN structure and they use both multiplicative and additive neuron models in their architecture. PS-ANNs produce superior forecasting performance because of their high order
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Siamese-Based Architecture for Cross-Lingual Plagiarism Detection in English–Hindi Language Pairs Big Data (IF 4.6) Pub Date : 2022-10-18 Basant Agarwal, Mukesh Kumar Gupta, Harish Sharma, Ramesh Chandra Poonia
The cross-lingual plagiarism detection (CLPD) is a challenging problem in natural language processing. Cross-lingual plagiarism is when a text is translated from any other language and used as it is without proper acknowledgment. Most of the existing methods provide good results for monolingual plagiarism detection, whereas the performances of existing methods for the CLPD are very limited. The reason