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Artificial Intelligence and Cognitive Computing: Methods, Technologies, Systems, Applications and Policy Making

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (31 October 2019) | Viewed by 66226

Special Issue Editors

Prof. Dr. Miltiadis D. Lytras

E-Mail Website
Guest Editor
Computer Science Department, College of Engineering, Effat University, Jeddah, Saudi Arabia
Interests: cognitive computing; artificial intelligence; data science; bioinformatics; innovation; big data research; data mining; emerging technologies; information systems; technology driven innovation; knowledge management; semantic web
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Anna Visvizi

E-Mail Website
Guest Editor
1. Institute of International Studies (ISM), SGH Warsaw School of Economics, Al. Niepodległości 162, 02-554 Warsaw, Poland
2. Effat College of Business, Effat University, Jeddah 21551, Saudi Arabia
Interests: smart cities; smart villages; international political economy (IPE); information and communication technology (ICT)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cognitive computing has received increasing attention from academia and industries as it brings cognitive science and computing together for the development of new computational platforms, infrastructures, systems, and algorithms. Artificial intelligence and computational intelligence are key elements to success in cognitive computing.

To this end, the Guest Editors of this Special Issue seek papers that address, but are not limited to, the following issues related to the diverse aspects of artificial intelligence and cognitive computing research:

  • Artificial Intelligence and Cognitive Computing Topics
    • Cognitive innovations;
    • Artificial intelligence and cognitive computing based on deep learning and reinforcement algorithms;
    • Artificial intelligence and cognitive computing approaches to crafting, evaluating, and intervening for immersive, networked user experiences;
    • Artificial intelligence and cognitive computing for smart cities research;
    • Artificial intelligence and cognitive computing for computational social science theory and applications;
    • Artificial intelligence and cognitive computing for integrating analytics with online texts, courseware, and learning environments to measure student progress and interaction;
    • Data analytics platform for detailed reporting, assessment, and collaboration;
    • Visual analytics to identify patterns and processes for mining large educational datasets;
    • New business models for next-generation AI;
    • Context awareness;
    • Advanced locomotion and navigation;
    • Machine learning approaches for advanced decision making.
  • Artificial Intelligence and Cognitive Computing Technologies
    • Semantics web and cognitive computing;
    • Artificial intelligence/computational intelligence in cognitive computing;
    • Emerging platforms, infrastructures, and systems;
    • Sophisticated reasoning/natural language processing/speech recognition/human–computer interaction;
    • Optimization design in cognitive computing.
  • Artificial Intelligence and Cognitive Computing/industry Applications
    • Healthcare;
    • Education;
    • Sustainability;
    • Food science.
  • Policy Making on AI and Cognitive Computing

Prof. Miltiadis D. Lytras
Prof. Anna Visvizi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Artificial intelligence
  • Cognitive computing
  • Machine learning
  • Deep learning
  • Big data
  • Data analytics
  • Visual analytics
  • Case studies
  • Conceptual approaches
  • International collaboration

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (15 papers)

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Editorial

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3 pages, 164 KiB  
Editorial
Artificial Intelligence and Cognitive Computing: Methods, Technologies, Systems, Applications and Policy Making
by Miltiadis D. Lytras and Anna Visvizi
Sustainability 2021, 13(7), 3598; https://doi.org/10.3390/su13073598 - 24 Mar 2021
Cited by 23 | Viewed by 3929
Abstract
n/a Full article
(This article belongs to the Special Issue Artificial Intelligence and Cognitive Computing: Methods, Technologies, Systems, Applications and Policy Making)

Research

Jump to: Editorial

16 pages, 5637 KiB  
Article
Application of Machine Learning in Evaluation of the Static Young’s Modulus for Sandstone Formations
by Ahmed Abdulhamid Mahmoud, Salaheldin Elkatatny and Dhafer Al Shehri
Sustainability 2020, 12(5), 1880; https://doi.org/10.3390/su12051880 - 2 Mar 2020
Cited by 36 | Viewed by 3821
Abstract
Prediction of the mechanical characteristics of the reservoir formations, such as static Young’s modulus (Estatic), is very important for the evaluation of the wellbore stability and development of the earth geomechanical model. Estatic considerably varies with the change in the [...] Read more.
Prediction of the mechanical characteristics of the reservoir formations, such as static Young’s modulus (Estatic), is very important for the evaluation of the wellbore stability and development of the earth geomechanical model. Estatic considerably varies with the change in the lithology. Therefore, a robust model for Estatic prediction is needed. In this study, the predictability of Estatic for sandstone formation using four machine learning models was evaluated. The design parameters of the machine learning models were optimized to improve their predictability. The machine learning models were trained to estimate Estatic based on bulk formation density, compressional transit time, and shear transit time. The machine learning models were trained and tested using 592 well log data points and their corresponding core-derived Estatic values collected from one sandstone formation in well-A and then validated on 38 data points collected from a sandstone formation in well-B. Among the machine learning models developed in this work, Mamdani fuzzy interference system was the highly accurate model to predict Estatic for the validation data with an average absolute percentage error of only 1.56% and R of 0.999. The developed static Young’s modulus prediction models could help the new generation to characterize the formation rock with less cost and safe operation. Full article
(This article belongs to the Special Issue Artificial Intelligence and Cognitive Computing: Methods, Technologies, Systems, Applications and Policy Making)
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18 pages, 1406 KiB  
Article
LSTM-Based Deep Learning Model for Predicting Individual Mobility Traces of Short-Term Foreign Tourists
by Alessandro Crivellari and Euro Beinat
Sustainability 2020, 12(1), 349; https://doi.org/10.3390/su12010349 - 1 Jan 2020
Cited by 46 | Viewed by 7809
Abstract
The increasing availability of trajectory recordings has led to the mining of a massive amount of historical track data, allowing for a better understanding of travel behaviors by revealing meaningful motion patterns. In the context of human mobility analysis, the problem of motion [...] Read more.
The increasing availability of trajectory recordings has led to the mining of a massive amount of historical track data, allowing for a better understanding of travel behaviors by revealing meaningful motion patterns. In the context of human mobility analysis, the problem of motion prediction assumes a central role and is beneficial for a wide range of applications, including for touristic purposes, such as personalized services or targeted recommendations, and sustainability studies related to crowd management and resource redistribution. This paper tackles a particular case of the trajectory prediction problem, focusing on large-scale mobility traces of short-term foreign tourists. These sparse trajectories, short and non-repetitive, lack spatial and temporal regularity, making prediction analysis based on individual historical motion data unreliable. To face this issue, we hereby propose a deep learning-based approach, taking into account the collective mobility of tourists over the territory. The underlying semantics of motion patterns are captured by means of a long short-term memory (LSTM) neural network model trained on pre-processed location sequences, aiming to predict the next visited place in the trajectory. We tested the methodology on a real-world big dataset, demonstrating its higher feasibility with respect to traditional approaches. Full article
(This article belongs to the Special Issue Artificial Intelligence and Cognitive Computing: Methods, Technologies, Systems, Applications and Policy Making)
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18 pages, 2204 KiB  
Article
Entropy-Based Face Recognition and Spoof Detection for Security Applications
by Francisco A. Pujol, María José Pujol, Carlos Rizo-Maestre and Mar Pujol
Sustainability 2020, 12(1), 85; https://doi.org/10.3390/su12010085 - 20 Dec 2019
Cited by 16 | Viewed by 4416
Abstract
Nowadays, cyber attacks are becoming an extremely serious issue, which is particularly important to prevent in a smart city context. Among cyber attacks, spoofing is an action that is increasingly common in many areas, such as emails, geolocation services or social networks. Identity [...] Read more.
Nowadays, cyber attacks are becoming an extremely serious issue, which is particularly important to prevent in a smart city context. Among cyber attacks, spoofing is an action that is increasingly common in many areas, such as emails, geolocation services or social networks. Identity spoofing is defined as the action by which a person impersonates a third party to carry out a series of illegal activities such as committing fraud, cyberbullying, sextorsion, etc. In this work, a face recognition system is proposed, with an application to the spoofing prevention. The method is based on the Histogram of Oriented Gradients (HOG) descriptor. Since different face regions do not have the same information for the recognition process, introducing entropy would quantify the importance of each face region in the descriptor. Therefore, entropy is added to increase the robustness of the algorithm. Regarding face recognition, our approach has been tested on three well-known databases (ORL, FERET and LFW) and the experiments show that adding entropy information improves the recognition rate significantly, with an increase over 40% in some of the considered databases. Spoofing tests has been implemented on CASIA FASD and MIFS databases, having obtained again better results than similar texture descriptors approaches. Full article
(This article belongs to the Special Issue Artificial Intelligence and Cognitive Computing: Methods, Technologies, Systems, Applications and Policy Making)
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16 pages, 3275 KiB  
Article
Intelligent Prediction of Minimum Miscibility Pressure (MMP) During CO2 Flooding Using Artificial Intelligence Techniques
by Amjed Hassan, Salaheldin Elkatatny and Abdulazeez Abdulraheem
Sustainability 2019, 11(24), 7020; https://doi.org/10.3390/su11247020 - 9 Dec 2019
Cited by 32 | Viewed by 4302
Abstract
Carbon dioxide (CO2) injection is one of the most effective methods for improving hydrocarbon recovery. The minimum miscibility pressure (MMP) has a great effect on the performance of CO2 flooding. Several methods are used to determine the MMP, including slim [...] Read more.
Carbon dioxide (CO2) injection is one of the most effective methods for improving hydrocarbon recovery. The minimum miscibility pressure (MMP) has a great effect on the performance of CO2 flooding. Several methods are used to determine the MMP, including slim tube tests, analytical models and empirical correlations. However, the experimental measurements are costly and time-consuming, and the mathematical models might lead to significant estimation errors. This paper presents a new approach for determining the MMP during CO2 flooding using artificial intelligent (AI) methods. In this work, reliable models are developed for calculating the minimum miscibility pressure of carbon dioxide (CO2-MMP). Actual field data were collected; 105 case studies of CO2 flooding in anisotropic and heterogeneous reservoirs were used to build and evaluate the developed models. The CO2-MMP is determined based on the hydrocarbon compositions, reservoir conditions and the volume of injected CO2. An artificial neural network, radial basis function, generalized neural network and fuzzy logic system were used to predict the CO2-MMP. The models’ reliability was compared with common determination methods; the developed models outperform the current CO2-MMP methods. The presented models showed a very acceptable performance: the absolute error was 6.6% and the correlation coefficient was 0.98. The developed models can minimize the time and cost of determining the CO2-MMP. Ultimately, this work will improve the design of CO2 flooding operations by providing a reliable value for the CO2-MMP. Full article
(This article belongs to the Special Issue Artificial Intelligence and Cognitive Computing: Methods, Technologies, Systems, Applications and Policy Making)
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18 pages, 3968 KiB  
Article
Comparative Analysis between International Research Hotspots and National-Level Policy Keywords on Artificial Intelligence in China from 2009 to 2018
by Jie Gao, Xinping Huang and Lili Zhang
Sustainability 2019, 11(23), 6574; https://doi.org/10.3390/su11236574 - 21 Nov 2019
Cited by 18 | Viewed by 6929
Abstract
In the last decade, artificial intelligence (AI) has undergone many important developments in China and has risen to the level of national strategy, which is closely related to the areas of research and policy promotion. The interactive relationship between the hotspots of China’s [...] Read more.
In the last decade, artificial intelligence (AI) has undergone many important developments in China and has risen to the level of national strategy, which is closely related to the areas of research and policy promotion. The interactive relationship between the hotspots of China’s international AI research and its national-level policy keywords is the basis for further clarification and reference in academics and political circles. There has been very little research on the interaction between academic research and policy making. Understanding the relationship between the content of academic research and the content emphasized by actual operational policy will help scholars to better apply research to practice, and help decision-makers to manage effectively. Based on 3577 English publications about AI published by Chinese scholars in 2009–2018, and 262 Chinese national-level policy documents published during this period, this study carried out scientometric analysis and quantitative analysis of policy documents through the knowledge maps of AI international research hotspots in China and the co-occurrence maps of Chinese policy keywords, and conducted a comparative analysis that divided China’s AI development into three stages: the initial exploration stage, the steady rising stage, and the rapid development stage. The studies showed that in the initial exploration stage (2009–2012), research hotspots and policy keywords had a certain alienation relationship; in the steady rising stage (2013–2015), research hotspots focused more on cutting-edge technologies and policy keywords focused more on macro-guidance, and the relationship began to become close; and in the rapid development stage (2016–2018), the research hotspots and policy keywords became closely integrated, and they were mutually infiltrated and complementary, thus realizing organic integration and close connection. Through comparative analysis between international research hotspots and national-level policy keywords on AI in China from 2009 to 2018, the development of AI in China was revealed to some extent, along with the interaction between academics and politics in the past ten years, which is of great significance for the sustainable development and effective governance of China’s artificial intelligence. Full article
(This article belongs to the Special Issue Artificial Intelligence and Cognitive Computing: Methods, Technologies, Systems, Applications and Policy Making)
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14 pages, 1947 KiB  
Article
Application of Artificial Intelligence Techniques to Predict the Well Productivity of Fishbone Wells
by Amjed Hassan, Salaheldin Elkatatny and Abdulazeez Abdulraheem
Sustainability 2019, 11(21), 6083; https://doi.org/10.3390/su11216083 - 1 Nov 2019
Cited by 19 | Viewed by 3695
Abstract
Fishbone multilateral wells are applied to enhance well productivity by increasing the contact area between the bottomhole and reservoir region. Fishbone wells are characterized by reduced operational time and a competitive cost in comparison to hydraulic fracturing operations. However, limited models are reported [...] Read more.
Fishbone multilateral wells are applied to enhance well productivity by increasing the contact area between the bottomhole and reservoir region. Fishbone wells are characterized by reduced operational time and a competitive cost in comparison to hydraulic fracturing operations. However, limited models are reported to determine the productivity of fishbone wells. In this paper, several artificial intelligence methods were applied to estimate the performance of fishbone wells producing from a heterogeneous and anisotropic gas reservoir. The well productivity was determined using an artificial neural network, a fuzzy logic system and a radial basis network. The models were developed and validated utilizing 250 data sets, with the inputs being the permeability ratio (Kh/Kv), flowing bottomhole pressure and lateral length. The results showed that the artificial intelligence models were able to predict the fishbone well productivity with an acceptable absolute error of 7.23%. Moreover, a mathematical equation was extracted from the artificial neural network, which is able to provide a simple and direct estimation of fishbone well productivity. Actual flow tests were used to evaluate the reliability of the developed model, and a very acceptable match was obtained between the predicted and actual flow rates, wherein an absolute error of 6.92% was achieved. This paper presents effective models for determining the well performance of complex multilateral wells producing from heterogeneous reservoirs. The developed models will help to reduce the uncertainty associated with numerical methods, and the extracted equation can be inserted into commercial software, thereby significantly reducing deviation between the actual data and simulated results. Full article
(This article belongs to the Special Issue Artificial Intelligence and Cognitive Computing: Methods, Technologies, Systems, Applications and Policy Making)
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15 pages, 3307 KiB  
Article
Evaluation of the Total Organic Carbon (TOC) Using Different Artificial Intelligence Techniques
by Ahmed Abdulhamid Mahmoud, Salaheldin Elkatatny, Abdulwahab Z. Ali, Mohamed Abouelresh and Abdulazeez Abdulraheem
Sustainability 2019, 11(20), 5643; https://doi.org/10.3390/su11205643 - 13 Oct 2019
Cited by 48 | Viewed by 5281
Abstract
Total organic carbon (TOC) is an essential parameter used in unconventional shale resources evaluation. Current methods that are used for TOC estimation are based, either on conducting time-consuming laboratory experiments, or on using empirical correlations developed for specific formations. In this study, four [...] Read more.
Total organic carbon (TOC) is an essential parameter used in unconventional shale resources evaluation. Current methods that are used for TOC estimation are based, either on conducting time-consuming laboratory experiments, or on using empirical correlations developed for specific formations. In this study, four artificial intelligence (AI) models were developed to estimate the TOC using conventional well logs of deep resistivity, gamma-ray, sonic transit time, and bulk density. These models were developed based on the Takagi-Sugeno-Kang fuzzy interference system (TSK-FIS), Mamdani fuzzy interference system (M-FIS), functional neural network (FNN), and support vector machine (SVM). Over 800 data points of the conventional well logs and core data collected from Barnett shale were used to train and test the AI models. The optimized AI models were validated using unseen data from Devonian shale. The developed AI models showed accurate predictability of TOC in both Barnett and Devonian shale. FNN model overperformed others in estimating TOC for the validation data with average absolute percentage error (AAPE) and correlation coefficient (R) of 12.02%, and 0.879, respectively, followed by M-FIS and SVM, while TSK-FIS model showed the lowest predictability of TOC, with AAPE of 15.62% and R of 0.832. All AI models overperformed Wang models, which have recently developed to evaluate the TOC for Devonian formation. Full article
(This article belongs to the Special Issue Artificial Intelligence and Cognitive Computing: Methods, Technologies, Systems, Applications and Policy Making)
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22 pages, 7505 KiB  
Article
A Hybrid Artificial Intelligence Model to Predict the Elastic Behavior of Sandstone Rocks
by Ahmed Gowida, Tamer Moussa, Salaheldin Elkatatny and Abdulwahab Ali
Sustainability 2019, 11(19), 5283; https://doi.org/10.3390/su11195283 - 25 Sep 2019
Cited by 22 | Viewed by 3245
Abstract
Rock mechanical properties play a key role in the optimization process of engineering practices in the oil and gas industry so that better field development decisions can be made. Estimation of these properties is central in well placement, drilling programs, and well completion [...] Read more.
Rock mechanical properties play a key role in the optimization process of engineering practices in the oil and gas industry so that better field development decisions can be made. Estimation of these properties is central in well placement, drilling programs, and well completion design. The elastic behavior of rocks can be studied by determining two main parameters: Young’s modulus and Poisson’s ratio. Accurate determination of the Poisson’s ratio helps to estimate the in-situ horizontal stresses and in turn, avoid many critical problems which interrupt drilling operations, such as pipe sticking and wellbore instability issues. Accurate Poisson’s ratio values can be experimentally determined using retrieved core samples under simulated in-situ downhole conditions. However, this technique is time-consuming and economically ineffective, requiring the development of a more effective technique. This study has developed a new generalized model to estimate static Poisson’s ratio values of sandstone rocks using a supervised artificial neural network (ANN). The developed ANN model uses well log data such as bulk density and sonic log as the input parameters to target static Poisson’s ratio values as outputs. Subsequently, the developed ANN model was transformed into a more practical and easier to use white-box mode using an ANN-based empirical equation. Core data (692 data points) and their corresponding petrophysical data were used to train and test the ANN model. The self-adaptive differential evolution (SADE) algorithm was used to fine-tune the parameters of the ANN model to obtain the most accurate results in terms of the highest correlation coefficient (R) and the lowest mean absolute percentage error (MAPE). The results obtained from the optimized ANN model show an excellent agreement with the laboratory measured static Poisson’s ratio, confirming the high accuracy of the developed model. A comparison of the developed ANN-based empirical correlation with the previously developed approaches demonstrates the superiority of the developed correlation in predicting static Poisson’s ratio values with the highest R and the lowest MAPE. The developed correlation performs in a manner far superior to other approaches when validated against unseen field data. The developed ANN-based mathematical model can be used as a robust tool to estimate static Poisson’s ratio without the need to run the ANN model. Full article
(This article belongs to the Special Issue Artificial Intelligence and Cognitive Computing: Methods, Technologies, Systems, Applications and Policy Making)
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18 pages, 4373 KiB  
Article
Real-Time Prediction of the Rheological Properties of Water-Based Drill-In Fluid Using Artificial Neural Networks
by Salaheldin Elkatatny
Sustainability 2019, 11(18), 5008; https://doi.org/10.3390/su11185008 - 12 Sep 2019
Cited by 24 | Viewed by 3483
Abstract
The rheological properties of drilling fluids are the key parameter for optimizing drilling operation and reducing total drilling cost by avoiding common problems such as hole cleaning, pipe sticking, loss of circulation, and well control. The conventional method of measuring the rheological properties [...] Read more.
The rheological properties of drilling fluids are the key parameter for optimizing drilling operation and reducing total drilling cost by avoiding common problems such as hole cleaning, pipe sticking, loss of circulation, and well control. The conventional method of measuring the rheological properties are time-consuming and require a high effort for equipment cleaning, so they are only measured twice a day. There is a need to develop an automated system to measure the rheological properties in real-time based on the frequent measurements of mud density, Marsh funnel time, and solid percent. The main objective of this paper is to apply a modified self-adaptive differential evolution technique to determine the optimum combination of an artificial neural network’s variables to precisely predict the rheological properties of water-based drill-in fluid using the frequent measuring of mud density, Marsh funnel time, and solid percent. The second objective is whitening the black box of an artificial neural network by developing five new empirical correlations to determine the rheological properties without the need for the artificial neural network models. Actual field measurements (900 data points) were used to train, test, and validate the artificial neural network models and the developed empirical correlations. The optimization process illustrated that the best training function was Bayesian regularization backpropagation (trainbr), and the best transferring function was Elliot symmetric sigmoid (elliotsig). The optimum number of neurons was 30 for the plastic viscosity and the flow consistency index, while it was 29 for apparent viscosity, yield point, and the flow behavior index. The developed artificial neural network models and empirical correlations predicted the rheological properties with high accuracy. The correlation coefficient (R) was more than 90%, and the average absolute percentage error was less than 8.6%. The new technique for rheological properties estimation is an example of the new development which will help the new generation to discover and extract oil and gas with less cost and with safer operations. Full article
(This article belongs to the Special Issue Artificial Intelligence and Cognitive Computing: Methods, Technologies, Systems, Applications and Policy Making)
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16 pages, 2994 KiB  
Article
Development of Output Correction Methodology for Long Short Term Memory-Based Speech Recognition
by Recep Sinan Arslan and Necaattin Barışçı
Sustainability 2019, 11(15), 4250; https://doi.org/10.3390/su11154250 - 6 Aug 2019
Cited by 16 | Viewed by 4092
Abstract
This paper presents a correction methodology for Long Short Term Memory (LSTM) based speech recognition. A strategy that validates with a reference database was developed for LSTM. It is conceptually simple but requires a large keyword database to match test templates. The correction [...] Read more.
This paper presents a correction methodology for Long Short Term Memory (LSTM) based speech recognition. A strategy that validates with a reference database was developed for LSTM. It is conceptually simple but requires a large keyword database to match test templates. The correction method is based on the “most matching method” that is finding the word in which the system output is closest among the “Referenced Template Database”. Each LSTM model recognition output was corrected with the proposed new concept. Thus, system recognition performance was improved by correcting faulty outputs. The effectiveness, efficiency, and contribution of this approach to system performance were demonstrated by experiments. Tests carried out using different speech-text datasets and LSTM models yielded an average performance increase of 2.25%. With some advanced models, this ratio rises to 3.84%. Full article
(This article belongs to the Special Issue Artificial Intelligence and Cognitive Computing: Methods, Technologies, Systems, Applications and Policy Making)
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28 pages, 24627 KiB  
Article
AirInsight: Visual Exploration and Interpretation of Latent Patterns and Anomalies in Air Quality Data
by Huijie Zhang, Ke Ren, Yiming Lin, Dezhan Qu and Zhenxin Li
Sustainability 2019, 11(10), 2944; https://doi.org/10.3390/su11102944 - 23 May 2019
Cited by 7 | Viewed by 3598
Abstract
Nowadays, huge volume of air quality data provides unprecedented opportunities for analyzing pollution. However, due to the high complexity, most traditional analytical methods focus on abstracting data, so these techniques discard the original structure and limit the understanding of the results. Visual analysis [...] Read more.
Nowadays, huge volume of air quality data provides unprecedented opportunities for analyzing pollution. However, due to the high complexity, most traditional analytical methods focus on abstracting data, so these techniques discard the original structure and limit the understanding of the results. Visual analysis is a powerful technique for exploring unknown patterns since it retains the details of the original data and gives visual feedback to users. In this paper, we focus on air quality data and propose the AirInsight design, an interactive visual analytic system for recognizing, exploring, and summarizing regular patterns, as well as detecting, classifying, and interpreting abnormal cases. Based on the time-varying and multivariate features of air quality data, a dimension reduction method Composite Least Square Projection (CLSP) is proposed, which allows appreciating and interpreting the data patterns in the context of attributes. On the basis of the observed regular patterns, multiple abnormal cases are further detected, including the multivariate anomalies by the proposed Noise Hierarchical Clustering (NHC) method, abruptly changing timestamps by Time diversity (TD) indicator, and cities with unique patterns by the Geographical Surprise (GS) measure. Moreover, we combine TD and GS to group anomalies based on their underlying spatiotemporal correlations. AirInsight includes multiple coordinated views and rich interactive functions to provide contextual information from different aspects and facilitate a comprehensive understanding. In particular, a pair of glyphs are designed that provide a visual representation of the temporal variation in air quality conditions for a user-selected city. Experiments show that CLSP improves the accuracy of Least Square Projection (LSP) and that NHC has the ability to separate noises. Meanwhile, several case studies and task-based user evaluation demonstrate that our system is effective and practical for exploring and interpreting multivariate spatiotemporal patterns and anomalies in air quality data. Full article
(This article belongs to the Special Issue Artificial Intelligence and Cognitive Computing: Methods, Technologies, Systems, Applications and Policy Making)
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15 pages, 1128 KiB  
Article
A Hybrid Model for Addressing the Relationship between Financial Performance and Sustainable Development
by Yanfang Zhang and Mushang Lee
Sustainability 2019, 11(10), 2899; https://doi.org/10.3390/su11102899 - 22 May 2019
Cited by 3 | Viewed by 2864
Abstract
Measuring financial performance has become an essential topic due to the potential decimating impacts on the corporation itself as well as to whole societies during financial turmoil. In order to provide an overarching description of the multidimensional nature for measuring a corporation’s operations, [...] Read more.
Measuring financial performance has become an essential topic due to the potential decimating impacts on the corporation itself as well as to whole societies during financial turmoil. In order to provide an overarching description of the multidimensional nature for measuring a corporation’s operations, it is preferable to employ data envelopment analysis (DEA). Different from prior research that merely focuses on a singular DEA performance rank, this study extends it to multiple DEA specifications (i.e., it combines inputs and outputs in several different ways) so as to make judgments more complete and robust. We also execute fuzzy visualization technique (i.e., nonlinear fuzzy robust principal component analysis, NFRPCA) to represent the main characteristics of data so that non-specialists can have better access to the results. The analyzed result is then fed into the restricted Boltzmann machine (RBM) to establish a model to forecast a firm’s operating performance. Even a fraction of accuracy improvement can result in considerable future savings to a firm and investors. When examined using real cases, the model is a promising alternative for operating performance forecasting and can assist both internal and external market participants. Full article
(This article belongs to the Special Issue Artificial Intelligence and Cognitive Computing: Methods, Technologies, Systems, Applications and Policy Making)
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26 pages, 9469 KiB  
Article
A Hybrid Unequal Clustering Based on Density with Energy Conservation in Wireless Nodes
by Tao Han, Seyed Mostafa Bozorgi, Ayda Valinezhad Orang, Ali Asghar Rahmani Hosseinabadi, Arun Kumar Sangaiah and Mu-Yen Chen
Sustainability 2019, 11(3), 746; https://doi.org/10.3390/su11030746 - 31 Jan 2019
Cited by 21 | Viewed by 3503
Abstract
The Internet of things (IoT) provides the possibility of communication between smart devices and any object at any time. In this context, wireless nodes play an important role in reducing costs and simple use. Since these nodes are often used in less accessible [...] Read more.
The Internet of things (IoT) provides the possibility of communication between smart devices and any object at any time. In this context, wireless nodes play an important role in reducing costs and simple use. Since these nodes are often used in less accessible locations, recharging their battery is hardly feasible and in some cases is practically impossible. Hence, energy conservation within each node is a challenging discussion. Clustering is an efficient solution to increase the lifetime of the network and reduce the energy consumption of the nodes. In this paper, a novel hybrid unequal multi-hop clustering based on density (HCD) is proposed to increase the network lifetime. In the proposed protocol, the cluster head (CH) selection is performed only by comparing the status of each node to its neighboring nodes. In this new technique, the parameters involving energy of nodes, the number of neighboring nodes, the distance to the base station (BS), and the layer where the node is placed in are considered in CH selection. So, in this new and simple technique considers energy consumption of the network and load balancing. Clustering is performed unequally so that cluster heads (CHs) close to BS have more energy for data relay. Also, a hybrid dynamic–static clustering was performed to decrease overhead. In the current protocol, a distributed clustering and multi-hop routing approach was applied between cluster members (CMs), to CHs, and CHs to BS. HCD is applied as a novel assistance to cluster heads (ACHs) mechanism, in a way that a CH accepts to use member nodes with suitable state to share traffic load. Furthermore, we performed simulation for two different scenarios. Simulation results showed the reliability of the proposed method as it was resulted in a significant increase in network stability and energy balance as well as network lifetime and efficiency. Full article
(This article belongs to the Special Issue Artificial Intelligence and Cognitive Computing: Methods, Technologies, Systems, Applications and Policy Making)
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21 pages, 983 KiB  
Article
Decision-Making Method based on Mixed Integer Linear Programming and Rough Set: A Case Study of Diesel Engine Quality and Assembly Clearance Data
by Wenbing Chang, Xinglong Yuan, Yalong Wu, Shenghan Zhou, Jingsong Lei and Yiyong Xiao
Sustainability 2019, 11(3), 620; https://doi.org/10.3390/su11030620 - 24 Jan 2019
Cited by 4 | Viewed by 3460
Abstract
The purpose of this paper is to establish a decision-making system for assembly clearance parameters and machine quality level by analyzing the data of assembly clearance parameters of diesel engine. Accordingly, we present an extension of the rough set theory based on mixed-integer [...] Read more.
The purpose of this paper is to establish a decision-making system for assembly clearance parameters and machine quality level by analyzing the data of assembly clearance parameters of diesel engine. Accordingly, we present an extension of the rough set theory based on mixed-integer linear programming (MILP) for rough set-based classification (MILP-FRST). Traditional rough set theory has two shortcomings. First, it is sensitive to noise data, resulting in a low accuracy of decision systems based on rough sets. Second, in the classification problem based on rough sets, the attributes cannot be automatically determined. MILP-FRST has the advantages of MILP in resisting noisy data and has the ability to select attributes flexibly and automatically. In order to prove the validity and advantages of the proposed model, we used the machine quality data and assembly clearance data of 29 diesel engines of a certain type to validate the proposed model. Experiments show that the proposed decision-making method based on MILP-FRST model can accurately determine the quality level of the whole machine according to the assembly clearance parameters. Full article
(This article belongs to the Special Issue Artificial Intelligence and Cognitive Computing: Methods, Technologies, Systems, Applications and Policy Making)
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