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Artificial Intelligence in Biological Sciences. Life (Basel) 2022; 12:life12091430. [PMID: 36143468 PMCID: PMC9505413 DOI: 10.3390/life12091430] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 08/25/2022] [Accepted: 09/10/2022] [Indexed: 12/03/2022] Open
Abstract
Artificial intelligence (AI), currently a cutting-edge concept, has the potential to improve the quality of life of human beings. The fields of AI and biological research are becoming more intertwined, and methods for extracting and applying the information stored in live organisms are constantly being refined. As the field of AI matures with more trained algorithms, the potential of its application in epidemiology, the study of host–pathogen interactions and drug designing widens. AI is now being applied in several fields of drug discovery, customized medicine, gene editing, radiography, image processing and medication management. More precise diagnosis and cost-effective treatment will be possible in the near future due to the application of AI-based technologies. In the field of agriculture, farmers have reduced waste, increased output and decreased the amount of time it takes to bring their goods to market due to the application of advanced AI-based approaches. Moreover, with the use of AI through machine learning (ML) and deep-learning-based smart programs, one can modify the metabolic pathways of living systems to obtain the best possible outputs with the minimal inputs. Such efforts can improve the industrial strains of microbial species to maximize the yield in the bio-based industrial setup. This article summarizes the potentials of AI and their application to several fields of biology, such as medicine, agriculture, and bio-based industry.
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Research on Rice Yield Prediction Model Based on Deep Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:1922561. [PMID: 35515497 PMCID: PMC9064530 DOI: 10.1155/2022/1922561] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 03/18/2022] [Accepted: 03/24/2022] [Indexed: 11/30/2022]
Abstract
Food is the paramount necessity of the people. With the progress of society and the improvement of social welfare system, the living standards of people all over the world are constantly improving. The development of medical industry improves people's health level constantly, and the world population is constantly climbing to a new peak. With the continuous development of deep learning in recent years, its advantages are constantly displayed, especially in the aspect of image recognition and processing, it drives into the distance. Thanks to the superiority of deep learning in image processing, the combination of remote sensing images and deep learning has attracted more attention. To simulate the four key factors of rice yield, this article tries a regression model with a combination of various characteristic independent variables. In this article, the selection of the best linear and nonlinear regression models is discussed, the prediction performance and significance of each regression model are analyzed, and some thoughts are given on estimation of actual rice yield.
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Digital Transformation in Smart Farm and Forest Operations Needs Human-Centered AI: Challenges and Future Directions. SENSORS 2022; 22:s22083043. [PMID: 35459028 PMCID: PMC9029836 DOI: 10.3390/s22083043] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 04/06/2022] [Accepted: 04/13/2022] [Indexed: 02/01/2023]
Abstract
The main impetus for the global efforts toward the current digital transformation in almost all areas of our daily lives is due to the great successes of artificial intelligence (AI), and in particular, the workhorse of AI, statistical machine learning (ML). The intelligent analysis, modeling, and management of agricultural and forest ecosystems, and of the use and protection of soils, already play important roles in securing our planet for future generations and will become irreplaceable in the future. Technical solutions must encompass the entire agricultural and forestry value chain. The process of digital transformation is supported by cyber-physical systems enabled by advances in ML, the availability of big data and increasing computing power. For certain tasks, algorithms today achieve performances that exceed human levels. The challenge is to use multimodal information fusion, i.e., to integrate data from different sources (sensor data, images, *omics), and explain to an expert why a certain result was achieved. However, ML models often react to even small changes, and disturbances can have dramatic effects on their results. Therefore, the use of AI in areas that matter to human life (agriculture, forestry, climate, health, etc.) has led to an increased need for trustworthy AI with two main components: explainability and robustness. One step toward making AI more robust is to leverage expert knowledge. For example, a farmer/forester in the loop can often bring in experience and conceptual understanding to the AI pipeline—no AI can do this. Consequently, human-centered AI (HCAI) is a combination of “artificial intelligence” and “natural intelligence” to empower, amplify, and augment human performance, rather than replace people. To achieve practical success of HCAI in agriculture and forestry, this article identifies three important frontier research areas: (1) intelligent information fusion; (2) robotics and embodied intelligence; and (3) augmentation, explanation, and verification for trusted decision support. This goal will also require an agile, human-centered design approach for three generations (G). G1: Enabling easily realizable applications through immediate deployment of existing technology. G2: Medium-term modification of existing technology. G3: Advanced adaptation and evolution beyond state-of-the-art.
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Deep Prediction Model Based on Dual Decomposition with Entropy and Frequency Statistics for Nonstationary Time Series. ENTROPY 2022; 24:e24030360. [PMID: 35327871 PMCID: PMC8947407 DOI: 10.3390/e24030360] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 01/14/2022] [Accepted: 02/25/2022] [Indexed: 12/25/2022]
Abstract
The prediction of time series is of great significance for rational planning and risk prevention. However, time series data in various natural and artificial systems are nonstationary and complex, which makes them difficult to predict. An improved deep prediction method is proposed herein based on the dual variational mode decomposition of a nonstationary time series. First, criteria were determined based on information entropy and frequency statistics to determine the quantity of components in the variational mode decomposition, including the number of subsequences and the conditions for dual decomposition. Second, a deep prediction model was built for the subsequences obtained after the dual decomposition. Third, a general framework was proposed to integrate the data decomposition and deep prediction models. The method was verified on practical time series data with some contrast methods. The results show that it performed better than single deep network and traditional decomposition methods. The proposed method can effectively extract the characteristics of a nonstationary time series and obtain reliable prediction results.
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PFVAE: A Planar Flow-Based Variational Auto-Encoder Prediction Model for Time Series Data. MATHEMATICS 2022. [DOI: 10.3390/math10040610] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Prediction based on time series has a wide range of applications. Due to the complex nonlinear and random distribution of time series data, the performance of learning prediction models can be reduced by the modeling bias or overfitting. This paper proposes a novel planar flow-based variational auto-encoder prediction model (PFVAE), which uses the long- and short-term memory network (LSTM) as the auto-encoder and designs the variational auto-encoder (VAE) as a time series data predictor to overcome the noise effects. In addition, the internal structure of VAE is transformed using planar flow, which enables it to learn and fit the nonlinearity of time series data and improve the dynamic adaptability of the network. The prediction experiments verify that the proposed model is superior to other models regarding prediction accuracy and proves it is effective for predicting time series data.
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Deep-Stacking Network Approach by Multisource Data Mining for Hazardous Risk Identification in IoT-Based Intelligent Food Management Systems. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:1194565. [PMID: 34804137 PMCID: PMC8598327 DOI: 10.1155/2021/1194565] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 08/23/2021] [Accepted: 10/07/2021] [Indexed: 12/17/2022]
Abstract
Food quality and safety issues occurred frequently in recent years, which have attracted more and more attention of social and international organizations. Considering the increased quality risk in the food supply chain, many researchers have applied various information technologies to develop real-time risk identification and traceability systems (RITSs) for preferable food safety guarantee. This paper presents an innovative approach by utilizing the deep-stacking network method for hazardous risk identification, which relies on massive multisource data monitored by the Internet of Things timely in the whole food supply chain. The aim of the proposed method is to help managers and operators in food enterprises to find accurate risk levels of food security in advance and to provide regulatory authorities and consumers with potential rules for better decision-making, thereby maintaining the safety and sustainability of food product supply. The verification experiments show that the proposed method has the best performance in terms of prediction accuracy up to 97.62%, meanwhile achieves the appropriate model parameters only up to 211.26 megabytes. Moreover, the case analysis is implemented to illustrate the outperforming performance of the proposed method in risk level identification. It can effectively enhance the RITS ability for assuring food supply chain security and attaining multiple cooperation between regulators, enterprises, and consumers.
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7
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Ren C, Jung H, Lee S, Jeong D. Coastal Waste Detection Based on Deep Convolutional Neural Networks. SENSORS 2021; 21:s21217269. [PMID: 34770576 PMCID: PMC8586973 DOI: 10.3390/s21217269] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 10/25/2021] [Accepted: 10/27/2021] [Indexed: 12/22/2022]
Abstract
Coastal waste not only has a seriously destructive effect on human life and marine ecosystems, but it also poses a long-term economic and environmental threat. To solve the issues of a poor manual coastal waste sorting environment, such as low sorting efficiency and heavy tasks, we develop a novel deep convolutional neural network by combining several strategies to realize intelligent waste recognition and classification based on the state-of-the-art Faster R-CNN framework. Firstly, to effectively detect small objects, we consider multiple-scale fusion to get rich semantic information from the shallower feature map. Secondly, RoI Align is introduced to solve positioning deviation caused by the regions of interest pooling. Moreover, it is necessary to correct key parameters and take on data augmentation to improve model performance. Besides, we create a new waste object dataset, named IST-Waste, which is made publicly to facilitate future research in this field. As a consequence, the experiment shows that the algorithm’s mAP reaches 83%. Detection performance is significantly better than Faster R-CNN and SSD. Thus, the developed scheme achieves higher accuracy and better performance against the state-of-the-art alternative.
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Kumar A, Kolnure SN, Abhishek K, Fadi-Al-Turjman, Nerurkar P, Ghalib MR, Shankar A. Advanced deep learning algorithms for infectious disease modeling using clinical data- A Case Study on CoVID-19. Curr Med Imaging 2021; 18:570-582. [PMID: 34503419 DOI: 10.2174/1573405617666210908125911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 06/28/2021] [Accepted: 07/02/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND Infectious disease happens when an individual is defiled by a micro-organism/virus from another person or an animal. It is troublesome that causes hurt at both individual and huge scope scales. CASE PRESENTATION The ongoing episode of COVID-19 ailment brought about by the new coronavirus first distinguished in Wuhan China, and its quick spread far and wide, revived the consideration of the world towards the impacts of such plagues on individual's regular daily existence. We attempt to exploit this effectiveness of Advanced deep learning algorithms to predict the Growth of Infectious disease based on time series data and classification based on (symptoms) text data and X-ray image data. CONCLUSION Goal is identifying the nature of the phenomenon represented by the sequence of observations and forecasting.
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Affiliation(s)
- Ajay Kumar
- Dept. of Computer Science & Engineering, NIT Patna, Bihar. India
| | | | - Kumar Abhishek
- Dept. of Computer Science & Engineering, NIT Patna, Bihar. India
| | - Fadi-Al-Turjman
- Research Centre for AI and IoT, Department of Artificial Intelligence Engineering, Near East University, Nicosia, Mersin 10. Turkey
| | - Pranav Nerurkar
- Dept. of CE & IT, VJTI Dept. of Data Science, MPSTME, NMIMS University, Mumbai. India
| | | | - Achyut Shankar
- Department of CSE, Amity School of Engineering and Technology, Amity University, Uttar Pradesh. India
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Yang H, Liu S. A prediction model of aquaculture water quality based on multiscale decomposition. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:7561-7579. [PMID: 34814263 DOI: 10.3934/mbe.2021374] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
In the field of intensive aquaculture, the deterioration of water quality is one of the main factors restricting the normal growth of aquatic products. Predicting water quality in real time constitutes the theoretical basis for the evaluation, planning and intelligent regulation of the aquaculture environment. Based on the design principles of decomposition, recombination and integration, this paper constructs a multiscale aquaculture water quality prediction model. First, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is used to decompose the different water quality variables at different time scales step by step to generate a series of intrinsic mode function (IMF) components with the same characteristic scale. Then, the sample entropy of each IMF component is calculated, the components with similar sample entropies are combined, and the original data are recombined into several subsequences through the above operations. In this paper, a prediction model based on a long short-term memory (LSTM) neural network is constructed to predict each recombination subsequence, and the Adam optimization algorithm is used to continuously update the weight of neural network to train and optimize the prediction performance. Finally, the predicted value of each subsequence is superimposed to predict the original water quality data. The dissolved oxygen and pH data of an aquaculture base were collected for prediction experiments, the results of which show that the proposed model has a high prediction accuracy and strong generalization performance.
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Affiliation(s)
- Huanhai Yang
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
- Co-innovation Center of Shandong Colleges and Universities: Future Intelligent Computing, Shandong Technology and Business University, Yantai, China
| | - Shue Liu
- Binzhou Medical University, Yantai, China
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Huang GQ, Tsai FS. Social Innovation for Food Security and Tourism Poverty Alleviation: Some Examples From China. Front Psychol 2021; 12:614469. [PMID: 34017277 PMCID: PMC8129493 DOI: 10.3389/fpsyg.2021.614469] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 04/06/2021] [Indexed: 12/31/2022] Open
Abstract
The COVID-19 pandemic has brought hunger to millions of people around the world. Social distancing measures coupled with national lockdowns have reduced work opportunities and the overall household incomes. Moreover, the disruption in agricultural production and supply routes is expected to continue into 2021, which may leave millions without access to food. Coincidentally, those who suffer the most are poor people. As such, food security and tourism poverty alleviation are interlinked when discussing social problems and development. While the corporate interest in tourism poverty alleviation is as old as the industrial revolution, little research has been conducted to show how social innovation can be leveraged to reinforce food security and alleviate poverty. Thus, this case study examines the food industry in rural China to establish how it conducts social innovation in food production and distribution to facilitate social development and mitigate poverty.
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Affiliation(s)
- Guo-Qing Huang
- College of Economics and Management, Southwest University, Chongqing, China
| | - Fu-Sheng Tsai
- North China University of Water Resources and Electric Power, Zhengzhou, China.,Department of Business Administration, Cheng Shiu University, Kaohsiung, Taiwan.,Center for Environmental Toxin and Emerging-Contaminant Research, Cheng Shiu University, Kaohsiung, Taiwan.,Super Micro Mass Research and Technology Center, Cheng Shiu University, Kaohsiung, Taiwan
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11
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Abstract
Internet of Things (IoT) is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The IoT for Smart Cities has many different domains and draws upon various underlying systems for its operation. In this paper, we provide a holistic coverage of the Internet of Things in Smart Cities. We start by discussing the fundamental components that make up the IoT based Smart City landscape followed by the technologies that enable these domains to exist in terms of architectures utilized, networking technologies used as well as the Artificial Algorithms deployed in IoT based Smart City systems. This is then followed up by a review of the most prevalent practices and applications in various Smart City domains. Lastly, the challenges that deployment of IoT systems for smart cities encounter along with mitigation measures.
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The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods. SENSORS 2021; 21:s21062085. [PMID: 33809743 PMCID: PMC8002332 DOI: 10.3390/s21062085] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 03/05/2021] [Accepted: 03/10/2021] [Indexed: 11/23/2022]
Abstract
State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.
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13
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Deep-Learning Forecasting Method for Electric Power Load via Attention-Based Encoder-Decoder with Bayesian Optimization. ENERGIES 2021. [DOI: 10.3390/en14061596] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Short-term electrical load forecasting plays an important role in the safety, stability, and sustainability of the power production and scheduling process. An accurate prediction of power load can provide a reliable decision for power system management. To solve the limitation of the existing load forecasting methods in dealing with time-series data, causing the poor stability and non-ideal forecasting accuracy, this paper proposed an attention-based encoder-decoder network with Bayesian optimization to do the accurate short-term power load forecasting. Proposed model is based on an encoder-decoder architecture with a gated recurrent units (GRU) recurrent neural network with high robustness on time-series data modeling. The temporal attention layer focuses on the key features of input data that play a vital role in promoting the prediction accuracy for load forecasting. Finally, the Bayesian optimization method is used to confirm the model’s hyperparameters to achieve optimal predictions. The verification experiments of 24 h load forecasting with real power load data from American Electric Power (AEP) show that the proposed model outperforms other models in terms of prediction accuracy and algorithm stability, providing an effective approach for migrating time-serial power load prediction by deep-learning technology.
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Ali Shah SA, Aziz W, Almaraashi M, Ahmed Nadeem MS, Habib N, Shim SO. A hybrid model for forecasting of particulate matter concentrations based on multiscale characterization and machine learning techniques. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:1992-2009. [PMID: 33892534 DOI: 10.3934/mbe.2021104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Accurate prediction of particulate matter (PM) using time series data is a challenging task. The recent advancements in sensor technology, computing devices, nonlinear computational tools, and machine learning (ML) approaches provide new opportunities for robust prediction of PM concentrations. In this study, we develop a hybrid model for forecasting PM10 and PM2.5 based on the multiscale characterization and ML techniques. At first, we use the empirical mode decomposition (EMD) algorithm for multiscale characterization of PM10 and PM2.5 by decomposing the original time series into numerous intrinsic mode functions (IMFs). Different individual ML algorithms such as random forest (RF), support vector regressor (SVR), k-nearest neighbors (kNN), feed forward neural network (FFNN), and AdaBoost are then used to develop EMD-ML models. The air quality time series data from Masfalah air station Makkah, Saudi Arabia are utilized for validating the EMD-ML models, and results are compared with non-hybrid ML models. The PMs (PM10 and PM2.5) concentrations data of Dehli, India are also utilized for validating the EMD-ML models. The performance of each model is evaluated using root mean square error (RMSE) and mean absolute error (MAE). The average bias in the predictive model is estimated using mean bias error (MBE). Obtained results reveal that EMD-FFNN model provides the lowest error rate for both PM10 (RMSE = 12.25 and MAE = 7.43) and PM2.5 (RMSE = 4.81 and MAE = 3.02) using Misfalah, Makkah data whereas EMD-kNN model provides the lowest error rate for PM10 (RMSE = 20.56 and MAE = 12.87) and EMD-AdaBoost provides the lowest error rate for PM2.5 (RMSE = 15.29 and MAE = 9.45) using Dehli, India data. The findings also reveal that EMD-ML models can be effectively used in forecasting PM mass concentrations and to develop rapid air quality warning systems.
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Affiliation(s)
- Syed Ahsin Ali Shah
- Department of Computer Science & IT, University of Azad Jammu and Kashmir, King Abdullah Campus, Muzaffarabad 13100, AJK, Pakistan
| | - Wajid Aziz
- Department of Computer Science & IT, University of Azad Jammu and Kashmir, King Abdullah Campus, Muzaffarabad 13100, AJK, Pakistan
- College of Computer Science & Engineering, University of Jeddah, Jeddah 23890, Saudi Arabia
| | - Majid Almaraashi
- College of Computer Science & Engineering, University of Jeddah, Jeddah 23890, Saudi Arabia
| | - Malik Sajjad Ahmed Nadeem
- Department of Computer Science & IT, University of Azad Jammu and Kashmir, King Abdullah Campus, Muzaffarabad 13100, AJK, Pakistan
| | - Nazneen Habib
- Department of Sociology & Rural Development, University of Azad Jammu Kashmir, Muzaffarabad 13100, AJK, Pakistan
| | - Seong-O Shim
- College of Computer Science & Engineering, University of Jeddah, Jeddah 23890, Saudi Arabia
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15
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Jin XB, Yu XH, Su TL, Yang DN, Bai YT, Kong JL, Wang L. Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy. ENTROPY (BASEL, SWITZERLAND) 2021; 23:219. [PMID: 33670098 PMCID: PMC7916859 DOI: 10.3390/e23020219] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 02/03/2021] [Accepted: 02/07/2021] [Indexed: 11/16/2022]
Abstract
Trend prediction based on sensor data in a multi-sensor system is an important topic. As the number of sensors increases, we can measure and store more and more data. However, the increase in data has not effectively improved prediction performance. This paper focuses on this problem and presents a distributed predictor that can overcome unrelated data and sensor noise: First, we define the causality entropy to calculate the measurement's causality. Then, the series causality coefficient (SCC) is proposed to select the high causal measurement as the input data. To overcome the traditional deep learning network's over-fitting to the sensor noise, the Bayesian method is used to obtain the weight distribution characteristics of the sub-predictor network. A multi-layer perceptron (MLP) is constructed as the fusion layer to fuse the results from different sub-predictors. The experiments were implemented to verify the effectiveness of the proposed method by meteorological data from Beijing. The results show that the proposed predictor can effectively model the multi-sensor system's big measurement data to improve prediction performance.
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Affiliation(s)
- Xue-Bo Jin
- Artificial Intelligence College, Beijing Technology and Business University, Beijing 10048, China; (X.-H.Y.); (Y.-T.B.); (J.-L.K.)
- China Light Industry Key Laboratory of Industrial Internet and Big Data Beijing Technology and Business University, Beijing 10048, China
| | - Xing-Hong Yu
- Artificial Intelligence College, Beijing Technology and Business University, Beijing 10048, China; (X.-H.Y.); (Y.-T.B.); (J.-L.K.)
- China Light Industry Key Laboratory of Industrial Internet and Big Data Beijing Technology and Business University, Beijing 10048, China
| | - Ting-Li Su
- Artificial Intelligence College, Beijing Technology and Business University, Beijing 10048, China; (X.-H.Y.); (Y.-T.B.); (J.-L.K.)
- China Light Industry Key Laboratory of Industrial Internet and Big Data Beijing Technology and Business University, Beijing 10048, China
| | - Dan-Ni Yang
- Electrical and Information Engineering College, Tianjin University, Tianjin 300072, China;
| | - Yu-Ting Bai
- Artificial Intelligence College, Beijing Technology and Business University, Beijing 10048, China; (X.-H.Y.); (Y.-T.B.); (J.-L.K.)
- China Light Industry Key Laboratory of Industrial Internet and Big Data Beijing Technology and Business University, Beijing 10048, China
| | - Jian-Lei Kong
- Artificial Intelligence College, Beijing Technology and Business University, Beijing 10048, China; (X.-H.Y.); (Y.-T.B.); (J.-L.K.)
- China Light Industry Key Laboratory of Industrial Internet and Big Data Beijing Technology and Business University, Beijing 10048, China
| | - Li Wang
- Artificial Intelligence College, Beijing Technology and Business University, Beijing 10048, China; (X.-H.Y.); (Y.-T.B.); (J.-L.K.)
- China Light Industry Key Laboratory of Industrial Internet and Big Data Beijing Technology and Business University, Beijing 10048, China
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16
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Shi Z, Bai Y, Jin X, Wang X, Su T, Kong J. Parallel deep prediction with covariance intersection fusion on non-stationary time series. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2020.106523] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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17
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An Adaptive Filter for Nonlinear Multi-Sensor Systems with Heavy-Tailed Noise. SENSORS 2020; 20:s20236757. [PMID: 33255987 PMCID: PMC7729753 DOI: 10.3390/s20236757] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 10/27/2020] [Accepted: 11/23/2020] [Indexed: 11/17/2022]
Abstract
Aiming towards state estimation and information fusion for nonlinear systems with heavy-tailed measurement noise, a variational Bayesian Student's t-based cubature information filter (VBST-CIF) is designed. Furthermore, a multi-sensor variational Bayesian Student's t-based cubature information feedback fusion (VBST-CIFF) algorithm is also derived. In the proposed VBST-CIF, the spherical-radial cubature (SRC) rule is embedded into the variational Bayes (VB) method for a joint estimation of states and scale matrix, degree-of-freedom (DOF) parameter, as well as an auxiliary parameter in the nonlinear system with heavy-tailed noise. The designed VBST-CIF facilitates multi-sensor fusion, allowing one to derive a VBST-CIFF algorithm based on multi-sensor information feedback fusion. The performance of the proposed algorithms is assessed in target tracking scenarios. Simulation results demonstrate that the proposed VBST-CIF/VBST-CIFF outperform the conventional cubature information filter (CIF) and cubature information feedback fusion (CIFF) algorithms.
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18
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Navarro E, Costa N, Pereira A. A Systematic Review of IoT Solutions for Smart Farming. SENSORS 2020; 20:s20154231. [PMID: 32751366 PMCID: PMC7436012 DOI: 10.3390/s20154231] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 07/24/2020] [Accepted: 07/27/2020] [Indexed: 02/02/2023]
Abstract
The world population growth is increasing the demand for food production. Furthermore, the reduction of the workforce in rural areas and the increase in production costs are challenges for food production nowadays. Smart farming is a farm management concept that may use Internet of Things (IoT) to overcome the current challenges of food production. This work uses the preferred reporting items for systematic reviews (PRISMA) methodology to systematically review the existing literature on smart farming with IoT. The review aims to identify the main devices, platforms, network protocols, processing data technologies and the applicability of smart farming with IoT to agriculture. The review shows an evolution in the way data is processed in recent years. Traditional approaches mostly used data in a reactive manner. In more recent approaches, however, new technological developments allowed the use of data to prevent crop problems and to improve the accuracy of crop diagnosis.
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Affiliation(s)
- Emerson Navarro
- School of Technology and Management, Computer Science and Communication Research Centre, Polytechnic Institute of Leiria, Campus 2, Morro do Lena—Alto do Vieiro, Apartado 4163, 2411-901 Leiria, Portugal; (E.N.); (N.C.)
| | - Nuno Costa
- School of Technology and Management, Computer Science and Communication Research Centre, Polytechnic Institute of Leiria, Campus 2, Morro do Lena—Alto do Vieiro, Apartado 4163, 2411-901 Leiria, Portugal; (E.N.); (N.C.)
| | - António Pereira
- School of Technology and Management, Computer Science and Communication Research Centre, Polytechnic Institute of Leiria, Campus 2, Morro do Lena—Alto do Vieiro, Apartado 4163, 2411-901 Leiria, Portugal; (E.N.); (N.C.)
- INOV INESC Inovação, Institute of New Technologies, Leiria Office, Campus 2, Morro do Lena—Alto do Vieiro, Apartado 4163, 2411-901 Leiria, Portugal
- Correspondence:
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19
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Autonomous Sensor Network for Rural Agriculture Environments, Low Cost, and Energy Self-Charge. SUSTAINABILITY 2020. [DOI: 10.3390/su12155913] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Over the last years, existing technologies have been applied to agricultural environments, resulting in new precision agriculture systems. Some of the multiple profits of developing new agricultural technologies and applications include the cost reduction around the building and deployment of them, together with more energy-efficient consumption. Therefore, agricultural precision systems focus on developing better, easier, cheaper, and overall more efficient ways of handling agricultural monitoring and actuation. To achieve this vision, we use a set of technologies such as Wireless Sensor Networks, Sensors devices, Internet of Things, or data analysis. More specifically, in this study, we proposed a combination of all these technologies to design and develop a prototype of a precision agriculture system for medium and small agriculture plantations that highlights two major advantages: efficient energy management with self-charging capabilities and a low-cost policy. For the development of the project, several prototype nodes were built and deployed within a sensor network connected to the cloud as a self-powered system. The final target of this system is, therefore, to gather environment data, analyze it, and actuate by activating the watering installation. An analysis of the exposed agriculture monitoring system, in addition to results, is exposed in the paper.
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20
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Chen Z, Yan Z, Jiang H, Que Z, Gao G, Xu Z. Detecting Coal Pulverizing System Anomaly Using a Gated Recurrent Unit and Clustering. SENSORS 2020; 20:s20113271. [PMID: 32521793 PMCID: PMC7309027 DOI: 10.3390/s20113271] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 06/05/2020] [Accepted: 06/07/2020] [Indexed: 01/22/2023]
Abstract
The coal pulverizing system is an important auxiliary system in thermal power generation systems. The working condition of a coal pulverizing system may directly affect the safety and economy of power generation. Prognostics and health management is an effective approach to ensure the reliability of coal pulverizing systems. As the coal pulverizing system is a typical dynamic and nonlinear high-dimensional system, it is difficult to construct accurate mathematical models used for anomaly detection. In this paper, a novel data-driven integrated framework for anomaly detection of the coal pulverizing system is proposed. A neural network model based on gated recurrent unit (GRU) networks, a type of recurrent neural network (RNN), is constructed to describe the temporal characteristics of high-dimensional data and predict the system condition value. Then, aiming at the prediction error, a novel unsupervised clustering algorithm for anomaly detection is proposed. The proposed framework is validated by a real case study from an industrial coal pulverizing system. The results show that the proposed framework can detect the anomaly successfully.
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Affiliation(s)
- Zian Chen
- College of Control Science and Engineering, Zhejiang University, Hangzhou 310000, China; (Z.C.); (Z.Y.); (G.G.); (Z.X.)
| | - Zhiyu Yan
- College of Control Science and Engineering, Zhejiang University, Hangzhou 310000, China; (Z.C.); (Z.Y.); (G.G.); (Z.X.)
| | - Haojun Jiang
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310000, China;
| | - Zijun Que
- College of Control Science and Engineering, Zhejiang University, Hangzhou 310000, China; (Z.C.); (Z.Y.); (G.G.); (Z.X.)
- Correspondence: ; Tel.: +86-1876-712-0084
| | - Guozhen Gao
- College of Control Science and Engineering, Zhejiang University, Hangzhou 310000, China; (Z.C.); (Z.Y.); (G.G.); (Z.X.)
| | - Zhengguo Xu
- College of Control Science and Engineering, Zhejiang University, Hangzhou 310000, China; (Z.C.); (Z.Y.); (G.G.); (Z.X.)
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21
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Reflections and Methodological Proposals to Treat the Concept of "Information Precision" in Smart Agriculture Practices. SENSORS 2020; 20:s20102847. [PMID: 32429504 PMCID: PMC7287785 DOI: 10.3390/s20102847] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Revised: 05/08/2020] [Accepted: 05/14/2020] [Indexed: 11/26/2022]
Abstract
Smart Agriculture (SA) is an evolution of Precision Farming (PF). It has technological basis very close to the paradigms of Industry 4.0 (Ind-4.0), so that it is also often referred to as Agriculture 4.0. After the proposal of a brief historical examination that provides a conceptual frame to the above terms, the common aspects of SA and Ind-4.0 are analyzed. These are primarily to be found in the cognitive approaches of Knowledge Management 4.0 (KM4.0, the actual theoretical basis of Ind-4.0), which underlines the need to use Integrated Information Systems (IIS) to manage all the activity areas of any production system. Based upon an infological approach, “raw data” becomes “information” only when useful to (or actually used in) a decision-making process. Thus, an IIS must be always designed according to such a view, and KM4.0 conditions the way of collecting and processing data on farms, together with the “information precision” by which the production system is managed. Such precision needs, on their turn, depend on the hierarchical level and the “Macrodomain of Prevailing Interest” (MPI) related to each decision, where the latter identifies a predominant viewpoint through which a system can be analyzed according to a prevailing purpose. Four main MPIs are here proposed: (1) physical and chemical, (2) biological and ecological, (3) productive and hierarchical, and (4) economic and social. In each MPI, the quality of the knowledge depends on the cognitive level and the maturity of the methodological approaches there achieved. The reliability of information tends to decrease from the first to the fourth MPI; lower the reliability, larger the tolerance margins that a measurement systems must ensure. Some practical examples are then discussed, taking into account some IIS-monitoring solutions of increasing complexity in relation to information integration needs and related data fusion approaches. The analysis concludes with the proposal of new operational indications for the verification and certification of the reliability of the information on the entire decision-making chain.
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22
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Khaki S, Pham H, Han Y, Kuhl A, Kent W, Wang L. Convolutional Neural Networks for Image-Based Corn Kernel Detection and Counting. SENSORS (BASEL, SWITZERLAND) 2020; 20:E2721. [PMID: 32397598 PMCID: PMC7249160 DOI: 10.3390/s20092721] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Revised: 05/01/2020] [Accepted: 05/07/2020] [Indexed: 12/04/2022]
Abstract
Precise in-season corn grain yield estimates enable farmers to make real-time accurate harvest and grain marketing decisions minimizing possible losses of profitability. A well developed corn ear can have up to 800 kernels, but manually counting the kernels on an ear of corn is labor-intensive, time consuming and prone to human error. From an algorithmic perspective, the detection of the kernels from a single corn ear image is challenging due to the large number of kernels at different angles and very small distance among the kernels. In this paper, we propose a kernel detection and counting method based on a sliding window approach. The proposed method detects and counts all corn kernels in a single corn ear image taken in uncontrolled lighting conditions. The sliding window approach uses a convolutional neural network (CNN) for kernel detection. Then, a non-maximum suppression (NMS) is applied to remove overlapping detections. Finally, windows that are classified as kernel are passed to another CNN regression model for finding the ( x , y ) coordinates of the center of kernel image patches. Our experiments indicate that the proposed method can successfully detect the corn kernels with a low detection error and is also able to detect kernels on a batch of corn ears positioned at different angles.
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Affiliation(s)
- Saeed Khaki
- Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA 50011-3611, USA;
| | - Hieu Pham
- Syngenta, Slater, IA 50244, USA; (H.P.); (Y.H.); (A.K.); (W.K.)
| | - Ye Han
- Syngenta, Slater, IA 50244, USA; (H.P.); (Y.H.); (A.K.); (W.K.)
| | - Andy Kuhl
- Syngenta, Slater, IA 50244, USA; (H.P.); (Y.H.); (A.K.); (W.K.)
| | - Wade Kent
- Syngenta, Slater, IA 50244, USA; (H.P.); (Y.H.); (A.K.); (W.K.)
| | - Lizhi Wang
- Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA 50011-3611, USA;
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