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The detection of spatial and temporal changes (or change detection) in remote sensing images is essential in any decision support system about natural phenomena such as extreme weather conditions, climate change, and floods. In this paper, a new method is proposed to determine the inference process parameters of boundary point, rule coefficient, defuzzification coefficient, and dependency coefficient and present a new FWADAM+ method to train that set of parameters simultaneously. The initial data are clustered simultaneously according to each data group. This result will be the basis for determining a suitable set of parameters by using the FWADAM+ concurrent training algorithm. Eventually, these results will be inherited in the following data groups to build other complex fuzzy rule systems in a shorter time while still ensuring the model's efficiency. The weather imagery database of the United States Navy (US Navy) is used to evaluate and compare with some related methods using the root-mean-squared error (RMSE), R-squared (R2) measures, and the analysis of variance (ANOVA) model. The experimental results show that the proposed method is up to 30% better than the SeriesNet method, and the processing time is 10% less than that of the SeriesNet method. [ABSTRACT FROM AUTHOR]

There have been various researches regarding routing in wireless sensor networks for many years, but underground wireless communication in complex environments like soil-air or water-air is a new and tough problem for researchers. Replacement of the battery of sensor nodes is already a challenging task under normal conditions. However, the difficulty increases significantly when deploying them in environments like cultivation soils; therefore maintaining the network lifetime as well as the total number of living nodes in such an adverse setting is a challenging task for researchers. In this paper, we propose a new energy-efficient routing model in wireless underground sensor networks (WUSN) that considers the path loss energy of the energy model and the energy efficiency of the total network. The proposed FCM-WUSN model is compared against the well-known fuzzy routing method namely FCM in terms of network lifetime, energy consumption, total node alive, and underground node alive. It has been observed that the network lifetime of the proposed method is 1.5 × longer than that of the FCM, and the number of alive nodes including the underground nodes are 1.6 × higher than that of the FCM. The results are better even when running data sets with more sensors in the network and sensor positioning based on the depth of sensors deployed underground. [ABSTRACT FROM AUTHOR]

This paper introduces a semi-automated approach for the prioritization of software features in medium- to large-sized software projects, considering stakeholders' satisfaction and dissatisfaction as key criteria for the incorporation of candidate features. Our research acknowledges an inherent asymmetry in stakeholders' evaluations, between the satisfaction from offering certain features and the dissatisfaction from not offering the same features. Even with systematic, ordinal scale-based prioritization techniques, involved stakeholders may exhibit hesitation and uncertainty in their assessments. Our approach aims to address these challenges by employing the Binary Search Tree prioritization method and leveraging the mathematical framework of Intuitionistic Fuzzy Sets to quantify the uncertainty of stakeholders when expressing assessments on the value of software features. Stakeholders' rankings, considering satisfaction and dissatisfaction as features prioritization criteria, are mapped into Intuitionistic Fuzzy Numbers, and objective weights are automatically computed. Rankings associated with less hesitation are considered more valuable to determine the final features' priorities than those rankings with more hesitation, reflecting lower indeterminacy or lack of knowledge from stakeholders. We validate our proposed approach with a case study, illustrating its application, and conduct a comparative analysis with existing software requirements prioritization methods. [ABSTRACT FROM AUTHOR]

In this study, a new dynamic fuzzy group recommender system (DFGRS) is investigated, which aims to deal with some critical challenges in the group recommender system (GRS), including the user preference uncertainty, item attractiveness tendency, and group members’ interaction and fairness. The proposed approach of the DFGRS applies intuitionistic fuzzy sets (IFSs) to represent user preferences on the considered items by taking into account user hesitancy and inconsistency in ratings. It also incorporates a novel dynamic similarity mechanism for taking into account users’ preference tendency and item attractiveness in predicting unknown user-item preferences. The model’s parameters are optimized using a modified Bayesian method. This combination creates a more realistic and concise prediction of unknown user ratings. Then, to form a balanced solution in the consensus phase of the DFGRS, an intuitionistic fuzzy Choquet integral aggregation operation (IFCAO) is suggested. This approach enables the DFGRS to generate recommendations that balance between optimal average group users’ preferences and fairness among them. Furthermore, the proposed IFCAO includes the group members’ interaction impact due to the proposed capacity function. In addition, several other intuitionistic fuzzy aggregation operations can be utilized that inherit features of common aggregation strategies defined for a traditional GRS. The experimental results in this paper demonstrate the performance of various aggregation approaches for the consensus phase of DFGRS. The obtained performance analysis results highlight the advantage and the potential of the DFGRS based on the proposed Choquet integral aggregation operation. Overall, the proposed DFGRS presents a novel approach for addressing challenges in group recommendation and could have important practical applications in various areas. [ABSTRACT FROM AUTHOR]

Transfer learning (TL) is further investigated in computer intelligence and artificial intelligence. Many TL methodologies have been suggested and applied to figure out the problem of practical applications, such as in natural language processing, classification models for COVID-19 disease, Alzheimer's disease detection, etc. FTL (fuzzy transfer learning) is an extension of TL that uses a fuzzy system to pertain to the vagueness and uncertainty parameters in TL, allowing the discovery of predicates and their evaluation of unclear data. Because of the system's increasing complexity, FTL is often utilized to further infer proper results without constructing the knowledge base and environment from scratch. Further, the uncertainty and vagueness in the daily data can arise and modify the process. It has been of great interest to design an FTL model that can handle the periodicity data with fast processing time and reasonable accuracy. This paper proposes a novel model to capture data related to periodical phenomena and enhance the quality of the existing inference process. The model performs knowledge transfer in the absence of reference or predictive information. An experimental stage on the UCI and real-life dataset compares our proposed model against the related methods regarding the number of rules, computing time, and accuracy. The experimental results validated the advantages and suitability of the proposed FTL model. [ABSTRACT FROM AUTHOR]

Identification and repairing holes in ireless sensor networks (WSNs) are challenging with full and low complexity. A local healing approach based on distributed virtual forces will make the healing area of the hole possible. The construction of repairing design is a locally allocated and constructed methodology that will enhance the coverage area in WSNs. In this paper, an enhanced border and hole detection (EBHD) algorithm is proposed for the energy containment of WSN. The performance of the proposed EBHD methodology is compared with those of the existing methods through Network Simulator 2 (NS2). The simulation results prove that the proposed EBHD achieves a high packet reception ratio while improving the residual energy and reducing end-to-end delay. [ABSTRACT FROM AUTHOR]

Periapical Inflammation (PI) is one of the most popular diseases in adults due to complication of endodontitis or dental trauma with corresponding consequences to quality-of-life like tiredness and signs of infection. Specifically, patients having severe PI are often tiredness and high fever accompanied by signs of infection such as dry lips, dirty tongue, lymph node reaction in the area under the jaw. In X-Ray images, PI could be recognized by vague boundaries with signs of periapical ligament extensions. It is necessary to design a computerized diagnosis system based on the Deep Learning models for supporting clinicians in diagnosis of PI from X-Ray images. In this paper, we propose a new medical system called VNU for diagnosis of PI from X-Rays images. The VNU system uses Deep Learning to classify whether X-Ray images being PI or not. The Residual Neural Network (ResNet) with 34 layers is utilized with proper data augmentation and learning algorithms. The system is designed based on 7-layer enterprise architecture (User, Business, Application, Data, Technology, Infrastructure, and Security). It is used by both the clinicians and IT operators. The system has been validated on real data from Hanoi Medical University, Vietnam consisting of 900 images with PI and 500 normal images. Two scenarios of validation namely hyperparameter selection and performance comparison with other CNN-based Deep Learning models have been performed. It has been found from the experiments that the proposed system has better performance than the others in terms of sensitivity and specificity with the corresponding values of 96.70% and 93.87%. The system is deployed on the web interface that offers flexibility for clinicians in diagnosis and training. [ABSTRACT FROM AUTHOR]

In this world, travelling is the daily routine to reach the desired places such as schools, hospitals, offices and so on. Everyone makes use of their vehicles like two-wheelers or four-wheelers to reach the destination. Vehicle condition plays a major role which includes many parameters such as tyre condition, engine overheating, low fuel mileage. This paper concentrates on the examination of tyre track design for deformities. The mileage of a vehicle relies upon different components. However, it depends on different parameters like street condition, tyre weight and numerous others. For smooth driving and for safe traffic reasons, the auspicious maintenance of tyres plays a vital task. Consequently, the likelihood of prediction of tyre imperfections is assumed to play a vital role to fleet proprietors, government and tyre makers. Researchers make use of recent technologies such as machine learning and deep learning concepts to provide a solution to various real-life problems. The creation of a weighted quality-based convolutional neural network with the purpose of discovery of imperfections on tyres by the examination of pictures of tyre track designs is being proposed. The proposed system with the accessible data-set made public by different tyre producers is included for evaluation. The consequences of our tests demonstrate that it is conceivable to predict the deformities and durability of a tyre from its track designs through a weighted quality-based convolutional neural network. The proposed methodology is used to identify the group of feature values from the tyre image. These kinds of feature values are utilized to implement the dissimilarity of the feature related pixels. The number of epochs is varied as a callback mechanism has been used to validate the proposed model. Binary accuracy and mean absolute error are the best metrics to evaluate the performance which are discussed in the results section. Also, precision, recall and F-measure are used as validation parameters for the proposed model. [ABSTRACT FROM AUTHOR]

Production of electricity from the burning of fossil fuels has caused an increase in the emission of greenhouse gases. In the long run, greenhouse gases cause harm to the environment. To reduce these gases, it is important to accurately forecast electricity production, supply and consumption. Forecasting of electricity consumption is, in particular, useful for minimizing problems of overproduction and oversupply of electricity. This research study focuses on forecasting electricity consumption based on time series data using different artificial intelligence and metaheuristic methods. The aim of the study is to determine which model among the artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), least squares support vector machines (LSSVMs) and fuzzy time series (FTS) produces the highest level of accuracy in forecasting electricity consumption. The variables considered in this research include the monthly electricity consumption over the years for different countries. The monthly electricity consumption data for seven countries, namely, Norway, Switzerland, Malaysia, Egypt, Algeria, Bulgaria and Kenya, for 10 years were used in this research. The performance of all of the models was evaluated and compared using error metrics such as the root mean squared error (RMSE), average forecasting error (AFE) and performance parameter (PP). The differences in the results obtained via the different methods are analyzed and discussed, and it is shown that the different models performed better for different countries in different forecasting periods. Overall, it was found that the FTS model performed the best for most of the countries studied compared to the other three models. The research results can allow electricity management companies to have better strategic planning when deciding on the optimal levels of electricity production and supply, with the overall aim of preventing surpluses or shortages in the electricity supply. [ABSTRACT FROM AUTHOR]

Flood is one of the deadliest natural hazards worldwide, with the population affected being more than 2 billion between 1998–2017 with a lack of warning systems according to WHO. Especially, flash floods have the potential to generate fatal damages due to their rapid evolution and the limited warning and response time. An effective Early Warning Systems (EWS) could support detection and recognition of flash floods. Information about a flash flood can be mainly provided from observations of hydrology and from satellite images taken before the flash flood happens. Then, predictions from satellite images can be integrated with predictions based on sensors' information to improve the accuracy of a forecasting system and subsequently trigger warning systems. The existing Deep Learning models such as UNET has been effectively used to segment the flash flood with high performance, but there are no ways to determine the most suitable model architecture with the proper number of layers showing the best performance in the task. In this paper, we propose a novel Deep Learning architecture, namely PSO-UNET, which combines Particle Swarm Optimization (PSO) with UNET to seek the best number of layers and the parameters of layers in the UNET based architecture; thereby improving the performance of flash flood segmentation from satellite images. Since the original UNET has a symmetrical architecture, the evolutionary computation is performed by paying attention to the contracting path and the expanding path is synchronized with the following layers in the contracting path. The UNET convolutional process is performed four times. Indeed, we consider each process as a block of the convolution having two convolutional layers in the original architecture. Training of inputs and hyper-parameters is performed by executing the PSO algorithm. In practice, the value of Dice Coefficient of our proposed model exceeds 79.75% (8.59% higher than that of the original UNET model). Experimental results on various satellite images prove the advantages and superiority of the PSO-UNET approach. [ABSTRACT FROM AUTHOR]

Data acquisition problem on large distributed wireless sensor networks (WSNs) is considered as a challenge in the growth of Internet of Things (IoT). Recently, the combination of compressive sensing (CS) and routing techniques has attracted much attention of researchers. An open question of this combination is how to integrate these techniques effectively for specific tasks. On the other hand, CS data reconstruction process is considered as one of the CS challenges because it requires to recover N data from only M measurement where M<

The decision-making problems based on fuzzy inference systems have received much attention from the worldwide scientific community. The M-CFIS-FKG model is considered one of the best models to solve classification problems based on uncertain and amplitude input datasets. It can infer and find the output labels of new samples that are not in the fuzzy rule base. Recently, the FKG-Pairs model has been considered an extension of FKG in the M-CFIS-FKG model by combining attribute pairs to infer and find the output labels in the cases of input datasets with incomplete gathering. It has overcome the limitation of the M-CFIS-FKG model. However, with real-time large input datasets or too-small training datasets, the FKG-Pairs model has also revealed limitations as it takes too much time to compute, and the accuracy is still relatively low. This paper has proposed a decision-making model in extreme cases (called FKG-Extreme) by using the Q-learning-based FKG-Pairs approach to enrich the fuzzy rule base after each time step with the cumulative mechanism of new rules. The proposed FKG-Extreme model has overcome the FKG-Pairs model's limitation in the extreme case. It has significantly improved the system's accuracy, while the computation time is acceptable. To validate the proposed model, we conducted experiments based on the standard UCI datasets, and the experimental results demonstrated that the system's performance in terms of accuracy is superior to the other reliable models in case of too small training data. Furthermore, the results of the two-way ANOVA also proved that the FKG-Extreme model is better than the FIS and FKG-Pairs models. [ABSTRACT FROM AUTHOR]