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Every music composition has a composer at the core of its building block, molding it into a style of their own. The creative compositional style of a composer varies dynamically with every composer which is perishable and inimitable but cannot be preserved. This paper proposes a hybrid style transfer model which is an end-to-end approach to transfer style, compose as well as evaluate the style transfer accuracy in the domain of the target music composer. The paper focuses on 3 composer maestros Liszt, Chopin and Schubert taken from the Maestro dataset. The main step of music composer style transfer is accomplished in 3 systematic steps, training composer classifiers according to feature sets, generating a model of a composition in a particular composer’s style from noise, and finally style transfer and its evaluation. The 3 steps are interconnected in a way that each step is the building block for the next step. The GAN architecture of the style transfer step is built out of the GAN architecture of the second step. The compositions generated from each architecture is finally evaluated by the common pre-trained classifier of the first step. The highest accuracy obtained is 80% for composer classification using the maestro dataset, 77.27% for the classification of the generated style transferred version of a composition into the target composer class using the pre-trained classifiers.

This paper presents the Storybox Methodology which combines a novel framework for structuring knowledge and conversations around a story (D-PAF), with a live chatroom-based training approach that builds the conversation knowledge base via live chatroom interactions. Chatbots have achieved success as intelligent interfaces in education, health, sales and support, but their move towards mainstream adoption has been hindered by the large amount of development resources required, in terms of data collection, preparation, user testing and technical knowledge. The complexity of the development task often necessitates both a system author and a domain expert working effectively together, adding further complexity and risk. Overcoming these barriers could increase feasibility of chatbots in a range of expert contexts. In education, there are groups of learners who do not enjoy reading and writing. Storytelling chatbots might be able to introduce these groups to enjoyable new ways to read and write, having a beneficial impact on their education and future prospects. This paper proposes the Storybox Methodology for the rapid development of storytelling chatbots. Storybox is evaluated by creating, training and testing ‘The Ghost’, a chatbot enacting Hamlet's Ghost character from William Shakespeare's dramatic tragedy. The results showed that after a period of live chatbot training of only 25 training conversations, The Ghost was able to conduct convincing conversations with participants.

Lithology identification is an important part of petroleum drilling engineering. Accurate identification of lithology is the foundation to ensure the smooth operation of drilling engineering. Conventional lithology recognition mainly relies on human experience. The recognition accuracy depends on the level of technical personnel and the recognition response time is lagging. It is difficult to meet the demand. How to achieve rapid and intelligent recognition of lithology is one of the core technical problems faced by oil drilling. In order to solve this problem, this paper uses the mechanical specific energy and other ground parameters to establish a rapid and intelligent recognition method of lithology based on the simulated annealing optimization support vector machine model. In order to improve the accuracy of the model recognition, a large number of methods have been developed from two classes and multiple classes, simulation analysis results show that the lithology recognition model based on the principle of mechanical specific energy and the simulated annealing optimization support vector machine algorithm can predict a priori unknown data with a prediction accuracy of over 90%. Compared with the support vector machine model and the K-means model, simulated annealing optimization support vector machine is used for comparative analysis, the algorithm to establish a lithology recognition model has better performance and higher accuracy. The intelligent identification model of lithology based on the principle of mechanical specific energy and simulated annealing optimization support vector machine algorithm established in this paper can quickly and accurately identify lithology, and provide new technical support for oil drilling formation analysis.

Nowadays the use of visual navigation in orchard is rapidly growing, while current research mainly focuses on orchard row detection or obstacle detection separately. The economic, fast and reliable navigation vision system that completing orchard row following and obstacle avoidance simultaneously and the obstacle avoidance algorithm for vision system without complex multiple navigation systems combining are needed further research. In this paper, a trinocular vision system for orchard vehicle is set up and the obstacle avoidance algorithm based on coupling of geometric constraint and virtual force field is designed for its vision system. First of all, based on analyzing the different vision system characteristics and the detection demand of orchard obstacle avoidance the trinocular vision system for orchard vehicle is set up based on a wide-angle camera and binocular stereo vision system. Then, the image processing algorithm of orchard row and obstacle detection is designed. For orchard row detection, the trunk regions enhancement algorithm is designed based on grayscale morphology filtering the trunk regions prediction algorithm is designed based on optical flow method to improve the trunk regions detection speed and accuracy. For obstacle detection, the background equalization algorithm based on H-channel image characteristics and the interference weaken algorithm with G-channel image characteristics are designed. Based on the row and obstacle detection results the algorithm for obstacle avoidance in orchard based on coupling of geometric constraint and virtual force field is designed. In the obstacle avoidance process, the virtual force field method is used as the mainly control of the vehicle obstacle avoidance process, and the geometric constraint between visual model of trinocular vision system and obstacle position is coupling with it to fulfill the avoidance demand of the obstacle with special shape and other condition that influence the virtual force field calculation. The experimental results show that the trinocular vision system for orchard vehicle built in this paper has strong adaptability to the actual environment which is accurate and stable for orchard row detection and obstacle detection simultaneously. The average deviation is 4.76 cm and 7.05 cm at 0.5 m/s and 1.0 m/s respectively, the average deviation of obstacle distance detection in the Z-axis direction is 3.18 cm, and in the X-axis direction is 0.45 cm. And the obstacle avoidance algorithm designed in this paper is effective in the orchard which can meet the actual production requirements. The research results will lay a foundation for the development of intelligent equipment and unmanned management in orchard.

This paper addresses a bi-objective dynamic multiprocessor open shop scheduling problem in which the simultaneous objectives of minimizing both the mean weighted flow time and the makespan are considered. This problem is commonly encountered in maintenance and healthcare diagnostic systems. Since it is NP-hard for both objectives, efficient heuristics are needed to quickly generate a set of non-dominated solutions that a decision maker would choose from. For this sake, two metaheuristic approaches based on the non-dominated sorting genetic algorithm (NSGA-II) and the multi-objective grey wolf optimizer (MOGWO) are developed in this paper. Both metaheuristics are hybridized with simulated annealing (SA) local search. Parameter tuning computational experiments are conducted first on a set of 30 small instances from the literature for which Pareto optimal solutions are known. Then, computational experiments on large randomly generated instances are conducted. Computational results for small instances show that the NSGA-II is capable of generating non-dominated solutions that are very close to the optimal Pareto front. Results also reveal that the performance of the NSGA-II is better in most of the cases compared to the MOGWO under different settings of the studied problem for both small and large instances. However, for large instances with large number of workstations and jobs, low loading level and high percentage of busy machines at the beginning of the schedule, the difference in performance between both metaheuristics is minor.

Financial services industries, such as insurance, increasingly use data from their broad cross-section of customers and follow these customers over time. In other areas such as medicine, engineering, and communication systems, it is well known that following subjects over time may result in biased data, for example, the so-called ”dropout effect”. This paper introduces techniques to address dropout commonly encountered in the insurance domain. Specifically, in the insurance context, multivariate claims outcomes may be related to a customer’s dropout or decision to lapse a policy. Insurance claims outcomes are also naturally a hybrid with both discrete and continuous components, which adds complexity to model calibration. Decision makers in the insurance industry will find our work provides helpful guidance in integrating customer loyalty, especially with bundled coverages. This paper introduces a generalized method of moments technique to estimate dependence parameters where associations are represented using copulas. This is especially useful for large data sets. The paper describes how the joint model provides new information that insurers can use to better manage their portfolios of risks. An application to a Spanish insurer data set is presented.

The research and practice of information systems have progressed by leaps and bounds due to the emergence of big data analytics and machine learning. The paper undertakes a bibliometric study to analyze the contributions of major authors, universities/organizations, and countries in terms of productivity, citations, and bibliographic coupling. A sample of 2160 articles from the Scopus for the period 2006–2020 is the basis of the study. The publications are grouped into five clusters, of which Cluster 1 is consistently dominant in the information systems publication landscape. Cluster 2 includes published studies on the Internet of Things, security, and cloud computing, which have also been widely researched. Cluster 3, the third-largest cluster, has attempted to investigate social media analytics. Cluster 4 aims to look into the impact of classification and predictive, which is found to have sustained research interest. Topics with scant coverage in terms of papers are primarily in Cluster 5, indicating saturation in the area and the need for conducting inter-disciplinary studies. The results of our study provide valuable insights for potential contributors and global audiences in terms of emerging topics for research.

Due to speckle noise, changes in kidney shape and size between patients, and similar regions, segmenting kidneys in ultrasound images is challenging. To alleviate this challenge, we proposed a novel CNN model, namely multi-scale fusion network of structural features and detailed features (SDFNet), to segment kidneys accurately and robustly. Specifically, the architecture includes structure feature extraction network (S-Net), detail information extraction network (D-Net) and multi-scale fusion block (MCBlock), which are in charge of extracting structural features, capturing texture details and merging features, respectively. In S-Net, we designed a boundary detection (BD) module to obtain more complete kidney structural features. In addition, this paper also designed a step-by-step training mechanism to improve the generalization ability of the SDFNet. We validated the proposed method and compared the same kidney ultrasound dataset with several state-of-the-art methods using six quantitative indicators. The results demonstrate that the proposed approach achieves the best overall performance and outperforms all other approaches on kidney ultrasound image segmentation. It is worth noting that this paper quantitatively analyzes the loss function of segment kidney ultrasound images. This work is a good reference for future research.

This short communication provides a discourse emerged after reading “Stacking ensemble based deep neural networks modeling for effective epileptic seizure detection, Expert Systems with Applications, 148, 113239, 2020.” The discussed paper proposes a novel application of stacking-based ensemble for seizure detection, where several deep neural networks were used as base classifiers. The ensemble design and experimental results presented by the author show some weaknesses, which is indicated by, one of which, an inability of the proposed model to outperform previous studies. In this note, controversy of the discussed paper is explained and an improved version of stacking-based deep neural network is also further introduced and detailed to prevent it in the future.

Building facilities condition assessment is considered a fundamental aspect of an effective decision-making maintenance management plan to fulfill service requirements. A noticeable dearth of studies is believed to have delivered condition assessment approaches for existing buildings; however, these approaches are still deemed premature, with some limitations in demand enhancement. This paper presents a novel physical condition assessment framework for existing educational buildings that contribute to the body of knowledge by offering a state-of-the-art approach incorporating an Artificial Neural Network (ANN) predictive model and a Structural Equation Model (SEM). The ANN predictive model aims to forecast the future condition-rating states for each facility component in various building spaces. Simultaneously, the SEM determines the proportionate weights of building facilities components. The primary objectives of this paper are to prioritize building components for maintenance purposes and record the potential effects of several parameters influencing the condition state of building components. These objectives can be achieved via four sequential modules: 1) scan to BIM module; 2) condition assessment prediction module; 3) proportionate weight determination module; and 4) entire space rating value module. Condition-monitoring data on six different buildings' internal components are analyzed to anticipate their future condition. The components carried out are: 1) wooden flooring tiles; 2) gypsum board ceiling tiles; 3) wooden doors; 4) wooden windows, 5) split air conditioner units; and 6) desktop computers. The overall coefficient of determination (R2) of the developed ANN models for the predicted six components conditions are 0.99, 0.99, 0.927, 0.88, 0.97, and 0.972, respectively.

This paper addresses the problem of how to represent education system measurement definitions and the data used to derive them. ISO 37120 is standard for measuring city performance. It defines 100 indicators of which seven focus on Education. As cities adopt the standard and begin publishing their indicator values, citizens, city bureaucrats and politicians will be able to see how well their education systems perform relative to other cities. Hence, education systems will be subject to greater scrutiny and will have to provide evidence-based explanations for their performance. In order to enable the automated analysis of city performance, four problems must be solved: 1) how are indicator definitions represented? 2) how is the data used to derive an indicator value represented? 3) how is educational system specific knowledge represented, and 4) how is city specific educational system knowledge represented? In this paper we present an Education Ontology designed to represent ISO 37120 education indicators and the information delineated above. The ontology is designed to enable an intelligent agent to perform consistency and root cause analysis of city indicators. It can also be used by cities to publish their education indicators and data, using Semantic Web standards, on their Open Data portals.

Missing values and the inconsistency of the measures of the knowledge economy remain vexing problems that hamper policy-making and future research in developing and emerging economies. This paper contributes to the new and evolving literature that seeks to advance better understanding of the importance of the knowledge economy for policy and further research in developing and emerging economies. In this paper we use a supervised machine deep learning neural network (DLNN) approach to predict the knowledge economy index of 71 developing and emerging economies during the 1995–2017 period. Applied in combination with a data imputation procedure based on the K-closest neighbor algorithm, DLNN is capable of handling missing data problems better than alternative methods. A 10-fold validation of the DLNN yielded low quadratic and absolute error (0,382 +- 0,065). The results are robust and efficient, and the model’s predictive power is high. There is a difference in the predictive power when we disaggregate countries in all emerging economies versus emerging Central European countries. We explain this result and leave the rest to future endeavors. Overall, this research has filled in gaps due to missing data thereby allowing for effective policy strategies. At the aggregate level development agencies, including the World Bank that originated the KEI, would benefit from our approach until substitutes come along.

Advances in neuroimaging techniques have enabled early diagnosis and better understanding of neuro-psychological disorders using resting state - functional Magnetic Resonance Images (rs-fMRI). However, factors like the large intra-class variability, high homogeneity, limited sample size and varied acquisition methodologies in aggregated datasets have limited their classification performance. These issues especially are highly prevalent in the automatic diagnosis of Attention Deficit Hyperactivity Disorder (ADHD). In this paper, a Discriminative Spatial Filtering Method (DSFM) is developed to improve the separability between the two classes in the dataset by automatically identifying regions of the brain with abnormal activities. Further, DSFM uses an orthogonal projection to extract highly separable features from raw rs-fMRI data of the identified regions of the brain. Using a projection based learning classifier DSFM achieved a classification accuracy of 73.83 % on the publicly available ADHD200 dataset. The results show that, selective inclusion of data from the regions of the brain with discriminant activity identified using DSFM removes the redundant features and improved the classification performance (improvement of 3.5 % over baseline). Further, the brain activity maps derived by inverse mapping of the DSFM spatial filters describe the nature of the abnormal brain activities and identify the corresponding regions responsible for the cognitive symptoms in neuro-psychological disorders like ADHD. The results in this paper clearly show that DSFM is a reliable diagnostic tool with interpretable results critical for the accurate understanding the etiological factors of neuro-psychological disorders like ADHD using rs-fMRI.

Music radio data is currently underutilised in radio program management. Software tools that listen to and analyse music airplay are in many markets nonexistent, limited, or unaffordable. In this paper we present a novel knowledge discovery and visualisation framework for broadcast radio, ZeitMetric. The ZeitMetric framework uses machine learning and music information retrieval techniques to label radio audio automatically for knowledge discovery. The framework incorporates a novel music dataset collection technique (MusiGrab) to leverage online music services for ground-truth data, as well as a novel knowledge visualisation and presentation technique based on self-organizing maps (ZeitViz). The framework is compared to what little literature relating to this topic exists, and a set of requirements for a high-quality broadcast radio knowledge discovery is developed. MusiGrab specifically is compared to an existing static music information retrieval dataset and shown to offer superior results in this context. Future research directions using and extending the framework are also discussed. On acceptance of the paper, code for a use-case of the MusiGrab dataset collection technique will be released on GitHub.

Recently, more and more real life problems are solved using artificial intelligence (AI) algorithms. One of the biggest challenges when working with AI is the selection and tuning of the best algorithm for solving the problem. The results generated by a given AI algorithm heavily depend on the way in which its hyperparameters are set. In most cases the process of algorithm selection and tuning is a manual, time consuming process in which the developer, based on experience and intuition tries to find the best solution from quality and execution time perspective. In this paper we present a method for solving the problem of AI algorithm selection and tuning, without human intervention, in a fully automated way. The method is a hybrid approach, a combination between particle swarm optimization and simulated annealing. We compare our approach with other similar tools like Auto-sklearn or Hyperopt-sklearn. We demonstrate the time efficiency and high accuracy of this method with some experiments on some known datasets. The paper also presents a platform for AI processing that include a set of procedures and services necessary in case of automatic processing of big datasets as well as the method for AI algorithm selection and tuning. This platform is useful for researchers and developers in an incipient phase of application development, when the best solution must be decided; it is also useful for specialists in different domains (physics, industry, economy) with less experience in using AI algorithms, but which has to process huge amount of data in an automated way.

Convolutional neural network is a prominent innovation in computer vision but is often troubled by problems such as dark light, turbidity, blur and high similarity to the background when applied to underwater object detection. Underwater object detection is one of the basic techniques of underwater grasping automation which plays a very important role in ocean detection and fishery of aquatic products. This paper presented an automatic detection method of underwater sea cucumber based on deep learning, which will provide effective technical support for the automated breeding and harvesting of sea cucumber. The Shortcut Feature Pyramid Network (S-FPN) proposed in this paper improves the existing multi-scale feature fusion strategy through shortcut connection. The ablation experimental results show that the mean average precision (mAP) of S-FPN reaches 91.5% which outperforms the baseline Feature Pyramid Network (88.6%), YOLO v3 (83.7%) and SVM-HOG (61.6%). To resolve the problem of complex environmental background interference of ocean floor, we proposed a Piecewise Focal Loss (PFL) function for balancing the positive and negative samples such that the algorithm can focus on the training difficulty of hard (i.e., positive) samples. And the ablation experimental results show that the mAP of PFL reaches 92.3% which outperforms the baseline Cross Entropy (91.5%) and Focal Loss (91.8%). Also, we chose Exponential Linear Unit as the optimization strategy, and Adaptive Moment Estimation as the activation function by ablation research, finally the mAP reached 94%.

This paper presents a novel multi-pooling architecture generated by combining the advantages of wavelet and max-pooling operations in convolutional neural networks (CNNs), focusing on semantic segmentation tasks. CNNs often use pooling to reduce the number of parameters, improve invariance to certain distortions, and enlarge the receptive field. However, pooling can cause information loss and thus is detrimental to further operations such as feature extraction and analysis. This problem is particularly critical for semantic segmentation, where each pixel of an image is assigned to a specific class to divide the image into disjoint regions of interest. To address this problem, pooling strategies based on wavelets-operations have been proposed with the promise to achieve a better trade-off between receptive field size and computational efficiency. Previous works have confirmed the superiority of wavelet pooling over the traditional one in semantic segmentation tasks. However, we have observed in our computational experiments that the expressive gains reported from the use of wavelet pooling in other segmentation tasks were not observed in the scope of aerial imagery due to imprecision in the segmentation of image details. The combination of wavelet pooling and max-pooling, a solution not yet reported in the literature, can address that issue. Such gap observed in the pooling area motivated the two proposals that are the main contributions of this paper: (a) A new multi-pooling strategy combining wavelet and traditional pooling in a new network structure suitable for aerial image segmentation tasks; (b) Two-stream architectures using the traditional max-pooling and wavelet pooling as streams. These proposals were implemented using the Segnet, a known architecture for semantic segmentation. The computational experiments, based on the IRRG images from the Potsdam and Vaihingen data sets, demonstrated that the proposed architectures surpassed the original Segnet architecture’s performance with results comparable to state-of-the-art approaches.

The variety and plethora of research papers available on the Web motivated researchers to propose models that could assist users with personalized citation recommendations. In recent years, citation recommendation models using Network Representation Learning (NRL) methods have shown promising results. Nevertheless, existing NRL-based models are limited in terms of exploiting semantic relations and contextual information between the objects of bibliographic papers’ networks. Additionally, these models cannot adequately explore the structure of heterogeneous information networks, topical relevance, and relevant semantics. Consequently, they suffer from network sparsity and inadequate personalization problems. To overcome these shortcomings, we present a network embedding model termed as Global Citation Recommendation employing Generative Adversarial Network (GCR-GAN). The proposed model exploits the Heterogeneous Bibliographic Network (HBN) to generate personalized citation recommendations. In particular, the proposed model utilizes semantic relations corresponding to the objects of the heterogeneous bibliographic network and captures network structure proximity employing the Scientific Paper Embeddings using Citation-informed Transformers (SPECTER) and Denoising Auto-encoder networks to learn semantic-preserving graph representations. Compared to baseline models, the recommendations generated by our model over the DBLP and ACM datasets prove that it outperforms baseline methods by gaining almost 11% and 12% improvement in terms of Mean Average Precision (MAP) and Normalized Discounted Cumulative Gain (nDCG) metrics, respectively. Furthermore, we analyzed the effectiveness of the proposed model considering network sparsity issue, where our model gains almost 7% better recall@100 score against the second-best counterpart.

In the Authorship Attribution (AA) task, the most likely author of textual documents, such as books, papers, news, and text messages and posts are identified using statistical and computational methods. In this paper, a new computational approach is presented for identifying the most likely author of text documents. The proposed solution emphasizes lazy profile-based classification and, by using the Term Frequency-Inverse Document Frequency (TF_IDF) scheme, introduces a new measure for identifying important terms of documents. The importance of the terms is then used to calculate the similarity between an anonymous document and known documents. The proposed solution works with raw text documents and does not require any NLP tools for preprocessing, which makes it language-independent. The efficiency of the proposed solution has been evaluated by conducting several experiments on two English and Persian datasets, each of which contains six corpora with different number of authors. The obtained results demonstrate that the proposed solution outperforms state-of-the-art stylometric features, employed by seven well-known classifiers, by obtaining 0.902 accuracy for the English dataset and 0.931 accuracy for the Persian dataset. In addition, supplementary experiments have been conducted to evaluate the effects of documents’ length on the accuracy of the proposed solution, to examine the computation time of the proposed solution and competitive classifiers, and to identify the most effective stylometric features and classifiers.

Crowdsourcing as an important computing resource for the Crowd-based Cooperative, crowdsourcing workers, due to the complexity of their personnel composition and the ambiguity of group characteristics, make it difficult to accurately recommend suitable worker groups for the task. Recommending crowdsourcing tasks to suitable workers can greatly improve the efficiency and quality of the execution of crowdsourcing tasks. The current crowdsourcing recommendation algorithm is based on the single characteristics of the workers, ignoring the multi-dimensional characteristics of the workers and the community characteristics of the crowd. This paper proposes a worker group recommendation method based on multi-community collaboration (GRMBC) by utilizing the worker characteristics information extracted from crowdsourcing platform. Based on the characteristics of workers'reputation, preference and activity, the method divides the worker group into several characteristics communities with similar behavior to discover the potential multi-community structure in the crowdsourcing worker group, and then selects Top-N worker group for recommendation through the interaction among multi-communities. At the same time, we also proposed two mitigation strategies for the cold start problem of data in crowdsourcing. This paper uses the public data collected by AMT to do the experiments, and compares the aggregation results of the recommendations generated by different algorithms. The results show the recommendations generated by the GRBMC algorithm proposed in this paper performs the best comprehensively. When the GRBMC algorithm that considers both worker attributes and communi-ty characteristics has an accuracy improvement of 0.03 ~ 0.04 compared with not using the recommendation algorithm on each index, the accuracy of 0.01 ~ 0.02 is improved com-pared with the method considering single characteristic. Moreover, compared with the individual recommendation method of the workers, the recommendation method of the workers' group is more in line with the platform requirements, and can well reflect the wisdom of crowd wisdom.

In the field of medicine, decision-making has traditionally been carried out based on the best available scientific information and the experience of specialists using data found in analog formats such as radiographies, medical reports, and handwritten notes, among others. In this sense, the Big Data phenomenon is changing the world of medicine since the technologies that have been developed have made available to researchers and clinicians enormous amounts of data in digital formats that can be used to complement or help in complex tasks such as mentioned decision making. A key element in this process is data analysis techniques, since without them it is not possible to exploit the information. Currently the most used techniques are based on algorithms in the area of artificial intelligence and more specifically machine learning. This paper focuses on a specific domain of medicine, renal replacement therapies for end-stage renal disease, where machine learning is beginning to be used as a complementary tool to predict or make decisions. This paper provides a narrative review of the main machine learning methods that are being used to conduct end-stage renal disease treatment analyses.

This paper develops smoothing data association based on integrated probabilistic data association (FLIPDA-S) tracker to identify a multicopter UAV (MUAV) and estimate its trajectory in an uncertain and cluttered environment. Recently, a number of unidentified MUAVs have been flown in prohibited airspace and a radar system has been utilized to identify the MUAV and track the trajectory. However, target tracking methods could have difficulties to track the target MUAV through randomly distributed and moving objects. The vehicle state estimation (VSE) algorithm, which utilize FLIPDA-S in this paper, adopts UAV kinematics and dynamics for tracking a UAV in clutter and false targets with significant UAV identification performance. The performance of the method has been validated with and without an initial position of the target MUAV. Both numerical simulation and experiments are demonstrated to verify the effectiveness and accuracy of FLIPDA-S with VSE algorithm to track UAVs in an uncertain and clutter environment.

In this paper, we propose a novel hybrid fuzzy–metaheuristic approach with the aim of overcoming premature convergence when solving multimodal single and multi-objective optimization problems. The metaheuristic algorithm used in our proposed approach is based on the imperialist competitive algorithm (ICA), a population-based method for optimization. The ICA divides its population into sub-populations, known as empires. Each empire is composed of a high fitness solution—the imperialist—and some lower fitness solutions—the colonies. Colonies move towards their associated imperialist in each empire to achieve better status (higher fitness). The most powerful empire tends to attract weaker colonies. These competitions and movements can be enhanced for better algorithm performance. In our hybrid approach, a global learning strategy is devised for each colony to learn from its best-known position, its associated imperialist and the global best imperialist. A fast-evolutionary elitism local search is used to enhance the collaborative search mechanism (competition) in each empire, and thus the overall optimization performance may be improved. Other main evolutionary operators include velocity adaptation and velocity divergence. To address parameterization and computational cost evaluation issues, two fuzzy inferencing mechanisms are designed and used in parallel: one is a learning strategy adaptor in each run, and the other is a smart evolution selector in each running window. For Pareto front approximation, fast-elitism non-dominated sorting is applied to the solutions, and a novel penalized sigma diversity index is designed to estimate the diversity (power) of solutions in the same rank. Comprehensive experimental results based on 22 well-known single-objective and 25 multi-objective benchmark instances clearly show that our proposed approach has better solution quality compared with other popular metaheuristics and state-of-the-art ICA variants. The proposed approach can be used as an optimization module in any intelligent decision-making systems to enhance efficiency and accuracy. The designed fuzzy inferencing mechanisms can also be incorporated into any single- or multi-objective optimizers for parameter tuning purposes, to make the optimizers more adaptive to new problems or environments.

In this paper, I use the MTS-Pathfinder approach to construct the value migration network and then study the network’s topological properties. This is the first attempt to build a network that includes in its structure the process of enterprise value migration on the stock exchange. Based on data from companies listed in the US market in December 2019 and applying a rigorous approach I demonstrate that the in- and out-degree distribution follow a power-law model. The study also reveals that the value migration network presents a disassortative behavior. Furthermore, I show that the relationship between the company's central position in the value migration network and return on shares has a positive impact for companies in the inflow phase and negative for companies in the outflow phase.

The elicitation of expert knowledge in a Bayesian model is a big challenge. Elicitation requirements increase exponentially with the size of the model, making the expert’s task more difficult and generating problems of quality or coherence of the elicited result. In this paper, we develop an extended Cluster-WSA method using several clusters as an elicitation method. A general formula to calculate the number of information to elicit and a generic algorithm for this method are developed. A selection procedure of some elicitation methods is established and the usefulness of the Cluster-WSA method is verified. The case study of parcel delivery with a drone is used to apply our method, to build a decision support system for diagnosis and prediction, and to make a performance indicator system.

In this paper, we propose a novel framework for transferred deep learning-based anomaly detection in hyperspectral images. The proposed framework includes four main steps. Firstly, the image2019s spectral dimension is reduced by applying the principal component analysis (PCA) to decrease computational time. Secondly, a deep convolutional neural network (CNN) is trained using only one image to learn the pixels2019 similarities in a picture. Consequently, a novel and well-designed algorithm entitled object area filtering (OAF) is employed to benefit from this learned similarity for extracting objects in the image. The OAF removes irrelevant objects by comparing their area to an acceptable anomaly area range. Lastly, the final result is obtained by multiplying the network output and binary map of anomalies. The receiver operating characteristic (ROC) is employed to evaluate the proposed framework. Extensive experimental evaluations demonstrate that the proposed framework substantially outperforms a significant number of comparable state-of-the-art methods. Finally, we empirically verify that the deep network exhibits excellent domain adaptability.

Satellite images have become more easily accessible due to government programs and the availability of commercial earth observation satellites. The increased volume of data and the recent advancement in deep learning-based techniques have made possible the development of fast and accurate methods for satellite image segmentation, which aims at automatically giving each pixel in the satellite image a label of the underlying object/ground type. However, despite the abundant supply of raw satellite images, the labelling of these images is a manual task and is therefore time and labour intensive. When a large labelled dataset is absent, resorting to a smaller data source significantly hampers the performance of the machine learning model. Therefore, an approach which mitigates the effects of using small labelled datasets should be investigated. This paper provides a solution to the problem of insufficient data samples for satellite image segmentation. To this end, two methods are developed and compared with the benchmark U-Net model: the use of transfer learning on an Xception architecture, and the use of steerable filters. The first model transfers knowledge from a pre-trained Xception model and predicts with a multi-resolution feature fusion module, specifically designed to recover fine details. The latter further improves the capability of our neural network model in handling data with high rotational variation. Both models are experimentally shown to significantly enhance the segmentation performance despite a small-sized training dataset.

This paper proposes a novel nature-inspired meta-heuristic optimizer, called Reptile Search Algorithm (RSA), motivated by the hunting behaviour of Crocodiles. Two main steps of Crocodile behaviour are implemented, such as encircling, which is performed by high walking or belly walking, and hunting, which is performed by hunting coordination or hunting cooperation. The mentioned search methods of the proposed RSA are unique compared to other existing algorithms. The performance of the proposed RSA is evaluated using twenty-three classical test functions, thirty CEC2017 test functions, ten CEC2019 test functions, and seven real-world engineering problems. The obtained results of the proposed RSA are compared to various existing optimization algorithms in the literature. The results of the tested three benchmark functions revealed that the proposed RSA achieved better results than the other competitive optimization algorithms. The results of the Friedman ranking test proved that the RSA is a significantly superior method than other comparative methods. Finally, the results of the examined engineering problems showed that the RSA obtained better results compared to other various methods.

Nowadays, cloud customers use cloud services increasingly to satisfy their demands. Usually, a significant number of customers are immature and inexpert and cannot express their needs accurately and numerically. They usually express their needs verbally and in the form of linguistic terms. On the other hand, the experienced customers express their needs numerically and accurately. In this situation, a recommendation system can be considered as one of the most useful ideas to support all type of customers. However, current recommendation systems (e.g., collaborative filtering based recommendations) meet customer requests that are accurately and numerically expressed. To support all types of customers, the construction of a strong recommendation system to analysis the demands expressed by customers (experienced and inexperienced) and to recommend suitable services is vital. As another important matter, cloud customers and services have been geographically distributed. Identifying the location of customers and services has a significant effect on the quality of services offered to customers. Therefore, the recommendation system should consider the location of customers and services in order to provide better services. In this paper, we introduce an efficient method to construct a powerful recommendation system which can provide suitable services considering the preferences of the customer and their location. The proposed recommendation system comprises two algorithms. The first algorithm is a fuzzy clustering algorithm, named FCA, that can well classify the location of customers and services. The second algorithm is an iterative adaptive neural-fuzzy algorithm, named IANFRA, which receives the preferences of the customer along with their location and identifies suitable services based on the locations clustered by FCA and the demands of customers (experienced and inexperienced). Finally, the feasibility of the proposed method has validated in terms of accuracy and scalability through conducting extensive experiments on a real distributed service quality dataset WS-DREAM. The evaluation results illustrate that both the service recommendation accuracy in the prediction of quality of services and the scalability, when the volume of the dataset is huge, have been improved.

Today’s organizations are witnessing a growing complexity in making interconnected decisions. Where individuals have a wider range of decisions to influence, the consequence of decisions far more propagate across the system, and the business environment continually influences the status of the system. Predicting the cascading effects of decisions in such situations would be very problematic yet can have several implications for managers and executives to think beyond organizational silos and make local decision with a bigger picture of emergent consequences in mind. A prominent challenge within this realm is the ever-increasing complexity of decision propagations, especially when incorporating the role and influence of people involved in decision-making. This paper tackles this challenge from an engineering change perspective, with the focus on computing the compound risk of decisions when their consequences concurrently propagate across the system. We introduce an interactive tool called Virtual Net Propagator, which incorporates organizational dynamics into decision analysis, with the aim to identify change opportunities and effective set of interventions. Illustrated by a field engineering case study, it is demonstrated that the proposed tool can provide detailed knowledge on how decisions are interconnected and how systemic (cascading) effects of a decision propagate through causal pathways, so highlighting key influencers along with role and influence of interfacing (hidden) players.

In this paper, we present a fraud detection framework for publicly traded firms using an optimization approach integrated with a meta-heuristic algorithm known as Beetle Antennae Search (BAS). Existing techniques include human resources, like financial experts and audit teams, to determine the ambiguities or financial frauds in the companies based on financial and non-financial ratios. It is a laborious task, time-consuming, and prone to errors. We designed an optimization problem to minimize the loss function based on a non-linear decision function combined with the maximization of recall (Sensitivity and Specificity). We solved the optimization problem iteratively using the BAS. It is a nature-inspired algorithm and mimics the beetle’s food-searching nature. It includes a single searching particle to find an optimal solution to the optimization problem in n -dimensional space. We used a benchmark dataset collected from SEC’s Accounting and Auditing Enforcement Releases (AAERs) for the simulation. It includes 28 raw financial variables and the data collected between 1991- 2008 . For the comparison, we evaluated the performance of BAS with the recently proposed approach using RUSBoost. We also compared it with some additional algorithms, i.e., Logit and SVM-FK. The results showed that BAS is comparable with these algorithms and outperformed them in time consumption.

Automatic detection of mass in mammograms is a big challenge and plays a crucial role in assisting radiologists for accurate diagnosis. Applying excellent deep learning based detection networks to medical image is a direction. In this paper, an anchor-free YOLOv3 network is proposed for mass detection in mammogram. The main improvements are as follows. Anchor-based (AB) detection scheme may involve too many negative boxes, which may aggravate the unbalance between positive boxes and negative boxes. Aiming at this problem, a novel anchor-free (AF) detection scheme is developed for box regression. To alleviate the problem caused by using mean square error (MSE) as box regression loss, generalized intersection over union (GIoU) loss is adopted to train bounding box regression. Focal loss is adopted as the objectness prediction loss, which can prevent the network degradation caused by the vast number of easy negatives. Moreover, a new feature aggregation manner by summing is designed and employed in the up to down pathway. Comparative experiments are performed on two databases (publicly available dataset INbreast and private dataset TXMD). For INbreast dataset, the proposed method achieved 0.95 true positive rate (TPR) with 1.7 false positives per image (FPs/I). For TXMD dataset, the proposed method achieved 0.94 TPR with 5.75 FPs/I. Experimental results demonstrate that the proposed method outperforms YOLOv3 network and gets a promising result compared with existing mass detection methods.

Deep learning tasks rely heavily on a large amount of training data, but collecting and annotating data daily is not practical. Therefore, Zero-shot learning (ZSL) has become important for the applications, where no labeled data is available during training. ZSL aims at recognizing unseen classes by semantic transfer of information from seen to unseen classes. In this paper, we have proposed an identifiable VAE (iVAE) based generative model to address conventional and generalized ZSL. The key to our approach is learning disentangled representations, where each dimension is statistically independent and responsible for generating data. Thus, VAE is a commonly used model for learning disentangled independent factors of variation from the data. Our goal is to learn a latent space representing significant information, that approximates the actual data distribution. Extensive experiments on five benchmark datasets, i.e. CUB, AWA1, AWA2, SUN and aPY, are performed for further evaluation in both settings.

Forecasting the box offices of movies is indispensable, whether it is for the stock investment decisions of the movie company, the movie sales strategy design of the advertising company, or the movie screening schedules of cinemas. However, this task is very challenging because it is affected by many complicated factors. In view of this complexity, we propose a method for forecasting the box offices of movies coming soon based on social media analysis. First, movie-related online reviews and their numbers are collected from microblogs. Second, several important features, i.e., the sentiment value of microblog texts, the consumers’ RFM value and the degree of moviegoers’ attention, are extracted or calculated. Subsequently, we use the extracted features to expand the parameters of the Bass model, and build two improved Bass models for forecasting the box offices of movies in the initial week and during the diffusion cycle, respectively. Finally, through fitting data progressively, we realize the forecasting task. The experimental results show that compared with the traditional forecast models and existing studies, the improved Bass method proposed in this paper has better performance, overall.

Recommendation systems rely on the historic data of users' purchases and their feedbacks to profile their preferences and make future recommendations. Most of these systems usually employ Collaborative Filtering (CF) models to analyze users’ ratings and infer the latent factors which represent the user and item features in k-dimensional latent space. However, the historical rating data used for recommendations are usually sparsed and unbalanced. Various approaches have been used to resolve these issues by combining the user’s ratings and reviews to better capture the user’s sentiments and make accurate recommendations. Other challenges comprise changes in users’ preferences and items’ perceptions over time. Therefore, this paper presents a new Sentiment Scoring Model (SSM) based on Long-/Short-Term Memory and a combination function that catches the sentiment bias between user rating and review to relieve the sparsity and unbalanced dataset. Next, we proposed an Adaptive LSTM (ALSTM) method that can model the drifting of user and item features to improve the recommendation accuracy. We show the performance of our model on the three real-world rating datasets from Amazon reviews, which comprises Fine Food, Baby, and Cell-phone & Accessories categories. The result shows the superiority of our proposed model over the existing static and dynamic models. The statistical test shows that all the performance gains are significant at p

Thermal cameras work well in harsh environments, but the quality of infrared images is not as high as visible light. Thermal to visible image translation can get rid of the image modal differences caused by various spectral characteristics. Nowadays, Generative Adversarial Network (GAN) can transform the images from one domain to another domain, but the generated images are still in a single channel in case of facial thermal to visible. In this paper, we propose a claw connection-based generative adversarial networks framework named ClawGAN for the facial thermal images to RGB visible images translation. We proposed the mismatch metric ( MM ) to measure the mapping relationship of paired images and use template matching to reduce MM of the dataset. Based on the CycleGAN framework, the synthesized loss and the generative reconstructed loss are added to the adversarial loss and the cycle-consistency loss to form a new objective function. And a claw-connected network is invoked to replace the U-net network structure of the generator for more feature preservation. The model is judged from subjective evaluation and objective evaluation based on image quality metrics such as PSNR (Peak Signal to Noise Ratio), SSIM (Structural Similarity), FID (Frechet inception distance), and face recognition accuracy. We divided the open datasets into bright light and dark light to research the effect of illumination. The experiments show that the proposed method has the lowest FID and the highest face recognition accuracy compared to the state-of-the-art methods. The proposed ClawGAN retains the structural features of thermal images while not only enhancing the quality of images but also effectively improving the observability of image translation results in both bright and dark light. The code is available at https://github.com/Luoyi3819/ClawGAN.

The prediction of stock price has always been a main challenge. The time series of stock price tends to exhibit very strong nonlinear characteristics. In recent years, with the rapid development of deep learning, the ability to automatically extract nonlinear features has significantly attracted scholars’ attention. However, the majority of the relevant studies have concentrated on prediction of the changes of stock market based on the data of the specific stock (e.g., transaction data, financial data, etc.), while those studies ignored the interaction between stocks of different industries, especially the interaction between the stocks of upstream enterprises and downstream enterprises in the industrial chain. This paper aims to propose a combination of transfer learning of industrial chain information and deep learning models, including multilayer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), for stock market prediction. These models are used to predict the trend of the 379 stock market indices by industry in China, and the DM test was employed for validation of the prediction results. It can be concluded that RNNs are not necessarily such an optimal choice for the prediction when dealing with specific time series data, and it could be justified by using the local interpretable model-agnostic explanations (LIME) algorithm. Hence, the MLP was selected to effectively improve the accuracy of the prediction of the stock market indices based on the transfer learning of industrial chain information. The investment strategy is constructed according to the prediction results, and the yield of maturity exceeds that of the buy-and-hold strategy.

Spherical fuzzy sets (SFSs) have recently become more popular in various fields. It was proposed as a generalization of picture fuzzy sets and Pythagorean fuzzy sets in order to deal with uncertainty and fuzziness information. The similarity measure is one of the beneficial tools to determine the degree of similarity between objects. It has many crucial application areas such as decision making, data mining, medical diagnosis, and pattern recognition. In the short time since their first appearance, some different distance and similarity measures of SFSs have been proposed, but they are limited through the literature. In this study, some novel distances and similarity measures of spherical fuzzy sets are presented. Then, we propose the Minkowski, Minkowski-Hausdorff, Weighted Minkowski and Weighted Minkowski-Hausdorff distances for SFSs. In addition, trigonometric and f-similarity measures are developed based on the proposed distances in this paper. The newly defined similarity measures are applied to medical diagnosis problem for COVID-19 virus and results are discussed. A comparative study of new similarity measures was established and some advantages of the proposed work are discussed.

In recent years, there has been a surge of interest in applying machine learning to graphs and networks that already exist in the world around us. The approach has been successfully used for domains as diverse as traffic management, e-commerce recommendation and public opinion monitoring. A critical aspect of representation learning for applied machine learning is feature engineering. Deep learning-based research in representation learning has developed methods for automatically learning a large number of potentially correlated features from original networks. However, most of these methods cannot be applied to heterogeneous networks, which are true expressions of the real-world. This is because they do not adequately capture the structure and semantics of different types of nodes in heterogeneous networks and the links between them. They also struggle to represent higher-order heterogeneous patterns of connection. This paper proposes a generalized motif-based higher-order representation learning method, MBRep, that learns triangle motif embedding in a network, on the basis of which it can obtain the embedding and representation of nodes in a heterogeneous network. Statistically, significant motif structures are extracted from the original heterogeneous network and combined with the heterogeneity of the nodes. A weight-biased random walk is then applied to the motif level higher-order network, using a SkipGram model to embed the motifs. After this, the embedding of the original network nodes is calculated using weighted averages and feature alignment. This can then be used for link prediction. We confirmed the effectiveness of MBRep by comparing its AUC and MRR performance with other state-of-the-art methods on three real-world datasets. Its adaptability was also validated by conducting a cold-start test.

This paper addresses the problem of classification when target data are subject to feature uncertainties. A robust approach based on Sobol sensitivity analysis is proposed to improve the robustness of support vector machine (SVM) models. SVM is a supervised machine learning method for pattern recognition whose performance depends on the definition of its hyperparameters and the quality of data. The proposed approach analyzes the impact of the uncertainties on the predictive performance of SVM based on Sobol’ sensitivity analysis. Afterwards, a new parameter is introduced to improve the robustness of SVM to the impact of uncertainties. The efficiency of this approach is evaluated by applying it to six real-world datasets. The results are then discussed and analyzed.

Development and improvement of region proposal algorithms have rapidly become one of the most critical research areas over recent years. The perfect accuracy of region-based recognition techniques has led to the use of proposal algorithms as an imperative core in various recognition problems. The main purpose of these algorithms is to extract effective regions of an image with an appropriate number that will reduce the search space and increase detection accuracy. The early development of these algorithms was based on a set of hand-crafted features. Recently, with advances in deep learning techniques, they have been widely and successfully applied to the region proposals. This paper reviews region proposal algorithms, theory, and evaluation metrics and also addresses the existing challenges. In addition, we present a classification for generating proposals, including classical and advanced methods based on hand-crafted features and deep learning, respectively. Both categories are described in details, and an extensive review of recent works is presented. The proposal improvement methods, including ranking algorithms, are also described. In total, more than 60 different algorithms have been studied and classified, and we also point out several applications based on region proposals.

Object detection with high accuracy is becoming increasingly important for industrial processes, such as producing printed circuit boards (PCBs). A false or missed detection during production can lead to serious quality issues. Therefore, an efficient detector that can maintain high quality is required for industrial applications. This paper proposes a method for improving detection accuracy while supporting real-time operations using the baseline YOLOv4. A new loss function for box regression called Gaussian intersection of union (GsIoU) is explored, which merges the predicted boxes under different anchors at the same position using a Gaussian function to calculate the box regression loss, improving the accuracy of the final box regression. The proposed PCB Component (PCBC) dataset is a benchmark comprising 18,948 images, 24 categories (containing the same component in different directions), and 508,313 components. Taking YOLOv4 as the experimental baseline on the PCBC dataset, the mean average precision (mAP) using the GsIoU has reached 86.9%, which is improved by 3.3% and 2.2% compared to using the CIoU and GIoU loss functions, respectively. The experiments were conducted on the COCO dataset to verify the generalization of the GsIoU. The detection accuracy of the GsIoU exceeds that GIoU and CIoU by 0.6% on AP50 and 0.6% on AP50:95, achieving 65.6% and 46.0% on the COCO test-val2017, respectively. The detection efficiency of the proposed method is the same as that of the baseline in the testing process and is slightly reduced in the training process owing to the synchronous calculation of the IoU, variance, and Gaussian operations on the network output. The experiments indicate that GsIoU is effective and efficient.

Face sketch synthesis plays a crucial role in face recognition for law enforcement applications. However, the current face sketch synthesis approaches generate sketches from photos based on a model trained by a certain database that is usually collected from individuals of the same ethnicity, and therefore such sketches merely inherit distinct facial distributions (shape and texture) of this database. This also makes such models inapplicable for real-world applications which mainly include multiple photo variations such as pose, lighting, skin color, and ethnic origin. In this paper, a unified face sketch synthesis model considering ethnicity issue as well as photo variations is proposed. A new deep learning scheme is designed to handle the generic visual representation and global structure of the face. Towards the final objective, the recent success of deep residual blocks is exploited and incorporated into a plain feedforward network, termed as DResNet, to learn a regression model for face sketch synthesis. A heterogeneous database containing photos with lighting, ethnicity, hair and skin variations is utilized for training the DResNet model. Extensive subjective and objective evaluations showed the superiority of the proposed DResNet method on state-of-the-art face sketch synthesis methods. Experimental results also demonstrated that the proposed DResNet method can be generalized to face sketch synthesis for real-world applications.

Recent deep learning based facial expression recognition (FER) methods are mostly driven by the availability of large amount of training data. However, availability of such data is not always possible for FER in the wild where the infeasibility of obtaining sufficient training samples for each emotion category. Therefore, in this paper, we introduce the few-shot learning to construct a deep learning model named Convolutional Relation Network (CRN) for FER in the wild, which is learnt by exploiting a feature similarity comparison among the sufficient samples of the emotion categories to identify new classes with few samples. Specifically, our method learns a metric space in which classification can be performed by computing distances to capitalize on powerful discriminative ability of deep expression features to generalize the predictive power of the network. To achieve this, the features are constrained to maximize the distance between the features of different classes and discover the commonality of the same classes. Extensive experiments on three challenging in-the-wild datasets demonstrate that the proposed model significantly outperforms state-of-the-art methods.

Image classification is a critical and meaningful task in image retrieval, recognition and object detection. In this paper, three-side efforts are taken to accomplish this task. First, visual features with multi-instance representation are extracted to characterize the image due to the merits of bag-of-words representations. And a new distance function is designed for bags, which measures the relationship between bags more precisely. Second, the idea of multi-view learning is implemented since multiple views encourage the classifier to be more consistent and accurate. Last but not least, the metric learning technique is explored by optimizing the joint conditional probability to pursue view-dependent metrics and the importance weights of the newly-designed distance in multi-view scenario. Therefore, we propose a multi-view multi-instance metric learning method named MVMIML for image classification, which integrates the advantages of the multi-view multi-instance representation and metric learning into a unified framework. To solve MVMIML, we adopt the alternate iteration optimization algorithm and analyze the corresponding computational complexity. Numerical experiments verify the advantages of the new distance function and the effectiveness of MVMIML.

Class-imbalance learning is an important research area and draws continued attention in various real-world applications for many years. Undersampling is a key method of class-imbalance learning in order to obtain a balanced class distribution, but it may discard potentially crucial samples and may be influenced by outliers or noises in imbalanced data. Multiview learning methods have shown that models trained on different views can help each other to improve their performances and robustness, but the existing imbalance learning approaches rely only on single-view samples. In this paper, we propose a m ultiview feature imbalance sampling method via m eta s elf- p aced l earning (M2SPL) to effectively choose high-quality samples and separate adjacent features to improve the robustness of the trained model. There are two advantages of our proposed method: (1) An adaptive reweight generation process acts as a pivotal part in our M 2 SPL. The adaptive density-based reweight samples learning mechanism considers noisy and intractable samples to improve the robustness of model. (2) The multiview feature learning can avoid the large value of the loss function to learn a robust model from original data, and can enhance the discrimination capability of the model. Comparison with the existing sampling approaches shows that our proposed M2SPL approach significantly improves the classification performance, with increases in the F1-score and G-mean of 15.4% and 12.5%, respectively, on average. Finally, our experimental results pass the Friedman and Holm tests, indicating that our model has a significant improvement over existing methods.

Interpretable machine learning is trending as it aims to build a human-understandable decision process. There are two main types of machine learning systems: white-box and black-box models. White-box models are inherently interpretable but commonly suffer from under-fitting phenomena; on the other hand, black-box models perform quite well in a wide range of application domain problems, but their reasoning behind a decision is hard or even impossible to understand. In the soft-computing area, fuzzy inference systems are rule-based systems that use fuzzy reasoning, bringing human perception modeling and computing with word capability. These rule-based systems are designed either manually or automatically but are commonly optimized to fit better some phenomena’ data (in a supervised learning task). After the optimization process, the initial semantic meaning of fuzzy sets is modified (slightly, in the best cases), creating a gray-box model. The principal objective of the proposed methodology in this paper is to extract a high-quality rule in terms of comprehensibility, accuracy and fidelity. This is accomplished by using a fuzzy linguistic interpretable model from an optimized neuro-fuzzy model, considering the initial knowledge context with which it was built. A grammar-guided genetic algorithm is used as the optimization process to find the interpretable description of the model. A collection of 16 datasets for classification tasks were used to evaluate our proposal, obtaining an f1-score of 0.814 with 0.026 standard deviation in the optimized model; the obtained fidelity, in terms of similarity from the interpretable model to the optimized one, was 0.93 of mean with 0.018 standard deviation. Obtained results show that neuro-fuzzy systems could play an important role in interpretable machine learning, providing natural language explanations from previous knowledge.

Intelligent reflecting surfaces (IRSs) are considered as a promising candidate enabling technology for future green wireless communication. Such intelligent surfaces have real-time reconfigurable properties that beamform the signal to a desired destination. In this paper, our objective is to study the performance optimization in an IRS-assisted wireless network. An array of reflecting elements is individually configured in IRS system set up, which is installed between the source and the destination to assist the communication between both without any obstacle. IRS-assisted network achieves significant performance gain as compared to wireless networks without IRS. However, there are severe practical challenges with IRSs including hardware impairments, size of IRS, and distribution of reflecting elements in the IRS. To tackle these issues, we analyzed the performance of the network for maximum achievable rates, minimum transmit power, and maximum energy efficiency (EE). The selection of optimal number of reflecting elements in the IRS enables huge savings in energy to achieve target rate with minimal power. We show that by incorporating optimal number of reflecting elements in IRSs, high EE can be achieved, which outperforms the direct transmission method. Moreover, we show the impact of increasing the number of reflecting elements on the trade-off between EE and achievable rates.

Connectivity and different coverage types including target, area, and barrier coverages are among the most critical challenges of wireless sensor networks (WSN). This paper investigates node deployment for the challenges of target coverage, area coverage, and connectivity over randomly distributed homogeneous and heterogeneous WSNs, and suggests a new method in both centralized and distributed modes which (i) covers all targets by the required number of sensors, (ii) provides maximum area coverage and connectivity over the network, and (iii) manages sensors' movement from the initial locations to the final ones. Specifically, in the case of target coverage, a new analytical deployment method using the steepest descend (SD) algorithm with Armijo and Wolf rules is proposed instead of evolutionary methods. The results demonstrate this method outperforms over Genetic algorithm (GA) in managing sensors' movement towards targets, guaranteeing 100% coverage in targets, and providing a significant reduction- more than 40% in the worst condition-in algorithm complexity. However, when area coverage and connectivity are also considered, a new hybrid deployment method, which first uses GA to extract the optimal sensors' coordinates via analyzing these challenges simultaneously and then employs SD algorithm to move sensors towards these locations, is suggested. Numerical results confirm the complexity of this two-step method is approximately identical to the GA method of existing research; however, it provides sensors' trajectory and more accuracy for network coverage and connectivity.

Machine learning method has been widely applied in industrial fault diagnosis, especially the deep learning method. In the field of industrial fault diagnosis, deep learning is mostly used to extract features of vibration signals to achieve end-to-end fault diagnosis systems. Due to the complexity and variety of actual industrial datasets, some deep learning models are designed to be complicated. However, designing neural network architectures requires rich professional knowledge, experience, and a large number of experiments, increasing the difficulty of developing deep learning models. Fortunately, Neural Architecture Search (NAS), a branch of Automated Machine Learning (AutoML), is developing rapidly. Given a search space, NAS can search for networks that perform better than manually designed. In this paper, a one-shot NAS method for fault diagnosis is proposed. The one-shot model is a supernet that contains all candidate networks in a given search space. The supernet is trained to evaluate the actual performance of candidate networks by measuring the difference between its output probability and the true labels. According to the prediction of supernet, the networks with excellent performance can be searched, using some common search methods such as random search or evolutionary algorithm. Finally, the searched network is trained by reusing the weights of the supernet. To evaluate the proposed method, two search spaces are designed, ResNet and Inception search spaces, to search on PHM 2009 Data Challenge gearbox dataset. The state-of-the-art results are obtained, and accuracies of searched ResNet-A and Inception-A are 84.11% and 83.81%, which are 3.29% and 10.88% higher than Reinforcement Learning based NAS.