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Bird's nest defect is an important cause of transmission line faults. To achieve accurate detection of bird nest defects in complex scenarios, a bird nest defect detection model for transmission lines was proposed that combines domain knowledge and occlusion reasoning networks. On the one hand, the model utilized the domain knowledge of the location of the bird's nest, using edge detection to obtain tower area information to constrain the location of candidate frames. This helps to reduce the false detection caused by complex backgrounds. On the other hand, on the basis of analyzing the occlusion characteristics of bird nests, the model employed occlusion reasoning networks that randomly erase features at the feature level to simulate the occlusion of bird nests in real scenes and improve the model's detection capability for occluded targets. Additionally, a multi‐scale feature fusion algorithm was designed in this paper to adapt the model to the scale variations of bird nests in aerial images. Experimental results demonstrate that the model outperforms advanced target detection models and other bird nest defect detection methods, with an AP50 of 78.8% and an AR10 of 72.4% for defect detection. [ABSTRACT FROM AUTHOR]

In the field of artificial intelligence, pattern recognition is widely used to extract the abstract information in those high dimensional inputs of image, voice, or video. However, the interpretability of pattern recognition still remains understudied. The incomplete features extracted from system input still limit the recognition performance. To reject the disturbance of feature incompleteness, an error compensation is realized into the pattern recognition model under a quantum computation framework. The quantum-based recognition system fulfills the information transmission from input to output with the transformation of quantum states. Then, a compensation for the quantum state is used to reject those intermediate errors in the pattern recognition task. The experiment results in this paper indicate an effectiveness of the proposed method, with which the compensated Quantum Neural Network obtains a better performance. The proposed method brings a more robust recognition system under unknown disturbances. [ABSTRACT FROM AUTHOR]

The goal of the research described in this paper is the design of a system to automatically extract information from paper-based maps and answer queries related to spatial features and structure of geographic data. The foundation to such a system is a set of image analysis algorithms to extract spatial features from images of paper-based maps. Efficient algorithms to detect symbols, identify and track various types of lines, follow closed contours, compute distances, find shortest paths, etc., from simplified map images have been developed. A query processor analyzes the queries presented by the user in a predefined syntax, controls the operation of the image processing algorithms, and interacts with the user. The query processor is written in Lisp and calls image analysis routines written in Fortran. [ABSTRACT FROM AUTHOR]

This paper expounds the application and perceived impact of Artificial Intelligence (AI) in academic libraries in Nigeria. Libraries especially in developing countries may become moribund in the 21 century unless they begin to harness new, smart and intelligent technologies for improved operations and service delivery. This research adopted a literature-based approach to x-ray the applications and perceived impact of Artificial Intelligence (AI) in academic libraries in Nigeria. Through a systematic analysis and review of literature, the study brought to limelight the current state of AI integration in academic libraries in Nigeria and its possible impact on library services, collections, users, professionals and general library operations and services. The application of Artificial Intelligence (AI) in academic libraries has the potential to revolutionize library operations and services. Some of the identifies AI tools include: Natural Language Recognition, Robotics, Big Data, Data Mining, Chatbot, Machine Learning, Pattern Recognition and Expert system. Findings from the study revealed that the application of AI in academic libraries have the potentials to increases productivity, improved customer satisfaction through personalization, easy availability and accessibility of information, easy collaboration and knowledge sharing, virtual assistance and chatbots, and ultimately increase overall operational effectiveness. This paper also explored some of the challenges associated with the application of AI technologies in academic libraries in Nigeria such as poor ICT skills and technical expertise, high initial costs of implementation, phobia for job displacement, epileptic power supply, poor maintenance culture, resistance to change, poor network connectivity, privacy and ethical implications, etc. To maximize the potential benefits of AI applications in academic libraries in Nigeria, it is crucial for these libraries to implement appropriate planning, guidelines and regulations on AI use as well as training and retraining of academic librarians to acquire the required ICT skills, knowledge and competence in order to adapt in the present digital and changing library environment. [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) is widely used in many fields. To further improve the accuracy of FER, this paper proposes a method based on double‐code LBP‐layer spatial‐attention network (DLSANet). The backbone model for the DLSANet is an emotion network (ENet), which is modified with a double‐code LBP (DLBP) layer and a spatial attention module. The DLBP layer is at the front of the first convolutional layer. More valuable features can be extracted by inputting the image processed by DLBP into convolutional layers. The JAFFE and CK+ datasets are used, which contain seven expressions: happiness, anger, disgust, neutral, fear, sadness, and surprise. The average of fivefold cross‐validation shows that DLSANet achieves a recognition accuracy of 93.81% and 98.68% on the JAFFE and CK+ datasets. The experiment reveals that the DLSANet can produce better classification results than state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

There has been a lot of research on edge-computing task offloading in deep reinforcement learning (DRL). Deep reinforcement learning is one of the important algorithms in the current AI field, but there is still room for improvement in the time cost and adaptive correction ability of the algorithm. This paper studies the application of DRL algorithms in edge-computing task offloading, and its key innovation is to propose an MADRLCO algorithm, which is based on the design idea of the Actor–Critic framework, uses the DNN model to act as an Actor, and can more accurately locate the initial decision through iterative training, and use the LSTM model to optimize the Critic, which can be more accurate. The optimal decision can be located in a short period of time. The main work of this paper is divided into three parts: (1) The AC algorithm of the Actor–Critic framework in DRL is proposed to be applied to edge-computing task offloading. (2) To address the weak generalization ability of the basic version of the Actor–Critic algorithm in multi-objective optimization, the sequential quantitative correction and adaptive correction parameter K method are used to optimize the Critic frame, thereby improving the generalization ability of the model in multi-objective decision-making and improving the rationality of decision-making results. (3) Aiming at the problem of large time cost in the critical framework of the model, a search algorithm for resource allocation-related parameters based on the time-series prediction method is proposed (time-series forecasting is a research branch of pattern recognition), which reduces the time overhead of the algorithm and improves the adaptive correction capability of the model. The algorithm in this paper can adapt to not only the time-varying network channel state, but also the time-varying number of device connections. Finally, it is proved by experiments that compared with the DRL calculation offloading algorithm based on DNN plus binary search, the MADRLCO algorithm reduces the model training time by 66.27%, and in the environment of the time-varying number of devices in the metasystem, the average model average. The standard calculation rate is 0.0403 higher than that of the current optimal algorithm. [ABSTRACT FROM AUTHOR]

In recent years, a third-generation neural network, namely, spiking neural network, has received plethora of attention in the broad areas of Machine learning and Artificial Intelligence. In this paper, a novel differential-based encoding method is proposed and new spike-based learning rules for backpropagation is derived by constraining the addition of bias voltage in spiking neurons. The proposed differential encoding method can effectively exploit the correlation between the data and improve the performance of the proposed model, and the new learning rule can take complete advantage of the modulation properties of bias on the spike firing threshold. We experiment with the proposed model on the environmental sound dataset RWCP and the image dataset MNIST and Fashion-MNIST, respectively, and assign various conditions to test the learning ability and robustness of the proposed model. The experimental results demonstrate that the proposed model achieves near-optimal results with a smaller time step by maintaining the highest accuracy and robustness with less training data. Among them, in MNIST dataset, compared with the original spiking neural network with the same network structure, we achieved a 0.39% accuracy improvement. [ABSTRACT FROM AUTHOR]

Insect pests and crop diseases are considered the major problems for agricultural production, due to the severity and extent of their occurrence causing significant crop losses. To increase agricultural production, it is significant to protect the crop from harmful pests which is possible via soft computing techniques. The soft computing techniques are based on traditional machine and deep learning-based approaches. However, in the traditional methods, the selection of manual feature extraction mechanisms is ineffective, inefficient, and time-consuming, while deep learning techniques are computationally expensive and require a large amount of training data. In this paper, we propose an efficient pest detection method that accurately localized the pests and classify them according to their desired class label. In the proposed work, we modify the YOLOv5s model in several ways such as extending the cross stage partial network (CSP) module, improving the select kernel (SK) in the attention module, and modifying the multiscale feature extraction mechanism, which plays a significant role in the detection and classification of small and large sizes of pest in an image. To validate the model performance, we develop a medium-scale pest detection dataset that includes the five most harmful pests for agriculture products that are ants, grasshopper, palm weevils, shield bugs, and wasps. To check the model's effectiveness, we compare the results of the proposed model with several variations of the YOLOv5 model, where the proposed model achieved the best results in the experiments. Thus, the proposed model has the potential to be applied in real-world applications and further motivate research on pest detection to increase agriculture production. [ABSTRACT FROM AUTHOR]

This paper presents the development and applications of a new, open-source toolbox that aims to provide automatic identification and classification of hydroclimatic patterns by their spatial features, i.e., location, size, orientation, and shape, as well as the physical features, i.e., the areal average, total volume, and spatial distribution. The highlights of this toolbox are: (1) incorporating an efficient algorithm for automatically identifying and classifying the spatial features that are linked to hydroclimatic extremes; (2) use as a frontend for supporting AI-based training in tracking and forecasting extremes; and (3) direct support for short-term nowcasting of extreme rainfall via tracking rainstorm centres and movement. The key design and implementation of the toolbox are discussed alongside three case studies demonstrating the application of the toolbox and its potential in helping build machine learning applications in hydroclimatic sciences. Finally, the availability of the toolbox and its source code is included. [ABSTRACT FROM AUTHOR]

The unceasing evolution of analytical instrumentation determines an exponential increase of data production, which in turn boosts new cutting-edge analytical challenges, requiring a progressive integration of artificial intelligence (AI) algorithms into the instrumental data treatment software. Machine learning, deep learning, and computer vision are the most common techniques adopted to exploit the information potential of advanced analytical chemistry measures. In this paper, our primary focus is on elucidating the remarkable advantages of leveraging AI tools for comprehensive two-dimensional gas chromatography data (pre)processing. We illustrate how AI techniques can efficiently explore the complex datasets derived from multidimensional platforms combining comprehensive two-dimensional separations with mass spectrometry in the challenging application area of food-omics. Pattern recognition based on image processing, computer vision, and AI smelling are discussed by introducing the principles of operation, reviewing available tools and software solutions, and illustrating their potentials and limitations through selected applications. • Artificial Intelligence boost the informative potential of GC × GC. • Computer Vision based on chromatogram images. • AI smelling machine with GC × GC-MS predicts food aroma properties. • Advancing the explorative potential of comprehensive two-dimensional chromatography. • Augmented visualization combines visual features with chemical information. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has come a long way in the last several years, both in hardware implementation and software algorithms and applications. This paper examines recent advances in artificial intelligence applications in biomedicine, such as living aid, illness diagnosis, biomedical research, and biomedical information processing. It also reviews deep learning as a technique in Artificial intelligence in healthcare and compares it to traditional methods. This review will better comprehend technology availability, keep the pace of new scientific developments, appreciate the enormous potential of artificial intelligence in biomedicine, and inspire researchers working in related domains. It is fair to say that the use of AI in biomedicine is in its early stages, like artificial intelligence itself. Artificial intelligence will continue to push the boundaries and broaden the scope of its applications as new improvements and discoveries are made, and substantial advancements are projected shortly. [ABSTRACT FROM AUTHOR]

This paper is the editorial of the virtual special issue (VSI) "Pattern recognition and artificial intelligence techniques for cultural heritage", of which the authors of this paper have been the guest editors. It aims to bring together the work of experts from the fields of pattern recognition and artificial intelligence and that of cultural heritage. This multidisciplinary subject covers a wide spectrum spanning from the study of the cultural heritage to the development of tools based on PR/AI techniques for cultural heritage analysis, reconstruction and understanding. The papers included in this special issue allowed us to highlight the advances on this subject from a wide-angle perspective, as well as to stimulate new theoretical and applied researches for better characterizing the state of the art in this domain. [ABSTRACT FROM AUTHOR]

Artificial intelligence evolved as a powerful tool in condition monitoring of the induction motor for early diagnosis of bearing faults. This paper attempted to identify the features to improve the accuracy of diagnosis using the benchmark database of the Case Western Reserve University (CWRU) and the Machinery Fault Prevention Technology (MFPT). In this paper, 18 time-domain features are extracted constituting six proposed time-dependent spectral features (TDSFs) and 12 statistical time-domain features. For the first time, a set of six TDSFs are extracted to diagnose the bearing faults. This involves extracting frequency-domain features using the relationship between Parseval’s theorem and the Fourier transform directly from the time domain. Particle swarm optimization (PSO) and wheel-based differential evolution (WBDE) feature selection algorithms are also implemented to identify four prominent features. It is found that three of the four selected features are TDSF features, and results of selected features revealed the attainment of 90.92% for 48 class identifications of CWRU database and 100% for 17 class identifications of the MFPT database, respectively. [ABSTRACT FROM AUTHOR]

Forecasting sporadic or intermittent demand presents significant challenges in supply chain management, primarily due to the frequent occurrence of zero demand values and the inherent difficulty in capturing underlying hidden patterns in sporadic dataset. Although various parametric and non-parametric methods are available for sporadic demand forecasting, they often fail to detect these hidden patterns, underscoring the need for artificial intelligence (AI) algorithms which are effective in identifying irregular patterns. Among AI algorithms, the k -Nearest Neighbor (k -NN) is particularly good at identifying patterns with limited data. In the past, k -NN based frameworks have been recognized for their ability to effectively forecast sporadic demand, particularly when it is anticipated that patterns will reoccur in subsequent non-zero demand values. However, this assumption does not always hold true in different real-world scenario. In sporadic time series data, zero values often comprise a significant proportion (>30%). To address this, this paper proposes a "zero-inclusive k -NN framework" that leverages both zero and non-zero demand data to identify patterns. The proposed framework offers two significant features: it enables industrial managers to utilize a large number of nearest neighbors and provides adaptability in demand-vector length. Numerical investigations with both synthetic and real datasets affirm the superior forecasting performance of the proposed k -NN method when compared to existing k -NN framework and conventional parametric benchmark methods. The implications of our findings extend to domains where sporadic or intermittent demand forecasting plays a vital role. [ABSTRACT FROM AUTHOR]

Aerial image-based target detection has problems such as low accuracy in multiscale target detection situations, slow detection speed, missed targets and falsely detected targets. To solve this problem, this paper proposes a detection algorithm based on the improved You Only Look Once (YOLO)v3 network architecture from the perspective of model efficiency and applies it to multiscale image-based target detection. First, the K-means clustering algorithm is used to cluster an aerial dataset and optimize the anchor frame parameters of the network to improve the effectiveness of target detection. Second, the feature extraction method of the algorithm is improved, and a feature fusion method is used to establish a multiscale (large-, medium-, and small-scale) prediction layer, which mitigates the problem of small target information loss in deep networks and improves the detection accuracy of the algorithm. Finally, label regularization processing is performed on the predicted value, the generalized intersection over union (GIoU) is used as the bounding box regression loss function, and the focal loss function is integrated into the bounding box confidence loss function, which not only improves the target detection accuracy but also effectively reduces the false detection rate and missed target rate of the algorithm. An experimental comparison on the RSOD and NWPU VHR-10 aerial datasets shows that the detection effect of high-efficiency YOLO (HE-YOLO) is significantly improved compared with that of YOLOv3, and the average detection accuracies are increased by 8.92% and 7.79% on the two datasets, respectively. The algorithm not only shows better detection performance for multiscale targets but also reduces the missed target rate and false detection rate and has good robustness and generalizability. [ABSTRACT FROM AUTHOR]

Highlights • Ensemble classifier system is applied to biometric verification. • For each base classifier, the classifier's competence is separately calculated. • A lip geometrical features measurements are proposed. • Experiments confirmed our method outperforms other state-of-the-art methods. Abstract Machine learning approaches are largely focused on pattern or object classification, where a combination of several classifier systems can be integrated to help generate an optimal or suboptimal classification decision. Multiple classification systems have been extensively developed because a committee of classifiers, also known as an ensemble, can outperform the ensemble's individual members. In this paper, a classification method based on an ensemble of binary classifiers is proposed. Our strategy consists of two phases: (1) the competence of the base heterogeneous classifiers in a pool is determined, and (2) an ensemble is formed by combining those base classifiers with the greatest competences for the given input data. We have shown that the competence of the base classifiers can be successfully calculated even if the number of their learning examples was limited. Such a situation is particularly observed with biometric data. In this paper, we propose a new biometric data structure, the Sim coefficients, along with an efficient data processing technique involving a pool of competent classifiers chosen by dynamic selection. [ABSTRACT FROM AUTHOR]

Road networks are the skeleton of a city, its pattern recognition plays an important role in urban landscape analysis, municipal planning, and traffic flow analysis. Road networks pattern recognition is an intelligent recognition problem that combines road geometric features, semantic features, and contextual relationships. The current road pattern recognition methods are mainly using geometric measures and statistical indicators to detect pattern recognition rules, and based on the edge ⁃node graph structure and the polygon group structure of road network.As the expression of road patterns is influenced by human spatial cognition and visual psychology, rule⁃based reasoning and statistical analysis are difficult to obtain results consistent with human judgment. Driven by artificial intelligence technology, this paper introduces a novel deep learning model for graph structure, namely graph convolution network (GCN), to identify the grid ⁃pattern in a road network. Firstly, to construct the graph structure, the road intersections are taken as nodes and the roads are taken as edges, all nodes are classified into grid and non⁃grid to act as the label and prediction target of the model. Road segments are then broken into consecutive linear units to extract the characteristics of the nodes which are also the input of the model. Then, the grid pattern is recognized by extracting the roads whose endpoints are all predicted to be a grid. The experimental results show that the accuracy of the method can reach 89.2% for road network’s node classification and 86.1% for road segment classification, so it is effective for identifying grid pattern in a road network. [ABSTRACT FROM AUTHOR]

In this paper, we experimentally and analytically evaluate the reliability of two state-of-the-art neural networks for linear regression and pattern recognition (multilayer perceptron and single-layer perceptron) implemented in a system-on-chip composed of a field-programmable gate array (FPGA) and a microprocessor. We have considered, for each neural network, three different activation function complexities, to evaluate how the implementation affects FPGAs reliability. As we show in this paper, higher complexity increases the exposed area but reduces the probability of one failure to impact the network output. In addition, we propose to distinguish between critical and tolerable errors in artificial neural networks. Experiments using a controlled heavy-ions beam show that, for both networks, only about 30% of the observed output errors actually affect the outputs correctness. We identify the causes of critical errors through fault injection, and found that faults in initial layers are more likely to significantly affect the output. [ABSTRACT FROM PUBLISHER]

With the advent of the era of big data, people are eager to extract valuable knowledge from the rapidly expanding data, so that they can more effectively use these massive storage data. The traditional data processing technology can only achieve basic functions such as data query and statistics, and cannot achieve the goal of extracting the knowledge existing in the data to predict the future trend. Therefore, along with the rapid development of database technology and the rapid improvement of computer's computing power, data mining (DM) came into existence. Research on DM algorithms includes knowledge of various fields such as database, statistics, pattern recognition and artificial intelligence. Pattern recognition mainly extracts features of known data samples. The DM algorithm using pattern recognition technology is a better method to obtain effective information from massive data, thus providing decision support, and has a good application prospect. Support vector machine (SVM) is a new pattern recognition algorithm proposed in recent years, which avoids dimension disaster by dimensioning and linearization. Based on this, this paper studies the DM algorithm based on pattern recognition, and proposes a DM algorithm based on SVM. The algorithm divides the vector of the SV set into two different types and iterates through multiple iterations to obtain a classifier that converges to the final result. Finally, through the cross-validation simulation experiment, the results show that the DM algorithm based on pattern recognition can effectively reduce the training time and solve the mining problem of massive data. The results show that the algorithm has certain rationality and feasibility. [ABSTRACT FROM AUTHOR]

Abstract—The paper addresses almost half a century's development of pattern recognition algorithms' application for solving the problem of determination of strong earthquake-prone areas. This approach was named Earthquake-Prone Areas (EPA). The pattern recognition algorithms applied for this purpose, the studied regions, and the methods for assessing the reliability of the obtained results including the theory of dynamic and limit recognition problems are considered. A recently developed alternative method for solving the problem by identifying the clusters of earthquake epicenters is also presented. This method is based on the approaches of the Discrete Mathematical Analysis (DMA) and implemented in the form of the algorithmic system named Formalized Clustering and Zoning (FCAZ). The comparison of the results obtained by the EPA approach and FCAZ system shows their good consistency which provides an additional argument in favor of their reliability. The possibilities for further development and joint application of the EPA approach and FCAZ system and creating, on this basis, an integrated method for systems analysis with the inclusion of artificial intelligence are outlined. In case of success, this method is expected to be used in seismic hazard assessment and planning of earthquake-resistant construction. [ABSTRACT FROM AUTHOR]

While conventional calibrated photometric stereo methods assume that light intensities and sensor exposures are known or unknown but identical across observed images, this assumption easily breaks down in practical settings due to individual light bulb's characteristics and limited control over sensors. This paper studies the effect of unknown and possibly non-uniform light intensities and sensor exposures among observed images on the shape recovery based on photometric stereo. This leads to the development of a “semi-calibrated” photometric stereo method, where the light directions are known but light intensities (and sensor exposures) are unknown. We show that the semi-calibrated photometric stereo becomes a bilinear problem, whose general form is difficult to solve, but in the photometric stereo context, there exists a unique solution for the surface normal and light intensities (or sensor exposures). We further show that there exists a linear solution method for the problem, and develop efficient and stable solution methods. The semi-calibrated photometric stereo is advantageous over conventional calibrated photometric stereo in accurate determination of surface normal, because it relaxes the assumption of known light intensity ratios/sensor exposures. The experimental results show superior accuracy of the semi-calibrated photometric stereo in comparison to conventional methods in practical settings. [ABSTRACT FROM AUTHOR]

In recent past, there has been a steep increase in the use of online platforms for the search of desired products. Real estate industry is no exception and has started initiating rent/sale of houses through online platforms. In this paper, we propose a deep neural network framework to facilitate automatic search of homes based on their floor plans. The salient features of this framework are that the query can be either an image (existing floor plan) or a sketch through a sketch pad interface. Our proposed framework automatically determines the type of query (image or sketch) and retrieves similar floor plan images from the database. The critical contributions of our proposed approach are: (1) a novel unified floor plan retrieval framework using multimodal query, i.e., an intuitive and convenient sketch query mode as well as a query by example mode ; (2) a conjunction of autoencoder, Cyclic GAN and CNN for the task of domain mapping and floor plan image retrieval. We have reported results of extensive experimentation and comparison with baseline results to establish the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

This paper describes a roadmap for the development of the SP Machine , based on the SP Theory of Intelligence and its realization in the SP Computer Model. The SP Machine will be developed initially as a software virtual machine with high levels of parallel processing, hosted on a high-performance computer. The system should help users visualize knowledge structures and processing. Research is needed into how the system may discover low-level features in speech and in images. Strengths of the SP System in the processing of natural language may be augmented, in conjunction with the further development of the SP System's strengths in unsupervised learning. Strengths of the SP System in pattern recognition may be developed for computer vision. Work is needed on the representation of numbers and the performance of arithmetic processes. A computer model is needed of SP-Neural , the version of the SP Theory expressed in terms of neurons and their interconnections. The SP Machine has potential in many areas of application, several of which may be realized on short-to-medium timescales. [ABSTRACT FROM AUTHOR]

This paper describes a process of semi-automatic systematic reviews in scientific research. On the one hand, it is presented a software prototype based on artificial intelligence, semi-automatic systematic reviews tool. On the other hand, a study case of a phd thesis project shows the maps and information with aggregate value. In addition, the software prototype were evaluated by 40 students of master and Ph.D projects, they report the utility of the software tool by a survey. This work presents the modules and functionalities, software architecture and software deployment. Furthermore, a comparative study of a set of similar software tools has been conducted according to the following criteria: input files, analysis and mapping, and software license. Finally, it is discussed the results of the comparative study, the implementation of artificial intelligence techniques, the software scalability and the easiness of include new analysis and maps. [ABSTRACT FROM AUTHOR]

The correct and consistent identification of the petrographic properties of coal is an important issue for researchers in the fields of mining and geology. As part of the study described in this paper, investigations concerning the application of artificial intelligence methods for the identification of the aforementioned characteristics were carried out. The methods in question were used to identify the maceral groups of coal, i.e. vitrinite, inertinite, and liptinite. Additionally, an attempt was made to identify some non-organic minerals. The analyses were performed using pattern recognition techniques (NN, kNN), as well as artificial neural network techniques (a multilayer perceptron – MLP). The classification process was carried out using microscopy images of polished sections of coals. A multidimensional feature space was defined, which made it possible to classify the discussed structures automatically, based on the methods of pattern recognition and algorithms of the artificial neural networks. Also, from the study we assessed the impact of the parameters for which the applied methods proved effective upon the final outcome of the classification procedure. The result of the analyses was a high percentage (over 97%) of correct classifications of maceral groups and mineral components. The paper discusses also an attempt to analyze particular macerals of the inertinite group. It was demonstrated that using artificial neural networks to this end makes it possible to classify the macerals properly in over 91% of cases. Thus, it was proved that artificial intelligence methods can be successfully applied for the identification of selected petrographic features of coal. [ABSTRACT FROM AUTHOR]

In recent years, more machine learning algorithms have been applied to odor classification. These odor classification algorithms usually assume that the training data sets are static. However, for some odor recognition tasks, new odor classes continually emerge. That is, the odor data sets are dynamically growing while both training samples and number of classes are increasing over time. Motivated by this concern, this paper proposes a deep nearest class mean (DNCM) model based on the deep learning framework and the nearest class mean method. The proposed model not only leverages deep neural network to extract deep features but also able to dynamically integrate new classes over time. In our experiments, the DNCM model was initially trained with 10 classes, then 25 new classes are integrated. Experiment results demonstrate that the proposed model is very efficient for incremental odor classification, especially for new classes with only a small number of training examples. [ABSTRACT FROM AUTHOR]

The essential goal of this paper is to extend the functionality of the bio-inspired intelligent HTM (Hierarchical Temporal Memory) network towards two capabilities: (i) object recognition in color images, (ii) detection of multiple objects located in clutter color images. The former extension is based on the development of a novel scheme for the application of three parallel HTM networks that separately process color, texture, and shape information in color images. For the latter HTM extension, we propose a novel system in which HTM is combined with a modified model of computational visual attention. We adopt the results of Bi et al. (2010), Hu et al. (2005), and Kučerová (2011) and add new elements for the calculation of image saliency maps. The proposed algorithm enables one to automatically locate individual objects in clutter images. For computer experiments, a special image database is created to simulate ideal single object images and cluttered images with multiple objects on an inhomogeneous background. The recognition performance of HTM alone and in combination with the salient-region detection method is evaluated. We show that the attention subsystem is able to satisfactorily locate multiple objects in clutter color images with an inhomogeneous background. We also perform benchmark calculations for two selected computer vision methods used for object detection in color clutter images. Namely, the cascade detector and template matching methods are used. Our study confirms that the proposed attention system can improve the capabilities of HTM for object classification in cluttered images. The compound system of visual attention and HTM outperforms the compared methods in both criteria (recall and correct detection rate). However, as expected, the system cannot match the recognition accuracy achieved by HTM for single object images, and thus, further research is needed. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is proving to be an efficient alternative approach to classical modeling techniques. AI refers to the branch of computer science that develops machines and software with human-like intelligence. Compared to traditional methods, AI offers advantages to deal with problems associated with uncertainties and is an effective aid to solve such complex problems. In addition, AI-based solutions are good alternatives to determine engineering design parameters when testing is not possible, thus resulting in significant savings in terms of human time and effort spent in experiments. AI is also able to make the process of decision making faster, decrease error rates, and increase computational efficiency. Among the different AI techniques, machine learning (ML), pattern recognition (PR), and deep learning (DL) have recently acquired considerable attention and are establishing themselves as a new class of intelligent methods for use in structural engineering. The objective of this review paper is to summarize techniques concerning applications of the noted AI methods in structural engineering developed over the last decade. First, a general introduction to AI is presented and the importance of AI in structural engineering is described. Thereafter, a review of recent applications of ML, PR, and DL in the field is provided, and the capability of such methods to address the restrictions of conventional models are discussed. Further, the advantages of employing such algorithmic methods are discussed in detail. Finally, potential research avenues and emerging trends for employing ML, PR, and DL are presented, and their limitations are discussed. [ABSTRACT FROM AUTHOR]

In recent years, Deep Learning (DL), such as the algorithms of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Generative Adversarial Networks (GAN), has been widely studied and applied in various fields including agriculture. Researchers in the fields of agriculture often use software frameworks without sufficiently examining the ideas and mechanisms of a technique. This article provides a concise summary of major DL algorithms, including concepts, limitations, implementation, training processes, and example codes, to help researchers in agriculture to gain a holistic picture of major DL techniques quickly. Research on DL applications in agriculture is summarized and analyzed, and future opportunities are discussed in this paper, which is expected to help researchers in agriculture to better understand DL algorithms and learn major DL techniques quickly, and further to facilitate data analysis, enhance related research in agriculture, and thus promote DL applications effectively. [ABSTRACT FROM AUTHOR]

In light of increasing alerts about limited energy sources and environment degradation, it has become essential to search for alternatives to thermal engine-based vehicles which are a major source of air pollution and fossil fuel depletion. Hybrid electric vehicles (HEVs), encompassing multiple energy sources, are a short-term solution that meets the performance requirements and contributes to fuel saving and emission reduction aims. Power management methods such as regulating efficient energy flow to the vehicle propulsion, are core technologies of HEVs. Intelligent power management methods, capable of acquiring optimal power handling, accommodating system inaccuracies, and suiting real-time applications can significantly improve the powertrain efficiency at different operating conditions. Rule-based methods are simply structured and easily implementable in real-time; however, a limited optimality in power handling decisions can be achieved. Optimization-based methods are more capable of achieving this optimality at the price of augmented computational load. In the last few years, these optimization-based methods have been under development to suit real-time application using more predictive, recognitive, and artificial intelligence tools. This paper presents a review-based discussion about these new trends in real-time optimal power management methods. More focus is given to the adaptation tools used to boost methods optimality in real-time. The contribution of this work can be identified in two points: First, to provide researchers and scholars with an overview of different power management methods. Second, to point out the state-of-the-art trends in real-time optimal methods and to highlight promising approaches for future development. [ABSTRACT FROM AUTHOR]

The timing of spikes plays an important role in the information processing of brain. However, for temporal-based learning algorithms, the temporally precise spike as learning target is not able to fit with the variety of stimuli. The performance of spiking neural networks (SNNs) is adversely affected by fixed target spikes. This paper proposes a new learning rule called relative ordering learning (ROL) for SNNs. In contrast to the existing algorithms that drive a neuron to fire or not fire or to fire at precise times, the ROL rule trains the neuron to emit the first spike to respond to the associated stimuli. Without the limitation of specified precise firing times, the ROL rule can adjust output spikes to fit with the feature of input stimuli dynamically and adaptively. Our experiments have demonstrated the great generalization ability of the ROL rule and its robustness against noise. With a few neurons and ultra-low training cost, the network trained by the ROL rule achieved the classification accuracies of 98.1% and 93.8% for training on the Iris and MNIST datasets, respectively, and the accuracies for testing are 94.2% and 90.3%. The comparison with MLP network also shows the great attraction of the SNN trained by the ROL rule as a solution for fast and efficient recognition tasks. [ABSTRACT FROM AUTHOR]

A considerable amount of research has focused on monitoring structural damage using Structural Health Monitoring (SHM) technologies, which has had recent advances. However, it is important to note the challenges and unresolved problems that disqualify currently developed monitoring systems. One of the frontline SHM technologies, the Electromechanical Impedance (EMI) technique, has shown its potential to overcome remaining problems and challenges. Unfortunately, the recently developed neural network algorithms have not shown significant improvements in the accuracy of rate and the required processing time. In order to fill this gap in advanced neural networks used with EMI techniques, this paper proposes an enhanced and reliable strategy for improving the structural damage detection via: (1) Savitzky-Golay (SG) filter, using both first and second derivatives; (2) Probabilistic Neural Network (PNN); and, (3) Simplified Fuzzy ARTMAP Network (SFAN). Those three methods were employed to analyze the EMI data experimentally obtained from an aluminum plate containing three attached PZT (Lead Zirconate Titanate) patches. In this present study, the damage scenarios were simulated by attaching a small metallic nut at three different positions in the aluminum plate. We found that the proposed method achieves a hit rate of more than 83%, which is significantly higher than current state-of-the-art approaches. Furthermore, this approach results in an improvement of 93% when considering the best case scenario. [ABSTRACT FROM AUTHOR]

Abstract: The paper presents an automatic approach to the recognition of the industrial tools on the basis of their image registered by the camera. The solved tasks include: the automatic localization of the tools in the image, preprocessing of the image (binarization, noise removal, filling the holes, normalization, etc.), generation of the numerical descriptors, verification of descriptors in the role of the diagnostic features, selection of the features and final stage of classification of the tools. The efficiency of the developed system has been verified on the example of exemplary set of industrial tools and the results of this verification are presented and discussed in the paper. [Copyright &y& Elsevier]

There is an issue with the timely application of artificial intelligence to humankind, particularly to the disabled. In this paper, we propose a surface electromyography signal recognition system for prosthetic application to persons with disabled hands, which achieves simplicity, reliability, accuracy and a short response time by increasing the performance of each part of the system and the coordination between the interconnected components. First, a surface electromyography signal acquisition system was designed on the basis of the cost and processing speed. Second, a method called the ‘extreme value’ was carried out on the original signal containing five continuous movements, by separating the signal into isolated segments representing different postures, which made application of the system for daily use possible. Third, on the basis of time-domain, chaos-theory and time–frequency-domain analysis methods, four features, namely the average amplitude, fractal dimension, maximum Lyapunov exponent and wavelet coefficient, were extracted from four possible arm locations to be determined. Furthermore, the average amplitude from the extensor digitorum and the wavelet coefficient from the flexor pollicis longus were determined as the final features after comparing the clustering effects of the extracted features. Finally, a new strategy for classifying the different postures based on a back-propagation neural network was introduced to obtain an average system accuracy of 82.77% for five continuous movements. [ABSTRACT FROM AUTHOR]

Detection transformer (DETR) combines convolutional neural network (CNN) with transformer, providing a more advanced idea. In this paper, an object detection model based on DETR is proposed for fire and smoke detection. Compared with other methods that based on deep learning, the proposed one simplifies the pipeline of detection and builds an end-to-end detector. At the same time, the original DETR model usually requires long training time and large amount of computation, resulting in relatively poor performance in detection speed and accuracy, and it shows to be not friendly for small or early fire detection. Therefore, when designing the proposed network model, a normalization-based attention module is added in the feature extraction stage to highlight the effective features, being beneficial to the process of encoding. A multiscale deformable attention is also used in the encoder–decoder structure, accelerating therefore, the process of convergence during the training of the model, which includes the enhancement of the detection of small objects. Also, considering the cost of computation, the number of layers in the encoder–decoder structure is redefined to reduce the complexity of the model, and that also reduces the requirements of the application equipment. Detailed experiments are conducted on three self-built datasets and two public video sets. The results show that, the proposed method has an excellent performance on all of the datasets considered here. [ABSTRACT FROM AUTHOR]

Abstract: The capability of fuzzy systems to solve different kinds of problems has been demonstrated in several previous investigations. Genetic fuzzy systems (GFSs) hybridize the approximate reasoning method of fuzzy systems with the learning capability of evolutionary algorithms. The objective of this paper is to design and analysis of various kinds of genetic fuzzy systems to deal with intrusion detection problem as a new real-world application area which is not previously tackled with GFSs. The resulted intrusion detection system would be capable of detecting normal and abnormal behaviors in computer networks. We have presented three kinds of genetic fuzzy systems based on Michigan, Pittsburgh and iterative rule learning (IRL) approaches to deal with intrusion detection as a high-dimensional classification problem. Experiments were performed with DARPA data sets which have information on computer networks, during normal and intrusive behaviors. The paper presents some results and compares the performance of different generated fuzzy rule sets in detecting intrusion in a computer network according to three different types of genetic fuzzy systems. [Copyright &y& Elsevier]

This paper investigates diagnostic techniques for electrical machines with special reference to induction machines and to papers published in the last ten years. A comprehensive list of references is reported and examined, and research activities classified into four main topics: 1) electrical faults; 2) mechanical faults; 3) signal processing for analysis and monitoring; and 4) artificial intelligence and decision-making techniques. [ABSTRACT FROM AUTHOR]

In this paper, we introduce feedback-based associative learning in self-organized learning arrays (SOLAR). SOLAR structures are hierarchically organized networks of sparsely connected neurons that define their own functions and select their interconnections locally. This paper provides a description of neuron self-organization and signal processing. Feedforward processing is used to make necessary correlations and learn the input patterns. Discovered associations between neuron inputs are used to generate feedback signals. These feedback signals, when propagated to the primary inputs, can establish the expected input values. This can be used for heteroassociative (HA) and autoassociative (AA) learning and pattern recognition. Example applications in HA learning are given. [ABSTRACT FROM AUTHOR]

Abstract: This paper introduces a method of classifying partial discharges of unknown origin. The innovative trend of using Artificial Neural Network (ANN) towards classification of Partial Discharge (PD) patterns is cogent and discernible. The Adaptive Resonance Theory (ART), a type of neural network which is suitable for PD pattern recognition is explained here. To ensure the suitability and reliability of chosen network for PD pattern recognition, the network is tested with the well known Iris plant database and alphabet character for recognition & classification. Further more the network is trained with various combinations of ϕ–q–n distributions of PD patterns and tested. It is shown that the ART 2 network is able to classify the PD patterns. The paper ends with analyzing the efficacy of multifarious features selected in the measurement space. Also the validation of input features is done using ‘Hold-One-Out’ method and partial set training technique [Copyright &y& Elsevier]

Abstract: This paper describes a rose analysis and recognition system and presents the main principles used to realize the recognition system. The major principles presented in this paper are the mathematical description methods for rose features such as shape, size and color of the flower, petal, leaf, etc, and the object-oriented pattern recognition (OOPR) approach which mathematically deals with how to comprehensively use all different rose features rationally in the recognition scheme. The recognition system is described and some of its experimental results are given which demonstrate the efficiency of our methods. [Copyright &y& Elsevier]

Abstract: Pattern recognition has a long history within electrical engineering but has recently become much more widespread as the automated capture of signal and images has been cheaper. Very many of the application of neural networks are to classification, and so are within the field of pattern recognition and classification. In this paper, we explore how probabilistic neural networks fit into the earlier framework of pattern recognition of partial discharge patterns since the PD patterns are an important tool for diagnosis of HV insulation systems. Skilled humans can identify the possible insulation defects in various representations of partial discharge (PD) data. One of the most widely used representation is phase resolved PD (PRPD) patterns. Also this paper describes a method for the automated recognition of PRPD patterns using a novel complex probabilistic neural network system for the actual classification task. The efficacy of composite neural network developed using probabilistic neural network is examined. [Copyright &y& Elsevier]

Abstract: This paper shows the design and use of feed-forward neural networks and the support vector machines (SVM) in the coordinate-free mathematical system of the Clifford geometric algebra. We compare the McCulloch–Pitts neuron and the geometric neuron. An interesting case of the geometric neuron is the conformal neuron which can be used for RBF networks and SVM. The paper presents the generalization of the real- and complex-valued multilayer perceptron (MLP) to the Clifford valued multilayer perceptron. The paper studies also the multivector support vector machines (MSVM) which are SVMs for processing multivectors. For that we design kernels involving Clifford products. The resultant kernel resembles a sort of polynomial kernel using a multivector representation. In the context of SVMs an important contribution of the paper is the generalization of the real- and complex-valued SVM classifiers over the hyper-complex numbers. This Clifford valued SVM accepts multiple multivector inputs and it is a multi-class classifier. For the preprocessing the authors introduce a promising geometric method utilizing Clifford moments. This method is applied together with geometric MLPs for tasks of 2D pattern recognition. The experimental part shows applications of SVM using the conformal neuron and Clifford kernels. We include challenging applications of the Clifford SVM classifier for nonlinear separable problems. The authors believe that the use of the MLPs and SVMs in the geometric algebra framework expands their sphere of applicability for multivector learning in graded spaces. [Copyright &y& Elsevier]

Original Hebbian encoding scheme of bidirectional associative memory (BAM) provides a poor pattern capacity and recall performance. Based on Rosenblatt's perceptron learning algorithm, the pattern capacity of BAM is enlarged, and perfect recall of all training pattern pairs is guaranteed. However, these methods put their emphases on pattern capacity, rather than error correction capability which is another critical point of BAM. This paper analyzes the sensitivity to noise in RAM and obtains an interesting idea to improve noise immunity of BAM. Some researchers have found that the noise sensitivity of BAM relates to the minimum absolute value of net inputs (MAV). However, in this paper, the analysis on failure association shows that it is related not only to MAV but also to the variance of weights associated with synapse connections. In fact, it is a positive monotone increasing function of the quotient of MAV divided by the variance of weights. This idea provides an useful principle of improving error correction capability of RAM. Some revised encoding schemes, such as small variance learning for RAM (SVBAM), evolutionary pseudorelaxation learning for BAM (EPRLAB) and evolutionary bidirectional learning (EBL), have been introduced to illustrate the performance of this principle. All these methods perform better than their original versions in noise immunity. Moreover, these methods have no negative effect on the pattern capacity of BAM. The convergence of these methods is also discussed in this paper. If there exist solutions, EPRLAB and EBL always converge to a global optimal solution in the senses of both, pattern capacity and noise immunity. However, the convergence of SVBAM may be affected by a preset function. [ABSTRACT FROM AUTHOR]

Development of information technologies is growing steadily. With the latest software technologies development and application of the methods of artificial intelligence and machine learning intelligence embededs in computers, the expectations are that in near future computers will be able to solve problems themselves like people do. Artificial intelligence emulates human behavior on computers. Rather than executing instructions one by one, as theyare programmed, machine learning employs prior experience/data that is used in the process of system's training. In this state of the art paper, common methods in AI, such as machine learning, pattern recognition and the natural language processing (NLP) are discussed. Also are given standard architecture of NLP processing system and the level thatisneeded for understanding NLP. Lastly the statistical NLP processing and multi-word expressions are described. [ABSTRACT FROM AUTHOR]

The invariant recognition of 2D binary images is the main subject of the paper. Two methods for invariant pattern recognition based on 2D Fourier power spectrum with guaranteed translation invariance are proposed. First method introduce the features invariant to translation, scaling, rotation and mirroring (TSO invariance). The second method introduces the features invariant to general affine transform (A invariance). The methods are used to obtain TSO/A invariant spectra except of the rotation effect which are analysed on circular paths with fixed radii. Harmonic analysis of power fluctuations around paths generates Fourier coefficients and their square absolute values are used as TSO/A invariant descriptors. The proposed methods were tested on two large sets of 2D digital images of tree leaves. After TSO/A invariant processing of thresholded digital images, kernel Support Vector Machine or self-organizing neural network were used for leaf categorisation. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of recognizing group activities of human objects based on their motion trajectory analysis. In order to resolve the complexity and ambiguity problems caused by a large number of human objects, we propose a Group Interaction Zone (GIZ) to detect meaningful groups in a scene to effectively handle noisy information. Two novel features, Group Interaction Energy (GIE) feature and Attraction and Repulsion Features, are proposed to better describe group activities within a GIZ. We demonstrate the performance of our method in two ways by (i) comparing the performance of the proposed method with the previous methods and (ii) analyzing the influence of the proposed features and GIZ-based meaningful group detection on group activity recognition using public datasets. [ABSTRACT FROM AUTHOR]

Recognition of power quality events by analyzing voltage waveform disturbances is a very important task for power system monitoring. This paper presents a hybrid intelligent scheme for the classification of power quality disturbances. The proposed algorithm is realized through three main steps: feature extraction, feature selection and feature classification. The feature vectors are extracted using S-transform (ST) and Wavelet transform (WT) which are very powerful time-frequency analysis tools. In order to avoid large dimension of feature vector, three different approaches are applied for feature selection step, namely Sequential Forward Selection (SFS), Sequential Backward Selection (SBS) and Genetic Algorithm (GA). In the next step, the most meaningful features are applied to Probabilistic Neural Network (PNN) as classifier core. Various transient events, such as voltage sag, swell, interruption, harmonics, transient, sag with harmonics, swell with harmonics, and flicker, are tested. Sensitivity of the proposed algorithm under different noisy conditions is investigated in this article. Results show that the classifier can detect and classify different power quality signals, even under noisy conditions, with high accuracy. [ABSTRACT FROM AUTHOR]