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We draw on a substantial body of theoretical and empirical research on movement and behavior tracking tools, remote sensing and geospatial artificial intelligence technologies, and metaverse assets and services. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout May 2023, with search terms including "Web3-powered metaverse worlds" + "neural network-based recognition and virtual simulation algorithms," "interactive 3D geo-visualization and neuromorphic computing systems," and "tactile sensing and cognitive modeling technologies." As we analyzed research published in 2022 and 2023, only 187 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 41, chiefly empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Distiller SR, ROBIS, and SRDR. [ABSTRACT FROM AUTHOR]

In this paper, a two-stage stochastic mathematical model is developed for an asset protection routing problem under a wildfire. The main aim of this study is to reduce the negative impact of a wildfire. Some parameters, such as travel and service times, obtaining profit by protecting an asset, and upper bounds of time windows, are considered as stochastic parameters. Generating proper scenarios for uncertain parameters has a large impact on the accuracy of the obtained solutions. Therefore, artificial neural networks are employed to extract possible scenarios according to previous actual wildfire events. The problem cannot be solved by exact solvers for large instances, so two matheuristic algorithms are proposed in this study to solve the problem in a reasonable time. In the first algorithm, a set of feasible routes is generated based on a heuristic approach, then a route-based mathematical model is used to obtain the final solution. Also, another matheuristic algorithm based on adaptive large neighbourhood search (ALNS) is proposed. In this algorithm, routing decisions are determined using the ALNS algorithm while other decisions are achieved by solving an intermediate mathematical model. The numerical analysis confirms the efficiency of both proposed algorithms; however, the first algorithm performs more efficiently. [ABSTRACT FROM AUTHOR]

Photocatalysts have made great contributions to the degradation of pollutants to achieve environmental purification. The traditional method of developing new photocatalysts is to design and perform a large number of experiments to continuously try to obtain efficient photocatalysts that can degrade pollutants, which is time-consuming, costly, and does not necessarily achieve the best performance of the photocatalyst. The rapid development of photocatalysis has been accelerated by the rapid development of artificial intelligence. Intelligent algorithms can be utilized to design photocatalysts and predict photocatalytic performance, resulting in a reduction in development time and the cost of new catalysts. In this paper, the intelligent algorithms for photocatalyst design and photocatalytic performance prediction are reviewed, especially the artificial neural network model and the model optimized by an intelligent algorithm. A detailed discussion is given on the advantages and disadvantages of the neural network model, as well as its application in photocatalysis optimized by intelligent algorithms. The use of intelligent algorithms in photocatalysis is challenging and long term due to the lack of suitable neural network models for predicting the photocatalytic performance of photocatalysts. The prediction of photocatalytic performance of photocatalysts can be aided by the combination of various intelligent optimization algorithms and neural network models, but it is only useful in the early stages. Intelligent algorithms can be used to design photocatalysts and predict their photocatalytic performance, which is a promising technology. [ABSTRACT FROM AUTHOR]

To further reduce the size of the neural network model and enable the network to be deployed on mobile devices, a novel fusion pruning algorithm based on information entropy stratification is proposed in this paper. Firstly, the method finds similar filters and removes redundant parts by Affinity Propagation Clustering, then secondly further prunes the channels by using information entropy stratification and batch normalization (BN) layer scaling factor, and finally restores the accuracy training by fine-tuning to achieve a reduced network model size without losing network accuracy. Experiments are conducted on the vgg16 and Resnet56 network using the cifar10 dataset. On vgg16, the results show that, compared with the original model, the parametric amount of the algorithm proposed in this paper is reduced by 90.69% and the computation is reduced to 24.46% of the original one. In ResNet56, we achieve a 63.82%-FLOPs reduction by removing 63.53% parameters. The memory occupation and computation speed of the new model are better than the baseline model while maintaining a high network accuracy. Compared with similar algorithms, the algorithm has obvious advantages in the dimensions of computational speed and model size. The pruned model is also deployed to the Internet of Things (IoT) as a target detection system. In addition, experiments show that the proposed model is able to detect targets accurately with low reasoning time and memory. It takes only 252.84 ms on embedded devices, thus matching the limited resources of IoT. [ABSTRACT FROM AUTHOR]

Salp swarm algorithm (SSA) is a swarm intelligence algorithm inspired by the swarm behavior of salps in oceans. In this paper, a adaptive multi-group salp swarm algorithm (AMSSA) with three new communication strategies is presented. Adaptive multi-group mechanism is to evenly divide the initial population into several subgroups, and then exchange information among subgroups after each adaptive iteration. Communication strategy is also an important part of adaptive multi-group mechanism. This paper proposes three new communication strategies and focuses on promoting the performance of SSA. These measures significantly improve the cooperative ability of SSA, accelerate convergence speed, and avoid easily falling into local optimum. And the benchmark functions confirm that AMSSA is better than the original SSA in exploration and exploitation. In addition, AMSSA is combined with prediction of wind power based on back propagation (AMSSA-BP) neural network. The simulation results show that the AMSSA-BP neural network prediction model can achieve a better prediction effect of wind power. [ABSTRACT FROM AUTHOR]

Simple Summary: Melanoma is the most dangerous type of skin cancer worldwide. Early detection of melanoma is crucial for better outcomes, but this often can be challenging. This research explores the use of artificial intelligence (AI) techniques combined with non-invasive imaging methods to improve melanoma detection. The authors aim to evaluate the current state of AI-based techniques using tools including dermoscopy, optical coherence tomography (OCT), and reflectance confocal microscopy (RCM). The findings demonstrate that several AI algorithms perform as well as or better than dermatologists in detecting melanoma, particularly in the analysis of dermoscopy images. This research highlights the potential of AI to enhance diagnostic accuracy, leading to improved patient outcomes. Further studies are needed to address limitations and ensure the reliability and effectiveness of AI-based techniques. Background: Melanoma, the deadliest form of skin cancer, poses a significant public health challenge worldwide. Early detection is crucial for improved patient outcomes. Non-invasive skin imaging techniques allow for improved diagnostic accuracy; however, their use is often limited due to the need for skilled practitioners trained to interpret images in a standardized fashion. Recent innovations in artificial intelligence (AI)-based techniques for skin lesion image interpretation show potential for the use of AI in the early detection of melanoma. Objective: The aim of this study was to evaluate the current state of AI-based techniques used in combination with non-invasive diagnostic imaging modalities including reflectance confocal microscopy (RCM), optical coherence tomography (OCT), and dermoscopy. We also aimed to determine whether the application of AI-based techniques can lead to improved diagnostic accuracy of melanoma. Methods: A systematic search was conducted via the Medline/PubMed, Cochrane, and Embase databases for eligible publications between 2018 and 2022. Screening methods adhered to the 2020 version of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Included studies utilized AI-based algorithms for melanoma detection and directly addressed the review objectives. Results: We retrieved 40 papers amongst the three databases. All studies directly comparing the performance of AI-based techniques with dermatologists reported the superior or equivalent performance of AI-based techniques in improving the detection of melanoma. In studies directly comparing algorithm performance on dermoscopy images to dermatologists, AI-based algorithms achieved a higher ROC (>80%) in the detection of melanoma. In these comparative studies using dermoscopic images, the mean algorithm sensitivity was 83.01% and the mean algorithm specificity was 85.58%. Studies evaluating machine learning in conjunction with OCT boasted accuracy of 95%, while studies evaluating RCM reported a mean accuracy rate of 82.72%. Conclusions: Our results demonstrate the robust potential of AI-based techniques to improve diagnostic accuracy and patient outcomes through the early identification of melanoma. Further studies are needed to assess the generalizability of these AI-based techniques across different populations and skin types, improve standardization in image processing, and further compare the performance of AI-based techniques with board-certified dermatologists to evaluate clinical applicability. [ABSTRACT FROM AUTHOR]

This paper investigates the stochastic optimization problem focusing on developing scalable parallel algorithms for deep learning tasks. Our solution involves a reformation of the objective function for stochastic optimization in neural network models, along with a novel parallel computing strategy, coined the weighted aggregating stochastic gradient descent (WASGD). Following a theoretical analysis on the characteristics of the new objective function, WASGD introduces a decentralized weighted aggregating scheme based on the performance of local workers. Without any center variable, the new method automatically gauges the importance of local workers and accepts them by their contributions. Furthermore, we have developed an enhanced version of the method, WASGD+, by (1) implementing a designed sample order and (2) upgrading the weight evaluation function. To validate the new method, we benchmark our pipeline against several popular algorithms including the state-of-the-art deep neural network classifier training techniques (e.g., elastic averaging SGD). Comprehensive validation studies have been conducted on four classic datasets: CIFAR-100, CIFAR-10, Fashion-MNIST, and MNIST. Subsequent results have firmly validated the superiority of the WASGD scheme in accelerating the training of deep architecture. Better still, the enhanced version, WASGD+, is shown to be a significant improvement over its prototype. [ABSTRACT FROM AUTHOR]

Landslides, widespread and highly dangerous geological disasters, pose significant risks to humankind and the ecological environment. Consequently, predicting landslides is vital for disaster prevention and mitigation strategies. At present, the predominant methods for predicting landslide susceptibility are evolving from conventional machine learning techniques to deep learning approaches. At present, the predominant methods for predicting landslide susceptibility are evolving from conventional machine learning techniques to deep learning approaches. Prior studies have shown that in the context of landslide susceptibility, these models frequently underperform relative to tree-based machine learning algorithms. This shortcoming has restricted the application of deep learning in this domain. To overcome this challenge, this study presents the TabNet algorithm, which combines the interpretability and selective feature extraction of tree models with the representation learning and comprehensive training capabilities of neural network models. This paper explores the potential of employing the TabNet algorithm for landslide susceptibility analysis in China's WanZhou region and evaluates its performance against traditional machine learning techniques. The experimental data indicate that the TabNet algorithm achieves a recall score of 0.898 and an AUC of 0.915, demonstrating a generalization capability that is comparable to that of classical machine learning algorithms. [ABSTRACT FROM AUTHOR]

Intelligent video analysis has broad application prospects. How to automatically analyze and identify human behavior in video has attracted extensive attention from researchers at home and abroad. Moreover, researching effective video behavior recognition algorithms and designing efficient behavior recognition systems has important theoretical and practical value. This paper studies the nonlinear classification technique and applies the video behavior recognition algorithm to basketball recognition. Moreover, this paper studies the classical convolutional neural network model and several improvements. In addition, this paper explains the advantages of convolutional neural networks in feature extraction compared with traditional neural networks and analyzes the performance of the algorithm by designing actual experiments. The research results show that the algorithm can quickly identify multiple players on the field, and the method can effectively deal with occlusion and other issues with high accuracy and real-time. [ABSTRACT FROM AUTHOR]

A two-stage lightweight online dereverberation algorithm for hearing devices is presented in this paper. The approach combines a multi-channel multi-frame linear filter with a single-channel single-frame post-filter. Both components rely on power spectral density (PSD) estimates provided by deep neural networks (DNNs). By deriving new metrics analyzing the dereverberation performance in various time ranges, we confirm that directly optimizing for a criterion at the output of the multi-channel linear filtering stage results in a more efficient dereverberation as compared to placing the criterion at the output of the DNN to optimize the PSD estimation. More concretely, we show that training this stage end-to-end helps further remove the reverberation in the range accessible to the filter, thus increasing the early-to-moderate reverberation ratio. We argue and demonstrate that it can then be well combined with a post-filtering stage to efficiently suppress the residual late reverberation, thereby increasing the early-to-final reverberation ratio. This proposed two-stage procedure is shown to be both very effective in terms of dereverberation performance and computational demands, as compared to, e.g., recent state-of-the-art DNN approaches. Furthermore, the proposed two-stage system can be adapted to the needs of different types of hearing-device users by controlling the amount of reduction of early reflections. [ABSTRACT FROM AUTHOR]

Fuzzy English recognition is affected by many factors, which leads to certain accuracy problems in intelligent recognition results. In order to improve the automatic recognition efficiency of fuzzy English, based on machine learning technology, this study constructs a neural network model. At the same time, this paper analyzes the research status and existing problems of handwritten character recognition, analyzes the model, and adopts multiple modules for automatic English recognition. In addition, the system is built on the basis of algorithms and model support, which makes fuzzy English recognition intelligent. Finally, in order to study the algorithm and model performance, the fuzzy English recognition is carried out through experiments. The research shows that the model constructed in this paper has certain recognition effect, which can be applied to practice, and can provide theoretical reference for subsequent related research. [ABSTRACT FROM AUTHOR]

The new image de-nosing algorithm based on improved deep convolutional neural network in the monogenic wavelet domain is proposed in this paper. The monogenic wavelet transform was employed to describe the amplitude and phase information of the noisy image. Then, the amplitude and phase information are simultaneously used as input of proposed improved convolutional neural network for denoising. Finally, the monogenic wavelet inverse transform is used to obtain the denoised image. The experimental results illustrate that the proposed algorithm achieves superior performance both in visual quality and objective peak signal-to-noise ratio values, compared with other state-of-the-art de-noising algorithms. [ABSTRACT FROM AUTHOR]

Artificial Neural Networks (ANNs) are intricate mathematical models inspired by the human neural system, holding great promise in the development of artificial intelligence. In the context of biotechnological applications within logistics, ANNs offer robust adaptability and fault tolerance. However, to unlock their full potential, the integration of swarm intelligence algorithms for parameter optimization has gained attention. This paper addresses the intersection of artificial intelligence, biotechnology, and logistics. Specifically, we focus on the critical task of efficiently constructing neural network models to enhance their performance in biotechnological logistics applications. Two key research areas are explored: Innovative Optimization Strategies: We propose novel strategies for optimizing ANNs using swarm intelligence algorithms. These strategies incorporate inverse learning techniques and neighborhood perturbation operations with dynamic probabilities. They expedite the escape from local optima and enhance the accurate exploration of adjacent regions. Such optimization is vital for effectively adapting ANNs to the complex challenges presented by biotechnological logistics. Freight Volume Forecasting in Biotechnology Logistics: Our research investigates the multifaceted factors influencing freight volume in the context of biotechnological logistics. We consider both the demands of the biotechnology sector and the logistical supply chain, identifying key indicators with strong correlations to freight volume. This analysis aids in refining predictions and optimizing resource allocation in biotechnological logistics. This paper underscores the convergence of artificial neural networks and swarm intelligence algorithms, emphasizing their application within the biotechnological logistics domain. The proposed optimization strategies aim to elevate the performance of ANNs in addressing the unique challenges posed by biotechnological logistics, ultimately fostering efficiency and precision. We outline future research directions to further harness the potential of these techniques for biotechnological applications in logistics. [ABSTRACT FROM AUTHOR]

In this paper, we study and analyze the generalized inverse mixed variational inequality. The existence and uniqueness of the solution of such problem are proposed. The neural network associated with the generalized inverse mixed variational inequality is presented, and moreover, the Wiener-Hopf equation which the solution of the equation is equivalent to the solution of the generalized inverse mixed variational inequality, is considered. The stability and existence of solution of such neural network are proved. Finally, we introduce some algorithms which are constructed by the concept of the neural network and display a numerical example for understanding our results. [ABSTRACT FROM AUTHOR]

Elderly people are one of the greatest assets of our nation, and it suits all the countries. Due to the improper way of living, bad health habits, and because of many other factors, they need to be monitored. In these days of our lives, people are coming up with a lot of tools and technologies, but none of them suggested a good, sophisticated approach. Both human and technology has some downsides by themselves. This creates the need for an application that can fill all the downsides of the available approaches. Air Care is a cost-efficient, minimalistic, sophisticated approach to monitor elders by their activities. Mainly targets on loco-motions, high-level activities, restricting their motions, monitoring people who visit home and notify, and monitoring the attentiveness of caregivers like so. Accuracy rate of 89% gained through the process. The primary objective of our research project is to find a solution using Machine learning, and deep learning to overcome the monitoring issues. Therefore, this paper focuses on developing and deploying a monitoring application by clustering data and using algorithms (SVM & K-Means) and neural network in a minimalistic way. [ABSTRACT FROM AUTHOR]

Due to the strict personnel control measures in COVID-19 epidemic, the control system cannot be maintained and managed manually. This puts forward higher requirements for the accuracy of its fault-tolerant performance. The control system plays an increasingly important role in the rapid development of industrial production. When the sensor in the system fails, the system will become unstable. Therefore, it is necessary to accurately and quickly diagnose the faults of the system sensors and maintain the system in time. This paper takes the control system as the object to carry out the fault diagnosis and fault-tolerant control research of its sensors. A network model of wavelet neural network is proposed, and an improved genetic algorithm is used to optimize the weights and thresholds of the neural network model to avoid the deficiencies of traditional neural network algorithms. For the depth sensor of a certain system, an online fault diagnosis scheme based on RBF (Radial Basis Function) neural network and genetic algorithm optimized neural network was designed. The disturbance fault, "stuck" fault, drift fault and oscillation fault of the depth sensor are simulated. Simulation experiments show that both online fault diagnosis schemes can accurately identify sensor faults and the genetic algorithm optimized neural network is superior to RBF neural network in both recognition accuracy and training time under the influence of COVID-19. [ABSTRACT FROM AUTHOR]

Under the influence of COVID-19, an efficient Ad-hoc network routing algorithm is required in the process of epidemic prevention and control. Artificial neural network has become an effective method to solve large-scale optimization problems. It has been proved that the appropriate neural network can get the exact solution of the problem in real time. Based on the continuous Hopfield neural network (CHNN), this paper focuses on the study of the best algorithm path for QoS routing in Ad-hoc networks. In this paper, a new Hopfield neural network model is proposed to solve the minimum cost problem in Ad-hoc networks with time delay. In the improved version of the path algorithm, the relationship between the parameters of the energy function is provided, and it is proved that the feasible solution of the network belongs to the category of progressive stability by properly selecting the parameters. The calculation example shows that the solution is not affected by the initial value, and the global optimal solution can always be obtained. The algorithm is very effective in the prevention and control in COVID-19 epidemic. [ABSTRACT FROM AUTHOR]

This paper presents a subgradient-based neural network to solve a nonsmooth nonconvex optimization problem with a nonsmooth nonconvex objective function, a class of affine equality constraints, and a class of nonsmooth convex inequality constraints. The proposed neural network is modeled with a differential inclusion. Under a suitable assumption on the constraint set and a proper assumption on the objective function, it is proved that for a sufficiently large penalty parameter, there exists a unique global solution to the neural network and the trajectory of the network can reach the feasible region in finite time and stay there thereafter. It is proved that the trajectory of the neural network converges to the set which consists of the equilibrium points of the neural network, and coincides with the set which consists of the critical points of the objective function in the feasible region. A condition is given to ensure the convergence to the equilibrium point set in finite time. Moreover, under suitable assumptions, the coincidence between the solution to the differential inclusion and the "slow solution" of it is also proved. Furthermore, three typical examples are given to present the effectiveness of the theoretic results obtained in this paper and the good performance of the proposed neural network. [ABSTRACT FROM AUTHOR]

This paper presents a method of high-capacity and transparent watermarking based on the usage of deep neural networks with the adjustable subsquares properties algorithm to encode the data of a watermark in high-quality video using the H.265/HEVC (High-Efficiency Video Coding) codec. The aim of the article is to present a method of embedding a watermark in a video with HEVC codec compression by making changes in a video in a way that is not noticeable to the naked eye. The method presented here is characterised by focusing on ensuring the accuracy of the original image in relation to the watermarked image, providing the transparency of the embedded watermark, while ensuring its survival after compression by the HEVC codec. The article includes a presentation of the practical results of watermark embedding with a built-in variation mechanism of its capacity and resistance, thanks to the adjustable subsquares properties algorithm. The obtained PSNR (peak signal-to-noise ratio) results are at the level of 40 dB or better. There is the possibility of the complete recovery of a watermark from a single frame compressed in the CRF (constant rate factor) range of up to 16, resulting in a BER (bit error rate) equal to 0 for the received watermark. [ABSTRACT FROM AUTHOR]

Research has proven that having high level of emotional intelligence (EI) can reduce the chance of getting mental illness. EI, and its component, can be improved with training, but currently the process is less flexible and very time-consuming. Machine learning (ML), on the other hand, can analyse huge amount of data to discover useful trends and patterns in shortest time possible. Despite the benefits, ML usage in EI training is scarce. In this paper, we studied 92 journal articles to discover the trend of the ML utilisation in the study of EI and its components. This survey aims to pave way for future studies that could lead to implementation of ML in EI training, and to rope in researchers in psychology and computer science to find possibilities of having a generic ML algorithm for every EI's components. Our findings show an increasing trend to apply ML on EI components, and Support Vector Machine and Neural Network are the two most popular ML algorithms used in those researches. We also found that social skill and empathy are the least exposed EI components to ML. Finally, we provide recommendations for future research direction of ML in EI domain, and EI in ML. [ABSTRACT FROM AUTHOR]

This paper discusses the factors affecting quality of school educational process. It describes the application of artificial intelligence approaches (neural networks) into the educational process. The analysis of methods for tracking changes in the position of the head while studying at school or at home is presented. The results obtained make it possible to evaluate the psycho‐physiological, psycho‐emotional state of primary school students in the process of interacting with a computer when performing educational tasks. The goal is to create computer tools that monitors changes in head position using images from a laptop or tablet digital camera. This article presents the stages of developing a neural network for assessing head turns when performing a school assignment and the results of applying the program. A new tool is proposed for assessing the state of a student in the learning process, to determine the ability to perceive different types of educational information. [ABSTRACT FROM AUTHOR]

Interest in the use of microwave equipment for breast imagery is on the increase owing to its safety, ease of use and friendlier cost. However, some of the pertinent blights of the design and optimization of microwave antenna include intensive consumption of computing resources, high price of software acquisition and very large optimization time. This paper therefore attempts to address these concerns by devising a rapid means of designing and optimizing the performance of a 1×4 array of circular microwave patch antenna for breast imagery applications by deploying the adaptive gradient descent algorithm (AGDA) for a circumspectly designed artificial neural network. In order to cross validate the findings of this work, the results obtained using the adaptive gradient descent algorithm was compared with those obtained with the deployment of the much reported Levenberg-Marquardt algorithm for the same dataset over same frequency range and training constraints. Analysis of the performance of the AGDA neural network shows that the approach is a viable and accurate technique for rapid design and analysis of arrays of circular microwave patch antenna for breast imaging. [ABSTRACT FROM AUTHOR]

In this paper we proposed a novel procedure for training a feedforward neural network. The accuracy of artificial neural network outputs after determining the proper structure for each problem depends on choosing the appropriate method for determining the best weights, which is the appropriate training algorithm. If the training algorithm starts from a good starting point, it is several steps closer to achieving global optimization. In this paper, we present an optimization strategy for selecting the initial population and determining the optimal weights with the aim of minimizing neural network error. Teaching-learning-based optimization (TLBO) is a less parametric algorithm rather than other evolutionary algorithms, so it is easier to implement. We have improved this algorithm to increase efficiency and balance between global and local search. The improved teaching-learningbased optimization (ITLBO) algorithm has added the concept of neighborhood to the basic algorithm, which improves the ability of global search. Using an initial population that includes the best cluster centers after clustering with the modified k-mean algorithm also helps the algorithm to achieve global optimum. The results are promising, close to optimal, and better than other approach which we compared our proposed algorithm with them. [ABSTRACT FROM AUTHOR]

In many machine learning applications and algorithms, the algorithm performance and accuracy are highly dependent on the metric used to measure the distance between different samples. Therefore, learning a distance metric specific to the data can improve these algorithms' performance. This paper proposes an unrestricted deep metric learning framework based on neural networks' interaction for learning metrics in latent space. The proposed method is inspired by generative neural nets (GANs), in which two neural nets are working together to learn true data distribution. In our method, one network plays the role of a supervisor for another network, a feature learning auto-encoder. Its task is to learn transformation to latent space in which data have more meaningful distance and separability. i.e., the supervisor gets the output of the auto-encoder and sends feedback to modify its weights. They interact with each other interleavingly. Several experiments were conducted on four datasets, such as MNIST, GISETTE, Winnipeg Cropland Classification (WCC), and swarm behavior, from different application domains, to evaluate the proposed method's performance. The results show that we can force auto-encoder to learn label information to project data into a latent space with better separability by using our approach. In addition to better class discrimination, the proposed method is far faster than normal auto-encoders during feature learning and has much less training time in the classification phase. [ABSTRACT FROM AUTHOR]

A novel vertical handover algorithm based on multi-attribute and neural network for heterogeneous integrated network is proposed in this paper. The whole frame of the algorithm is constructed by setting the network environment in which we use the network resources by switching between UMTS, GPRS, WLAN, 4G, and 5G. Each network build their own three-layer BP (Back Propagation, BP) neural network model and then the maximum transmission rate, minimum delay, SINR (signal to interference and noise ratio, SINR), bit error rate, user moving speed, and packet loss rate which can affect the overall performance of the wireless network are employed as reference objects to participate in the setting of BP neural network input layer neurons and the training and learning process of subsequent neural network data. Finally, the network download rate is adopted as prediction target to evaluate performance on the five wireless networks and then the vertical handover algorithm will select the right wireless network to perform vertical handover decision. The simulation results on MATLAB platform show that the vertical handover algorithm designed in this paper has a handover success rate up to 90% and realizes efficient handover and seamless connectivity between multi-heterogeneous networks. [ABSTRACT FROM AUTHOR]

In this paper, the modified Spider Monkey Optimization (SMO) with Multi-Layer Perceptron (MLP) is utilized to solve the classification problem on five different datasets. The MLP is a widely used Neural Network (NN) variant which requires training on specific application to tackle the slow convergence speed and local minima avoidance. The original SMO with MLP experiences the problem of finding the optimal classification result; due to that, the SMO is enhanced by other meta-heuristics algorithm to train the MLP. Based on the concept of no free lunch theorem, there is always a possibility to improve the algorithm. With the same expectation, the performance of the SMO algorithm is improved by using Differential Evolution (DE) and Grey Wolf Optimizer (GWO) algorithm to train the MLP. Likewise, the SMO-DE and SMO-GWO are two different concepts employed to improve efficiency. The results of proposed algorithms are compared with other well-known algorithms such as BBO, PSO, ES, SVM, KNN, and Logistic Regression. The results show that the proposed algorithm performs better than others or they are more competitive. [ABSTRACT FROM AUTHOR]

Stochastic computation has recently been proposed for implementing artificial neural networks with reduced hardware and power consumption, but at a decreased accuracy and processing speed. Most existing implementations are based on pre-training such that the weights are predetermined for neurons at different layers, thus these implementations lack the ability to update the values of the network parameters. In this paper, a stochastic computational multi-layer perceptron (SC-MLP) is proposed by implementing the backward propagation algorithm for updating the layer weights. Using extended stochastic logic (ESL), a reconfigurable stochastic computational activation unit (SCAU) is designed to implement different types of activation functions such as the $tanh$ and the rectifier function. A triple modular redundancy (TMR) technique is employed for reducing the random fluctuations in stochastic computation. A probability estimator (PE) and a divider based on the TMR and a binary search algorithm are further proposed with progressive precision for reducing the required stochastic sequence length. Therefore, the latency and energy consumption of the SC-MLP are significantly reduced. The simulation results show that the proposed design is capable of implementing both the training and inference processes. For the classification of nonlinearly separable patterns, at a slight loss of accuracy by 1.32-1.34 percent, the proposed design requires only 28.5-30.1 percent of the area and 18.9-23.9 percent of the energy consumption incurred by a design using floating point arithmetic. Compared to a fixed-point implementation, the SC-MLP consumes a smaller area (40.7-45.5 percent) and a lower energy consumption (38.0-51.0 percent) with a similar processing speed and a slight drop of accuracy by 0.15-0.33 percent. The area and the energy consumption of the proposed design is from 80.7-87.1 percent and from 71.9-93.1 percent, respectively, of a binarized neural network (BNN), with a similar accuracy. [ABSTRACT FROM AUTHOR]

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]

Fourier ptychography is a new type of computational imaging technology, which uses a stack of low‐resolution images obtained from overlapped apertures (or equivalent) to reconstruct super‐resolved image. However, the accuracy of the aperture position will directly affect the quality and resolution of the reconstructed image. This paper proposes a new perspective for FP positional deviations correction using the neural network. We construct a trainable neural network to perform positional deviations correction along with object reconstruction. The real part and imaginary part of the object as well as the different and irregular positional deviations of each aperture are set as the weights of convolutional layer. The gradients over these weights are computed automatically, and gradient‐based optimization algorithm is employed to recover the object and find the correct aperture position. Simulation and experiment are performed to verify our algorithm. The results show that the proposed algorithm can accurately find the aperture position and improve the reconstruction quality. [ABSTRACT FROM AUTHOR]

This paper presents an empirical study of a formative neural network-based assessment approach by using mobile technology to provide pharmacy students with intelligent diagnostic feedback. An unsupervised learning algorithm was integrated with an audience response system called SIDRA in order to generate states that collect some commonality in responses to questions and add diagnostic feedback for guided learning. A total of 89 pharmacy students enrolled on a Human Anatomy course were taught using two different teaching methods. Forty-four students employed intelligent SIDRA (i-SIDRA), whereas 45 students received the same training but without using i-SIDRA. A statistically significant difference was found between the experimental group (i-SIDRA) and the control group (traditional learning methodology), with T (87) = 6.598, p < 0.001. In four MCQs tests, the difference between the number of correct answers in the first attempt and in the last attempt was also studied. A global effect size of 0.644 was achieved in the meta-analysis carried out. The students expressed satisfaction with the content provided by i-SIDRA and the methodology used during the process of learning anatomy (M = 4.59). The new empirical contribution presented in this paper allows instructors to perform post hoc analyses of each particular student's progress to ensure appropriate training. [ABSTRACT FROM AUTHOR]

As a consequence of increasing wind power penetration level, it will be a big challenge to control and operate the power system because of the inherent uncertainty of the wind energy. One of the ways to deal with the wind power variability is to predict it accurately and reliably. The traditional point forecasting-based technique cannot notably solve the uncertainty in power system operation. In order to compute the probabilistic forecasting, which yields information on the uncertainty of wind power, a novel hybrid intelligent method that incorporates the wavelet transform, neural network (NN), and improved krill herd optimization algorithm (IKHOA), is used in this paper. Also, the extreme learning machine is exerted to train NN and calculates point forecasts, and IKHOA is applied to forecast the noise variance. The robust method called bootstrap is regarded to create prediction intervals and calculate the model uncertainty. The efficiency of proposed forecasting engine is evaluated by usage of wind power data from the Alberta, Canada. [ABSTRACT FROM AUTHOR]

Curriculum is the basis of vocational training, its development level and teaching efficiency determine the realization of vocational training objectives, as well as the quality and level of major vocational academic training. Therefore, the development of curriculum is an important issue. And affect the school's teaching capacity building. The analysis of the latest developments in the main courses shows that there are some deviations or irrationalities in the curriculum in some colleges and universities, and the general problems of understanding the latest courses, such as lack of solid foundation in curriculum setting, unclear direction of objectives, unclear reform ideas, inadequate and systematic construction measures, lack of attention to the quality of education. This paper explains the rules for the establishment of first-level courses, clarifies the ideas and priorities of architecture, and explores strategies for building university-level courses using knowledge of artificial intelligence and neural network algorithms in order to gain experience from them. [ABSTRACT FROM AUTHOR]

Securing the network from intrusions becomes a more challenging task to conduct for system administrators, and the need for a more powerful and efficient intrusion detection system emerges with the continuous development of cyber-attacks exploring various methods and techniques. A performed survey in [1] show the various emerging attacks in cyber security accompanied with the exponential growth of the internet interconnections, the attacks are affecting the confidentiality, availability, and the integrity of the data in the cyber world, as more data is now available in electronic format, and more access is provided to end users, the challenge is to secure the network from any intrusion. Rather than following the traditional way of detecting attacks by looking for signatures of known intrusion attempts, machine learning can help detect nonconformities over the network. We propose the usage of artificial intelligence to build a sophisticated Network Intrusion Detection System able to be trained/self-trained using models and algorithms found in machine learning/deep learning to detect malicious network traffic. The aim of this paper is to present a new IDS model based on machine learning approach to detect malicious traffic and protect the network from cyber-attacks. The usage of machine learning will allow better accuracy in detection and faster response time. This technique can also be used to continuously update the IDS knowledge base for instant response through malicious packets rejection. In order to implement and measure the performance of our model, we used NSL-KDD dataset which contains records of various mimicked attacks on a real IDS system, after the preprocessing phase which consist of data summarization, cleaning, and normalization, we used the most relevant attributes for the classification process based on CfsSubsetEval technique with BestFirst approach as an attribute selection algorithm to remove the redundant attributes and to allow the usage of the most pertinent attributes of the dataset. To build our prediction model we used a comparative evaluation of three algorithms (K-means, AdaBoost and Multilayer Perceptron), the experimental results show that the MLP algorithm provides a high detection rate and reduces false alarm rate. Finally, a set of principles is concluded, which will set path for future research for implementing an efficient and performant IDS. To help researchers in the selection of IDS, several recommendations are provided with future directions for this research. [ABSTRACT FROM AUTHOR]

The information storage algorithm of a Hopfield-type network is a kind of self-correlation learning. This algorithm needs the condition in which vectors of learning patterns cross at right angles to each other. This paper proposes n method to make this condition easier. To this purpose, threshold is used. An investigation of how to decide quantity is included. This paper discusses a network with units whose state is 1 or 0. Fixing method of threshold has not been proposed before to this type of network. It is, therefore, proposed in this research; the goal is to facilitate learning patterns. [ABSTRACT FROM AUTHOR]

Artificial neural networks have been widely used in time series prediction. In this paper, multi-layer feedforward neural networks with optimized structures, using particle swarm optimization (PSO) algorithm, are used for hourly load prediction based on load time series of IEEE Reliability Test System. To have a small and appropriate feature subset, a hybrid method is used for feature selection in this paper. This hybrid method uses the combination of genetic algorithm (GA) and ant colony optimization (ACO) algorithm. The season, day of the week, time of the day and history load are considered as load influencing factors in this study based on the mentioned standard load dataset. The optimized number of neurons in the hidden layers of multi-layer perceptron (MLP) is determined using PSO algorithm. Experimental results show that the proposed hybrid model offers superior performance, in terms of mean absolute percentage error (MAPE), in time series prediction as compared to some recent researches in this field. [ABSTRACT FROM AUTHOR]

Rule extraction from artificial neural networks remains important task in complex diseases such as diabetes and breast cancer where the rules should be accurate and comprehensible. The quality of rules is improved by the improvement of the network classification accuracy which is done by the discretization of input attributes. In this paper, we developed a rule extraction algorithm based on multiobjective genetic algorithms and association rules mining to extract highly accurate and comprehensible classification rules from ANN's that have been trained using the discretization of the continuous attributes. The data pre-processing provides very good improvement of the ANN accuracy and consequently leads to improve the performance of the classification rules in terms of fidelity and coverage. The results show that our algorithm is very suitable for medical decision making, so an excellent average accuracy of 94.73 has been achieved for the Pima dataset and 99.36 for the breast cancer dataset. [ABSTRACT FROM AUTHOR]

In this paper, a novel control scheme is developed to solve an optimal containment control problem of unknown continuous-time multi-agent systems. Different from traditional adaptive dynamic programming (ADP) algorithms, this paper proposes an internal reinforcement ADP algorithm (IR-ADP), in which the internal reinforcement signals are added in order to facilitate the learning process. Then a distributed containment control law is designed for each agent with the internal reinforcement signal. The convergence of this IR-ADP algorithm and the stability of the closed-loop multi-agent system are analyzed theoretically. For the implementation of the optimal controllers, three neural networks (NNs), namely internal reinforcement NNs, critic NNs and actor NNs, are utilized to approximate the internal reinforcement signals, the performance indices and optimal control laws, respectively. Finally, some simulation results are provided to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Classification is needed in disaster investigation, traffic control, and land-use resource management. How to quickly and accurately classify such remote sensing imagery has become a popular research topic. However, the application of large, deep neural network models for the training of classifiers in the hope of obtaining good classification results is often very time-consuming. In this study, a new CNN (convolutional neutral networks) architecture, i.e., RSSCNet (remote sensing scene classification network), with high generalization capability was designed. Moreover, a two-stage cyclical learning rate policy and the no-freezing transfer learning method were developed to speed up model training and enhance accuracy. In addition, the manifold learning t-SNE (t-distributed stochastic neighbor embedding) algorithm was used to verify the effectiveness of the proposed model, and the LIME (local interpretable model, agnostic explanation) algorithm was applied to improve the results in cases where the model made wrong predictions. Comparing the results of three publicly available datasets in this study with those obtained in previous studies, the experimental results show that the model and method proposed in this paper can achieve better scene classification more quickly and more efficiently. [ABSTRACT FROM AUTHOR]

In this paper, we propose two hybrid models to release some limitations and enhancement of the results. In this regard, three popular GARCH-type models are utilized for more accurate estimating of volatility, as the most important parameter for option pricing. Furthermore, the two non-parametric models based on Artificial Neural Networks and Neuro-Fuzzy Networks tuned by Particle Swarm Optimization algorithm are proposed to price call options for the S&P 500 index. By comparing the results obtained using these models, we conclude that both Neural Network and Neuro-Fuzzy Network models outperform the Black–Scholes model. [ABSTRACT FROM AUTHOR]

This paper presents various applications of evolutionary computing approach for architectural space planning problem. As such the problem of architectural space planning is NP-complete. Finding an optimal solution within a reasonable amount of time for these problems is impossible. However for architectural space planning problem we may not be even looking for an optimal but some feasible solution based on varied parameters. Many different computing approaches for space planning like procedural algorithms, heuristic search based methods, genetic algorithms, fuzzy logic, and artificial neural networks etc. have been developed and are being employed. In recent years evolutionary computation approaches have been applied to a wide variety of applications as it has the advantage of giving reasonably acceptable solution in a reasonable amount of time. There are also hybrid systems such as neural network and fuzzy logic which incorporates the features of evolutionary computing paradigm. The present paper aims to compare the various aspects and merits/demerits of each of these methods developed so far. Sixteen papers have been reviewed and compared on various parameters such as input features, output produced, set of constraints, scope of space coverage-single floor, multi-floor and urban spaces. Recent publications emphasized on energy aspect as well. The paper will help the better understanding of the Evolutionary computing perspective of solving architectural space planning problem. The findings of this paper provide useful insight into current developments and are beneficial for those who look for automating architectural space planning task within given design constraints. [ABSTRACT FROM AUTHOR]

In this paper, a new channel assignment strategy named compact dynamic channel assignment (CDCA) is proposed. The CDCA differs from other strategies by consistently keeping the system in the utmost optimal state, and thus the scheme allows to determine a call succeeding or failing by local information instead of that of the whole network. It employs Hopfield neural networks for optimization which avoids the complicated assessment of channel compactness and guarantees optimum solutions for every assignment. A scheme based on Hopfield neural network is considered before; however, unlike others, in this algorithm an energy function is derived in such a way that for a neuron, the more a channel is currently being allocated in other cells, the more excitation the neuron will acquire, so as to guarantee each cluster using channels as few as possible. Performance measures in terms of the blocking probability, convergence rate and convergence time are obtained to assess the viability of the proposed scheme. Results presented show that the approach significantly reduces stringent requirements of searching space and convergence time. The algorithm is simple and straightforward, hence the efficient algorithm makes the real-time implementation of channel assignment based on neural network feasibility. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, a Group Method of Data Handling (GMDH)-type neural network algorithm with a feedback loop for structural identification of Radial Basis Function (RBF) neural network is proposed. In case of the GMDH-type neural network, the network architecture is automatically organized by heuristic self-organization. Optimum architecture is evolved using one of the criterions, defined as Akaike's Information Criterion (AIC) or Prediction Sum of Squares (PSS), for minimizing the prediction error. In the conventional multilayered neural network, prediction error criteria defined as AIC and PSS cannot be used to determine the neural network architecture. In case of the GMDH-type neural network proposed in this paper, structural parameters such as the number of neurons, relevant input variables and the number of feedback loop calculations are automatically determined so as to minimize AIC or PSS. Furthermore, the GMDH-type neural network can identify RBF neural network accurately, since the complexity of the neural network is increased gradually by feedback loop calculations. [ABSTRACT FROM AUTHOR]

Principal component analysis (PCA) using neural networks is an active research field with many applications to signal processing and data analysis. This paper presents a PCA neural network endowed with a novel learning algorithm, and an analysis of its features. As the basic discrete-time Oja's PCA neural network does not converge globally, it is important to derive a robust and globally convergent PCA learning algorithm. Based on previous works on the globally convergent PCA learning algorithm, a robust and globally convergent PCA learning algorithm is proposed in this paper. The behavior of this discrete-time learning algorithm is directly studied in this paper. We show that the algorithm is robust and globally convergent, The selection of the parameters of this algorithm will also be discussed in details. Finally, simulation results are provided to verify the theoretical results presented. Compared to other PCA learning algorithms, the proposed algorithm performs favorably in terms of robust stability, global convergence, speed and accuracy. [ABSTRACT FROM AUTHOR]

A near-optimum parallel algorithm for solving the one-dimensional gate assignment problem is presented in this paper, where the problem is NP-hard and one of the most fundamental layout problems in VLSI design. The proposed system is composed of n × n processing elements based on the artificial two-dimensional maximum neural network for (n + 2)-gate assignment problems. Our algorithm has discovered improved solutions in the benchmark problems compared with the best existing algorithms. The proposed approach is applicable to other VLSI layout problems such as the PLA (Programmable Logic Array) folding problem. © 1999 Scripta Technica, Electr Eng Jpn, 129(2): 71–77, 1999 [ABSTRACT FROM AUTHOR]

In cellular mobile communication, the same channel is shared by a number of cells. Up to now, the model has been considered where the same channel is used with a sufficient separation, so that the radiowave interference is sufficiently small for any call generated in the cell. Recently, on the other hand, a model has been proposed where the same channel is assigned independently of the specified cell separation, as long as the carrier-interference ratio is above a certain value. Although the assignment algorithm has been discussed for the traditional model, no report has been made on the detailed assignment procedure for the new model. With such a situation as the background, this paper proposes an application of Hopfield's neural net as an approach to the channel assignment in the new model and derives the energy function. Several results of computer simulation are shown where the proposed neural net is applied to the mobile communication model. It is not always true that the neural net produces the optimal solution, but it is interesting that an unexpected improvement of the channel utilization is observed in the communication traffic as far as the result of computer simulation is concerned. [ABSTRACT FROM AUTHOR]

This paper proposes a fast learning algorithm of neural networks and evaluates the performances of adaptive equalizers using neural networks trained by the proposed algorithm in a frequency-selective fading channel. The backpropagation (BP) algorithm which is used widely to train neural networks has a slow convergence rate because it is based on the gradient descent method. This paper presents a fast learning algorithm using the recursive least squares (RLS) algorithm which has a fast convergence rate as an adaptive algorithm for adaptive linear filters. In the proposed algorithm, the sum of the squared error between the actual total input and the desired total input is used as the cost function to apply the RLS algorithm. A simulation result on the exclusive-OR problem indicates that the proposed algorithm is about 8.8 times faster than the BP for the number of iterations required to converge. Recently, there has been interest in adaptive equalizers as an application field of neural networks. However, the performance of an adaptive equalizer using a neural network in a frequency-selective fading channel which is observed in land mobile communications has never been evaluated. Therefore, in this paper, the performances of adaptive equalizers using neural networks trained by the proposed algorithm in a frequency-selective fading channel are evaluated. Especially, an adaptive equalizer using the selectively unsupervised learning neural network proposed by the authors is considered. The adaptive equalizer can reject the false learning by carrying out learning selectively. It is shown that the adaptive equalizer is superior to the conventional one and the one using the conventional neural network. [ABSTRACT FROM AUTHOR]

The traditional neural system which conducts the paired association has the real coupling coefficients and is not suited to the implementation on digital processing LSI because of its analog property. From such a viewpoint, this paper applies the traditional idea of calculating the coupling coefficients on the real field to the binary field and proposes a system which conducts the paired association using only the logic operation. More precisely, it is noted that the associative law applies to the matrix operation on the real field as well as the matrix operation for the binary code. The coupling coefficients for the binary code for the paired association arc derived by applying the sweeping-out method to the matrix of binary numbers. By this approach, a paired-associate system is obtained by a simpler training algorithm with easy LSI implementation. This paper describes first the traditional solution of the paired-associate problem on the real field and then proposes a method of solution based on that approach for the paired-associate problem on the binary field. The existence condition for the solution of the paired- associate problem is discussed for the proposed method. Finally, the noise-reduction power of the proposed method is estimated. [ABSTRACT FROM AUTHOR]

Abstract In the last 20 years, and in particular in the last decade, the availability of propagation data for GNSS has increased substantially. In this sense, the ionosphere has been sounded with a large number of receivers that provide an enormous amount of ionospheric data. Moreover, the maturity of the models has also been increased in the same period of time. As an example, IGS has ionospheric maps from GNSS data back to 1998, which would allow for the correlation of these data with other quantities relevant for the user and space weather (such as Solar Flux and Kp). These large datasets would account for almost half a billion points to be analyzed. With the advent and explosion of Big Data algorithms to analyze large databases and find correlations with different kinds of data, and the availability of open source code libraries (for example, the TensorFlow libraries from Google that are used in this paper), the possibility of merging these two worlds has been widely opened. In this paper, a proof of concept for a single frequency correction algorithm based in GNSS GIM vTEC and Fully Connected Neural Networks is provided. Different Neural Network architectures have been tested, including shallow (one hidden layer) and deep (up to five hidden layers) Neural Network models. The error in training data of such models ranges from 50% to 1% depending on the architecture used. Moreover, it is shown that by adjusting a Neural Network with data from 2005 to 2009 but tested with data from 2016 to 2017, Neural Network models could be suitable for the forecast of vTEC for single frequency users. The results indicate that this kind of model can be used in combination with the Galileo Signal-in-Space (SiS) NeQuick G parameters. This combination provides a broadcast model with equivalent performances to NeQuick G and better than GPS ICA for the years 2016 and 2017, showing a 3D position Root Mean Squared (RMS) error of approximately 2 m. [ABSTRACT FROM AUTHOR]

Abstract With the rapid development of high-through technology, vast amounts of protein molecular data has been generated, which is crucial to advance our understanding of biological organisms. An increasing number of protein post translation modification sites identification approaches have been designed and developed to detect such modification sites among the protein sequences. Nevertheless, these methods are merely suitable for one type of modification site, their performance deteriorate rapidly when applied to other types of modification sites' prediction. In this paper, with the method of different types of neural network algorithm ensemble, a novel method, named CMSENN (http://121.250.173.184/) Computational Modification Sites with Ensemble Neural Network, was proposed to detect protein modification. The algorithm mainly consists of several steps: First, the predicted peptide sequences translate to the feature vectors. Second, the three types of employed amino acid residues properties should be normalized. Finally, various combination of features and classification model have been compared the performances with several current typical algorithms. The results demonstrate that the proposed model have well performance at the sensitivity, specificity, F1 score and Matthews correlation coefficient (MCC) value in the identification modification with the approach of the selected features and algorithm combination. Highlights • This paper proposed CMSENN to detect protein modification sites. • The predicted peptide sequences translate to the feature vectors. • And three types of employed amino acid residues properties should be normalized. • The results demonstrate that the proposed model have well performance. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) recognition is an important tool for online monitoring of gas insulated switchgear (GIS) and diagnosing existing defects. At present, there are two different types of data patterns used for analysis and evaluation of PD signals: phase resolved partial discharge (PRPD) mode and time resolved partial discharge (TRPD) mode. Using different types of data patterns separately can lead to inconsistent or even conflicted recognition results, but the two types of data patterns having complementary information between each other can be used together for data fusion. Dempster-Shafer (DS) evidence theory is introduced to address the problem of evidence conflict and low fusion efficiency. First, two sub-networks for PD recognition are established and compared employing the back propagation neural network (BPNN) learning algorithm under the two modes respectively. Secondly, to minimize (with possible elimination of) the possibility of misclassification, a new fusion decision-making system for PD recognition is proposed on the basis of fusing the results from the sub-networks. Finally, extensive field experiments on two artificial defects are conducted in order to evaluate the performance of the proposed method in this paper. The classification results reveal that the proposed method significantly outperforms the ways of using only single type of data patterns. The method proposed in this paper reduces the uncertainty and effectively improves the credibility of diagnosis. [ABSTRACT FROM AUTHOR]